Antoine Triller

Xiaojuan Yang

Christian G Specht
email: specht@biologie.ens.fr

Carlos B Duarte

Josef Kittler

Specht Cg

Invited member

Keywords: Synapses inhibitrices mixtes, GlyR, GABAAR, géphyrine, RIM1/2, cotransmission, domaine sous-synaptique (SSD), nanocolonne trans-synaptique, microscopie de localisation de molécule unique (SMLM), microscopie de reconstruction optique stochastique Super-resolution, single molecule localization microscopy (SMLM), two-color dSTORM, synaptic proteins, nanoclusters, correlation analysis subsynaptic domain (SSD), super-resolution microscopy, single molecule localization microscopy (SMLM), inhibitory receptors, gephyrin

Single molecule localization microscopy (SMLM) bypasses the diffraction limit by recording spatially and temporally separated single molecule signals, achieving a resolution of ~10-40 nm. Numerous studies of excitatory synapses using SMLM have revealed a heterogeneous intra-synaptic organization of synaptic proteins.

Synaptic proteins form sub-synaptic domains (SSDs), in which the molecule density is higher than in the adjacent areas. Furthermore, pre-and post-synaptic SSDs can align into a trans-synaptic nanocolumn, which is proposed to be a regulatory element for synaptic strength and plasticity at excitatory synapses.

At mixed inhibitory synapses, GlyRs and GABAARs co-exist within the same post-synaptic density (PSD). However, little is known about how the two types of Avec la méthodologie bicolore dSTORM, j'ai examiné l'organisation intrasynaptique des GlyRs, GABAARs, géphyrine et RIM1/2 aux synapses inhibitrices mixtes. Toutes ces composantes synaptiques présentent des distributions hétérogènes et forment des SSDs à l'intérieur de la synapse. Les GlyR et les GABAAR ne sont pas complètement mélangés, mais pouvent occuper des espaces différents aux synapses inhibitrices mixtes. De plus, les SSDs de RIM1/2 présynaptiques sont alignées avec les SSDs de géphyrine post-synaptique dans des nanocolonnes trans-synaptiques. J'ai également examiné l'effet de l'activité du réseau sur les relations spatiales des GlyRs, GABAARs, géphyrine et RIM1/2. La corrélation spatiale entre les GlyRs, les GABAARs et la géphyrine au niveau des PSD inhibiteurs mixtes a été augmentée lorsque l'activité excitatrice était élevée par un traitement par 4-AP. De plus, les GlyRs, mais pas les GABAARs, présentaient une corrélation spatiale plus élevée avec la RIM1/2 présynaptique après un traitement par 4-AP. Cependant, le nombre de SSD par synapse de ces protéines synaptiques n'est pas modifié par le traitement par 4-AP. Par conséquent, l'organisation spatiale des GlyRs et des GABAARs dans les synapses inhibitrices mixtes semble être régulée différemment par les changements de l'activité du réseau. Cette étude offre donc un nouvel angle de compréhension des mécanismes sous-jacents à la co-transmission GABAergique/glycinergique.

receptors are organized within the PSD, and whether their spatial relationship plays a role in the regulation of the GABAergic/glycinergic co-transmission. The inhibitory scaffold protein gephyrin provides binding sites for both GlyRs and GABAARs. Gephyrin has been shown to form SSDs at GABAergic synapses. However, it is unclear whether gephyrin forms SSDs at mixed inhibitory synapses, and if so, what roles these subdomains hold. Also, it is still unresolved whether trans-synaptic nanocolumns exist at inhibitory synapses and how GlyRs and GABAARs are aligned with pre-synaptic vesicle release sites.

To answer these open questions, I have adopted a two-color dSTORM imaging approach. My experimental results showed that the fluorescent dyes used for labeling had a strong impact on the characterization of synaptic clusters in dSTORM images. Thus, I carefully studied the methodology of two-color dSTORM imaging of synaptic clusters and developed an imaging and analysis strategy. I mainly adopted H-watershed method for SSD segmentation and intensity correlation quotient (ICQ) for evaluating the spatial relationships of synaptic proteins.

With the established two-color dSTORM methodology, I examined the intrasynaptic organization of GlyRs, GABAARs, gephyrin and RIM1/2 at mixed inhibitory synapses in a pair-wise manner. All these synaptic components exhibited heterogeneous distributions and formed SSDs within the synapse. GlyRs and GABAARs were not fully intermingled, but sometimes occupied different spaces at mixed inhibitory synapses. Moreover, pre-synaptic RIM1/2 SSDs were aligned with post-synaptic gephyrin SSDs into trans-synaptic nanocolumns.

I also examined the effect of network activity on the spatial relationships of GlyRs, GABAARs, gephyrin and RIM1/2. The spatial correlation between GlyRs, GABAARs and gephyrin at mixed inhibitory PSDs was increased when elevating the excitatory activity by 4-AP treatment. In addition, GlyRs, but not GABAARs, exhibited a higher spatial correlation with pre-synaptic RIM1/2 after 4-AP treatment. However, the SSD counts per synapse of these synaptic proteins were not changed by 4-AP treatment. Therefore, the spatial organization of GlyRs and GABAARs within the mixed inhibitory synapses appears to be differentially regulated by network activity. This study thus provides a new angle for understanding the mechanisms underlying GABAergic/glycinergic cotransmission.

Keywords

Mixed inhibitory synapses, GlyR, GABAAR, gephyrin, RIM1/2, co-transmission, subsynaptic domain (SSD), trans-synaptic nanocolumn, single molecule localization microscopy (SMLM), direct stochastic optical reconstruction microscopy (dSTORM), multi-color dSTORM.

Résumé

La microscopie de localisation de molécule unique (SMLM) contourne la limite de diffraction en enregistrant des signaux de molécule unique séparés dans l'espace et dans le temps, atteignant une résolution de ~10-40 nm. De nombreuses études de synapses excitatrices utilisant la SMLM ont révélé une organisation intra-synaptique hétérogène des protéines synaptiques. Il est intéressant de noter que les protéines synaptiques forment des domaines sous-synaptiques (SSD), dans lesquels la densité moléculaire est plus élevée que dans les zones adjacentes. En outre, les SSD pré-et post-synaptiques peuvent s'aligner dans une nanocolonne trans-synaptique, qui est proposée comme un élément régulateur de la force et de la plasticité synaptique aux synapses excitatrices.

Aux synapses inhibitrices mixtes, les GlyRs et les GABAARs coexistent dans la même densité post-synaptique (PSD). Cependant, on sait peu de choses sur la façon dont les deux types de récepteurs sont organisés au sein de la PSD et si leur relation spatiale joue un rôle dans la régulation de la co-transmission GABAergique/glycinergique. La géphyrine, protéine inhibitrice d'échafaudage, fournit des sites de liaison pour les GlyRs et les GABAARs. Il a été démontré que la géphyrine forme des SSD aux synapses GABAergique. Cependant, il n'est pas clair si la géphyrine forme des SSD aux synapses inhibitrices mixtes et, dans l'affirmative, quels rôles ces sous-domaines jouent. De plus, on ne sait toujours pas s'il existe des nanocolonnes trans-synaptiques aux synapses inhibitrices et comment les GlyR et les GABAAR sont alignés avec les sites de libération vésiculaire présynaptique.

Pour répondre à ces questions ouvertes, j'ai adopté une approche d'imagerie dSTORM bicolore. Mes résultats expérimentaux ont montré que les colorants fluorescents utilisés pour le marquage avaient un fort impact sur la caractérisation des amas synaptiques dans les images dSTORM. Ainsi, j'ai soigneusement étudié la méthodologie d'imagerie dSTORM bicolore des grappes synaptiques et j'ai développé une stratégie d'imagerie et d'analyse. J'ai principalement adopté la méthode H-Watershed pour la segmentation SSD et le quotient de corrélation d'intensité (ICQ) pour évaluer les relations spatiales des protéines synaptiques. Table 1. Summary of the changes in synaptic molecule amount at mixed synapses with altered neuronal activity ……………………………………………………………………………………………101 Table 2. Summary of the changes in the spatial correlation between protein pairs with altered neuronal activity, measured by ICQ ……………………………………………….………………101 Table 3. Size and protein copy numbers of SSDs and PSDs obtained with different experimental techniques ……………………………………………………………………………………………105

List of Tables

Chapter 1 1 Introduction

In this chapter, I first introduced the two main inhibitory neurotransmitter receptors in the central nervous system, gamma aminobutyric acid (GABA) subtype A receptors (GABAARs) and glycine receptors (GlyRs). Their main scaffold protein at the inhibitory post-synaptic density (PSD), gephyrin, was also introduced. Then, I focused on the evidence for GABA/glycine co-release and GABAAR/GlyR co-existence at mixed inhibitory synapses. The molecular mechanisms of GABA/glycine co-transmission and its implications in neuronal function were also discussed.

In the second part of the introduction, I introduced super-resolution optical microscopy, specifically single molecule localization microscopy (SMLM). In particular, I discussed the challenges in the application of SMLM to synaptic structures, which are imposed by both the inherent limitations of SMLM and the intrinsic properties of synapses. Despite of these limitations and pitfalls, detailed insights into the structures of small compartments such as synapses have been achieved. Therefore, I summarized the current studies on the intra-synaptic organization of synaptic proteins (e.g. subsynaptic domains, also referred to as SSDs), and talked about their potential roles in regulating synaptic plasticity.

Finally, based on the available evidence, I discussed the possible existence of SSDs at mixed inhibitory synapses, and their potential functional implications. This chapter closes with the aims of this study.

1.1 GABAARs and GlyRs: the two main inhibitory neurotransmitter receptors 1.1.1 The subunit composition of GABAARs and GlyRs GABAARs and GlyRs belong to the superfamily of Cys-loop ligand-gated ion channels (pGLICs) [START_REF] Kasaragod | Structure-Function Relationships of Glycine and GABAA Receptors and Their Interplay With the Scaffolding Protein Gephyrin[END_REF][START_REF] Moss | Constructing inhibitory synapses[END_REF].

Their subunits have a characteristic structure, which is composed of an extracellular N-terminus, four transmembrane domains (TM1-4) and a short extracellular C-terminus (Figure 1.A). A large intracellular loop is formed between TM3 and TM4. Functional GABAARs and GlyRs are pentamers. The five TM2 subunits facing the center form the lining of the ion channel, which selectively allows the passage of chloride ions (Figure 1.B). The influx of negatively charged chloride ions leads to a reduction of membrane potential, thus counteracting the effect of depolarizing (excitatory) currents.

GABAARs have a large variety of subunits. Nineteen subunits of eight different types have been identified, including α1-6, β1-3, γ1-3, δ, ε, θ, π, and ρ1-3.

GABAARs are mainly heteropentamers, consisting of two α (α1-6), two β (β1-3)

and either one γ (γ1-3) or one δ subunit [START_REF] Fritschy | Molecular and functional heterogeneity of GABAergic synapses[END_REF][START_REF] Olsen | International Union of Pharmacology. LXX. Subtypes of gamma-aminobutyric acid(A) receptors: classification on the basis of subunit composition, pharmacology, and function[END_REF][START_REF] Sigel | Structure, function, and modulation of GABAA receptors[END_REF]. The GABA binding sites are located at the interface of α and β subunits. Therefore, GABAAR pentamers encompass two GABA binding sites. The most common subunit composition in the mammalian brain is α1β2γ2, followed by α2β3γ2 and α3β3γ2 [START_REF] Knoflach | GABA A receptor-mediated neurotransmission: Not so simple after all[END_REF][START_REF] Nutt | GABAA receptors: subtypes, regional distribution, and function[END_REF]. The synaptic clustering of GABAARs is dependent on the direct interaction of GABAAR α1-3 subunits and β2-3 subunits with gephyrin, the main inhibitory scaffold protein (Kowalczyk et al., 2013; H. M. [START_REF] Maric | Molecular basis of the alternative recruitment of GABA A versus glycine receptors through gephyrin[END_REF]H. Maric, Mukherjee, Tretter, Moss, & Schindelin, 2011;G. Meyer, Kirsch, Betz, & Langosch, 1995;[START_REF] Mukherjee | The residence time of GABAARs at inhibitory synapses is determined by direct binding of the receptor α1 subunit to gephyrin[END_REF][START_REF] Tretter | The Clustering of GABA(A) Receptor Subtypes at Inhibitory Synapses is Facilitated via the Direct Binding of Receptor α2 Subunits to Gephyrin[END_REF][START_REF] Tretter | Molecular Basis of the γ-Aminobutyric Acid A Receptor α3 Subunit Interaction with the Clustering Protein Gephyrin[END_REF][START_REF] Tretter | Gephyrin, the enigmatic organizer at GABAergic synapses[END_REF].

Moreover, another study showed that GABAAR α5 subunits also localize at synaptic sites through direct interaction with gephyrin [START_REF] Brady | Synaptic Localization of α5 GABA(A) Receptors via Gephyrin Interaction Regulates Dendritic Outgrowth and Spine Maturation[END_REF].

GABAAR γ2 subunits do not bind gephyrin directly, but they are essential for the synaptic clustering of GABAAR [START_REF] Essrich | Postsynaptic clustering of major GABA A receptor subtypes requires the γ2 subunit and gephyrin[END_REF]. Recent studies have shown that γ2-containing GABAARs cluster at inhibitory synapses through a gephyrinindependent mechanism. More specifically, GABAAR γ2 subunits interact with neuroligin-2 via GABAAR regulatory Lhfpl (GARLH) [START_REF] Davenport | An Essential Role for the Tetraspanin LHFPL4 in the Cell-Type-Specific Targeting and Clustering of Synaptic GABAAReceptors[END_REF][START_REF] Heller | The Biochemical Anatomy of Cortical Inhibitory Synapses[END_REF][START_REF] Yamasaki | GARLH Family Proteins Stabilize GABAA Receptors at Synapses[END_REF]. While the majority of γ2 subunits localize at the synaptic sites, they are also found at extra-synaptic sites, as well as α1-2 and β3 subunits [START_REF] Kasugai | Quantitative localisation of synaptic and extrasynaptic GABA A receptor subunits on hippocampal pyramidal cells by freeze-fracture replica immunolabelling[END_REF]. In contrast, δ subunit-containing

GABAARs are believed to be exclusively located at extra-synaptic sites. These synaptic and extra-synaptic receptor populations produce two types of currents: phasic and tonic. Presynaptic release of GABA first activates synaptic receptors, inducing fast and transient (phasic) currents. The extracellular, diffuse GABA can then activate extra-synaptic receptors, producing persistent (tonic) inhibitory currents.

GlyRs have relatively simple structure and composition. Only five subunits have been identified, including four α subunits (α1-4) and one β subunit. The GlyR heteropentamers consists of α and β subunit, of which the stoichiometry is proposed to be either 2α:3β or 3α:2β [START_REF] Burzomato | Stoichiometry of recombinant heteromeric glycine receptors revealed by a pore-lining region point mutation[END_REF][START_REF] Durisic | Stoichiometry of the Human Glycine Receptor Revealed by Direct Subunit Counting[END_REF][START_REF] Grudzinska | The beta Subunit Determines the Ligand Binding Properties of Synaptic Glycine Receptors[END_REF][START_REF] Langosch | Conserved quaternary structure of ligand-gated ion channels: the postsynaptic glycine receptor is a pentamer[END_REF]Patrizio et al. 2017;[START_REF] Yang | Stoichiometry and subunit arrangement of α1β glycine receptors as determined by atomic force microscopy[END_REF]. GlyR β subunit has a strong affinity for the scaffold protein gephyrin, but none of the four α subunits directly binds to gephyrin. The β subunit therefore underlies the synaptic location of GlyRs, by its strong binding to the gephyrin scaffold [START_REF] Kim | Deciphering the structural framework of glycine receptor anchoring by gephyrin[END_REF]. GlyRs can also form homopentamers with only α

subunits, which localize at extra-synaptic sites and mediate tonic current [START_REF] Dutertre | Inhibitory glycine receptors: An update[END_REF][START_REF] Legendre | The glycinergic inhibitory synapse[END_REF][START_REF] Lynch | Molecular structure and function of the glycine receptor chloride channel[END_REF]).

1. 1.2 The lateral diffusion of the receptors underlying inhibitory synaptic plasticity Single Quantum-Dot tracking of GlyRs at the neuronal plasma membrane revealed that synaptic and extra-synaptic receptors are not stationary but undergo lateral diffusion in the membrane with different dynamics [START_REF] Dahan | Diffusion Dynamics of Glycine Receptors Revealed by Single-Quantum Dot Tracking[END_REF].

Lateral diffusion also leads to the translocation of the receptors between extrasynaptic and synaptic sites. Lateral diffusion is powered by both thermal Brownian diffusion and reversible trapping through receptor-scaffold interactions (Choquet and Triller 2013;[START_REF] Salvatico | Synaptic receptor dynamics: From theoretical concepts to deep quantification and chemistry in cellulo[END_REF]. At synaptic sites, the steric hindrance in the crowed environment and trapping by interacting with scaffold proteins can lower the synaptic receptor mobility, compared to extra-synaptic receptors [START_REF] Li | Protein Crowding within the Postsynaptic Density Can Impede the Escape of Membrane Proteins[END_REF][START_REF] Renner | Diffusion Barriers Constrain Receptors at Synapses[END_REF]. Given that there is a substantial amount of extra-synaptic receptors [START_REF] Kasugai | Quantitative localisation of synaptic and extrasynaptic GABA A receptor subunits on hippocampal pyramidal cells by freeze-fracture replica immunolabelling[END_REF], the lateral diffusion of membrane receptors provides a mechanism for the regulation of synaptic receptor content and synaptic plasticity, in addition to endocytosis and exocytosis [START_REF] Gerrow | Synaptic stability and plasticity in a floating world[END_REF][START_REF] Triller | New Concepts in Synaptic Biology Derived from Single-Molecule Imaging[END_REF]. It has been proposed that the fast trafficking of membrane receptors by lateral diffusion can replace the desensitized receptors at synaptic sites and thus participate in the high frequency synaptic activity, although this concept is still debated [START_REF] Choquet | Fast AMPAR trafficking for a high-frequency synaptic transmission[END_REF]Delgado & Selvin, 2018). Nevertheless, the study of receptor dynamics has provided new ideas about synaptic plasticity [START_REF] Dumoulin | Cellular transport and membrane dynamics of the glycine receptor[END_REF][START_REF] Renner | Molecular dynamics of postsynaptic receptors and scaffold proteins[END_REF].

In hippocampal neurons, increased excitatory activity by several different pharmacological treatments resulted in larger diffusion coefficient of GABAAR γ2

subunits at both synaptic and extra-synaptic sites [START_REF] Bannai | Activity-Dependent Tuning of Inhibitory Neurotransmission Based on GABAAR Diffusion Dynamics[END_REF]. In addition, the mobility of synaptic GABAARs can be modulated by their agonists, antagonists and allosteric modulators. For instance, the GABAAR agonist muscimol increases the diffusion coefficient of GABAAR α1, α2, α5 and γ2 subunits, while the positive allosteric modulator diazepam abolishes the effect of muscimol and reduces the GABAAR diffusion coefficient [START_REF] Gouzer | Benzodiazepine-dependent stabilization of GABA A receptors at synapses[END_REF].

During sustained neuronal activity by 4-AP treatment, diazepam reduced the lateral diffusion and stabilized GABAARs at synapses [START_REF] Lévi | Benzodiazepine ligands rapidly in fluence GABAA receptor diffusion and clustering at hippocampal inhibitory synapses[END_REF]. It is noteworthy that different regulation of GABAAR subunit mobility was reported in these studies. This was also shown in a recent study revealing that the diffusion properties of GABAARs are dependent on the subunit composition [START_REF] Hannan | GABAAR isoform and subunit structural motifs determine synaptic and extrasynaptic receptor localisation[END_REF]. Moreover, excitatory synaptic activity has been shown to modulate the diffusion dynamics of GlyRs. Reduced excitatory activity increased the diffusion coefficient of GlyR α1 subunits, but not GABAAR γ2 subunits in spinal cord neurons [START_REF] Lévi | Homeostatic Regulation of Synaptic GlyR Numbers Driven by Lateral Diffusion[END_REF]. Furthermore, increased microglia activity reduced the diffusion coefficient of GlyR α1 subunits, but did not affect GABAAR γ2 subunits (Cantaut-Belarif et al., 2017). Taken together, the diffusion dynamics of GABAARs and GlyRs are differently regulated in spinal cord neurons, where both types of receptors can co-exist at the same inhibitory PSDs (see section 1.3).

These data suggest that the lateral diffusion of inhibitory synaptic receptors indeed participates in the regulation of inhibitory neurotransmission.

The physiological functions of GABAARs and GlyRs and their implications in neurological disorders

During development, the intracellular chloride ion level is elevated in immature neurons. Therefore, the opening of the ion channels of GABAARs and GlyRs leads to chloride efflux. This causes depolarization of the neurons and increases the neuronal excitability (reviewed in [START_REF] Bowery | GABA and glycine as neurotransmitters: A brief history[END_REF][START_REF] Dutertre | Inhibitory glycine receptors: An update[END_REF][START_REF] Legendre | The glycinergic inhibitory synapse[END_REF][START_REF] Spitzer | How GABA generates depolarization[END_REF]. During the second postnatal week, a transition from depolarizing to hyperpolarizing occurs. This is due to the increased expression of the potassium-chloride co-transporter (KCC2), which pumps chloride out of the cytosol [START_REF] Kaila | Cationchloride cotransporters in neuronal development, plasticity and disease[END_REF]. In mature neurons, the activation of GABAARs and GlyRs thus causes chloride ion influx and therefore hyperpolarization of the membrane potential. The membrane hyperpolarization reduces the excitability of the neurons, thus inducing neuronal inhibition.

A balanced excitation and inhibition in the neuronal circuits is important for the brain function, and its disruption may underlie various neurological disorders. For example, an imbalance of excitation and inhibition is a shared pathophysiological mechanism for autism spectrum disorders and schizophrenia (reviewed in [START_REF] Gao | Common Mechanisms of Excitatory and Inhibitory Imbalance in Schizophrenia and Autism Spectrum Disorders[END_REF][START_REF] Nelson | Excitatory/Inhibitory Balance and Circuit Homeostasis in Autism Spectrum Disorders[END_REF]. Disrupted inhibitory control via GABAergic neurotransmission plays important roles in these pathologies (reviewed in [START_REF] Pizzarelli | Alterations of GABAergic Signaling in Autism Spectrum Disorders[END_REF][START_REF] Selten | Inhibitory control of the excitatory/inhibitory balance in psychiatric disorders[END_REF]. Moreover, the reduction in GABAergic neurotransmission is also a key feature of epilepsy, in addition to an elevated glutamatergic activity (reviewed in [START_REF] Bonansco | Plasticity of Hippocampal Excitatory-Inhibitory Balance: Missing the Synaptic Control in the Epileptic Brain[END_REF][START_REF] Fukata | Epilepsy and synaptic proteins[END_REF][START_REF] Treiman | GABAergic mechanisms in epilepsy[END_REF][START_REF] Wong | Too Much Inhibition Leads to Excitation in Absence Epilepsy[END_REF]. The reduction in GABAergic inhibition leads to abnormally high neuronal activity, thus seizures. Promoting GABAergic inhibition, such as by benzodiazepines that are positive allosteric modulators of GABAARs can suppress seizures. GABAARs have been extremely important therapeutic targets for these neurological diseases.

GlyRs are mainly expressed in the hindbrain and spinal cord. They are also implicated in numerous neurological disorders involving the motor control and sensory information processing. For example, several missense mutations in the GlyR α1 subunit gene underlie human hyperekplexia, also known as startle syndrome or stiff baby syndrome [START_REF] Legendre | The glycinergic inhibitory synapse[END_REF][START_REF] Lynch | Molecular structure and function of the glycine receptor chloride channel[END_REF][START_REF] Schaefer | Impaired Glycine Receptor Trafficking in Neurological Diseases[END_REF]. Hyperekplexia is characterized by exaggerated startle response to mild stimuli and muscle rigidity. Moreover, the GlyR α3 subunits has been implicated in the inflammatory pain sensation in the spinal cord dorsal horn [START_REF] Betz | Glycine receptors: Recent insights into their structural organization and functional diversity[END_REF][START_REF] Harvey | Glycine transporters as novel therapeutic targets in schizophrenia, alcohol dependence and pain[END_REF]. GlyR α3 knock out animals not only had lower glycinergic neurotransmission in response to inflammatory signals, but also reduced pain sensation [START_REF] Harvey | GlyR α3: An Essential Target for Spinal PGE 2-Mediated Inflammatory Pain Sensitization[END_REF]. So far, GlyRs are not as well investigated as GABAARs as therapeutic targets.

1.2 Gephyrin: a master regulator at inhibitory synapses Gephyrin is expressed in almost all organisms, across bacteria, plants and animals.

In mammals, it is detected in various non-neuronal tissues, including liver, heart, stomach and so on [START_REF] Nawrotzki | Expression and subcellular distribution of gephyrin in non-neuronal tissues and cells[END_REF]. Gephyrin is a moonlighting protein as it has multiple functions in the non-neuronal organs and the central nervous system. In non-neuronal cells, gephyrin is responsible for the last step of molybdenum cofactor (Moco) synthesis [START_REF] Schwarz | Molybdenum cofactors, enzymes and pathways[END_REF]. Recently, gephyrin was also implicated in the regeneration of pancreatic β cell from α cells induced by the antimalarial drug artemisinins [START_REF] Li | Artemisinins Target GABA(A) Receptor Signaling and Impair alpha Cell Identity[END_REF]. Though gephyrin is critical in non-neuronal cells, I will not further discuss this topic. Instead, I will focus on its roles in the central nervous system, where gephyrin is a key regulator at inhibitory synapses. Among all the PTMs, the identified phosphorylation sites are almost exclusively located in the C-domain, including the prominent phosphorylation site at the serine amino acid residue at the position 270 (S270). The binding of the monoclonal antibody mAb7a is dependent on S270 phosphorylation [START_REF] Kuhse | Phosphorylation of gephyrin in hippocampal neurons by cyclindependent kinase CDK5 at Ser-270 is dependent on collybistin[END_REF]. Moreover, gephyrin undergoes alternative splicing that controls its folding, receptor binding and synaptic clustering [START_REF] Herweg | Splice-specific Glycine Receptor Binding, Folding, and Phosphorylation of the Scaffolding Protein Gephyrin[END_REF]. The splicespecific control of gephyrin clustering is thought to act through conformational changes in the C-domain.

Biochemical studies have shown that gephyrin molecules can form stable trimers through their G-domain interaction, and that weak E-domain interactions can also induce dimerization [START_REF] Saiyed | Molecular Basis of Gephyrin Clustering at Inhibitory Synapses[END_REF][START_REF] Sola | Structural basis of dynamic glycine receptor clustering by gephyrin[END_REF]. Therefore, it has been proposed that gephyrin molecules form a hexagonal lattice under the post-synaptic membrane (Kneussel & Betz, 2000a, 2000b). This hypothesis is consistent with the observation of a confined gephyrin distribution in a thin layer in electron microscopy [START_REF] Triller | Distribution of glycine receptors at central synapses: an immunoelectron microscopy study[END_REF]. Structural analysis of gephyrin revealed that the trimers can have different compactness by the different extension status of the C-domain (Figure 2.B). This gives room to the possibility that synaptic gephyrin can be distributed heterogeneously in the planar lattice (Figure 2.C). Indeed, super-resolution optical imaging revealed the heterogeneous distribution of gephyrin molecules at the inhibitory PSDs (Pennacchietti et al., 2017a).

(A) (B) (C)
The fluorescence recovery after photobleaching (FRAP) of live cells has revealed the dynamics of synaptic scaffolds (reviewed in [START_REF] Specht | The dynamics of synaptic scaffolds[END_REF]. FRAP imaging has shown that excitatory scaffold proteins such as PSD-95, GKAP and Shank are unstable at the synapses and have different turnover rates [START_REF] Kuriu | Differential Control of Postsynaptic Density Scaffolds via Actin-Dependent and -Independent Mechanisms[END_REF]. Moreover, the dendritic spines undergo morphological changes regulated by actin skeleton in a calcium-dependent manner (reviewed in [START_REF] Oertner | Calcium regulation of actin dynamics in dendritic spines[END_REF]. In addition, the spine head, PSD and pre-synaptic bouton exhibit correlated enlargement induced by two-photon glutamate uncaging [START_REF] Meyer | Balance and stability of synaptic structures during synaptic plasticity[END_REF].

Similarly, inhibitory synaptic scaffolds also undergo dynamic morphological changes. Venus tagged gephyrin clusters exhibit rapid lateral motion along the dendritic shafts on the time scale of seconds, and this movement is calcium dependent and controlled by the cytoskeleton [START_REF] Hanus | Activity-Dependent Movements of Postsynaptic Scaffolds at Inhibitory Synapses[END_REF]. Interestingly, YFP-tagged gephyrin clusters show fusing and splitting behavior that are resulted from their lateral movement on a time scale of hours [START_REF] Dobie | Inhibitory Synapse Dynamics: Coordinated Presynaptic and Postsynaptic Mobility and the Major Contribution of Recycled Vesicles to New Synapse Formation[END_REF].

Importantly, the lateral movement of gephyrin clusters is closely correlated with the pre-synaptic bouton marked by vesicular GABA transporter (VGAT) labeling [START_REF] Dobie | Inhibitory Synapse Dynamics: Coordinated Presynaptic and Postsynaptic Mobility and the Major Contribution of Recycled Vesicles to New Synapse Formation[END_REF][START_REF] Kuriu | Activity-dependent coordinated mobility of hippocampal inhibitory synapses visualized with presynaptic and postsynaptic tagged-molecular markers[END_REF]. In other words, gephyrin scaffolds are not stationary, but mobile entities on the dendritic shaft. Recent super-resolution optical imaging further revealed the continuous morphological changes within gephyrin clusters (Rodriguez et al., 2017;Specht et al. 2013), which will be discussed in detail later.

The roles of gephyrin in the regulation of synaptic GlyRs and GABAARs

Gephyrin has been shown to play an important role in the regulation of GlyRs and GABAARs (reviewed in Alvarez 2017; Groeneweg et al. 2018;[START_REF] Kasaragod | Structure-Function Relationships of Glycine and GABAA Receptors and Their Interplay With the Scaffolding Protein Gephyrin[END_REF][START_REF] Tyagarajan | Gephyrin: a master regulator of neuronal function?[END_REF]. The phosphorylation, nitrosylation and palmitoylation of gephyrin has been implicated in the synaptic re-organization and plasticity of GABAergic synapses (Dejanovic et al. 2014;Dejanovic and Schwarz 2014;[START_REF] Zacchi | Gephyrin phosphorylation in the functional organization and plasticity of GABAergic synapses[END_REF]. Gephyrin has a strong binding affinity for GlyR β subunits, but does not directly interact with GlyR α subunits [START_REF] Kim | Deciphering the structural framework of glycine receptor anchoring by gephyrin[END_REF]. As a scaffold protein, gephyrin provides common binding sites for GlyR β subunits and GABAAR α1-3 and β2-3 subunits, with lower affinity for the GABAAR subunits (Kowalczyk et al., 2013;[START_REF] Maric | Molecular basis of the alternative recruitment of GABA A versus glycine receptors through gephyrin[END_REF]Maric et al., 2011;[START_REF] Meyer | Identification of a gephyrin binding motif on the glycine receptor b subunit[END_REF][START_REF] Mukherjee | The residence time of GABAARs at inhibitory synapses is determined by direct binding of the receptor α1 subunit to gephyrin[END_REF][START_REF] Tretter | The Clustering of GABA(A) Receptor Subtypes at Inhibitory Synapses is Facilitated via the Direct Binding of Receptor α2 Subunits to Gephyrin[END_REF][START_REF] Tretter | Molecular Basis of the γ-Aminobutyric Acid A Receptor α3 Subunit Interaction with the Clustering Protein Gephyrin[END_REF]. The direct binding of receptors with gephyrin is important for their synaptic clustering and is dependent on the oligomerization of gephyrin [START_REF] Calamai | Gephyrin Oligomerization Controls GlyR Mobility and Synaptic Clustering[END_REF][START_REF] Mukherjee | The residence time of GABAARs at inhibitory synapses is determined by direct binding of the receptor α1 subunit to gephyrin[END_REF]. Moreover, the receptor binding pocket of gephyrin is in close proximity with the E-domain dimerization motif [START_REF] Grünewald | Sequences Flanking the Gephyrin-Binding Site of GlyRβ Tune Receptor Stabilization at Synapses[END_REF].

Thus receptor binding may also contribute to the gephyrin clustering at synaptic sites.

Glycinergic and GABAergic mixed inhibitory synapses

The co-transmission of multiple neurotransmitters from pre-synaptic neurons is a common phenomenon in the mammalian central nervous system (reviewed in [START_REF] Granger | Multi-transmitter neurons in the mammalian central nervous system[END_REF][START_REF] Tritsch | Mechanisms and functions of GABA co-release[END_REF][START_REF] Vaaga | Dual-transmitter neurons: Functional implications of co-release and co-transmission[END_REF]. The co-release of GABA with other neurotransmitters such as glycine, glutamate and dopamine, as well as the cross-talk of GABAARs with the corresponding receptors, is likely to have a role in fine-tuning of synaptic transmission and plasticity (reviewed in [START_REF] Shrivastava | GABA(A) receptors: postsynaptic co-localization and cross-talk with other receptors[END_REF][START_REF] Tritsch | Mechanisms and functions of GABA co-release[END_REF]. The disruption of the balanced cotransmission may underlie neurological disorders. For instance, the GABA/glutamate co-release has been implicated in animal models of depression as well as cocaine withdrawal [START_REF] Meye | Shifted pallidal co-release of GABA and glutamate in habenula drives cocaine withdrawal and relapse[END_REF][START_REF] Shabel | Mood regulation. GABA/glutamate co-release controls habenula output and is modified by antidepressant treatment[END_REF], and the cotransmission of GABA/dopamine in the olfactory bulb might be modulated in Parkinson disease [START_REF] Doty | Olfaction in Parkinson's disease and related disorders[END_REF][START_REF] Ross | Association of olfactory dysfunction with risk for future Parkinson's disease[END_REF]. However, the implications of co-transmission in neurological diseases are so far still poorly described and require further investigation.

In the context of my study, I am particularly interested in the co-release of glycine and GABA, which is a particular case of co-transmission. In this section, I particularly introduced the evidence of GABA/glycine co-release and GABAAR/GlyR co-existence at the same synapses, which are referred to as mixed inhibitory synapses. I also discussed about our current knowledge of the mechanisms underlying the regulation of mixed inhibitory synapses.

1.3.1 The evidence of GABAAR/GlyR colocalization and GABA/glycine corelease GABA and glycine are the two main inhibitory neurotransmitters mediating fast inhibition in the central nervous system (see review [START_REF] Bowery | GABA and glycine as neurotransmitters: A brief history[END_REF]. It was first described in spinal cord neurons that glycine and GABA are co-released from the same pre-synaptic terminal, and that their respective receptors, GlyRs and GABAARs, co-exist at the same post-synaptic density (Figure 3.A; [START_REF] Bohlhalter | Inhibitory neurotransmission in rat spinal cord: co-localization of glycine-and GABAA-receptors at GABAergic synaptic contacts demonstrated by triple immunofluorescence staining[END_REF][START_REF] Lévi | Synaptic Control of Glycine and GABAA Receptors and Gephyrin Expression in Cultured Motoneurons[END_REF][START_REF] Mitchell | An immunocytochemical study of glycine receptor and GABA in laminae I-III of rat spinal dorsal horn[END_REF][START_REF] Todd | Colocalization of GABA, glycine, and their receptors at synapses in the rat spinal cord[END_REF][START_REF] Triller | Gamma-Aminobutyric Acidcontaining Terminals Can Be Apposed to Glycine Receptors at Central Synapses[END_REF]. Here, I refer to these GABAergic and glycinergic synapses as mixed inhibitory synapses.

