dataesr/scientific-paragraphs-categorization huggingface dataset:
1c ). This database is integrating information on both multiple subunits of ligands and receptors, and a classification into molecular families/subfamilies. Development of a computational pipeline to dissect intercellular communication. In the ICELLNET framework, we developed an automatized tool in R script to infer communication between multiple cell types by integrating: 1) prior knowledge on ligand-receptor interactions (Fig. 1 ); 2) computation of a communication score between pairs of cells based on their transcriptomic profiles, and; 3) several visualization modes to guide results interpretation. Quantification of intercellular communication was achieved by scoring the intensity of each ligand-receptor interaction between two cell types from their expression profiles (Fig. 2 ). From each transcriptomic profile, all genes or only differentially expressed genes could be used, and no filtering threshold was applied to gene expression.
Taking advantage of the ICELLNET database, the genes coding for ligands/receptors were selected from all 380 interactions to compute the score, but it is also possible to restrict the database to specific families of molecules, depending on the biological question. A unique feature and strength of ICELLNET is its ability to infer cell-to-cell communication even from an individual cell population-based transcriptome of interest (the "central cell"). The user should define this cell type of interest, and provide its transcriptional profile, which for example may be selected from different primary cell subsets, or from the same cell type cultured in different biological conditions (Fig. 2  top-left). ICELLNET separately considers other cell types with known transcriptomic profiles (hereafter called « partner cells ») that can connect to the central cell.
These can be cell types coming from the same dataset as the central cell, or from any other transcriptomic dataset. The ICELLNET pipeline has integrated reference transcriptomic profiles by using the Human Primary Cell Atlas  17, 18. This public dataset includes transcriptomic profiles of 31 human cell types including immune cells, stromal cells, neural cells, and tissuespecific cell types, all generated with the same Affymetrix technology (Fig. 2  top-right). Human Primary Cell Atlas has been downloaded and added to ICELLNET framework, in order to be used as reference transcriptomic profiles of partner cell types (Supplementary Data 2). The user always has the possibility of providing original transcriptional profiles for central and partner cells, or to use the reference transcriptomic profiles included in ICELLNET R package to represent putative « partner » cells.
Establishment of a score to assess the communication between cells. From the transcriptomic profiles, we selected the genes coding for the ligands and the receptors in our database. We designed the tool to enable the user to focus on the ligands and/or receptors that are differentially expressed between conditions of study, or to use the entire ligand-receptor database to compute the communication score. Since cell-to-cell communication is directional, we considered ligand expression from the central cell, and receptor expression from the partner cells in order to assess outward communication. Conversely, we then selected receptor expression from the central cell, and ligand expression from partner cells in order to assess inward communication (Fig. 2 middle ). For each gene, expression levels were scaled by maximum of gene expression in the dataset, in order to avoid a communication score predominantly driven by highly expressed genes.
Indeed, bioactivity of communication molecules varies a lot. Some cytokines, such as IL-12 and IL-4, are very bioactive at low concentrations, and often expressed at very low levels both in transcript and protein. Conversely, many chemokines are produced at much higher levels, without necessarily having a higher bioactivity. Not scaling the data before inferring a communication score would systematically favor a few highly expressed molecules, and would not allow detecting the contribution of important molecules expressed at much lower levels. In the ICELLNET framework, quantification of intercellular communication consists of scoring the intensity of each ligand-receptor interaction between two cell types with known expression profiles. Whenever relevant, we took into account multiple ligand subunits, or receptor chains, using logical rules to impose their co-expression in order to consider functionality.
2 ). Thus, the ICELLNET framework allowed us to identify specific communication channels revealing potential interactions between CAF-S4 and TME components. Lupus nephritis cell-cell communication network inferred from single-cell RNA-seq datasets using ICELLNET. Single-cell technologies are now largely employed in various biological fields to better characterize immune cell diversity and cell phenotypes. They also offer insightful datasets to reconstruct cell-cell interactions between different cell populations from the same sample or tissue. We applied ICELLNET to a published single-cell dataset of immune cells from lupus nephritis patients  20. This dataset included several immune cell subpopulations of T and B lymphocytes, but also natural killer cells, macrophages, and dendritic cell populations  20.
