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Predicting drivers of proximal tubule cell state through regularized regression analysis of single cell multiomic sequencing

ABSTRACT: In this study, we generated a single nucleus multiomic (snRNA-seq and snATAC-seq) dataset of adult human kidney. We developed a bioinformatic tool to analyze this dataset by identifying key cis-regulatory elements and transcription factors associated with specific cell types and states. We applied this tool to identify transcription factors implicated in proximal tubule cell injury and failed repair states. We demonstrate this tool can be applied to single cell multiomic datasets to derive insight into cell type- and state-specific gene regulatory networks.

ORGANISM(S): Homo sapiens

PROVIDER: GSE220222 | GEO | 2023/01/02

REPOSITORIES: GEO

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Predicting drivers of proximal tubule cell state through regularized regression analysis of single cell multiomic sequencing

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