Numerous subsequent studies have investigated the GABA/glycine co-release in various regions in the central nervous system, including the auditory network [START_REF] Fischl | Glycinergic transmission modulates GABAergic inhibition in the avian auditory pathway[END_REF][START_REF] Lim | GABA mediates presynaptic inhibition at glycinergic synapses in a rat auditory brainstem nucleus[END_REF][START_REF] Lu | Glycinergic Transmission Shaped by the Corelease of GABA in a Mammalian Auditory Synapse[END_REF][START_REF] Moore | Corelease of Inhibitory Neurotransmitters in the Mouse Auditory Midbrain[END_REF][START_REF] Nabekura | Developmental switch from GABA to glycine release in single central synaptic terminals[END_REF][START_REF] Nerlich | Dynamic Fidelity Control to the Central Auditory System: Synergistic Glycine/GABAergic Inhibition in the Cochlear Nucleus[END_REF], respiratory network [START_REF] Rahman | Mixed miniature postsynaptic currents resulting from co-release of glycine and GABA recorded from glycinergic neurons in the neonatal respiratory network[END_REF], brain stem motor nucleus [START_REF] Muller | Developmental dissociation of presynaptic inhibitory neurotransmitter and postsynaptic receptor clustering in the hypoglossal nucleus[END_REF][START_REF] O'brien | The Nonuniform Distribution of the GABA(A) Receptor α1 Subunit Influences Inhibitory Synaptic Transmission to Motoneurons within a Motor Nucleus[END_REF][START_REF] Russier | GABA and glycine co-release optimizes functional inhibition in rat brainstem motoneurons in vitro[END_REF], cerebellum [START_REF] Dugué | Target-Dependent Use of Coreleased Inhibitory Transmitters at Central Synapses[END_REF][START_REF] Dumoulin | IPSC Kinetics at Identified GABAergic and Mixed GABAergic and Glycinergic Synapses onto Cerebellar Golgi Cells[END_REF][START_REF] Rousseau | Mixed Inhibitory Synaptic Balance Correlates with Glutamatergic Synaptic Phenotype in Cerebellar Unipolar Brush Cells[END_REF], and even in a small population of hippocampal neurons [START_REF] Lévi | Gephyrin Is Critical for Glycine Receptor Clustering But Not for the Formation of Functional GABAergic Synapses in Hippocampal Neurons[END_REF]. The prevalence of the GABA/glycine co-transmission in the hindbrain and spinal cord emphasizes its functional importance for inhibitory regulation of the motor and sensory systems.

In the spinal cord, GABA/glycine co-transmission has been reported in the lamina I-II interneurons in the dorsal horn, and in the motor neurons and interneurons in the ventral horn [START_REF] Geiman | Glycine and GABAA receptor subunits on renshaw cells: Relationship with presynaptic neurotransmitters and postsynaptic gephyrin clusters[END_REF]Gonzalez-Forero et al. 2005;Jonas et al., 1998;[START_REF] Keller | Region-specific developmental specialization of GABA-glycine cosynapses in laminas I-II of the rat spinal dorsal horn[END_REF][START_REF] Lévi | Synaptic Control of Glycine and GABAA Receptors and Gephyrin Expression in Cultured Motoneurons[END_REF][START_REF] O'brien | The Nonuniform Distribution of the GABA(A) Receptor α1 Subunit Influences Inhibitory Synaptic Transmission to Motoneurons within a Motor Nucleus[END_REF][START_REF] Russier | GABA and glycine co-release optimizes functional inhibition in rat brainstem motoneurons in vitro[END_REF]. The co-transmission mostly occurs during developmental stages, and starts to fade away during postnatal maturation [START_REF] Geiman | Glycine and GABAA receptor subunits on renshaw cells: Relationship with presynaptic neurotransmitters and postsynaptic gephyrin clusters[END_REF]Gonzalez-Forero and Alvarez, 2005;[START_REF] Keller | Region-specific developmental specialization of GABA-glycine cosynapses in laminas I-II of the rat spinal dorsal horn[END_REF]. However, though less prominent, GABA/glycine co-release is still observed in the adult animals [START_REF] Chéry | Junctional versus Extrajunctional Glycine and GABA A Receptor-Mediated IPSCs in Identified Lamina I Neurons of the Adult Rat Spinal Cord[END_REF][START_REF] Coull | Trans-synaptic shift in anion gradient in spinal lamina I neurons as a mechanism of neuropathic pain[END_REF][START_REF] Dufour | Mixed GABA-glycine synapses delineate a specific topography in the nucleus tractus solitarii of adult rat[END_REF]. Furthermore, different isoforms of GlyRs and GABAARs have distinct expression levels. In the spinal cord dorsal horn, GlyR α1 and β subunits are highly expressed while the expression of α2 and α3 subunits is relatively low [START_REF] Anderson | Different forms of glycine-and GABAA -receptor mediated inhibitory synaptic transmission in mouse superficial and deep dorsal horn neurons[END_REF]. GlyR α3 expression is restricted to a population of neurons in the spinal cord dorsal horn [START_REF] Harvey | GlyR α3: An Essential Target for Spinal PGE 2-Mediated Inflammatory Pain Sensitization[END_REF]. GABAAR α2, β3 and γ2 subunits have relatively higher expression in the spinal cord neurons than the other GABAAR subunits such as α1 and β2 subunits [START_REF] Anderson | Different forms of glycine-and GABAA -receptor mediated inhibitory synaptic transmission in mouse superficial and deep dorsal horn neurons[END_REF]. These GABAAR subunits also exhibit region-specific expression levels between superficial dorsal horn and deep dorsal horn. Taken together, the evidence points out that the GABA/glycine cotransmission may play important roles in the sensory and motor control.

Electrophysiology experiments have also further demonstrated the co-release of GABA and glycine from the same pre-synaptic terminals and the same individual vesicles [START_REF] Chéry | Junctional versus Extrajunctional Glycine and GABA A Receptor-Mediated IPSCs in Identified Lamina I Neurons of the Adult Rat Spinal Cord[END_REF]Jonas et al., 1998;[START_REF] O'brien | Cotransmission of GABA and Glycine to Brain Stem Motoneurons[END_REF]. Glycinergic and GABAergic mIPSCs have distinct kinetics, with glycinergic mIPSCs exhibiting larger amplitudes and fast decay, while GABAergic mIPSCs have a lower amplitude and a longer decay time (Figure 3.B). The combination of glycinergic and GABAergic transmission leads to elevated amplitudes and prolonged decay times, producing a stronger inhibition.

The mixed glycinergic/GABAergic transmission may thus underlie the precise control of synaptic inhibition (Alvarez, 2017). Therefore, it is not surprising that the malfunction in the co-transmission of GABA and glycine has been implicated

in numerous pathologies such as neuropathic pain, analgesia and amyotrophic lateral sclerosis [START_REF] Coull | Trans-synaptic shift in anion gradient in spinal lamina I neurons as a mechanism of neuropathic pain[END_REF][START_REF] Imlach | Glycinergic dysfunction in a subpopulation of dorsal horn interneurons in a rat model of neuropathic pain[END_REF][START_REF] Medelin | Altered development in GABA co-release shapes glycinergic synaptic currents in cultured spinal slices of the SOD1G93A mouse model of amyotrophic lateral sclerosis[END_REF][START_REF] Takazawa | Inhibition Mediated by Glycinergic and GABAergic Receptors on Excitatory Neurons in Mouse Superficial Dorsal Horn Is Location-Specific but Modified by Inflammation[END_REF][START_REF] Takazawa | Synaptic pathways and inhibitory gates in the spinal cord dorsal horn[END_REF][START_REF] Zeilhofer | GABAergic analgesia: new insights from mutant mice and subtype-selective agonists[END_REF].

The synaptic control of GABA/glycine co-transmission

Our current knowledge about mixed inhibitory synapses mostly comes from electrophysiological studies, and relates to the pre-synaptic control of GABA and glycine neurotransmitters. There are only a handful of studies about the regulation of the respective receptors, GlyRs and GABAARs, at mixed inhibitory PSDs.

The pre-synaptic control of GABA and glycine neurotransmitter loading and release has been relatively well investigated (see review Aubrey 2016). The cytosolic supply of GABA and glycine in the pre-synaptic terminal is regulated by separate mechanisms. The supply of GABA is achieved by a combination of enzymatic biosynthesis from glutamate and the uptake by the relevant membrane transporters, while the supply of glycine is mostly dependent on its membrane transport (Figure 3.A). In contrast, the loading of GABA and glycine to the synaptic vesicles depends on the same vesicular transporter known as vesicular inhibitory amino acid transporter (VIAAT, or VGAT) [START_REF] Chaudhry | The Vesicular GABA Transporter, VGAT, Localizes to Synaptic Vesicles in Sets of Glycinergic as Well as GABAergic Neurons[END_REF][START_REF] Dumoulin | Presence of the vesicular inhibitory amino acid transporter in GABAergic and glycinergic synaptic terminal boutons[END_REF][START_REF] Gasnier | The loading of neurotransmitters into synaptic vesicles[END_REF][START_REF] Wojcik | A Shared Vesicular Carrier Allows Synaptic Corelease of GABA and Glycine[END_REF]. The sharing of a common vesicular transporter indicates that the release of GABA and glycine from the pre-synaptic terminals is under the same control by calcium signals.

Therefore, the GABA/glycine content in individual vesicles and the number and distribution of the respective receptors at the post-synaptic density may be the main determinants of co-transmission. Indeed, the relative content of GABA and glycine within the vesicles has a large variability, with pure glycine-containing, pure GABA-containing and GABA/glycine-containing vesicles existing in the same pre-synaptic terminals (Aubrey and Supplisson, 2018;[START_REF] Katsurabayashi | Distinct profiles of refilling of inhibitory neurotransmitters into presynaptic terminals projecting to spinal neurones in immature rats[END_REF]. Moreover, acute changes of cytosolic GABA or glycine concentrations rapidly change the IPSC amplitudes in an activity-independent manner [START_REF] Apostolides | Rapid, Activity-Independent Turnover of Vesicular Transmitter Content at a Mixed Glycine/GABA Synapse[END_REF]. This suggests that the vesicular GABA/glycine is not saturated, and that rapid modulation of evoked IPSCs can be achieved by modulating the vesicular and cytosolic neurotransmitter content. The cytosolic GABA and glycine content is most likely regulated by the glycine transporters (GlyT2) and glutamate transporters (EAAT3) [START_REF] Apostolides | Rapid, Activity-Independent Turnover of Vesicular Transmitter Content at a Mixed Glycine/GABA Synapse[END_REF][START_REF] Aubrey | The Transporters GlyT2 and VIAAT Cooperate to Determine the Vesicular Glycinergic Phenotype[END_REF][START_REF] Ishibashi | Dynamic regulation of glycine-GABA co-transmission at spinal inhibitory synapses by neuronal glutamate transporter[END_REF]. Therefore, the cause for the various profiles of vesicular content can be the different affinity of VIAAT for GABA and glycine [START_REF] Juge | Vesicular inhibitory amino acid transporter is a Cl-/γ-aminobutyrate cotransporter[END_REF][START_REF] Mcintire | Identification and characterization of the vesicular GABA transporter[END_REF][START_REF] Sagné | Cloning of a functional vesicular GABA and glycine transporter by screening of genome databases[END_REF]. Another possibility could be that the expression level of VIAAT is different across individual vesicles, which remains to be examined.

At the mixed PSDs, the lateral mobility and synaptic accumulation of GlyRs, but not GABAARs, is regulated by NMDAR-dependent excitatory activity [START_REF] Lévi | Homeostatic Regulation of Synaptic GlyR Numbers Driven by Lateral Diffusion[END_REF]. Moreover, microglia can tune the lateral mobility of GlyRs and glycinergic IPSCs through prostaglandin E2, while it has no effect on GABAARs (Cantaut-Belarif et al., 2017). This evidence suggests that GlyRs and GABAARs are differentially regulated at mixed inhibitory synapses. Furthermore, gephyrin antisense oligonucleotides strongly reduce the amplitudes (and frequency) of glycinergic currents, but not GABAergic amplitudes [START_REF] Van Zundert | Glycinergic and GABAergic synaptic transmission are differentially affected by gephyrin in spinal neurons[END_REF].

This indicates that gephyrin differentially modulates synaptic GlyRs and GABAARs. Since GlyRs and GABAARs bind to the same receptor binding sites of gephyrin with different affinities (see above), there is possibly a direct competition between GlyRs and GABAARs for the synaptic binding sites (Specht et al., 2013). However, other gephyrin-independent clustering mechanisms must also be taken into account.

The molecular mechanisms underlying mixed inhibitory synapses are still elusive, especially regarding the cooperation or competition between GlyRs and GABAARs. How GlyRs and GABAARs are differently regulated at the same PSD and what role gephyrin plays in this process is still poorly understood. their ultra-structural details (reviewed in [START_REF] Hell | The 2015 super-resolution microscopy roadmap[END_REF][START_REF] Laine | From single-molecule spectroscopy to super-resolution imaging of the neuron: A review[END_REF]Schermelleh et al., 2010;Sieben et al. 2018;[START_REF] Sydor | Super-Resolution Microscopy: From Single Molecules to Supramolecular Assemblies[END_REF]Turkowyd et al., 2016;[START_REF] Vangindertael | Methods and Applications in Fluorescence -An introduction to optical super-resolution microscopy for the adventurous biologist[END_REF]. STED microscopy adopts donut-shaped illumination with excitation light in the center and a longer wavelength light to deplete the emission fluorescence at the perimeter (Klar et al., 2000). Thus, STED illumination only excites and records the signals from the fluorophores at the center of the illumination, and the super-resolution is achieved by tuning the laser powers to minimize the donut center. Super-resolution images are reconstructed after scanning the whole structure. SIM uses a different type of illumination structure with a known, high spatial frequency pattern to scan the samples (Gustafsson, 2000). This produces moiré fringe, from which the substructure information can be extracted and super-resolution images is therefore reconstructed. SIM and STED microscopy both circumvent the diffraction limit and achieve better resolution than conventional microscopy.

SMLM includes several related techniques, e.g. stochastic optical reconstruction microscopy (STORM), photo-activated localization microscopy (PALM) and universal point accumulation imaging in nanoscale topography (uPAINT) (Betzig et al. 2006;Giannone et al. 2010;[START_REF] Heilemann | Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes[END_REF]Hess et al., 2006;Rust et al., 2006).These different forms of SMLM all share the same basic principles. A detailed discussion about the advantages and limitations inherent to each form is written in a book chapter in the forthcoming book Single Molecule Microscopy in Neurobiology edited by Yasushi Okada and Nobuhiko Yamamoto (also see Appendix 5). Here, I only discuss about STORM and PALM, which is more related in my project.

In The main difference between STORM and PALM is the labeling technique, where organic dyes are used in STORM and fluorescent proteins are used in PALM to label the target proteins (Betzig et al., 2006;[START_REF] Heilemann | Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes[END_REF]Hess et al., 2006;Rust et al., 2006). Both organic dyes and fluorescent proteins can be induced to blink under different conditions. In STORM, the blinking of fluorescent dyes requires particular imaging buffers and high illumination intensity. In PALM, the blinking of fluorescent proteins is achieved by sequential photo-activation/conversion with UV light in buffered salt solutions. PALM is thus preferable for live-cell imaging, because the STORM imaging buffers are detrimental to living cells. However, labeling with fluorescent proteins requires the expression of recombinant fusion proteins, which may potentially change the target protein's function. Moreover, organic dyes usually have narrower excitation/emission spectra and higher photon emission compared to fluorescent proteins. Therefore, STORM is more suitable for multi-color imaging and to gain a better localization precision.

There are two versions of STORM imaging, N-STORM (Rust et al., 2006) and direct STORM (dSTORM) [START_REF] Heilemann | Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes[END_REF]. N-STORM adopts a pair of dyes, one with a short wavelength (green) and another with a longer wavelength (red), to act as activator and reporter, respectively. The pair of dyes are conjugated to the same antibody at close proximity. During imaging, a red laser is used to excite the red dyes and to switch them into the OFF state, while a green laser at low intensity excites the green dyes that in turn activate the red fluorophores. Note that no UV light is needed in N-STORM. For dual-color imaging, the two target proteins are labeled with the same reporter dye (e.g. Alexa Fluor 647), but different activator dyes are used to selectively visualize the two targets (Bates et al., 2007). There are, however, a few drawbacks of using activator/reporter dye pairs in STORM. Firstly, it is difficult to control precisely the ratio of activator and reporter dyes on the same antibody. Secondly, by using two wavelengths for a single target protein, N-STORM leaves limited options of dyes for multi-color imaging. Thirdly and most importantly, the commonly used reporter dye Alexa

Fluor 647 can blink spontaneously in the imaging buffer without laser activation, making N-STORM susceptible to contaminating detections from the other channel. This is particularly problematic for dense structures such as synaptic protein assemblies, because there is no efficient way to identify and remove the contaminating detections. In dSTORM, only single fluorophores (reporter dyes) are used for one color imaging, and their activation is tuned by a low dose of UV light and reducing agents in the imaging buffer. The spectral separation of the fluorophores affords a larger choice of dyes, making dSTORM more favorable for implementing dual-color super-resolution imaging of synaptic proteins.

The challenges of applying two-color dSTORM to synapses

The superior resolving power of SMLM makes it a powerful tool to study small structures such as synapses. However, its application to synapses is accompanied by many challenges arising from the techniques themselves and from the particular properties of synapses.

Challenges posed by technical limitations

Several technical limitations are inherent to SMLM (Figure 5.A). Ideally, the target structures are densely labeled with fluorophores, and single molecule detections of these fluorophores are precisely localized. From the abundant localizations, the fine structure is then reconstructed (Figure 5. A1). This ideal scheme of SMLM can be hampered by many factors during experimental practice. First, the localization precision is generally limited in a range of 10-40 nm (Schermelleh et al., 2010;Sieben et al., 2018;Turkowyd et al., 2016). Limited localization precision can blur the fine structure of the targets and attenuate the resolution (Figure 5.A2). Second, the labeling density of fluorophores needs to be extremely high in order to obtain abundant localizations (Baddeley and Bewersdorf, 2018;Deschout et al., 2014;[START_REF] Van De Linde | The effect of photoswitching kinetics and labeling densities on super-resolution fluorescence imaging[END_REF][START_REF] Shivanandan | Accounting for Limited Detection Efficiency and Localization Precision in Cluster Analysis in Single Molecule Localization Microscopy[END_REF]. This requirement is often compromised in biological samples due to the inefficiency of the labeling strategies, for example due to epitope masking. Sparse labeling leads to insufficient detections and therefore poorly represented target structures (Figure 5.A3). Third, many labeling strategies also suffer from the bulky size of the probes such as antibodies (Maidorn et al., 2016). The large size of the antibodies inevitably dis-locates the fluorophores from the epitopes, and consequently smears the detected structures structures, many images can be combined and averaged to gain detailed information (B1). For well-isolated complex structures, image averaging is not suitable and complex characterization approaches are needed (B2). For closely-spaced complex structures, little information can be extracted (B3). (Figure 5.A4). Fourth, the photo-blinking behavior of fluorophores is a major concern in SMLM. The blinking performance of fluorophores is dependent on their photo-physical and photo-chemical properties and can be modulated with imaging buffer components and by adjusting the strength of the laser illumination (Annibale et al., 2011;Dempsey et al., 2011;Endesfelder et al., 2011;[START_REF] Ha | Photophysics of Fluorescence Probes for Single Molecule Biophysics and Super-Resolution Imaging[END_REF][START_REF] Lehmann | Novel organic dyes for multicolor localization-based super-resolution microscopy[END_REF][START_REF] Van De Linde | The effect of photoswitching kinetics and labeling densities on super-resolution fluorescence imaging[END_REF]van de Linde et al., , 2011;;Nahidiazar et al., 2016;Pennacchietti et al., 2017b;[START_REF] Wang | Characterization and development of photoactivatable fluorescent proteins for single-molecule-based superresolution imaging[END_REF]. Sub-optimal imaging conditions cause clustering artefacts that lead to false representation of the target structure (Annibale et al., 2011a;Burgert et al, 2015;[START_REF] Endesfelder | Art and artifacts in single-molecule localization microscopy: beyond attractive images[END_REF].

Besides, the photo-switching of fluorophores can give rise to repetitive detections of the same molecule, and the stochasticity of the blinking behavior adds to the uncertainty in the reconstructed structures. As a result, targets with identical structure may not give rise to the same spatial representation or number of detections (Figure 5.B1).

In the scheme of multicolor SMLM, each channel submits to the same challenges mentioned above. However, additional sources of errors due to the multicolor scheme increase the difficulties in SMLM, such as the registration of two channels [START_REF] Annibale | Identification of the factors affecting co-localization precision for quantitative multicolor localization microscopy[END_REF]. Specifically, among different sources of artifacts in two-color dSTORM, the compatibility of fluorescent dyes in the same imaging system is a key player during data acquisition (Dempsey et al. 2011;Nahidiazar et al. 2016;[START_REF] Xu | Stochastic optical reconstruction microscopy (STORM)[END_REF]. Even under optimized imaging conditions, various dyes can produce different representations of the same structure (Dempsey et al., 2011). In conclusion, two-color dSTORM is faced with greater challenges and optimizations need to be taken for reliable data acquisition and interpretation.

Challenges posed by the characters of synapses

Different biological structures have distinctive features that introduce particular concerns in SMLM (Figure 5.B). Baddeley and Bewersdorf classified the analyzed structures into three categories (Baddeley and Bewersdorf, 2018). For well-isolated stereotypic structures such as the nuclear pore complex (NPC), many detected structures can be combined and averaged to gain finer structural information (Figure 5.B1). This averaging strategy can compensate for the technical limitations in SMLM in order to resolve the accurate structures, lowering the demands for detection efficiency. However, this requires accurate averaging algorithms during the image processing. For well-isolated complex structures such as gene loci and synapses, each detected structure needs to be treated as individuals rather than be averaged due to the large variability of these structures (Figure 5.B2). To segment these individual complexes from the whole image and to characterize their variable structural features is very demanding in terms of image processing and statistical analysis (Nicovich et al., 2017). For closely spaced complex structures such as condensed chromatin, resolving the structure is difficult and only limited information can be extracted from the SMLM data (Figure 5.B3). This classification of analyzed structures is a good way to illustrate the different difficulties in SMLM applications.

The study of synapses using SMLM imaging is particularly challenging due to the technical limitations discussed above, as well as the special properties of synapses.

From an ultra-structural point of view, the unique character of synaptic protein assemblies pose particular challenges to the acquisition of reliable SMLM data and to the data interpretation. First of all, the size of synapses is of the same order as the diffraction limit, with only a few hundred nanometers in diameter, which makes it difficult to obtain temporally and spatially separated sparse blinking events during imaging (Harris and Stevens, 1989;[START_REF] Harris | Ultrastructure of Synapses in the Mammalian Brain[END_REF]Specht et al., 2013).

Even when the overall blinking in the field of view (FOV, here referring to the whole images under the recording) is sparse, there can still be many overlapping blinking events at synaptic sites. The assignment of multiple signals to a single detection necessarily causes wrong representations in the super-resolved image.

Second, synapses have large variability in the cluster morphology and protein copy numbers (Fukazawa and Shigemoto 2012;Masugi-Tokita et al. 2007;Nusser et al., 1997Nusser et al., , 1998;;Sheng and Kim 2011;Specht et al. 2013;Sugiyama et al. 2005;Tarusawa et al. 2009). As a consequence, individual synaptic clusters cannot be combined and population averaging is not applicable. Each synaptic cluster needs to be treated individually, which increases the difficulties of data analysis.

Furthermore, given the stochastic nature inherent to dSTORM, the variations in detection efficiency and localization density of synaptic clusters in the same images (i.e. FOVs) can be large, not to mention the variations among different images (i.e.

FOVs). Third, even though synapses are densely packed compartments, the copy numbers of many of their components are quite small, with receptors in the order of tens and scaffold proteins in order of hundreds of molecules per. Small numbers of target proteins make it difficult to attain sufficiently dense labeling and hence efficient sampling of the synapse in order to reconstruct a representative superresolution image. Fourth, synaptic proteins undergo dynamic changes and reorganization during synaptic plasticity, which adds to the difficulties of SMLM data interpretation. Given the variability and plasticity of synapses, high throughput analysis of large number of synapses are needed for meaningful data interpretation.

Fifth, the characterization of the internal synaptic organization from the dSTORM data is subject to the difficulties faced by the third category structures (closely spaced complex structures) (Figure 5.C).

In addition to the aspects mentioned above, the detection of two synaptic components in parallel dramatically increases the complexity of the experiments.

On the one hand, it requires the achievement of good imaging quality of two channels, which is depend on the compatibility of fluorescent dyes, imaging buffer, laser and microscope setups. On the other hand, it demands the implementation of more complicated data processing and analysis tools. In summary, extraordinary scrutiny is required to assess the quality of the imaging data and the image processing methods in the study of synaptic structures.

Intra-synaptic heterogeneity and sub-synaptic domains as regulators of synaptic functions

Despite of all the challenges and difficulties, synapses are of great interest in SMLM due to their small size and important roles in neuronal activity. SMLM investigations have provided important insights into synaptic function. Dani and colleagues have examined pre-and post-synaptic protein assemblies with SMLM, confirming a layered molecular organization of different synaptic components (Dani et al., 2010). This arrangement may explain the different kinetic properties of excitatory scaffold proteins, where proteins that are located at a greater distance from the synaptic membrane have faster exchange rates [START_REF] Kuriu | Differential Control of Postsynaptic Density Scaffolds via Actin-Dependent and -Independent Mechanisms[END_REF][START_REF] Sharma | Postsynaptic protein mobility in dendritic spines: Long-term regulation by synaptic NMDA receptor activation[END_REF]. SMLM has also been used to determine the absolute numbers of scaffold proteins and neurotransmitter receptor complexes at synapses, providing new types of quantitative information about the regulation of synaptic structure and function [START_REF] Fricke | One, two or three? Probing the stoichiometry of membrane proteins by single-molecule localization microscopy[END_REF]Patrizio et al. 2017;[START_REF] Specht | Quantitative Nanoscopy of Inhibitory Synapses : Counting Gephyrin Molecules and Receptor Binding Sites[END_REF].

A very interesting discovery is the observation of sub-synaptic domains (SSDs)

with super-resolution optical microscopy. In 2013, several groups reported independently that different synaptic proteins are distributed heterogeneously at synapses (MacGillavry et al., 2013;Nair et al., 2013;Specht et al., 2013). SMLM images showed that the excitatory scaffold protein PSD-95 occupies subdomains within the post-synaptic density that regulate AMPAR clustering (MacGillavry et al., 2013;Nair et al., 2013). The existence of PSD-95 subdomains was confirmed with STED microscopy both in vitro and in vivo (Broadhead et al., 2016;Dzyubenko et al., 2016;Hruska et al., 2018;Masch et al., 2018;Wegner et al., 2018). Likewise, sub-synaptic domains of gephyrin were shown to play a role in inhibitory plasticity at GABAergic synapses (Pennacchietti et al., 2017a). These findings point towards a mechanism whereby sub-synaptic domains drive the recruitment of neurotransmitter receptors to specific locations within the PSD, thus regulating synaptic transmission. Furthermore, using multicolor 3D-STORM,

Tang and colleagues demonstrated that the sub-synaptic domains of several presynaptic proteins are aligned with those of post-synaptic proteins, an arrangement that is referred to as trans-synaptic nanocolumn (Tang et al., 2016). Therefore, it was proposed that the subsynaptic spatial organization can regulate the synaptic strength and plasticity (Figure 6.A). Synaptic adhesion molecules are likely to coordinate these trans-synaptic complexes. SMLM and STED microscopy have also shown that pre-synaptic proteins of the active zone (AZ) and synaptic adhesion proteins display sub-synaptic distributions (Chamma et al., 2016 a, b;Glebov et al. 2017;Haas et al. 2018;Perez de Arce et al. 2015;Tang et al. 2016).

These exciting observations not only demonstrate the power of SMLM to visualize the ultra-structures of synapses, but also point towards possible roles of sub-synaptic domains in synaptic function (reviewed in Biederer et al., 2017;Chen et al., 2018;Liu et al, 2017;Scheefhals and MacGillavry 2018). Electron microscopy of symmetric synapses has revealed a discontinuous network of filaments at the inhibitory PSD and in the synaptic cleft (High et al., 2015;Linsalata et al., 2014). Super-resolution optical microscopy confirmed that the inhibitory scaffold protein gephyrin forms heterogeneous distributions and subsynaptic domains (Figure 6.B; Dzyubenko et al. 2016;Pennacchietti et al., 2017a;Specht et al. 2013). SMLM imaging in cultured hippocampal neurons further revealed that extra-synaptic gephyrin molecules are recruited to synaptic sites during NMDA-induced inhibitory long-term potentiation (Pennacchietti et al., 2017a). The increase in molecule density was accompanied by an increased fraction of gephyrin clusters with multiple SSDs. More recently, Crosby et al conducted a comprehensive analysis of pre-and postsynaptic components using 3D-SIM, reaching a resolution of ~120 nm laterally and ~300 nm axially (Crosby et al., 2019). It was shown that GABAARs form SSDs with an average diameter of ~300 nm that are closely associated with SSDs of gephyrin and pre-synaptic RIM1/2. This implies the existence of trans-synaptic nanocolumns as an organizing principle of inhibitory synapses. Given that the measured size of the SSDs was close to the resolution limit, the concept of nanocolumns at inhibitory synapses will require further validation. Nonetheless, these studies strongly suggest that the internal organization of inhibitory synapses plays an important role in regulating synaptic transmission.

At mixed inhibitory synapses, GlyRs and GABAARs co-exist at the same PSDs that are juxtaposed to the pre-synaptic terminals where GABA and glycine are coreleased from the same vesicles (Figure 3). Once released from the pre-synaptic terminals, the neurotransmitters can diffuse quickly in the synaptic cleft. The effective range for the neurotransmitters to activate their receptors is about 125 nm in radius (Delgado & Selvin, 2018). This creates a particular situation, where the exact position of GlyRs and GABAARs relative to the presynaptic vesicle release site can have a strong impact on the efficacy of the agonists and thus the activity of the receptors. However, we do not yet know whether GlyRs and GABAARs are totally intermingled or whether they form SSDs at mixed synapses.

And if they do form distinct SSDs, how are they related to the SSDs of gephyrin, as well as the pre-synaptic machinery? Through its capacity to resolve the spatial organization at single molecule level, SMLM may provide answers to these open questions.

Aims of this study

As discussed above, my goal was to investigate the spatial relationship between GlyRs and GABAARs, as well as between the receptors and gephyrin at mixed inhibitory synapses. Due to the co-existence of GlyRs and GABAARs at the same PSDs, the spatial relationship between the two receptors and the pre-synaptic vesicle release sites can be important for tuning the efficacy of GlyRs and GABAARs. Thus, I have also examined the spatial relationship between the receptors and the vesicle release sites. My ultimate objective is to understand how neuronal activity regulates and is regulated by the spatial relationship of inhibitory synaptic components.

To realize this investigation, I first adopted the primary spinal cord neuron culture as an appropriate model for mixed inhibitory synapses. In cultured spinal cord neurons, about 70% of the inhibitory synapses are mixed inhibitory synapses [START_REF] Dumoulin | Formation of mixed glycine and GABAergic synapses in cultured spinal cord neurons[END_REF]. Secondly, I developed an optimized two-color dSTORM imaging and data analysis protocol that is suitable for the synaptic structures.

Thirdly, based on this methodology of two-color dSTORM imaging of synapses, I carefully examined the ultra-structure of synaptic protein assemblies and their spatial relationships at mixed inhibitory synapses. I was especially interested in the changes of the spatial relationship between synaptic components during altered neuronal network activity. To this aim, I used pharmacology approaches to change the excitatory activity. My experimental results provide a novel perspective for understanding the mechanisms underlying mixed inhibitory synapses.

Chapter 2 Primary spinal cord neuron cultures were prepared from embryonic Sprague Dawley rats on day 14 of gestation (E14) as described previously (Specht et al., 2013), with some modifications.

Glass coverslips (18 mm

A = ε c l
where A is the absorption at the indicated wavelength , ε the molar extinction coefficient, c the concentration and l the path length (1 mm). A molar extinction coefficient of 195000 M -1 cm -1 was estimated for IgG and 130000 M -1 cm -1 for the Cy3B dye. From the molar concentration, the number of dyes per IgG was calculated. The estimated dye/IgG ratio was between 3 and 5 for all the experiments. Five minutes after the administration of the drug, another 3-min long movie was recorded. A third movie was recorded between 18 and 21 minutes.

The calcium signals were measured by drawing mask regions of the cell bodies manually and then measuring the integrated fluorescence intensity in the regions using Fiji [START_REF] Schindelin | Fiji: An opensource platform for biological-image analysis[END_REF] Ten or twenty images were taken with an exposure time of 50 ms, and always in the order of far-red, followed by the red and the green channels. For a given set of experiments, all the imaging parameters were kept constant.

The stacks of images were combined by average projection of the ten (or twenty) frames in Fiji. Then, binary masks of the clusters were produced with the wavelet function of the Spot detector in Icy, using a size filter to remove the clusters smaller than 3 pixels (De [START_REF] Chaumont | Icy: An open bioimage informatics platform for extended reproducible research[END_REF]. The integrated intensity within the binary masks were measured by a lab-made program [START_REF] Hennekinne | Differential Control of Thrombospondin over Synaptic Glycine and AMPA Receptors in Spinal Cord Neurons[END_REF] written in Matlab (MathWorks). Cluster intensities were not corrected for background. For comparing the relative amount the proteins, binary clusters produced from one of the channels were used as masks for all the channels imaged.

2.6 Two-color dSTORM imaging and analysis

Microscope setup

The stochastic optical reconstruction microscopy (STORM) setup is built on the inverted Nikon Eclipse Ti microscope with a perfect focusing system (PFS) to minimize the axial drift of the microscope stage, and equipped with a 100× oil immersion objective (HP APO TIRF 100× oil, NA 1.49, Nikon). The setup includes several continuous laser lines with emission wavelengths at 640 nm, 561 nm, 532 nm, 488 nm and 405 nm (Coherent), with nominal maximum power of 1W, 1W, 500 mW, 500 mW and 120 mW, respectively. The lasers are combined in an external platform and controlled via an acousto-optic tunable filter (AOTF)

to apply fast illumination pulses and to set the laser intensity. The laser beam is expanded with a total internal reflection fluorescence (TIRF) arm that focuses the beam on the back focal plane of the objective and that controls the inclination angle of the excitation. The emission light is captured with an electron multiplying charge-coupled device (EMCCD) camera using a multiple wavelength dichroic mirror and appropriate band pass filters (Semrock FF02-684/24 for A647, FF01-607/36 for Cy3B) in the emission light path. Conventional fluorescence images were taken with a mercury lamp (Intensilight C-HGFIE, Nikon) that emits a broad spectrum of wavelengths, using the corresponding band pass filters (FF01-650/13 for A647, FF01-560/25 for Cy3B) in the excitation light path (see 2.5). All the elements of the microscope are controlled by NIS-Elements software (Nikon).

Sequential two-color dSTORM imaging

The direct STORM (dSTORM) imaging workflow is illustrated in Figure 7.A. On the day of imaging, immuno-labeled neurons on glass coverslips were incubated with 100 nm multicolor beads (Tetraspeck, ThermoFisher, Cat. No. T7279) diluted in PBS for 5-30 minutes (concentration at ~ 10 9 particles/ml). After washing with PBS to remove the unattached beads, the coverslips were mounted on glass slides with a cavity (diameter 15-18 mm, depth 0.6-0.8 mm) containing freshly prepared Gloxy imaging buffer (0.5 mg/ml glucose oxidase, 40 μg/ml catalase, 0.5 M D-glucose, 50 mM β-mercaptoethylamine (MEA) in PBS, pH 7.4). Gloxy buffer was freshly prepared on every day of imaging, and degased with N2. The coverslips were sealed on the glass slides with silicone rubber (Picodent twinsil speed 22).

Under the microscope, we searched for field of view (FOV) containing several synaptic clusters and multicolor beads that served as fiducial markers. During this time, the sample was illuminated with the mercury lamp instead of the lasers to minimize the bleaching of the fluorophores. Reference images (epifluorescence images) were taken in the far-red and in the red channel with the lamp, and also in the green channel when triple labeling was performed. The effective magnification of these images was 100x, resulting in a pixel size of 160 nm.

