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CAT-seq: A Chemical Strategy for Subcellular-Resolved RNA Profiling and Synchronous Multi-omics Investigation

GSE255007 Homo sapiens; Mus musculus Expression profiling by high throughput sequencing; Other 38 samples 2025/06/24 GPL28330GPL24247GPL29480GPL24676
Summary
Understanding cellular functions in health and disease requires dissecting spatiotemporal variations in the subcellular transcriptome. Mitochondria, with their independent RNA metabolism, play pivotal roles in numerous biological processes. Existing methods for mitochondrial RNA profiling suffer from limitations such as low resolution, contamination, and dependence on genetic manipulation. Here, we present CAT-seq, a bioorthogonal photocatalytic labeling strategy that enables high-resolution, in situ profiling of mitochondrial RNA in living cells without genetic manipulation. Through systematic exploration of quinone methide warheads, we identified an efficient RNA labeling probe. Rigorous validation and optimization enabled CAT-seq to successfully profile mitochondrial RNA, track RNA dynamics in HeLa cells, and even the challenging RAW 264.7 macrophages, achieving ~60% specificity. Furthermore, leveraging the unique reactivity of distinct quinone methides, we established an orthogonal labeling system enabling synchronous RNA and protein multi-omics profiling within the same sample. This allows us to unravel the intricate link between mitochondrial RNA and protein changes. Applying synchronous multi-omics to RAW 264.7 cells during immune response revealed an underlying mitochondrial remodeling mechanism behind oxidative phosphorylation pathway reduction. By integrating bioorthogonal photocatalytic chemistry with proximity-based labeling, CAT-seq offers a general, catalytic, and non-genetic approach for subcellular RNA and multi-omics investigations. This opens up exciting avenues for studying diverse physiological and pathological processes with RNA involvement.
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NCBI GEO page ↗ Paper (PMID 40957953) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more RNA-seq datasets →
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