Dissection of intercellular communication using the transcriptome-based framework ICELLNET

Cell-to-cell communication can be inferred from ligand-receptor expression in cell transcriptomic datasets. However, important challenges remain: 1) global integration of cell-to-cell communication, 2) biological interpretation, and 3) 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 3 datasets generated through RNA-seq, single-cell RNA-seq, and microarray. ICELLNET reveal 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 profile(s). transcriptomes of 6 different donors (biological replicates) were studied at 2 different time points : H4 and H8. 4 conditions were generated at both time points : medium + IgG, LPS + IgG, LPS + anti-TNFR, LPS + anti-IL10R. Microarray analysis were performed at both time points to decipher the contribution of TNF and IL-10 auto-regulatory loops in the control of cell-cell communication