4d  and Supplementary Data 4). Similar results were observed for CT0a and CT3b cluster subsampling. This demonstrated the relevance of using the average of cluster gene expression to assess communication, rather than unique cells. We also used the Human Primary Cell Atlas transcriptomic profiles of cDC to compute the communication scores between dendritic cells and the same T-cell clusters from the lupus nephritis dataset (CT0a and CT3b) (Supplementary Fig. 3a ). Even if the cDC transcriptomic profile came from a different technology, it led to similar communication scores when comparing with the ones computed exclusively from the original single cell dataset (Fig. 4b, c , and Supplementary Fig. 3 ). The 122 ligand-receptor pairs contributing to the communication score in the single-cell dataset were also found using Human Primary Cell Atlas dataset (Supplementary Fig.
3c ). The slight differences can be explained by the fact that the cells were not in the same biological state but also some genes were not captured with the Affymetrix technology. Nevertheless, it demonstrated that the Human Primary Cell Atlas can be very valuable to infer communication potential with a cell type that is not represented in a studied dataset. Application of ICELLNET to in vitro-generated immune cell states. We then focused on a controlled in vitro system to assess whether ICELLNET would be able to predict cell-cell communication. We addressed the communication and underlying mechanisms in resting and activated DC. We generated an original microarray dataset, and applied ICELLNET to assess intercellular communication. LPS-activated human monocytederived DC produce two important autocrine cytokines, TNF and IL-10, which play a major role in regulating inflammation.
We applied ICELLNET to reconstruct the intercellular networks between DCs and putative target cells. The network representation demonstrated an increase of the global communication score in all 12 channels, when comparing 8-hours LPS-activated DC to resting (medium) DC (Fig. 5b ). Importantly, these maps revealed that blocking the IL-10 loop determined the largest amplification of DC communication with all 12 cellular targets, while blocking the TNF loop in LPSactivated DCs had a negligible effect on the global communication score (Fig. 5b , Supplementary Fig. 4b  and Supplementary Data 5). IL-10 controls an intercellular communication module in LPSactivated dendritic cells. We compared the transcriptomic profiles of each condition (aTNFR and aIL-10R) to the LPS-alone condition to extract the differentially expressed genes (DEG) (Supplementary Data 5).
We then screened the IL-10 and TNF DEG to identify ligands and receptors included in the database. We were able to extract 27 ligands and 23 receptors which were differentially regulated from the aIL-10R condition, while there were only 12 ligands and 10 receptors differentially regulated from the aTNFR condition (Fig. 5c ). ICELLNET barplots suggested that cytokines were driving the increase in the communication score when blocking IL-10R. We looked at the subfamilies of cytokines to precisely identify the key communication channels (Supplementary Fig. 4b ) Type 1 and TNF subfamilies were increased in aIL-10R condition compared to others. This was confirmed by individual channel communication scores (Fig. 5d ). To confirm the hypothesis that IL-10 controls cytokine-mediated DC communication, we selected four important immunoregulatory molecules from the IL-6-and IL-12-families, and further validated expression at the protein level in 24 h culture DC supernatants using cytometric bead array (CBA) and ELISA (Fig.
Discussion The majority of studies which aim to reconstruct intercellular communication from transcriptomic datasets integrate prior knowledge in the form of a ligand-receptor interaction database. This provides a straightforward manner to infer communication when a match is identified between a ligand and a cognate receptor for two respective cell types. The largest of such databases 9 integrated over 2500 ligand-receptor pairs through literature mining and computational analysis, and has been exploited in multiple computational tools for predicting cell-tocell communication  5, 8, 34, 35. However, this approach lacks experimental validation of predicted ligand-receptor interactions, and it does not take into account the different subunits of ligands or receptors. With ICELLNET, we have developed a fully manually curated database, combining biological relevance, ease of use, and experimental validation.
