Diego Amarante-Silva

Emmanuel Bruet

Rémy Gars

Margaux Piechon

Tatiana Gorojankina

Jérôme Bignon

Sophie E Creuzet
email: sophie.creuzet@cnrs.fr

Neural crest mural cells of forebrain meninges harbor innate immune functions during early brain development and exhibit different responses to septic and toxic insults

Keywords: αSMA, alpha-Smooth Actin Muscle, CitH, Citrullinated Histone, CNC, cephalic neural crest, CNS, central nervous system, DAMP, Damage Associated Molecular Pattern, E, embryonic day, ET, Extracellular Traps, HH, Hamburger-Hamilton stage, IFN, interferon, IL, interleukin, LPS, Lipopolysaccharides, PADI4, Peptidyl Arginine Deiminase 4, PDGFRβ, Platelet-Derived Growth Factor Receptor β, NG2, Neuron-Glial Antigen 2, R, arginine convertible into citrulline, Tumor Necrosis Factor,

In vertebrates, the cephalic neural crest (CNC), an embryonic multipotent cell population, gives rise to meningeal derivatives, covering and protecting the developing forebrain. In postnatal life, the meninges exert a prime role in developing and maintaining the central nervous system's homeostasis and help modulate the immune capacities of resident cells and the blood-brain barrier. However, their role in the embryo remains elusive before the formation of brain barriers. Here, we explore whether meningeal CNC-mural cells participate in immunosurveillance during the early stages of embryogenesis. Using primary cultures of E8 chicken forebrain meningeal mural cells, immuno-phenotyping, and flow cytometry analyses, we demonstrated the expression of specific receptors, including TLRs and MHC class II molecules typically expressed in myeloid cells. Our experiments showed that when challenged by bacterial particles, CNC mural cells also have phagocytic capacities. Transcriptomic analysis revealed activation of inflammatory pathways leading to increased expression of myeloid surface markers and the production of cytokines and interleukins in the presence of bacterial particles. Our in vitro assays also show the capacities of mural cells to present and transfer antigen particles and recruit cooperant cells, both locally and at a distance. Moreover, we revealed that pericytes could deploy DNA traps like neutrophils to stop pathogens. We show that mural cells use extracellular DNA shedding against pathogenic conditions and communicate with each other to mobilize cell cooperation.

Transcriptomic analyses revealed that exposure to bacterial particles leads to severe impairment of morphogenetic and neuro-developmental pathways and shed light on the possible detrimental effect of bacterial infection on brain development and maturation of cognitive functions. To reinforce the possible link between neurodevelopmental defects and immune activation, we challenged the immune capacities of CNC pericytes after exposure to toxic medication. We used Valproate (VPA), an antiepileptic drug associated with developmental disorders in newborns when administered during pregnancy. We show that VPA injection into chicken embryos triggered exacerbated myeloid traits and DNA trap releases in CNC pericytes. Our studies show that CNC mural cells are professional innate immune cells capable of (which was not certified by peer review) is the author/funder.

Introduction

The neural crest is a phylogenetic innovation which has emerged at the dawn of vertebrate evolution and which turned out to be essential for vertebrate radiation [LeDouarin, 1982;[START_REF] Gans | Neural crest and the origin of vertebrates: a new head[END_REF][START_REF] Hall | The neural crest as a fourth germ layer and vertebrates as quadroblastic not triploblastic[END_REF][START_REF] Green | Evolution of vertebrates as viewed from the crest[END_REF]. During vertebrate development, the neural crest cells give rise to multiple cell types with distinct differentiation potential along the anteroposterior axis. While in the trunk, the neural crest mostly provides Schwann cells and melanocytes, the cephalic neural crest (CNC) gives rise to a rich and versatile ectomesenchyme endowed with skeletal, connective, perivascular, and meningeal differentiation capacities [LeDouarin et al., 2004;Creuzet, 2009a;LeDouarin and Creuzet, 2009]. During chordate evolution, the emergence of CNC-derived mesenchyme has supplanted the cephalic mesoderm in providing dermal, meningeal and skeletal, mesenchymal derivatives to sculpt the vertebrate head [START_REF] Couly | The developmental fate of the cephalic mesoderm in quail-chick chimeras[END_REF]. Therefore, a developmental shift occurs around the transverse level of the adenohypophysis, between the anterior cephalic domain, where the CNC mesenchymal contribution predominates, and the rest of the body, where mesodermal contribution prevails [Couly et al;1992;[START_REF] Couly | The triple origin of skull in higher vertebrates: a study in quail-chick chimeras[END_REF]Creuzet, 2009b]. The concomitant emergence of the CNC-derived ectomesenchyme and the expansion of the prosencephalic vesicles pointed to decisive functional advantages yielded by the CNC cells through the derivatives they form, and presumably also, the mechanisms they control. Developmental studies have shown that CNC cells empower the development of the anterior brain: they provide pro-encephalic signaling to promote encephalization, and convey morphogenetic signaling to coordinate the forebrain patterning [Creuzet, 2009;[START_REF] Garcez | Combinatorial activity of Six1-2-4 genes in cephalic neural crest cells controls craniofacial and brain development[END_REF][START_REF] Aguiar | The facial neural crest controls fore-and midbrain patterning by regulating Foxg1 expression through Smad1 activity[END_REF].

Anteriorly, the CNC forms the entire muscular-connective tissue wall of the large arteries down to the heart, where they contribute to the semilunar valves, the conotruncus, and the inter-ventricular septum.

In the ascendant vascular system, while the endothelium is made of mesodermal cells, the mural cells -pericytes or perivascular cells -have an exclusive neural crest origin [START_REF] Couly | The angiogenic potentials of the cephalic mesoderm and the origin of brain and head blood vessels[END_REF] [START_REF] Korn | Neuroectodermal origin of brain pericytes and vascular smooth muscle cells[END_REF]. In this ascending vascular sector, emerging from the aortic outflow tract, the CNC-mural cells line the endothelium of the aorta, of the great arteries emerging and its brachio-cephalic branches, to the very thin ramifications of the vasculature which irrigate the face, the oro-nasal, pharyngeal, tracheal mucosae, and the pulmonary veins [START_REF] Etchevers | The cephalic neural crest provides pericytes and smooth muscle cells to all blood vessels of the face and forebrain[END_REF]. CNC-mural cells also contribute to the vasculature of the anterior brain. In the anterior brain, the CNC is the source of the primary leptomeninges wrapping the forebrain, where CNC cells predominantly differentiate into mural cells that line the endothelium of pial blood vessels and capillaries, and also form the perivascular cells in the forebrain parenchyma -including olfactory bulb, hemispheres, thalamus, epi-and hypothalamus-, along with the stroma of choroid plexus of the lateral and third ventricles [START_REF] Etchevers | The cephalic neural crest provides pericytes and smooth muscle cells to all blood vessels of the face and forebrain[END_REF][START_REF] Löscher | Basic pharmacology of valproate: a review after 35 years of clinical use for the treatment of epilepsy[END_REF]. They ensure the tuning adjustment of the vascular wall to the hemodynamic constraints and changes in blood pressure imposed by the pulsatile systolic blood supply to support forebrain viability [START_REF] Etchevers | Anterior cephalic neural crest is required for forebrain viability[END_REF][START_REF] Creuzet | Negative effect of Hox gene expression on the development of the neural crest-derived facial skeleton[END_REF].

At a given transverse level, the mural cells share a common embryonic origin [START_REF] Etchevers | The cephalic neural crest provides pericytes and smooth muscle cells to all blood vessels of the face and forebrain[END_REF] and similar properties and molecular markers, like the accumulation of alpha-Smooth Muscle Actin (aSMA) [START_REF] Skalli | A monoclonal antibody against alpha-smooth muscle actin: a new probe for smooth muscle differentiation[END_REF][START_REF] Alarcon-Martinez | Capillary pericytes express α-smooth muscle actin, which requires prevention of filamentous-actin depolymerization for detection[END_REF]. Other cytoskeletal proteins such as nestin, tropomyosin, non-muscle myosin, desmin, and vimentin participate in their functions as smooth muscle cells. However, they are not exclusive markers, and their accumulation may vary between organs [START_REF] Van Dijk | The complex mural cell: pericyte function in health and disease[END_REF]. In addition, Platelet-Derived Growth Factor Receptor (PDGFR-β) is regarded as a specific marker since animals deficient in PDGFR-β show an absence of pericytes, defects in vascularization, and die during development [START_REF] Lindahl | Pericyte loss and microaneurysm formation in PDGF-Bdeficient mice[END_REF]]. However, some authors pointed out that PDGFR-β is widely expressed throughout the embryo and precludes its relevance for developmental studies, but is extensively used to analyze the fate of mural cells in adulthood [Guimaraes-Camboa et al., 2017;[START_REF] Prazeres | Perivascular cell αv integrins as a target to treat skeletal muscle fibrosis[END_REF]. The chondroitin sulfate proteoglycan 4, known as Neuron-Glial Antigen 2 (NG2), also characterizes activated mural cells [START_REF] Ozerdem | NG2 proteoglycan is expressed exclusively by mural cells during vascular morphogenesis[END_REF]. However, NG2 is widely expressed by other embryonic derivatives like adipocytes, chondroblasts, and

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Schwann cells, and by oligodendrocytes and microglia, which prevents its extensive use for developmental studies [START_REF] Fukushi | Expression of NG2 proteoglycan during endochondral and intramembranous ossification[END_REF][START_REF] Castro | Specific labeling of synaptic schwann cells reveals unique cellular and molecular features[END_REF][START_REF] Armulik | Pericytes: developmental, physiological, and pathological perspectives, problems, and promises[END_REF].

