GEO series

Kinetics-seq enables comprehensive profiling of single-cell RNA kinetics in vivo to reveal dynamic tumor heterogeneity

GSE311996 Mus musculus (mouse) Single-cell RNA-seq 13 samples Submitted 2026/03/01 Platform GPL21273

At a glance

Single-cell RNA-seq · mouse · 13 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 13 samples: 6 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.

Summary (written by the submitters)

Tumor tissue is a dynamic system governed by complex transcriptional kinetics. Although scRNA-seq has revolutionized cellular profiling, it captures static expression snapshots, lacking direct access to transcriptional dynamics. Here, we present Kinetics-seq, a time-resolved single-cell method that integrates in vivo metabolic labeling strategy with scRNA-seq to construct a comprehensive transcriptional kinetic landscape within tissues.

Read the full summary ↓

This approach enables transcriptome-wide quantification of RNA abundance, turnover, synthesis, and degradation rates at single-cell resolution. By incorporating RNA kinetic parameters, Kinetic-seq introduces a dynamic dimension to cellular profiling, revealing heterogeneities not only across cell types but also within individual populations. Through joint modeling of RNA synthesis and degradation rates, Kinetics-seq uncovers gene-specific regulatory strategies and pronounced kinetic diversity among tumor cells. Moreover, RNA kinetics serves as a screen tool to identify transcriptionally active gene subsets, revealing pathways such as cholesterol homeostasis, myogenesis, and complement that display stronger temporal associations with tumor progression than abundance-based analyses. Collectively, KineMap-seq provides a powerful tool for refining cellular taxonomy, elucidating RNA regulatory strategies, and identifying actively regulated genes that affect tumor ecosystem, while also offering new opportunities for mechanistic investigation, biomarker discovery, and therapeutic intervention.

This dataset

Open GSE311996 in finder → Find more mouse 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 GSE311996_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 PRJNA1372032 and SRA study SRP650271. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly in duplicate

NC ×2 D5 ×2 D7 ×2 D9 ×2 D11 ×2 D20 ×2 D ×1

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 13 sample titles; they account for 12 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

GSM9335821 NC_1 NC
GSM9335822 NC_2 NC
GSM9335823 D3
GSM9335825 D5_1 D5
GSM9335826 D5_2 D5
GSM9335827 D7_1 D7
GSM9335828 D7_2 D7
GSM9335829 D9_1 D9
GSM9335830 D9_2 D9
GSM9335831 D11_1 D11
GSM9335833 D11_2 D11
GSM9335834 D20_1 D20
GSM9335835 D20_2 D20

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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.