SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics

Abstract Single-cell transcriptomics offers unprecedented opportunities to infer the ligand–receptor (LR) interactions underlying cellular networks. We introduce a new, curated LR database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted LR interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison of related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.

[1]  S. Shen-Orr,et al.  Social network architecture of human immune cells unveiled by quantitative proteomics , 2017, Nature Immunology.

[2]  Damian Szklarczyk,et al.  The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored , 2010, Nucleic Acids Res..

[3]  Bo Wang,et al.  Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning , 2016, Nature Methods.

[4]  Sophia Hober,et al.  A human protein atlas based on antibody proteomics. , 2006, Current opinion in molecular therapeutics.

[5]  M. Tosolini,et al.  Single-Cell Signature Explorer for comprehensive visualization of single cell signatures across scRNA-seq datasets , 2019, bioRxiv.

[6]  Lucas E. Wange,et al.  Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq , 2018, Nature Communications.

[7]  Davis J. McCarthy,et al.  Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation , 2012, Nucleic acids research.

[8]  M. Junttila,et al.  Influence of tumour micro-environment heterogeneity on therapeutic response , 2013, Nature.

[9]  H. Binder,et al.  Multilineage communication regulates human liver bud development from pluripotency , 2017, Nature.

[10]  J. Buhmann,et al.  Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry , 2014, Nature Methods.

[11]  Raphael Kopan,et al.  The Canonical Notch Signaling Pathway: Unfolding the Activation Mechanism , 2009, Cell.

[12]  Mirjana Efremova,et al.  CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes , 2020, Nature Protocols.

[13]  J. Ajani,et al.  iTALK: an R Package to Characterize and Illustrate Intercellular Communication , 2019, bioRxiv.

[14]  Koki Tsuyuzaki,et al.  Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data , 2019, bioRxiv.

[15]  Michael P Snyder,et al.  Two methods for full-length RNA sequencing for low quantities of cells and single cells , 2012, Proceedings of the National Academy of Sciences.

[16]  Grace X. Y. Zheng,et al.  Massively parallel digital transcriptional profiling of single cells , 2016, Nature Communications.

[17]  Andreas Prlic,et al.  Ensembl 2008 , 2007, Nucleic Acids Res..

[18]  Yoshihiro Yamanishi,et al.  KEGG for linking genomes to life and the environment , 2007, Nucleic Acids Res..

[19]  Daniel A. Skelly,et al.  Single-Cell Transcriptional Profiling Reveals Cellular Diversity and Intercommunication in the Mouse Heart. , 2018, Cell reports.

[20]  G. Lemke,et al.  Identification of Axl as a downstream effector of TGF-β1 during Langerhans cell differentiation and epidermal homeostasis , 2012, The Journal of experimental medicine.

[21]  Rachael P. Huntley,et al.  The UniProt-GO Annotation database in 2011 , 2011, Nucleic Acids Res..

[22]  Joseph L. Herman,et al.  Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis , 2015, Nature Methods.

[23]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

[24]  J. Krueger,et al.  The skin as an immune organ: Tolerance versus effector responses and applications to food allergy and hypersensitivity reactions. , 2019, The Journal of allergy and clinical immunology.

[25]  Allon M. Klein,et al.  Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.

[26]  Cathy H. Wu,et al.  The Universal Protein Resource (UniProt): an expanding universe of protein information , 2005, Nucleic Acids Res..

[27]  Maria Kasper,et al.  Single-Cell Transcriptomics Reveals that Differentiation and Spatial Signatures Shape Epidermal and Hair Follicle Heterogeneity , 2016, Cell systems.

[28]  Tony Pawson,et al.  β-Catenin and TCF Mediate Cell Positioning in the Intestinal Epithelium by Controlling the Expression of EphB/EphrinB , 2002, Cell.

[29]  Izhar Ben-Shlomo,et al.  Signaling Receptome: A Genomic and Evolutionary Perspective of Plasma Membrane Receptors Involved in Signal Transduction , 2003, Science's STKE.

[30]  Xun Zhu,et al.  DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data , 2019, Genome Biology.

[31]  R. Imbesi,et al.  Immunolocalization of HB-EGF in Human Skin by Streptavidin-Peroxidase (HRP) Conjugate Method , 2011 .

[32]  Oscar Franzén,et al.  PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data , 2019, Database J. Biol. Databases Curation.

[33]  Kieran R. Campbell,et al.  Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling , 2019, Nature Methods.

[34]  R. Satija,et al.  Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression , 2019, Genome Biology.

[35]  F. Nestle,et al.  The multitasking organ: recent insights into skin immune function. , 2011, Immunity.

[36]  Gary D. Bader,et al.  Pathway Commons, a web resource for biological pathway data , 2010, Nucleic Acids Res..

[37]  N. Slavov,et al.  SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation , 2017, Genome Biology.

[38]  Fabian J Theis,et al.  Current best practices in single‐cell RNA‐seq analysis: a tutorial , 2019, Molecular systems biology.

[39]  R. Flavell,et al.  Abrogation of TGFβ Signaling in T Cells Leads to Spontaneous T Cell Differentiation and Autoimmune Disease , 2000 .

[40]  L. Zon,et al.  Hematopoiesis: An Evolving Paradigm for Stem Cell Biology , 2008, Cell.

[41]  Alasdair J. G. Gray,et al.  The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY , 2017, Nucleic Acids Res..

[42]  Charles H. Yoon,et al.  Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq , 2016, Science.

[43]  Shawn M. Gillespie,et al.  Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer , 2017, Cell.

[44]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[45]  Theo Knijnenburg,et al.  Extracting Intercellular Signaling Network of Cancer Tissues using Ligand-Receptor Expression Patterns from Whole-tumor and Single-cell Transcriptomes , 2017, Scientific Reports.

[46]  B. Tucker,et al.  PyMINEr Finds Gene and Autocrine-Paracrine Networks from Human Islet scRNA-Seq , 2019, Cell reports.

[47]  Rong Li,et al.  Single-Cell RNA-Seq Analysis Maps Development of Human Germline Cells and Gonadal Niche Interactions. , 2017, Cell stem cell.

[48]  J. Aerts,et al.  SCENIC: Single-cell regulatory network inference and clustering , 2017, Nature Methods.

[49]  Douglas A. Lauffenburger,et al.  Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics , 2018, Cell reports.

[50]  Piero Carninci,et al.  A draft network of ligand–receptor-mediated multicellular signalling in human , 2015, Nature Communications.

[51]  Thawfeek M. Varusai,et al.  The Reactome Pathway Knowledgebase , 2017, Nucleic acids research.

[52]  Catalin C. Barbacioru,et al.  mRNA-Seq whole-transcriptome analysis of a single cell , 2009, Nature Methods.

[53]  Inna Kuperstein,et al.  Fibroblast Heterogeneity and Immunosuppressive Environment in Human Breast Cancer. , 2018, Cancer cell.

[54]  R. Paus,et al.  Tumour Necrosis Factor Alpha, Interferon Gamma and Substance P Are Novel Modulators of Extrapituitary Prolactin Expression in Human Skin , 2013, PloS one.

[55]  Kerstin B. Meyer,et al.  Single-cell reconstruction of the early maternal–fetal interface in humans , 2018, Nature.

[56]  A. Zlotnik,et al.  Chemokine ligand–receptor interactions critically regulate cutaneous wound healing , 2018, European Journal of Medical Research.

[57]  Holger Gerhardt,et al.  Basic and Therapeutic Aspects of Angiogenesis , 2011, Cell.

[58]  Akhilesh Pandey,et al.  Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology. , 2009, Methods in molecular biology.