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

GSE255007 Homo sapiens (human) RNA-seq (gene expression) 38 samples Submitted 2025/06/24 Platform GPL28330Platform GPL24247Platform GPL29480Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 38 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
38 samples: 18 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Photocatalytic labelling-enabled subcellular-resolved RNA profiling and synchronous multi-omics investigationNature chemistry · 2025
Summary (written by the submitters)
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.
Read the full summary ↓
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.
Published in
Photocatalytic labelling-enabled subcellular-resolved RNA profiling and synchronous multi-omics investigation
Bi Y, Yu L, Deng Q et al. · Nature chemistry 2025 · PMID 40957953 · doi:10.1038/s41557-025-01946-1
This dataset
Open GSE255007 in finder → Find more RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE255007_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1073189 and SRA study SRP488014. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40957953) ↗
Study design
18 conditions, mostly in duplicate
RAW264.7 cells, Sync-label Enrich, Blank, ×3 RAW264.7 cells, Sync-label Enrich, LPS, ×3 HeLa cells, CAT-seq Input, ×2 HeLa cells, CAT-seq Enrich, ×2 HeLa cells, Sync-label Input, ×2 HeLa cells, Sync-label Enrich, ×2 RAW264.7 cells, CAT-seq Enrich, Blank, ×2 RAW264.7 cells, CAT-seq Enrich, LPS, ×2 +10 more

Supports a between-group comparison across 38 samples.

How we read this: 18 replicated groups read from 38 sample titles; they account for 38 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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