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

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.