Dissection of intercellular communication using the transcriptome-based framework ICELLNET

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.

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

“…Figure 11). The combinations involving NATMI also clustered by method, with the only exceptions being the Kirouac2010 38 and ICELLNET 21 resources, which were the smallest resources (Supp. Table 2).…”

“…The available prior knowledge resources, largely composed of ligand-receptor, extracellular matrix, and adhesion interactions, are typically distinct but o en show partial overlap 2,20 . Some of these resources also provide additional details for the interactions such as information about protein complexes 2,7,13,21,22 , subcellular localisation 2,13 , and classification into signalling pathways and categories 13,21 (Supp. Table 1).…”

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

“…5). Standard algorithms for predicting the ligand-receptor interaction pairs involved in intercellular communication primarily incorporate scRNA-seq data and a database of known ligand-receptor interactions [120][121][122][123][124][125][126][127] . When a landmark of interest is known, such as the tumour leading edge 33 , the co-expression of ligands and receptors at proximal spots in the region can be evaluated for statistical significance above the background to extend insight beyond the cell subtypes present to the level of the potential proteins they use to communicate with each other to drive local phenotypic features.…”

T helper cell-licensed mast cells promote inflammatory Th17 cells

“…Because these results suggested that MC TH increased their ability to communicate with Th cells, we analyzed, based on our transcriptomic dataset, the up-regulation of ligands in MC TH and that of their cognate receptors based on CD3/CD28-activated memory CD4 + T cell dataset (LaMere et al, 2017). We computed a communication score for each L-R pair in a publicly available database by adapting the computational method described in the ICELLNET framework (Noel et al, 2021). This score is based on both expression level thresholding method (to determine active L-R pair) and on R-L expression product method (to rank the active L-R pair) (Armingol et al, 2021) with MCs as sender cells and activated Th cells as receiver cells.…”