Except for one study  5  , ICELLNET is the only database offering a classification of predicted interactions into biological families. Similar to CellPho-neDB  10  , ICELLNET takes into account the multiple subunits of ligands and receptors, by introducing logical rules for coexpression of protein subunits. A systematic comparison of cytokine interactions revealed 14 interactions included in ICELLNET but not in CellPhoneDB, such as MIF/CXCR2 and MIF/CXCR4 36. Although ICELLNET includes a relatively small number of interactions compared to other existing databases, it is very specific and exhaustive for cytokine interactions, and will in time be extended to all chemokine and checkpoint interactions, thus providing a unique resource to study intercellular communication within the immune system.
To make ICELLNET a valuable resource, we have established a strategy to keep the database updated and integrate missing knowledge. A significant number of interactions have been established in the past 20 years, but there are still receptors without known ligands, such as DR6 (TNFRSF21), RELT, TROY and NGFR from TNF receptor family  37  , and ligands without known receptors such as IL17D  16. New receptors for existing ligand-receptor pairs can also be uncovered using this approach. For example, even though it was already known that IL34 and M-CSF could separately activate M-CSFR  38  it was then described that IL34/M-CSF heterodimer was also capable of activating M-CSFR  39. We will apply a PubMed alert strategy to cover all new interactions that could be described on these pre-identified ligand and/or receptor partners.
Existing tools infer communication between cells from singlecell RNA-seq datasets  4, 5, 7. We have designed ICELLNET as a versatile tool, which can be applied to bulk cell profiles (Affymetrix or RNA-seq) widely available in public databases, but also to fully documented single-cell RNA-seq datasets to infer communication between clusters or groups of cells. This could be also easily adapted to other types of data such as flow cytometry data. By using the Human Primary Cell Atlas as a reference for transcriptomic profiles  17, 18  , ICELLNET allows us to integrate cell communication partners (sender or receiver) not included in a given original experimental dataset. We identified the Human Primary Cell Atlas as a particularly suitable resource, as it integrates transcriptional profiles of over thirty human primary cell types generated with the same Affymetrix platform  18.
While previous applications of this atlas enabled the identification of specific tissue-related genes  40, 41  , we developed an original use for this resource to simulate cell cross-talks in diverse microenvironments. In addition, ICELLNET can accommodate other original RNA-seq datasets of cell populations  42, 43  as reference profiles to infer intercellular communication. Hence, ICELLNET is a flexible tool, which can be easily adapted depending on the biological question, by offering the possibility to select communication molecules families and cell types of interest. Interesting approaches that integrate downstream signaling pathways and target gene expression have been explored as a surrogate of an effective communication  7, 44. Even if very relevant, these approaches can lead to false positive and false negative predictions, the frequency of which is almost impossible to predict.
Yet, how they communicate with neighboring cells is not well described. Applying ICELLNET to breast cancer fibroblasts' bulk cell transcripts revealed potentially novel interactions between CAF subsets and tumor microenvironment components. The CXCL12/CXCR4 interaction that we found within CAF-S1-to-Tregs (Fig. 3d ) was also described in other studies  19, 48  , and contributes to the immunosuppressive phenotype displayed by CAF-S1. ICELLNET also highlighted interactions specific to CAF-S4 subset such as JAG1 with Notch receptors (NOTCH1, NOTCH2), and expression of PDGF proteins interacting with their cognate receptor. These proteins have never been associated specifically to CAF-S4 subset at the transcriptomic level and warrant further experimental validation studies. Other studies have shown that IL-10 regulates DC-derived inflammatory cytokines and chemokines, in particular IL-12  49  , and that IL-10 secreted by LPS-activated DC controls a communication channel in an autocrine manner  50.
Through our systems approach, we could demonstrate that endogenous DCderived IL-10 governs the global connectivity of DC with multiple cell types. This original in vitro dataset generated in a controlled system allowed us to experimentally validate four intercellular communication channels, which established the robustness and biological relevance of ICELLNET predictions. We did not find this level of experimental validation in any of the other cell-cell communication inference methods to date  5, 10, 35. In conclusion, ICELLNET is an adaptable tool that allows us to gain insight into communication channels between cells from one bulk transcriptomic profile of a cell population. By focusing on specific cell types or families of molecules, ICELLNET provides several representation modes to help the interpretation of the results.
