bioRxivGenomicsPreprint
Spatial mapping of RNA turnover kinetics and regulatory landscapes of mRNA stability in the mammalian brain
Qi QiuiD, Hongjie Zhang, Zijie XiaiD, William N. D. GaoiD, Julie Leu, Dongming LiangiD, Ying Li, Yijing SuiD, Emily R. FeiermaniD, Van Horn, Guo‐li MingiD, Erica KorbiD, Hongjun SongiD, Zhaolan ZhouiD, Hao WuiD
Abstract
Spatial and activity-dependent gene regulation in the mammalian brain requires coordinated control of RNA synthesis and degradation 1,2, yet spatially resolved measurement of RNA turnover kinetics in complex tissues remains technically challenging 3. Here, we present spatial NT-seq, an approach that integrates transgenesis-free metabolic RNA labeling with in situ chemical recoding to spatially co-map RNA abundance and turnover kinetics in the mouse brain. By distinguishing newly synthesized from pre-existing RNAs, this method reveals spatially resolved transcriptional and post-transcriptional responses to electroconvulsive stimulation (ECS), a treatment for refractory depression. We uncover pronounced spatial heterogeneity in RNA turnover, with the dentate gyrus (DG) exhibiting elevated basal RNA turnover and robust ECS-induced responses. These findings reveal a "kinetics scaling" mechanism of coordinated regulation of RNA synthesis and decay, by which DG cells can rapidly remodel their transcript pools in responses to external stimuli or differentiation signals 4,5. Machine learning applied to in vivo RNA kinetics landscapes further identifies sequence features and post-transcriptional regulators underlying region-and cell-type-specific control of mRNA stability. Together, this integrated experimental and computational framework, in vivo Timescope, enables transcriptome-wide mapping of RNA turnover kinetics and the regulatory architecture of RNA stability across spatial and cellular contexts, providing new insights into the spatiotemporal regulation of RNA dynamics in brain function and disease.
- organism: mouse
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- Citations
- 0
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