As smooth muscle cells, mural cells regulate blood vessel compliance by controlling vessel diameter, from vasoconstriction to vasodilation, and adjusting pro-angiogenic signaling to hypoxic conditions. They exert critical regulatory functions in vasculogenesis by controlling endothelial proliferation, vascular permeability, and vascular matrix assembly [START_REF] Gaengel | Endothelial-mural cell signaling in vascular development and angiogenesis[END_REF][START_REF] Stratman | Interactions between mural cells and endothelial cells stabilize the developing zebrafish dorsal aorta[END_REF][START_REF] Andrews | Fibronectin Produced by Cerebral Endothelial and Vascular Smooth Muscle Cells Contributes to Perivascular Extracellular Matrix in Late-Delayed Radiation-Induced Brain Injury[END_REF]. Through their mechanical roles and the dynamics of blood flow they act on, mural cells coordinate the homeostasis of the organs they reside in by timely adapting oxygenation, nutrient supply, and draining of metabolic waste [Birbair, 2018]. In the adult brain, mural cells assume pivotal adaptation at the interface of two fluidic compartments, the blood flow and the cerebrospinal fluid [START_REF] Greitz | Cerebrospinal fluid circulation and associated intracranial dynamics. A radiologic investigation using MR imaging and radionuclide cisternography[END_REF][START_REF] Bergsneider | The relationship of pulsatile cerebrospinal fluid flow to cerebral blood flow and intracranial pressure: a new theoretical model[END_REF]]. They are in close contact with the endothelium, astrocytic end-feet, microglia, and neuronal parenchyma to form the blood-brain-barrier, a barrier crucial to the central nervous system (CNS) homeostasis [START_REF] Banks | Physiology and pathology of the blood-brain barrier: implications for microbial pathogenesis, drug delivery and neurodegenerative disorders[END_REF][START_REF] Rustenhoven | Brain Pericytes As Mediators of Neuroinflammation[END_REF] [START_REF] Balabanov | CNS microvascular pericytes express macrophage-like function, cell surface integrin alpha M, and macrophage marker ED-2[END_REF]. In the context of cerebral ischemia, oxygen deficit, or glucose deprivation, pericytes repress perivascular markers and prompt the expression of microglia-like markers [START_REF] Sakuma | Brain pericytes serve as microglia-generating multipotent vascular stem cells following ischemic stroke[END_REF]. Upon immune-challenged conditions, pericytes can produce inflammatory molecules, including some cytokines, interleukins (ILs), TNF-α, and interferon-γ (IFN-γ) signals, which lead to the recruitment of cells within the neurovascular unit, particularly astrocytes and microglia, into a pro-inflammatory state [START_REF] Kovac | Brain microvascular pericytes are immunoactive in culture: cytokine, chemokine, nitric oxide, and LRP-1 expression in response to lipopolysaccharide[END_REF]. However, depending on the context, pericytes may also secrete anti-inflammatory molecules such as IL-33 and fractalkine to prevent microglia activation and exert a neuroprotective role [START_REF] Rustenhoven | Brain Pericytes As Mediators of Neuroinflammation[END_REF][START_REF] Hattori | The Multiple Roles of Pericytes in Vascular Formation and Microglial Functions in the Brain[END_REF].

Furthermore, upon trauma or infection, they concur to leucocyte extravasation to resorb inflammation or limit and defeat pathogen intrusion [START_REF] Rudziak | Role and Molecular Mechanisms of Pericytes in Regulation of Leukocyte Diapedesis in Inflamed Tissues[END_REF]Gribl et al., 2018;Julia et al., 2022].

However, the immune properties of pericyte during embryonic development, before the maturation of the immune system and formation of brain barriers, remains an open question. Furthermore, the immune properties of CNC cells still need to be characterized: the overall concept until now specifies that only mesodermal cells can perform immune functions. Here, we explored whether CNC-mural cells can exert immune surveillance at the early stages of brain development. We sought to understand how CNC mural cells respond to pathogenic stimulation and characterize the arsenal of markers and tools they express when challenged by bacterial particles to protect the brain against harmful pathogens. In addition, we aimed to identify the converging mechanisms of toxic and septic shock on the physiology of meningeal CNC mural cells. To do so, we chose to work with chicken embryos where the respective contribution of the CNC and mesoderm lineages to the cephalic vasculature has been accurately mapped [START_REF] Couly | The angiogenic potentials of the cephalic mesoderm and the origin of brain and head blood vessels[END_REF][START_REF] Etchevers | Anterior cephalic neural crest is required for forebrain viability[END_REF]2001;[START_REF] Korn | Neuroectodermal origin of brain pericytes and vascular smooth muscle cells[END_REF], and the sequential development of lymphoid organs and maturation of the professional immune cells extensively documented [START_REF] Garcia | The Chicken Embryo Model: A Novel and Relevant Model for Immune-Based Studies[END_REF]. Therefore, we focused our study on E8, bearing in mind that at this stage, microglia and astrocytes are not mature, and the brain barriers do not exist as in adults.

Moreover, this model, which develops without placenta, allowed us to scrutinize the early intrinsic immune functions of forebrain meninges without any septic variation conveyed by a maternal environment that could impinge on the immune status of the embryo [START_REF] Guma | Differential effects of early or late exposure to prenatal maternal immune activation on mouse embryonic neurodevelopment[END_REF].

… … / … …

Results

CNC-mural cells exhibit a stable perivascular phenotype

To explore the intrinsic immune capacities of CNC-mural cells, we established the optimal conditions for native meningeal cell primary cultures. Meninges peeled from the surface of the telencephalon and diencephalon of chicken embryos at Hamburger-Hamilton stage 32 (HH32, corresponding to E8) were dissociated in a TrypLE express solution, rinsed, and seeded on fibronectin-coated chamber slides, then grown in vitro for 72 hours until reaching confluence (Fig. 1A). First, we analyzed the expression of the perivascular phenotype in CNC-mural cell mass culture by testing the accumulation of αSMA, as a marker of smooth muscle cells through immunocytochemistry [START_REF] Skalli | A monoclonal antibody against alpha-smooth muscle actin: a new probe for smooth muscle differentiation[END_REF].

CNC-mural cells display some surface myeloid markers and harbor phagocytic capacity

To test if CNC-mural cells harbor myeloid features, we examined the expression of early myeloid markers by immunocytochemistry and cytometry, CSFR1 corresponding to CD115, which consists in the earliest marker expressed in macrophage cell lineage [START_REF] Garceau | Pivotal Advance: Avian colony-stimulating factor 1 (CSF-1), interleukin-34 (IL-34), and CSF-1 receptor genes and gene products[END_REF], F4/80, a macrophage-specific glycoprotein, identified in microglia [START_REF] Lawson | Heterogeneity in the distribution and morphology of microglia in the normal adult mouse brain[END_REF], and Mac1, corresponding to an integrin subunit, CD11b/CD18, identified as a macrophage differentiation antigen [START_REF] Springer | Mac-1: a macrophage differentiation antigen identified by monoclonal antibody[END_REF].

Co-immunostaining of primary cultures with these macrophage markers and αSMA antibody revealed that, among αSMA -positive cells, 73% were CSF1R-positive, 44% positive for F4/80, and 47% positive for Mac1 (Fig. 1B). Flow cytometry corroborated these results by showing that, among Phalloidinstained cells, 66% were CSF1R-positive, 28% F4/80-positive, and 56% Mac1-positive (Fig. 1B). The immunocytochemistry and flow cytometry analyses indicated that the majority of CNC-mural cells exhibited the essential macrophage markers, CSF1R, F4/80, and Mac1, suggesting that they could harbor some immune capacities.

Since the CNC is the exclusive source of the prosencephalon leptomeninges, at odds with the rest of the CNS meninges, we examined whether mesoderm-derived meningeal cells have similar myeloid traits by isolating and culturing meningeal cells taken from E8-spinal cords (S3A,B Fig) . We evaluated the expression of macrophage markers by immunocytochemistry and found that the percentage of spinal cord mural cells positive for CSF1R, F4/80, and Mac1 was lower than their CNC-derived counterparts. These observations indicated that spinal mural cells also express macrophage markers, albeit to a lesser extent, and suggested that the CNC-mural cells have a more significant immune potential (S3A,C,D Fig).

Next, we explored the immunological potency of CNC-mural cells by challenging their response to pathogen exposure. To test their ability to phagocytosis, we exposed CNC-mural cells to pHrodo Red E. coli BioParticles. This conjugate showed no fluorescent signal at a neutral pH. However, upon internalization, the ingested E. coli particles when exposed to the acidic bactericidal pH of the phagolysosome, activated the bright fluorescence of the pHrodo fusion protein.

To investigate if CNC-mural cells harbored intrinsic phagocyte activity, the CNC-mural cells, once reaching the confluence, were exposed to the pHrodo Red E. coli BioParticles for 1 h before fixation in 4% formaldehyde, then co-immunostaining with AlexaFluor-633-Phalloidin and DAPI. Confocal imaging revealed that CNC-mural cells had internalized many fluorescent E. coli particles in their cytoplasm and filopodia (Fig. 1C). Some fluorescent E. coli bacterial particles were also anchored to the cell membrane and accumulated at the cell surface. Interestingly, despite ingesting many E. coli particles, the phagocytic CNC-mural cells did not acquire ameboid morphology. Still, they kept their elongated multipolar morphology and formed a dense network with long branching processes. Flow cytometry analysis allowed the quantification of phagocytic capacities in these cells. It showed that most CNCmural cells expressing macrophage markers, with the respective proportion of 64.88%, 75.34%, 69.46% of CSF1-R-positive, F4/80-positive, or Mac1-positive cells, could phagocyte and internalize E. coli particles (Fig. 1D,E

CNC-mural cells have a specific transcriptomic immune signature

To further characterize the immune profile of the CNC-mural cells, we performed a pairwise analysis comparing the transcriptome of E. coli-challenged cells to the control ones. We explored changes in gene expression in the presence or absence of treatment with E. coli by RNA-sequencing. When challenged by E. coli particles, CNC-mural cells showed variations in the expression of more than 600 genes (Fig. 2A). Gene ontology analyses revealed that transcriptomic enrichments coincided with inflammatory responses and cytokine activation (S5 Fig) Even though only subtle and non-significant changes impacted the expression of MHC class I and class II antigens, many targets belonging to the TNF pathway, involving TNF-interacting proteins, TNIP2 and TNIP1, along with TNF-induced proteins, TNFAIP3, TNFAIP6 and TNFAIP2, were strongly upregulated. Transcriptomic variations in C-C motif ligands revealed significant enrichment in CCL20, CCL4, CCL17, and CCL19 chemokines. In addition, IL1B, IL8, IL6, IL17C, and IL18 were significantly augmented in CNC-mural cells when exposed to E. coli particles. Similarly, IFN-γ IRF7, and IRF1 expression augmented while IRF6 diminished. Interestingly, the expression of two cytokine-inducible intracellular molecules, SOCS3 and SOCS1, which act as a suppressor of cytokine signaling, known to temper the inflammatory response of resident immune cells in the adult brain [START_REF] Baker | SOCS1 and SOCS3 in the control of CNS immunity[END_REF] and to stimulate macrophage polarization [START_REF] Wilson | SOCS Proteins in Macrophage Polarization and Function[END_REF], were both significantly augmented (Fig. 2A).

