ArticleNature communications2024
Dyna-vivo-seq unveils cellular RNA dynamics during acute kidney injury via in vivo metabolic RNA labeling-based scRNA-seq.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- scMOG: A graph neural network method for regulatory relationship-preserving single-cell multi-omics integration.Quantitative biology (Beijing, China) · 2026Article
- Spatial mapping of RNA turnover kinetics in the mouse brain.Nature neuroscience · 2026Article
- Therapeutic rewiring of ceRNA networks: a computational pipeline for drug repurposing in acute kidney injury via circRNA-miRNA-mRNA axis disruption.Human genomics · 2026Review
- Spatial mapping of RNA turnover kinetics and regulatory landscapes of mRNA stability in the mammalian brain.bioRxiv : the preprint server for biology · 2026Article
- A convenient single-cell assay for the newly synthesized transcriptome reveals FLI1 regulon downregulation during T cell activation.Cell reports methods · 2026Article
- Single-cell immune landscape of acute kidney injury after cardiac surgery with cardiopulmonary bypass.Renal failure · 2025Article
- Toward Mapping Spatiotemporally Resolved Transcriptomes and RNA-Protein Interactions.Wiley interdisciplinary reviews. RNAReview
Corrections and comments
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Authors and funding
13 authors.
Funding
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.
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Registered trials
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