Sequential two-color dSTORM was then carried out first in the far-red channel for A647 signals, followed by the red channel for Cy3B. In the beginning of each recording, the field of view was briefly pre-bleached to push the fluorophores into the OFF state and to dampen the brightness of the beads. Then, 10000 to 30000 frames were recorded with an exposure time of 50 milliseconds. The frame numbers were adjusted and kept constant for each set of experiments to ensure comparability of the recordings. We used highly inclined laser illumination rather than total internal reflection fluorescence (TIRF) to reduce the background fluorescence while avoiding interferences and inhomogeneous illumination. The estimated illumination intensity on the field of view was ~1 kW/cm 2 for the 640 nm laser, and ~2 kW/cm 2 for the 561 nm laser. The blinking of Cy3B was supported by low-intensity activation with the 405 nm laser (~ 0.03 to 0.30 kW/cm 2 ). The total acquisition time needed for each ROI was about half an hour to one hour.

dSTORM image processing and data analysis

The most critical processing steps of the raw imaging data comprise single particle detection, drift correction, two-channel registration and image reconstruction (Figure 7). Single particle detection was realized by using an adapted version of the multiple-target tracing (MTT) program [START_REF] Sergé | Dynamic multitarget tracing to probe spatiotemporal cartography of cell membrane[END_REF] in Matlab 2012b (MathWorks), as described by Izeddin et al [START_REF] Izeddin | Super-resolution dynamic imaging of dendritic spines using a low-affinity photoconvertible actin probe[END_REF].

In this program, a Gaussian fitting was applied to each fluorophore signal to determine its coordinates and intensity (number of photons collected). The localization precision of A647 and Cy3B signals was estimated from the signal intensities, using the detections of individual fluorophores at synapses imaged under the two-color dSTORM mode as follows:

/ √n, where is the width (standard deviation) of the Gaussian fit of the detections, and n is the number of photons. The localization precision obtained was 6 ± 3 nm for A647 signals, and 7 ± 2 nm for Cy3B signals (n = 12 synapses).

The lateral drift was corrected with the program PALMvis [START_REF] Lelek | Superresolution imaging of HIV in infected cells with FlAsH-PALM[END_REF] in Matlab 2012b using the multicolor beads as fiducial markers. A sliding window of 100 to 1000 frames was applied to calculate the average position of the beads throughout the recording. The systematic x/y drift over time was thus estimated with a temporal resolution of 5-50 s based on the average displacement of the beads (Figure 8). The calculated drift of the stage was then applied to the whole image to correct the coordinates of all the single fluorophore localizations in each image frame. At this stage, we obtain the final coordinates of the localizations in both channels. The coordinates-based data was then imported into the SRclusters program (Delestro and Genovesio, IBENS, unpublished) for the two-channel registration by aligning the positions of the beads in the two channels, followed by further cluster analysis (Figure 12). Cluster analysis in SRclusters includes cluster size measurement, spatial correlation analysis and SSD segmentation (see section

3.1.2.3).
Alternatively, the drift-corrected coordinates-based data are converted into rendered super-resolution images by representing each localization as a Gaussian function with the same intensity and standard deviation. The standard deviation σ = 15 nm was used even though the estimated localization precision was 6-7 nm, in order to account for the less bright detections. Super-resolution images are generally displayed with a pixel size of 10 nm to balance file size versus representation precision. Finally, the two super-resolution images were aligned using the TurboReg plugin [START_REF] Thevenaz | A Pyramid Approach to Sub-Pixel Registraion based on Intensity[END_REF] in Fiji before further cluster analysis (Figure 10, see section 3.1.2.1).

Statistical analysis

All statistical analysis was performed in Microsoft Excel (Microsoft). Student's t-

test

Chapter 3 3 Results

As discussed in the introduction, the application of two-color dSTORM to synaptic structures is faced with challenges from both inherent technical limitations and biological constraints. In my project, the first step was to obtain an optimized two-color dSTORM imaging protocol and analysis tools that are suited for synaptic structures. Then this methodology was applied to investigating the ultrastructure of mixed inhibitory synapses, in order to understand the mechanisms underlying the inhibitory co-transmission. The results are presented in two parts in the following, methodological results followed by results on mixed inhibitory synapses.

Methodological evaluation: two-color dSTORM of synapses

We adopted two-color dSTORM imaging to study the endogenous synaptic proteins Here, the optimized protocol from imaging to data analysis is introduced, supported by experimental data. These results address the importance of performing control experiments in SMLM and our strategies presented here can be easily adapted to other SMLM studies.

An optimized sequential two-color dSTORM imaging protocol

After some trials and failures in testing the fluorescent dye pairs, imaging buffers, laser illumination schemes and relevant imaging parameters, one configuration stood out to be a good choice (Figure 7). We chose Alexa Fluor 647 (A647) in the far-red channel and Cy3B in the red channel (see Methods for sample preparation and imaging procedure). A647 and Cy3B are both carbocyanine dyes and are very sensitive to oxygen bleaching and to UV activation (Dempsey et al., 2011). The imaging buffer contains an oxygen-scavenging system for the removal of oxygen achieved by Glucose Oxidase and Catalase, and also the reducing agent βmercaptoethylamine (MEA) to help the photo-switching of the dyes (Gloxy buffer) (Dempsey et al., 2011;[START_REF] Izeddin | Super-resolution dynamic imaging of dendritic spines using a low-affinity photoconvertible actin probe[END_REF]Nahidiazar et al., 2016). We found that A647 dyes generally have more photo-switching cycles and can produce good detections of the synapses without UV activation in the Gloxy buffer compared to Cy3B. Hence, in our sequential two-color imaging procedure, we first record A647 signals without UV activation and then record Cy3B signals with fine-tuned UV activation. Moreover, in order to correct the lateral drift and to accurately register the two channels in the imaging data, we incubated the samples with fluorescent beads before mounting the coverslip on the microscope. The beads were detected continuously during the image recording, and were used as fiducial markers for drift correction and two-channel registration.

Fluorescent beads as fiducial marker

Drift correction and two-channel registration are important factors for reconstructing super-resolution images as well as co-localization analysis of synaptic components [START_REF] Annibale | Identification of the factors affecting co-localization precision for quantitative multicolor localization microscopy[END_REF]. Since the recordings can take up to 30 minutes, the microscope stage can drift dramatically in the x/y plane due to mechanical and thermal effects (Figure 8.A). The perfect focusing system of our Nikon Ti microscope minimizes the drift in the axial direction. However, the lateral drift can still lead to false structural representations that demand correction. For stereotypic or densely labeled structures, the drift correction can be achieved by using the structures themselves as landmarks. But in the case of sparse synaptic clusters, there are too few detections for precise drift correction using the clusters themselves. Beads which are stably attached to the coverslip and continuously detected during the recording track the lateral drift of the stage precisely and therefore are better option for drift correction in fixed samples (Figure 8.B). For sequential two-color dSTORM, two-channel registration is also mandatory, because both lateral drift and spectral differences result in a mismatch between the reconstructed images in the two channels. Using beads as independent fiducial markers are more precise in the two-channel alignment than using the synaptic clusters. In conclusion, beads are important tools in our two-color dSTORM protocol.

Performance evaluation of chosen dyes

In addition to the signal to noise ratio (SNR), the dSTORM image quality depends largely on the photo-blinking behavior of the dyes, which is influenced by many factors (e.g. chemical traits of the dyes, imaging buffer composition, laser power).

The evaluation of the individual factors is important for understanding their influence on the quality of the dSTORM data. However, the final image quality depends on the combined effect of these factors that are heavily inter-dependent. To assess the performance of the dyes, we carried out the optimized two-color dSTORM protocol using two secondary antibodies, conjugated with either A647 or Cy3B.

Clean glass coverslips were incubated for one hour with A647-conjugated donkey anti-mouse IgG and Cy3B-conjugated donkey anti-rabbit IgG that were diluted in 3% BSA and mounted with beads and Gloxy buffer. We then performed sequential two-color dSTORM imaging of these secondary antibodies. We characterized the clusters produced by single secondary antibodies in the two channels. Both A647-IgG and Cy3B-IgG produced nanometer scale clusters with a wide variability in the number of detections per cluster as well as cluster size (Figure 9.A). From the temporal profiles of the blinking events of a single secondary antibody probe, we can infer the photophysical behavior of the dyes. For A647, the blinking events are brief and dispersed across the entire recording (Figure 9.B). Cy3B fluorophores on the other hand display relatively few blinking events across the recording. Some

Cy3B dyes showed persistent ON times, resulting in bursts of repetitive detections of a single blinking event, contributing to artifactual cluster identification. As a consequence, Cy3B produces detection densities that are less quantitative than those of A647. The diameter of the clusters produced by single IgG in both channels is around 40 to 120 nm (Figure 9.C), which corresponds to the localization precision of the fluorophores. These apparent clusters are also similar to the size of the subsynaptic domains (SSDs) proposed for synaptic proteins (Haas et al., 2018;Macgillavry et al., 2013;Nair et al., 2013;Tang et al., 2016). Therefore, the detection density and the size of the clusters from single probes have to be taken into account during data analysis and data interpretation when characterizing subsynaptic domains.

Two-color dSTORM data analysis protocol

The first-hand data produced from SMLM recordings are the coordinates of the single molecule detections and the fluorescence intensity of the single signals. From the coordinates of the single molecules, an image containing the super-resolved fine structures can be rendered, with the pixel intensity representing the localization density [START_REF] Coltharp | Quantitative analysis of singlemolecule superresolution images[END_REF]. Both types of datasets are very useful for statistical analysis. Nevertheless, each type has its own strengths and drawbacks during analysis in practical terms.

The coordinates-based data in principle make the best use of single molecule localizations. However, the localization precision is usually neglected in the analytical methods developed so far. Moreover, cluster segmentation, feature extraction and spatial correlation analysis on the coordinates-based data demand qualified programming skills. On the other hand, the rendered super-resolution images give more intuitive information, and can be analyzed using common image processing tools that are widely known to researchers. More importantly, in the rendered images, pixel values represent the density of the localizations, taking into account the average localization precision. Since the SMLM coordinates are not the actual positions of the fluorophores but only calculated positions, they can also be understood as localization probabilities. The inclusion of the localization precision in image rendering thus produces more realistic representations. In addition, pixels that do not contain any localizations have the value of zero. In other words, there is no background in the rendered super-resolution images. Thus, there is no need for background subtraction or thresholding, which is often one of the major difficulties in fluorescent image processing.

In terms of available analysis tools, there are some open-source software for coordinates-based data, such as Clus-Doc and LAMA [START_REF] Andronov | ClusterViSu , a method for clustering of protein complexes by Voronoi tessellation in super-resolution microscopy[END_REF][START_REF] Levet | SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data[END_REF]Malkusch & Heilemann, 2016;[START_REF] Pageon | Clus-DoC: a combined cluster detection and colocalization analysis for single-molecule localization microscopy data[END_REF]. However, these tools are not very suited to the specific demands of synaptic clusters. In our case, we did not find any available analysis tools that would allow us to perform individual synapse-based analysis, which is essential for the comparison of two synaptic proteins at the same synapse (e.g. GlyRs and GABAARs at mixed synapses). As for the rendered images, individual synaptic clusters can be obtained and characterized with commonly used image processing tools, such as Fiji [START_REF] Schindelin | Fiji: An opensource platform for biological-image analysis[END_REF] and Icy [START_REF] De Chaumont | Icy: An open bioimage informatics platform for extended reproducible research[END_REF]. Image-based analysis of synaptic clusters is relatively straightforward and less demanding on programming skills. Therefore, we have built up a complete analysis workflow for image-based data analysis (Figure 10). In addition, we also developed a coordinate-based data analysis program together with the bioinformatics platform in our institution (Delestro and Genovesio, IBENS, Figure 12).

Image-based data analysis workflow

There are three main parts in this workflow: image alignment, segregation of the image into individual synaptic clusters, and synaptic cluster characterization (Figure 10.A).

Images requiring alignment include the rendered images from the two channels and the mask images produced from the epifluorescence images (Figure 10.B). The rendered images from the two channels have substantial mismatch due to the chromatic difference between the two channels and also the drift correction. The conventional epifluorescence images captured right before dSTORM imaging are used as references in order to identify synaptic clusters and to create binary masks for synaptic clusters. We adopt the combined mask image from the binary images to ensure the inclusion of the whole synaptic clusters from both channels (Figure 10.B). In this part of the workflow, the rendered images and the combined mask images are aligned with the aid of TurboReg plugin [START_REF] Thevenaz | A Pyramid Approach to Sub-Pixel Registraion based on Intensity[END_REF] in Fiji.

In the second part, the regions of interest (ROIs) representing individual synaptic clusters are chosen from the aligned mask images. These ROIs are applied to the aligned rendered images in order to crop the individual synaptic clusters in Fiji. On the images of individual synaptic clusters, regions outside the ROIs are cleared and the clusters with saturated pixels are removed from the analysis. The clusters with saturated pixels could be artefacts on the coverslip, or they could be caused by repetitive detections of some persistent blinking events (e.g. beads). At this point, we obtained individual synaptic cluster pairs of two synaptic proteins (e.g. GlyR and gephyrin), on which we can perform further cluster characterization.

The third part includes measuring the super-resolution synaptic cluster size, the spatial correlation between the two channels and the internal synaptic organization of the two target proteins.

To measure the cluster size, we applied a mild thresholding on the individual synaptic clusters and used Fiji Measure function. The purpose of thresholding is to reduce the impact from outlier detections around the synapses while remaining the most of the synaptic cluster area.

To characterize the spatial correlation of two synaptic proteins, we employed the intensity correlation analysis (ICA) plugin [START_REF] Khanna | N type Ca2 + channels and RIM scaffold protein covary at the presynaptic transmitter release face but are components of independent protein complexes[END_REF][START_REF] Li | A Syntaxin 1, Gαo, and N-Type Calcium Channel Complex at a Presynaptic Nerve Terminal: Analysis by Quantitative Immunocolocalization[END_REF].

The ICA plugin incorporates several different correlation analysis methods, including intensity correlation quotient (ICQ), Pearson's correlation coefficient (PCC), Mander's overlap coefficient (MOC) and Mander's correlation coefficient (MCC). ICQ measures the frequency of pixels which co-vary in intensity relative to their mean intensity in the two channels. ICQ values range between -0.5 and +0.5, where -0.5 indicates a mutually exclusive distribution, zero a random distribution, and +0.5 a perfectly positive correlation between the two proteins [START_REF] Li | A Syntaxin 1, Gαo, and N-Type Calcium Channel Complex at a Presynaptic Nerve Terminal: Analysis by Quantitative Immunocolocalization[END_REF].

PCC and MOC measure the linear correlation of the pixel intensity from the two channels [START_REF] Dunn | A practical guide to evaluating colocalization in biological microscopy[END_REF][START_REF] Mcdonald | Statistical tests for measures of colocalization in biological microscopy[END_REF] To characterize the internal synaptic organization of target proteins, we set out to segment the synaptic clusters into subsynaptic domains (SSD). An H-watershed method was adopted to segment SSDs by using the interactive H-watershed plugin developed by Benoit Lombardot (http://imagej.net/Interactive_Watershed) in Fiji.

H-watershed finds local maxima with one parameter, H, allowing the detection of intensity peaks and the segmentation of the subdomains from the whole synapse.

After SSD segmentation, the features of the binary SSDs are extracted using extended particle analyzer (Brocher, 2014) in Fiji. The number of SSDs per synapse and the size of the SSDs are obtained, which reflect the intra-synaptic protein organization.

Verification of the different intensity correlation approaches with an exemplary dataset

The methods provided in ICA plugin (i.e. PCC, MOC, MCC, ICQ) all have their own limitations in measuring the correlation of fluorescent signals in the two channels. Therefore, we tested the analytical capability of these methods using our experimental data.

We stained the synapses for RIM1/2 and gephyrin and then conducted two-color dSTORM on these samples. Because RIM1/2 is not specific to inhibitory synapses but also exists at excitatory synapses, we can divide the synapses into two groups: conditions. PCC and MOC did not detect a difference of spatial correlation between the two groups. Given that the detections of gephyrin clusters in group two are very few, as also seen from the cluster size, it is reasonable that the spatial correlation between gephyrin clusters and RIM1/2 clusters in group two could be lower than that of group one. Thus, the impact of few detections in one channel on the spatial correlation was detected by ICQ and MCC. However, the pixels with intensity close to the mean and the pixels with much higher intensity are treated as the same. MCC provides a more straightforward colocalization measurement and it is easy to interpret. The major drawback of MCC is that it is very sensitive to background noise, because it takes all the nonzero pixels in one channel to produce the mask for the other channel. Taken together with the results in our test, we employed the ICQ and MCC tests in our following experiments as a measure of spatial correlation.

The impact of low protein copy number on the imaging data

The low copy number of synaptic proteins impede the requirement of dense labeling in STORM. This challenge can be more evident when the synaptic protein is downregulated under certain conditions such as altered neuronal network activities.

When the synaptic cluster is weakly labeled with fluorescent dyes and the dyes do not have many blinking cycles, the majority of fluorescent signals can be lost during the pre-bleaching step and only a few detections are obtained during the recording.

When rendered, the few localizations will give rise to very weak clusters. For instance, we compared the rendered clusters of gephyrin and GABAARs both labeled with Cy3B. In this case, gephyrin clusters were densely labeled and had good brightness and contrast, while GABAAR clusters were weakly labeled and had poor brightness and contrast (Figure 13). Given the different blinking behaviors of the various dyes, the same structures may be detected and represented differently in the super-resolution images (Dempsey et al., 2011). Indeed, we found that the reconstructed structures of the same gephyrin clusters labeled with both A647 and Cy3B showed similar but not identical cluster patterns (Figure14.A). In this experiment, gephyrin was labeled with both A647 and Cy3B through a common primary antibody and two secondary antibodies in fact is caused by repetitive detections of a few blinking events only.

GPHN-A647 GPHN-Cy3B

The profile of localizations in GPHN-A647 cluster

The profile of localizations in GPHN-Cy3B cluster

The profile of localizations in the subdomain of GPHN-Cy3B cluster

GPHN-A647 GPHN-Cy3B GPHN-A647/Cy3B A B C
subdomain in the gephyrin-Cy3B cluster, we found that most of the localizations in this subdomain were concentrated in a few bursts. The bursts of detections likely resulted from the persistent fluorescence of the Cy3B dyes during the ON phase of the blinking cycles. It is noteworthy that the profiles of the localizations from A647 and Cy3B shown here corresponds well with their blinking behavior characterized with secondary antibodies (Figure 9). In sum, different dyes may produce nonidentical cluster patterns even for the same object.

Next, we quantitatively assessed the discrepancy of the cluster patterns detected with A647 and Cy3B. GlyRs were labeled, via one common primary antibody and two secondary antibodies conjugated with either A647 or Cy3B. GlyR clusters were well stained with good brightness and contrast in both channels according to the epifluorescence images (Figure 15.A). In the dSTORM images, GlyR-A647 clusters were generally well reconstructed. However, not all the GlyR-Cy3B clusters were properly reconstructed in the dSTORM image (Figure 15.A). As expected, the reconstructed GlyR clusters from A647 and Cy3B detections showed similar overall shape but differential internal signal distribution (Figure 15.B).

However, the measured size of the same GlyR clusters is much larger with A647 (0.107 ± 0.052 µm 2 , mean ± SD) than with Cy3B (0.044 ± 0.036 µm 2 ) (Figure 15.C).

We further looked into the internal organization of the GlyR clusters detected by these two dyes. We applied H-watershed to segment the subsynaptic domains (SSDs)

in GlyR-A647 and GlyR-Cy3B clusters, with the same parameters for both channels (Figure 15.D). The SSD numbers per synaptic cluster in GlyR-A647 and GlyR-Cy3B had a moderate correlation (Pearson's method, R 2 = 0.302). The average number of SSDs per synapse in the two channels were similar (GlyR-A647: 2.31 ± 1.23; GlyR-Cy3B: 2.20 ± 1.40). Notably, the measured size of the SSDs in GlyR-A647 (0.031 ± 0.023 µm 2 ) is much larger than those in GlyR-Cy3B (0.011 ± 0.011 µm 2 ). Altogether, our data showed that the different photophysical properties of A647 and Cy3B dyes have strong impact on the quantification of synaptic clusters in dSTORM images.

In the two experiments discussed above, we cannot rule out that the competition between the two secondary antibodies for the primary antibody binding sites can influence this observed discrepancy. However, we did not observe exclusive spatial We used two different primary antibodies which recognized different epitopes on gephyrin. Primary mouse monoclonal mAb7a (m7a) and rabbit polyclonal antibodies against gephyrin (rbGPHN) were labeled, in one case with Cy3Bconjugated donkey anti-mouse IgG and A647-conjugated donkey anti-rabbit IgG (condition 1), and in the other case with A647-conjugated donkey anti-mouse IgG and Cy3B-conjugated donkey anti-rabbit IgG (condition 2) (Figure 16). In agreement with our observations in Figure 15, gephyrin clusters detected with A647 and Cy3B exhibited similar overall shape but differential internal organization (Figure 16.A). Also, the size of the gephyrin clusters detected with A647 was consistently larger than those with Cy3B, regardless of the primary antibodies (rbGPHN-A647: 0.090 ± 0.047 µm 2 ; m7a-Cy3B: 0.040 ± 0.022 µm 2 ; m7a-A647: 0.079 ± 0.036 µm 2 ; rbGPHN-Cy3B: 0.051 ± 0.027 µm 2 ). Importantly, the spatial correlation in the second condition was significantly stronger than that in the first condition, as judged from the ICQ values (Figure 16.B). MCC analysis also revealed differences in the level of overlap from one channel to the other between the two labeling conditions. This raised alert to that the spatial correlation level can be changed simply by reversing the dyes on the target proteins.

On the other hand, we segmented the subsynaptic domains (SSDs) of gephyrin clusters (Figure 16.C). Taking into account the effects of the fluorophores as revealed in Figure 15, we adopted different H-watershed parameters that were optimized for A647 and Cy3B channels. In each labeling condition, there was a weak correlation of the SSD counts per synapse in the two channels (Pearson's, R 2 = 0.103 for condition 1, R 2 = 0.096 for condition 2). In fact, the SSD numbers per antibodies are influenced by the dyes different (rbGPHN-A647: 1.5 ± 0.8; m7a-Cy3B: 1.9 ± 1.1; m7a-A647: 2.1 ± 1.1; rbGPHN-Cy3B: 2.2 ± 1.2). SSD area detected by rbGPHN-A647 were significantly larger than by m7a-Cy3B (t-test, p < 0.00001), while the difference was not detected with the other antibody combination (t-test, p = 0.064).

synapse measured in the different conditions were very different (rbGPHN-A647:

1.5 ± 0.8; m7a-Cy3B: 1.9 ± 1.1; m7a-A647: 2.1 ± 1.1; rbGPHN-Cy3B: 2.2 ± 1.2) (Figure 16.D). Interestingly, the sizes of SSDs probed with rbGPHN-A647 were much larger than with m7a-Cy3B in the first condition. However, this difference was not seen in the second condition with reversed dye labeling. Therefore, the spatial correlation measurement and the SSD characterization can be influenced simply by reversing the dyes for the labeling.

To further validate our conclusions from the image-based analysis, the coordinatebased dataset from the same experiments was analyzed using SRclusters (Figure 17.A). The hull area of rbGPHN-A647 clusters was much larger than m7a-Cy3B clusters, while m7a-A647 clusters and rbGPHN-Cy3B clusters did not show much difference in size (rbGPHN-A647: 0.200 ± 0.072 µm 2 ; m7a-Cy3B: 0.090 ± 0.050 µm 2 ; m7a-A647: 0.121 ± 0.055 µm 2 ; rbGPHN-Cy3B: 0.120 ± 0.066 µm Apart from the impacts of the dyes on the detected clusters, these experiments also suggested that the gephyrin populations targeted by the two primary antibodies are not identical. If we assume that rbGPHN and m7a antibodies probe the same population of gephyrin molecules, it would be expected that the two labeling conditions would yield similar results in the spatial correlation and SSD measurements. However, it was not the case in our results. In fact, there was an obvious difference in the spatial correlation and SSD size between the two labeling counts per cluster are affected by the dyes, but to a lesser extent with the segmentation method in SRclusters (rbGPHN-A647: 1.9 ± 1.2; m7a-Cy3B: 1.7 ± 1.0; m7a-A647: 1.9 ± 1.1; rbGPHN-Cy3B: 2.0 ± 1.1) than that with H-Watershed (See Figure 16.D). The SSD hull area of rbGPHN-A647 (0.021 ± 0.016 µm 2 ) is much larger than that of m7a-Cy3B (0.012 ± 0.010 µm 2 ) in condition 1 (t-test, p < 0.00001). In condition 2, rbGPHN SSD area (0.014 ± 0.012 µm 2 ) is larger than that of m7a SSD (0.013 ± 0.010 µm 2 ) as well, but only with a small trend (t-test, p = 0.261). Note that the overall trend is similar as that in Figure 16.D. The dataset in this figure is the same as in Figure 16.

conditions. One possible explanation is that rbGPHN and m7a antibodies probe different sub-populations of gephyrin molecules. Indeed, the polyclonal rbGPHN antibodies target at the gephyrin E-domain, while the monoclonal m7a antibodies are known to target at a phosphorylated epitope (pS270) in gephyrin C-domain [START_REF] Kuhse | Phosphorylation of gephyrin in hippocampal neurons by cyclindependent kinase CDK5 at Ser-270 is dependent on collybistin[END_REF]. In addition, Cy3B dyes tend to produce more granular clusters, while A647 dyes tend to produce more homogeneous clusters. Taken together the effects of the dyes and the primary antibodies, the different results of labeling condition one and condition two suggest that not all the gephyrin S270 sites are phosphorylated and that the pS270 sites may be distributed heterogeneously at the gephyrin lattice at the PSDs. In cultured spinal cord neurons, the majority of inhibitory synapses contain GlyRs [START_REF] Dumoulin | Formation of mixed glycine and GABAergic synapses in cultured spinal cord neurons[END_REF]. Gephyrin as a scaffold protein for GlyRs has been described to have highly heterogeneous distribution and form SSDs (Pennacchietti et al., 2017a;Specht et al. 2013). Given the strong binding between GlyRs and gephyrin, we would expect that GlyR distribution exhibits similar heterogeneity at synaptic sites. To investigate the intra-synaptic organization of GlyRs at the PSD, we conducted two-color dSTORM imaging of GlyRs labeled with Cy3B and gephyrin with A647. The reconstructed clusters of GlyRs and gephyrin revealed the heterogeneity in the distribution of both proteins at synaptic sites (Figure 18.A). Generally speaking, the distribution of GlyRs matched well with gephyrin clusters. Meanwhile, from the intra-synaptic view, GlyRs form synaptic subdomains. The GlyR SSDs sometimes co-localize with gephyrin SSDs, and sometimes locate beside the gephyrin SSDs. To some extent, this mismatch can be explained by the three dimensional orientation of the synapses. Some of the PSDs were parallel to the imaging focal plane, exhibiting en face view, while others were seen from the side view (Figure 18.A). Given that only pS270 gephyrin molecules were labeled in this experiment, the mismatch of GlyR SSDs and gephyrin SSDs suggests that the formation of GlyR SSDs may not be fully dependent on gephyrin phosphorylation. The GlyR SSDs which are beside the gephyrin SSDs in these synapses may be the ones stabilized by other nonphosphorylated gephyrin molecules. In other words, despite the direct interaction between GlyRs and gephyrin, their distribution at the PSDs is not perfectly matched.

We further characterized the number of SSDs for both GlyR and gephyrin clusters. Most of the gephyrin synaptic clusters had one to three SSDs, while the majority of GlyR synaptic clusters had one to four SSDs (Figure 18.B). The SSD counts for gephyrin and GlyR clusters showed a weak positive correlation.

Moreover, the SSD counts of either gephyrin or GlyR synaptic clusters positively correlated with their synaptic cluster size (Figure 18.C), which is consistent with previous studies (Crosby et al., 2019;Fukata et al., 2013;Hruska et al., 2018;Nair et al., 2013). To summarize, our data suggest that GlyRs are organized heterogeneously and form SSDs at the inhibitory PSDs in spinal cord neurons.

GABAARs and GlyRs can occupy different spaces at mixed inhibitory

PSDs

In cultured spinal cord neurons, GABAARs and GlyRs co-exist at ~70% of the inhibitory PSDs [START_REF] Dumoulin | Formation of mixed glycine and GABAergic synapses in cultured spinal cord neurons[END_REF]. Whether these two types of receptors are homogeneously intermingled or they exclusively occupy different spaces at the PSD is not known. To investigate how these two types of receptors are organized at the same PSDs, we conducted two-color dSTORM imaging of GABAARs and GlyRs at the mixed inhibitory synapses. GABAARs were labeled with A647 and GlyRs with Cy3B. The reconstructed dSTORM images revealed that GABAARs have heterogeneous distribution and form SSDs at the inhibitory PSDs (Figure 19). The reconstructed structures of GlyR clusters further confirmed that GlyRs In sum, our data provided the first evidence for the intra-synaptic organization of the key proteins at mixed inhibitory synapses, namely GABAARs, GlyRs and gephyrin. These three synaptic proteins are heterogeneously distributed and form

#1 GABA A R GlyR #2 GABA A R GlyR #3 GABA A R GlyR #4 GABA A R GlyR #5 GABA A R GlyR #6 GABA A R GlyR #7 GABA A R GlyR
SSDs. And they have different spatial organization patterns at the PSD. Their distinct spatial organization patterns suggest that intra-synaptic protein redistribution might serve as a regulator for neuronal activity. Previous studies have

shown that GABAARs and GlyRs can be differentially regulated by neuronal activity in spinal cord neurons (Cantaut-Belarif et al., 2017;[START_REF] Lévi | Synaptic Control of Glycine and GABAA Receptors and Gephyrin Expression in Cultured Motoneurons[END_REF][START_REF] Lévi | Homeostatic Regulation of Synaptic GlyR Numbers Driven by Lateral Diffusion[END_REF]. Therefore, we next undertook an investigation of the intra-synaptic spatial organization of GABAARs and GlyRs under different neuronal network activities. The intensity ratio of gephyrin/GlyR showed that the intensity of gephyrin was reduced after 4-AP treatment, with respect to GlyRs at the same PSD. The lower intensity ratio of GABAAR/GlyR indicated that GABAARs were removed to a larger extent than GlyRs from the same PSD in response to 4-AP treatment. The intensity ratio of GABAAR/gephyrin was increased after 4-AP treatment. This suggested that gephyrin was reduced to a larger extent than GABAARs at the same PSD. In conclusion, increased neuronal activity by 4-AP treatment differentially regulated GABAAR and GlyR amounts at mixed inhibitory synapses, while reduced neuronal activity by TTX treatment did not have a strong effect on their synaptic contents.

Gephyrin phosphorylation level but not the total molecular amount was reduced by increased neuronal activity

The primary antibody m7a that was used in the previous experiments detects gephyrin molecules which are phosphorylated at S270 sites in the C-domain [START_REF] Kuhse | Phosphorylation of gephyrin in hippocampal neurons by cyclindependent kinase CDK5 at Ser-270 is dependent on collybistin[END_REF]. The phosphorylation at S270 sites has been shown to play an important role in regulating receptor clustering and inhibitory signaling (Groeneweg et al., 2018). The result in Figure 21 revealed that gephyrin immunoreactivity at synapses was reduced by 4-AP treatment. However, it did not determine whether gephyrin molecules were removed from synaptic sites or only the phosphorylation level was reduced in response to 4-AP treatment. Therefore, we conducted double immunocytochemistry of gephyrin with m7a antibody and another antibody recognizing gephyrin E-domain (rbGPHN). In principle, Gephyrin/GlyR rbGPHN antibodies should detect the entire gephyrin content (total gephyrin), while m7a antibodies detect the S270 phosphorylated gephyrin (p-gephyrin).

As expected, gephyrin clusters stained with rbGPHN and m7a matched very well with each other (Figure 22.A). We quantified the colocalization level of gephyrin clusters stained with the two different primary antibodies. For the large puncta, the binary clusters from the two channels always overlapped. The small puncta generally had low intensity and sometimes low contrast. Thus some of the small puncta were not detected in the second channel. Overall, the percentage of total gephyrin puncta overlapped with p-gephyrin puncta was around 85%, while that of p-gephyrin with total gephyrin was around 50% (Figure 22.B). The rbGPHN antibodies might also stain the extra-synaptic gephyrin molecules, increasing the fluorescence in the dendrites and decreasing the contrast of synaptic gephyrin clusters. On the other hand, the m7a antibodies might have high efficiency in recognizing their epitopes at gephyrin clusters. Thus, many small gephyrin clusters would be quantified with m7a staining but not with rbGPHN staining, which could result in a lower percentage in m7a (rbGPHN+) than in rbGPHN (m7a+) colocalization. Note that the treatment did not have dramatic effect on the degree of colocalization. Furthermore, we quantified the integrated intensity of the gephyrin clusters stained with these two primary antibodies. The total gephyrin stained with rbGPHN was not changed with either TTX or 4-AP treatment, while the gephyrin phosphorylation level was reduced by 4-AP treatment compared to TTX (Figure 22.C). The intensity ratio of p-gephyrin over total gephyrin per synapse was also reduced by 4-AP treatment versus TTX, confirming that gephyrin was dephosphorylated rather than removed from the synapses after 4-AP treatment. In addition, TTX treatment did not have much effect on the gephyrin cluster intensity compared to control condition.

In conclusion, increased neuronal activity by 4-AP treatment differentially regulates different synaptic proteins at mixed inhibitory synapses in spinal cord neurons. The GABAAR amount was reduced by 4-AP treatment while the GlyR amount was not changed. Gephyrin remained at synaptic sites after 4-AP treatment. However, its phosphorylation level at the S270 site was dramatically decreased. These results suggest that gephyrin molecules probably form a stable lattice at mixed inhibitory synapses, providing binding sites for receptors.

Meanwhile, the phosphorylation level of gephyrin is closely regulated by neuronal activity. And the accumulation of GABAARs at mixed synapses is probably more associated with gephyrin phosphorylation level than that of GlyRs.

As shown before, GABAARs, GlyRs and gephyrin are heterogeneously distributed at mixed inhibitory PSDs. Since their copy numbers and phosphorylation status are differentially regulated in response to neuronal network activity, they may also undergo spatial re-organization during the altered activity. Next, we employed two-color dSTORM to investigate the intra-synaptic organization and the relative spatial correlation of the receptors and gephyrin after altering the neuronal activity by TTX and 4-AP.

Intra-synaptic re-organization of GlyRs, GABAARs and p-gephyrin during neuronal activity

The treatment of cultured spinal cord neurons with TTX and 4-AP was done as above. The fixed and immuno-stained samples were subjected to two-color dSTORM imaging afterwards. We compared the spatial distribution of GlyRs, GABAARs and gephyrin in a pair-wise manner as described in the methodology section. Note that the uncertainty of the neuronal baseline activity level in the non-treated coverslips was observed in our experiments and shown in Figure 21 and Figure 22. Therefore, we did not use the non-treated cells as control condition in the following two-color dSTORM experiments. Instead, we directly compared 4-AP treatment to TTX treatment conditions as representative of high and low neuronal activity levels, respectively.

The spatial association of synaptic GlyRs and p-gephyrin was increased after 4-AP treatment

We first performed two-color dSTORM imaging of p-gephyrin labeled with A647

and GlyRs labeled with Cy3B after treatment. Both gephyrin and GlyR clusters

showed heterogeneous distribution at synapses and exhibited subsynaptic domains (SSDs), consistent with our earlier description (Figure 23.A, also see Figure 18). As discussed in section 3.1.4, different dyes can have a strong impact on the detected synaptic clusters, influencing the spatial association and SSD measurements. In the case of p-gephyrin labeled with A647 and GlyRs with Cy3B, gephyrin clusters would be detected with less heterogeneity and more completeness due to the superior blinking properties of A647. This could potentially have an impact on the comparison between the two experimental conditions. Therefore, we reversed the dyes on gephyrin and the GlyRs for twocolor dSTORM imaging to validate the effects of the treatment.

