Article ; Online: InterCellar enables interactive analysis and exploration of cell-cell communication in single-cell transcriptomic data.
Communications biology
2022 Volume 5, Issue 1, Page(s) 21
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 ... ...
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. |
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MeSH term(s) | COVID-19 ; Transcriptome |
Language | English |
Publishing date | 2022-01-11 |
Publishing country | England |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ISSN | 2399-3642 |
ISSN (online) | 2399-3642 |
DOI | 10.1038/s42003-021-02986-2 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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