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Dissection of intercellular communication using the transcriptome-based framework ICELLNET.

Floriane NoelLucile Massenet-RegadIrit Carmi-LevyAntonio CappuccioMaximilien GrandclaudonColine TrichotYann KiefferFatima Mechta-GrigoriouVassili Soumelis
Published in: Nature communications (2021)
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.
Keyphrases
  • single cell
  • rna seq
  • cell therapy
  • emergency department
  • dendritic cells
  • mesenchymal stem cells
  • bone marrow
  • poor prognosis
  • immune response
  • dna methylation
  • long non coding rna
  • binding protein