In the reversed color experiments, p-gephyrin was labeled with Cy3B and GlyR with A647 for dSTORM imaging (Figure 24.A). As expected, the cluster size was dramatically affected by the dyes (Figure 24.B). Different from the previous measurements, gephyrin cluster size was not decreased by 4-AP treatment in this case, while GlyR cluster size was slightly increased by 4-AP treatment. The spatial correlation measurements showed a trend towards higher ICQ values, which was consistent with the previous results (Figure 24.C). However, MCC measured an increase of gephyrin overlap with GlyRs by 4-AP treatment, in contrast to the earlier MCC measurements. On the other hand, the numbers of SSDs per gephyrin or GlyR cluster did not differ between the two treatment groups, consistent with earlier results (Figure 24.D). Moreover, the SSD area was strongly affected by the dyes as well, but did not show a difference between conditions. In fact, the SSD area always showed similar trend as the synaptic cluster area. This means that the dyes have an impact not only on the synaptic cluster size measurement, but also on the SSD size measurement.

In summary, by reversing the dyes on the target proteins, we again manifested the strong impact of the dyes on the synaptic cluster measurements. Favorably, ICQ measurements were consistent in the two scenarios with reversed dyes. This suggests that the spatial correlation between GlyRs and gephyrin increased after 4-AP treatment, and that ICQ values are good read-out for these ultra-structural changes. In addition, SSD counts for gephyrin and GlyR clusters per synapse were not changed in both scenarios by the treatment. It is necessary to point out that some analysis methods may not be robust enough for the synaptic colocalization measurement in dSTORM images. Even though MCC was able to indicate the colocalization difference between RIM1/2 with gephyrin at synapses and RIM1/2 without gephyrin (Figure 11), it failed to produce consistent results when the dyes were reversed on gephyrin and GlyRs (Figure 23 and Figure 24). Moreover, the measured synaptic cluster size and SSD size were strongly affected by the dyes.

Therefore, in the following experiments, we did not take the MCC and cluster size measurements into consideration for understanding the synaptic organization. But we included these data in the figures in order to fully present the results.

Taken together these results, the spatial correlation between GlyRs and pgephyrin was increased by 4-AP treatment as measured with ICQ. The SSD counts of GlyR and p-gephyrin clusters did not change. As shown in Figure 22, the gephyrin phosphorylation level was largely reduced by 4-AP treatment while the total gephyrin molecules stayed constant. Therefore, the SSD counts likely reflect the internal synaptic heterogeneity, rather than the structural units as synaptic building blocks. In conclusion, these results suggest that GlyRs and pgephyrin may undergo intra-synaptic re-distribution toward a stronger spatial association during increased neuronal activity.

GABAARs exhibit increased spatial association with both p-gephyrin and

GlyRs during increased neuronal activity

The synaptic GABAAR content was significantly lower after increasing the neuronal activity with 4-AP treatment, compared to the TTX treated condition (Figure 21). GABAAR molecules might be removed from the PSD sites either randomly or from particular sites. In order to resolve which population of GABAARs were removed from the synaptic sites, we examined the spatial relationship of GABAARs with p-gephyrin and with GlyRs by pairwise two-color dSTORM experiments.

First, we conducted two-color dSTORM imaging of p-gephyrin labeled with These results suggest that GABAAR molecules were not removed randomly from the PSD. GABAARs may be lost at the sites where p-gephyrin level is decreased, thus leaving the remaining GABAARs and p-gephyrin with higher spatial correlation. Another possibility is that the retained GABAARs may undergo redistribution to associate more closely with the remained p-gephyrin sites.

Next, we conducted two-color dSTORM imaging of GlyRs labeled with A647 and GABAARs with Cy3B (Figure 26.A), in order to examine the change in their spatial correlation. ICQ measurements indicated that the spatial correlation of GABAARs and GlyRs was also increased by the 4-AP treatment (Figure 26.C).

The GABAAR SSD and GlyR SSD counts were again not changed (Figure 26.D).

Given that the spatial association of p-gephyrin with GlyRs and GABAARs were both increased after 4-AP treatment, it is not surprising that GlyRs and GABAARs have a stronger correlation as well after 4-AP treatment. Because the GlyR amount is not changed and the GABAAR amount is reduced at synaptic sites by 4-AP treatment, it is possible that GABAARs may be prevented to leave from the ICQ measured an increase of spatial correlation by 4-AP treatment compared to TTX (t-test, p < 0.01), while MCC showed an increase of GlyRs overlapping with GABA A Rs (t-test, p < 0.001) and no difference of GABA A Rs overlapping with GlyRs.

(D) The SSD measurements of GlyR and GABA A R clusters in the dSTORM images.

No difference in the SSD counts was detected between the two conditions. GlyR SSD area is smaller in TTX than in the 4-AP condition (t-test, p<0.00001). Number of synapses: n=123 in TTX, n=178 in 4-AP conditions, from two independent experiments.

synaptic sites in those regions that are also occupied by GlyRs, which would also explain the higher spatial correlation.

Summary of intra-synaptic re-organization at mixed inhibitory PSDs during neuronal activity

To recapitulate, the earlier epifluorescence microscopy experiments demonstrated that the synaptic content of GABAARs and p-gephyrin are reduced after increasing neuronal activity by 4-AP treatment, while the amount of GlyRs and total gephyrin are not changed. Two-color dSTORM experiments further revealed that the spatial correlation among GABAARs, p-gephyrin and GlyRs is increased after the 4-AP treatment. Taken together these results, we hypothesize that gephyrin molecules form a stable lattice as scaffold for the neurotransmitter receptors at inhibitory PSDs, providing strong binding sites for GlyRs and relatively weak binding sites for GABAARs. When the neuronal activity is increased, the phosphorylation level of gephyrin is reduced at specific subsynaptic sites. This does not have an effect on the GlyR content, due to their strong binding to the gephyrin scaffold. GlyRs are therefore less affected by the dephosphorylation of gephyrin. In contrast, GABAARs appear to be more sensitive to gephyrin de-phosphorylation and are thus removed from the sites where gephyrin is dephosphorylated. At the same time, the remaining GABAARs and GlyRs may undergo re-distribution to better associate with the remaining pgephyrin sites, in order to maintain certain level of inhibitory strength. This interpretation is further explored in the discussion.

Following our observations of the intra-synaptic re-organization at mixed inhibitory PSDs, an interesting question is: how does this relate to pre-synaptic components and influence the synaptic transmission? At the pre-synaptic active zone (AZ), the vesicle releasing site is dependent on a few key proteins [START_REF] Südhof | The Presynaptic Active Zone[END_REF]. RIM1/2 is an important component to indicate the vesicle releasing site, owning to its proximity to the pre-synaptic membrane and its heterogeneous subsynaptic distribution at the AZ (Dani et al., 2010;Glebov et al., 2017). A recent study has shown that RIM1/2 indeed dictates the evoked vesicle fusion sites and that post-synaptic PSD-95 SSDs are aligned with pre-synaptic RIM1/2

SSDs into trans-synaptic nanocolumns at excitatory synapses in hippocampal neurons (Tang et al., 2016). As a common pre-synaptic protein for both excitatory and inhibitory synapses, RIM1/2 may play an important role in regulating the inhibitory synaptic transmission as well. However, it is not clear yet how p- investigated the spatial correlation of the inhibitory receptors and RIM1/2 using the same pharmacological approach and two-color dSTORM as described above. Similar to GlyRs, GABAAR SSDs can also be aligned with RIM1/2 SSDs (Figure 31.A). However, no difference in the spatial association of GABAARs and RIM1/2 was detected by ICQ measurement after the treatment (Figure 31.B). The SSD numbers of GABAAR and RIM1/2 clusters were also not changed by the treatment (Figure 31.C). Given that GABAARs are removed from the synaptic sites after 4-AP treatment, the loss of GABAARs seems to have no effect on their spatial relationship with pre-synaptic RIM1/2. Therefore, it is possible that GABAAR is generally less associated with RIM1/2 than GlyRs at mixed inhibitory synapses.

gephyrin

Summary of the results on mixed inhibitory synapses

We investigated the intra-synaptic organization of the key proteins at mixed inhibitory synapses (GlyR, GABAAR, gephyrin, RIM1/2) in dissociated spinal cord neurons, with the optimized protocol of two-color dSTORM. Furthermore, we examined the implications of their intra-synaptic organization in the neuronal activity with the two-color dSTORM. We also examined the changes in their synaptic accumulation during altered neuronal network activity, with conventional microscopy. Here, we recapitulated the results of the experiments on the mixed inhibitory synapses.

In the conventional microscopy experiments, the synaptic content of p-gephyrin and GABAARs was reduced by 4-AP treatment, compared to the TTX condition, while that of GlyR, total gephyrin and RIM1/2 was not changed (Table 1). We also found that TTX treatment did not have strong impact on the synaptic accumulation of these synaptic components, compared to the non-treated control condition. Therefore, we used TTX conditions as comparison with 4-AP treatment in the two-color dSTORM experiments. To get an overview of the basal spatial correlation level of the synaptic components, we put together the ICQ measurements of paired synaptic components from non-treated conditions or TTX treated conditions (Figure 32).

The basal spatial correlation of a pre-and post-synaptic component pair is consistently lower than that of a post-synaptic component pair, except that of

GlyRs and GABAARs. The relatively low basal spatial correlation of GlyRs and

GABAARs may indicate their competition for gephyrin binding sites and synaptic space. The ICQ measurement for p-gephyrin and total gephyrin is generally higher than that for gephyrin and GlyRs, which is as expected. These data not only showed the different spatial correlation levels between different synaptic components, but also proved the feasibility of using ICQ measurement to detect the changes in the spatial correlation.

Using ICQ method, we measured the changes in the spatial correlation of the synaptic proteins during increased neuronal network activity by 4-AP treatment.

The spatial correlation between GlyRs and p-gephyrin, GABAARs and pgephyrin, GlyRs and GABAARs was increased at the post-synaptic compartment after 4-AP treatment, compared to the TTX condition (Table 2). Moreover, GlyRs appeared to have stronger correlation with pre-synaptic RIM1/2 after 4-AP treatment. However, the spatial correlation of GABAARs and RIM1/2 was not changed. In this chapter, I first critically review the subject of subsynaptic domain (SSD).

Then I discuss my biological findings and their implications in the regulation of mixed inhibitory synaptic activity. Finally, I also discuss the technical and analytical challenges that I encountered and propos alternative approaches.

A critical review of sub-synaptic domains (SSDs)

We have observed SSDs of GlyRs, GABAARs, gephyrin and RIM1/2 at inhibitory synapses. The SSD counts per cluster are generally in the range of that has been reported in the literature. However, we found that the SSD segmentation and characterization can be affected by the dyes and the analysis methods. Also, there exists some ambiguity in the scientific literature about the definition of SSDs.

Therefore, I reviewed the literature in detail and critically discuss the multiple aspects of SSDs. Our review article on this topic has been recently published [START_REF] Yang | Subsynaptic Domains in Super-Resolution Microscopy: The Treachery of Images[END_REF](also see Appendix 6).

What is a subsynaptic domain? Terminology and definition

A major source of confusion is that different names have been used in the literature to describe subsynaptic domains. Among these, the terms nanodomain, nanocluster, subcluster, subdomain and nanomodule have been used in an interchangeable manner (e.g. Broadhead et al. 2016;Haas et al. 2018;Hruska et al. 2018;Macgillavry et al. 2013;Nair et al. 2013). The lack of a clear and unified terminology has made it difficult to refer to specific molecular structures and to be aware of the differences and similarities between studies. Regarding the choice of words, the term cluster should best be avoided, because it can also refer to the clustering algorithms that are widely used for image analysis of SMLM data (Nicovich et al., 2017). The prefix nano is redundant, because synapses themselves have diameters of only a few hundred nanometers. Furthermore, nanodomain has been widely used to describe the high Ca2 + ion concentrations in the proximity of an open calcium channel (Augustine et al., 2003;Eggermann et al. 2013;Ghelani and Sigrist 2018).

We therefore refer to these structures as subsynaptic domain or SSD (Crosby et al., 2019) for the following reasons: (1) the term is self-explanatory, referring to a space that is smaller than the whole synaptic compartment and that is occupied by a given type of molecules; ( 2) it is flexible in that it can be equally applied to membrane receptors, scaffold and signaling proteins, whether they are presynaptic or post-synaptic. We define SSD as a sub-compartment of the synapse in which the density of a specific synaptic protein is higher than in the surrounding area, and that is typically observed with super-resolution microscopy. We believe that the term subsynaptic domain could thus provide some clarity in defining specific molecular entities at synapses.

SSD size and protein copy numbers

The most basic feature of SSDs that holds biologically relevant information is their size and the copy number of proteins that they contain. A wide range of sizes was detected by SMLM and STED microscopy (Table 3). For instance, SSDs of excitatory scaffold proteins in cultured hippocampal neurons have a diameter of ~80 nm as judged by coordinate-based SMLM analysis (MacGillavry et al., 2013), whereas an average diameter of 120 nm was measured in reconstructed super-resolution images (Nair et al., 2013). STED microscopy detected SSDs of PSD-95 with a diameter of 200 nm (Fukata et al., 2013). These differences in SSD size are likely due to the different resolution of the imaging systems and the application of a threshold during image processing. A comparative study of PSD-95 in hippocampal tissue using PALM and STED determined median SSD diameters of 126 nm and 158 nm, respectively, exemplifying the impact of the imaging approach (Broadhead et al., 2016). The typical diameter of the whole PSD in hippocampal neurons ranges from 100 nm to 800 nm, with a mean of about 300 nm (Arellano et al., 2007;Harris & Stevens, 1989). Therefore, the lower limit of SSD sizes of ~50 nm reflects the image resolution of the superresolution imaging techniques, while the upper limit corresponds to the size of the entire synapse. Given that synapse sizes vary substantially across the central nervous system, an interesting question is whether SSDs of different synaptic proteins have stereotypical sizes that are the same at different types of synapses (see Crosby et al. 2019).

Information about protein copy numbers is essential to establish the structural basis of SSD formation. To date, there are hardly any quantitative data about SSD molecule numbers. Subsynaptic domains of AMPARs have been estimated to contain an average of ~20 receptor complexes (Nair et al., 2013). Due to the limited accessibility of the epitopes for immunolabelling, however, the actual number of receptors per SSD could be higher. This could have an effect on the role of SSDs in synaptic function, since the number of active receptors is directly related to the strength of synaptic transmission (Fukazawa & Shigemoto, 2012;Masugi-Tokita et al., 2007;Tarusawa et al., 2009). Broadhead et al., 2016;Chamma et al., 2016 a,b;Haas et al., 2018;MacGillavry et al., 2013;Nair et al., 2013 130-760 * STED Excitatory, hippocampal and cortical Broadhead et al., 2016;Hruska et al.,2018;Nair et al., 2013 ~ Fukazawa & Shigemoto, 2012;Masugi-Tokita et al., 2007;Tarusawa et al., 2009 30 (0-200) GABA A Rs # Electrophysiology, EM Inhibitory, cerebellar and hippocampal Nusser et al.,1997Nusser et al., , 1998 30 (40-500) 30 (40-500) PSD-95 # Biochemistry, TIRF microscopy Excitatory, various brain regions [START_REF] Sugiyama | ) Gephrin # SMLM (decay recordings) Inhibitory[END_REF]Sugiyama et al. 2005 30 (40-500) Gephrin # SMLM (decay recordings) Inhibitory, spinal cord Patrizio et al. 2017;Specht et al., 2013 * Mean values obtained in the cited studies; # mean (range in brackets). Values were taken directly or calculated from those reported in the cited studies.

Number of SSDs per synapse

Most synapses contain only one SSD or no SSD at all. More specifically, a single SSD was detected in 50% to 80% of synapses imaged with SMLM, SIM or STED microscopy, less than 20% had more than three SSDs, and six SSDs was the upper limit (Broadhead et al. 2016;Chamma et al., 2016 a, b;Crosby et al. 2019;Hruska et al. 2018;MacGillavry et al. 2013;Nair et al. 2013;Pennacchietti et al., 2017a).

It is likely that the different imaging techniques and analyses again have an effect on the detection of multiple SSDs. This raises the question whether the SSD simply reflects the center of mass of the protein assembly, and if so, whether the presence of single or multiple SSDs actually matter for the regulation of synaptic function.

There exists a positive correlation between the number of SSDs and the size of the PSD or the dendritic spine (Crosby et al., 2019;Fukata et al., 2013;Hruska et al., 2018;Nair et al., 2013). EM studies have revealed a large variability in PSD area, ranging from 100 to 800 nm in diameter (Table 3). More than half of the PSDs are small (<0.05 µm 2 ), which is similar to the fraction of synapses with only one SSD (Arellano et al., 2007). Moreover, the number of AMPAR molecules is positively correlated with the PSD size, and large complex PSDs have a higher density of AMPARs than small, non-perforated PSDs (Fukazawa & Shigemoto, 2012;Ganeshina et al., 2004;Shinohara et al., 2008). Together, these data indicate that SSDs may only play a role at large PSDs, reflecting the superior strength of these synapses.

Trans-synaptic nanocolumns

From the viewpoint of neuron connectivity, pre-synaptic and post-synaptic SSDs can be aligned to form trans-synaptic structural units that regulate synaptic function (Biederer et al., 2017;Chen et al., 2018). Such an organization has been observed at excitatory synapses using 3D-SMLM, and was suitably named transsynaptic nanocolumn (Tang et al., 2016). SMLM studies have further shown that synaptic adhesion complexes such as neuroligin and neurexin are also organized in SSDs, suggesting that they contribute to the formation of trans-synaptic nanocolumns (Haas et al., 2018;Perez de Arce et al., 2015). The term nanocolumn therefore refers to a specific concept, namely the alignment of preand post-synaptic SSDs that brings together different functional elements. Future studies are expected to explore the possible role of nanocolumns in synaptic plasticity.

The dynamics of SSDs

The hypothesis that SSDs regulate synaptic transmission implies that SSDs adapt dynamically to changes in synaptic strength. Indeed, live SMLM in cultured neurons has revealed the mobility and morphological changes of SSDs. Synaptic scaffolds undergo dynamic changes on a timescale of 5-10 minutes, displaying marked differences in the number, position and shape of SSDs at different time points (Nair et al., 2013;Rodriguez et al., 2017;Specht et al., 2013). STED microscopy further showed that these morphological changes occurred both in vitro and in vivo (Hruska et al., 2018;Wegner et al., 2018). The dynamics of SSDs are in agreement with the exchange of individual proteins at synaptic and extra-synaptic sites, which is a hallmark of the dynamic synapse (Choquet & Triller, 2013;Delgado & Selvin, 2018). Therefore, SSDs are momentary representations of the protein distribution and need to be viewed as dynamic snapshots rather than rigid structural units.

How to detect SSDs with SMLM

The identification of SSDs consists in detecting small numbers of densely packed molecules in a confined space with a high local background from neighboring molecules with lower density. Despite these challenges, SMLM is well suited to resolve the internal organization of small structures such as synapses at single molecule level. In the following, we discuss the relevant factors of the image acquisition and data analysis that have an impact on the identification of SSDs. photo-physical and photo-chemical properties, and it can be modulated by the laser power and the composition of the imaging buffer (Dempsey et al., 2011;Endesfelder et al., 2011;Nahidiazar et al., 2016;van de Linde et al., 2011). Suboptimal imaging conditions such as inefficient laser illumination or an incompatible buffer system can result in artificial clustering [START_REF] Annibale | Quantitative Photo Activated Localization Microscopy: Unraveling the effects of photoblinking[END_REF]Burgert et al., 2015;Nahidiazar et al., 2016). Even with an optimized imaging protocol, different fluorophores will produce different representations of the analyzed structure (Baddeley & Bewersdorf, 2018;Dempsey et al., 2011). The evaluation of the number and the size of SSDs is therefore strongly dependent on the fluorophores, and control experiments with different fluorophores are crucial to validate the experimental findings [START_REF] Yang | Practical guidelines for two-color SMLM of synaptic proteins in cultured neurons[END_REF]. In addition to the fluorophores, attention should also be drawn to the labelling strategies used for sample preparation. The distance between the fluorophores and the actual positions of the target molecules (e.g. due the size of antibodies used for labelling), and under-sampling due to a limited labelling efficiency can add to the uncertainties in the identification of SSDs (Deschout et al., 2014;Maidorn et al., 2016).

Image segmentation

Depending on the type of SMLM data (pointillist or reconstructed superresolution images), different algorithms have been adopted for segmenting SSDs.

For coordinates-based data, a local density threshold is generally applied. The local density can for instance be defined as the number of detections within a radius of five times the mean nearest neighbor distance of all the detections within each synapse, and SSDs are identified as regions above a certain threshold (Macgillavry et al. 2013;Pennacchietti et al., 2017a;Tang et al. 2016). As regards the reconstructed images, an intensity threshold may be adopted instead. For example, wavelet segmentation has been used to identify SSDs at synapses in the whole field of view (Chamma et al., 2016 a,b;Nair et al. 2013). Similarly, watershed segmentation can be employed to segment SSDs of individual synapses in reconstructed SMLM images or deconvoluted STED images (Broadhead et al., 2016;Dzyubenko et al., 2016). In my own experiments, I have generally applied H-watershed segmentation to generate SSDs of receptor and scaffold proteins at inhibitory synapses. The difficulty of all these approaches is that the detected size and the number of SSDs are directly dependent on the algorithms and the chosen parameters, which makes an accurate identification of SSDs challenging.

Dealing with small molecule numbers and the variability of synapses Synapses exhibit a large variability not only in size, but also in terms of molecule numbers. Neurotransmitter receptors such as AMPARs or GABAARs have relatively low copy numbers, with an average of ~50 receptor complexes per synapse (ranging up to 200 copies; Table 3). The main scaffold proteins at excitatory and inhibitory synapses outnumber the receptors by a factor of four to five. PSD-95 and gephyrin molecules amount to 40-500 per synapse, with an average of ~300 copies [START_REF] Patrizio | Alpha subunit-dependent glycine receptor clustering and regulation of synaptic receptor numbers[END_REF][START_REF] Sugiyama | ) Gephrin # SMLM (decay recordings) Inhibitory[END_REF]Specht et al., 2013;Sugiyama et al., 2005). The low copy numbers of synaptic proteins, especially receptors, makes the identification of SSDs with SMLM challenging, since the labelling of the structures is often rather faint. At the same time, the high local density of synaptic proteins can further reduce the efficiency of immunolabelling due to epitope masking. The overall receptor density at synapses is in the order of 700 AMPARs/µm 2 for the whole PSD (50 AMPARs / 0.07 µm 2 ).

An average SSD with a diameter of 70 nm (area of 0.0038 µm 2 ) contains about 20AMPARs, resulting in an estimated density of ~5000 AMPAR complexes/µm 2 (Nair et al., 2013). Considering the molecular size of the receptor complexes (10 nm × 20 nm; Patriarchi et al., 2018), 20 AMPARs would occupy a membrane area of at least 0.004 µm 2 . This means that the receptors are very densely packed inside the SSD, adding to the uncertainties that result from the stochasticity of the immunolabelling and fluorophore detection.

Alternative approaches

Given the rapid advances in super-resolution imaging technologies, promising alternatives for the investigation of complex structures such as synapses are quickly emerging. Among these, smaller probes such as nanobodies have been produced to bypass the limitations of labelling density and to minimize the distance between the fluorophores and the target proteins (Chamma et al., 2016a;Maidorn et al. 2016). DNA-PAINT allows multi-color SMLM imaging (Nieves et al., 2018). DNA origami standards provide a more precise way for calibrating protein copy numbers given that the absolute quantification of molecules at SSDs is faced with large stochasticity of the imaging technique (Zanacchi et al., 2017).

Furthermore, new algorithms are being developed to segment synaptic clusters in coordinates-based datasets more efficiently (Baddeley & Bewersdorf, 2018;Nicovich et al., 2017). In collaboration with Felipe Delestro and Auguste Genovesio (IBENS), we have developed a versatile approach based on DBSCAN for cluster identification and the analysis of dual-color pointillist dSTORM datasets (SRclusters, see Figure 17 and Appendix 3).

4.2

The intra-synaptic spatial organization of mixed inhibitory synapses and its functional implications I observed that gephyrin, GABAARs and RIM1/2 are heterogeneously distributed at inhibitory synapses and form SSDs in dissociated spinal cord neurons. This is similar with the recent studies on GABAergic inhibitory synapses in dissociated hippocampal neurons (Crosby et al. 2019;Pennacchietti et al., 2017a). Moreover, I found that GlyRs also exhibit similar heterogeneous distribution and SSDs at inhibitory PSDs. At mixed inhibitory synapses, GlyRs and GABAARs are not fully intermingled and their SSDs can occupy different spaces. This gives rise to the possibility that the intra-synaptic organization of the receptors can serve as a regulator for synaptic activity.

The intra-synaptic organization of GlyRs and GABAARs at mixed PSDs is differentially regulated

The cultured spinal cord neurons showed low basal network activity in our calcium imaging experiments. TTX application blocked the basal calcium signals completely, while 4-AP increased the frequency of the calcium signals. This effect was persistent for at least 20 minutes. In the conventional microscopy experiments, the synaptic amount of GlyRs, GABAARs or gephyrin was not changed after one hour of TTX treatment. However, the amount of GABAARs and p-gephyrin was reduced at the post-synaptic sites, while that of GlyRs and total gephyrin was not changed (Table 1). In the two-color dSTORM experiments, the spatial correlation between GlyRs, GABAARs and p-gephyrin was increased after 4-AP treatment compared to TTX treatment (Table 2). Given that the amount of both GABAARs and p-gephyrin was reduced, the elevated spatial correlation between these three proteins may indicate an intra-synaptic re-organization at the mixed PSDs during elevated neuronal network activity.

The accumulation of GlyRs and GABAARs at mixed PSDs is differentially regulated by excitatory activity

In my experiments, 4-AP treatment reduced the synaptic amount of GABAARs and p-gephyrin, consistent with previous studies in our laboratory using hippocampal neurons [START_REF] Bannai | Activity-Dependent Tuning of Inhibitory Neurotransmission Based on GABAAR Diffusion Dynamics[END_REF][START_REF] Niwa | Gephyrin-Independent GABAAR Mobility and Clustering during Plasticity[END_REF]. However, the synaptic amount of GlyRs and total gephyrin was not changed by the 4-AP treatment (Table 1). On the other hand, the synaptic amount of gephyrin, GlyRs and GABAARs did not change with TTX treatment compared to the non-treated condition. This is not surprising, because the baseline activity in our spinal cord cultures was generally low, and variations in the baseline activity level can be large (Figure [START_REF] Macgillavry | Nanoscale scaffolding domains within the postsynaptic density concentrate synaptic ampa receptors[END_REF][START_REF] Nair | Super-Resolution Imaging Reveals That AMPA Receptors Inside Synapses Are Dynamically Organized in Nanodomains Regulated by PSD95[END_REF]. Therefore, in my experiments, the 4-AP and TTX treatments were taken as two representative conditions for high and low activity level respectively. In conclusion, the synaptic accumulation of GlyRs and GABAARs were differentially regulated by network activity.

A previous study in our laboratory showed that the diffusion coefficient of GlyRs was increased by TTX treatment in dissociated spinal cord neurons, while that of GABAAR γ2 subunits was not changed [START_REF] Lévi | Homeostatic Regulation of Synaptic GlyR Numbers Driven by Lateral Diffusion[END_REF]. Interestingly, in hippocampal neurons where inhibitory synapses accommodate only GABAARs, TTX treatment reduces the diffusion coefficient of synaptic GABAAR γ2 subunits [START_REF] Bannai | Activity-Dependent Tuning of Inhibitory Neurotransmission Based on GABAAR Diffusion Dynamics[END_REF]. Different baseline activity in these cultures might contribute to the discrepancy in these observations. Alternatively, it is possible that the receptor dynamics may be differentially regulated at pure GABAergic synapses and mixed inhibitory synapses. On the other hand, 4-AP treatment increased the diffusion coefficient of synaptic GABAAR γ2 subunits in hippocampal neurons [START_REF] Bannai | Activity-Dependent Tuning of Inhibitory Neurotransmission Based on GABAAR Diffusion Dynamics[END_REF]. However, it is not yet known if and how 4-AP treatment influences the GABAAR and GlyR mobility at mixed inhibitory synapses in spinal cord neurons. Given that TTX treatment changes the diffusion coefficient [START_REF] Lévi | Homeostatic Regulation of Synaptic GlyR Numbers Driven by Lateral Diffusion[END_REF] but not the synaptic accumulation of GlyRs in spinal cord neurons, the diffusion coefficient may be not directly related to the synaptic accumulation of receptors.

Furthermore, TTX treatment was shown to reduce synaptic amount of GABAAR α2 subunits in dissociated spinal cord neurons, compared to the non-treated condition (Specht et al., 2013). However, in my experiments, TTX treatment did not change the synaptic amount of GABAAR β3 subunits compared to the nontreated condition. In addition, a recent study has shown that an alanine point mutation of gephyrin at serine 270 sites (S270A) increased the synaptic amount of GABAAR α2 subunits and gephyrin molecules in hippocampal neurons [START_REF] Battaglia | Activity-dependent inhibitory synapse scaling is determined by gephyrin phosphorylation and subsequent regulation of GABA A receptor diffusion[END_REF]. However, the reduced phosphorylation at S270 sites by 4-AP treatment decreased the synaptic GABAAR β3 subunits in my study. This suggests that different GABAAR subunits may be differently regulated in response to neuronal network activity. GABAAR β3 subunits have been shown to play crucial roles in inhibitory synaptic transmission and their deficiency is involved in several pathogenesis [START_REF] Culiat | Deficiency of the beta3 subunit of the type A gammaaminobutyric acid receptor causes cleft palate in mice[END_REF][START_REF] Ferguson | New insight into the role of the β3 subunit of the GABAA-R in development, behavior, body weight regulation, and anesthesia revealed by conditional gene knockout[END_REF][START_REF] Houston | Distinct Regulation of beta2 and beta3 Subunit-Containing Cerebellar Synaptic GABA(A) Receptors by Calcium/Calmodulin-Dependent Protein Kinase II[END_REF][START_REF] Houston | CaMK-II modulation of GABAA receptors expressed in HEK293, NG108-15 and rat cerebellar granule neurons[END_REF][START_REF] Nguyen | The GABAAReceptor β Subunit Is Required for Inhibitory Transmission[END_REF][START_REF] Petrini | Synaptic recruitment of gephyrin regulates surface GABAA receptor dynamics for the expression of inhibitory LTP[END_REF][START_REF] Saliba | Activity-dependent phosphorylation of GABA(A) receptors regulates receptor insertion and tonic current[END_REF]. This distinguishes GABAAR β3 subunits as important subjects for future studies.

Increased intra-synaptic spatial correlation of GlyRs, GABAARs and gephyrin at mixed PSDs by excitatory activity

In hippocampal neurons, chemical induction of inhibitory long-term potentiation (iLTP) increased the number of gephyrin SSDs (Pennacchietti et al., 2017a). In a recent study using structured illumination microscopy (SIM), both GABAAR SSD counts and gephyrin SSD counts were increased after bicuculline treatment (Crosby et al., 2019). Bicuculline, as a GABAAR antagonist, blocks inhibition and increases neuronal activity. When the gephyrin clusters were disrupted by dominant negative expression of gephyrin, both gephyrin SSD and GABAAR SSD counts were reduced. In addition, the SSD size was not changed in both experimental manipulations (Crosby et al., 2019). Therefore, it was suggested that SSDs may be building blocks of inhibitory synapses.

In my experiments, I did not observe any changes in the SSD counts of the inhibitory synaptic components after the pharmacological treatments. However, I observed changes in the SSD size due to the treatments. These different observations may be attributed to the differences between hippocampal neurons and spinal cord neurons, or to the differences in the pharmacological treatments adopted in these studies. Another important factor is the choice of imaging methods and analytical approaches (see below). Despite the fact that SIM improves the resolution by two-fold without suffering from the stochasticity of fluorescent blinking, it may not be sufficient to detect the true SSD size. In fact, the average SSD size was about 300 nm in diameter measured with SIM (Crosby et al., 2019). This SSD size is close to the size of inhibitory PSDs (~350 nm) measured by electron microscopy (Bourne & Harris, 2011;Santuy et al., 2018). In our study, we found that the SSD size was strongly influenced by the choice of dyes, which impedes a conclusion about the SSD size measured in our experiments. On the other hand, the analytical approaches may influence the results of SSD segmentation. Pennacchietti and colleagues used coordinate-based data derived from PALM imaging to segment SSDs from the synaptic clusters, by applying a threshold to the local density (Pennacchietti et al., 2017a). In our study, we applied H-watershed to the synaptic clusters in the rendered images for SSD segmentation. Because weak signals may be enhanced during image rendering, the SSD counts were probably overestimated in our results, especially in the images with down-regulated protein levels.

Therefore, the hypothesis that SSDs are building blocks for inhibitory synapses requires further investigation. There is still room for improvements in the SSD detection (see below). However, given the changes in the spatial correlation of GlyRs, GABAARs and p-gephyrin, what we can conclude is that the spatial relationships of inhibitory synaptic proteins plays a role in regulating synaptic activity. The heterogeneous distribution of GlyRs and GABAARs at mixed inhibitory synapses may not be a random phenomenon, but appears to be closely regulated by neuronal activity.

Given the changes in the synaptic amount of p-gephyrin and its spatial correlation with the receptors in our experiments, gephyrin phosphorylation at S270 sites may be a key regulator for the intra-synaptic organization of GlyRs and GABAARs. In addition, other auxiliary proteins, such as the GABAAR regulatory protein Lhfpl (GARLH) and the GDP/GTP-exchange factor collybistin, are also important in regulating synaptic clustering of gephyrin and GABAARs [START_REF] Davenport | An Essential Role for the Tetraspanin LHFPL4 in the Cell-Type-Specific Targeting and Clustering of Synaptic GABAAReceptors[END_REF][START_REF] Heller | The Biochemical Anatomy of Cortical Inhibitory Synapses[END_REF][START_REF] Kins | Collybistin, a newly identified brainspecific GEF, induces submembrane clustering of gephyrin[END_REF][START_REF] Papadopoulos | The Role of Collybistin in Gephyrin Clustering at Inhibitory Synapses: Facts and Open Questions[END_REF][START_REF] Saiepour | Complex Role of Collybistin and Gephyrin in GABAA Receptor Clustering[END_REF][START_REF] Yamasaki | GARLH Family Proteins Stabilize GABAA Receptors at Synapses[END_REF]. Their potential roles in regulating receptors at mixed inhibitory synapses remain to be studied in the future.

Trans-synaptic nanocolumns at inhibitory synapses and potential roles of adhesion proteins

Trans-synaptic nanocolumns have been reported at excitatory synapses, incorporating the SSDs of pre-synaptic Bassoon, RIM1/2 and post-synaptic AMPARs, PSD-95, GKAP, Shank and Homer into one trans-synaptic unit (Tang et al., 2016). Similarly, I observed a trans-synaptic alignment of RIM1/2 SSDs with p-gephyrin SSDs at inhibitory synapses in spinal cord neurons (Figure 27).

Given that the majority of inhibitory synapses in dissociated spinal cord neurons are mixed synapses [START_REF] Dumoulin | Formation of mixed glycine and GABAergic synapses in cultured spinal cord neurons[END_REF], the trans-synaptic nanocolumns most likely exist at mixed inhibitory synapses. When the neuronal network activity was increased by 4-AP treatment, the p-gephyrin level was reduced at synaptic sites.

How the trans-synaptic alignment of RIM1/2 SSDs and p-gephyrin SSDs is affected by the altered activity remains an open question. Moreover, gephyrin phosphorylation at S270 does not occupy the entire gephyrin lattice (see section 3.1.4.2). Therefore, it will also be interesting to further investigate how the gephyrin lattice as a whole is correlated to pre-synaptic RIM1/2 SSDs.