CNC-mural cells can interact at distance for cell cooperation and antigen presentation

The above results indicated that native CNC-mural cells harbor immune abilities and are capable of pathogens' recognition and interception, leading to the activation of pathogen-responding pathways. As evoked previously, CNC-mural cells phagocyting pHrodo E. coli exhibited many bacterial fragments attached to the outer surface of their cell extensions (Fig. 2B, arrows). To explore how E. colichallenged CNC-mural cells could interact and to investigate if these interactions could underpin antigen presentation, we used a two-well insert system that allowed the cultivation of two pools of cells separated by a silicone barrier (Fig. 2C). Once the cells reached confluence, the silicon barrier removal created a 500 µm-width cell-free gap to become progressively colonized by the converging migration of the cells coming from the two wells (Fig. 2D). CNC-mural cells were seeded either in one well, only, or in both. After 48h of cell expansion, pHrodo E. coli particles or fresh vehicle medium were added to the contralateral well. Following the silicone barrier removal, the cells were further grown for 24 hours. After fixation, a combined solution of Alexa Fluor 405 Phalloidin and Sytox TM Green stained at once CNCmural cell cytoskeleton and nucleus, respectively (Fig 2D). The quantification of the cells in the gap reflected the propensity of the cells to interact at a distance and to migrate when stimulated or not by their environment. First, we tested the ability of CNC-mural cells to migrate into the gap without any cell in the contralateral well. The analysis of the migrating cells in the 500 µm cell-free GAP showed that E. coli particles alone were not significantly attractive for the CNC cells (Fig. 2E). Next, we plated CNCmural cells in both wells and their respective contribution to the colonization of the gap was comparable and limited as previously. However, when E. coli particles unilaterally challenged CNC-mural cells before the barrier removal, the number of E. coli-treated cells migrating in the gap towards the non-treated ones was 1.4-fold increased over control experiments, suggesting a cooperation between exposed and non-exposed cells. More striking, the non-treated cells massively migrated into the gap, attracted by the CNC-mural cells exposed to E. coli particles; their migration and gap colonization was 3.4-fold higher than in control conditions. These experiments showed that a focal infection stimulated CNC-mural migration and prompted the distant cell interactions between E. coli-treated and non-treated cells, presumably underpinning antigen presentation and cell cooperation. To get insight into this process, we used super-resolution imaging and 3D rendering of pathogens phagocyting cells (Fig. 2F). On the exposed side, both entire E. coli bacteria and small fragments of bacterial particles co-habited in the cytoplasm and filopodia of phagocyting cells, showing that the CNC-mural cells could degrade pathogens (Fig. 2F On the contralateral side, we did not retrieve any intact bacteria in the cytoplasm of the non-exposed cells. However, many small bacterial fragments were incorporated and internalized to their filopodia, casting light on bacterial material transmission between E. coli-exposed and non-exposed cells (S8 Fig) .   Overall, these observations indicated that the E. coli bacteria were degraded and transferred to the nonexposed cells once engulfed by the exposed cells.

To further characterize the effects of E. coli exposure on CNC-mural cells and document the molecular determinants underlying the cooperation between the E. coli-exposed and non-exposed cells, we performed a refined transcriptomic analysis using a Nanostring-specific code set. This strategy offered a unique, sensitive quantification of the expression of genes of interest based on the amplification-free detection of low-abundance transcripts (Fig. 2G). This analysis revealed that in control native conditions, CNC-mural cells exhibited a basal expression of TLR1B, TLR2A, TLR4, TLR5, TLR7, TLR15, and TLR21, the exclusive expression of the MHC Class I, BF1 factor (no transcript detection for BF2), the combined expression of HLA-DMA and HLA-DMB, a slight expression of CD13 and CD18, and most importantly, CD44, a marker shared by CNC and hematopoietic lineages [START_REF] Corbel | Expression of CD44 during early development of the chick embryo[END_REF][START_REF] Cao | The role of CD44 in fetal and adult hematopoietic stem cell regulation[END_REF]. When subjected to E. coli, CNC-mural cells reduced the expression of TLRs except for TLR15, which was strongly increased. Similarly, while all the CDs and MHC class I and II markers decreased, CD44 expression was 1.5-fold higher than in control cells. Using the same strategy, we explored the expression of cytokines TNF-alpha, IL1B, IL6, IL10, and the nuclear factor NF-KB1. Under control conditions, CNC-mural cells exhibited a basal expression of TNF-alpha, IL6, and NF-KB1. However, upon E. coli exposure, CNC-mural cells strongly augmented the expression of IL1B and IL6, along with NF-KB1 (Fig. 2G). According to the paradigm, when exposed to LPS, CNC-mural cells activated a similar set of immune genes, with a subtle difference in response amplitude to the pathogens or its

CNC-mural cells can decondense their DNA like neutrophils do

In 2004, Brinkmann et al. reported the capacity of Neutrophils to form DNA traps to intercept and inactivate circulating pathogens [START_REF] Brinkmann | Neutrophil extracellular traps kill bacteria[END_REF]. Ever since, the capacity to form a DNA web has become an innate immune response against bacterial or fungal intruders was extended to others immune circulating cells. However, this process, so far perceived as the prerogative of adult blood cells, has never been evidenced in mesenchymal cells. Furthermore, the capacity to shed DNA is defective in neonates, casting light on the possible vulnerability of newborn immunity [START_REF] Yost | Impaired neutrophil extracellular trap (NET) formation: a novel innate immune deficiency of human neonates[END_REF]. So, to step further towards the demonstration that CNC-mural cells are professional immune cells, we explored if these cells have the capacity to form extracellular traps (ETosis) and shed DNA web.

In the first approach, we performed DNA staining with DAPI of CNC mural cell cultures. Under control conditions, a fraction of the CNC-mural cells, representing about 12%, showed spontaneous decondensation of their nucleus and formation of long of extracellular DNA fragments, released unidirectionally into the extracellular space (Fig. 3A, left panel). When subjected to a septic stress caused by exposure to pHrodo E. coli particles, cells exhibited escalating DNA shedding (Fig. 3A, right panel). Under these conditions, the percentage of DNA-shedding cells rose to 25.55% (Fig. 3A, left panel). Furthermore, the quantification of E. coli particles bound to extracellular DNA filaments indicated a positive correlation between the percentage of cells exhibiting DNA decondensation and the number of E. coli particles in contact with the DNA (Fig. 3A, center graph). In addition, we evidenced that under control conditions, the DNA decondensation was primarily unidirectional, respective to the shedding nucleus. By contrast, upon E. coli exposure, the DNA shedding became abounding, tangled and multidirectional (Fig. 3A, left graph). These findings indicated that CNC-mural cells may play a role in the immune response to bacterial infections by generating DNA traps. Bacterial stimuli enhanced the DNA shedding, leading to a distinct configuration of the DNA webs shed in E. coli-treated cells and control conditions. By reducing their surface charges, citrullinated histones (CitH) fail to maintain cohesive interactions with the chromatin and prompt nucleosome disintegration [Leshner et al., 2009]. We tested the accumulation of CitH3 (bearing three sites of citrullination along their tail on R2, R8, and R17) as an immunomarker of intense citrullination and effective DNA shedding. In our experiments, the accumulation of CitH3 in control cells appeared as faint staining of the nuclei, shedding unidirectional DNA strands (Fig. 3B, left panel). When subjected to E. Coli particles, CitH3 immunostaining revealed dense and multidirectional extracellular webs trapping E. coli particles and interconnecting many adjacent nuclei (Fig. 3B, right panel). We used Sytox TM Green staining and CitH3 immunodetection to quantify the DNA shedding by cytometry. Due to its incapacity to enter intact cell membranes, Sytox TM Green, a DNA binding dye used for cell viability assays, was a resolutive strategy to evidence the shedding nuclei and extracellular DNA exclusively. We observed that 5.4% of living cells were spontaneously releasing DNA traps, which was significantly 1.3-fold higher in CNC-mural cell cultures treated with E. coli particles (Fig. 3C, right panel).

In parallel, the quantification of cell culture grown on the Tecan microplate showed that E. coli exposure significantly augmented the percentage of adherent cells undergoing ETosis. In addition, when the supernatant of the cell culture treated with E. coli particles was harvested and analyzed on the Tecan microplate previously coated with CitH3 antibody, this approach revealed an increase of the complex associating CitH3 and extracellular DNA shed in their supernatant, even higher than at the intra-cellular level (Fig. 3C, left panel).

Overall, our observations support with the demonstration that CNC-mural cells harbor native immune functions early in brain development, suggesting that these capacities may operate as a first line of defense against pathogens at a stage when brain barriers and the cognate professional immune cells are not mature.

Valproate, a toxic medication for brain development impedes the native immune functions of CNC-mural cells

To explore if this function could be targeted by toxic medications leading to severe post-natal behavior impairment, we challenged the CNC-mural cells with Valproate (VPA), an anticonvulsant drug widely administered to manage seizures, as well as an efficient medication and mood stabilizer alleviating with a broad spectrum of conditions including bipolar disorders and schizophrenia [START_REF] Löscher | Basic pharmacology of valproate: a review after 35 years of clinical use for the treatment of epilepsy[END_REF][START_REF] Bowden | Anticonvulsants in bipolar disorders: current research and practice and future directions[END_REF][START_REF] Emrich | Effect of sodium valproate on mania. The GABA-hypothesis of affective disorders[END_REF]. However, aside from its powerful effects on adult brain disorders, this drug was embryotoxic when administrated during pregnancy. Exposure to VPA during development had severe drawbacks on fetal development, leading to an increased risk of congenital malformations like spina bifida and exencephaly, as well as post-natal behavioral deficits and autistic manifestations [START_REF] Nau | Valproic acid-induced neural tube defects in mouse and human: aspects of chirality, alternative drug development, pharmacokinetics and possible mechanisms[END_REF][START_REF] Christensen | Prenatal valproate exposure and risk of autism spectrum disorders and childhood autism[END_REF].

To investigate the consequences of toxic medication on CNC-mural cells, we treated meningeal cells in the embryo before being collected for in vitro cell culture. In mass culture, the cells showed alterations in the expression of macrophage markers CSFR1, F4/8, and Mac1. For all these markers, we noted an increase of their expressions since 86% of the cells expressed CSFR1, 64% F4/80, and 76% Mac1.

