Abstract
Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes. Next, we discuss how new techniques for single-cell epigenomics, such as single-cell ATAC-seq and single-cell DNA methylation profiling, can be used to decipher gene regulatory programmes. We finally look forward to the application of single-cell multi-omics and perturbation techniques that will likely play important roles for GRN inference in the future.
Highlights
Gene regulatory networks define and maintain cell-type specific transcriptional states, which in turn underlie cellular morphology and function
Each cell type or stable state is defined by a particular combination of active transcription factors that interact with a set of cis-regulatory regions in the genome –in an interplay with chromatin structure– to produce a specific gene expression profile [1]
The combinations of active transcription factors (TFs) and their target genes are usually represented as Gene Regulatory Networks (GRNs)
Summary
Gene regulatory networks define and maintain cell-type specific transcriptional states, which in turn underlie cellular morphology and function. To studying how different regulatory layers affect one another or looking at cellular heterogeneity between these different levels, most of these studies provide good examples on how single-cell multi-omics techniques improve our understanding of the role of epigenetic modifications of cis-elements in GRNs. For instance, Angermueller et al [82], studied associations between changes in methylation and expression of individual genes. By detecting footprints of TFs, scNOMe-seq predicts TF activity in individual cells, and how this is affected by DNA methylation, studying cell-to-cell variation at the regulatory level These examples illustrate a great potential for single-cell multiomics to build complex models of gene regulation. Methods to map GRNs from scRNA-seq data are emerging quickly; methods to unravel regulatory programs from single-cell epigenomics data are lagging behind
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