InterCellar enables interactive analysis and exploration of cell\u2212cell communication in single-cell transcriptomic data

Marta Interlandi,Kornelius Kerl,Martin Dugas

doi:10.1038/s42003-021-02986-2

Abstract

Deciphering cell−cell communication is a key step in understanding the physiology and pathology of multicellular systems. Recent advances in single-cell transcriptomics have contributed to unraveling the cellular composition of tissues and enabled the development of computational algorithms to predict cellular communication mediated by ligand−receptor interactions. Despite the existence of various tools capable of inferring cell−cell interactions from single-cell RNA sequencing data, the analysis and interpretation of the biological signals often require deep computational expertize. Here we present InterCellar, an interactive platform empowering lab-scientists to analyze and explore predicted cell−cell communication without requiring programming skills. InterCellar guides the biological interpretation through customized analysis steps, multiple visualization options, and the possibility to link biological pathways to ligand−receptor interactions. Alongside convenient data exploration features, InterCellar implements data-driven analyses including the possibility to compare cell−cell communication from multiple conditions. By analyzing COVID-19 and melanoma cell−cell interactions, we show that InterCellar resolves data-driven patterns of communication and highlights molecular signals through the integration of biological functions and pathways. We believe our user-friendly, interactive platform will help streamline the analysis of cell−cell communication and facilitate hypothesis generation in diverse biological systems.

Highlights

Deciphering cell−cell communication is a key step in understanding the physiology and pathology of multicellular systems
In order to demonstrate InterCellar’s exploration functionalities implemented in the three biological domains, we considered a publicly available scRNA-seq dataset composed of metastatic melanoma samples from Tirosh et al.[18]
We retained the cell type labels assigned by the authors, namely melanoma-malignant cells, T cells, B cells, macrophages (Macro), endothelial cells (Endo), cancer-associated fibroblasts (CAF), and natural killer (NK) cells

Summary

Introduction

Deciphering cell−cell communication is a key step in understanding the physiology and pathology of multicellular systems. Despite the existence of various tools capable of inferring cell−cell interactions from single-cell RNA sequencing data, the analysis and interpretation of the biological signals often require deep computational expertize. InterCellar guides the biological interpretation through customized analysis steps, multiple visualization options, and the possibility to link biological pathways to ligand−receptor interactions. By analyzing COVID-19 and melanoma cell−cell interactions, we show that InterCellar resolves data-driven patterns of communication and highlights molecular signals through the integration of biological functions and pathways. Many computational tools exist for the inference of cell−cell communication[10,13,14], the majority only provides limited downstream analysis functionalities, hindering the biological interpretation of the predicted interactions. The second data-driven analysis implements functionalities to compare interactions across multiple datasets, highlighting patterns of communication that are uniquely found in each of the conditions considered. We present InterCellar’s main features and demonstrate its general applicability on two datasets, concerning melanoma and coronavirus disease 2019 (COVID-19)

Methods

Results

Conclusion