Flow cytometry analyses corroborated these results and showed a significant increase for each marker considered. (Fig. 4A) The RNA sequence analysis revealed a transcriptomic profile distinct from the one revealed after pathogenic exposure. Some targets appear significantly increased, such as TRIL, MHC class I, and BF2 (while it is not detected on native cells or exposed to E. coli). Similarly, the activation of molecules related to TNF signaling occurred, characterized by an increase in LITAF, C1QTNF5, and TNIP1, and a decrease in TRAF3 and TRAF6. Exposure to VPA also induced an increase in chemokines, CCL4, and CXCR1, and a modest increase in IL6 and some interleukins-related secondary targets (but no activation of IL1B and IL8, as with exposure to E. coli particles). Also, the activation of interferon molecules selectively mobilized IFI6, IFI35, and IRF7. When VPA-treated cells were grown in two-well inserts, we found that they had a restricted ability to migrate spontaneously, either with control cells or with homologous cells also treated with VPA (Fig. 4C). These observations show that VPA prevents the capabilities of CNC-mural cells from interacting and cooperating remotely. On the other hand, the immunostaining performed with Sytox TM Green and CitH3 showed that the treated cells have a significantly higher propensity to deploy their DNA than the control cells. (Fig. 4D). Transcript analysis performed using specific Nanostring code set approach showed that VPA significantly reduces TLR expression on the surface of CNC-mural cells and TNF-alpha signaling. On the other hand, VPA exacerbates CD44 expression while reducing the expression of the self-recognition marker BF1 (Fig. 4E).

Dual septic and toxic insults exacerbate inflammatory pathways in CNC-mural cells

When VPA-treated CNC-mural cells were exposed to E. coli particles, they demonstrated the ability to phagocyte and present bacterial particles (Fig. 5A). Furthermore, compared to CNC-mural cells from control embryos, the VPA treatment significantly increased the percentage of CNC-mural cells phagocytosing the E. coli particles (Fig. 5A).

When primed with VPA and exposed to E. coli particles, CNC-mural cells exhibited intense DNA decondensation (Fig. 5A). The combination of VPA treatment and E. coli exposure considerably enhanced the DNA decondensation and shedding, as 42% of the CNC-mural cells underwent ETosis compared with the control.

In addition to the observed increase in the percentage of pericytes presenting DNA decondensation in response to E. coli particles, further analysis revealed that the percentage of cells with E. coli particles associated with DNA decondensation was 1.7-fold higher in CNC-mural cells from VPA-treated embryos compared to those from control embryos (Fig. 5A). Flow cytometry analysis showed an increased percentage of VPA-treated cells and subjected to E. coli particles, undergoing ETosis, compared to controls (Fig. 5A). Consistent with these findings, the conditioned media of CNC-mural cells from VPAtreated embryos contained significantly more CitH3-DNA complexes (Fig. 5A). These results indicated that CNC-mural cells from VPA-treated embryos exhibit increased DNA decondensation and extracellular DNA web release when exposed to E. coli particles.

The RNA sequencing revealed that the CNC-mural cells from VPA-treated embryos had disrupted gene expression in response to E. coli treatment. The volcano plot in Fig. 5B highlighted a significant number of up-regulated genes. The analysis of immune response genes showed a similar response to E. colitreated cells from both control embryos (Fig. 2A) and VPA-treated embryos (Fig. 5B).

However, the results of cell migration and colonization of the 500 µm-gap demonstrated that the VPAtreated -CNC mural cells had reduced capacity to migrate and interact with their neighboring cells.

These observations suggested that VPA rendered the CNC-meningeal cells less responsive to the inflammatory signals produced by E. coli-treated cells (Figs. 5C,D). Altogether, these results suggest that VPA-treated CNC-mural cells can respond to infections but are less attracted to infection or inflammation sites to cooperate.

The Nanostring transcriptomic analysis validated that CNC-mural cells from VPA-treated embryos expressed genes typically involved in the immune response. Furthermore, when exposed to E. coli, particles, CNC-mural cells up-regulated several genes, such as TLR15, CD44, NF-KB1, IL1B, and IL6.

These observations indicated that the VPA-treated CNC-mural cells could develop a specific immune response and that exposure to E. coli enhanced this response [Fig. 5E].

Material and methods

Ethics statement

The Centre National de la Recherche Scientifique (CNRS) and Paris-Saclay University adhere to the ethical use of laboratory animals, under which prehatching avian embryos are not considered live vertebrate animals. As such, this study did not require any animal approval.

Chicken embryos

The experiments were conducted in chicken embryos. Fertilized eggs acquired from Les Bruyeres (Dangers, France) were incubated at 38±0.5°C with increasing humidity and ventilation until reaching the stages (HH) [START_REF] Hamburger | A series of normal stages in the development of the chick embryo[END_REF] required for experiments.

To test if and how a toxic insult could affect the immune capacities of CNC-mural cells, embryos were injected with a VPA (24 ug /40 mg) or a PBS-vehicle (control) solution into the forebrain region using ultrathin glass needles. After injection and sealing eggshells with tape, embryos were preincubated until reaching the desired HH34 stage to harvest their meninges.

CNC-mural cell isolation and in vitro culture

In vitro experiments were performed on primary cultures of pericytes. VPA and control-injected chicken embryos heads were collected at HH34 stage and placed in PBS solution supplemented with 0.5 % PS and 0.5% Fungizone. The skin was removed and the meninges from the prosencephalon region were collected into 1.5 ml Eppendorf tubes with PBS (1 embryo/tube). The CNC meninges were dissociated with TrypLE (Gibco) for 30 min at 37°C and pelleted by speed decantation. After counting cells to adjust their concentration, CNC-mural cells were seeded on 4-well chamber Polymer slides (Nunc™ Lab-Tek™) coated with fibronectin (5µg/cm 2 , Sigma-Aldrich), and seeded at the concentration of 15.000 cells /cm 2 . Cells were cultured in DMEM/F-12 medium, containing 10 % FBS, 1 % PS, and 1% Fungizone at 37 °C in a humidified atmosphere with 5% CO2. After 3 days of incubation, 25 μL of pHrodo Red E. coli BioParticles (Thermo Fisher Scientific) were added into several wells for 1 hour.

Cells were then washed with PBS and fixed with 4% formaldehyde (20 min) for further immunostaining.

Cell-cell interaction assays

To analyze cell-cell interactions and their cooperation at distance, cell cultures were performed as described above, with some adaptations. After isolation, the CNC-mural cells were seeded in the 4-well plates containing self-inserted culture-inserts, with 2 wells separated by a 500 µm removable silicon wall (Ibidi). During the second day of culture, the E. coli particles were added to the cells unilaterally, leaving the cells in contralateral well unexposed. After 1h of treatment, the inserts were removed, the cells were rinsed, and a fresh medium was added. Migration of cells into 500µm cell-free gap was analyzed after further incubation for 24h. Cells were then fixed and stained with Phalloidin blue and Sytox TM Green. The cell migration and interactions were observed in fluorescent microscope and the images were analyzed with the ImageJ software.

Immunocytochemistry

Briefly, after fixation, cells were washed 3 times with PBS and permeabilized with 0.1% Triton X-100 (Sigma, 15 min). Cells were incubated with primary and secondary antibodies overnight at 4°C. Primary antibodies used include αSMA (MA5-11547; Invitrogen), NG2 (sc33666; SantaCruz), PDGFRß (MA5-15143; Invitrogen), CSFR1 (sc692; SantaCruz), F4/80 (sc-377009; SantaCruz), Mac1 (NB110-89-474; Novis Biotechne), CitH3 (ab5103; Abcam), CitH4 (07-596; Sigma Aldrich). Primary antibodies were detected using anti -rabbit-and anti-mouse-Alexa Fluor conjugated secondary antibodies (Invitrogen).

Cell nuclei were stained with 4′, 6-diamidino-2-phenylindole dihydrochloride (DAPI, Sigma) and/or with Sytox TM Green Nucleic Acid Stain (1:30.000, Invitrogen). Cells were imaged with a fluorescence stereomicroscope (Leica DM2000) or a confocal microscope (Leica TCS SP8). The images were quantified using the ImageJ software (National Institutes of Health) and processed with Photoshop software (Adobe).

Cytometry

Primary pericyte cultures were prepared as described above. After 3 days in vitro, the cells were detached from the culture plates with TrypLE (Gibco), counted and approximately 1 x 10 5 cells were transferred into 1.5-ml tubes. The tubes were maintained in ice during all the procedures before the FACS analysis. Cells were incubated first with primary antibodies overnight at 4°C and then with secondary antibodies overnight at 4°C. For identification of living cells co-expressing Cit-H3 and Sytox TM Green, cells were incubated with agitation with primary anti-Cit-H3 antibody (1:250) for 1 h, washed then further incubated for 1 h with secondary antibodies (goat anti-rabbit IgG Alexa Fluor 633, 1:500). Finally, the Sytox TM Green (1:30.000), a plasma membrane-impermeable DNA-binding dye, was added for 15 min and cells were subjected to FACS analysis. The cells were analyzed with CytoFlex (Beckman-Coulter) bench Flow Cytometer and the data were processed using the CytExpert software.

DNA and Cit-H3-DNA complex quantification

Primary pericyte cultures were performed as described above, with some modifications to analyze the culture's supernatant. On the second day of culturing, the medium was changed and cell incubation continued. On the third day the cell cultures were treated with 25µl E. coli BioParticles (pHrodo, Thermo Fisher Scientific) alone or combined with a PAD inhibitor Cl-amidine (200µM, Calbiochem, Merk) for 1 hour [Chaaban, et al., 2020]. This cell-permeable compound acts as an inhibitor of DNA decondensation and extracellular DNA trap formation. Then supernatants were collected into the Eppendorf tubes and centrifugated to eliminate cell debris (300XG for 5 min). The DNA concentrations in the supernatants was determined by NanoVue nanodrop (GE Healthcare). Supernatants were stocked at -20°C before further utilization. The cells recovered from these experiments were used for the above-described living cells FACS analysis.

DNA traps are accompanied by CitH3 and CitH4. To detect the DNA quantity associated with CitH3 in collected supernatants, the latter were incubated overnight at 4° in 96 Well Black Plate (Ibidi) previously -incubated overnight with anti-Cit-H3 antibody. The day after, the wells were treated with the nucleic acid-binding fluorescent reagent Sytox TM Green to access the Cit-H3-DNA complexes. The plates were then read using a Tecan Genios Microplate reader, with excitation at 485 nm and emission measured at 535 nm. Results are expressed in arbitrary fluorescent units. Supernatant samples treated with DNAse (20U/mL) served as negative control.

