Dyna-vivo-seq: Unveiling Cellular RNA Dynamics via in vivo Metabolic RNA Labeling-based Single-cell RNA-sequencing

A fundamental objective of genomics is to track variations in gene expression programs that define cell state progression during development, differentiation, and response to stimuli. While metabolic RNA labeling-based single-cell RNA sequencing offers insights into temporal biological processes, its limited applicability 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 simultaneously examining new and old RNAs. Leveraging Dyna-vivo-seq, we characterized the heterogeneity of RNA dynamics and quantified turnover rates of 21, 231 genes in 24, 237 single cells. The new RNAs can offer an additional dimension to facilitate the discernment of cellular heterogeneity. Based on new RNAs, we discerned two distinct high and low metabolic labeling populations among proximal tubular (PT) cells. Leveraging the enhanced sensitivity of new RNA-based analysis, we identified 90 rapidly responded transcription factors (TFs) and explored the heterogeneous response of PT cells for the AKI, highlighting that PT cells with high RNA metabolic activity exhibit heightened susceptibility to injury. Dyna-vivo-seq has introduced a temporal dimension to traditional otherwise static measurements in vivo, providing a powerful tool to the characterization of dynamic transcriptome changes at single cell leveles in living organisms and holding great promise for a wide range of biomedical applications.