Bereket Abera Yilma

Najib Aghenda

Marcelo Romero

Yannick Naudet
email: yannick.naudet@list.lu

Hervé Panetto
email: herve.panetto@univ-lorraine.fr

Personalised Visual Art Recommendation by Learning Latent Semantic Representations

In Recommender systems, data representation techniques play a great role as they have the power to entangle, hide and reveal explanatory factors embedded within datasets. Hence, they influence the quality of recommendations. Specifically, in Visual Art (VA) recommendations the complexity of the concepts embodied within paintings, makes the task of capturing semantics by machines far from trivial. In VA recommendation, prominent works commonly use manually curated metadata to drive recommendations. Recent works in this domain aim at leveraging visual features extracted using Deep Neural Networks (DNN). However, such data representation approaches are resource demanding and do not have a direct interpretation, hindering user acceptance. To address these limitations, this work introduces an approach for Personalised Recommendation of Visual arts based on learning latent semantic representation of paintings. Specifically, we trained a Latent Dirichlet Allocation (LDA) model on textual descriptions of paintings. Our LDA model manages to successfully uncover non-obvious semantic relationships between paintings whilst being able to offer explainable recommendations. Experimental evaluations demonstrate that our method performs better than other approaches based on the use of visual features extracted using pre-trained Deep Neural Networks.

I. INTRODUCTION

Paintings are important pieces in visual art that bring together complex elements such as drawings, gestures, narration, composition, abstraction, ...etc [START_REF] Mayer | The Artist's Handbook of Materials and Techniques, Third Edition[END_REF]. These elements carry deeper semantics beyond their usual categorizations (i.e. time period, material, size, color, ...etc.) Paintings are also perceived differently by people as they trigger different emotional and cognitive reflections depending on the background and personality of a person [START_REF] Mayer | The Artist's Handbook of Materials and Techniques, Third Edition[END_REF]. Personalised recommendation of visual arts often suggest similar contents to those users have already seen or previously indicated that they liked. On the contrary, a recent work proposed by [START_REF] Frost | ART I DON'T LIKE: AN ANTI-RECOMMENDER[END_REF] presents an Anti-Recommendation approach called "Art I don't like". This anti-recommender exposes users to a variety of content and suggests artworks that are dissimilar to the ones users selected aiming to allow serendipity and exploration. In comparison to other areas such as movie and music recommendation, Visual art has received little attention [START_REF] Messina | Exploring Content-based Artwork Recommendation with Metadataand Visual Features[END_REF]. Nevertheless, the huge potential and benefit of personalised recommendations of visual arts has been seen in the works of [START_REF] Messina | Exploring Content-based Artwork Recommendation with Metadataand Visual Features[END_REF], [START_REF] Esman | The World's Strongest Economy? The Global Art Market[END_REF] [START_REF] He | Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation[END_REF]and [START_REF] Naudet | Personalisation in Cyber Physical and Social Systems: the Case of Recommendations in Cultural Heritage Spaces[END_REF]. Similarities and relationships among paintings can normally be inferred based on common high level features such as colour, material, style, artist...etc. However, such features are not expressive enough as they can not fully capture abstract concepts that are latent/hidden in the paintings. In order to capture non-obvious contexts by a machine, effective data representation is very crucial [3]. In general prominent works in visual art recommendation rely on ratings and manually curated metadata (i.e. color, style, mood,...etc) in order to drive recommendations. Recently works such as [START_REF] He | Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation[END_REF] started to use latent visual features extracted using deep neural networks (DNN) and also use pre-trained models for making visual art recommendations. According to results reported by [START_REF] Messina | Exploring Content-based Artwork Recommendation with Metadataand Visual Features[END_REF] DNN-based visual features perform better than manually curated metadata. However, these latent visual features do not have a direct interpretation and can not be used to explain recommendations [START_REF] Apaza | Online Courses Recommendation based on LDA[END_REF]. Hence they hinder user acceptance.

In this work we propose a personalised VA recommendation strategy by learning latent semantic representations. In particular we adopt a Latent Dirichlet Allocation (LDA) based representation learning approach [START_REF] Blei | Latent Dirichlet Allocation Michael I. Jordan[END_REF]. LDA is a topic analysis model that is known to be successful in the domain of Natural Language Processing (NLP) for uncovering hidden semantic relationships among documents. In this approach paintings are represented by documents containing their detailed textual descriptions. Our LDA model is trained on 2368 paining collections from the National Gallery, London and employed for a personalised recommendation task. The objective of this work is two fold. Primarily it introduces a strategy of leveraging textual data for visual art work recommendations. Consequentlyy it offers a method for driving explainable recommendations since the latent topics uncovered though LDA are directly interpretable. The rest of this paper is organised as follows Section II covers a brief literature review. Section III presents the problem formulation. Section IV illustrates the proposed solution introducing the LDA training and recommendation strategy. Section V presents experimental evaluation of the proposed approach and a comparative analysis against visual feature based recommendations extracted using Deep Neural Networks. Finally Section V presents a concluding discussion.

II. RELATED WORK

The earliest works in Information Retrieval (IR) and Natural language processing (NLP) have been using vector space models to represent documents as a vector of key words [3]. However, such representations offered very limited reduction of description length and had a limited ability to capture inter/intera-document structures. To this end further techniques have been developed to tackle the curse of dimensionality by capturing hidden semantic structures in document modeling. In 2003 an unsupervised generative probabilistic model called Latent Dirichlet Allocation (LDA) was probpsed [START_REF] Blei | Latent Dirichlet Allocation Michael I. Jordan[END_REF]. LDA demonstrated superiority overseveral other proposed models. Following this Latent variable models became widely accepted strategies to make inference about hidden semantic relationship between variables. Particularly in Recommendation systems LDA has been applied on several tasks such as Online Courses Recommendation [START_REF] Apaza | Online Courses Recommendation based on LDA[END_REF], personalized hashtag recommendation [START_REF] Zhao | A personalized hashtag recommendation approach using LDA-based topic model in microblog environment[END_REF], Scientific Paper Recommendation [START_REF] Amami | An LDA-Based Approach to Scientific Paper Recommendation[END_REF], Similar TV User Grouping and TV Program Recommendation [START_REF] Pyo | LDA-Based Unified Topic Modeling for Similar TV User Grouping and TV Program Recommendation[END_REF]. LDA has been proven to be successful over several recommendation tasks. Hence, in this work we propose a strategy to leverage LDA in the domain of Visual Art recommendation.

III. PROBLEM FORMULATION

Let P = {p 1 , p 2 , ..., p m } be the set of paintings in a museum, D = {d 1 , d 2 , ..., d m } be the set of documents containing textual descriptions of the paintings in P and P u = {p u 1 , p u 2 , ..., p u n } be the set of paintings a user u has rated where P u ∈ P . W u = {w u 1 , w u 2 , ..., w u n } are the weights representing the preference of u for the paintings in P u . The task of personalised painting recommendation is to recommend a ranked list of paintings based on what paintings the user have liked or rated. However, considering the realistic scenario in a museum, access to user preferences is usually very limited. Hence, providing a personalised recommendation service in such settings needs to rely on content based approaches that can work well under limited preference information. In this work we propose a Latent Dirichlet Allocation (LDA) model, to build a content based personalised painting recommendation system. Our trained LDA model is capable of uncovering hidden semantic structures in the paintings to derive explainable recommendations. The LDA training and recommendation strategy is discussed in the next section.

IV. PROPOSED APPROACH

A. LDA Model

Latent Dirichlet allocation (LDA) is an unsupervised generative probabilistic model for collections of text corpora proposed by [START_REF] Blei | Latent Dirichlet Allocation Michael I. Jordan[END_REF]. The intution behind LDA is that documents are represented as random mixtures over latent topics, where each topic is characterized by a distribution over words. Many works have shown that LDA is capable of capturing latent semantic information from a collection of documents. Particularly it can identify similar documents by uncovering abstract topics that occur in a collection of documents. [START_REF] Jelodar | Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey[END_REF][6][7] [START_REF] Misra | Text segmentation: A topic modeling perspective[END_REF]. Our pating recommender system is built by creating a document description for each painting in the collection and training an LDA model on the corpus that can uncover semantic structures hidden in the paintings. This refers to everything that is not known a priori and hidden in the paintings. Then the trained LDA model can be queried to recommend most similar paintings for paintings a user have liked. The intuition used in LDA is that each document can be seen as a combination of multiple topics. If we take paintings as an example, paintings can be described as a mixture of several concepts such as religion, nudity, portrait,... etc. In LDA each document is characterized by a predefined set of latent topics. In essence, each document is a distribution of topics and each topic is a distribution of words. This means each word in each document comes from a topic and the topic is selected from a per-document distribution over topics. Prominent words in each latent topic explain the nature of the topic and prominent latent topics related to each document explain the nature of the document (i.e. paintings). For instance, let us assume that latent topics are "religion", "still life", and "landscape". A painting may have the following distribution over the topics : 70% "religion", 10% "still life" and 20% "landscape". Moreover, each topic has a distribution over the words in the vocabulary. For topic "religion", the probability of the word "Saint" would be higher than in the topic "landscape". The LDA model is represented as a probabilistic graphical model in Figure 1. α is the per-document topic distribution, β is the per-topic word distribution, θ is the topic distribution for a document d, ϕ is the word distribution for the topic K and z the topic for the n th word in the document d and finally, W is a word. In LDA, each topic is a Multinomial distribution over the vocabulary in the collection of documents. To represent a topic, only the top-n words are considered based on their probability. The procedure of building LDA model for a collection of paintings is explained as follows:

• Pre-processing: we construct a collection of documents that contain detailed textual description of the paintings.