Transcriptomic analysis with specific Nanostring code-set and bulk RNA-seq

The pericyte cultures were performed as described above, and the total RNA from control, VPA and E. coli-treated samples was extracted using the Tri Reagent (Sigma-Aldrich) according to the manufacture instructions. The samples were sent to the genomics platform of the Curie Institute for Nanostring nCounter analysis. The panel of analyzed genes is shown in the supplemental table. Analysis of differential gene expression was performed using the nSolver 4.0 software.

For the RNA-Seq analysis, the RNA samples were extracted in a similar way and were sent to the Novogene Co., LTD (Cambridge, England). Genes with adjusted p-value ≤ 0.05 and log2 (FoldChange) > 0 were considered as differentially expressed.

Statistical Analysis

Statistical differences between samples were evaluated by unpaired Student's t-test using GraphPad Prism 8 software (GraphPad Software, Inc.). The level of significance was set at p ≤ 0.05. The results represent the mean of at least three independent experiments.

LEGEND TO FIGURES

  al., 2017; Ganguli and Chavali, 2021]. Through their strategic position between the CNS and the blood [Daneman et al., 2010; Rustenhoven et al., 2017], they control the permeability of the blood-brain-barrier [Daneman et al., 2010; Hill et al., 2015] and regulate the blood flow in the CNS [Carlsson et al., 2023]. Growing pieces of evidence pointed to the implication of mural cells as modulators of the brain-immune interface [Hurtado-Alvarado et al., 2014] upon inflammation, ischemia, oxygen and nutriment shortage, or trauma [Rustenhoven et al., 2017]. Under inflammatory conditions, pericytes can respond to inflammatory stimuli such as Interleukin-1ß (IL-1β) and Tumor Necrosis Factor (TNF-α), Lipopolysaccharides (LPS), and are then able to secrete inflammatory molecules and Damage Associated Molecular Pattern (DAMP) molecules, exhibit transcriptional changes following immunomodulatory stimuli [Pieper et al., 2014; Umehara et al., 2018; Brown et al., 2019], and can engulf pathogen-coated beads

  Following a rapid adherence to the substratum, the CNC mural cells formed a dense network of multipolar elongated cells strongly positive for αSMA, sharing long interconnected extensions (S1A Fig). Similar phenotypes resulted from either AlexaFluor TM 405-Phalloidin or AlexaFluor TM 633-Phalloidin after fluorescent staining. Quantification revealed that nearly 100% of CNC-mural cells exhibited a perivascular αSMApositive profile in primary cultures. This phenotype was robust and stable enough to be maintained over ten passages (S1B Fig). In parallel, we tested whether CNC-mural cells also expressed two other mural cell markers, PDGFRß and NG2 [Lindahl et al., 1997; Ozerdem et al., 2001]. Both PDGFRß-(S2A Fig) and NG2-(S2B Fig) positive immunolabeling approached 100% of cells, further confirming the perivascular and pericyte identity of CNC-mural cells.

  ; S4 Fig).

  First, we evidenced the up-regulation of TLR4, a sensor of LPS, and significant enrichment in TLR15, a receptor unique to birds and reptiles [Boyd et al., 2012], which activation accounts for the virulence of pathogen proteases [de Zoete et al., 2011].

  ; S6 Fig). To confirm the effectiveness and dynamics of E. coli bacteria proceeding in the exposed cells, we performed holotomography live imaging with correlative red fluorescence detection of E. coli particles in the exposed cells. A 15-minute time-lapse video recording showed the efficiency of the CNC-mural cells in fragmenting the ingested E. coli particles (S7 Fig).

  surface motif. By contrast, exposure to Zymosan demonstrated that CNC mural cells activated a specific immune response the pathogens recognized; the CNC mural cells could activate stereotyped immune responses (S09 Fig). Notably, apart from these inflammatory cytokines, we did not detect any transcript of the counter-regulatory cytokine IL10 in either E. coli-, LPS-, or Zymosan-exposed or non-exposed cells.

E

  . coli exposure enhances DNA web shedding from CNC-mural cells The process of extracellular DNA shedding involves the release of chromatin, initiated by the enzyme Peptidyl Arginine Deiminase (PADI) 4, which induces the citrullination of histones [Thiam et al., 2020].

Figure 1 :

 1 Figure 1: CNC-mural cells harbor immune and phagocytic capacities. (A) Experimental design. Brain from 34HH stage chicken embryo contrasting the prosencephalon with the vasculature and the meninges covering it. The meninges were harvested and dissociated in TrypLE. After cell dissociation, cells were seeded and maintained in culture plates for 72h before fixation, immunostaining, and imaging. (B) Immunostaining showing αSMA-positive (in green) CNC-mural cells expressing macrophage markers (CSF-1R, F4/80, and Mac1respectivily (red) in vitro, in the control condition. DAPI stains nuclei in blue, and insets highlight the expression of macrophage markers alone. The numbers correspond to the mean percentage of double-positive cells quantified with ImageJ software. Below are each marker's cytometry analysis and the mean percentage of double-marked cells (pericyte-specific marker phalloidin and macrophage markers). (C) CNC-mural cells (Phalloidin-stained; in gray) phagocyte E. coli particles (red) in vitro after only 1 h of exposure to the bacterial particles. DAPI stains nuclei in blue. (D) From the left to the right, flow cytometry quantification of Phalloidinstained cells and positive for the macrophage markers, CSF1-R (64.88%), F4/80 (75.34%) and Mac1 (69.46%), having engulfed fluorescent E. coli particles (bar outlined in red), in comparison to E. coli-free cells (bars outlined in grey). Error bars represent ± SEM. **p ≤ 0.01; *p ≤ 0.05; ns not significant. (E) Higher magnification of CNC-mural cells showing engulfed E. coli red fluorescent particles inside the cell. For a detailed dynamic 3D rendering of a video presented in S04 Fig).

Figure 2 :

 2 Figure 2: Exposure to E. coli particles enhances the immune functions of CNC-mural cells. (A) Volcano plot of Differentially Expressed Genes (DEG) between control and E. coli exposed cells, with the upregulated genes shaded in red. Scatter plots displaying the results of RNA-seq analysis of DEG involved in the immune response comparing control and E. coli-treated cells. ***p ≤ 0.001; **p ≤ 0.01; *p ≤ 0.05; ns not significant. (A) Immunostaining showing pericytes tagged with the specific marker Phalloidin (green) and E. coli particles (red). A 3D reconstruction is shown in the middle panel, and the left panel displays a magnification of filopodial tips with colocalization of E. coli particles (indicated by arrows). (B) Representation of the experimental design for the inserts assay. Meningealpericytes derived from neural crest cells were cultured for 48h in 2-well inserts. E. coli particles were added to one well of the inserts, and after 1h, the inserts were removed. The cells were then further cultured for an additional 24 hours before being analyzed, focusing on the 500 µm gap. (C) Immunostaining of pericytes tagged with the specific marker Phalloidin (blue) and the cell nuclei tagged with Sytox TM Green (green). The 500 µm cell-free gap delineated with the dashed lines 24h after insert removal allows quantifying the number of cells that migrated from each insert well (the left side

Figure 3 :

 3 Figure 3: Meningeal NC-derived pericytes present DNA decondensation and produce extracellular traps (ET) in vitro. (A) Pericytes cell culture illustrating DNA decondensation where nuclei appear in blue and phagocyted bacteria in red in both control and E. coli-treated (stimulated) cells. The accompanying graphs compare the percentage of cells displaying DNA decondensation in control and E. coli-stimulated conditions. The graphs also show the percentage of E. coli-stimulated cells displaying DNA decondensation associated with 0, 1-2, or ≥3 E. coli particles and the percentage of unidirectional or multidirectional DNA decondensation in control and E. coli-stimulated cells. **p ≤ 0.01; *p ≤ 0.05; ns not significant. (B) Mural cells immunostained for CitH3 (green), nuclei (blue), and E. coli particles (red) show CitH3 colocalization with decondensed DNA in control conditions. In pericytes treated with E. coli, extracellular CitH3 formed a web-like structure associating many E. coli particles and other cell nuclei. (C) Double staining of Sytox TM green and CitH3 in unfixed pericytes cell culture treated or not treated with E. coli particles. The numbers in the figure represent the mean of double positive cells as determined by cytometry analysis. The first graph illustrates the relative co-expression of Sytox TM green and CitH3 in pericytes treated or not treated with E. coli. The second graph represents TECAN analysis results, displaying the relative expression of the CitH3-DNA complex in the conditioned media of pericytes treated or not treated with E. coli particles. **p ≤ 0.01; *p ≤ 0.05.

Figure 4 :

 4 Figure 4: Exposure to VPA modulates the immune profile of meningeal CNC mural cell culture in vitro. (A) Immunostaining showing a αSMA (+) (green) cells expressing macrophage markers (CSF-1R, F4/80, and Mac1 respectively -red) in vitro. DAPI stains nuclei in blue, and insets highlight the expression of macrophage markers alone. The numbers correspond to the mean percentage of doublepositive cells quantified with ImageJ software. The center panels below are each marker's cytometry analysis and the mean percentage of double-marked cells (pericyte-specific marker a αSMA and macrophage markers). The graph panels below compare the relative co-expression of the pericytesspecific marker αSMA and the macrophage markers between control and VPA-treated cells (cytometry). *p ≤ 0.05. (B) Volcano plot of DEG between control cells and VPA-treated cells, with the upregulated genes in red. Scatter plots below display the results of RNA-seq analysis of DEG involved in the immune response comparing control cells and VPA cells*. ***p ≤ 0.001; **p ≤ 0.01; *p ≤ 0.05; ns not significant. (C) Immunostaining of pericytes tagged with the specific marker Phalloidin (gray) and the cell nuclei labeled with DAPI (blue). The 500 µm, large cell-free gap delineated between the dashed lines; the cells migrated during the 24h following dragging the insert. The upper panel on the right quantifies the number of migrating cells in the 500 µm GAP. Each graph line corresponds to a different condition, and the numbers indicate the mean cell number. The graph below compares the relative number of cells found in the 500 µm gap and demonstrates that VPA-treated cells migrate less towards control cells than other VPA-treated cells. Error bars indicate ± SEM. **p ≤ 0.01; ns not significant. (D) The graph on the left compares the percentage of cells displaying DNA decondensation between control and VPAtreated cells. The next one illustrates the relative co-expression of Sytox TM green and CitH3 in control and VPA-treated cells (cytometry). The last graph represents TECAN analysis results, displaying the relative expression of the CitH3-DNA complex in the conditioned media of control and VPA cells. *p ≤ 0.05; ns not significant. (E) Nanostring analysis of the transcriptional patterns of immune response genes in control and VPA-treated mural cells (below) cell culture.

