Dissection of intercellular communication using the transcriptome-based framework ICELLNET

Floriane Noël, Lucile Massenet-Regad, Irit Carmi-Levy, Antonio Cappuccio, Maximilien Grandclaudon, Coline Trichot, Yann Kieffer, Fatima Mechta-Grigoriou and Vassili Soumelis ()
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Floriane Noël: INSERM U976, Equipe labellisée par la Ligue Nationale contre le Cancer
Lucile Massenet-Regad: INSERM U976, Equipe labellisée par la Ligue Nationale contre le Cancer
Irit Carmi-Levy: Institut Curie
Antonio Cappuccio: Institut Curie
Maximilien Grandclaudon: Institut Curie
Coline Trichot: INSERM U976, Equipe labellisée par la Ligue Nationale contre le Cancer
Yann Kieffer: Institut Curie
Fatima Mechta-Grigoriou: Institut Curie
Vassili Soumelis: INSERM U976, Equipe labellisée par la Ligue Nationale contre le Cancer

Nature Communications, 2021, vol. 12, issue 1, 1-16

Abstract: Abstract Cell-to-cell communication can be inferred from ligand–receptor expression in cell transcriptomic datasets. However, important challenges remain: global integration of cell-to-cell communication; biological interpretation; and application to individual cell population transcriptomic profiles. We develop ICELLNET, a transcriptomic-based framework integrating: 1) an original expert-curated database of ligand–receptor interactions accounting for multiple subunits expression; 2) quantification of communication scores; 3) the possibility to connect a cell population of interest with 31 reference human cell types; and 4) three visualization modes to facilitate biological interpretation. We apply ICELLNET to three datasets generated through RNA-seq, single-cell RNA-seq, and microarray. ICELLNET reveals autocrine IL-10 control of human dendritic cell communication with up to 12 cell types. Four of them (T cells, keratinocytes, neutrophils, pDC) are further tested and experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from individual or multiple cell-based transcriptomic profiles.

Date: 2021
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DOI: 10.1038/s41467-021-21244-x

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