Dyna-vivo-seq unveils cellular RNA dynamics during acute kidney injury via in vivo metabolic RNA labeling-based scRNA-seq

Kun Yin, Yiling Xu, Ye Guo, Zhong Zheng, Xinrui Lin, Meijuan Zhao, He Dong, Dianyi Liang, Zhi Zhu, Junhua Zheng (), Shichao Lin (), Jia Song () and Chaoyong Yang ()
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Kun Yin: Shanghai Jiao Tong University
Yiling Xu: Xiamen University
Ye Guo: Xiamen University
Zhong Zheng: Shanghai Jiao Tong University
Xinrui Lin: Shanghai Jiao Tong University
Meijuan Zhao: Xiamen University
He Dong: Xiamen University
Dianyi Liang: Xiamen University
Zhi Zhu: Xiamen University
Junhua Zheng: Shanghai Jiao Tong University
Shichao Lin: Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM)
Jia Song: Shanghai Jiao Tong University School of Medicine
Chaoyong Yang: Shanghai Jiao Tong University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract A fundamental objective of genomics is to track variations in gene expression program. While metabolic RNA labeling-based single-cell RNA sequencing offers insights into temporal biological processes, its limited applicability only to in vitro models challenges the study of in vivo gene expression dynamics. Herein, we introduce Dyna-vivo-seq, a strategy that enables time-resolved dynamic transcription profiling in vivo at the single-cell level by examining new and old RNAs. The new RNAs can offer an additional dimension to reveal cellular heterogeneity. Leveraging new RNAs, we discern two distinct high and low metabolic labeling populations among proximal tubular (PT) cells. Furthermore, we identify 90 rapidly responding transcription factors during the acute kidney injury in female mice, highlighting that high metabolic labeling PT cells exhibit heightened susceptibility to injury. Dyna-vivo-seq provides a powerful tool for the characterization of dynamic transcriptome at the single-cell level in living organism and holds great promise for biomedical applications.

Date: 2024
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DOI: 10.1038/s41467-024-54202-4

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