Methods Human primary cell atlas dataset. The dataset contains 745 samples of over thirty human primary cell types in different biological conditions (rested or activated). Included in BioGPS platform, all the samples have been generated with the same Affymetrix technology (Human Genome U133 Plus 2. 0 arrays). For this study, an already processed and normalized dataset has been downloaded and added to ICELLNET package. This dataset is described in Supplementary Data 2. CAFs RNA-seq data processing. The dataset contains 77 samples from Luminal (Lum) and Triple Negative Breast Cancers (TNBC) from 16 patients (10 Lum, 6 TNBC)  19. The samples correspond either to tumor tissue or juxtatumoral tissue. Cells corresponding to CAF-S1 and CAF-S4 have been isolated, collected, and sequenced. Average sequencing depth was 30 million for paired-end reads, with a read length of 100 bp.
Reads were mapped on the reference genome (hg19/GRCh37 from UCSC genome release) using Tophat_2. 0. 6 algorithm. Duplicates were removed and gene expression quantification was performed using HTSeq-Count and featuresCount. Only genes with five reads in at least 25% of all samples were kept for further analyses. Normalization was done using the method implemented in DESeq2 R package (version 1. 26. 0). In this study, only 6 biologically independent samples of CAF-S1 and 3 biologically independent samples of CAF-S4 from TNBC were considered in the analyses. Purification of peripheral blood mononuclear cells (PBMCs) from adult blood. Fresh blood samples were collected from healthy donors and obtained from Hôpital Crozatier Établissement Français du Sang (EFS), Paris, France. A contract (convention) has been established between French Blood establishment (EFS) and Institut Curie, in conformity with national regulations and ethical guidelines.
0 arrays. If multiple probes corresponded to the same receptor, we selected the optimal probe based on the Jetset optimality condition  51. Curation of the ligand/receptor database. Surveying the literature for any potential interactions, we manually curated a ligand-receptor database using STRING (https://string-db. org/), Ingenuity (https://www. ingenuity. com/), and BioGRID (https://thebiogrid. org) online tools to verify protein-protein interactions, as well as Reactome (https://reactome. org) and CellPhoneDB (https://www. cellphonedb. org) databases, already dedicated to ligand-receptor interactions. As robustness of an interaction, we considered interactions described in at least two independent published resources (reviews, original papers, existing ligand-receptor database), written by different authors for reviews and original articles.
Consistency criterion was used to compare interactions described in different resources, checking that same protein subunits are involved. "Experimental validation" criterion was in particular useful to check for specific interactions with few descriptions or no consistency between resources. Original articles demonstrating the interaction were reported in PubMedID column of the database. The interactions were classified into families of molecules based on the known biological function of the ligand and the receptor. The subfamilies of cytokines were defined based on molecular structures, as defined in the literature  3, [14] [15] [16]. The database of ligand-receptor interactions is contained in the Supplementary Data 1. Gene expression matrix scaling method. After selecting the genes corresponding to the ligands and/or receptors from the transcriptional profiles, each ligand/ receptor gene expression is scaled by maximum of gene expression among all the conditions and then multiplied by 10, to have values ranging from 0 to 10.
For each gene, the maximum value (  10 ) is defined as the mean of expression of the 5% highest values of expression for RNA-seq and microarray datasets. Outliers are rescaled at 10 if above maximum value. Intercellular communication score computation. To score the intensity of a particular ligand-receptor interaction between a central cell and a given partner cell, we considered the product of the expression of the ligand in the central cell and of the cognate receptor in the partner cells. Formally, if l i j is the average expression level of ligand i by the central cell in the experimental condition j, and r i k is the average expression of the corresponding receptor by cell type k, the intensity s i j;k of the corresponding interaction was quantified by s i j;k ¼ l i j Á r i k : For interactions requiring multiple components of the ligand and/or of the receptor, we considered a geometric average of the receptor components.