• Initialization: 1) We assume there is a defined number of topics k in the collection of documents 2) Attribute a topic to each word W in the collection of documents where θ i ∼ Dir(α) with i ∈ {1, ..., M } and Dir(α) is a Dirichlet distribution. This initializes a topic model

• Learning: 1)
We assume that the topic assigned to a word W is wrong but that all the others are correct which consists in computing the conditional probabilities p(topic t | document d) (probability that the document d is assigned to the topic t) and p(word w | topic t) (probability that the topic t is assigned to the word w) 2) We update the topic of the document which is now the topic that has the highest probability to be assigned to this document (p

(topic t | document d) • p(word w | topic t))
Assigning the right number of topics as well as the hyperparameters however is not a trivial task. In literature Gibbs sampling algorithm is widely used to estimate parameters of LDA [START_REF] Ganguly | Paper2vec: Combining Graph and Text Information for Scientific Paper Representation[END_REF][10] [START_REF] Ng | dna2vec: Consistent vector representations of variable-length kmers[END_REF]. In our implementation we used an (optimized version of) collapsed gibbs sampling from MALLET [START_REF] Graham | Getting Started with Topic Modeling and MALLET[END_REF]. We refer the reader to the detailed discussion about LDA formulation in [START_REF] Blei | Latent Dirichlet Allocation Michael I. Jordan[END_REF] and [START_REF] Jelodar | Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey[END_REF]. Figure 2 and 3 illustrate the intuition behind our Painting LDA model construction procedure. As it is illustrated in the figures a collection of documents is used as an input to the LDA algorithm. LDA creates k topics that can be seen as clusters of words. Each document of the collection is represented as a distribution of the topics which are themselves, distributions of words.

B. Personalised Paining Recommendation

Once the LDA model is trained over the entire corpus we get a matrix of documents by topics which expresses latent topic distribution of each painting. From this we can generate an M xM similarity matrix for all the paintings in the dataset. In our study user preferences towards paintings are modeled by a normalized weight transformed from a 5 star user ratings. (i.e. we assign a weight w u i ∈ [0, 1] for every painting p u i a user has rated). The task of personalised Painting recommendation is to recommend most similar paintings to a user based on few of the paintings a user has rated. This is done by expanding user preferences towards unseen paintings and predicting a similarity score for the paintings in the detest. The predicted score S(p i , u) for a painting p i in the dataset, according to the preferences of user u is calculated based on the weighted average similarity score (distance) from all other paintings that

S(p i , u) = 1 N N j=1 w j * d(p i , p j ) (1)
where d(p i , p j ) is the similarity between painting p i and p j , p j ∈ p u according to the LDA similarity matrix. The summation is taken over all user rated paintings. This scoring strategy is illustrated on Figure 4. Once the scoring procedure is complete the paintings are sorted in a descending order and the first K paintings are selected to generate a recommendation list. Our LDA based personalised painting recommendation procedure is sketched in Algorithm 1.

Algorithm

S(p i , u) ← COMPUTE( 1 N N j=1 w j * d(p i , p j )) 14:
end for 15:

Sort P← DESCENDING(S(p i , u))

16:

return {p 1 ,... p k } ← SELECT TOP K(R) 17: end procedure

V. EXPERIMENT & DISCUSSION

This section presents the experiments and discussions addressing the obtained results. First, we briefly describe the dataset. subsequently, we introduce the metric used to evaluate the proposed topic model. Then, we present an analysis regarding the capability of our approach in terms of explainability. Finally, we describe our method to assess the quality of our approach in the context of a recommendation task.

A. Dataset

In this study we used a dataset containing 2368 paintings from the National Galary of London. Each painting is represented by a set of attributes which are summarized in Table I. For our approach we specifically focus on painting description attribute. These descriptions are provided by museum experts and carry complementary information about the paintings such as stories and narratives that can be exploited to capture the semantic of a painting. These descriptions provide concise information about each painting. For the task of topic modeling using LDA, we decided to enrich the descriptions dataset D by concatenating the paintings descriptions with keyword attributes such as the artist name, the painting title, the technique used, the publication date, the format (landscape, portrait) and the size (small, medium, ...) and also additional information from other sources to better train the model. Hence we generated a second dataset DE enriched with additional textual descriptions and stories related to the paintings, collected from various sources such as Wikipedia and books. To avoid "Garbage in, garbage out" we performed textual preprocessing on both datasets (i.e. removal of punctuation, stop words, bi-grams, Lemmatization) as they do not add any value to the topic models.

B. Model Evaluation

In topic modeling, Topic Coherence is a commonly used technique to evaluate topic models. It is defined as the sum of pairwise similarity scores on the words w 1 , ..., w n used to describe the topic, usually the top n words by frequency p(w|t) [START_REF] Jelodar | Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey[END_REF], [START_REF] Newman | Automatic Evaluation of Topic Coherence[END_REF].

Coherence = i<j Score(w i , w j ) (2)
Ideally, a good model should generate coherent topics. (i.e the higher the coherence score the better the topic model is [START_REF] Newman | Automatic Evaluation of Topic Coherence[END_REF]). In order to identify the optimal number of topics as well as which data set D or DE generates the best topic model, our implementation resorted the topic coherence pipeline from gensim library CoherenceModel. Figure 5 shows the evolution of the topic coherence as a function of the number of topics for each of the two dataset. From the analysis presented in figure 5 we can make two observations. Firstly, the data set DE (orange) generally gives slightly better topic coherence score and thus, a better topic model. Secondly, we can observe that with 10 topics we obtained a topic coherence of approximately 0.46 for the data set DE which is the best score for this window. Having too many topics requires more resources as well as time for a result that is not significantly better. Thus, we decided to limit the number of topics to 10 and choose to work with the dataset DE since DE provides slightly better results than D. We believe that it is due to the fact that the Moreover, what is it worth noticing that a mixture of keywords and paintings descriptions seems to generate a better topic model than paintings descriptions used alone.

In addition to the evaluation of the topic coherence, we visualize the topics by using a visualization tool called pyLDAvis.

In figure 6, we see each topic represented by a circle annotated with a digit from 0 to 9. The size of the circle represents the prevalence of a topic, i.e, the popularity of a topic among the paintings. The distance represent the similarity between topics.

In fact it is an approximation to the original topic similarity because we are using a 2-dimensional scatter plot to best represent the spatial distribution of the topics. The objective here is to have topics that are overlapping as little as possible.

With 10 topics, we can observe that the topics are evenly popular while being sufficiently distinct from each other. This means that the topics are sufficiently different which is what it is intended.

C. Explainability

In figure 7.a and 7.b we present two paintings 1 . The painting in 7.b) is the most similar painting to the painting 7.a) based on our LDA topic model. We observe in figure 7.c and 7.d that their topic distribution is very similar and one particular topic stands out: topic 8. The fact that this topic stands out from the others for these paintings implies that the words found in this topic are more likely to be found in the paintings descriptions than the words from the other topics. When we have a closer look at the topics descriptions in Table II, we can see that the topic 8 is very well defined as it is obvious that there is a coherence between the words that are used. In fact, topic 8 can be described as the "christian" topic since many of the words in this topic are usually found in christian corpora. When we look at the paintings, it is obvious that there are many references to Christianity and therefore, we can assume that their descriptions contain vocabulary that refers to a religious context. On the other hand, when we look at the descriptions of topics 0 and 5, what is interesting is that the words found in these topics do not seem to share the same semantic at first sight. However, the underlying intuition behind LDA is that words are in the same topics because they are frequently found together and therefore, they are used in a same context. Thus, words "Paris" and "flower" found in the topic 5 that do not seem to have any obvious relation are nonetheless in the same topic because many paintings descriptions in our data set describe Paris and flowers together. LDA is able to find relations between words and at a broader extent, paintings that are not always obvious to us but explainable. Therefore for the task of recommendation using LDA we can easily identify prominent topics and reason out shared semantics among recommendations in order to help users better understand recommendations. In our experiment we offered explanations to users as a word cloud of prominent concepts shared among the recommendations matching their personal preferences. Fig. 8 an example of interest word cloud.

