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New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data.

Xin ShaoXiaoyan LuJie LiaoHuajun ChenXiaohui Fan
Published in: Protein & cell (2020)
For multicellular organisms, cell-cell communication is essential to numerous biological processes. Drawing upon the latest development of single-cell RNA-sequencing (scRNA-seq), high-resolution transcriptomic data have deepened our understanding of cellular phenotype heterogeneity and composition of complex tissues, which enables systematic cell-cell communication studies at a single-cell level. We first summarize a common workflow of cell-cell communication study using scRNA-seq data, which often includes data preparation, construction of communication networks, and result validation. Two common strategies taken to uncover cell-cell communications are reviewed, e.g., physically vicinal structure-based and ligand-receptor interaction-based one. To conclude, challenges and current applications of cell-cell communication studies at a single-cell resolution are discussed in details and future perspectives are proposed.
Keyphrases
  • single cell
  • rna seq
  • cell therapy
  • high throughput
  • high resolution
  • gene expression
  • stem cells
  • dna methylation
  • mesenchymal stem cells
  • bone marrow
  • artificial intelligence
  • deep learning
  • high density