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

Nicolas Ledru,Parker C. Wilson,Yoshiharu Muto,Yasuhiro Yoshimura,Haojia Wu,Dian Li,Amish Asthana,Stefan G. Tullius,S. Waikar,Giuseppe Orlando,Benjamin D. Humphreys

Published 2024 in Nature Communications

ABSTRACT

Renal proximal tubule epithelial cells have considerable intrinsic repair capacity following injury. However, a fraction of injured proximal tubule cells fails to undergo normal repair and assumes a proinflammatory and profibrotic phenotype that may promote fibrosis and chronic kidney disease. The healthy to failed repair change is marked by cell state-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq sequencing offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We develop a regularized regression approach to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generate a single nucleus multiomic dataset from seven adult human kidney samples and apply our method to study drivers of a failed injury response associated with kidney disease. We demonstrate that our approach is a highly effective tool for predicting key cis- and trans-regulatory elements underpinning the healthy to failed repair transition and use it to identify NFAT5 as a driver of the maladaptive proximal tubule state.

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