ArticleNature communications2021
Dissection of intercellular communication using the transcriptome-based framework ICELLNET.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers.
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138 citing papers in PubMed.
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- Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE.bioRxiv : the preprint server for biology · 2026Article
- Review
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- Robust identification of cell-cell communication heterogeneity in single cells.bioRxiv : the preprint server for biology · 2026Article
- Accelerating Leigh syndrome drug discovery through deep learning screening in brain organoids.Nature communications · 2026Article
- A High-Fiber, Plant-Based Diet in Myeloma Precursor Disorders: Results from the NUTRIVENTION Clinical Trial and Preclinical Vk*MYC Model.Cancer discovery · 2026Article
- scComm: a contrastive learning framework for deciphering cell-cell communications at single-cell resolution.Genome biology · 2026Article
- Spatial transcriptomics reveals immune-stromal crosstalk within the synovium of patients with juvenile idiopathic arthritis.JCI insight · 2026Article
- Development and Validation of a Six-Gene Signature of Myeloid Antigen Presentation Dysfunction Based on Single-Cell and Multi-Cohort Transcriptomics for Predicting Prognosis and Recurrence of Hepatocellular Carcinoma.Cancer informatics · 2026Article
78 more citing papers are in PubMed but not listed here.
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9 authors.
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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.
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