It is important to point out that the synapses are dynamic structures and are continuously changing in living neurons. After fixation of the cells, we only get a snapshot of a certain status of the synapses. In live neurons, gephyrin clusters can move laterally on the dendritic shaft in the range of micrometers, and the pre-synaptic compartments also move accordingly [START_REF] Dobie | Inhibitory Synapse Dynamics: Coordinated Presynaptic and Postsynaptic Mobility and the Major Contribution of Recycled Vesicles to New Synapse Formation[END_REF][START_REF] Hanus | Activity-Dependent Movements of Postsynaptic Scaffolds at Inhibitory Synapses[END_REF][START_REF] Kuriu | Activity-dependent coordinated mobility of hippocampal inhibitory synapses visualized with presynaptic and postsynaptic tagged-molecular markers[END_REF]. This means that some trans-synaptic signals are needed to maintain the coordinated pre-and post-synaptic mobility. Synaptic cell adhesion molecules may be the key regulators in anchoring pre-and post-synaptic compartments during this movement. Many synaptic adhesion molecules are known to play important roles in synaptic function [START_REF] Missler | Synaptic Cell Adhesion[END_REF].

However, which adhesion molecules are responsible for the coordinated mobility at inhibitory synapses is still unknown.

In addition to the mobility of the whole synapses, SMLM and STED microscopy have revealed that the intra-synaptic organization also changes over time, exhibiting differences in the synaptic morphology and SSD organization (Hruska et al., 2018;Nair et al., 2013;Rodriguez et al., 2017;Specht et al., 2013;Wegner et al., 2018). Under these circumstances, whether the trans-synaptic nanocolumns are maintained through the morphological changes and the lateral mobility of synapses remains to be investigated. It is likely that the trans-synaptic nanocolumns are maintained by the synaptic adhesion molecules. Synaptic cell adhesion molecule 1 (SynCAM1) and Ephrin type-B receptor 2 (EphB2) were shown to form SSDs as well (Perez de Arce et al., 2015). SynCAM1 SSDs mostly are located at the periphery of excitatory synapses, while EphB2 is enriched deeper within the PSD. Another study has shown that neuroligin-1 and leucinerich repeat transmembrane neuronal protein 2 (LRRTM2) can also form SSDs at excitatory synapses (Chamma et al., 2016a). Neuroligin-1 molecules are generally located closer to the PSDs than LRRTM2. Owing to the different locations of these different synaptic adhesion molecules, they might play different roles in maintaining the alignment of the whole synapses and the trans-synaptic nanocolumns. A recent study has revealed that neuroligin-1 SSDs were integrated into the trans-synaptic nanocolumns of RIM1/2 and AMPARs at excitatory synapses (Haas et al., 2018). Expression of a C-terminally truncated neuroligin-1 disrupted the trans-synaptic nanocolumns and impaired the synaptic transmission. However, little is known so far about the roles of synaptic adhesion molecules in inhibitory trans-synaptic nanocolumns. The synaptic adhesion molecules responsible for the coordinated synapse mobility and the trans-synaptic nanocolumns at inhibitory synapses remain to be investigated.

Differential trans-synaptic alignment of GlyRs and GABAARs to presynaptic vesicle releasing sites

We found that 4-AP or TTX treatment did not change the amount of synaptic RIM1/2 compared to the non-treatment condition (Table 1). However, the spatial correlation between RIM1/2 and GlyRs was increased by 4-AP compared to TTX treatment, while the spatial correlation between RIM1/2 and GABAARs did not change (Table 2). This finding suggests that the relative locations of GlyRs and GABAARs to pre-synaptic vesicle release sites could be differentially regulated.

We know that the synaptic amount of GABAARs and p-gephyrin are decreased by 4-AP treatment compared to the TTX condition, but not that of GlyRs and total gephyrin. Under these circumstances, if the trans-synaptic nanocolumns are somehow maintained, it may imply that GlyRs rather than GABAARs are recruited to the trans-synaptic nanocolumns after 4-AP treatment.

A recent study using SIM showed that GABAAR SSDs are closely correlated with p-gephyrin SSDs as well as RIM1/2 SSDs at inhibitory synapses in hippocampal neurons (Crosby et al., 2019). This suggests that GABAARs may be incorporated in the trans-synaptic nanocolumns at GABAergic synapses. However, at mixed inhibitory synapses, a different mechanism may exist to differentially incorporate GlyRs and GABAARs into the trans-synaptic nanocolumns.

At excitatory synapses, AMPARs and NMDARs also exist at the same PSDs.

Interestingly, it appears that AMPARs are more enriched in the PSD-95 SSDs than NMDARs (MacGillavry et al., 2013). In fact, there is evidence showing that AMPARs are incorporated into trans-synaptic nanocolumns (Tang et al., 2016). At these synapses, AMPARs and NMDARs share the same neurotransmitters, glutamate, released from the same pre-synaptic terminal. This suggests that they may compete for glutamate binding after vesicle release. It is known that the activation and neurotransmission of AMPARs and NMDARs are different in several ways, such as ligand binding affinity, current kinetics and voltagedependency. However, it is possible that their spatial organization within the synapses also takes part in the receptors' access to the ligand and thus channel opening. At mixed inhibitory synapses, the increased spatial correlation of GlyRs, but not GABAARs, with both p-gephyrin and RIM1/2 by 4-AP treatment suggests that GlyRs might be closely incorporated into trans-synaptic nanocolumns.

Moreover, GlyRs and GABAARs are activated by two different neurotransmitters, glycine and GABA respectively. This suggests that they do not compete for ligand binding in the same way as the excitatory receptors. There is still some way to go before we can fully understand the role of intra-synaptic organization of two types of receptors at the same PSDs both for inhibitory and excitatory synapses.

Limitations in the current study and alternative approaches

Since the application of SMLM on synaptic clusters was first published in 2010 (Dani et al., 2010), SMLM has been adopted in many studies of the ultra-structure of synapses and proven to be a powerful tool. The ten-fold improvement of image resolution is appealing. However, it is still a young technique, especially in the field of investigating synaptic clusters. Due to the intrinsic stochasticity of the signals in SMLM and the complexity of synaptic structures (see section 1.2.2), we are faced with many challenges in the data acquisition and analysis. In this section, I discussed the limitations of my experiments as a result of the technical constraints, and also proposed alternative approaches and directions for future improvements.

First of all, I found that the impact of dyes on the detection of synaptic clusters in dSTORM is not trivial. In fact, the dyes have strong influence on the characterization of synaptic cluster size, the number of SSDs per clusters and the SSD size . This raises the question: can we actually measure the size of synaptic clusters and SSDs with dSTORM? An estimation of the clusters size and a comparison of the size between different experimental conditions may be more feasible. However, we found that the changes in the clusters size by the treatment are sometimes not consistent, when the same protein is labeled with a different dye . Therefore, despite the powerful resolution of dSTORM, there is still much room for improvement for a more precise estimation of synaptic cluster and SSD size.

Secondly, the data processing and analysis approaches can also have major effect on the cluster characterization. Both coordinate-based and image-based data analysis revealed the strong effect of the dyes on the synaptic cluster and SSD sizes (Figure [START_REF] Loschberger | Super-resolution imaging visualizes the eightfold symmetry of gp210 proteins around the nuclear pore complex and resolves the central channel with nanometer resolution[END_REF][START_REF] Szymborska | Nuclear Pore Scaffold Structure Analyzed by Super-Resolution Microscopy and Particle Averaging[END_REF]. However, different analytical techniques resulted in different measurements of SSD counts per synaptic cluster in the same dataset. In the rendered images, the pixel intensity of the clusters can be differently enhanced between images during the rendering step. As a consequence, synaptic clusters rendered from small number of localizations in one image may have the same intensity with other clusters rendered from many localizations in another image. This can cause an over-estimation of SSD counts with the H-watershed analysis, especially for the images with relative low number of localizations. In the results presented here, the SSD segmentation was mostly done with H-watershed using image-based datasets. Therefore, a different approach to segment SSDs is preferable in order to verify the observations in my experiments. For that purpose, we have developed the program SRclusters which makes use of coordinate-based data. In SRclusters, we adopted a weighted DBSCAN approach for SSD segmentation (see Appendix 3). This analysis will be applied to my dataset in the near future. Alternatively, a thresholding of the local density of localizations in the synaptic clusters using the coordinate-based data can potentially be implemented for SSD segmentation (also see section 4.3;Macgillavry et al. 2013;Pennacchietti et al., 2017a).

For the spatial correlation of synaptic clusters in the two channels, we mostly employed ICQ measurements using image-based datasets. Since the ICQ method measures the number of pixels in which the intensity in two channels changes in synchrony, rather than the total intensity of the clusters in the two channels, it is less affected by the image rendering. ICQ thus proved to be quite efficient in our experiments. However, there are still some concerns when using ICQ for the spatial correlation measurement of pre-and post-synaptic proteins. The separation of pre-and post-synaptic clusters can be evident in the dSTORM images, especially when the synapses are seen from the side in cross-section. Under these circumstances, the ICQ method may fall short in the precise measurement of the spatial correlation level. In SRclusters, a distance score is used to indicate the spatial correlation of two clusters in the two channels. This may be a better way to measure the correlation between structures that are separated by a small distance (i.e. the synaptic cleft or the plasma membrane).

This brings me to the last point. Synaptic clusters are three dimensional structures.

When imaged in two dimensional mode, the 3-D structures are projected to 2-D.

This causes the loss of information in the third dimension. When the synapses are seen from en face view, we may have better focus of one protein than the other in the two-color dSTORM imaging. This effect may be further enhanced by the inclined illumination. When the synapses are seen in side view, the 2-D projection of 3-D structures may cause the merging of different SSDs. Therefore, 3-D imaging with two-color dSTORM is certainly preferable for the synaptic clusters.

We have set up a 3-D two-color dSTORM imaging protocol (see Appendix 4).

However, these techniques are challenging and require further optimization. I have therefore not yet used 3D imaging systematically. I plan to do the experiments in 3D to confirm the most important findings of my research. Future development of 3-D image processing and cluster characterization approaches will allow me to accomplish a quantitative study of 3-D synaptic clusters.

Future directions and perspectives

At mixed inhibitory synapses, GlyRs and GABAARs undergo re-organization during altered neuronal activity, however, their SSD counts did not change. The differential changes in the molecular amount and spatial organization between GlyRs and GABAARs during the altered neuronal activity may underlie the precise control of the synaptic activity. For example, the increased spatial correlation of GlyRs and RIM1/2 might indicate an increase in the glycinergic miniature IPSCs in the neurons. Further electrophysiology and simulation studies can potentially validate this hypothesis. More studies on the GlyR/GABAAR cotransmission in the future can be foreseen, providing more insights into their precise functional regulations.

We have also demonstrated the existence of SSDs of GlyRs, GABAARs, gephyrin and RIM1/2 at inhibitory synapses in spinal cord neurons. RIM1/2 and p-gephyrin SSDs formed trans-synaptic nanocolumns at inhibitory synapses. These observations are in line with what has been seen at excitatory synapses, where RIM1/2 and PSD-95 SSDs, together with other proteins in the active zone and the post-synaptic density, form trans-synaptic nanocolumns. Inhibitory and excitatory synapses may therefore share similar structural organization, as SSDs could be important regulators of synaptic function. SSDs as dynamic structural units provide another angle to interpret synaptic functions. However, the identification and characterization of SSDs is complicated by technical difficulties. Future development of analytical methods will therefore be required to understand the formation of SSDs and their functions at synapses. SMLM is a powerful tool to study synaptic structures. Yet, the complexity in the application of SMLM to synaptic structures calls for more attention to evaluating the strategies in practice. I believe that more attention has to be paid to the technical and analytical development in this particular application. Future developments of SMLM methodologies will certainly promote the study of the ultra-structure of synapses.

Appendix Table 2. List of two-color dSTORM dataset

# indicates the third labeling, which was not subject to dSTORM imaging, but only used as a marker. * indicates this labeling was done in one of the repeating experiments. To interact with all the figures, change '%matplotlib inline' to '%matplotlib nbagg'. Note that the program will run very slowly on the interactive mode.

To save images, right click on the image and save.

In [], it always goes as [channel 1, channel 2].

Step01 -Cluster and Register:

Plot color code: blue-first channel (channel 0), red-second channel (channel 1).

In names = [ch0, ch1], Do not use '_', use '-' instead, otherwise errors will occur later, because the program extract the channel names from the file name based on the separation by '_'.

Set names, paths, DBSCAN variables (distance, amount), intensity threshold, beads amount.

Step02 -Remove beads:

Set persistence threshold: when the value <1, it is the percentage; when the value >1, it is the number of frames.

The persistence threshold can be arbitrarily adjusted for each image to include proper clusters, because the number of detections for different protein under different conditions can be dramatically different, in addition to the stochasticity of dSTORM detection.

Step3 -Paring clusters:

Set path, max_dist (distance between the centroid of paired clusters), min_points (the sum of the points from two clusters in a pair).

The min_points can be arbitrarily adjusted to include all the proper synapses.

Step04 -Feature extraction:

(1) Set path and process_list = [], choosing the good synaptic pairs according to step03 and reference images.

Parameter "min_sample" is adjusted by the density of the cluster:

---(min_sample input) * (Hull density).

Subclusters (number of subclusters segmented from each cluster, using modified DBSCAN with density).

Parameters output:

Copy "zipfiles.ipynb" in to the data folder and run the script. The zipped file is saved under the same path. Download the zipped file. (If the saving process was done by several times, the second batch of zipped files is compressed into the previous zipped file.)

List of output files: - ------------------------------------------------------------------------------------------------------------ Subcluster features: npoints, Hull Area, Density, Eigen ratio. For each channel.

…Red arrows indicate the final data files. ----------------------------------------------------------------------------------------------------------------- Statistics--- [Global

Conclusion

The two-color 3D dSTORM imaging allows us to image the synaptic clusters in three dimension, which can avoid the imaging artefacts due to the 2D projection in the 2D imaging mode. It will be interesting to continue the 3D imaging, in order to further confirm the important findings in my work presented in the thesis.

Introduction 1.1 Overview of SMLM techniques

In conventional optical microscopy, the lateral resolution is limited by the wavelength (λ) and the numerical aperture (NA) of the objective. The minimal distance between two resolvable points is several hundred nanometers due to the diffraction limit (d ≈ 0.61 λ/NA).

Conventional fluorescence microscopy therefore cannot gain detailed information on sub- and N is the number of collected photons [START_REF] Thompson | Precise Nanometer Localization Analysis for Individual Fluorescent Probes[END_REF], [START_REF] Huang | Super-resolution optical microscopy: multiple choices[END_REF]. Note that the localization precision is also affected by background noise, in other words, the signal to noise ratio (SNR). The second concept is the localization density, which is the number of localizations per spatial unit. It relies on the labeling density, as well as the blinking properties of the fluorophores. It has been proposed that the labeling density should be sufficiently high to meet the Nyquist-Shannon criterion [START_REF] Izeddin | Super-resolution dynamic imaging of dendritic spines using a lowaffinity photoconvertible actin probe[END_REF], [START_REF] Van De Linde | Direct stochastic optical reconstruction microscopy with standard fluorescent probes[END_REF]. According to this criterion, the sampling must be at least two times denser than the achieved spatial resolution. However, this is not always the case in SMLM, because fluorophore localizations are not classical samples as in conventional microscopy but stochastic events [5]. The detection of redundant information due to multiple switching cycles of the fluorophores means that the number of detections is much higher than the actual sampling of independent targets. Baddeley and Bewersdorf demonstrated that significantly higher localization density, empirically exceeding by 5-fold the Nyquist-Shannon criterion, are therefore needed to attain the best spatial resolution [5]. The third definition is the spatial resolution, which is not as clear a concept any more as in conventional microscopy.

Canonically, it refers to how well two objects can be distinguished, which is strongly dependent on the wavelength and the objective. In SMLM, it usually means the resolving power for two objects in the reconstructed images. Then, both the localization precision as well as the localization density are determinants of the spatial resolution.

SMLM techniques include stochastic optical reconstruction microscopy (STORM) [START_REF] Rust | Stochastic optical reconstruction miscroscopy (STORM) provides sub-diffraction-limit image resolution[END_REF] and photo-activated localization microscopy (PALM) [START_REF] Betzig | Imaging Intracellular Fluorescent Proteins at Nanometer Resolution[END_REF], [START_REF] Hess | Ultra-High Resolution Imaging by Fluorescence Photoactivation Localization Microscopy[END_REF]. The main difference between STORM and PALM is the labeling technique, where endogenous proteins are stained with organic dyes in STORM and target proteins are tagged with fluorescent proteins in PALM, respectively. Both organic dyes and fluorescent proteins can be induced to blink under different conditions. In STORM, the blinking of fluorescent dyes requires particular imaging buffers and high illumination intensity. In PALM, the blinking of fluorescent proteins is achieved by sequential photo-activation/conversion with UV light in buffered salt solutions.

PALM is thus preferable for live-cell imaging, because the STORM imaging buffers are detrimental to living cells. However, labeling with fluorescent proteins requires the expression of recombinant fusion proteins, which may potentially change the target protein's function. Moreover, organic dyes usually have narrower excitation/emission spectra and higher photon emission compared to fluorescent proteins. Therefore, STORM is more suitable for multi-color imaging and to gain a better localization precision.

There are two versions of STORM imaging, N-STORM [START_REF] Rust | Stochastic optical reconstruction miscroscopy (STORM) provides sub-diffraction-limit image resolution[END_REF] and direct STORM (dSTORM) [START_REF] Heilemann | Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes[END_REF]. N-STORM adopts a pair of dyes, one with a short wavelength (green) and another with a longer wavelength (red), to act as activator and reporter, respectively. The pair of dyes are conjugated to the same antibody at close proximity. During imaging, a red laser is used to excite the red dyes and to switch them into the OFF state, while a green laser at low intensity excites the green dyes that in turn activate the red fluorophores. Note that no UV light is needed in N-STORM. For dual-color imaging, the two target proteins are labeled with the same reporter dye (e.g. Alexa Fluor 647), but different activator dyes are used to selectively visualize the two targets [START_REF] Bates | Multicolor Super-Resolution Imaging with Photo-Switchable Probes Fluorescent[END_REF]. There are, however, a few drawbacks of using activator/reporter dye pairs in STORM. Firstly, it is difficult to control precisely the ratio of activator and reporter dyes on the same antibody. Secondly, by using two wavelengths for a single target protein, N-STORM leaves limited options of dyes for multi-color imaging. Thirdly and most importantly, the commonly used reporter dye Alexa Fluor 647 can blink spontaneously in the imaging buffer without laser activation, making N-STORM susceptible to contaminating detections from other channels. This is particularly problematic for dense structures such as synaptic protein assemblies, because there is no efficient way to identify and remove the contaminating detections. In dSTORM, only single fluorophores (reporter dyes) are used for one color imaging, and their activation is tuned by a low dose of UV light and reducing agents in the imaging buffer. The spectral separation of the fluorophores affords a larger choice of dyes, making dSTORM more favorable for implementing dual-color super-resolution imaging of synaptic proteins.

SMLM applications in neurobiology

SMLM techniques have been successfully applied to many biological structures, providing new insights into their sub-cellular organization and functions [5], [START_REF] Zhong | Applying superresolution localization-based microscopy to neurons[END_REF], [START_REF] Sydor | Super-Resolution Microscopy: From Single Molecules to Supramolecular Assemblies[END_REF]. Baddeley and

Bewersdorf classified the analyzed structures into three categories with increasing difficulties:

(1) well-isolated repetitive or stereotypic structures, ( 2) more complex but still isolated structures, and (3) complex and closely spaced structures [5]. This classification is a good way to illustrate how the level of difficulty can vary for different structures, meaning that labeling, imaging and the data analysis need to be optimized for a given molecular complex.

Stereotypic structures allow combining the information from many structures to reconstruct composite images, and are hence less demanding in terms of the labeling density and detection efficiency. On the other hand, variable structures like synapses need to be treated individually. Therefore, it is not surprising that most SMLM studies to date have been conducted on stereotypic structures such as filaments and nuclear pore complex (NPC), and only limited numbers of studies have been carried out on complex structures such as gene loci or synapses.

One of the most striking discoveries that has been achieved thanks to SMLM imaging was the identification of periodic actin rings in neuronal dendrites and axons by Xu and colleagues [START_REF] Xu | Actin, Spectrin, and Associated Proteins Form a Periodic Cytoskeletal Structure in Axons[END_REF]. These actin rings are evenly distributed along axons and connected by spectrin tetramers, forming a periodic cytoskeletal structure. The periodic organization is also found in the axon initial segment (AIS), providing a robust structural scaffold [START_REF] Leterrier | Nanoscale Architecture of the Axon Initial Segment Reveals an Organized and Robust Scaffold[END_REF]. Similarly, the dendritic spine neck was shown to contain periodic actin structures, as demonstrated by another super-resolution imaging technique, namely stimulated emission depletion (STED)

microscopy [START_REF] Bär | Periodic F-actin structures shape the neck of dendritic spines[END_REF]. The discovery of the periodic actin cytoskeleton in neurons has led to substantial further research and has fundamental consequences for our understanding of the morphogenesis of neurons. Another successful application of SMLM is the description of the eight-fold symmetry of the NPC and the orientation of its Y-shaped component [START_REF] Loschberger | Super-resolution imaging visualizes the eightfold symmetry of gp210 proteins around the nuclear pore complex and resolves the central channel with nanometer resolution[END_REF]- [START_REF] Jordi Broeken | Resolution improvement by 3D particle averaging in localization microscopy[END_REF].

Due to their stereotypic structure and well-known features, NPCs as well as sparsely distributed microtubule filaments are commonly used to assess the performance of newly developed fluorophores, probes, algorithms, etc.

Synapses are of great interest in SMLM due to their small size and important roles in neuronal activity. SMLM investigations have provided important insights into synaptic function. Dani and colleagues have examined pre-and post-synaptic protein assemblies with SMLM, confirming a layered molecular organization of different synaptic components [START_REF] Dani | Super-resolution Imaging of Chemical Synapses in the Brain[END_REF]. This arrangement may explain the different kinetic properties of excitatory scaffold proteins, where proteins that are located at a greater distance from the synaptic membrane have faster exchange rates [START_REF] Kuriu | Differential Control of Postsynaptic Density Scaffolds via Actin-Dependent and -Independent Mechanisms[END_REF]. It has also been shown by SMLM that post-synaptic scaffold proteins are organized in nanodomains that undergo dynamic changes and regulate the strength of neuronal transmission at excitatory [START_REF] Macgillavry | Nanoscale scaffolding domains within the postsynaptic density concentrate synaptic ampa receptors[END_REF]- [START_REF] Haas | Pre-post synaptic alignment through neuroligin-1 tunes synaptic transmission efficiency[END_REF] and inhibitory synapses [START_REF] Specht | Quantitative Nanoscopy of Inhibitory Synapses : Counting Gephyrin Molecules and Receptor Binding Sites[END_REF]- [START_REF] Pennacchietti | Nanoscale Molecular Reorganization of the Inhibitory Postsynaptic Density Is a Determinant of GABAergic Synaptic Potentiation[END_REF]. This synaptic nano-architecture was also observed in vivo and has been confirmed using different superresolution imaging techniques [START_REF] Broadhead | PSD95 nanoclusters are postsynaptic building blocks in hippocampus circuits[END_REF], [START_REF] Dzyubenko | Colocalization of synapse marker proteins evaluated by STED-microscopy reveals patterns of neuronal synapse distribution in vitro[END_REF]. It was further proposed that pre-and post-synaptic nanodomains are aligned in so-called trans-synaptic nanocolumns [START_REF] Tang | A trans-synaptic nanocolumn aligns neurotransmitter release to receptors[END_REF]. Synaptic adhesion molecules are likely to coordinate these trans-synaptic complexes [START_REF] De Arce | Topographic Mapping of the Synaptic Cleft into Adhesive Nanodomains Report Topographic Mapping of the Synaptic Cleft into Adhesive Nanodomains[END_REF], [START_REF] Chamma | Mapping the dynamics and nanoscale organization of synaptic adhesion proteins using monomeric streptavidin[END_REF]. Finally, SMLM has been used to determine the absolute numbers of scaffold proteins and neurotransmitter receptor complexes at synapses, providing new types of quantitative information about the regulation of synaptic structure and function [START_REF] Specht | Quantitative Nanoscopy of Inhibitory Synapses : Counting Gephyrin Molecules and Receptor Binding Sites[END_REF], [START_REF] Fricke | One, two or three? Probing the stoichiometry of membrane proteins by single-molecule localization microscopy[END_REF], [START_REF] Patrizio | Alpha subunitdependent glycine receptor clustering and regulation of synaptic receptor numbers[END_REF].

Difficulties of applying two-color dSTORM to synaptic proteins

SMLM has shown great promise in revealing the nanometer-scale architecture of synapses and the dynamic re-organization during synaptic plasticity. However, SMLM is a complex technology and has some intrinsic limitations that need to be taken into consideration for the experimental design (see section 2). In addition, the unique character of synaptic protein clusters poses particular challenges to the acquisition of reliable SMLM data and to obtaining statistically meaningful results.

First of all, the size of synapses is of the same order as the diffraction limit, with only a few hundred nanometers in diameter, which makes it difficult to obtain resolvable sparse blinking events during imaging. Even when the overall blinking in the field of view (region of interest, ROI) is sparse, there can still be many overlapping blinking events at synaptic sites. The Owing to the complexity of the technology and the unique properties of any research target, each application of SMLM has to be optimized and the imaging quality closely controlled (section 4.3). We strongly recommend that a suitable biological question is formulated in the first place, and that factors such as the spatial resolution, sampling efficiency and data output of the SMLM recordings are weighed against the reproducibility and expected magnitude of the observed effect. In our case, we hypothesize that different neurotransmitter receptors and scaffold proteins occupy specific sub-domains at inhibitory synapses. Our aim is to compare the distributions of these inhibitory synaptic proteins on a scale of tens of nanometers using dual-color dSTORM imaging.

In spinal cord neurons, fast inhibition is mainly mediated by two types of receptors, glycine receptors (GlyR) and γ-aminobutyric acid (GABA) type A receptors (GABA A R). The neurotransmitters glycine and GABA are co-released from the same presynaptic terminals [START_REF] Jonas | Corelease of two fast neurotransmitters at a central synapse[END_REF], [START_REF] Aubrey | Presynaptic control of inhibitory neurotransmitter content in VIAAT containing synaptic vesicles[END_REF] and the two types of receptors coexist at the same postsynaptic densities (PSD) [START_REF] Triller | Gamma-Aminobutyric Acid-containing Terminals Can Be Apposed to Glycine Receptors at Central Synapses[END_REF]- [START_REF] Shrivastava | GABAA Receptors: Post-Synaptic Co-Localization and Cross-Talk with Other Receptors[END_REF]. At the PSD, both GlyRs and GABA A Rs bind to the inhibitory synaptic scaffold protein gephyrin. They share a common binding site but have different binding affinities for gephyrin [START_REF] Meyer | Identification of a Gephyrin Binding Motif on the Glycine Receptor β Subunit[END_REF]- [START_REF] Alvarez | Gephyrin and the regulation of synaptic strength and dynamics at glycinergic inhibitory synapses[END_REF]. Besides, the accumulation of the two types of receptors at synapses can be differentially regulated; for example, microglial prostaglandin E2 modulates GlyR diffusion dynamics but not GABA A Rs [START_REF] Cantaut-Belarif | Microglia control the glycinergic but not the GABAergic synapses via prostaglandin E2 in the spinal cord[END_REF]. GlyR-and GABA A R-mediated postsynaptic currents have different kinetic properties, with GlyR currents exhibiting fast decay times and GABA A Rs decaying more slowly [START_REF] Aubrey | Presynaptic control of inhibitory neurotransmitter content in VIAAT containing synaptic vesicles[END_REF], [START_REF] Alvarez | Gephyrin and the regulation of synaptic strength and dynamics at glycinergic inhibitory synapses[END_REF], [START_REF] Russier | GABA and glycine co-release optimizes functional inhibition in rat brainstem motoneurons in vitro[END_REF]. Together, the co-transmission and the independent regulation of GlyRs and GABA A Rs at mixed inhibitory synapses may modulate the time course and amplitude of fast inhibition in spinal cord neurons. Recently, superresolution fluorescence microscopy has revealed that gephyrin molecules form nanodomains that are dynamically re-organized during inhibitory synaptic plasticity [START_REF] Specht | Quantitative Nanoscopy of Inhibitory Synapses : Counting Gephyrin Molecules and Receptor Binding Sites[END_REF], [START_REF] Pennacchietti | Nanoscale Molecular Reorganization of the Inhibitory Postsynaptic Density Is a Determinant of GABAergic Synaptic Potentiation[END_REF], [START_REF] Dzyubenko | Colocalization of synapse marker proteins evaluated by STED-microscopy reveals patterns of neuronal synapse distribution in vitro[END_REF]. On the other hand, it was shown that excitatory scaffold proteins are also organized in nanodomains that in turn regulate the distribution of excitatory neurotransmitter receptors [START_REF] Macgillavry | Nanoscale scaffolding domains within the postsynaptic density concentrate synaptic ampa receptors[END_REF], [START_REF] Nair | Super-Resolution Imaging Reveals That AMPA Receptors Inside Synapses Are Dynamically Organized in Nanodomains Regulated by PSD95[END_REF].

Therefore, our goal is to investigate the spatial relationship between GlyRs, GABA A Rs and gephyrin at mixed inhibitory synapses. Whether GlyRs and GABA A Rs occupy the same or separate sub-synaptic compartments in relation to the gephyrin distribution has profound consequences for the understanding of the mechanism of co-transmission at mixed inhibitory synapses.

The intrinsic challenges that we encountered when applying SMLM to inhibitory synaptic proteins led us to the method described here (Fig. 1B,C). We found that a practical guideline for biologists to set up quality-controlled SMLM experiments was imperative yet missing. In this article, we describe the crucial considerations for implementing two-color dSTORM of synaptic proteins and provide biology-oriented strategies to assess the imaging quality. We also present a workflow for data analysis with freely-accessible and ready-to-use software.

We hope that these guidelines can help biologists set up their own workflow to study synaptic structures or similarly complex cellular compartments.

Sample preparation for two-color dSTORM

Labeling strategies

With a localization precision of single molecules in the order of 10 nm, the size of the probes used for labeling can impact the quality of dSTORM data in several ways (Fig. 2).

Commercial primary and secondary antibodies are easy to access and convenient for immunostaining, hence they are commonly used to label endogenous proteins in SMLM experiments. However, the average size of full-length IgG is about 10 nm, which is similar to the single molecule localization precision in dSTORM. The combination of primary and secondary antibodies can add a distance of up to 20 nm between the epitope and the fluorophores. If the orientation of the antibodies bound to the target proteins is random, the detected synaptic clusters can be blurred and the resolution of the super-resolution images is lessened. Moreover, the bulky size of antibodies can limit the labeling density due to the masking of epitopes in the crowded synaptic environment, which reduces the sampling efficiency. And finally, the cross-linking effect of primary and secondary antibodies may potentially induce clustering artefacts that only become apparent on the nanometer scale, especially when using live-staining of membrane proteins such as neurotransmitter receptors (Fig. 2A).

Different approaches can be adopted to overcome the drawbacks of using conventional antibodies in dSTORM. For example, antigen-binding fragments (Fab) can be used instead of whole IgG as secondary labels. Primary antibodies can also be conjugated directly with fluorescent dyes, omitting secondary antibodies altogether [START_REF] Chamma | Mapping the dynamics and nanoscale organization of synaptic adhesion proteins using monomeric streptavidin[END_REF]. Alternatively, single-domain antibody fragments derived from camelid or shark antibodies, so-called nanobodies, have recently caught a lot of attention due to their favorable small size [START_REF] Maidorn | Tools and limitations to study the molecular composition of synapses by fluorescence microscopy[END_REF] (Fig. 2B). If combined with specific protein tags, dye-conjugated nanobodies can be used as a modular detection system that generates less epitope-displacement and can give better spatial resolution [START_REF] Ries | A simple , versatile method for GFP-based microscopy via nanobodies[END_REF]- [START_REF] Pleiner | Nanobodies : site-specific labeling for super-resolution imaging , rapid epitope-mapping and native protein complex isolation[END_REF], however, this requires modification and recombinant expression of the target proteins.

Unfortunately, none of these approaches can be easily implemented. Coupling primary antibodies with organic dyes is prone to reduce the binding efficacy and may even affect antibody specificity, whereas the production of specific nanobodies for a target protein can be time-consuming. Nevertheless, the use of small sized probes is certainly preferable whenever there is availability.

Due to the practical limitations mentioned above, we propose to start using conventional immunostaining with primary and secondary antibodies that have been previously used and validated by classical fluorescent microscopy, which is also the case in our experiments. Even though we obtain a slightly lower resolution with conventional antibody labeling of GlyR and gephyrin clusters than with smaller probes, we expect that any significant changes in the synaptic distribution of the receptors and scaffold proteins in response to regulatory processes can still be detected. In addition to using commercial dye-conjugated secondary antibodies, we have also coupled unconjugated secondary antibodies with succinimidyl ester (NHS ester) derivatives of fluorescent dyes, in order to better control the dye/protein ratio of this fluorophore (D/P ratio ranging from 1 to 4). By reducing the amount of redundant information arising from different dyes on the same antibody, this approach increases the sampling efficiency of independent target proteins.

Choice of fluorescent dyes and imaging buffers

Fluorescent dyes are the most critical factors for the imaging quality of multi-color dSTORM.

As shown in Figure 1A, fluorophores can switch between the ON and the OFF state. The excited fluorophores (S1) can either emit red-shifted light and return to ground state (S0) or enter into the OFF state. Fluorophores in the OFF state can enter the ON state spontaneously (with low efficiency), and the activation can be accelerated by activation lasers and reducing agents in the imaging buffer. Generally, all dyes can be activated by light of shorter wavelength and almost all dyes can be activated by UV light with different efficiency. The ON/OFF switching of fluorophores is stochastic, and the duration that fluorophores remain in the OFF state can last from sub-milliseconds to minutes or even hours. Since the ability to separate blinking events both spatially and temporally during imaging is key to obtaining accurate dSTORM data, the photophysical properties of the dyes have a decisive impact on the experimental outcomes. Artifacts that occur in the reconstructed images as a consequence of an insufficient separation of blinking events are difficult to be identified and removed during data processing, and can lead to false conclusions [START_REF] Annibale | Identification of clustering artifacts in photoactivated localization microscopy[END_REF]- [START_REF] Burgert | Artifacts in single-molecule localization microscopy[END_REF].

The blinking behavior of the dyes is dependent on the illumination intensity and reducing agents in the imaging buffer. In dSTORM imaging, a high power of excitation laser light is needed to keep most fluorophores in the OFF state while inducing efficient and sparse blinking of the fluorophores [START_REF] Van De Linde | The effect of photoswitching kinetics and labeling densities on super-resolution fluorescence imaging[END_REF], [START_REF] Van De Linde | Photoinduced formation of reversible dye radicals and their impact on superresolution imaging[END_REF]. The presence of oxygen in the imaging buffer causes the irreversible bleaching of fluorophores and is therefore deleterious for dSTORM imaging.

It has also been demonstrated that the blinking performance of many dyes is superior in low oxygen buffers containing reducing agent such as thiols to assist the activation from the OFF to the ON state [START_REF] Van De Linde | Photoinduced formation of reversible dye radicals and their impact on superresolution imaging[END_REF]- [START_REF] Ha | Photophysics of Fluorescence Probes for Single Molecule Biophysics and Super-Resolution Imaging[END_REF]. Therefore, most dSTORM imaging buffers contain an enzymatic oxygen scavenging system as well as millimolar concentrations of the thiols βmercaptoethylamine (MEA) or β-mercaptoethanol (see Appendix for buffer compositions).

Most commonly, oxygen removal is achieved with a glucose oxidase based enzymatic system (Gloxy buffer), which works optimally for carbocyanine dyes [START_REF] Dempsey | Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging[END_REF]. Rhodamine dyes usually do not blink well without oxygen, thus an oxyrase-based buffer (OxEA) permitting low level oxygen is preferred for multicolor dSTORM imaging with rhodamine dyes [START_REF] Nahidiazar | Optimizing imaging conditions for demanding multi-color super resolution localization microscopy[END_REF]. Meanwhile, novel organic dyes are being developed in an attempt to further improve the fluorophore performance [START_REF] Qinsi Zheng | Ultra-stable organic fluorophores for single-molecule research[END_REF]- [START_REF] Lehmann | Novel organic dyes for multicolor localization-based super-resolution microscopy[END_REF], and there are quite a range of commercially available dyes for dSTORM. However, due to their photochemical differences in addition to their spectral properties, identifying a pair of compatible dyes that behave equally well in the same imaging system is not trivial [START_REF] Dempsey | Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging[END_REF], [START_REF] Nahidiazar | Optimizing imaging conditions for demanding multi-color super resolution localization microscopy[END_REF], especially when both target proteins require the best possible imaging qualities.