Figure 5 :

 5 Figure 5: Exposure to VPA and to E. coli increases the inflammatory profile of meningeal CNC mural cell culture in vitro.

(

  A) VPA-treated mural cell culture exhibit DNA decondensation after E. coli exposure, where cell nuclei appeared in blue and E. coli particles were in red. From left to right, the graphs show cytometry quantification of the percentage of control and VPA-treated cells expressing the specific pericyte marker Phalloidin and fluorescent E. coli particles after 1h of exposure to E. coli particles. The following graph compares the percentage of cells displaying DNA decondensation in control and VPA-treated cells LEGEND TO SUPPLEMENTARY INFORMATION S01: Meningeal NC-derived pericytes from 34HH chicken embryos provide a homogeneous αSMA positive cell population grown on fibronectin-coated cell culture plates. (A) Immunostaining for the pericyte marker αSMA (green) confirms the homogeneity of the meningeal NCderived pericyte culture in vitro. (B) Percentage of αSMA-positive cells in meningeal NC-derived pericytes culture at various passages (up to 10). S02: Meningeal NC-derived pericytes co-express αSMA and other specific pericyte markers. (A) Immunostaining for pericyte markers αSMA (gray) and NG2 (green) in meningeal NC-derived pericyte culture. Nuclei are stained with DAPI (blue). Co-expression of both markers is demonstrated. The percentage of cells expressing each marker is quantified using ImageJ software and presented in the graph. (B) Immunostaining for pericyte markers αSMA (gray) and PDGFRβ (green) in meningeal NCderived pericyte culture. Nuclei are stained with DAPI (blue). Co-expression of both markers is demonstrated. The percentage of cells expressing each marker is quantified using ImageJ software and presented in the graph. S03: Meningeal spinal cord mesoderm-derived pericytes express macrophage markers at a lower level than meningeal prosencephalon NC-derived pericytes. Immunostaining for mural cells with Phalloidin (green) and the macrophage markers CSF-1R (A), F4/80 (B), and MAC1 (C) on CNC meningeal cells (left column) and mesodermal meninges (right column)). Cell nuclei are stained with DAPI (blue). The insets show the expression of macrophage markers. Graphs on the right present the percentage of cells co-expressing pericyte and macrophage markers, as quantified by Fiji. (A') On a 34HH stage chicken embryo, the dotted lines indicate the region from which the vertebral column was excised. The spinal cord meninges were collected and subjected to tissue dissociation before being seeded in cell culture plates, as detailed in the materials and methods section. S04: Meningeal NC-derived pericytes exhibit phagocytic activity towards E. coli particles in vitro. The video demonstrates the immunostaining of Phalloidin (blue) and E. coli particles (red), in which the E. coli particles can be seen associated with the actin cytoskeleton within the cell cytoplasm. Related to Figure 01 E. S05: RNA-seq analysis of DEGs in pericytes treated with E. coli and control pericytes.

  ; Etchevers(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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	et al., 2001;
				6 : 36

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with E. coli particles. Then, the graph illustrates the relative co-expression of Sytox TM green and CitH3 in control and VPA-and E. coli -treated cells (cytometry). The last graph represents TECAN analysis results, displaying the relative expression of the CitH3-DNA complex in the conditioned media of control and VPA-and E. coli -treated cells. ***p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05; ns not significant. (B) Volcano plot of DEG between control cells and VPA cells* exposed to E. coli, with the upregulated genes in red. Scatter plots display the results of RNA-seq analysis of DEG involved in the immune response comparing control and VPA-and E. coli -treated cells. The plots show that nearly all genes appeared upregulated after exposure to VPA and to E.


Omar Adjali

Romaric Besançon

Olivier Ferret

Hervé Le Borgne

Brigitte Grau

Multimodal entity linking for tweets

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Entity linking (EL) is a crucial task for natural language processing applications that require disambiguating named mentions within textual documents. It consists in mapping ambiguous named mentions to entities defined in a knowledge base (KB). To address the EL problem, most of the stateof-the-art approaches use some form of textual representations associated with the target mention and its corresponding entity. [START_REF] Bunescu | Using encyclopedic knowledge for named entity disambiguation[END_REF] first proposed to link a named mention to an entity in a knowledge base, followed by several other local approaches [START_REF] Cucerzan | Large-scale named entity disambiguation based on Wikipedia data[END_REF][START_REF] Milne | Learning to link with wikipedia[END_REF][START_REF] Dredze | Entity disambiguation for knowledge base population[END_REF][START_REF] Eshel | Named Entity Disambiguation for Noisy Text[END_REF][START_REF] Daher | Supervised learning of entity disambiguation models by negative sample selection[END_REF] where mentions are individually disambiguated using lexical mention-entity measures and context features extracted from their surrounding text. In contrast, global approaches [START_REF] Ratinov | Local and global algorithms for disambiguation to wikipedia[END_REF][START_REF] Guo | Entity linking with a unified semantic representation[END_REF][START_REF] Pershina | Personalized page rank for named entity disambiguation[END_REF][START_REF] Amir Globerson | Collective entity resolution with multi-focal attention[END_REF] use global features at the document-level to disambiguate all the mentions within the document and can also exploit semantic relationships between entities in the KB. These approaches have shown their effectiveness on standard EL datasets such as TAC KBP [START_REF] Heng | Overview of the TAC 2010 knowledge base population track[END_REF], CoNLL-YAGO [START_REF] Hoffart | Robust disambiguation of named entities in text[END_REF], and ACE [START_REF] Bentivogli | Extending English ACE 2005 corpus annotation with ground-truth links to Wikipedia[END_REF] which contain structured documents that provide a rich context for disambiguation. However, EL becomes more challenging when a limited, informal, textual context is available, such as in social media posts. On the other hand, social media posts often include images to illustrate the text that could be exploited to improve the disambiguation by adding a visual context.

In this paper, we address the problem of multimodal entity linking (MEL), by leveraging both semantic textual and visual information extracted from mention and entity contexts. We propose to apply MEL on Twitter posts as they form a prototypical framework where the textual context is generally poor and visual information is available. Indeed, an important mechanism in Twitter communication is the usage of a user's screen name (@UserScreenName) in a tweet text. This helps to explicitly mention that the corresponding user is somehow related to the posted tweet. One observation we made is that some Twitter users tend to mention other users without resorting to screen names but rather use their first name, last name, or acronyms (when the Twitter user is an organization). Consider the following example tweet (see Figure 1):

Andrew explains how embracing AI doesn't have to be daunting Figure 1: An illustrative example of multimodal Entity Linking on our Twitter dataset. The example depicts a subset of (text, image) pairs representing the entity @AndrewNg (left), and some example of (text, image) pairs related to the mention Andrew (right).

In this example, the mention Andrew refers to the user's screen name @AndrewYNg. Obviously, the mention Andrew could have referred to any Twitter user whose name includes Andrew. Such a practice, when it occurs, may lead to ambiguities that standard EL systems may not be able to resolve, as most of Twitter users do not have entries in standard knowledge bases. We thus propose to link ambiguous mentions to a specific Twitter KB, composed of Twitter users.

Our contributions are: (i) we investigate the multimodal entity linking task. To the best of our knowledge, this is the first EL work that combines textual and visual contexts on a Twitter dataset; (ii) we construct a new large-scale dataset for multimodal EL and above all, define a method for building such datasets at convenience; (iii) we present a new EL model-based for learning a multimodal joint representation of tweets and show, on the proposed dataset, the interest of adding visual features for the EL task. Code and data are available at https://github.com/OA256864/MEL Tweets1 .

Related Work

Entity Linking on Twitter Recently, several research efforts proposed to meet the challenges posed by the EL task on Twitter media posts. Collective approaches are preferred and leverage global information about tweets in relation with the target mention. [START_REF] Liu | Entity linking for tweets[END_REF] collectively resolves a set of mentions by aggregating all their related tweets to compute mention-mention and mention-entity similarities while [START_REF] Huang | Collective tweet wikification based on semi-supervised graph regularization[END_REF] takes advantage of both semantic relatedness and mention co-referencing. In a more original way, [START_REF] Shen | Linking named entities in tweets with knowledge base via user interest modeling[END_REF] determines the user's topics of interest from all its posted tweets in order to collectively link all its named entity mentions. Similarly [START_REF] Hua | Microblog entity linking with social temporal context[END_REF] considers social (user's interest + popularity) and temporal contexts. Other collective approaches include in their EL model additional non-textual features. For example, [START_REF] Fang | Entity linking on microblogs with spatial and temporal signals[END_REF] and [START_REF] Chong | Collective entity linking in tweets over space and time[END_REF] use global information of tweets that are close in space and time to the tweet of the target mention. Finally, [START_REF] Dredze | Twitter at the grammys: A social media corpus for entity linking and disambiguation[END_REF] proposes a joint cross-document co-reference resolution and disambiguation approach including temporal information associated with their corpus to improve EL performance. While these works yield interesting results using non-textual features, they often depend on the availability of social data and do not exploit visual information. Multimodal Representation Learning Joint multimodal representations are used in several multimodal applications such as visual question answering [START_REF] Jabri | Revisiting visual question answering baselines[END_REF][START_REF] Agrawal | VQA: Visual question answering[END_REF], text-image retrieval [START_REF] Wang | Learning deep structure-preserving image-text embeddings[END_REF][START_REF] Chami | Amecon: Abstract meta-concept features for text-illustration[END_REF][START_REF] Chowdhury | Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval[END_REF] and image captioning [START_REF] Karpathy | Deep visual-semantic alignments for generating image descriptions[END_REF][START_REF] Johnson | Densecap: Fully convolutional localization networks for dense captioning[END_REF] and exploits different approaches e.g., Canonical Correlation Analysis [START_REF] Quynh | Aggregating Image and Text Quantized Correlated Components[END_REF], linear ranking-based models and non-linear deep learning models to learn projecting image features and text features into a joint space [START_REF] Fukui | Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding[END_REF]. In our work, we take inspiration from [START_REF] Wang | Learning two-branch neural networks for image-text matching tasks[END_REF] for our joint representation learning model as it showed satisfying results for multimodal learning.

