Biology subjects

Chembazhi, U. V.

Publications and source records attributed to Chembazhi, U. V..

2 recordsLinked to original sources

bioRxiv2020

Cellular plasticity balances the metabolic and proliferation dynamics of a regenerating liver

The adult liver has exceptional ability to regenerate, but how it sustains normal metabolic activities during regeneration remains unclear. Here, we use partial hepatectomy (PHx) in tandem with single-cell transcriptomics to track cellular transitions and heterogeneities of ~22,000 liver cells through the initiation, progression, and termination phases of mouse liver regeneration. Our results reveal that following PHx, a subset of hepatocytes transiently reactivates an early-postnatal-like gene expression program to proliferate, while a distinct population of metabolically hyperactive cells appears to compensate for any temporary deficits in liver function. Importantly, through combined analysis of gene regulatory networks and cell-cell interaction maps, we find that regenerating hepatocytes redeploy key developmental gene regulons, which are guided by extensive ligand-receptor mediated signaling events between hepatocytes and non-parenchymal cells. Altogether, our study offers a detailed blueprint of the intercellular crosstalk and cellular reprogramming that balances the metabolic and proliferation requirements of a regenerating liver.

genomics↗

bioRxiv2020

SimiC: A Single Cell Gene Regulatory Network Inference method with Similarity Constraints

Single-cell RNA-Sequencing has made it possible to infer high-resolution gene regulatory networks (GRNs), providing deep biological insights by revealing regulatory interactions at single-cell resolution. However, current single-cell GRN analysis methods produce only a single GRN per input dataset, potentially missing relationships between cells from different phenotypes. To address this issue, we present SimiC, a single-cell GRN inference method that produces a GRN per phenotype while imposing a similarity constraint that forces a smooth transition between GRNs, allowing for a direct comparison between different states, treatments, or conditions. We show that jointly inferring GRNs can uncover variation in regulatory relationships across phenotypes that would have otherwise been missed. Moreover, SimiC can recapitulate complex regulatory dynamics across a range of systems, both model and non-model alike. Taken together, we establish a new approach to quantitating regulatory architectures between the GRNs of distinct cellular phenotypes, with far-reaching implications for systems biology.

bioinformatics↗