D. Recommendation Quality

In order to evaluate the performance of our algorithm on a recommendation task we performed a controlled experiment with 15 real users. Each user provided a rating for 80 paintings from the dataset to be used for profiling. As a baseline recommender we used visual features extracted using Residual Networks (ResNet-50) [START_REF] He | Deep residual learning for image recognition[END_REF]. ResNet-50 is a 50-layer deep convolutional neural network trained on more than a million images from the ImageNet database [START_REF]ImageNet[END_REF]. Thus, it has learned rich feature representations for a wide range of images. We used the pre-trained ResNet-50 to extract latent visual features from our painting dataset. The extracted features are then used to identify similar paintings to user preferred ones through similar scoring mechanism used for LDA. The User-Centric evaluation was done through a questionnaire where participants had to express their opinion in a Five point Likert scale to four of the following statements. "The recommendations match my personal preferences and interests" (Predictive accuracy); "The recommender helped me discover paintings I did not know before" (Novelty); "The recommender helped me discover surprisingly interesting paintings I might not known otherwise." (Serendipity); "The recommended paintings are diverse" (Diversity). Additionally users were asked if explanations offered by LDA helped them to better understand recommendations. Interestingly all . Fig. 9. Comparison of LDA Vs ResNet-50 interms of Accuracy, Novelity, Serendipity and Diversity participants responded "Yes", this shows that explainable recommendations have a positive impact on user experience. Figure 9 summarizes the average values of user ratings for the two recommendation pipelines. As reported on the figure, LDA achieved significantly higher diversity values (4/5) comapred to ResNet-50 (2.3/5). This is due to the fact that the notion of similarity in LDA is directly related to semantically dominant concepts shared among paintings. i.e. LDA also uncovers relationship between paintings that do not necessarily have a resembling visual features. Hence, LDA captures semantic similarities that can not be discovered through latent visual features. This can also justify the higher values of serendipity and Novelty since LDA based recommendation can contain visually diverse but semantically related paintings. In terms of matching user preferences, LDA also performs significantly better (4.1/5) than ResNet-50 (3.1/5).

VI. CONCLUSION

In this work we proposed a personalised Visual Art recommendation strategy based on learning latent semantic representations. In particular we trained an LDA based algorithm which leverages textual descriptions of paintings to uncover latent semantic relationships among paintings. Our Algorithm compares favourably with state of the art VA recommendation method based on visual features extracted using Deep Neural Networks. The latent features extracted with LDA can directly be interpreted by humans. Thus, it enables a personaliser to offer explainable recommendation which is proved to be helpful for users to better understand recommendations. Globally it is evident from the controlled experiment that, LDA outperforms the baseline in all the evaluation matrices. As a future work, further validation with larger set of user groups and also a hybrid approach combining visual features could be a relevant extension.

Fig. 1 .

 1 Fig. 1. Latent Dirichlet Allocation plate diagram

Fig. 2 .

 2 Fig. 2. The intuition behind Painting LDA model

Fig. 3 .

 3 Fig. 3. Topic modeling with LDA

Fig. 4 .

 4 Fig. 4. Painting Scoring Strategy

Fig. 5 .Fig. 6 .

 56 Fig. 5. Comparative topic coherence analysis of the two data sets

Fig. 7 .

 7 Fig.7. Target painting (Top left), its most similar painting (Top right), Topic distribution of the target (bottom left) and its most similar painting (bottom right).

TABLE I .

 I ATTRIBUTES & DESCRIPTIONS OF THE PAINTINGS DATASET

TABLE II .

 II DESCRIPTION OF THREE TOPICS

These paintings are available under Creative Commons License


Ms Justine Pandard

Ms Na Pan

Ms Dina

H Ebene

Dr Thomas Le Saux

Mr Eric Ait- Yahiatène

Dr Xiaoqing Liu

Dr Laurence Grimaud

Dr Olivier Buriez

Pr Eric Labbé

Dr Frédéric Lemaître

Dr Manon Guille-Collignon

A Fluorescent False Neurotransmitter as a Dual Electrofluorescent Probe for Secretory Cell Models

Keywords: electrochemistry, exocytosis, fluorescent false neurotransmitters, indium tin oxide, photophysics

In this work, a new dual electrofluorescent probe (FFN42) belonging to Fluorescent False Neurotransmitter family was rationally designed for investigating cell secretion. This coumarin including one amino and two hydroxy groups is very promising due to its electroactive and fluorescent properties. The optimal excitation and emission wavelengths (380 nm and 470 nm respectively) make this probe adapted to its use in fluorescence microscopy. Photophysical properties of FFN42 were investigated by means of UV/Vis absorption and fluorescence spectroscopies and gave access to quantum yield (0.18), molar absorption coefficient (12000 M - 1 .cm -1 ) and pKas (5.4 and 6.7 for hydroxy groups). Electroactivity of FFN42 was investigated on carbon fiber and ITO electrodes at relatively low oxidation potentials (0.24 V and 0.45 V vs Ag/AgCl respectively). Epifluorescence observations evidenced that FFN42 was accumulated into secretory vesicles of PC12 and N13 cells. Toxicity tests further revealed that FFN42 had no lethal effect on these cells. Amperometric data obtained on carbon fiber electrodes proved that the probe is released by N13 cells. As a consequence, this bimodal probe opens new fields in the investigation of biological applications of secretion with electrofluorescent probes.

Introduction

Communication between cells is a fundamental process involved in numerous normal healthful and pathologic functioning phenomena. In this respect, vesicular exocytosis is a ubiquitous pathway of intercellular communication where an emitting cell delivers water soluble molecules contained in secretory granules. This process notably occurs during synaptic transmission and hormone secretion. [1] Briefly, an intracellular vesicle moves to the cell membrane through vesicle-trafficking steps via cytoskeleton network (actin, microtubule and motor proteins) to reach the cell membrane. Steps of vesicles tethering and docking then occur and aim at maintaining the vesicle at the close vicinity of the cell membrane thanks to proteic anchors. [2] Vesicular fusion with cell membrane begins with the merging of the two membranes before the subsequent formation of a narrow fusion pore (~ nm) through which the first encapsulated biomolecules start to diffuse. A more massive release of biochemical molecules can be then achieved via the consecutive melting of the granule with the plasma membrane. [3] Electrochemistry and fluorescence techniques are the most widely employed techniques for investing exocytosis in real time at the single cell level. Fluorescent microscopies are based on the use of fluorescent markers (FM 1-43, acridine orange, GFP...) that can be located in the secretory vesicles (membrane, vesicular content...). As a consequence, the secretory vesicles that undergo fusion can be imaged in real time and this monitoring gives access to the status of the vesicles before and during fusion (displacements, fusion location...). Among fluorescence imaging techniques, total internal reflection fluorescence microscopy (TIRFM) is particularly adapted. An induced evanescent wave at the bottom of an adherent biological sample confines the excitation to a tiny thickness in the close vicinity of the cell membrane. This avoids the background fluorescence from non-focal planes and enhances the signal/noise ratio and the axial resolution. For example, fluorescent-labelled vesicles within an adherent cultured cell on a microscope coverslip can be imaged in a region typically less than 150 nm thick. The mapping of secretory sites on cell surface or the tracking of vesicles can then be observed. Conversely, amperometry gathers quantitative information on the amount of electroactive species released with an excellent temporal resolution (~ms) that perfectly meets the requirement of the fast kinetics of exocytosis. Indeed, many biogenic endogenous monoamine neuromolecules (serotonin, dopamine, norepinephrine, epinephrine; Figure S1) are electroactive species. Their oxidation at a microelectrode surface located in the close vicinity of a single cell results in an electrical current that features the release dynamics. [4] Adding fluorescence measurements is thus an important improvement of the electrochemical technique. It thus would combine the excellent temporal resolution of amperometry with the location of individual exocytotic events, thus offering the possibility to monitor whole exocytotic events. [5] This is why we recently implemented a new combining strategy that gathers both analytical techniques to study exocytosis on specific transparent ITO (Indium Tin Oxide) microchips that allowed the simultaneous monitoring of the vesicular release by amperometry and fluorescence.

As a proof of concept, the dual detection was demonstrated with a stable clone of enterochromaffin BON cells (BC21) that expresses GFP-tagged neuropeptide-Y (NPY-GFP) and release the electroactive serotonin. [5b, 6] However, the use of two independent probes (fluorescent and electroactive) prevented a quantitative analysis of the release due to a lack of control on the relative proportion of both probes within vesicles (simultaneous optical and electrochemical signals reached only ~20 %). A unique dual electrofluorescent probe, FFN102, was then introduced. This probe belongs to the family of fluorescent false neurotransmitter (FFN), i.e. a synthesized analogue of a biogenic neurotransmitters initially proposed as a versatile synaptic imaging platforms. [7] Indeed, FFN102 was shown to be electroactive on ITO and carbon electrodes as well as recognized by VMAT (vesicular monoamine transporter which carries monoamine neurotransmitters from the cytoplasm into synaptic vesicles) of BON N13 and PC12 cells. [5c, 5d, 8] However, its high oxidation potential (+900 mV vs Ag/AgCl on ITO) induces electrical noise on the ITO semiconductor material that restricts its advantages. [5c] In this work, we report on an innovative dual electrofluorescent probe called FFN42 capable of acting as optical/electrochemical dual reporter in the coupling method, [5a] especially with longer excitation/emission wavelengths and/or lower oxidation potential. This approach could be of high interest to counterbalance the drawbacks of FFN102. In this way, the bimodal probe FFN42 was designed and characterized by electrochemistry, photophysics and with biological tests of uptake/toxicity and ability to be released, thus showing it compensates defaults of previous dual probes of FFN family like FFN102 for the purpose of the coupling technology (amperometry-TIRFM).