To our knowledge, [START_REF] Moon | Multimodal Named Entity Disambiguation for Noisy Social Media Posts[END_REF] is the only other work that leverages multimodal information for an entity disambiguation task in a social media context. However, they use a dataset of 12K annotated imagecaption pairs from Snapchat (which is not made available), whereas our work relies on a much larger base (85K samples for the benchmark and 2M to build the KB) and is more in line with the state-of-theart which mostly uses Twitter as case study for EL on social media. Furthermore, they use Freebase as KB, which does not contain image information and does not allow a multimodal representation at the KB level, leading to a very different disambiguation model.

Problem Formulation

In Twitter terms, each user account has a unique screen name (@AndrewYNg), a user name (Andrew Ng), a user description (Stanford CS adjunct faculty) and a timeline containing all the tweets (text+image) posted by the user (see Figure 1). On this basis, we consider the following definitions:

• an entity e corresponds to a Twitter user account u (generally associated with a person or an organization);

• a mention m corresponds to an ambiguous textual occurrence of an entity e j mentioned in a tweet t that does not belong to the timeline of e j ;

• the knowledge base is the set of entities (i.e. Twitter accounts).

Formally, we denote the knowledge base KB = {e j } as a set of entities, each entity being defined as a tuple e j = (s j , u j , T L j ) including their screen name s j , user name u j and timeline T L j (we did not use the user description in the representation of the entity). The timeline contains both texts and images. A mention m j is defined as a pair (w i , t i ) composed of the word (or set of words) w i characterizing the mention and the tweet t i in which it occurs: t i contains both text and images.

The objective of the task consists in finding the most similar entity e * (m i ) to the mention m i in the KB according to a given similarity. From a practical point of view, we do not compute the similarities between the mention and all entities in the KB. We first select a subset of the entities that are good candidates to disambiguate m i . It is defined as Cand(m i ) = {e j ∈ KB|w i ∼ u j } where w i ∼ u j indicates that the mention words w i are close to the entity name u j . In our case, due to the nature of the dataset, we only use a simple inclusion (i.e. the mention words are present in the entity name) but a more complex lexical distance that takes into account more variations could be considered [START_REF] Moon | Multimodal Named Entity Disambiguation for Noisy Social Media Posts[END_REF]. The disambiguation of m i is then formalized as finding the best multimodal similarity measure between the tweet containing the mention and the timeline of the correct entity, both containing text and images:

e * (m i ) = argmax ej ∈Cand(mi)

sim(t i , T L j ) (1)
4 Twitter-MEL Dataset

One important contribution of our work is a novel dataset for multimodal entity linking on short texts accompanied by images derived from Twitter posts. The process to build this dataset is mostly automatic and can, therefore, be applied to generate a new dataset at convenience. More details and further analysis on the dataset can be found in [START_REF] Adjali | Building a multimodal entity linking dataset from tweets[END_REF].

All the tweets and user's metadata were collected using the Twitter official API2 . The dataset creation process comprises two phases: the construction of the knowledge base and the generation of ambiguous mentions. As mentioned in Section 1, our multimodal EL task aims at mapping mentions to entities (i.e. twitter users) from our Twitter knowledge base, which are characterized by a timeline, namely the collection of the most recent tweets posted by a given user.

As a first step, we established a non-exhaustive initial list of Twitter user's screen names using Twitter lists in order to have users that are likely to produce a sufficient number of tweets and be referred by a sufficient number of other users. A Twitter list is a curated set of Twitter accounts3 generally grouped by topic. From this initial list of users, we started building the KB by collecting the tweets of each user's timeline along with its meta-information ensuring that both re-tweets and tweets without images were discarded. Moreover, as explained previously, users tend to create ambiguous mentions in tweets when they employ any expression (for example first or last name) other than Twitter screen names to mention other users in their post (see Section 1). Consequently, we have drawn inspiration from this usage to elaborate a simple process for both candidate entity and ambiguous mention generation.

Selection of Possibly Ambiguous Entities

To ensure that the KB contains sufficiently ambiguous entities, i.e. entities with possible ambiguous mentions, to make the EL task challenging, we expanded the KB from the initial lists with ambiguous entities. More precisely, we first extracted the last name from each Twitter account name of the initial list of users. Then, we used these names as search queries in the Twitter API user search engine to collect data about similar users4 . Users that have been inactive for a long period, non-English users and non-verified user accounts were filtered. Furthermore, to ensure more diversity in the entities and not only use person names, we manually collected data about organization accounts. We relied on Wikipedia acronym disambiguation pages to form groups of ambiguous (organization) entities that share the same acronym.

Generation of Ambiguous Mentions

After building the KB, we used the collected entities to search for tweets that mention them. The Twitter Search API5 returns a collection of relevant tweets matching the specified query. Thus, for each entity in the KB, (i) we set its screen name (@user) as the query search; (ii) we collect all the retrieved tweets; (iii) we filtered out tweets without images. Given the resulting collections of tweets mentioning the different entities of the KB, we systematically replaced the screen name mentioned in the tweet with its corresponding ambiguous mention: last names for person entities and acronyms for organization entities. Finally, we kept track of the ground truths of each tweet in the dataset reducing the cost of a manual annotation task and resulting in a dataset composed of annotated pairs of text and image. Although ambiguous mentions are synthetically generated, they are comparable to some extent with real-world ambiguous mentions in tweets. We applied a named entity recognition (NER) system [START_REF] Devlin | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[END_REF] on the tweets of the dataset which achieved a 77% accuracy score on the generated mentions. This suggests that these latter are somehow close to real-world named mentions.

Dataset Statistics and Analysis

Altogether, we collected and processed 14M tweets, 10M timeline tweets for entity characterization and 4M tweets with ambiguous mentions (mention tweets) covering 20k entities in the KB. Filtering these tweets drastically reduced the size of our data set. Regarding mention tweets, a key factor in the reduction is the elimination of noisy tweets where mentions have no syntactic role in the tweet text (e.g. where the mention is included in a list of recipients of the message). Discarding these irrelevant tweets as well as tweets without image left a dataset of 2M timeline tweets and 85k mention tweets. After 3 months of data collection, we found that only 10% of tweet posts are accompanied by images.

In the end, we randomly split the set of mention tweets into training (40%), validation (20%) and test (40%) while ensuring that 50% of mention tweets in the test set correspond to entities unseen during the training. Table 1 shows the timeline tweet distribution of all entities in our KB. As noted by [START_REF] Hua | Microblog entity linking with social temporal context[END_REF], this distribution reveals that most Twitter users are information seekers, i.e. they rarely tweet, in contrast to users that are content generators who tweet frequently. Along with user's popularity, this has an influence on the number of mention tweets we can collect. We necessarily gathered more mention tweets from content generator entities, as they are more likely to be mentioned by others than information seeker entities.

Proposed MEL Approach

Visual and textual representations of mentions and entities are extracted with pre-trained networks. We then learn a two-branch feed-forward neural network to minimize a triplet-loss defining an implicit joint feature space. This network provides a similarity score between a given mention and an entity that is combined with other external features (such as popularity or other similarity measures) as the input of a multi-layer perceptron (MLP) which performs a binary classification on a (mention, entity) pair.

Features

Textual context representation We used the unsupervised Sent2Vec [START_REF] Pagliardini | Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features[END_REF] model to learn tweet representations, pre-trained on large Twitter corpus. We adopted this model as training on the same type of data (short noisy texts) turns out to be essential for performing well on the MEL task (see Sec. 6.2). The Sent2Vec model extends the CBOW model proposed by [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF] to learn a vector representation of words. More precisely, it learns a vector representation of a sentence S by calculating the average of the embeddings of the words (unigrams) making up the sentence S and the n-grams present in S :

V S = 1 |R(S)| w∈R(S) v w (2)
where R(S) is the set of n-grams (including unigrams) in the sentence S, and v w is the embedding vector of the word w. Therefore, the textual context of a mention m i within the tweet t i is represented by the sentence embedding vector of t i . We produce then for each mention two continuous vector representations (D=700), a sentence embedding U (i) m inferred using only tweet unigrams and a sentence embedding B (i) m inferred using tweet unigrams and bigrams. Combining their vectors is generally beneficial [START_REF] Pagliardini | Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features[END_REF]. An entity context being represented by a set of tweets (see Section 3), given an entity e i , we average the unigram and bigram embeddings of all e i 's timeline tweets yielding two average embedding vectors

U (i) e , B (i)
e representing the entity textual context used as features.

BM25 features

Given that the disambiguation task aims at finding the correct entity for a tweet, it can be viewed as an IR problem, where we try to associate a given tweet with the most relevant timeline in the KB. We, therefore, consider, as a baseline, a tf-idf -like model to match the mention with the entity: in our case, both the tweet and the timeline are represented as bag-of-words vectors and we used the standard BM25 weighting scheme to perform the comparison. Popularity features Given an entity e representing a Twitter user u, we consider 3 popularity features represented by: N f o the number of followers, N f r the number of friends and N t the number of tweets posted by u. Visual context features The visual features are extracted with the Inception v3 model [START_REF] Szegedy | Rethinking the inception architecture for computer vision[END_REF], prelearned on the 1.2M images of the ILSVRC challenge [START_REF] Russakovsky | ImageNet Large Scale Visual Recognition Challenge[END_REF]. We use its last layer (D = 1,000), which encodes high-level information that may help discriminating between entities. For an entity e i , we retain a unique feature vector that is the average of the feature vectors of all the images within its timeline, similarly to the process for the textual context. The visual feature vector of a mention is extracted from the image of the tweet that contains the mention.

Joint Multimodal Representation Learning

The proposed model measures the multimodal context similarity between a mention and its candidate entities. Figure 2 shows the architecture of the proposed joint representation learning model. It follows a triplet loss structure with two sub-models as in [START_REF] He | Multi-perspective sentence similarity modeling with convolutional neural networks[END_REF][START_REF] Rao | Noise-contrastive estimation for answer selection with deep neural networks[END_REF], one processing the mention contexts and the other processing the entity contexts. Each sub-model has a structure resembling the similarity model proposed by [START_REF] Wang | Learning two-branch neural networks for image-text matching tasks[END_REF], i.e., it comprises three branches (unigram embedding, bigram embedding, image feature), each in turn including 2 fully connected (FC) layers with a Rectified Linear Unit (ReLU) activation followed by a normalization layer [START_REF] Lei Ba | Layer normalization[END_REF]. Then, the output vectors of the three branches are merged using concatenation, followed by a final FC layer. Other merging approaches exist such as element-wise product and compact bilinear pooling [START_REF] Fukui | Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding[END_REF][START_REF] Wang | Learning two-branch neural networks for image-text matching tasks[END_REF]. However, in our work, simple concatenation showed satisfying results as a first step. Moreover, the mention and entity inputs differ in our task, a mention being characterized by one (text, image) pair and an entity by a large set of (text, image) pairs. Thus, we investigated the performance of our model when the parameters of the two sub-models are partially or fully shared. We found that shared parameters yielded better accuracy results on the validation set, thus the weight of the FC layers of both branches are shared.

