Single-cell transcriptomics unveils gene regulatory network plasticity

Genome Biol. 2019 Jun 4;20(1):110. doi: 10.1186/s13059-019-1713-4.


Background: Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies.

Results: We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer's disease. Using tools from graph theory, we compute an unbiased quantification of a gene's biological relevance and accurately pinpoint key players in organ function and drivers of diseases.

Conclusions: Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Alzheimer Disease / genetics
  • Animals
  • Diabetes Mellitus, Type 2 / genetics
  • Feasibility Studies
  • Gene Regulatory Networks*
  • Genes, Essential
  • Genomics / methods*
  • Humans
  • Mice, Transgenic
  • Single-Cell Analysis
  • Transcriptome*