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Single-cell transcriptomics unveils gene regulatory network plasticity.

ABSTRACT: 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.

SUBMITTER: Iacono G

PROVIDER: S-EPMC6547541 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Single-cell transcriptomics unveils gene regulatory network plasticity.

Iacono Giovanni G Massoni-Badosa Ramon R Heyn Holger H

Genome biology 20190604 1

<h4>Background</h4>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.<h4>Results</h4>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 tha ...[more]

PMID: 31159854

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Project description:Recent analysis of single-cell transcriptomic data has revealed a surprising organization of the transcriptional variability pervasive across individual neurons. In response to distinct combinations of synaptic input-type, a new organization of neuronal subtypes emerged based on transcriptional states that were aligned along a gradient of correlated gene expression. Individual neurons traverse across these transcriptional states in response to cellular inputs. However, the regulatory network interactions driving these changes remain unclear. Here we present a novel fuzzy logic-based approach to infer quantitative gene regulatory network models from highly variable single-cell gene expression data. Our approach involves developing an a priori regulatory network that is then trained against in vivo single-cell gene expression data in order to identify causal gene interactions and corresponding quantitative model parameters. Simulations of the inferred gene regulatory network response to experimentally observed stimuli levels mirrored the pattern and quantitative range of gene expression across individual neurons remarkably well. In addition, the network identification results revealed that distinct regulatory interactions, coupled with differences in the regulatory network stimuli, drive the variable gene expression patterns observed across the neuronal subtypes. We also identified a key difference between the neuronal subtype-specific networks with respect to negative feedback regulation, with the catecholaminergic subtype network lacking such interactions. Furthermore, by varying regulatory network stimuli over a wide range, we identified several cases in which divergent neuronal subtypes could be driven towards similar transcriptional states by distinct stimuli operating on subtype-specific regulatory networks. Based on these results, we conclude that heterogenous single-cell gene expression profiles should be interpreted through a regulatory network modeling perspective in order to separate the contributions of network interactions from those of cellular inputs.

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Single-cell transcriptomics unveils gene regulatory network plasticity.

Publications

Single-cell transcriptomics unveils gene regulatory network plasticity.

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