The characterization of the blinking performance of the dyes is therefore indispensable and should be taken into account for the experimental design. Empirically, the performance of dyes can be assessed according to the following principles: ( 1 As a starting point, we strongly recommend the carbocyanine dye Alexa Fluor 647 in the farred channel. Alexa 647 is commonly used in dSTORM due to its excellent photophysical properties that satisfy the five requirements mentioned above. Moreover, appropriate blinking of Alexa 647 can be achieved in various buffer conditions, and there is generally very little background fluorescence in biological samples in the far-red channel. To determine the second color for two-color dSTORM, we have tested several rhodamine dyes (Atto 488, Alexa Fluor 488, Alexa Fluor 568) and carbocyanine dyes (Cy3B) emitting in the green or the red channel in different imaging buffers, to evaluate their compatibility with Alexa 647 in sequential two-color dSTORM. As expected, Gloxy and OxEA buffers stand out of merely MEA-containing buffers and PBS. In the green channel, Atto 488 performed better than Alexa 488 in Gloxy buffer. The concern of using green dyes is that biological samples often have a high level of fluorescence background in this part of the spectrum, and that the higher energy of the green light accelerates the bleaching of the fluorescent beads used as fiducial markers (section 2.3). In the red channel, Alexa 568 blinks nicely in OxEA buffer but less well in Gloxy buffer. Cy3B on the other hand works better in Gloxy buffer. Also, Alexa 647 performs optimally in Gloxy buffer but fails to exhibit sufficient spontaneous blinking in OxEA buffer without UV activation. Therefore, we chose Alexa 647 and Cy3B in Gloxy buffer as the optimal imaging system. Since our two-color dSTORM recordings are done sequentially (Fig. 1B,C), this combination of dyes allows us to omit UV activation during the imaging of Alexa 647 due to its spontaneous blinking, thus avoiding any undesired bleaching of Cy3B or the beads. In the second stage, Cy3B is imaged in the red channel with an increasing 405 nm illumination to dynamically adjust the number of fluorophores in the ON state. It is recommended to prepare fresh imaging buffer before the dSTORM recordings, since aging buffers tend to induce less and slower blinking events. Optionally, a very stable glucose-oxidase based imaging buffer is commercially available from Abbelight (Smart kit).

Fluorescent beads as fiducial markers

Drift correction and two-channel registration are important factors for reconstructing superresolution images as well as co-localization analysis of synaptic components [START_REF] Paolo Annibale | Identification of the factors affecting co-localization precision for quantitative multicolor localization microscopy[END_REF]. Fluorescent beads are generally used as fiducial markers in two-color dSTORM imaging for both drift correction and image registration. Since the recordings can take up to 30 minutes, the microscope stage can drift dramatically in the x/y plane by up to tens of micrometers due to mechanical and thermal effects (Fig. 3). Though the perfect focusing system of our Nikon Ti microscope minimizes the drift in the axial direction, the lateral drift can lead to false structural representations that demand correction. For stereotypic or densely labeled structures, the drift correction can also be achieved by using the structures themselves as landmarks. However, in the case of sparse synaptic clusters, there are too few detections for drift correction using the clusters themselves. Using beads for drift correction is therefore our best option. For sequential two-color dSTORM, two-channel registration is also mandatory, because both lateral drift and spectral differences result in a mismatch between the reconstructed images in the two channels. Using beads as independent fiducial markers induces less bias in the two-channel alignment than using the synaptic clusters.

During sample preparation, we attach multicolor beads (100 nm, TetraSpeck microspheres, ThermoFisher) non-specifically to the coverslips. To this aim, the fixed and labeled samples are incubated for about one minute with an excess of beads (~ 10 6 µl -1 in PBS) and rinsed, before mounting the coverslips in imaging buffer. The number of attached beads strongly depends on the density of cellular material on the coverslip, and can be controlled by adjusting the dilution of the beads, the incubation duration and the amount of rinsing. Too few beads will make it difficult to find suitable ROIs and too many beads can mask the signals coming from the fluorophores (Fig. 1C). The attachment of beads on the coverslip is nonspecific, meaning that the beads are not always stationary. This is seen as jittering of the beads during imaging, and sometimes the beads can even detach and move out of the field of view (Fig. 3B, bead #1). Beads are usually very bright and produce precisely localized detections. However, when their signals are saturated, the positions of the beads are not fitted accurately, introducing errors in the drift correction. Consequently, strong laser illumination is needed at the beginning of the recording to partially bleach the beads. Note that the prebleaching of the beads can result in a loss of signals from the sample fluorophores. In short, saturated or moving beads should be avoided as markers, and using more than one bead in the ROI can improve the precision of drift correction (Fig. 3). In the ideal case, we aim to record about two to three stable and non-saturated beads per field of view.

Primary spinal cord neuron culture and immunocytochemistry

All procedures using animals follow the regulations of the French Ministry of Agriculture and the Direction départamentale des services vétérinaires de Paris (Ecole Normale Supérieure, Animalerie des Rongeurs, license B 75-05-20). Primary spinal cord neuron cultures were prepared from embryonic Sprague Dawley rats on day 14 of gestation (E14) as described previously [START_REF] Specht | Quantitative Nanoscopy of Inhibitory Synapses : Counting Gephyrin Molecules and Receptor Binding Sites[END_REF], with some modifications. Cells were plated on 18 mm glass coverslips (thickness 0. were coupled with Cy3B mono-reactive NHS ester (PA63101, GE Healthcare) according to the supplier's protocol, purified using size exclusion columns (Illustra NAP-5 columns, #17085302, GE Healthcare) and used at a dilution of 1:50 to 1:100 for immunolabeling.

The choice of fixatives, concentrations of primary and secondary antibodies, as well as buffering solutions and incubation times can all affect dSTORM imaging [START_REF] Richter | Glyoxal as an alternative fixative to formaldehyde in immunostaining and super-resolution microscopy[END_REF]- [START_REF] Whelan | Image artifacts in Single Molecule Localization Microscopy : why optimization of sample preparation protocols matters[END_REF]. Even though the primary neuron culture and the immunocytochemistry procedures are standard protocols, optimization of the sample preparation is important for dSTORM, since the requirements are not the same as for conventional fluorescence microscopy. For example, Cy3B-tagged secondary antibodies with low dye/protein ratio (D/P » 1-4) give more discrete blinking profiles in our dSTORM recordings, even if the total fluorescence intensity of the labeled synapses is reduced. Fixatives such as PFA can contribute to background fluorescence. We have therefore quenched the fluorescence background with 50 mM NH 4 Cl after fixation. Membrane molecules may retain a certain level of residual mobility after chemical fixation that can be detected in super-resolution images [START_REF] Tanaka | Membrane molecules mobile even after chemical fixation[END_REF]. In these cases, it is recommended to increase fixation times, as long as this does not interfere with the access of the antibodies to the epitopes. Where necessary, membrane proteins such as neurotransmitter receptors can also be stained by live-cell immunocytochemistry to enhance epitope recognition. However, care should be taken to avoid the artificial clustering of the membrane proteins due to the crosslinking effect of secondary antibodies [START_REF] Brünig | Intact sorting, targeting, and clustering of γ-aminobutyric acid A receptor subtypes in hippocampal neurons in vitro[END_REF]. Finally, samples should always be protected from light and kept at 4°C for no longer than two days prior to dSTORM imaging.

Sequential two-color dSTORM imaging

Microscope setup

The STORM setup is built on an inverted Nikon Eclipse Ti microscope with a perfect focusing system (PFS) to minimize the axial drift of the microscope stage, and equipped with a 100× oil immersion objective (HP APO TIRF 100× oil, NA 1.49, Nikon). The setup includes several continuous laser lines with emission wavelengths at 640 nm, 561 nm, 532 nm, 488 nm and 405 nm (Coherent), with nominal maximum power of 1W, 1W, 500 mW, 500 mW and 120 mW, respectively (Fig. 4). The lasers are combined in an external platform and controlled via an acousto-optic tunable filter (AOTF) to apply fast illumination pulses and to set the laser intensity. The laser beam is expanded with a total internal reflection fluorescence (TIRF) arm that controls the inclination angle of the excitation light, and is focused on the back focal plane of the objective. The emission of the fluorescent dyes is captured with an electron multiplying charge-coupled device (EMCCD) camera (Andor iXon 897 Ultra, 512 x 512 pixels chip) using a multiple wavelength dichroic mirror and appropriate band pass filters (Semrock FF02-684/24 for Alexa 647, FF01-607/36 for Cy3B) in the emission light path. Conventional fluorescence images were taken with a mercury lamp (Intensilight C-HGFIE, Nikon) that emits a broad spectrum of wavelengths, using the corresponding band pass filters (FF01-650/13 for Alexa 647, FF01-560/25 for Cy3B) in the excitation light path. All the elements are controlled by NIS-Elements software (Nikon).

Imaging procedure

The dSTORM imaging workflow is illustrated in Figure 1B and C. On the day of imaging, labeled neuron cultures on glass coverslips are incubated with 100 nm beads, and mounted on glass slides with a cavity (diameter 15-18 mm, depth 0.6-0.8 mm) containing freshly prepared Gloxy imaging buffer. Coverslips are sealed with silicone rubber (Picodent twinsil speed 22).

We search for ROIs containing several synaptic clusters and fiducial beads under the illumination of the mercury lamp instead of lasers to minimize the bleaching of the fluorophores. Reference images are taken in the far-red and in the red channel with the lamp.

The effective magnification of these images is 100x, resulting in a pixel size of 160 nm.

Sequential two-color dSTORM is then carried out first in the far-red channel for Alexa 647 signals, followed by the red channel for Cy3B. In the beginning of each recording, the field of view is briefly pre-bleached to push the fluorophores into the OFF state and to dampen the brightness of the beads. At least 20000 frames with an exposure time of 50 milliseconds are recorded in each channel. We use highly inclined illumination rather than TIRF to reduce the background fluorescence while avoiding interferences and inhomogeneous illumination. In the far-red channel, the 640 nm laser is used for illumination at an intensity of approximately 2.7 kW/cm 2 , resulting in the spontaneous blinking of Alexa 647 fluorophores in the absence of 405 nm laser activation in Gloxy buffer. In the red channel, Cy3B dyes are imaged with the 561 nm laser at an intensity of 4 kW/cm 2 . The blinking of Cy3B is supported by low-intensity activation with the 405 nm laser (~ 0.01 kW/cm 2 power). The total acquisition time needed for each ROI is about one hour.

Determining the length of imaging

The primary goal of SMLM imaging is to record as many blinking events as possible from the fluorophores. However, it is practically impossible to detect all the signals in dSTORM because of the random ON/OFF switching cycles of the organic dyes. Some fluorophores can stay in the OFF state for long periods that exceed the recording time. In fact, the multiple switching cycles produce redundant data where each fluorophore can be detected several times. The target proteins are usually labeled with several fluorophores through primary and secondary antibody staining, which produces further redundant information about the position of the same target protein. In addition, the number of detection artefacts (i.e. non-specific fluorescence background) increases with the number of image frames, whereas the actual fluorophore blinking events decrease over time. Therefore, it is neither necessary nor advisable to extend the duration of the recordings and aim to detect all the blinking events from the fluorophores. It is only important to record sufficient independent localizations from which representative structures can be reconstructed. We have therefore adopted the following reasoning to determine the imaging length of our recordings: what is the minimal number of frames from which the reconstructed structures have visibly good agreement with the images reconstructed from much longer recordings?

For two-color dSTORM imaging of GlyRs and gephyrin labeled with Alexa 647 and Cy3B, respectively, we recorded 30000 frames in the far-red and 40000 frames in the red channel, until there remained few blinking events at the end of the recordings (Fig. 5). We then split the whole stacks into consecutive sub-stacks of 10000 frames from which we reconstructed separate images and compared their similarity. GlyR clusters reconstructed from the first 10000 frames agree well with those in the second and third sub-stacks. Image reconstructions made of the first 20000 frames give very similar representation as 30000 frames. This illustrates that Alexa 647 fluorophores undergo multiple ON/OFF switching cycles that give rise to relatively stable long-term blinking, and that the sub-synaptic distribution of labeled GlyRs at synapses can be captured within 20000 frames (Fig. 5A). In the red channel, the gephyrin clusters from the first two sub-stacks of 10000 images are again quite similar, however, the reconstruction of the third sub-stack produces a less detailed representation.

Clearly, Cy3B fluorophores have fewer blinking cycles, meaning that the number of independent detections declines over time, despite the use of the 405 nm activation laser. As a result, the gephyrin clusters reconstructed from 30000 and from 40000 frames are essentially indistinguishable. We therefore set the recording time in the red channel to 30000 frames (Fig. 5B). From the temporal profile of the reconstructed clusters in the two channels, we can also gain some insights into the blinking performance of the dyes. If the clusters reconstructed from the sub-stacks are similar, this implies that the switching cycles of the fluorophores are relatively dispersed throughout the recording (also see Fig. 6A), confirming that the two dyes are suitable for dSTORM imaging of synaptic proteins.

Data analysis and quality assessment

Image pre-processing

Critical processing steps of the raw imaging data include single particle detection, drift correction, two-channel registration and image reconstruction (Fig. 1B). Single particle detection is realized by applying Gaussian fitting of the PSF of each fluorophore signal to determine the molecule coordinates. To this aim, we use an adapted version of the multipletarget tracing (MTT) program [START_REF] Arnauld Serge | Dynamic multi-target tracing to probe spatiotemporal cartography of cell membrane[END_REF] in Matlab 2012b (MathWorks), as described by Izeddin et al [START_REF] Izeddin | Super-resolution dynamic imaging of dendritic spines using a lowaffinity photoconvertible actin probe[END_REF]. The lateral drift is corrected with the program PALMvis [START_REF] Lelek | Superresolution imaging of HIV in infected cells with FlAsH-PALM[END_REF] in Matlab 2012b using beads as fiducial markers. A sliding window of 100 to 1000 frames is applied to calculate the average position of the beads, and the systematic x/y drift over time is estimated with a temporal resolution of 5-50 s based on the average displacement of the beads (Fig. 3). The calculated drift of the stage is then applied to the whole image to correct the coordinates of all the single fluorophore localizations. At this stage, we obtain the final coordinates of the localizations, and the two-channel registration can now be done by aligning the beads that were chosen as fiducial markers. Nevertheless, we can go one step further and render the SMLM images as density-based representations of the fluorophore detections. The driftcorrected coordinates are converted into rendered images by representing each localization as a Gaussian function with the same intensity and with a standard deviation (σ = 15 nm) that is derived from the average localization precision of the detections. Super-resolution images are generally displayed with a pixel size of 10 nm to balance file size versus representation precision. In the rendered super-resolution images, the pixel intensities ultimately represent the density of localizations and the images have zero background. Finally, the two superresolution images are aligned using the TurboReg plugin [START_REF] Thevenaz | A Pyramid Approach to Sub-Pixel Registraion based on Intensity[END_REF] in Fiji [START_REF] Schindelin | Fiji: An open-source platform for biological-image analysis[END_REF].

Alternative tools are available for all the processing steps described above [START_REF] Sage | Quantitative evaluation of software packages for single-molecule localization microscopy[END_REF]. For example, ThunderSTORM [START_REF] Ovesný | ThunderSTORM: A comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging[END_REF] and QuickPALM [START_REF] Henriques | QuickPALM: 3D real-time photoactivation nanoscopy image processing in ImageJ[END_REF] are commonly used for signal detection and SMLM image reconstruction, and are freely accessible as plugins in Fiji. For more information about the software and programs designed for SMLM, please also see the website of the Biomedical Imaging Group of the EPFL (Lausanne, Switzerland; http://bigwww.epfl.ch/smlm/software).

Statistical analysis

Both types of datasets from SMLM can be used for statistical analysis, namely the coordinate- Different algorithms and software are available for analyzing coordinate-based pointillist data.

For instance, density-based spatial clustering of applications with noise (DBSCAN) and Voronoï tessellation have been exploited for spatial clustering and segmentation of SMLM data [START_REF] Nicovich | Turning single-molecule localization microscopy into a quantitative bioanalytical tool[END_REF]. The advantage of the two algorithms is that they do not make any assumptions about the shape of the structures, which is very suitable for segmenting synaptic clusters in SMLM data. To quantify the co-localization of two proteins, coordinate-based co-localization (CBC) [START_REF] Malkusch | Coordinate-based colocalization analysis of single-molecule localization microscopy data[END_REF], [START_REF] Georgieva | Nanometer resolved single-molecule colocalization of nuclear factors by two-color super resolution microscopy imaging[END_REF] enables single molecule correlation analysis of dual-color pointillist data.

These algorithms have been implemented in different open-source software for SMLM data analysis, such as Clus-Doc, LAMA and so on [START_REF] Pageon | Clus-DoC : a combined cluster detection and colocalization analysis for single-molecule localization microscopy data[END_REF]- [START_REF] Andronov | ClusterViSu , a method for clustering of protein complexes by Voronoi tessellation in super-resolution microscopy[END_REF]. However, using these tools can be problematic, because they are often not suited to the specific demands. In our case, we did not find any suitable analysis tool that would allow us to perform individual cluster-based analysis, which is essential for the comparison of synaptic GlyR and gephyrin clusters. In contrast, individual synaptic clusters in the rendered super-resolution images can be easily obtained with classical image processing. With the powerful image analysis platforms Fiji [START_REF] Schindelin | Fiji: An open-source platform for biological-image analysis[END_REF] and Icy [START_REF] Chaumont | Icy: An open bioimage informatics platform for extended reproducible research[END_REF], image-based analysis of individual synaptic clusters is relatively straightforward. Therefore, we suggest to begin with image-based SMLM analysis.

In the following, we describe an image-based analysis workflow using exclusively open source tools that allow biologists to extract relevant information. With the help of simple macros in Fiji [START_REF] Schindelin | Fiji: An open-source platform for biological-image analysis[END_REF] to record commands and construct basic programs, a set of experimental data, namely two-color recordings of ten cells with about one hundred synapses, can be analyzed in one day using this workflow.

First, the reference images are converted into binary masks with the Icy spot detector plugin, choosing appropriate parameters to select well focused and brightly labeled synaptic puncta.

The combined area of synaptic masks in the two channels is obtained by calculating the sum of the binary images with Fiji and used to identify the synaptic clusters in the super-resolution images. Since out-of-focus synapses and background fluorescence can also appear as clusters in dSTORM images, using the mask to pre-select synaptic clusters is necessary. However, it should be kept in mind that this process can potentially introduce a certain bias in the analysis, since small and weakly labeled synaptic clusters may be lost. This is not an issue in our case, because our aim is to directly compare the sub-synaptic distributions of different synaptic components within the same synapse. The dual-color dSTORM recordings can therefore be considered as paired data. Therefore, the selection of synaptic clusters based on the labeling intensity is a legitimate approach.

Since our super-resolution images are reconstructed from detection coordinates, there may be a substantial mismatch between the two super-resolution images and the reference images.

Drift correction and chromatic differences add to the mismatch between the images.

Therefore, in the second step, the super-resolution images and the combined mask image are aligned with TurboReg plugin in Fiji. Also, the pixel size of the low resolution mask (160 nm)

has to be artificially enhanced to match that of the rendered dSTORM images (10 nm). After image registration, the masks for individual synapses are applied to the super-resolution images to crop the individual synaptic clusters in Fiji. At this point, we obtain individual synaptic cluster pairs of GlyRs and gephyrin, on which we can perform further cluster analysis.

To characterize the spatial correlation of GlyR and gephyrin clusters, we employ the intensity correlation analysis (ICA) plugin in Fiji as described by Li et al [START_REF] Li | A Syntaxin 1, Gαo, and N-Type Calcium Channel Complex at a Presynaptic Nerve Terminal: Analysis by Quantitative Immunocolocalization[END_REF]. In this analysis, the variations of the intensities for each pixel between two images are compared in order to identify correlated changes that reflect the co-localization of the two proteins. Intensity correlation quotients (ICQ) between -0.5 and +0.5 are calculated for each synaptic cluster, where -0.5 indicates a mutually exclusive distribution, zero a random distribution, and +0.5 a perfect positive correlation between the two proteins. Moreover, the ICA plugin also calculates the Pearson correlation coefficient (Pearson's R) which measures the linear correlation of the intensity of each pixel between the two images, providing additional information.

To characterize the internal synaptic distribution of GlyRs and gephyrin, we carry out Hwatershed analysis to segment the subclusters using the interactive H-watershed plugin developed by Benoit Lombardot (http://imagej.net/Interactive_Watershed) in Fiji. Hwatershed finds local maxima with one parameter, H, allowing the detection of subcluster peaks and the segmentation of the subclusters from each other. After segmentation, the features of the binary synaptic subclusters are extracted using extended particle analyzer hand display relatively few blinking events, some of which have persistent ON times, resulting in large bursts of repetitive detections during a single blinking event. As a consequence, Cy3B produces detection densities that are less quantitative than those of Alexa 647. The diameter of the clusters produced by single IgG is around 40 to 120 nm, which is similar to the size of the nanodomains proposed for synaptic proteins [START_REF] Macgillavry | Nanoscale scaffolding domains within the postsynaptic density concentrate synaptic ampa receptors[END_REF]- [START_REF] Haas | Pre-post synaptic alignment through neuroligin-1 tunes synaptic transmission efficiency[END_REF], [START_REF] Tang | A trans-synaptic nanocolumn aligns neurotransmitter release to receptors[END_REF].

Therefore, the detection density and the size of the clusters from single probes used for labeling has to be taken into account during data analysis and interpretation when characterizing nanodomains of synaptic proteins.

Next, we assessed the consistency of two-color dSTORM imaging of synaptic proteins by labeling GlyR clusters in spinal cord neurons with the same antibody against the GlyR α1 subunit and with two different secondary antibodies, namely Alexa 647-conjugated donkey anti-rabbit and Cy3B-conjugated donkey anti-rabbit IgG. We then analyzed the spatial correlation of GlyR clusters detected in the two channels. As expected, both Pearson's coefficient and ICA showed strong positive correlation, demonstrating that Alexa 647 and Cy3B produce similarly good representations of the same GlyR cluster (Fig. 6B). We further looked into the internal organization of the GlyR clusters detected by the two dyes. The superresolution clusters of GlyR were segmented using H-watershed to detect the sub-synaptic GlyR distribution. The subcluster counts detected by either Alexa 647 or Cy3B showed moderate correlation and Cy3B tended to produce more subclusters. Concomitantly, the areas of the subclusters detected by Alexa 647 were significantly larger than the ones detected by Cy3B. This shows that the different photophysical properties of Alexa 647 and Cy3B dyes have considerable impact on the sub-synaptic structural representation in dSTORM.

Third, we tested the effect of dye reversal on the detection of synaptic clusters (Fig. 6C). We used two different primary antibodies that recognize the same synaptic protein, gephyrin.

Primary mouse monoclonal mAb7a (m7a) and rabbit polyclonal antibodies against gephyrin (rbGPHN) were labeled, in one case with Cy3B-conjugated donkey anti-mouse IgG and Alexa 647-conjugated donkey anti-rabbit IgG (condition 1), and in the other case with Alexa 647-conjugated donkey anti-mouse IgG and Cy3B-conjugated donkey anti-rabbit IgG (condition 2). We then compared the spatial correlation of the gephyrin clusters in the two channels between the two conditions, as well as the subclusters. The second experimental condition showed significantly stronger correlation than the first condition, as judged from the Pearson's R and ICQ values. As to the sub-synaptic organization of gephyrin, the subcluster counts per synapse detected by Cy3B were again higher than those of Alexa 647-labeled clusters in both conditions. Interestingly, the sizes of the subclusters probed with rbGPHN-A647 were much larger than with m7a-Cy3B in the first condition. However, this difference was not seen in the second condition with reversed dyes. Under the same circumstances, it would be expected that the two conditions give the same degree of co-localization and that gephyrin subclusters detected by Alexa 647 would generally occupy larger areas than with Cy3B, which is not the case in our results. Given that m7a and rbGPHN detect different epitopes of gephyrin, one possible explanation for this unexpected finding is that the two epitopes are not distributed homogeneously at synapses, which is part of our ongoing research. Our data also illustrate the necessity of control experiments with reversed dyes to validate the experimental findings.

Conclusion

SMLM is a powerful technology that allows the characterization of biological samples with a resolution on the nanometer scale. Based on our own experiences we strongly recommend to consider the technical difficulties and limitations of the technique in the experimental design, in particular when SMLM is applied to complex biological structures. The methodological framework discussed in this chapter is applicable to synapses and similarly complex structures alike. We also propose that the quality of the reconstructed images and the relevance of the extracted parameters be critically assessed before drawing far-reaching conclusions, and that the results of these assessments and control experiments should be included where possible in scientific publications. A. Long dSTORM recordings of 30000 frames were acquired under optimized imaging conditions until there were few remaining blinking events from GlyRs labeled with Alexa 647 (GlyR-A647). The reconstructed GlyR clusters from independent substacks of 10000 frames were compared to define the ideal length of recording. The good agreement between the three substacks indicates that 10000 frames are sufficient to reconstruct the synaptic clusters with Alexa 647 fluorophores.

with 767 subclusters for GlyR-A647 and 874 subclusters for GlyR-Cy3B, from two independent experiments). Scale: 200 nm.

C. Dye reversal experiment: two-color dSTORM imaging of gephyrin in spinal cord neurons (fixed with cold methanol) that were labeled with two different primary antibodies against gephyrin, followed by two combinations of secondary antibodies. The antibody combination rbGPHN-Cy3B/m7a-A647 (n = 174 synapses) gave a significantly higher spatial correlation of gephyrin clusters in the two channels than the combination with reversed dyes (rbGPHN-A647/m7a-Cy3B, n = 148), as shown by Pearson's coefficient (Mann-Whitney U test, p = 0.00262) and ICQ (p < 0.00001). Subcluster counts per synapse were systematically higher when clusters were detected with Cy3B than with Alexa 647 regardless of the primary antibodies (paired t-test, p < 0.0001 for both combinations, data are shown as mean ± SD).

Subcluster areas detected by rbGPHN-A647 were significantly larger than by m7a-Cy3B

(Kolgomorov-Smirnov test, p < 0.0001, n = 321 subclusters for rbGPHN-A647 and 403 for m7a-Cy3B), while the difference was not detected with the other antibody combination (p = 0.425, n = 578 for rbGPHN-Cy3B and 504 for m7a-A647). Data were from three imaging experiments using two different spinal cord cultures. Scale: 100 nm.

INTRODUCTION

Single molecule localization microscopy (SMLM) bypasses the diffraction limit by detecting signals from a sparse subset of molecules that are temporally separated, thus achieving a spatial resolution of single molecules of 10-40 nm (Schermelleh et al., 2010;Turkowyd et al., 2016;Sieben et al., 2018). SMLM includes several related techniques, namely STORM, PALM and uPAINT (Betzig et al., 2006;Hess et al., 2006;Rust et al., 2006;Giannone et al., 2010).

In 2010, Dani et al. (2010) measured the laminar distribution of synaptic proteins using multicolor three-dimensional (3D) STORM, demonstrating the capability of SMLM to visualize the ultra-structure of synapses [START_REF] Specht | Visualizing the ultrastructures and dynamics of synapses by single-molecule nanoscopy[END_REF]. This marks the beginning of super-resolution optical imaging of synaptic structures. Numerous studies have since applied SMLM to explore the heterogeneity and complexity of protein assemblies at synapses. Another type of super-resolution optical microscopy achieves sub-diffraction resolution by means of structured excitation, such as stimulated emission depletion (STED; Klar et al., 2000) and structured illumination microscopy (SIM; Gustafsson, 2000). Regardless of the different working principles, super-resolution microscopy techniques have yielded significant insights into the distribution of synaptic proteins on the nanometer scale. Given their wide-field, volumetric imaging strategies, three-dimensional and quantitative information can be gained from a large sample size.

In 2013, several groups reported independently that different synaptic proteins are distributed heterogeneously at synapses (MacGillavry et al., 2013;Nair et al., 2013;Specht et al., 2013). SMLM images showed that the excitatory scaffold protein PSD-95 occupies subdomains within the post-synaptic density (PSD) that regulate AMPAR clustering (MacGillavry et al., 2013;Nair et al., 2013). The existence of PSD-95 subdomains was confirmed with STED microscopy both in vitro and in vivo (Broadhead et al., 2016;Dzyubenko et al., 2016;Hruska et al., 2018;Masch et al., 2018;Wegner et al., 2018). Likewise, subsynaptic domains of gephyrin were shown to play a role in inhibitory plasticity at GABAergic synapses (Pennacchietti et al., 2017;Crosby et al., 2019). These findings point towards a mechanism whereby subsynaptic domains drive the recruitment of neurotransmitter receptors to specific locations within the PSD, thus regulating synaptic transmission.

SMLM and STED microscopy have also shown that pre-synaptic proteins of the active zone (AZ) and synaptic adhesion proteins display subsynaptic distributions (Perez de Arce et al., 2015;Chamma et al., 2016a,b;Tang et al., 2016;Glebov et al., 2017;Haas et al., 2018). Using multicolor 3D-STORM, Tang et al. (2016) demonstrated that subsynaptic domains of RIM1/2 are aligned with those of PSD-95, an arrangement that is referred to as trans-synaptic nanocolumn. The alignment of pre-and post-synaptic elements appears to be due to neuroligin/neurexin adhesion complexes (Perez de Arce et al., 2015;Haas et al., 2018). These exciting observations not only demonstrate the power of SMLM to visualize the ultrastructures of synapses but also point towards possible roles of subsynaptic domains in synaptic function (reviewed in Biederer et al., 2017;Liu et al., 2017;Chen et al., 2018;Scheefhals and MacGillavry, 2018).

Despite these advances, the concept of subsynaptic domains remains ambiguous, not least because the technical and biological limitations in identifying subsynaptic domains have not been sufficiently scrutinized. Here, we review the available evidence for the existence of subsynaptic domains, highlighting the factors that need to be taken into account in detecting small protein assemblies using SMLM. We then discuss the possible role of subsynaptic domains in the regulation of glycinergic and GABAergic co-transmission based on recent data from inhibitory synapses.

WHAT IS A SUBSYNAPTIC DOMAIN? Terminology and Definition

A major source of confusion is that different names have been used in the literature to describe subsynaptic domains. Among these, the terms nanodomain, nanocluster, subcluster, subdomain and nanomodule have been used in an interchangeable manner (e.g., MacGillavry et al., 2013;Nair et al., 2013;Broadhead et al., 2016;Haas et al., 2018;Hruska et al., 2018). The lack of a clear and unified terminology has made it difficult to refer to specific molecular structures and to be aware of the differences and similarities between studies. Regarding the choice of words, the term cluster should best be avoided, because it can also refer to the clustering algorithms that are widely used for image analysis of SMLM data (Nicovich et al., 2017). The prefix nano is redundant because synapses themselves have diameters of only a few hundred nanometers. Furthermore, nanodomain has been widely used to describe the high Ca 2+ ion concentrations in the proximity of an open calcium channel (Augustine et al., 2003;Eggermann et al., 2013;Ghelani and Sigrist, 2018).

We, therefore, refer to these structures as subsynaptic domain or SSD (Crosby et al., 2019) for the following reasons: (1) the term is self-explanatory, referring to a space that is smaller than the whole synaptic compartment and that is occupied by a given type of molecules; and (2) it is flexible in that it can be equally applied to membrane receptors, scaffold and signaling proteins, whether they are pre-synaptic or post-synaptic. We define SSD as a sub-compartment of the synapse in which the density of a specific synaptic protein is higher than in the surrounding area, and that is typically observed with super-resolution microscopy. We believe that the term SSD could thus provide some clarity in defining specific molecular entities at synapses.

SSD Size and Protein Copy Numbers

The most basic feature of SSDs that holds biologically relevant information is their size and the copy number of proteins that they contain. A wide range of sizes was detected by SMLM and STED microscopy (Table 1). For instance, SSDs of excitatory scaffold proteins in cultured hippocampal neurons have a diameter of ⇠80 nm as judged by coordinate-based SMLM analysis (MacGillavry et al., 2013), whereas an average diameter of 120 nm was measured in reconstructed super-resolution images (Nair et al., 2013). STED microscopy detected SSDs of PSD-95 with a diameter of 200 nm (Fukata et al., 2013). These differences in SSD size are likely due to the different resolution of the imaging systems and the application of a threshold during image processing. A comparative study of PSD-95 in hippocampal tissue using PALM and STED determined median SSD diameters of 126 nm and 158 nm, respectively, exemplifying the impact of the imaging approach (Broadhead et al., 2016). The typical diameter of the whole PSD in hippocampal neurons ranges from 100 nm to 800 nm, with a mean of about 300 nm (Harris and Stevens, 1989;Arellano et al., 2007). Therefore, the lower limit of SSD sizes of ⇠50 nm reflects the image resolution of the super-resolution imaging techniques, while the upper limit corresponds to the size of the entire synapse. Given that synapse sizes vary substantially across the central nervous system, an interesting question is whether SSDs of different synaptic proteins have stereotypical sizes that are the same at different types of synapses (see Crosby et al., 2019).

Information about protein copy numbers is essential to establish the structural basis of SSD formation. To date, there are hardly any quantitative data about SSD molecule numbers. SSDs of AMPARs have been estimated to contain an average of ⇠20 receptor complexes (Nair et al., 2013). Due to the limited accessibility of the epitopes for immunolabeling, however, the actual number of receptors per SSD could be higher. This could have an effect on the role of SSDs in synaptic function since the number of active receptors is directly related to the strength of synaptic transmission (Masugi-Tokita et al., 2007;Tarusawa et al., 2009;Fukazawa and Shigemoto, 2012).

Number of SSDs Per Synapse

Most synapses contain only one SSD or no SSD at all. More specifically, a single SSD was detected in 50% to 80% of synapses imaged with SMLM, SIM or STED microscopy, less than 20% had more than three SSDs, and six SSDs was the upper limit (MacGillavry et al., 2013;Nair et al., 2013;Broadhead et al., 2016;Chamma et al., 2016a,b;Pennacchietti et al., 2017;Hruska et al., 2018;Crosby et al., 2019). It is likely that the different imaging techniques and analyses again have an effect on the detection of multiple SSDs. This raises the question whether the SSD simply reflects the center of mass of the protein assembly, and if so, whether the presence of single or multiple SSDs actually matter for the regulation of synaptic function.

There exists a positive correlation between the number of SSDs and the size of the PSD or the dendritic spine (Fukata et al., 2013;Nair et al., 2013;Hruska et al., 2018;Crosby et al., 2019). EM studies have revealed a large variability in PSD area, ranging from 100 nm to 800 nm in diameter (Table 1). More than half of the PSDs are small (<0.05 µm 2 ), which is similar to the fraction of synapses with only one SSD (Arellano et al., 2007). Moreover, the number of AMPAR molecules is positively correlated with the PSD size, and large complex PSDs have a higher density of AMPARs than small, non-perforated PSDs (Ganeshina et al., 2004;Shinohara et al., 2008;Fukazawa and Shigemoto, 2012). Together, these data indicate that SSDs may only play a role at large PSDs, reflecting the superior strength of these synapses.

Trans-synaptic Nanocolumns

From the viewpoint of neuron connectivity, pre-synaptic and post-synaptic SSDs can be aligned to form trans-synaptic structural units that regulate synaptic function (Biederer et al., 2017;Chen et al., 2018). Such an organization has been observed at excitatory synapses using 3D-SMLM, and was suitably named trans-synaptic nanocolumn (Tang et al., 2016). SMLM studies have further shown that synaptic adhesion complexes such as neuroligin and neurexin are also organized in SSDs, suggesting that they contribute to the formation of trans-synaptic nanocolumns (Perez de Arce et al., 2015;Haas et al., 2018). The term nanocolumn, therefore, refers to a specific concept, namely the alignment of pre-and post-synaptic SSDs that brings together different functional elements. Future studies are expected to explore the possible role of nanocolumns in synaptic plasticity.

