Modeling gene expression cascades during cell state transitions

• Published in: iScience Vol. 27; no. 4; p. 109386
• Main Authors: Rosebrock, Daniel, Vingron, Martin, Arndt, Peter F.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 19.04.2024; Elsevier
• Subjects: Biological constraints; Cell biology; Classification of bioinformatical subject; Systems biology; Transcriptomics; Cell biology; Biological constraints; Classification of bioinformatical subject; Systems biology; Transcriptomics
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2024.109386

During cellular processes such as differentiation or response to external stimuli, cells exhibit dynamic changes in their gene expression profiles. Single-cell RNA sequencing (scRNA-seq) can be used to investigate these dynamic changes. To this end, cells are typically ordered along a pseudotemporal trajectory which recapitulates the progression of cells as they transition from one cell state to another. We infer transcriptional dynamics by modeling the gene expression profiles in pseudotemporally ordered cells using a Bayesian inference approach. This enables ordering genes along transcriptional cascades, estimating differences in the timing of gene expression dynamics, and deducing regulatory gene interactions. Here, we apply this approach to scRNA-seq datasets derived from mouse embryonic forebrain and pancreas samples. This analysis demonstrates the utility of the method to derive the ordering of gene dynamics and regulatory relationships critical for proper cellular differentiation and maturation across a variety of developmental contexts. [Display omitted] •Fitting pseudotime-ordered expression profiles to interpretable functional forms•Derivation of transcriptional cascades to define a pseudotime trajectory•Inference of directionality of regulatory interactions Biological constraints; Cell biology; Classification of bioinformatical subject; Systems biology; Transcriptomics