GEO series

Redirecting glucose flux during in vitro expansion generates epigenetically and metabolically superior anti-tumor T cells for adoptive cell therapies (CUT&Tag)

GSE249769 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 16 samples Submitted 2025/05/30 Platform GPL30172

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 16 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Mus musculus (mouse)
Samples: 16 samples: 16 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.
Paper: Redirecting glucose flux during in vitro expansion generates epigenetically and metabolically superior T cells for cancer immunotherapyCell metabolism · 2025

Summary (written by the submitters)

Cellular therapies are uniquely living drugs that must survive and persist within the body to achieve anti-tumor efficacy. In vitro culture of therapeutic T cells often relies on hypermetabolic conditions to maximize expansion. We show that this non-physiologic environment generates metabolically and functionally impaired T cells that more readily rely on aerobic glycolysis than T cells found in vivo.

Read the full summary ↓

To target this shift in glycolytic metabolism, we employ dichloroacetate (DCA) during in vitro expansion resulting in elevated mitochondrial capacity and stemness properties pre-infusion - ultimately improving anti-tumor efficacy. DCA-conditioned T cells surprisingly show no elevated intratumoral function, but have an immediate and long-term survival benefit. DCA conditioning decreases reliance on extracellular glucose by promoting the usage of more physiologic carbon sources. This metabolic shift increases histone acetylation at key longevity genes dependent on citrate export from mitochondria. Blockade of mitochondrial citrate export – and thus formation of cytosolic acetyl-coA – reverses the beneficial effects of DCA. Our data suggest a simple means of correcting in vitro expansion of adoptive cell therapies for enhanced anti-tumor efficacy.

Published in

Redirecting glucose flux during in vitro expansion generates epigenetically and metabolically superior T cells for cancer immunotherapy

Frisch AT, Wang Y, Xie B et al. · Cell metabolism 2025 · PMID 39879981 · doi:10.1016/j.cmet.2024.12.007

This dataset

Open GSE249769 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE249769_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1050305 and SRA study SRP476716. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39879981) ↗

Study design

16 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 16 sample titles: 16 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

GSM7963944 Pmel-1 T cells, untreated, Day 7, mouse 1, H3K27Ac
GSM7963945 Pmel-1 T cells, untreated, Day 7, mouse 2, H3K27Ac
GSM7963946 Pmel-1 T cells, untreated, Day 7, mouse 3, H3K27Ac
GSM7963947 Pmel-1 T cells, DCA-treated, Day 7, mouse 1, H3K27Ac
GSM7963948 Pmel-1 T cells, DCA-treated, Day 7, mouse 2, H3K27Ac
GSM7963949 Pmel-1 T cells, DCA-treated, Day 7, mouse 3, H3K27Ac
GSM7963950 Pmel-1 T cells, untreated, Day 7, mouse 1, H3K9Ac
GSM7963951 Pmel-1 T cells, untreated, Day 7, mouse 2, H3K9Ac
GSM7963952 Pmel-1 T cells, untreated, Day 7, mouse 3, H3K9Ac
GSM7963953 Pmel-1 T cells, DCA-treated, Day 7, mouse 1, H3K9Ac
GSM7963954 Pmel-1 T cells, DCA-treated, Day 7, mouse 2, H3K9Ac
GSM7963955 Pmel-1 T cells, DCA-treated, Day 7, mouse 3, H3K9Ac
GSM7963956 Pmel-1 T cells, untreated, Day 7, mouse 2, H3K9Lac
GSM7963957 Pmel-1 T cells, untreated, Day 7, mouse 3, H3K9Lac
GSM7963958 Pmel-1 T cells, DCA-treated, Day 7, mouse 2, H3K9Lac
GSM7963959 Pmel-1 T cells, DCA-treated, Day 7, mouse 3, H3K9Lac

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