In summary, using the extracted visual and textual features {U m ,B m ,I m }, {U e ,B e ,I e } respectively of a mention m i and an entity e i , our model is trained to project each modality through the branches into an implicit joint space. The resulting representations are concatenated into C m and C e and passed through the final FC layer yielding two multimodal vectors J m and J e . The objective is to minimize the following triplet loss function:

min W e -=e + max(0, 1 -f m (m), f e (e + ) -f m (m), f m (e -) )
where m, e + , e -, are the mention, positive and negative entities respectively. f m (•) is the mention subnetwork output function, f e (•) the entity sub-network function and • is the L 2 -norm. The objective aims at minimizing the L 2 distance between the multimodal representation of m and the representation of a positive entity p + , and maximizing the distance to the negative entity e -multimodal representation. For MEL, we calculate the cosine similarity between the two vectors of a pair (m i , e i ) given by f m (•) and f e (•) to represent their multimodal context similarity: sim(m i , e i ) = cosine(J

(i) m , J (i)
e ). Finally, we propose to integrate the popularity of the entity to the estimation of the similarity by combining the multimodal context similarity score and popularity features through a MLP. Other external features may also be combined at the MLP level, as in our case the BM25 similarity. This MLP is trained to minimize a binary cross-entropy, with label 0 (resp. 1) for negative (resp. positive) entities w.r.t the mention. Baseline feature where the most popular entity is selected; BM25 Standard textual context similarity with BM25 weighting; S2V-uni Similarity measured between the unigram embeddings extracted using the Sent2Vec language model; S2V-bi Similarity measured between the bigram embeddings extracted using the Sent2Vec language model; S2V

For easy readibility, we use the S2V notation to represent the combination of S2V-uni and S2V-bi; Img Similarity measured between the image features extracted using the pretrained Inception-V3 model; ET(X )

Combination of features X using an Extra-Trees classifier; JMEL(X )

Combination of features X with our joint multimodal representation model (see Section 5.2).

6 Experiments Analysis and Evaluation Setup

Parameter Settings

We initialized the weights of each FC layer using the Xavier initialization [START_REF] Glorot | Understanding the difficulty of training deep feedforward neural networks[END_REF]. Training is performed over 100 epochs, each covering 600k (mention, entity) pairs of which 35k are positive samples. The triplet loss is minimized by stochastic gradient descent, with a momentum of 0.9, an initial learning rate of 0.1 and a batch size of 256. The learning rate is divided by 10 when the loss does not change by more than 1e -4 during 6 epochs. After 50 epochs, if the accuracy on the validation set does not increase during 5 successive evaluations, the training is early stopped. Regarding the BCE branch, the MLP has two hidden layers with one more neuron than the number of inputs and tanh non-linearities. The BCE loss is minimized using L-BGFS with a learning rate of 10 -5 . The network is implemented in PyTorch [START_REF] Paszke | Automatic differentiation in PyTorch[END_REF]. Since our approach is not directly comparable with previous works [START_REF] Moon | Multimodal Named Entity Disambiguation for Noisy Social Media Posts[END_REF], we compare the results with different configurations and baselines. As 50% of mentions are unique in each split of our dataset, we do not take the standard most frequent entity prediction as a baseline, which links the entity that was seen most in training to its corresponding mention. We rather consider the popularity features and standard textual similarity measures (BM25), presented in section 5.1. Moreover, for comparison sake, we also report in Table 3 the performance of a baseline combination of the features using an Extra-Trees classifier [START_REF] Geurts | Extremely randomized trees[END_REF], compared with our Joint Multimodal Entity Linking approach (JMEL), which combines the textual and visual features at the representation level. Table 2 summarizes the features and the combination models used in these experiments.

In order to more thoroughly assess the contribution of textual and visual information in the MEL task, we compare the performance of the proposed model when the visual features are combined with sentence embeddings that are derived from different models the following notable models: Skip-Thought [START_REF] Kiros | Skip-thought vectors[END_REF] (D=4,800) trained on a large corpus of novels, Sent2Vec [START_REF] Pagliardini | Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features[END_REF] unigram and bigram vectors (D=700) trained respectively on English Wikipedia and on a Twitter corpus, InferSent [START_REF] Conneau | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data[END_REF] (D=2,048) Bi-LSTM+max pooling model trained on the Standford SNLI corpus, BERT [START_REF] Devlin | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[END_REF] trained on BooksCorpus [START_REF] Zhu | Aligning books and movies: Towards story-like visual explanations by watching movies and reading books[END_REF] and on English Wikipedia and ELMo [START_REF] Matthew E Peters | Deep contextualized word representations[END_REF] trained on the One Billion Word Benchmark [START_REF] Chelba | One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling[END_REF]. For BERT and ELMo, sentences are represented by the average word embedding.

Results

Table 3 reports the accuracy results on the validation and test sets for the binary classification task about the correctness of the first entity selected from the KB for a given mention. First, we note that the baseline points out an imbalance in our dataset, as 59% of the mentions in the test set correspond to the most popular entity among the candidate entities, compared to 36.9% in the validation set. Note that for some Entity linking datasets, popularity can achieve up to 82% accuracy score [START_REF] Dai | Entity Linking within a Social Media Platform: A Case Study on Yelp[END_REF]. We also observe that our popularity baseline outperforms the combination of textual and visual features with the Extra-Trees classifier: this indicates that the features extracted from the textual and visual contexts, when naively used and combined, produce poor results. In contrast, our model achieves significant improvements on both the validation and test sets compared with the popularity baseline and the Extra-Trees combination. We see that combining additional features in the JMEL model (popularity and BM25) also provides significant performance gain. Regarding the visual modality, although considering it alone leads to poor results, its integration in a global model always improve the performance, compared to text-only features.

Image and sentence representation impact analysis Table 4 reports the MEL accuracy on the validation and test sets with various sentence representation models. It shows that the integration of visual features always improves the performance of our approach, whatever the sentence embedding model we use, even though the level of improvement varies depending on the sentence model. For example, while the results of averaging BERT word embeddings and InferSent are comparable using images on the test set, InferSent performs significantly better than BERT using text only. This emphasizes the role of the visual context representation of mentions and entities to help in the EL task. If we look at the performance of the various sentence embeddings models, we can see that the sent2vec model trained on a Twitter corpus outperforms all other embeddings: this reveals the importance of training data in a transfer learning setting. Indeed, we observe that the sent2vec model trained on English Wikipedia produces worse results than the model trained on the target task data (Twitter). Hence, we can assume that other models that achieve good results when trained on a general corpus (such as InferSent or BERT) would get better results if trained on a Twitter collection.

Error analysis We identified several potential sources of errors that may be addressed in future work. First, in our approach we characterize entity contexts with a collection of text/image pairs and by taking their mean, we consider all these pairs equally important to represent an entity. However, by manually checking some entities, we note that each timeline may contain a subset of outlier pairs and more specifically, images that are not representative of an entity. Sampling strategies may be employed to select the most relevant images and to discard the misleading outliers. Moreover, our model fails on some difficult cases where the visual and textual contexts of entity candidates are indistinguishable.

For example, it fails on the mention "post", by linking it to the entity @nationalpost instead of @nypost, two entities representing news organizations whose posts cover various topics. One additional bias is that we restricted our dataset and KB to have only tweets with images. Meanwhile, we observed that tweets without images tend to have more textual context. Thus, it would be interesting to include tweets without images for further experiments.

Conclusion

We explore a novel approach that makes use of text and image information for the entity linking task applied to tweets. Specifically, we emphasize the benefit of leveraging visual features to help in the disambiguation. We propose a model that first extracts textual and visual contexts for both mentions and entities and learns a joint representation combining textual and visual features. This representation is used to compute a multimodal context similarity between a mention and an entity. Preliminary experiments on a dedicated dataset demonstrated the effectiveness of the proposed model and revealed the importance of leveraging visual contexts in the EL task. Furthermore, our work is all the more relevant with the emergence of social media, which offer abundant textual and visual information. In that perspective, we propose a new multimodal EL dataset based on Twitter posts and a process for collecting and constructing a fully annotated multimodal EL dataset, where entities are defined in a Twitter KB. Further exploration is still needed concerning certain points in our model: in particular, our future work includes exploring attention mechanisms for text and images and experimenting different sampling strategies for the triplet loss.

  B m , I m } {U e , B e , I e }

Figure 2 :

 2 Figure 2: Triplet ranking model for learning entity and mention joint representations.

Table 1 :

 1 Statistics on timeline and entity distributions in MEL dataset.

	Mean Median Max Min StdDev

Table 2 :

 2 Features and models used in our experiments.

	Features	Description (see Section 5.1)
	Popularity (Pop)

Table 3 :

 3 Multimedia Entity Linking results (accuracy).

	Valid.	Test

Table 4 :

 4 Impact of sentence embeddings on EL results.

			Valid		Test
	Sent. Embedding	Txt	Txt+Img	Txt	Txt+Img
	S2V-uni(Twitter)	0.592	0.611	0.698	0.708
	S2V-uni(Wiki)	0.499	0.538	0.625	0.654
	S2V-bi(Twitter)	0.616	0.637	0.709	0.716
	S2V-bi(Wiki)	0.511	0.547	0.639	0.663
	InferSent(GloVe)	0.559	0.579	0.666	0.683
	InferSent(fastText [30]) 0.551	0.570	0.671	0.689
	Avg. BERT	0.580	0.594	0.641	0.687
	Avg. ELMo	0.524	0.563	0.605	0.655
	Skip-Thought	0.464	0.511	0.575	0.605
	S2V(Twitter)	0.628	0.639	0.724	0.731
	S2V(Wiki)	0.524	0.551	0.652	0.666

forked at https://github.com/cea-list-lasti/MEL Tweets

https://dev.twitter.com

https://help.twitter.com/en/using-twitter/twitter-lists

Only the first 1,000 matching results are available with the Twitter API.

Twitter API searches within a sampling of tweets published in the past 7 days.

Acknowledgments. This research was partially supported by Labex DigiCosme (project ANR11LABEX-0045DIGICOSME) operated by ANR as part of the program "Investissements d'Avenir" Idex Paris Saclay (ANR11IDEX000302).