Results and discussion

Photophysical properties of FFN42

Photophysical properties of FFN42 (Figure 1A) were firstly investigated. Under physiological conditions (pH 7.4), optimal excitation and emission wavelengths were 380 nm and 470 nm, respectively (Figure 1B). In the case of FFN102 (SI Figure S1), optimal excitation and emission wavelengths were located at 371 nm and 456 nm, respectively. Thus, the value of maximum excitation wavelength of FFN42 is red shifted for 9 nm (see SI Figure S3). [9] Beyond this slight increase, using FFN42 rather than FFN102 globally shows advantages in terms of fluorescence.

At 405 nm excitation wavelength (commonly available in microscopy setups), fluorescence of FFN102 only reached 25% of the normalized fluorescence intensity (Figure S3). Conversely, 95% of the normalized intensity is reached for FFN42 due to its broader absorption spectrum (Figure 1B). Furthermore, fluorescence intensity significantly augments with the rise of pH values, especially from pH 6 to pH 7.4 where the emission intensity increases by a factor 13 (Figure 1C).

Considering the difference of pH between the intravesicular (pH 5~6) and extracellular media (pH 7.4), this pH-dependence is fully consistent with the required properties of a fluorescent probe for investigating exocytosis. [10] Checking the validity of Beer-Lambert's law with UV/Vis absorption spectra (Figure 1D) gave access to the molar absorption coefficient of FFN42 (12000 M -1 .cm -1 in a mixture of acetonitrile/phosphate buffer (50/50 v/v)) at 380 nm. This value is comparable to values of the coumarin nucleus at 311 nm in ethanol (5700 M -1 .cm -1 ) and umbelliferone (see structures in Figure S1) at 330 nm in ethanol (evaluated at 16800 M -1 .cm -1 ). [11] Fluorescence spectrum of FFN42 and of the quinone sulfate reference gave access to FFN42 quantum yield as mentioned in the equation ( 1) and a value of 0.18 was found for the fluorescence quantum yield of FFN42. [12] The brightness of FFN42 allowed fluorescence imaging with good signal to noise ratio (see below).

As described above, the absorption, excitation, and emission spectra of FFN42 are pH-dependent. This can be rationalized by the equilibrium between the protonated (phenol) and deprotonated (phenolate) forms in accordance with previous publications. [7] In the case of FFN42, pKas of the three acid functions (hydroxy groups in positions 6 and 7 respectively; amino group in position 4) were determined by acquisition of 32 UV-vis absorption spectra at different pH values lying between 3.57 and 11.05. Values were 9.8 for the primary amine (which refers to the ammonium ion/amine couple) in position 4, 6.7 for the hydroxy group in position 6 and 5.4 for the hydroxy group in position 7 (Figure 1A). [13] The value for the amino group is consistent with similar structures (for instance, pKa = 9.83 for phenethylamine [START_REF]CRC Handbook of Chemistry and Physics[END_REF] ). Furthermore, the pH-dependence of fluorescence for FFN42 is in agreement with its moderately acidic pKas of 5.4 and 6.7 for the two phenol functions. Indeed, at physiological pH, the concentration of the phenolate forms of the probe is significantly high to enhance the fluorescence level. Of note, the use of pHdependent probes for detection of exocytosis cell secretion avoids any confusion between the vesicular release and a return of the marked vesicle in the cytoplasm.

All in all, the optical properties (slight increase in maximum wavelength and quite broad absorption band) make FFN42 more suitable than FFN102 for fluorescence measurements.

Electrochemical properties of FFN42

The electrochemical behaviour of FFN42 was studied by cyclic voltammetry with a carbon fiber electrode (CFE; 10 µm diameter) between -0.2 and 0.8 V vs Ag/AgCl or an Indium Tin Oxide (ITO) microdevice (disk of 150 µm diameter) between 0.0 and 0.6 V vs Ag/AgCl (scan rate = 200 mV.s -1 ). As shown in Figure 2A and 2B, FFN42 is electroactive at relatively low potential on carbon fiber (oxidation peak at 0.24 V vs Ag/AgCl) and ITO (oxidation peak at 0.45 V vs Ag/AgCl) microelectrodes. Others endogenous neurotransmitters (dopamine, serotonin; see Figure S1) or dual electrofluorescent FFN probe like FFN102 are also electroactive. As for this kind of compounds, passivation of electrode surface was observed with FFN42 (see Figure S4). [5c] Nevertheless, such a fouling does not jeopardize the use of FFN42 in electrochemical monitoring of secretion at the cell level with amperometry since cyclic voltammetry conditions differ in terms of conditions (non-dynamic solution, different spatial and temporal scales…). As an example, dopamine and serotonin are two commonly neurotransmitters studied in amperometry though they show high passivation of the working electrodes in cyclic voltammetry. [15] Compared to FFN102, the detection of FFN42 on carbon and ITO surfaces at less anodic potentials is very likely due to the replacement of the chlorine group by a second hydroxy group in position 6 of the coumarin nucleus. This more electron-donating group is expected to promote easier oxidation. This low oxidation potential value will decrease the applied potential in amperometry and thus the electrical noise namely due to capacitive phenomena. This also does not limit the cell lines to "empty" cell models of neurotransmitters. This potential diversity of the biological object of study is of great importance, the properties of exocytosis being strongly dependent on the cellular model. [3] Further analyses require investigating of the FFN42 oxidation mechanism. Owing to its catecholbased structure, a 2e -/2H + mechanism is expected. The number of electrons exchanged during the oxidation can be estimated from two independent electrochemical measurements that helps to calculate unknown parameters (diffusion coefficient "D" and the number of electrons "n" involved in the process of type Ox + ne -= Red) from controlled parameters (concentration, electrode surface, scan rate...). In practice, a transient (cyclic voltammetry on a static millimeter electrode) and a steady state technique (rotating disk electrode ; RDE) are associated. [16] On the one hand, in cyclic voltammetry, the magnitude of the current spike is expressed by the Randles-Sevcik's law for a diffusion controlled process : [START_REF] Bard | Electrochemical Methods : Fundamentals and Applications[END_REF] at 25 °C (2)

with A is the electrode surface area (cm 2 ), D the diffusion coefficient (cm 2 .s -1 ), C* the electroactive species concentration (mol.cm -3 ), v the scan rate (V.s -1 ) and F = 96500 C.mol -1 .

On the other hand, experiments with a RDE lead to a steady state current that can be expressed according to Levich equation:

(

) 3
where ω is the angular rotation rate of the electrode (rad.s -1 ) and ν is the kinematic viscosity (ν= 10 -6 cm 2 .s -1 for water).

The values of "n" and "D" can then be deduced by combining equations ( 2) and ( 3) and measurements of the effects of "v" and "". The number of electrons exchanged during the oxidation process was found equal to n = 2.01 and the diffusion coefficient of FFN42 was estimated at D = 1.99.10 -5 cm².s -1 , a value comparable to the one of dopamine (D = 0.9.10 -5 cm².s - 1 on carbon fiber electrodes). [START_REF] Sujaritvanichpong | [END_REF] The knowledge of "n" is an essential parameter that allows one to calculate the amount or released molecules (Faraday's law) during the analysis of exocytotic signals in amperometry. [3] Investigation of pH influence on cyclic voltammograms (CVs) was then carried out in PBS over a pH range of 4.1 to 8.4 with a carbon fiber electrode (CFE ; scan rate = 200 mV.s -1 ). As displayed in Figure 2C, the FFN42 oxidation peak shifted to less anodic potential values when pH value increased. This result suggests that the oxidation of FFN42 probably involves the two hydroxy groups due to the formation of deprotonated phenolate forms at higher pH values which are easier to oxidize. Furthermore, the E p = f(pH) graph shows a straight line (Figure 2D, slope = 43 mV) .

In a general case (for a reversible process of the type Ox + mH + + ne -= Red), the expected slope is expressed according to: [START_REF] Compton | Understanding Voltammetry[END_REF] at 25 °C (4) This is indeed the case for FFN102 (Figure S5) where the slope (58.5 mV ~ 59 mV) is consistent with a phenol function involved in a rate-determining CE (Chemical-Electrochemical) process where m = n = 1. Conversely, FFN42 did not give the expected slope of 59 mV (m = n = 2). This result is not unusual for benzenes bearing hydroxy groups like quinols [START_REF] Wedege | [END_REF] and was especially evidenced in recent electrochemical studies of catechol (Figure S1). [21] In the latter case, theoretical and experimental analyses evidenced that, despite a whole 2e -/2H + mechanism, the catechol oxidation mechanism within a theoretical nine-species square scheme (including a oneelectron irreversible step) could account for different slopes ranging from 40 to 59 mV as a function of pH. However, contrary to catechol (pKas = 9.5 and 12.8 respectively), the two pKa values for FFN42 (5.4 and 6.7 respectively) are too close to afford a strong predominance of acidic/basic forms of FFN42 over the pH values investigated here. It thus results in an average behavior leading to the straight line displayed in Figure 2D. As a consequence, the general mechanism for the oxidation of FFN42 is probably similar to the one depicted for catechol, i.e. a full 2e -/2H + process described by a square scheme (Figure 3).