The Dynamics of SSDs

The hypothesis that SSDs regulate synaptic transmission implies that SSDs adapt dynamically to changes in synaptic strength. Indeed, live SMLM in cultured neurons has revealed the mobility and morphological changes of SSDs. Synaptic scaffolds undergo dynamic changes on a timescale of 5-10 min, displaying marked differences in the number, position and shape of SSDs at different time points (Nair et al., 2013;Specht et al., 2013;Rodriguez et al., 2017). STED microscopy further showed that these morphological changes occurred both in vitro and in vivo (Hruska et al., 2018;Wegner et al., 2018). The dynamics of SSDs are in agreement with the exchange of individual proteins at synaptic and extra-synaptic sites, which is a hallmark of the dynamic synapse (Choquet and Triller, 2013;Delgado and Selvin, 2018). Therefore, SSDs are momentary representations of the protein distribution and need to be viewed as dynamic snapshots rather than rigid structural units.

HOW TO DETECT SUBSYNAPTIC DOMAINS WITH SMLM

The identification of SSDs consists in detecting small numbers of densely packed molecules in a confined space with a high local background from neighboring molecules with lower density. Despite these challenges, SMLM is well suited to resolve the internal organization of small structures such as synapses at single molecule level. In the following, we discuss the relevant factors of the image acquisition and data analysis that have an impact on the identification of SSDs.

Image Acquisition

SMLM techniques aim to record large numbers of single fluorophore detections from densely labeled structures, while ensuring that the signals are sufficiently sparse to be well separated. STORM, PALM and uPAINT have all been employed for detecting SSDs. The three techniques have the same intrinsic challenges when it comes to the ultrastructure of synapses, chief among them being the fluorophore. Most fluorophores are detected repeatedly due to their fluorescence lifetime, photo-switching and blinking. This can create dense clusters of redundant detections that are easily mistaken for SSDs. The blinking behavior of the fluorophores (organic dyes or fluorescent proteins) is dependent on their photo-physical and photo-chemical properties, and it can be modulated by the laser power and the composition of the imaging buffer (Dempsey et al., 2011;Endesfelder et al., 2011;van de Linde et al., 2011;Nahidiazar et al., 2016). Sub-optimal imaging conditions such as inefficient laser illumination or an incompatible buffer system can result in artificial clustering (Annibale et al., 2011;Burgert et al., 2015;Nahidiazar et al., 2016). Even with an optimized imaging protocol, different fluorophores will produce different representations of the analyzed structure (Dempsey et al., 2011;Baddeley and Bewersdorf, 2018). The evaluation of the number and the size of SSDs is therefore strongly dependent on the fluorophores, and control experiments with different fluorophores are crucial to validate the experimental findings (Yang and Specht, in press). In addition to the fluorophores, attention should also be drawn to the labeling strategies used for sample preparation. The distance between the fluorophores and the actual positions of the target molecules (e.g., due to the size of antibodies used for labeling), and undersampling due to a limited labeling efficiency can add to the uncertainties in the identification of SSDs (Deschout et al., 2014;Maidorn et al., 2016).

Image Segmentation

Depending on the type of SMLM data (pointillist or reconstructed super-resolution images), different algorithms have been adopted for segmenting SSDs. For coordinates-based data, a local density threshold is generally applied. The local density can for instance be defined as the number of detections within a radius of five times the mean nearest neighbor distance of all the detections within each synapse, and SSDs are identified as regions above a certain threshold (MacGillavry et al., 2013;Tang et al., 2016;Pennacchietti et al., 2017). As regards the reconstructed images, an intensity threshold may be adopted instead. For example, wavelet segmentation has been used to identify SSDs at synapses in the whole field of view (Nair et al., 2013;Chamma et al., 2016a,b). Similarly, watershed segmentation can be employed to segment SSDs of individual synapses in reconstructed SMLM images or deconvoluted STED images (Broadhead et al., 2016;Dzyubenko et al., 2016). The difficulty of all these approaches is that the detected size and the number of SSDs are directly dependent on the algorithms and the chosen parameters, which makes an accurate identification of SSDs challenging.

Dealing With Small Molecule Numbers and the Variability of Synapses

Synapses exhibit a large variability not only in size but also in terms of molecule numbers. Neurotransmitter receptors such as AMPARs or GABA A Rs have relatively low copy numbers, with an average of ⇠50 receptor complexes per synapse (ranging up to 200 copies; Table 1). The main scaffold proteins at excitatory and inhibitory synapses outnumber the receptors by a factor of four to five. PSD-95 and gephyrin molecules amount to 40-500 per synapse, with an average of ⇠300 copies (Sugiyama et al., 2005;Sheng and Kim, 2011;Specht et al., 2013;Patrizio et al., 2017). The low copy numbers of synaptic proteins, especially receptors, makes the identification of SSDs with SMLM challenging since the labeling of the structures is often rather faint. At the same time, the high local density of synaptic proteins can further reduce the efficiency of immunolabeling due to epitope masking. The overall receptor density at synapses is in the order of 700 AMPARs/µm 2 for the whole PSD (50 AMPARs/0.07 µm 2 ). An average SSD with a diameter of 70 nm (area of 0.0038 µm 2 ) contains about 20 AMPARs, resulting in an estimated density of ⇠5,000 AMPAR complexes/µm 2 (Nair et al., 2013). Considering the molecular size of the receptor complexes (10 nm ⇥ 20 nm;

Alternative Approaches

Given the rapid advances in super-resolution imaging technologies, promising alternatives for the investigation of complex structures such as synapses are quickly emerging. Among these, smaller probes such as nanobodies have been produced to bypass the limitations of labeling density and to minimize the distance between the fluorophores and the target proteins (Chamma et al., 2016a;Maidorn et al., 2016). DNA-PAINT allows multi-color SMLM imaging (Nieves et al., 2018). DNA origami standards provide a more precise way for calibrating protein copy numbers given that the absolute quantification of molecules at SSDs is faced with large stochasticity of the imaging technique (Zanacchi et al., 2017). Furthermore, new algorithms are being developed to segment synaptic clusters in coordinates-based datasets more efficiently (Nicovich et al., 2017;Baddeley and Bewersdorf, 2018).

THE EMERGING ROLE OF SSDs IN INHIBITORY SYNAPTIC TRANSMISSION

Electron microscopy of symmetric synapses has revealed a discontinuous network of filaments at the inhibitory PSD and in the synaptic cleft (Linsalata et al., 2014;High et al., 2015). Super-resolution optical microscopy confirmed that the inhibitory scaffold protein gephyrin forms synaptic clusters of variable morphology that can undergo dynamic changes and may contain SSDs (Specht et al., 2013;Dzyubenko et al., 2016;Pennacchietti et al., 2017;Crosby et al., 2019). SMLM imaging in cultured hippocampal neurons further revealed that extra-synaptic gephyrin molecules are recruited to synaptic sites during NMDA-induced inhibitory long-term potentiation (Pennacchietti et al., 2017). The increase in molecule density was accompanied by an increased fraction of gephyrin clusters with multiple SSDs (Figure 1). More recently, Crosby et al. (2019) conducted a comprehensive analysis of pre-and postsynaptic components using 3D-SIM, reaching a resolution of ⇠120 nm laterally and ⇠300 nm axially. It was shown that GABA A Rs form SSDs with an average diameter of ⇠300 nm that are closely associated with SSDs of gephyrin and pre-synaptic RIM (Crosby et al., 2019). This implies the existence of transsynaptic nanocolumns as an organizing principle of inhibitory synapses. Given that the measured size of the SSDs was close to the resolution limit, the concept of nanocolumns at inhibitory synapses will require further validation. Nonetheless, these studies strongly suggest that the internal organization of inhibitory synapses plays an important role in regulating synaptic transmission.

Unlike the cortex and hippocampus where fast neuronal inhibition is mainly mediated by GABA A Rs, both glycine and GABA receptors coexist at synapses in the brainstem and the spinal cord. Gephyrin provides binding sites for the immobilization of both types of receptor (reviewed in [START_REF] Choii | Gephyrin: a central GABAergic synapse organizer[END_REF]Alvarez, 2017;Groeneweg et al., 2018;[START_REF] Specht | Fractional occupancy of synaptic binding sites and the molecular plasticity of inhibitory synapses[END_REF]. Several GABA A R subunits bind to gephyrin, albeit with a lower affinity than the GlyRb subunit (e.g., Maric et al., 2011;Kowalczyk et al., 2013). We do not yet know whether GlyRs and GABA A Rs form SSDs at mixed synapses, and if so, how they are related to the SSDs of gephyrin. Mixed inhibitory synapses are activated by the co-release of glycine and GABA from presynaptic vesicles (Jonas et al., 1998;Aubrey and Supplisson, 2018). This creates a situation, where the exact position of GlyRs and GABA A Rs relative to the pre-synaptic release site can have a strong impact on the efficacy of the agonists and thus the activity of the receptors. Through its capacity to resolve the spatial organization of mixed inhibitory synapses, SMLM may provide answers to these open questions.

OUTLOOK

The concept of SSDs as dynamic units underlying synaptic strength provides a new angle to interpret the function of synapses. SMLM and other super-resolution imaging techniques are powerful tools to investigate the internal organization of synapses. Given the intrinsic stochasticity of SMLM and the inherent variability of synaptic protein assemblies, however, the identification and characterization of SSDs demand great

1 27 2

 27 Introduction ..................................................................................................................... 1 1.1 GABAARs and GlyRs: the two main inhibitory neurotransmitter receptors ............. 1 1.1.1 The subunit composition of GABAARs and GlyRs .............................................. 1 1.1.2 The lateral diffusion of the receptors underlying inhibitory synaptic plasticity 4 1.1.3 The physiological functions of GABA A Rs and GlyRs and their implications in neurological disorders ................................................................................................ 5 1.2 Gephyrin: a master regulator at inhibitory synapses ............................................... 7 1.2.1 The structure of gephyrin complex and their dynamics .................................... 7 1.2.2 The roles of gephyrin in the regulation of synaptic GlyRs and GABA A Rs ........... 9 1.3 Glycinergic and GABAergic mixed inhibitory synapses ........................................... 10 1.3.1 The evidence of GABA A R/GlyR colocalization and GABA/glycine co-release .. 11 1.3.2 The synaptic control of GABA/glycine co-transmission ................................... 13 1.4 Heterogeneity of intra-synaptic organization revealed by super-resolution optical microscopy .................................................................................................................... 15 1.4.1 Super-resolution optical microscopy and stochastic optical reconstruction microscopy (STORM) ................................................................................................. 15 1.4.2 The challenges of applying two-color dSTORM to synapses ........................... 19 1.4.2.1 Challenges posed by technical limitations ................................................ 19 1.4.2.2 Challenges posed by the characters of synapses ...................................... 21 1.4.3 Intra-synaptic heterogeneity and sub-synaptic domains as regulators of synaptic functions ..................................................................................................... 23 1.5 Functional implications of intra-synaptic heterogeneity and SSDs in mixed inhibitory synapses ....................................................................................................... 26 1.6 Aims of this study .................................................................................................... Materials and methods .................................................................................................. 28 2.1 Primary spinal cord neuron culture ........................................................................ 28 2.2 Conjugation of Cy3B dye to secondary antibodies ................................................. 29 2.3 Pharmacological treatment and immunocytochemistry ........................................ 30 vi 2.4 Calcium imaging and data analysis .......................................................................... 2.5 Conventional microscopy and data analysis ........................................................... 2.6 Two-color dSTORM imaging and analysis ............................................................... 2.6.1 Microscope setup ............................................................................................. 2.6.2 Sequential two-color dSTORM imaging ........................................................... 2.6.3 dSTORM image processing and data analysis .................................................. 2.7 Statistical analysis .................................................................................................... 3 Results ............................................................................................................................ 3.1 Methodological evaluation: two-color dSTORM of synapses ................................. 3.1.1 An optimized sequential two-color dSTORM imaging protocol ....................... 3.1.1.1 Fluorescent beads as fiducial marker ........................................................ 3.1.1.2 Performance evaluation of chosen dyes ................................................... 3.1.2 Two-color dSTORM data analysis protocol ...................................................... 3.1.2.1 Image-based data analysis workflow ........................................................ 3.1.2.2 Verification of the different intensity correlation approaches with an exemplary dataset ................................................................................................. 3.1.2.3 SRclusters: coordinates-based data analysis tool ..................................... 3.1.3 The impact of low protein copy number on the imaging data ........................ 3.1.4 Dyes have a strong impact on the detected synaptic structure characterization ................................................................................................................................... 3.1.4.1 The impact of dyes on dSTORM synaptic clusters explained by their blinking profiles ..................................................................................................... 3.1.4.2 The impact of reversing the dyes on the synaptic clusters in dSTORM images ................................................................................................................... 3.2 The study of mixed inhibitory synapses with two-color dSTORM .......................... 3.2.1 Post-synaptic proteins are heterogeneously organized and exhibit SSDs at mixed inhibitory synapses in spinal cord neurons .................................................... 3.2.1.1 GlyR and gephyrin are heterogeneously distributed at inhibitory PSDs ... 3.2.1.2 GABA A Rs and GlyRs can occupy different spaces at mixed inhibitory PSDs ............................................................................................................................... 3.2.2 The copy numbers of synaptic GABA A Rs and GlyRs are differentially regulated by neuronal activity ................................................................................................... 3.2.2.1 Calcium imaging showed the effect of TTX and 4-AP on the network activity of cultured spinal cord neurons ................................................................ 3.2.2.2 GABA A Rs and gephyrin, but not GlyRs immuno-reactivity is regulated by changes in network activity ...................................................................................

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1. The basic structure of GABAARs and GlyRs ………………………………………………………2 Figure 2. Gephyrin as the scaffold protein at inhibitory PSDs …………………………………………8 Figure 3. Co-release and co-transmission of glycine and GABA at mixed inhibitory synapses ……………………………………………………………………………………….…………………...…...12

Figure 4 .

 4 Figure 4. The basic principles of stochastic optical re construction microscopy (STORM)…………………………………………………………………………………………………………………...….17

Figure 5 .Figure 6 . 25 Figure 7 .

 56257 Figure 5. The challenges of applying SMLM to synapses ………………………………………………20 Figure 6. Sub-synaptic domains (SSDs) as regulators of synaptic function …………….……..25 Figure 7. Sequential two-color dSTORM imaging of synaptic proteins ………………………….37

Figure 8 . 39 Figure 9 . 41 Figure 10 .Figure 11 .

 8399411011 Figure 8. Drift correction with beads as fiducial markers ……………………………………………..39 Figure 9. Blinking profiles of the dyes conjugated to secondary antibodies………….……….41 Figure 10. Image-based data analysis workflow ………………………………………………………..…44 Figure 11. Verification of the different intensity correlation approaches with an exemplary dataset ………………………………………………………………………………………… …………….48

Figure 12 . 52 Figure 13 . 54 Figure 14 . 56 Figure 15 .Figure 16 . 61 Figure 17 . 63 Figure 18 . 66 Figure 19 .

 1252135414561516611763186619 Figure 12. SRclusters: a coordinates-based data analysis tool ……………………………………..52 Figure 13. The effect of low copy numbers of synaptic proteins on the cluster integrity in dSTORM images …………………………………………………………………………………………………………..54 Figure 14. The impact of dyes on dSTORM synaptic clusters explained by their blinking profiles …………………………………………………………………………………………………………….………….56 Figure 15. Strong impact of fluorescent dyes on the reconstructed synaptic clusters in dSTORM ………………………………………………………………………………………………………………………58 Figure 16. Image-based analysis of dye reversal experiments of gephyrin in two-color dSTORM ………………………………………………………………………………………………………………….…..61 Figure 17. Coordinate-based analysis of dye reversal experiments on gephyrin using SRclusters ……………………………………………………………………………………….…………………………..63 Figure 18. The heterogeneous distribution of GlyR and gephyrin at the inhibitory PSDs in spinal cord neurons ……………………………………………………………………………………………………..66 Figure 19. The heterogeneous distribution of GABAARs and GlyRs at mixed inhibitory PSDs and their different spatial organization ……………………………………………………………….68

Figure 20 .Figure 21 .Figure 22 .Figure 23 . 78 Figure 24 . 80 Figure 25 .Figure 26 . 86 Figure 27 .Figure 28 .Figure 29 .Figure 30 .Figure 31 .Figure 32 .

 20212223782480252686272829303132 Figure 20. The effect of TTX and 4-AP on neuronal activity in cultured spinal cord neurons demonstrated by calcium imaging …………………….……………………………………………71 Figure 21. Reduced synaptic accumulation of GABAARs and gephyrin, but not GlyRs by 4-AP treatment …………………………………………………………………….…………………………………………73

Figure 1 .

 1 Figure 1. The basic structure of GABA A Rs and GlyRs. (A) Each subunit of GABA A Rs and GlyRs has the characteristic structure of the Cys-loop ligand-gated ion channel superfamily. It starts with an extracellular Nterminus, followed by four transmembrane domains (TM1-4) and ends with a short extracellular C-terminus. A long intracellular loop is formed between TM3 and TM4. (B) The functional receptor complex consist of five subunits, and TM2 is known to form the ion channel in the middle. From Moss and Smart 2001.

1. 2 . 1

 21 The structure of gephyrin complex and their dynamics Gephyrin molecules are composed of three domains, the N-terminal G-domain, a linker (C-domain), and the C-terminal E-domain (Figure2.A). Gephyrin is subject to many different types of post-translation modifications (PTMs), including phosphorylation, palmitoylation, SUMOylation, acetylation and nitrosylation.

Figure 2 .

 2 Figure 2. Gephyrin as the scaffold protein at inhibitory PSDs. (A) The structure of gephyrin consists of an N-terminal G-domain, a linker domain (L, also known as C-domain) and a C-terminal E-domain. Gephyrin is subject to different types of post-translational modifications at different sites, such as phosphorylation, acetylation, palmitoylation, SUMOylation, nitrosylation. Most identified phosphorylation sites locate to the linker domain. From Kasaragod and Schindelin 2018. (B) Gephyrin forms stable trimers through the G-domain trimerization. The trimers can exhibit different compactness due to the different extension status of their linker domains.FromSander et al., 2013. (C) The stable gephyrin trimers are thought to assemble sub-membranous lattice with distinct compactness (left). It has been recently reported that gephyrin clusters can form subdomains that contain higher molecule density than the neighboring regions (right). FromGroeneweg et al., 2018.

Figure 3 .

 3 Figure 3. Co-release and co-transmission of glycine and GABA at mixed inhibitory synapses.(A) The glycine and GABA mixed inhibitory synapses. GlyRs and GABA A Rs coexist at the same inhibitory PSDs. In the pre-synaptic terminals, the neurotransmitters glycine and GABA are loaded into the same vesicles, by a common vesicular transporter VGAT (also known as VIAAT). The cytosolic supplies of glycine and GABA are achieved through separate pathways. The cytosolic glycine is supplied by the uptake from the extracellular space through GlyT2. GABA is mostly synthesized from glutamate by the enzyme GAD. The uptake of GABA by GAT-1 can also contribute to its cytosolic content. In addition, the cytosolic glutamate can be transported from the extracellular space by EAAT-3, but mostly it is synthesized from glutamine by the enzyme PAG. Glutamine is transported into the cell by SNAT. From Aubrey 2016. (B) Glycinergic mIPSCs have lower peak amplitudes and a faster decay time, while glycinergic/GABAergic mixed mIPSCs have higher peak amplitudes and a slower decay time. In the peak-scaled average mIPSCs, mixed IPSCs exhibit a complex decay time course, containing a fast (f) and a slow (s) component.From Alvarez 2017.

1. 4

 4 Heterogeneity of intra-synaptic organization revealed by super-resolution optical microscopy 1.4.1 Super-resolution optical microscopy and stochastic optical reconstruction microscopy (STORM) In conventional optical microscopy, the lateral resolution is limited by the wavelength (λ) and the numerical aperture (NA) of the objective. The minimal distance between two resolvable points is several hundred nanometers due to the diffraction limit (d ≈ 0.61 λ/NA). Therefore, conventional fluorescence microscopy cannot gain detailed information on sub-diffraction-limited structures such as synapses, which have a size of only a few hundred nanometers. The advancement of several super-resolution optical microscopy techniques, such as stimulated emission depletion (STED), structured illumination microscopy (SIM) and single molecule localization microscopy (SMLM), have boosted the study of sub-diffraction-limited structures and provided us with novel insights into

Figure 4 .

 4 Figure 4. The basic principles of stochastic optical reconstruction microscopy (STORM). (A) A simplified Jablonski diagram illustrating the ON/OFF blinking cycles of fluorescent dyes. After excitation to the S1 state, the fluorophores can either return to the ground state (S0) by emitting longer wavelength light, or they enter the non-fluorescence OFF state. Fluorophores in the OFF state are reactivated by UV light or spontaneously in the presence of reducing agents in the imaging buffer. (B) The diagram shows the working principles of STORM. The samples are densely labeled with fluorophores. At the beginning of the STORM imaging, a strong dose of illumination laser (red) is applied to bleach the ON state fluorophores. Then a small dose of activation laser (green) is applied to activate a subset of spatially separated fluorophores. These sparsely activated fluorescent signals are recorded under the red laser illumination. Taking advantage of the stochastic blinking of the fluorophores, tens of thousands of frames are recorded. Post-processing of the recordings obtains the localizations of the blinking events, from which the superresolution dSTORM images are reconstructed. Form Rust et al., 2006.

Figure 5 .

 5 Figure 5. The challenges of applying SMLM to synapses. (A) The challenges imposed by the technical limitations of SMLM. Except in the ideal situation (A1), the quality of SMLM images can be compromised by insufficient localization precision (A2), labeling density (A3), and the dislocation of the fluorophores due to the bulky size of probes (A4). From Deschout et al., 2014. (B) Different types of target structures as subjects of SMLM impose different requirements and challenges. For stereotypic

From

  Baddeley and Bewersdorf, 2018. (C) The intra-synaptic organization of a PSD-95 cluster. Resolving its intra-synaptic organization is faced with both technical and biological challenges. Scale bar 100 nm. FromMacGillavry et al., 2013.

Figure 6 . 5

 65 Figure 6. Sub-synaptic domains (SSDs) as regulators of synaptic function. (A) Several important protein types at excitatory synapses are organized into sub-synaptic domains (SSDs), such as receptors, adhesion and scaffold proteins. The pre-synaptic SSDs and post-synaptic SSDs appear to be aligned in trans-synaptic nanocolumns. From Tang et al. 2016 and Chen et al., 2018. (B) At inhibitory synapses, gephyrin clusters are organized heterogeneously and can have several SSDs. Scale bar: 100 nm. From Pennacchietti et al., 2017.

  or Kolmogorov-Smirnov test was used for significance test. Paired t-test was used for the calcium imaging data to compare signals between different time points. Data are generally represented in the form of cumulative probability distributions, or as bar graphs showing the median and quartiles of the distribution (calcium imaging data), or the mean ± standard deviation.

  at mixed inhibitory synapses in dissociated spinal cord neurons. With respect to the multifaceted challenges arising from the technical limitations and the unique synaptic characters, we carefully optimized the protocol, involving the sample preparation, image acquisition and data analysis. A full discussion of the optimization concerning the choice of fluorophore pairs, labeling strategies, imaging parameters, quality assessment and data analysis is synthesized in a chapter in the forthcoming book Single Molecule Microscopy in Neurobiology edited by Yasushi Okada and Nobuhiko Yamamoto in Springer Neuromethods series (See appendix: YANG and Specht, Practical guidelines for two-color SMLM of synaptic proteins in cultured neurons).

Figure 7 .

 7 Figure 7. Sequential two-color dSTORM imaging of synaptic proteins. (A) Experimental workflow for two-color dSTORM imaging and data processing. (B) Representative images of two-color dSTORM recordings. From left to right: epifluorescence reference images, acquisition of image stacks with single fluorophore blinking events, pointillist dSTORM images reconstructed from single fluorophore localizations (after drift correction), and rendered super-resolution images in the far-red (A647) and the red (Cy3B) channels. Arrowheads point to beads in the region of interest (ROI) used for drift correction. Scale: 2 μm.

Figure 8 .

 8 Figure 8. Drift correction with beads as fiducial markers. (A) Apparent drift in the X and Y directions of three beads that were attached to the coverslip (red, green and blue traces). The bright green trace represents the average drift of the three beads, calculated with a sliding window of 500 frames. (B) Projection of the detection coordinates of the three beads before drift correction (left panels) and bead #3 after drift correction (right). Note that the lateral drift of the beads can differ; bead #1 is lost after frame 16000, and #2 is less bright and therefore has lower localization precision.

Figure 9 .

 9 Figure 9. Blinking profiles of the dyes conjugated to secondary antibodies. Two-color dSTORM imaging was done on sparsely distributed secondary antibodies coupled with Alexa Fluor 647 (A647-IgG) or Cy3B (Cy3B-IgG). (A) A large variability in the number of detections and cluster area for each dyecoupled IgG was observed. (B) Representative time traces of single A647-IgG and Cy3B-IgG detections in dSTORM show that the blinking of Alexa 647 is more dispersed than that of Cy3B (the upper boxes show the number of detections per frame, the lower boxes the cumulative detections). (C) Dyecoupled IgGs produce apparent nanoclusters with diameters ranging from 40 nm to 120 nm, as a result of multiple detections with limited localization precision.

FijiFigure 10 .

 10 Figure 10. Image-based data analysis workflow. (continued in next page)

  . They are mathematically similar, except that MOC uses absolute intensity values and PCC uses the deviation from the mean intensity. PCC values range from -1 to +1, with +1 indicating perfect positive correlation. Values that are negative or close to zero are difficult to interpret in fluorescent images. MOC values range from 0 to +1, with larger values indicating better correlation. MCC (MCC1 and MCC2) calculates the percentage of the total intensity in one channel which overlaps with the non-zero pixels in the second channel, and vice versa. These methods adopt different principles and have their own merits and drawbacks. The use of several methods together can compensate the drawbacks and provide more information about the data.

Figure 11 .Figure 11 .

 1111 Figure 11. Verification of the different intensity correlation approaches with an exemplary dataset. (continued in next page)

  Furthermore, we vertically flipped the gephyrin clusters in group one, in order to verify the robustness of these spatial correlation approaches. This flipping created a situation where the clusters were somewhat scrambled, mimicking a negative control with low spatial correlation. Indeed, the spatial correlation of group one was reduced after flipping the gephyrin clusters, which was shown by all the four methods (Figure11.C). This means that these methods are able to test the change in the spatial correlation between two synaptic clusters.MOC treats the pixels with low red and high green signals as equal to median red and median green signals, where the latter apparently has better correlation. Thus, MOC measures mostly co-occurrence. PCC is similarly calculated as MOC, except that PCC normalizes the signals to its mean in each channel. Though PCC values close to +1 can mean perfect positive correlation, values close to -1 do not mean negative correlation and values close to zero are difficult to interpret in fluorescent images. ICQ counts the pixels which increase or decrease in synchrony in the two channels, thus it measures the co-variation of signals.

3. 1 . 2 . 3

 123 SRclusters: coordinates-based data analysis tool Super-resolution cluster analysis (SRclusters) is a tool developed in collaboration with Delestro and Genovasio (IBENS) for the characterization of clusters using coordinate-based data (Figure12). It is written in Python language and based on a Jupyter Notebook. The main algorithms adopted here are the density-based spatial clustering of applications with noise (DBSCAN) and the nearest neighbor distance (NND). DBSCAN needs two pre-defined parameters: the search radius (ε) and the minimal number of points (minPts). NND needs one pre-defined parameter: the number of nearest neighbors (x). It then calculates the average distance between each point and its x neighbors. With these two algorithms, the segmentation of clusters and characterization of spatial correlation are enabled in SRclusters. A user manual is shown in the Appendix 3. There are four steps in the SRclusters program (see Appendix 3). Step one performs the segmentation of the clusters by DBSCAN and the registration of the two channels by aligning the bead as fiducial marker (Figure 12.A-B). Step two removes the beads from the clusters by thresholding the persistence of detections in the recording. Beads are generally detected throughout the whole recording and have high persistence of detections. Step three detects the cluster pairs from the two channels based on their centroid distance. Only the paired clusters are passed

Figure 12 .

 12 Figure 12. SRclusters: a coordinates-based data analysis tool. (A) Pointillist image showing gephyrin molecules detected with Ae647, and the cluster segmentation with DBSCAN. Color-coded clusters are segmented gephyrin clusters; small black clusters and grey points are considered as noise. (B) The aligned gephyrin cluster pair detected in the two channels, corresponding to the cluster indicated by the red arrow in (A). Scale bar: 100 nm. (C) The same gephyrin cluster pair as in (B) displayed separately, either as pointillist image (left) or density hot map (right). The colored subdomains in the left are gephyrin SSDs segmented with density-weighed DBSCAN. Scale bar: 100nm.

Figure 13 .

 13 Figure 13. The effect of low copy numbers of synaptic proteins on the cluster integrity in dSTORM images. (A) Gephyrin clusters that were labeled with IgG-Cy3B showed good brightness and contrast in the epifluorescence image, thus also produce nice super-resolution clusters in the dSTORM image (green arrow). (B) When the same secondary IgG-Cy3B was used to label GABA A R, some GABA A R clusters were only weakly stained, and had a poor contrast (top left). Synaptic GABA A R clusters could barely be identified from the dSTORM image, especially with many signals in the surroundings (top right). Gephyrin was stained with Alexa 647 to confirm the identity of synaptic clusters in the images (middle). Red arrow indicates an example synapse. Yellow arrowhead points to a bead in the ROI, showing that the images were properly aligned. Scale bar: 1 µm. Note that the brightness and contrast in the Cy3B channel was differently adjusted in (A) and (B) to ensure visibility of the clusters.

Figure 14 .

 14 Figure 14. The impact of dyes on dSTORM synaptic clusters explained by their blinking profiles. (A) The reconstructed dSTORM images of the same gephyrin cluster detected with A647 (top) and Cy3B (middle) are not identical. A647 or Cy3B conjugated secondary antibodies which recognize the same primary gephyrin antibody m7a were used. The merged image is rotated by 90º to the left. Scale bar: 100nm. (B) Pointillist images of the same two clusters. (C) The localization profiles of the clusters. Gephyrin cluster detections with A647 increase linearly over time (top), while GPHN-Cy3B localization profile shows gaps and bursts (middle). The dense subdomain in the GPHN-Cy3B cluster (orange circle in B)

Figure 15 .Figure 15 .

 1515 Figure 15. Strong impact of fluorescent dyes on the reconstructed synaptic clusters in dSTORM. (continued in next page)

Figure 16 .

 16 Figure 16. Image-based analysis of dye reversal experiments of gephyrin in two-color dSTORM. (A) Gephyrin was labeled with two different primary antibodies, followed by two combinations of secondary antibodies. Scale bar: 100 nm. (B) The antibody combination rbGPHN-Cy3B/m7a-A647 (condition 2, n = 174 synapses) gave a significantly higher spatial correlation of gephyrin clusters in the two channels than the combination with reversed dyes (condition 1, rbGPHN-A647/m7a-Cy3B, n = 148), as shown by ICQ (t-test, p < 0.001). The MCC test (MCC1 and MCC2) also shows a significant difference of the overlap between the two channels (t-test, p< 0.00001 for both). (C) The segmented SSDs by H-watershed. (D) SSD counts per cluster for rbGPHN and m7a

  2 ). It should be noticed that the hull area measured with SRclusters is about twice as large as the area measured in the image-based clusters. The reason is that the hull area includes all the outlier points surrounding the clusters, without thresholding. The spatial correlation level in the second labeling condition was consistently higher than in the first labeling condition as revealed by the score values (smaller score values indicate stronger spatial correlation) (Figure17.B). The SSD counts per synapse did not show differences among the different labeling conditions (rbGPHN-A647: 1.9 ± 1.2; m7a-Cy3B: 1.7 ± 1.0; m7a-A647: 1.9 ± 1.1; rbGPHN-Cy3B: 2.0 ± 1.1) (Figure17.C). However, the size of SSDs measured with the hull area confirmed the difference in size in the image-based analysis. In sum, the coordinate-based data analysis further confirmed that dyes have strong impacts on the characterizations of the detected synaptic clusters.

Figure 17 .

 17 Figure 17. Coordinate-based analysis of dye reversal experiments on gephyrin using SRclusters. (A) Pointillist images showing the aligned gephyrin clusters detected in the two channels and individual clusters segmented into SSDs in the two labeling conditions. Scale bar: 100 nm. (B) Smaller distance score in condition 2 (18 ± 21 nm) indicates a stronger spatial correlation of gephyrin clusters detected in the two channels than in condition 1 (38 ± 29 nm, t-test, p < 0.00001). (C) SSD

3. 2

 2 The study of mixed inhibitory synapses with two-color dSTORM 3.2.1 Post-synaptic proteins are heterogeneously organized and exhibit SSDs at mixed inhibitory synapses in spinal cord neurons 3.2.1.1 GlyR and gephyrin are heterogeneously distributed at inhibitory PSDs

  are heterogeneously distributed. Interestingly, GABAARs and GlyRs are not fully inter-mingled at the PSD, and they exhibit different patterns of spatial organization. GABAARs and GlyRs can occupy different spaces, as well as overlap with each other at the PSD. These observations raise the possibility that the spatial organization of GABAARs and GlyRs may be differentially regulated and can play a role in tuning the synaptic co-transmission.

Figure 18 .Figure 18 .

 1818 Figure 18. The heterogeneous distribution of GlyR and gephyrin at the inhibitory PSDs in spinal cord neurons. (continued in next page)

Figure 19 .

 19 Figure 19. The heterogeneous distribution of GABA A Rs and GlyRs at mixed inhibitory PSDs and their different spatial organization. Two-color dSTORM images of GABA A R clusters (red) and GlyR clusters (green) at mixed synapses. GABA A Rs were labeled with A647 and GlyRs with Cy3B. Scale bar: 200 nm.