For example, if a given interaction corresponding to ligand i required two chains of the receptor, the score was computed as l i j Á ffiffiffiffiffiffiffiffiffiffiffiffiffi r i;1 k :r i;2 k q , where r i;1 k and r i;2 k are the expression levels of the two receptor chains in cell type k. To assign a global score S j,k to the communication between the central cell in the condition j and cell type k, a composite score was defined by summing up the intensity of all the possible ligand-receptor interactions, i. e. , S j;k ¼ P N i¼1 s i j;k , N being the total number of interactions. If there is any technical effect in one dataset (central cell or partner cell) it is considered as a weight coefficient w: S j;k ¼ X N i¼1 w i j l i j w i k r i k ð1Þ S j;k ¼ w X N i¼1 s i j;k ð2Þ This weight is common for all cells selected from the dataset of central or partner cell and can be considered as a multiplication factor, so it only affects the range of the communication score but not the relative difference between them.
Regarding the four DC experimental conditions (Medium (j = 0), LPS (j = 1), blocking TNF loop (j = 2), blocking IL-10 loop), we normalized the global scores S j,k to the Medium condition (j = 0) across the four conditions. Thus, the final scores S j;k used to measure the communication intensity between DC in the condition j and the target cell k were computed using the following formula: S j;k ¼ S j;k =S 0;k ð3Þ S j;k ¼ P N i¼1 s i j;k P N i¼1 s i 0;k ð4Þ The score corresponding to each interaction and each target cell in the experimental condition of CAF subsets, lupus nephritis patients single-cell RNA-seq dataset, and the four DC experimental conditions are provided in Supplementary Data 3, 4, and 5 respectively. The generation of the inward connectivity maps was done by reversing the role of the central cell and their cellular targets.
For both cases, we then perform a Wilcoxon statistical test to compare the communication scores distributions. The p-values are adjusted with p. adjust() function from the R package « stats » (version 3. 6. 1) using the Benjamini & Hochberg 52 method in R. This returns the p-value matrix of statistical tests, that can be visualized in a heatmap representation with the pvalue. plot() function from « icellnet » R package. Statistical analysis of gene expression data. Expression data were normalized with Plier. Transcriptomics analysis was performed in Matlab (version R2010). For independent filtering, we used the function geneverfilter, which calculates the variance of each probe across the samples and identifies the ones with low variance. Probes with variance less than the 40th percentile were filtered out.
Cells were cultured with medium, LPS (100 ng/ml), or with DC supernatant diluted 1:10 for 4 h. Total RNA was extracted by RNeasy Mini kit (Qiagen). RNA was then transcribed to cDNA using Superscript II reverse transcriptase based on the manufacture's protocol (Invitrogen). The Taqman method was used for real-time PCR with primers from Life technologies (Supplementary Table  1 ). The expression of mRNA was normalized to the geometrical mean of 3 house-keeping genes: ACTB, GAPDH, and RPL34. All HaCaT cells were negative for Mycoplasma contamination, standardized and regular tests were performed by PCR for mycoplasma detection. Statistical analysis of DC-T-cell protein data. All analyses were generated with R 3. 1 or 3. 6. 3. For principal component analysis (PCA) of the T-cell secretion profile, a data matrix was formed whose rows corresponded to conditions and columns to the different cytokines (each column was scaled using zscore).
Acknowledgements We wish to thank  Melissa Saichi ,  Lilith Faucheux ,  Gaël Blivet-Bailly ,  Fanny Coffin ,  Philippe Hupé ,  Franck Perez ,  Sebastian Amigorena ,  Yong-Jun Liu  for insightful comments and discussions, and  Fivos Soumelis  for conceptual advice on the definition, functions, and formalization of communication in living systems. We thank the  Institut Curie Flow Cytometry facility (Z. Maciorowsky) , and the  Institut Curie Affymetrix facility  ( D. Gentien ). This work was supported by funding from  Institut National de la Santé et la Recherche Médicale (Inserm) , the  Institut Curie, Agence Nationale pour la Recherche (ANR) ,  Fondation pour la Recherche Médicale (FRM) , the  European Research Council  ( ERC starting grant   281987 ),  ANR-10-IDEX-0001-02   PSL ,  ANR-11-LABX-0043 ,  CIC IGR-Curie 1428  for V.