Uptake of FFN42 within secretory vesicles of cell models of exocytosis

We firstly used N13 cells that are a stable clone of BON cells to check the ability of FFN42 to penetrate the cytoplasm and then the secretory vesicles of the cells. BON cells derived from a carcinoid tumor and have been shown considerable attraction for their use as cellular model for exocytotic studies [22] although they need to be loaded with exogenous serotonin [23] or FFN102 to detect secretion by amperometry and GFP or FFN102 for TIRFM analysis. [5c, 5d, 8] The uptake and distribution of FFN42 in BON N13 were analyzed by epifluorescence. and50%). These results are consistent with the ability of several FFNs to globally mimic the intrinsic properties of monoamine neurotransmitters and to be transported by VMATs (vesicular monoamine transporter), [7b, 24] whose both isoforms are expressed by BON cells. [25] Finally, vesicular uptake of FFN42 within vesicles from PC12 cells was also investigated in order to ascertain on another well-known and used secretory cell model for exocytosis (Figure 5). The effects of FFN42 concentration and incubation time were analyzed with the same protocol than for N13 cells. It is worth mentioning that PC12 cells have smaller secretory vesicles by comparison with N13 cells (typically 75-120 nm radius), [26] making it more difficult for the identification of individual secretion vesicles. Nevertheless, fluorescence was observed in PC12 cells at optimal conditions of 20µM of FFN42 during 30 min with observation of blurry dots. The percentage of cells with fluorescence staining in PC12 cells loaded with FFN42 was around 60-70%. As a result, this shows that FFN42 can be uptaken in the vesicles of two usual cell models of exocytosis.

Cellular toxicity tests

A very important issue is that the electrofluorescent probe FFN42 did not lead to toxic effects. As described above, BON N13 cells were incubated with a 20 μM solution of FFN42 for a period of 20 min that were the optimal vesicular internalization conditions previously determined. Toxicity tests were achieved (see experimental part) by using a solution of 1 μM of Calcein and 1 μM of Ethidium Homodimer (from the LIVE / DEAD-Thermofischer Scientific kit) in PBS (pH 7.4).

Cell samples were rinsed with a solution of filtered PBS (pH 7.4), before their observation and further analyzed in this same solution. No dead (red) cells were observed on the whole set of experiments achieved under the best conditions of FFN42 uptake by BON N13 cells (Figure 6).

The same observation was obtained under control conditions (i.e. incubation with a solution without FFN42 during 20 min; data not shown). This experiment confirmed the non-toxicity of FFN42 as a fluorescent and electrochemical reporter in BON N13 cells at the tested concentrations.

Amperometry measurements of FFN42 exocytotic release

Beyond the investigations detailed above, it is important to demonstrate that the uptaken FFN42 molecule within vesicles can be released by the considered cells. This can be achieved by amperometry at CFE. The CFE is positioned in the close vicinity of a given BON N13 single cell previously incubated with FFN42 (see experimental part). After helding the CFE potential at 500 mV vs Ag/AgCl and stimulating the cell with ionomycin to trigger exocytosis, a chronoamperogramm is recorded (Figure 7). This corresponds to a usual electrochemical monitoring of exocytosis at the single cell level where each current spike reflects the release of the electroactive content of an individual vesicle. [3] Because no electroactive content can be detected at +500 mV vs Ag/AgCl from no pre-loaded BON N13 cells, [5c, 25c] the spikes necessarily result from the oxidation of FFN42 at the electrode surface that shows the ability of N13 cells to release the previously loaded FFN. Preliminary statistics were performed to calculate the exocytotic frequency (0.16  0.02 Hz) which is relatively low and consistent with this cell model. [25c] Furthermore, the mean parameters of amperometric spikes (n = 24) were estimated to : ). [5d, 25c] Whereas the duration of a vesicular event is expectedly similar for all, the amount of electroactive probe as well as the flux is significantly higher for FFN42. This is probably related to a less efficient serotonin loading [25c] and a one-electron oxidation for FFN102 while FFN42 oxidation involves a two-electron process (see above). In any event, this also evidenced that the magnitude of the amperometrical signals resulted from the FFN42 is higher thus significantly enhancing the signal/ratio of the electrochemical detection.

Conclusion

In this work, we reported on the photophysical and electrochemical properties of a promising dual probe FFN42 for the analysis of exocytosis with coupling optical/electrochemical method.

We show here that FFN42 presents key advantages compared to the previously used FFN102, like a lower oxidation potential on carbon and ITO and also a chemical formula, steric hindrance and diffusion coefficient very closed to the endogenous neurotransmitters. This new synthesized dual electrofluorescent probe also possesses interesting photophysical properties as well as ability to be recognized and uptaken in vesicles of BON N13 and PC12 cells and secreted by BON cells, that make it an ideal candidate for labeling secretory vesicles in fluorescence and electrochemistry. Future prospective works will focus on FFN42 potential use on coupled amperometric and TIRFM recordings on cells to provide a deeper understanding of the fluorescent optical signals in cells secretion thanks to the support of amperometric detection.

Experimental section

Synthesis 1

The starting substrate Boc-β-Ala-OH (1.00g, 5.28 mmol, 1.0 equiv) was added into a suspension of fresh 1,1'-carbonyldiimidazole (1.03 g, 11.63 mmol, 2.2 equiv) in distilled THF (15 mL) under

Argon. The resulting suspension was stirred for 4 hours. MgCl 2 (503 mg, 5.28 mmol, 1.0 equiv) and methyl potassium malonate (1.65 g, 10.6 mmol, 2.0 equiv) were added under Argon. The resulting solution was stirred vigorously overnight before H 2 O (15 mL) and a 1 M aqueous HCl solution (10 mL) were added. The aqueous phase was extracted with ethyl acetate (30 mL *3).

The collected organic phase was successively washed with a 1 M aqueous HCl solution (10 mL) and brine (8 mL), dried with anhydrous MgSO 4 , filtered, and concentrated under reduced pressure to give the product 1 as a light-yellow liquid (657 mg, 51%). 1 H-NMR (300 MHz,CDCl 3 ) δ (ppm): 4.96 (brs, 1H, NH), 3.74 (s, 3H, CH 3 ), 3.46 (s, 2H, CH 2 ), 3.37 (q, J = 5.9 Hz, 2H, CH 2 ), 2.77 (t, J = 5.9 Hz, 2H, CH 2 ), 1.42 (s, 9H, tBu). 13 C-NMR (75 MHz, CDCl 3 ) δ (ppm) : 202.5, 167.4, 156.0, 79.6, 52.6, 49.2, 43.2, 35.1, 28.5.

FFN42

To a mixture of 1, 2, 4-trihydroxybenzene (164 mg, 1.30 mmol, 1.3equiv) and product 1 (245 mg, 1.00 mmol, 1.0 equiv) was added methanesulfonic acid (0.65 mL, 10.00 mmol, 10.0 equiv) at 0 °C. The resulting mixture was stirred for 3 hours at room temperature before dilution with cold ethyl ether (10 mL). The residual solid was filtered off, washed with cold diethyl ether and recrystallized with 0.1 M aqueous HCl solution to give the desire product as a pale grey solid (FFN42) (191 mg, 74%).

Fabrication of carbon fiber electrodes

All CFEs (carbon fiber electrodes) were fabricated by sealing individual carbon fibers into pulled-glass capillaries. First, individual carbon fibers were aspirated (10-µm diameter, Thornel P-55S, Cytec) into glass capillary tubing (PG120T-10-Harvard Apparatus 1.2mm O.D x 0.93mm I.D), then through a vertical micropuller (PC-100; Narishige), the capillary was separated into two identical tubes with a short length of protruding carbon fiber at the tapered end. A drop of mercury or Galinstan (mix of Galium, Indium and Tin, Geratherm Medical AG, Germany) was back-filled into the capillary tubes to make an electrical contact. In order to limit a specific working area at the top of the carbon fiber, an insulating layer of poly(oxyphenylene) was electrochemically deposited carbon fiber according to a published method. [27] A mixed solution containing 0.40 M allylamine, 0.23 M 2-allylphenol, and 0.23 M 2-butoxyethanol in 1:l (v/v) water-methanol was used. To achieve polymer deposition, a potential of +4 V was applied to carbon fiber electrode for 3 min with a platinum counter electrode. After being washed within distilled water, the polymer insulated electrodes were cured for 3 h at 150 °C in order to completely remove the solvent. To perform electrochemical experiments, polymer insulated carbon fiber tip was cut to an appropriate length and was polished on a diamond particle whetstone microgrinder (EG-4; Narishige) at an angle of 45° for 2 min to produce an elliptical and conductive surface. Nichrome wire was finally inserted into conductive mercury to make an electrical contact.

