Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape

Jingyi Ren(Broad Institute), Haowen Zhou(Broad Institute), Hu Zeng(Broad Institute), Connie Kangni Wang(Broad Institute), Jiahao Huang(Broad Institute), Xiaojie Qiu(Whitehead Institute for Biomedical Research), Xin Sui(Broad Institute), Qiang Li(Harvard University), Xunwei Wu(Harvard University), Zuwan Lin(Broad Institute), Jennifer A. Lo(Broad Institute), Kamal Maher(Broad Institute), Yichun He(Broad Institute), Xin Tang(Broad Institute), Judson Lam(Massachusetts Institute of Technology), Hongyu Chen(Broad Institute), Brian Li(Broad Institute), David E. Fisher(Harvard University), Jia Liu(Harvard University), Xiao Wang(Broad Institute)
Nature Methods
April 10, 2023
Cited by 69Open Access
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Abstract

Spatiotemporal regulation of the cellular transcriptome is crucial for proper protein expression and cellular function. However, the intricate subcellular dynamics of RNA remain obscured due to the limitations of existing transcriptomics methods. Here, we report TEMPOmap-a method that uncovers subcellular RNA profiles across time and space at the single-cell level. TEMPOmap integrates pulse-chase metabolic labeling with highly multiplexed three-dimensional in situ sequencing to simultaneously profile the age and location of individual RNA molecules. Using TEMPOmap, we constructed the subcellular RNA kinetic landscape in various human cells from transcription and translocation to degradation. Clustering analysis of RNA kinetic parameters across single cells revealed 'kinetic gene clusters' whose expression patterns were shaped by multistep kinetic sculpting. Importantly, these kinetic gene clusters are functionally segregated, suggesting that subcellular RNA kinetics are differentially regulated in a cell-state- and cell-type-dependent manner. Spatiotemporally resolved transcriptomics provides a gateway to uncovering new spatiotemporal gene regulation principles.


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