3. 2 . 2

 22 The copy numbers of synaptic GABAARs and GlyRs are differentially regulated by neuronal activity Before the examination of the intra-synaptic protein organization at mixed inhibitory synapses, a fundamental question to be answered first is: how is the amount of the synaptic proteins regulated in response to different levels of neuronal activity? To answer this question, we adopted pharmacological methods to modulate the overall network activity in the cultured spinal cord neurons. We then used conventional epifluorescence microscopy to measure the total amount of synaptic proteins at inhibitory synapses. As widely used approach in biology, conventional fluorescence microscopy does not require the bleaching and switching of fluorescent signals. Therefore, conventional fluorescence imaging has less stochasticity in the measurement of synaptic protein quantity when compared to dSTORM imaging.To modify the neuronal activity, we used TTX and 4-AP as pharmacological agents in our experiments. Tetrodotoxin (TTX) potently blocks voltage-gated sodium channels, thus blocking the action potential and diminishing the neuronal activity[START_REF] Narahashi | Review Tetrodotoxin: A brief history[END_REF]. 4-aminopyridine (4-AP) blocks voltage-gated potassium channels which are responsible for the repolarization during action potential, thus prolonging the action potential and increasing the neuronal activity[START_REF] Zhang | guinea pig polyclonal RIM1/2 Synaptic system Cat.No. 140205 unconjugated donkey anti-mouse IgG (H+L) Jackson ImmunoResearch Cat.No. 715-005-151 unconjugated donkey anti-rabbit IgG (H+L) Jackson ImmunoResearch Cat.No. 711-005-152 unconjugated donkey anti-rat IgG (H+L) Jackson ImmunoResearch Cat.No. 712-005-153 unconjugated donkey anti-guinea pig IgG (H+L) Jackson ImmunoResearch Cat.No. 706-005-148 Alexa 647-conjugated goat anti-rat IgG (H+L) Invitrogen Cat.No. A21247, RRID: AB_141778 Alexa 647-conjugated donkey anti-rabbit IgG (H+L) Jackson ImmunoResearch Cat.No. 711-605-152, RRID: AB_2492288 Alexa 647-conjugated donkey anti-mouse IgG (H+L) Jackson ImmunoResearch Cat.No. 715[END_REF]. TTX and 4-AP thus modulate the neuronal activity in opposite directions without directly interfering with the inhibitory synaptic proteins. We first evaluated the neuronal activity of spinal cord neurons with TTX or 4-AP treatment using calcium imaging. Then, we investigated the effect of the altered neuronal activity on the protein copy numbers at mixed inhibitory synapses. 3.2.2.1 Calcium imaging showed the effect of TTX and 4-AP on the network activity of cultured spinal cord neurons We compared the calcium signals of cultured spinal cord neurons before and after the administration of either TTX (1 µM) or 4-AP (50 µM). Calcium signals were recorded before, 5 min after and 20 min after the drug administration, with each recording lasting 3 min. Before the application of TTX or 4-AP, neurons had moderate level of activity (Figure 20.A). Five minutes after the drug application, the activity levels were clearly reduced by TTX, while the activity levels were dramatically increased by 4-AP. The effect was still present after 20 min as shown in the example signal traces. Moreover, we quantified the amplitude and frequency of the calcium signals (Figure 20.B). 4-AP significantly increased the calcium signal frequencies, but did not have strong effect on the amplitude.Because TTX abolished all the neuronal activity, we only took into account the calcium signals before the treatment. The frequency and amplitude of the calcium signals before treatment were similar in the two conditions as expected. In sum, TTX and 4-AP as pharmacological agents and their concentration used here are suitable to modulate the neuronal activity level of spinal cord neurons.3.2.2.2 GABAARs and gephyrin, but not GlyRs immuno-reactivity is regulated by changes in network activityMature spinal cord neurons between DIV 14 and DIV 18 were used. TTX or 4-AP was diluted in the culture maintenance medium to the final concentration of 1 µM and 50 µM, respectively. Non-treated neurons from the same culture plate were used as control condition. After drug administration, cells were placed back in the incubator for one hour before fixation. We performed triple immunocytochemistry to stain GABAARs, GlyRs and gephyrin (m7a) simultaneously at mixed inhibitory synapses, which allowed us to compare directly their relative amount at the same synapses. Binary gephyrin clusters were used as masks to calculate the integrated intensity for all the three proteins.

Figure 20 .

 20 Figure 20. The effect of TTX and 4-AP on neuronal activity in cultured spinal cord neurons demonstrated by calcium imaging. (A) Calcium signals are decreased after TTX treatment and increased after 4-AP treatment (left). On the right, time-lapse imaging of calcium signals showed the changes in neuronal activity for different cells (represented by different colors; top: TTX, bottom: 4-AP). Scale bar: 20 µm. (B) Quantification of the amplitude and frequency of the calcium signals. TTX blocks all the neuronal activity after its application, as judged by the lack of calcium signals. Five minutes after 4-AP treatment, the frequency was greatly increased (paired t-test, p<0.00001) and the amplitude was slightly increased (paired ttest, p=0.025). Number of cells: n=117 in 4-AP treatment and n=70 in TTX treatment, from three independent experiments.

Figure 21 .

 21 Figure 21. Reduced synaptic accumulation of GABA A Rs and gephyrin, but not GlyRs by 4-AP treatment. (A) Triple staining of gephyrin, GlyRs and GABA A Rs at mixed inhibitory synapses. Scale bar: 5 µm. (B) Fluorescence intensity of the synaptic clusters reveals the reduction of gephyrin and GABA A Rs, but not GlyRs after 4-AP treatment. Kolmogorov-Smirnov test with alpha at 0.01, significant between TTX and 4-AP conditions for gephyrin, GlyRs and GABA A Rs. (C) The intensity ratio of gephyrin/GlyR and GABA A R/GlyR reveals the correlated change of these proteins at the same PSDs. Kolmogorov-Smirnov test with alpha at 0.01, significant between TTX and 4-AP conditions for gephyrin/GlyR, GABA A R/GlyR and GABA A R/gephyrin. Number of synapses: n=8856 in non-treated (CTRL) group, n=9416 in TTX condition, n=6949 in 4-AP condition; from three independent experiments.

Figure 22 .

 22 Figure 22. Reduced gephyrin phosphorylation level but not the total molecular amount at the inhibitory PSD by 4-AP treatment. (A) Double staining of gephyrin with one antibody (rbGPHN) recognizing the gephyrin E-domain and another one (m7a) recognizing S270 phosphorylated gephyrin. Arrows point to weakly stained synapses, of which one produces only a rbGPHN mask (red arrow) and the other one produces only m7a mask (green arrow). Scale bar: 5 µm. (B) The colocalization level of the binary synaptic masks of rbGPHN and m7a clusters. Even though the colocalization level of rbGPHN with m7a showed a difference (t-test, p < 0.05 between CTRL and TTX, p < 0.01 between TTX and 4-AP), the treatment did not have a strong effect on the colocalization between rbGPHN and m7a. (C) The fluorescence intensity of total gephyrin was not changed by the treatment, whereas the pgephyrin was reduced after 4-AP treatment (compared to TTX, Kolmogorov-Smirnov test, p < 0.01). The intensity ratio of p-gephyrin over total gephyrin shows that the gephyrin phosphorylation level was also reduced at the PSDs (Kolmogorov-Smirnov test, p < 0.01). Number of synapses: n=3946 in CTRL condition, n=4040 in TTX condition, n=3818 in 4-AP condition, from two independent experiments.

  The overall size of gephyrin clusters was reduced by 4-AP treatment compared to TTX condition, while GlyR cluster size was not changed(Figure 23.B). This is in line with the reduced immuno-reactivity of gephyrin clusters and sustained immuno-reactivity of GlyR clusters in our earlier results. The ICQ measurement indicated that the spatial association of GlyRs and gephyrin was increased by 4-AP treatment (Figure23.C). This increased correlation in ICQ measurement can be attributed to the specific re-organization of synaptic GlyRs and p-gephyrin, because low level of proteins in one channel actually cause a decrease in the ICQ values (see Figure11). MCC measurements did not indicate any differences between the two groups, suggesting that the overall overlap of one protein inside the boundary of the other protein assembly had not changed. Furthermore, we characterized the intra-synaptic distribution pattern of gephyrin and GlyRs by counting the SSDs. Neither gephyrin SSD nor GlyR SSD counts per synapse showed a difference between the two treatment groups (Figure23.D). However, the gephyrin SSD area was reduced and the GlyR SSD area was increased by the 4-AP treatment, which is in agreement with internal re-organization of synaptic proteins.

Figure 23 .Figure 24 .Figure 24 .

 232424 Figure 23. Increased spatial correlation of p-gephyrin and GlyRs by 4-AP treatment. (continued in next page)

  A647 and GABAARs with Cy3B (Figure 25.A). The higher ICQ values after 4-AP treatment indicated that the spatial correlation between GABAARs and p-gephyrin was increased (Figure 25.C). The increase in ICQ values is probably due to a specific re-organization of synaptic proteins, because low protein level in one channel actually causes a reduction in the ICQ values (see Figure 11). GABAAR SSD and p-gephyrin SSD numbers per synapse were not changed (Figure 25.D).

Figure 25 .Figure 25 .Figure 26 .

 252526 Figure 25. Increased spatial correlation of GABA A Rs and p-gephyrin by 4-AP treatment, but not their SSD numbers. (continued in next page)

Figure 27 .Figure 28 .

 2728 Figure 28.C), despite the variability of synaptic structures. This distance measurement accounts for the distance between RIM1/2 and pre-synaptic membrane(37 ± 4 nm, Dani et al. 2010), the synaptic cleft (~20 nm, Harris and Weinberg 2012), the distance between gephyrin and post-synaptic membrane(21.7 ± 8.5 nm, Triller et al. 1985), and the primary and secondary antibodies on each side (~10 nm of a single antibody,Maidorn et al. 2016). Due to the uncertainty of the orientations of the antibodies, we can count the size of one antibody on each side. Then the distance between the two fluorophores at pre-and post-synaptic compartments adds up to ~99 nm, which is in good agreement with our experimental data.

3. 2 . 5 . 1 Figure 29 .

 25129 Figure 29. No change in the synaptic accumulation of RIM1/2 at mixed inhibitory synapses by 4-AP treatment. (A) Triple staining of RIM1/2, GlyRs and GABA A Rs at mixed inhibitory synapses in spinal cord neurons. Scale bar: 5 µm. (B) The intensity of RIM1/2 was not changed (Kolmogorov-Smirnov test, p > 0.01) and GlyR clusters are marginally reduced (Kolmogorov-Smirnov test, p < 0.01), while that of GABA A Rs is decreased (p < 0.01) by 4-AP treatment, compared to TTX condition. (C) The intensity ratio of RIM1/2 / GlyR was not changed, that of GABA A R/GlyR was reduced (p < 0.01), and that of GABA A R / RIM1/2 was reduced (p < 0.01). Number of synapses: n=3972 in non-treated (CTRL) group, n=5406 in TTX condition, n=4408 in 4-AP condition; from two independent experiments.

Figure 31 .C

 31 Figure 31. No change in the spatial correlation of GABA A Rs and RIM1/2 by 4-AP treatment. (A) Synaptic clusters of GABA A Rs labeled with A647 and RIM1/2 labeled with Cy3B in dSTORM images. Scale bar: 100 nm. (B) ICQ measurements did not show a difference in the spatial correlation of GABA A Rs and RIM1/2 between 4-AP and TTX conditions (t-test, p=0.462). (C) The SSD measurements of GABA A R and RIM1/2 clusters in the dSTORM images. No difference was detected between the two treatments in the SSD counts. Number of synapses: n=82 in TTX, n=94 in 4-AP conditions, from two independent experiments.

Figure 32 .

 32 Figure 32. The spatial correlation of synaptic proteins measured by ICQ. Grey bars: without any treatment; blue bars: TTX treated conditions.

  is taken as control. Results are from two-color dSTORM experiments with TTX or 4-AP treatment.) project, I have adopted state-of-the-art super-resolution optical microscopy to study the internal structures of inhibitory synapses. Owing to the complexity of the technique and the particular properties of synapses, I developed an optimized two-color dSTORM protocol and carefully evaluated the influence of the dyes. With the obtained approaches, I then examined the intra-synaptic organization of GlyRs and GABAARs at the same PSD, and their re-organization in response to neuronal network activity. This is the first time that the different receptor types at mixed inhibitory synapses have been closely investigated in such detail at single molecule resolution using super-resolution optical microscopy.

  Image acquisition SMLM techniques aim to record large numbers of single fluorophore detections from densely labelled structures, while ensuring that the signals are sufficiently sparse to be well separated. STORM, PALM and uPAINT have all been employed for detecting SSDs. The three techniques have the same intrinsic challenges when it comes to the ultrastructure of synapses, chief among them being the fluorophore. Most fluorophores are detected repeatedly due to their fluorescence lifetime, photo-switching and blinking. This can create dense clusters of redundant detections that are easily mistaken for SSDs. The blinking behavior of the fluorophores (organic dyes or fluorescent proteins) is dependent on their

  features]. "_features.csv" Combined all the data into one excel file. #pairs filtered according to epi images. For '_features', removed 'ncenters', 'nperiphery', 'density', 'nedges', 'NormalityPValue'. Split the two channels [0, 1]. **Cluster size information: Hull Area. **Detection number information: npoints, Hull Density. **Subcluster counts. **Distributional heterogeneity: InnerDistMean, InnerDistStd, CoefficientOfVariation, NormalityStatistics.

  correlation: Mean0, std0, CoefVar0, Mean1, std1, CoefVar1, Score. Statistics ---[Subcluster features]."_features_sub0.csv" & "_features_sub1.csv"

  diffraction-limit structures such as synapses, which have a size of only a few hundred nanometers. The advancement of super-resolution fluorescence microscopy allows researchers to probe nanometer-scale structural details. Single molecule localization microscopy (SMLM) takes advantage of the blinking behavior of fluorophores (Fig. 1A) to calculate the precise position of individual molecules. During imaging, the fluorophores are first pushed into a non-fluorescent OFF state, then a sparse subset of fluorophores are reactivated by a low dose of UV light or spontaneously in the presence of reducing agents in the imaging buffer to gain a temporal separation of the signals. These sparse single detections are fitted mathematically during image processing, attaining a localization precision of ~10 nm. Super-resolution images can then be reconstructed by superimposing all the detections from the recording. SMLM differs from conventional optical microscopy very significantly and there are some definitions in SMLM that biologists should be aware of. The first one is the localization precision. It is the precision with which single detections are localized by fitting with a Gaussian distribution or other algorithms. The localization precision can be approximated by ∆/√N, where ∆ is the full width at half-maximum (FWHM) of the point spread function (PSF)

  assignment of multiple signals to a single detection necessarily causes wrong representations in the super-resolved image. Second, synapses have large structural variability in terms of cluster shape and molecule numbers. As a consequence, individual synaptic clusters cannot be combined and population averaging is not applicable. Each synaptic cluster needs to be treated individually, which increases the difficulties of data analysis. Furthermore, given the stochastic nature inherent to dSTORM, the variations in detection efficiency and localization density of synaptic clusters in the same ROI can be large, not to mention the variations among different ROIs. Therefore, extraordinary scrutiny is required to assess the quality of the imaging data. Thirdly, even though synapses are densely packed compartments, the copy numbers of many of their components are quite small, with receptors in the order of tens and scaffold proteins in order of hundreds of molecules per synapse. Small numbers of target proteins make it difficult to attain sufficiently dense labeling and hence efficient sampling of the synapse in order to reconstruct a representative super-resolution image. Fourth, synaptic proteins undergo dynamic changes and re-organization during synaptic plasticity, which adds to the difficulties of SMLM data interpretation. Given the variability and plasticity of synapses, large sample sizes (number of synapses) are needed for meaningful data interpretation. However, synapses are usually well separated along the dendrites and sometimes only a few synapses can be recorded in one ROI due to the high magnification used for imaging, which limits the data output and consequently increases the time required for the experiments. In addition to the aspects mentioned above, the detection of two synaptic components in parallel dramatically increases the complexity of the experiments, because it not only requires the achievement of good imaging quality of two channels, but also the implementation of more complicated data processing and analysis tools.

  ) at the beginning of the dSTORM recording, the activated fluorophores can be easily pushed into the OFF state in reducing and oxygen-depleted buffer conditions under strong illumination; (2) the fluorophores in the OFF state can be efficiently activated into the ON state by UV light or reducing agents; (3) the dyes can go through several ON/OFF switching cycles, which ensures that sufficient detections can be recorded; (4) the duration of the fluorescence emission should not be persistent (longer than a few image frames) in order to avoid repetitive detections of the same fluorophore during a single blinking event; (5) the dyes should be sufficiently bright to obtain a high localization precision of the single molecule signals. Since all of the above parameters are dependent on the imaging conditions, the optimization of the laser intensities and the buffer composition can substantially improve the blinking performance of the fluorescent dyes.

  based pointillist data and the rendered super-resolution image data. Each type of datasets has its own strengths and drawbacks for analysis. The coordinate-based pointillist data in principle make the best use of single molecule localizations. However, the localization precision is usually neglected in the analytical methods developed so far. Moreover, cluster segmentation, feature extraction and spatial correlation analysis based on the pointillist data may demand qualified programming skills. On the other hand, the rendered super-resolution images give more intuitive information, and can be analyzed using common image processing tools such as ImageJ that are widely known to researchers. More importantly, in the rendered images, pixel values represent the density of the localizations, taking into account the localization precision. Since the SMLM coordinates are not the actual positions of the fluorophores but only calculated positions, they can also be understood as localization probabilities. The inclusion of the localization precision in image rendering thus produces more realistic representations. In addition, pixels that do not contain any localizations have the value of zero. In other words, there is no background in the rendered super-resolution images. Thus there is no need for background subtraction or thresholding, which is one of the major difficulties in fluorescent image processing.

Figure 3 .

 3 Figure 3. Drift correction with beads as fiducial markers.

Figure 4 .

 4 Figure 4. dSTORM microscope setup with its main components.

Figure 5 .

 5 Figure 5. Determining the optimal length of dSTORM recordings.

  Fig.1

  Fig. 2A B

  Fig. 5

FIGURE 1 |

 1 FIGURE 1 | Pointillist images showing synaptic gephyrin clusters with one SSD (left) or four SSDs (right). The points represent the detections of single fluorophores from PALM imaging. Scale bar: 100 nm (adapted with permission from Pennacchietti et al., 2017).

2 Materials and methods

	2.1 Primary spinal cord neuron culture
	All procedures using animals follow the regulations of the French Ministry of
	Agriculture and the Direction départamentale des services vétérinaires de Paris
	(Ecole Normale Supérieure, Animalerie des Rongeurs, license B 75-05-20).

  We carried out the analysis workflow as described above. We first measured the size of the synaptic clusters to verify the two groups of synapses (Figure11.B). As expected, the area of gephyrin clusters from group two is dramatically smaller than that of group one, because there was none (~20% of the clusters) or very few detections of gephyrin in group two.Next, we compared the spatial correlation of the super-resolution clusters in the two channels between group one and group two (Figure11.C). ICQ measured a higher correlation in group one than group two. MCC measurement also showed a significant difference between the two groups. In the MCC test, the fraction of

	(group 1) and RIM1/2 without
	gephyrin (group 2). Group 1 includes all the clusters with both RIM1/2 and
	gephyrin well stained (yellow arrow), producing overlapping binary clusters;
	group 2 includes clusters with RIM1/2 well stained but no gephyrin staining (blue
	arrow) or very weak gephyrin staining (white arrow), thus producing no binary
	gephyrin clusters. RIM1/2 was labeled with A647 and gephyrin with Cy3B. Scale
	bar: 2 µm. (B) Individual synaptic cluster pairs were obtained and measured for

their area. Example synaptic cluster pairs from the two groups are shown on the top, and the cluster size is shown on the bottom. Scale bar 200 nm. (C) The spatial correlation of RIM1/2 and gephyrin clusters was measured with several approaches incorporated in the ICA plugin. The spatial correlation of RIM1/2 and gephyrin clusters in group 1 was significantly reduced when gephyrin clusters were flipped vertically, measured by all the approaches (t-test, p<0.00001).

Group 1 has stronger spatial correlation than group 2 as measured by ICQ (t-test, p<0.00001) and MCC (t-test, MCC1, p<0.00001; MCC2, p=0.015). MOC does not detect a difference between group 1 and group 2 (t-test, p=0.296), and PCC values are mostly negative or close to zero in the two groups. Not that in the ICQ measurement, about 2% of the values in group 2 are negative and are cut off from the curve. Number of synapses: group 1, n=536; group 2, n=709; from three independent experiments. cluster pairs in our test. RIM1/2 signals co-localized with gephyrin is much higher in group one than in group two. This is because there are strong gephyrin signals in group one to produce large overlaps with RIM1/2. On the other hand, the fraction of gephyrin signals colocalized with RIM1/2 is similar in the two groups, even though gephyrin signals were dramatically different. This is because RIM1/2 signals were strong in both groups and produced large overlaps to include most of the gephyrin signals in both

Table 1 .

 1 Summary of the changes in synaptic molecule amount at mixed synapses with altered neuronal activity.

	(Comparing 4-AP to TTX)	Synaptic molecule amount
	Total gephyrin	No change
	p-gephyrin	Decreased
	GlyRs	No change
	GABAARs	Decreased
	RIM1/2	No change
	(TTX condition is taken as control. Results are from the conventional microscopy
	experiments with TTX or 4-AP treatment.)

Table 2 .

 2 Summary of the changes in spatial correlation between synaptic proteins with altered neuronal activity, measured by ICQ.

Table 3 .

 3 Size and protein copy numbers of SSDs and PSDs obtained with different experimental techniques.

	SSD	50-130 *	Structure Diameter (nm)
			Molecule numbers
		SMLM	Technique
		Excitatory, hippocampal	Synapse type
			References

  (Note that opening the csv files in EXCEL may change the format and cause error when loading files to SRclusters in the Step01.)

	Two-color dSTORM of [2] SRclusters on Jupyter Notebook. Targets GlyR GlyR α1 http://bioclusts02.bioclust.biologie.ens.fr:7777/srclusters/login Dyes Alexa 647 Cy3B Password: srclusters	Treatments No treatment	Independent experiments
	Gephyrin	m7a rbGPHN	Alexa 647 or Cy3B Cy3B or Alexa 647	No treatment
	Gephyrin and GlyR	m7a GlyR α1	Alexa 647 Cy3B	No treatment
	Gephyrin and RIM1/2	m7a RIM1/2	Cy3B Alexa 647	No treatment
		m7a	Alexa 647
	Gephyrin and GlyR	GlyR α1	Cy3B	TTX or 4-AP
		GABA A R β3	Alexa 488 * #
		m7a	Cy3B
	Gephyrin and GlyR	GlyR α1	Alexa 647	TTX or 4-AP
		GABA A R β3	Alexa 488 * #
	Gephyrin and GABA A R	m7a GABA A R β3 GlyR α1	Alexa 647 Cy3B Alexa 488 * #	TTX or 4-AP
		GlyR α1	Alexa 647
	GlyR and GABA A R	GABA A R β3	Cy3B	TTX or 4-AP
		m7a	Alexa 488 * #
		GlyR α1	Alexa 647
	RIM1/2 and GlyR	RIM1/2	Cy3B	TTX or 4-AP
		GABA A R β3	Alexa 488 #
		GABA A R β3	Alexa 647
	RIM1/2 and GABA A R	RIM1/2	Cy3B	TTX or 4-AP
		GlyR α1	Alexa 488 #

  16 mm, No. 1.5, VWR #6310153) that were pre-washed with ethanol and coated with 70 μg/ml poly-DL-ornithine. Dissociated neurons were seeded at a density of 4 × 10 4 antibodies in blocking buffer for 1 hour at room temperature in the dark. In the experiments described here, the following primary antibodies were used: mouse

	monoclonal mAb7a (Synaptic Systems, #147011, at 1:500 dilution) and rabbit polyclonal
	(Synaptic Systems, #147002, 1:500) antibodies against gephyrin, as well as custom-made
	rabbit polyclonal antibody against the GlyR subunit a1 (A. Triller lab, #2353, 1:500 to
	1:1000). The secondary antibodies were Alexa Fluor 647-conjugated donkey anti-mouse IgG
	(Jackson ImmunoResearch, #715-605-151, 1:500 to 1:1000) and Alexa Fluor 647-conjugated
	donkey anti-rabbit IgG (Jackson ImmunoResearch, #711-605-152, 1:500 to 1:1000). In
	addition, unconjugated donkey anti-mouse IgG (Jackson ImmunoResearch, #715-005-151)
	and donkey anti-rabbit IgG (Jackson ImmunoResearch, #711-005-152) secondary antibodies
	cells/cm 2 in Neurobasal medium (ThermoFisher) supplemented with B-27, 2 mM glutamine,
	5 U/ml penicillin and 5 µg/ml streptomycin. Neurons were cultured at 37°C with 5% CO 2 , and
	the medium was replenished twice a week by replacing half of the volume with BrainPhys
	neuronal medium (Stemcell Technologies) supplemented with SM1 and antibiotics. Usually,
	spinal cord neurons developed mature neurites after two weeks in culture, at which point they
	were used for immunocytochemistry.
	Spinal cord neurons were fixed with 100% methanol at -20°C for 10 minutes or with pre-
	warmed (37°C) 4% paraformaldehyde (PFA) in PBS for 15 minutes. Unspecific binding sites

were blocked with 3% BSA in PBS (blocking buffer) for at least 30 minutes. The cells were then incubated with primary antibodies diluted in blocking buffer for 1 hour at room temperature or overnight at 4°C, followed by several washing steps in PBS and incubation with secondary

TABLE 1 |

 1 Size and protein copy numbers of SSDs and PSDs obtained with different experimental techniques.

	Synapse type
	Technique
	Structure Diameter (nm) Molecule numbers

Frontiers in Molecular Neuroscience | www.frontiersin.org 1 July 2019 | Volume 12 | Article 161

Frontiers in Molecular Neuroscience | www.frontiersin.org

July 2019 | Volume 12 | Article 161

Acknowledgement

We thank Manuel Maidorn and Felipe Opazo for the illustrations in Figure 2 [47], and Ignacio Izeddin for Figure 4. Our research is funded by grants (to Antoine Triller, IBENS, Paris) from the Agence Nationale de la Recherche (ANR-12-BSV4-0019-01, ANR-11-IDEX-0001-02, ANR-10-LABX-54) and the European Research Council (ERC, PlastInhib). XY is supported by the China Scholarship Council (CSC).

Appendix: imaging buffers

Gloxy buffer: 0.5 mg/ml glucose oxidase, 40 μg/ml catalase, 0.5 M D-glucose, 50 mM βmercaptoethylamine (MEA), in PBS pH 7.4, degased with N 2 [3].

OxEA buffer: 3% (v/v) oxyrase (EC-Oxyrase, Sigma-Aldrich), 1 M sodium DL-lactate, 50 mM MEA, in PBS pH 8.0 [59].

ACKNOWLEDGMENTS

We thank Andrea Barberis (IIT, Genoa) for the permission to reproduce the pointillist image in Figure 1.

The SRclusters program is self-explanatory. It is written in Python language based on Jupyter Notebook. The main algorithms adopted here are the density-based spatial clustering of applications with noise (DBSCAN) and the nearest neighbor distance (NND).

[1] Preparation of input files.

Matlab 2012b, SrInput2.

Both the MTT mat file and drift corrected file are needed, and in the same folder. The drift corrected file should be named as "###_DC.mat".

The transformation of mat file to SR_input csv file:

In mat file: coordinates (x, y, z) are in pixels, intensity is the alpha value from the Gaussian fitting.

In SR_input file: coordinates (x,y,z) are in nanometers, Intensity is the calculated photon number.

See the table below for the calculation details. Consult your microscope engineer when the setup is modified for the correct parameters (Pre-amp, EM gain, quantum efficiency, Gaussian fitting details for alpha values).

(2) Global synaptic cluster features: Features ---all features are imported from networkx, based on 'graph', except for npoints which are extracted independently from the input files.

Diameter (the maximum eccentricity): calculating by the nodes (which are localizations here), not in nanometer scale. Radius (the minimum eccentricity): by the nodes, not in nanometer scale. Ncenters (number of points where the eccentricity equal to radius). Nperiphery (number of points where the eccentricity is equal to the diameter). Density (is 0 for a graph without edges and 1 for a complete graph). Npoints (number of points/localizations in this cluster). Nedges (number of connections on the Delaunay triangulation). Hull area (area of the bounding polygon). Hull density (number of points/hull area). Eigen ratio (ratio between the biggest and smaller Eigen values of the co-variance matrix. A value of 1 would mean a perfectly round cluster, and 0 an infinitely elongated cluster).

Npoints: extracted from the coordinates by counting the number of coordinates for each cluster. Hull Area: corresponding to the Hull Volume in the graph, is the area of the cluster, in nm 2 . Hull density is calculated from the Npoints/Hull Area.

(3) Spatial correlation and distributional heterogeneity with NND: Set nneighbors = [], it is applied to both intra-cluster and inter-cluster NND measurement.

2-channel correlation: Score = absolute (intermean0-intermean1); for each pair.

Another score {abs (intermean0-intermean1) + abs (interSD0-interSD1)} has linear relationship with score {abs (intermean0-intermean1)}, thus not adopted.

Inter-cluster NND: Mean0, std0, CoefVar0, Mean1, std1, CoefVar1. (0=channel 0, 1= channel 1). Intra-cluster NND: Mean0, std0, CoefVar0, Mean1, std1, CoefVar1. (0=channel 0, 1= channel 1). Normality (statistics) ---from SciPy.org == (Skewness 2 + Kurtosis 2 ) ---Larger values, more normal distribution. For both intra-cluster and inter-cluster NND.

A two-color 3D dSTORM scheme

To enable the 3D imaging of synaptic clusters in my doctoral project, I have set up a two-color 3D dSTORM imaging procedure (Appendix Figure 2). A deformable mirror (MicAO 3DSR, Imagine Optics) was introduced into the emission light path on our microscope setup, instead of cylindrical lens. The deformable mirror can introduce astigmatism into the light path, and it can also be adjusted to minimize other optical aberrations. The two-color 3D dSTORM imaging procedure is basically similar to the 2D dSTORM Appendix Figure 2.C).

In addition to that, the deformable mirror needs to be optimized at the beginning of the recordings, then a user-defined astigmatism is introduced and a calibration recording with a bead is taken for producing the calibration curve.

Appendix (EPA) [START_REF] Brocher | Qualitative and Quantitative Evaluation of Two New Histogram Limiting Binarization Algorithms[END_REF] in Fiji. We can thus obtain detailed information about synaptic GlyR and gephyrin subclusters under various experimental conditions. For example, changes in the number and area of the subclusters per synapse reveal how the distribution of GlyRs and gephyrin within the PSD is regulated during inhibitory synaptic plasticity.

Strategies for imaging quality assessment

The evaluation of individual factors (dyes, buffers, probes, laser power and so on) is important for understanding their influence on the quality of the dSTORM data. However, the final image quality depends on the combined effect of these factors that are heavily interdependent. Therefore, a joined assessment of the imaging data is necessary to estimate the capability of the protocol to answer the biological question or hypothesis. We propose to carry out the following types of control experiments to evaluate the performance of the chosen experimental approach (Fig 6).

First, we recorded the blinking behavior of the two dyes conjugated to IgG in order to estimate the degree of redundant detections and the appearance of fake nanoclusters. We carried out two-color dSTORM imaging of two secondary antibodies, conjugated with either Alexa 647 or Cy3B dye. Clean glass coverslips were incubated for one hour with Alexa 647conjugated donkey anti-mouse IgG and Cy3B-conjugated donkey anti-rabbit IgG that were diluted in 3% BSA and mounted with beads and imaging buffer. We then performed sequential two-color dSTORM imaging as described above and characterized the clusters produced by single secondary antibodies in the two channels (Fig. 6A). Both A647-IgG and Cy3B-IgG produced nanometer scale clusters with a wide variability in the number of detections per cluster as well as cluster size. From the temporal profiles of the blinking events, we can infer the photophysical behavior of the dyes. For Alexa 647, the blinking events are brief and dispersed across the entire recording. Cy3B fluorophores on the other A. The large size of primary and secondary antibodies limits their access to all the epitopes and keeps the fluorophores at a distance from the target proteins. Live labeling with antibodies may also cause artificial clustering of membrane proteins.

B.

Small monovalent probes such as nanobodies circumvent these limitations, avoiding epitope masking, signal de-localization and crosslinking. The epitope-binding interface is shown in magenta, organic dyes in red, labeling probes in green, and target proteins in blue.

These illustrations were originally published by Maidorn, Rizzoli & Opazo in the Biochemical Journal (2016, https://doi.org/10.1042/BCJ20160366) [START_REF] Maidorn | Tools and limitations to study the molecular composition of synapses by fluorescence microscopy[END_REF], and were reproduced with the kind permission of the authors and the publisher.

B.

For Cy3B-labeled gephyrin clusters, the first two substacks were similar and matched the reconstructed image of the entire stack (40000 frames), however, the third substack showed incomplete sampling. To ensure sufficient detections of Cy3B signals, we determined an optimal imaging length of 30000 frames for this channel. Yellow boxes show the masks of individual synaptic clusters produced from the epifluorescence images. Scale: 500 nm. The application of super-resolution optical microscopy to investigating synaptic structures has revealed a highly heterogeneous and variable intra-synaptic organization. Dense subsynaptic protein assemblies named subsynaptic domains or SSDs have been proposed as structural units that regulate the efficacy of neuronal transmission. However, an in-depth characterization of SSDs has been hampered by technical limitations of super-resolution microscopy of synapses, namely the stochasticity of the signals during the imaging procedures and the variability of the synaptic structures. Here, we synthetize the available evidence for the existence of SSDs at central synapses, as well as the possible functional relevance of SSDs. In particular, we discuss the possible regulation of co-transmission at mixed inhibitory synapses as a consequence of the subsynaptic distribution of glycine receptors (GlyRs) and GABA A receptors (GABA A Rs).

LAY ABSTRACT

Super-resolution imaging strategies bypass the resolution limit of conventional optical microscopy and have given new insights into the distribution of proteins at synapses in the central nervous system. Neurotransmitter receptors and scaffold proteins appear to occupy specialized locations within synapses that we refer to as subsynaptic domains or SSDs. Interestingly, these SSDs are highly dynamic and their formation seems to be related to the remodeling of synapses during synaptic plasticity. It was also shown that SSDs of pre-and post-synaptic proteins are aligned in so-called nanocolumns, highlighting the role of SSDs in the regulation of synaptic transmission. Despite recent advances, however, the detection of SSDs with super-resolution microscopy remains difficult due to the inherent technical limitations of these approaches that are discussed in this review article. scrutiny in the experimental and analytical procedures. Superresolution techniques may still have some way to go before we can truly resolve the fast molecular processes at synapses.

AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

FUNDING

XY is supported by the China Scholarship Council (CSC).

Conflict of Interest Statement:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

ABSTRACT

Stochastic optical reconstruction microscopy (STORM) bypasses the diffraction limit by recording spatially and temporally separated single molecule signals, achieving a resolution of ~10-40 nm. In my study, I have developed a two-color dSTORM imaging and data analysis strategy, in order to investigate the ultrastructure of mixed inhibitory synapses. My results show that GlyRs, GABA A Rs, gephyrin and RIM1/2 exhibit a heterogeneous intra-synaptic organization and form sub-synaptic domains (SSDs). GlyRs and GABA A Rs were not fully intermingled, but sometimes occupied different spaces at the post-synaptic density (PSD). In addition, post-synaptic gephyrin SSDs were aligned with pre-synaptic RIM1/2 SSDs, forming trans-synaptic nanocolumns. During elevated neuronal activity by 4-AP treatment, the spatial correlation between GlyRs, GABA A Rs and gephyrin was increased at the PSD. Moreover, the spatial correlation of GlyRs and RIM1/2 was also increased, while that of GABA A Rs and RIM1/2 did not change. The number of SSDs per synapse for these synaptic proteins was not changed by 4-AP. My study thus provides a new angle for understanding the mechanisms underlying GABAergic/glycinergic co-transmission.

MOTS CLÉS

Synapses inhibitrices mixtes, GlyR, GABA A R, géphyrine, RIM1/2, co-transmission, domaine soussynaptique (SSD), nanocolonne trans-synaptique, microscopie de localisation de molécule unique (SMLM), microscopie de reconstruction optique stochastique directe (dSTORM), dSTORM multicouleur.

RÉSUMÉ

La microscopie optique stochastique de reconstruction (STORM) contourne la limite de diffraction en enregistrant des signaux monomoléculaires spatialement et temporellement séparés, atteignant une résolution de ~10-40 nm. Dans mon étude, j'ai développé une stratégie d'imagerie et d'analyse de données dSTORM bicolore afin d'étudier l'ultrastructure des synapses inhibitrices mixtes. Mes résultats ont montré que les GlyRs, les GABA A Rs, la géphyrine et RIM1/2 présentent une organisation intra-synaptique hétérogène et forment des domaines sous-synaptiques (SSDs). Les GlyR et les GABA A R ne sont pas complètement mélangés, mais pouvent occuper des espaces différents à la densité post-synaptique (PSD). De plus, les SSD de géphyrine postsynaptique sont alignées avec les SSD de RIM1/2 pré-synaptiques, formant des nanocolonnes trans-synaptiques. Au cours d'une activité neuronale élevée par traitement 4-AP, la corrélation spatiale entre les GlyRs, les GABA A Rs et la géphyrine a augmentée au PSD. De plus, la corrélation spatiale des GlyRs et RIM1/2 a également augmenté, tandis que celle des GABA A Rs et RIM1/2 n'a pas changé. Le nombre de SSD par synapse pour ces protéines synaptiques n'est pas modifié par 4-AP. Cette étude fourni un nouvel angle de compréhension des mécanismes sous-jacents à la co-transmission GABAergique/glycinergique. KEYWORDS Mixed inhibitory synapses, GlyR, GABA A R, gephyrin, RIM1/2, co-transmission, subsynaptic domain (SSD), trans-synaptic nanocolumn, single molecule localization microscopy (SMLM), direct stochastic optical reconstruction microscopy (dSTORM), multi-color dSTORM.