Fabrication of ITO microdevices

In order to define the electrode surface on circular glass substrate (170 µm in thickness and 4 cm in diameter, Thermo Scientific), a lithography step with a positive photosensitive resin AZ5214-E (Microchemicals) was used and deposited by spin-coating. A Ti-Primer was used in order to provide a better adhesion of the resin on glass. Glass substrates underwent an annealing step for a period of two minutes at a temperature of 110 °C. A step of UV irradiation on an aligner (SUSS MicroTecMJB4 Mask Aligner) was then carried out (Mode: First Exposure), using a designed mask (Selba, Versoix, Suisse) delimiting the surfaces of the future electrodes. A second annealing step was then carried out for 2 minutes at 125 °C, allowing the reticulation of the resin.

A second UV exposure was performed on the aligner (Mode: Flood Exposure mode), for 30 seconds, without any mask to create a reverse polarity of the resin which then became negative.

The substrates were immersed in a developer (AZ 726 MIF) for 30 seconds. Samples were rinsed with deionized water and dried with compressed air. ITO electrodes were then deposited on glass substrates by sputtering. A second platinum metallization was performed by hiding the central part of the electrodes, in order to increase the conductivity and to facilitate subsequent lateral connections. A lift-off step in an acetone bath was then carried out in order to remove the resin and to reveal the deposited metal. The annealing step of ITO semiconductor was essential in order to considerably increase the conductivity and the transparency of the raw deposit of ITO.

This procedure was carried out in a conventional oven, able to reach a temperature of 300 °C, in air and at atmospheric pressure. The oven was programmed in three steps; the first one was a linear 1h30 temperature ramp from ambient temperature up to 300 ° C, the second was a 300 °C temperature plateau of 3h30 and the last one was a linear ramp down to ambient temperature. The electrical contacts were made using tin-plated copper wires. A circular well of PDMS (3cm in diameter and 1.5 cm in height), allowed defining the volume of solution necessary for cell culture.

A rectangular shaped PDMS well (1 cm wide, 1.5 cm long and 0.5 cm high) delimited the active surface of the electrodes. The final device owned eight ITO electrodes (see Figure S2). Each ITO electrode was composed of one disk (diameter of 150 µm with one 50 µm width band to be connected to the sides of the sample) situated at 500 µm far from the edge of the PDMS rectangle.

Spectroscopic experiments

Samples were placed in quartz cuvettes (Hellma) of 1x1 cm. UV/Vis absorption spectra were recorded on a Cary 300 spectrophotometer (Agilent Technologies, Santa Clara, CA) equipped with a t2×2 Sport dual cuvette holder (Quantum Northwest, Liberty Lake, WA) thermoregulated at 293 K. Molar absorption coefficients were extracted using the linear relationship of the Beer-Lambert law. The evolution of the absorbance of FFN42 as a function of pH was measured between 3.57 and 11.05 in a Britton-Robinson buffer composed of 10 mM of acetic acid, 10 mM of CHES (2-(cyclohexylamino)ethanesulfonic acid) and 10 mM of sodium dihydrogen phosphate in 50 mM of NaCl. The pKa values of the three protonation sites of FFN42 were extracted using SPECFIT/32 global analysis system (version 3.0 for 32-bit Windows systems). Fluorescence measurements were performed at 293 K with a QuantaMaster spectrophotometer (PTI, Monmouth Junction, NJ) equipped with a TLC50 cuvette holder (Quantum Northwest). In the case of the quantum yield determination of FFN42, solutions for emission measurements were adjusted to concentrations such that the maximum absorption was kept below 0.15 in the useful wavelength range. The overall emission quantum yields after one-photon excitation Φ em values were calculated from the following relation:

(

where the subscript "ref" stands for standard samples, A ref (λexe) is the absorbance at the excitation wavelength, D is the integrated emission spectrum, and r i is the refractive index for the solvent.

The uncertainty in the experimental value of Φ em of FFN42 was estimated to be 10%. The standard fluorophore sample for the quantum yield measurements was quinine sulfate in 0.05M H 2 SO 4 with  ref = 0.5. [12] Electrochemical experiments

All the voltammetric analyses of molecules on ITO and CFE were performed with an EA162

Picostat (eDAQ, Australia) through an e-corder 401 system associated with EChem software inside a Faraday cage. Ag/AgCl wire was used as the reference electrode and the counter electrode was a Pt wire. The number of electrons for the oxidation of FFN42 was determined by means of two electrochemical methods. The steady state method was performed with a rotating disk electrode (RDE; glassy carbon electrode, diameter 3 mm). The transient method was cyclic voltammetry (glassy carbon electrode, diameter 1 mm). Both were recorded with a potentiostat EGG (Princeton Applied Research). They were performed in a solution of 75 µM of FFN42 in PBS (pH 7.4) at two scan rates of 200 and 500 mV.s -1 for cyclic voltammetry and at an angular rate equal to 5000 rpm for RDE experiment. The reference electrode was a classical saturated calomel electrode (SCE) and the counter electrode was a Pt wire.

Cell culture

The BON cell line was derived from a metastatic human carcinoid tumor of the pancreas. BON

Experiments on cells in the presence of FFN42

For their observation under epifluorescence microscopy, cells were studied in Petri dishes or in ITO devices. These Petri dishes (P50G-1.5-14-F, MatTek Cultureware, Ashland, MA) or ITO devices were previously treated with a collagen IV solution (0.1 mg.mL -1 ) before introducing the cells. 300 μL of collagen IV solution was used to cover the whole surface of the substrate to enable a satisfactory adhesion of cells, allowing their optimal optical observation. Six hours after samples preparation, collagen solution was removed from the surface of the substrates, which were subsequently cleaned with a filtered solution of PBS (pH 7.4). After the subculture stage (described above), cells were re-suspended in their complete growth medium. A dilution step of this high concentrated solution, allowed the acquisition of a final concentration within the sample of 10 5 cells.mL -1 . Finally, 4 mL are introduced into the different samples, previously treated with collagen IV solution. Samples are then maintained in the incubator (at 37 °C under an atmosphere of 5% CO 2 ), for a duration of 24 hours, before any experiments.

For the uptake and distribution of FFN42 in N13 and PC12 cells procedures, 24 hours after samples preparation, samples media were renewed, with complete growth media supplemented with various concentrations of FFN42 (10, 20 and 50 µM) and/or for different periods of time (20, 30 and 60 min). Then, samples were stored in an incubator (37 °C with a humidified atmosphere of 5 % CO 2 ). Afterwards, Petri dishes were rinsed three times with filtered PBS (7.4) and cells were observed under epifluorescence in PBS for PC12 cells and in Locke's x1 for N13 cells.

For the tests of the toxicity of FFN42 in N13 cells, cell samples were prepared 24 hours before experiments. BON N13 cells were coated with a collagen IV solution (0.1 mg / mL) for a better cellular adhesion on Petri dishes (P50G-1.5-14-F, MatTekCultureware, Ashland, MA) with a concentration of 75 000 cells.mL

Electrochemical experiments on cells in the presence of FFN42

Electrochemical measurements of exocytosis are nowadays a usual method. The experimental procedure was already described elsewhere (see [25c] as an example). Briefly, a microcapillary filled with a 10 µM ionomycin solution was positioned against the cell of interest to initiate the exocytotic secretion (N13 cells previously incubated with FFN42 30 min at 20 µM). To perform amperometric measurement in Locke's X1 solution supplemented with 2.5 mM CaCl 2 and 1.2 mM MgCl 2 , a constant potential value of +500 mV vs. Ag/AgCl (working as the reference electrode) was applied to a CFE working electrode (located at the top of the cell) by using a potentiostat AMU-130 (Radiometer). The resulting currents were recorded in real time and stored on a computer (e-corder 821, eDAQ converter; software eDAQ Chart; output digitized at 40 kHz).

Amperometric traces obtained during cell secretion was visually inspected and signals were designated as exocytotic spikes if their maximum current values were 3 times higher than the rms noise of the baseline current recorded prior to each signal. Note that only well-isolated amperometric spikes were taken into consideration whereas those overlapped ones were discarded during data treatment. Each spike characteristics, i.e., the maximum oxidation current I max (pA), the total electrical charge Q (fC), the half-width t 1/2 (ms) were determined using homemade software. All values are reported as the mean ± SEM of the data (I max ; Q; t 1/2 ). Proposed mechanism for oxidation of FFN42 based on reference [21] . A) Representative amperometric trace obtained after stimulation (10 µM ionomycin) of a single N13 BON cell preloaded with FFN42 (30 min at 20 µM). B) Isolated current spike with the three main extracted parameters (t 1/2 ; I max , Q).

