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scDUAL-seq Simultaneously Measures RNA Synthesis and Degradation Rates in Single Cells

GSE286521 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2025/07/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 24 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
24 samples: 24 samples, each with a distinct title — no replicated groupsGroups 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.
Summary (written by the submitters)
The interplay between RNA transcription and degradation tightly regulates the dynamic changes of RNA abundance. To unveil the underlying regulatory mechanism of RNA in shaping gene expression patterns, it is essential yet challenging to profile the coordinated regulation of RNA synthesis and degradation rates at the single-cell level during dynamic programs such as cell differentiation and cell state transition.
Read the full summary ↓
Herein, we propose scDUAL-seq, a double metabolic RNA labeling-based single-cell RNA sequencing method to simultaneously measure RNA synthesis and degradation rates in thousands of single cells during dynamic processes. In a cortisol response model of colorectal cancer cells, scDUAL-seq identifies the RNA regulatory strategies for 5,930 genes, discovers key regulons driving the strategy transitions, and disentangles the interplay between RNA synthesis and degradation that control distinct strategy transitions during cortisol response. We anticipate that scDUAL-seq will be broadly applied to record RNA kinetic rates and the underlying regulatory mechanism in diverse dynamic processes.
This dataset
Open GSE286521 in finder → Find more human 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 GSE286521_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1209856 and SRA study SRP556931. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 24 sample titles: 24 distinct titles with little repetition. 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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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.