Figures

Figures captions

Figure 4

 4 shows the effects of the FFN42 concentration and incubation time. Before observation, cells were rinsed three times and analyzed in PBS (pH 7.4). Images shown in Figure 4 indicate that the minimal concentration of FFN42 to observe clear visible dots in the cell corresponding to exocytotic granules is 20 µM. Kinetic incubation tests showed that an incubation time of 20 min is the optimal one for vesicular penetration of FFN42 in vesicles. No differences in terms of fluorescence intensity were seen from 20 to 60 min of incubation and the percentage of cells with fluorescent vesicles was independent of the exposure time with FFN42 (value ranging between 10

t 1 /

 1 2 = (26.0  1.9) ms ; Q = (421  55) fC ; I max = (9.6  1.3) pA. It is interesting to compare these data with those obtained with serotonin-loaded or FFN102-loaded vesicles of N13 cells (serotonin : t 1/2 = (32.0  0.5) ms ; Q = (182  11) fC ; I max = (4.9  0.5) pA ; FFN102 : t 1/2 = (30.2  1.2) ms ; Q = (209  15) fC ; I max = (5.3  0.5) pA

  N13 cells were provided by C.M. Townsend (University of Texas Medical Branch, Galveston, TX). The base medium for BON N13 cell line was DMEM/F12 (Dulbecco's modified eagle medium: nutrient mixture F-12). DMEM/ F-12 medium supplemented with 10% heat-inactivated fetal bovine serum as well as 1% penicillin/streptomycin solution was employed as complete growth medium filtered with 0.22 µm filtration membrane before any use or experiments. BON N13 cells were maintained in complete growth medium in a 37 °C incubator with a humidified atmosphere of 5% CO 2 . Cells were grown on 75 cm² cell culture flasks (Nunc, Roskilde, Denmark) and culture medium was renewed every 2 days. Subculture of BON N13 cells were conducted weekly by trypsinization when cell confluence reaching approximately 90%. After aspirating the culture medium out of the culture flask, confluent BON N13 cells were firstly rinsed twice with 5 mL sterile PBS solution in order to completely remove serum-containing culture medium which contained trypsin inhibitors. Then, 1 mL of trypsin-EDTA solution was employed to gently dissociate cells from the flask bottom. After being incubated in 37 °C incubator for during 3 min, almost all the cells were detached from the flask bottom by trypsin treatment. In order inhibit the trypsin effect, the trypsinized cells were thus re-suspended in 9 mL fresh complete culture medium and were gently aspirated to dissociate all cell clusters into single cell. Finally, 1 mL cell suspension was added into a new culture flask with 18 mL fresh medium. PC12 cells were purchased from the American Type Culture Collection (Manassas, VA). The cells were maintained in RPMI-1640 media, supplemented with 10% heated inactivated horse serum, 5% heated inactivated fetal bovine serum and 1% penicillin streptomycin solution (Life technologies) in a 5% CO 2 , 100% humidity atmosphere at 37 °C. Cells were sub-cultured approximately every 4-5 days. Throughout the cell cultures lifetime, the medium was refreshed every 2 days.

- 1 .

 1 Incubation of BON N13 cells were performed with the optimal conditions determined (20 µM of FFN42 during 20 min of incubation). The LIVE / DEAD-Thermofischer Scientific kit was used. Images of BON N13 cells were obtained under epifluorescence. The dead cells are characterized by a red coloration (Ethidium Homodimer-1, EthD-1, labeling). EthD-1 is a cell-impermeant viability indicator that has a high-affinity to nucleic acid with weak fluorescent until bound to DNA and then emits red fluorescence. It indicates the loss of the integrity of the plasma membrane. The living cells are characterized by a green coloration (Calcein AM labeling, to indicate intracellular esterase activity, sign of cell viability).

  Figure 1

A)

  Chemical structure of FFN42 with the pKa values for the three acidic functions, obtained thanks to SPECFIT/32 global analysis system simulations on UV-vis spectra (see text). B) UVvis absorption and fluorescence emission spectra (λ ex = 380 nm and λ em = 470 nm) of FFN42 at 10 µM in PBS (7.4). C) Fluorescence emission spectra of FFN42 at 10 µM in PBS (7.4) at different pH values. D) Molar absorption coefficient ɛ in M -1 cm -1 of FFN42 in a mixture of acetonitrile/phosphate buffer (50/50 v/v) without NaCl to prevent any aggregation of FFN42 and chloride ions.

Figure 2

 2 Figure 2 Cyclic voltammogram of FFN42 (100 µM) in PBS at pH 7.4 (scan rate = 200 mVs -1 ) on CFE (A) and on ITO (B). (C) pH responsive normalized CVs of FFN42 (100 µM) in PBS at 200 mV.s -1 on CFE. (D) Oxidation peak potential as a function of pH for FFN42 (E p,a = 0.5757 -0.0431pH; R 2 = 0.99; data extracted from Figure 2C).

Figure 3

 3 Figure 3

Figure 4

 4 Figure 4 Epi-fluorescence images of N13 cells incubated with FFN42. Top: Incubation tests at different concentrations of FFN42 during 20 min. Visible bright dots of the size of vesicles appeared at 20 µM concentration in FFN42. Bottom: Incubation tests at different time of incubation in presence of FFN42 at 20 µM. After 20 min of incubation fluorescence of vesicles was satisfactory. Scale bar: 10 µm.

Figure 5

 5 Figure 5 Epi-fluorescence images of PC12 cells incubated with FFN42. Top: Incubation tests at different concentrations of FFN42 during 30 min. Visible bright dots of the size of vesicles appeared clearly at 20 µM concentration in FFN42. Bottom: Incubation tests at different time of incubation

Figure 6

 6 Figure 6 Epifluorescence images of N13 cells in presence of 20 µM of FFN42 during 20 min. Toxicity tests were performed with the LIVE/DEAD-Thermofischer kit. Green cells are living cells. Red cells are dead cells. a) Images in bright field. Fluorescence images with a b) green (GFP) filter, c) green and red (GFP/rhodamine) filters, d) red (rhodamine) filter. Scale bar: 10 µm.

Figure 7

 7 Figure 7

Acknowledgements

This work has been supported in part by Sorbonne Université, CNRS (UMR 8640), Ecole Normale Supérieure (PSL Research University), French Ministry of Research and Chinese Scolarship Council. M. G-C thanks "Emergences Ville de Paris 2014" Grant and Institut Universitaire de France Junior 2015 Fellowship Program. Marie-Aude Plamont and Dr. Lihui Hu are warmly thanked for their help in the discussions about cells culture and cells experiments, respectively. Jérôme Delacotte is also thanked for fruitful discussion.

Conflict of interest

There are no conflicts of interest to declare.


PNAS Commentary

Title 1: Rethinking climate-context dependencies in biological terms Title 2: Accounting for biotic interactions and microclimatic processes Author: Jonathan Lenoir 1 1 UR "Ecologie et dynamique des systèmes anthropisés" (EDYSAN, UMR 7058 CNRS), Université de Picardie Jules Verne, 1 Rue des Louvels, 80037 Amiens cedex 1, France Main Text: To detect biodiversity changes, biologists can rely on time series of historical observations and resurveys [START_REF] Verheyen | Combining biodiversity resurveys across regions to advance global change research[END_REF][START_REF] Dornelas | BioTIME: a database of biodiversity time series for the Anthropocene[END_REF][START_REF] Comte | BioShifts: a global geodatabase of climate-induced species redistribution over land and sea[END_REF]. As global climate is warming, there is a staggering number of studies detecting population losses (i.e. local extinction or extirpation events), species range shifts (e.g. leading-edge expansion) and community reshuffling (e.g. biotic homogenization) [START_REF] Antão | Temperature-related biodiversity change across temperate marine and terrestrial systems[END_REF][START_REF] Lenoir | Species better track climate warming in the oceans than on land[END_REF][START_REF] Urban | Escalator to extinction[END_REF]. However, to attribute these detections to specific factors, with the aim to hone our predictive tools, is a more challenging task and still a nascent research area. Recent work suggests that not only the magnitude of climate change matters to explain the observed variation in biodiversity changes but also the ambient climate context from historical surveys (i.e. baseline climatic conditions) [START_REF] Bertrand | Ecological constraints increase the climatic debt in forests[END_REF] as well as the interaction between the two (4, 5) (Fig. 1). For instance, under similar velocities of climate change, marine species coming from initially warm waters are shifting their geographical range poleward much faster than those coming from initially cold waters [START_REF] Lenoir | Species better track climate warming in the oceans than on land[END_REF]. These complex interactions generate climate-context dependencies (Fig. 1) and thus complicate our understanding of biodiversity responses to anthropogenic climate change. Yet, the total contribution of climate-context dependencies, like the one example mentioned above to explain the observed variation in biodiversity redistribution under climate change, remains relatively low [START_REF] Lenoir | Species better track climate warming in the oceans than on land[END_REF], thus questioning the relevance of complicating models that rely on climatic predictors only. In a new study in this issue of PNAS, Vandvik et al. [START_REF] Vandvik | Biotic rescaling reveals importance of species interactions for variation in biodiversity responses to climate change[END_REF] propose to simplify the use of complex interaction terms between climatic variables in models to explain, and potentially predict, biodiversity responses to anthropogenic climate change by rethinking climate-context dependencies in biological terms.

To illustrate how biotic rescaling may capture climate-context dependencies, let us compare biodiversity responses to climate change between two distant ecosystems differing in ambient climate (Fig. 1). In a temperate forest ecosystem, the developed root system of trees combined with a dense network of stomata in the leaves allows water to circulate from the deep soil layers to the top of the canopy layer throughout transpiration, a process that creates and sustains its own forest microclimate [START_REF] Aussenac | Interactions between forest stands and microclimate: Ecophysiological aspects and consequences for silviculture[END_REF]. Thanks to that process, the upper tree canopy layer buffers the ambient climatic conditions such that forest-dwelling species living in the understory experience more stable climatic conditions than species living outside forests (10-12) (Fig. 1).

In the long term, forest microclimate dynamics can modify the velocity at which climate is changing in the understory, letting more time for forest biodiversity to adapt [START_REF] Zellweger | Forest microclimate dynamics drive plant responses to warming[END_REF]. Besides, the shade-casting ability of trees generates positive and negative biotic interactions for shade tolerant and light demanding vascular plant species, respectively. Hence, tree canopy height and density at temperate latitudes are good biotic proxies that may simultaneously capture microclimatic processes and biotic interactions contributing to the complex climate-context dependencies that trees create for forest biodiversity living in the understory, such as vascular plants in the herbaceous layer. At higher latitudes, wet tundra ecosystems are dominated by mosses and lichens [START_REF] Beringer | The representation of Arctic soils in the land surface model: the importance of mosses[END_REF], with vascular plants being clearly outnumbered therein. Mosses and lichens do not have roots and do not transpire enough moisture to dry out the soil, leading to constantly wet conditions which sustain suitable conditions to their own growth. This type of vegetation, albeit quite vulnerable to physical disturbances (e.g. fire or trampling and grazing from large herbivores), creates and sustains its own microclimatic environment which does not depend much on macroclimatic conditions (15) (Fig. 1). Locally, a dense and thick moss mat can considerably insulate the ground [START_REF] Beringer | The representation of Arctic soils in the land surface model: the importance of mosses[END_REF][START_REF] Soudzilovskaia | Dominant bryophyte control over high-latitude soil temperature fluctuations predicted by heat transfer traits, field moisture regime and laws of thermal insulation[END_REF][START_REF] Cannone | Influence of vegetation on the ground thermal regime in continental Antarctica[END_REF][START_REF] Gornall | Arctic mosses govern belowground environment and ecosystem processes[END_REF], which may have consequences on recruitment dynamics for vascular plants [START_REF] Soudzilovskaia | How do bryophytes govern generative recruitment of vascular plants?[END_REF]. Besides, mosses also have the capacity to sequester nutrients and thus prevent vascular plants to colonize the system, even hampering seedling recruitment and treeline expansion induced by climate warming [START_REF] Lett | Mosses modify effects of warmer and wetter conditions on tree seedlings at the alpine treeline[END_REF]. In other words, mosses have the capacity to mediate the impact of climate change on the recruitment dynamic of vascular plants at high latitudes. Here as well, the thickness and cover of moss mats can be used as biotic proxies to capture the complex climate-context dependencies that mosses create for vascular plants living at these high latitudes.

I used these two distant ecosystems involving contrasting ambient climates as two examples in which the main structuring elements of the vegetation (trees and mosses in the above-mentioned examples) (Fig. 1) not only exert control on biotic interactions in the system but also act as macroclimate modifiers throughout microclimatic dynamics which may differ in their respective abilities to mediate a similar amount of climate change velocity, thus generating climate-context dependencies. In other ecosystems too, both the cover and height of the most dominant vegetation can be used as meaningful and appropriate proxies for relevant community processes (biotic interactions and microclimate dynamics) that may capture complex climatecontext dependencies in more simple biological terms. That is exactly what Vandvik et al. [START_REF] Vandvik | Biotic rescaling reveals importance of species interactions for variation in biodiversity responses to climate change[END_REF] are advocating and testing throughout an experimental approach. 8) discovered that species assemblages in transplanted turfs slowly converged towards the community composition of the control turfs of the destination site, which was expected and consistent with the imposed climate change treatments. Yet, on average, the observed number of species colonizing the translocated turfs as well as the proportion of species being locally extirpated within the translocated turfs were systematically lower than what would have been expected if the translocated turfs were literally similar to the control turfs. These differences were the most pronounced for extirpation events, suggesting lagging effects consistent with the existing scientific literature [START_REF] Bertrand | Ecological constraints increase the climatic debt in forests[END_REF]. More important, the number of species colonizing the translocated turfs and the proportion of species being locally extirpated within the translocated turfs decreased and increased, respectively, as the height of the moss layer increased. The authors' statistical models offer evidence suggesting that the height of the moss layer is a meaningful and appropriate proxy which can help predict colonization and extirpation events of vascular plants in alpine grasslands while eliminating several interaction terms related to climate context-dependencies (Fig. 1), in line with the authors' initial assumptions.

The Vandvik et al. ( 8) study, although restricted to a boreal oceanic region, makes a critical step forward in demonstrating a significant reduction in model complexity by rethinking climate-context dependencies in biological terms (Fig. 1). Using simple proxies of the structure of the most dominant vegetation layer, such as moss canopy height in Artic-alpine grasslands, can replace complex climate-context dependencies to forecast future biodiversity changes. It provides an important insight for those trying to hone predictive models of biodiversity responses to anthropogenic climate change. A clear next step is to get a better mechanistic understanding of the relative contribution of biotic interactions and microclimatic processes underlying the use of such biotic proxies. Vandvik et al. [START_REF] Vandvik | Biotic rescaling reveals importance of species interactions for variation in biodiversity responses to climate change[END_REF] interpreted the effect of the biotic proxies they used for vegetation structure chiefly in terms of biotic interactions. Yet, as mentioned earlier, mosses not only affect the recruitment of vascular plants directly through altered competition for light and nutrients acquisitionwith the potential to hamper treeline expansion induced by climate warming (20)but it also indirectly affects recruitment by insulating soil from extreme air temperatures [START_REF] Soudzilovskaia | Dominant bryophyte control over high-latitude soil temperature fluctuations predicted by heat transfer traits, field moisture regime and laws of thermal insulation[END_REF][START_REF] Soudzilovskaia | How do bryophytes govern generative recruitment of vascular plants?[END_REF] (Fig. 1). Hence, biotic rescaling is a powerful approach to capture biotic interactions as well as important microclimatic processes which would otherwise require important amounts of microclimatic data [START_REF] Lembrechts | SoilTemp: A global database of near-surface temperature[END_REF].

In short, the finding of Vandvik et al. [START_REF] Vandvik | Biotic rescaling reveals importance of species interactions for variation in biodiversity responses to climate change[END_REF] highlights the successful use of simple biological proxies to capture climate-context dependencies and to simplify models relying solely on climatic predictors while not necessarily requiring to collect new field observations. Thanks to recent advances in remote sensing technology, some data on the height and cover of the most dominant vegetation layer are readily available and can be used as biotic proxies to improve predictions on biodiversity responses to climate change [START_REF] Zellweger | Advances in Microclimate Ecology Arising from Remote Sensing[END_REF].

  To simulate ongoing and future climate change impacts on Arctic-alpine grassland vegetation, Vandvik et al.[START_REF] Vandvik | Biotic rescaling reveals importance of species interactions for variation in biodiversity responses to climate change[END_REF] set up, in 2009, a thorough and well thought out macroecological experiment.More specifically, they transplanted an impressive total of 175 turfs, of 25 × 25 cm each, from different ambient climate origins (e.g. cold and dry, cold and wet, etc.) to warmer, wetter, simultaneously warmer and wetter climates as well as to similar climates (local transplants within blocks to account for transplantation effects). Then, during four years, Vandvik et al.[START_REF] Vandvik | Biotic rescaling reveals importance of species interactions for variation in biodiversity responses to climate change[END_REF] thoroughly monitored species composition changes within each of the 175 transplanted turfs, as well as in 60 additional untouched control non-transplanted turfs, to quantify two aspects of changes in species assemblages, namely the temporal trends in the number of species colonizing each of the transplanted and control turf as well as the proportion of species being locally extirpated (i.e. lost populations) within each of the transplanted and control turfs. Finally, Vandvik et al. (8) related these temporal trends in local colonization and extirpation events to both climate-related and biotic-related factors using two competing models. The first model included climate-related factors only (Fig. 1): climate change treatments (e.g. warmer and wetter conditions); ambient climate at the origin site (e.g. cold and dry alpine grasslands); and climate-context dependencies (i.e. the interaction term between climate change and ambient climate). The second model tested whether adding biotic factors, such as moss canopy height (Fig. 1) or novel colonist species cover, simplifies the climate-only model by capturing climatecontext dependencies. After monitoring the 175 translocated and 60 control turfs between 2009 and 2013, Vandvik et al. (

Caption for Figure 1: Climate-context dependencies involve complex interaction effects between the magnitude of climate change (CC) and different ambient (or baseline) climate (AC) contexts (e.g. temperate vs boreal). In this issue of PNAS, Vandvik et al. [START_REF] Vandvik | Biotic rescaling reveals importance of species interactions for variation in biodiversity responses to climate change[END_REF] suggest to rethink these climate-context dependencies in biological terms by using simple proxies, such as the cover or density of the most dominant layer, capturing both biotic interactions and microclimatic processes that are meaningful to predict the impact of climate change on vascular plant communities.


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