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Characterization and effective expansion of CD4-CD8- TCRab+ T cells from individuals living with type 1 diabetes

GSE285617 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/01/05 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 24 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
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.
Paper
Characterization and effective expansion of CD4(-)CD8(-) TCRαβ(+) T cells from individuals living with type 1 diabetesMolecular therapy. Methods & clinical development · 2025
Summary (written by the submitters)
CD4-CD8- TCRab+ (double-negative [DN]) T cells represent a rare T cell population that promotes immunological tolerance through various cytotoxic mechanisms. In mice, autologous transfer of DN T cells has shown protective effects against autoimmune diabetes and graft-versus-host disease. Here, we characterized humanDNT cells from people living with type 1 diabetes (PWT1D) and healthy controls.
Read the full summary ↓
We found that while DN T cells and CD8+ T cells share many similarities, DN T cells are a unique T cell population, both at the transcriptomic and protein levels. We also show that by using various cytokine combinations, human DN T cells can be expanded in vitro up to 1,000- fold (mean >250-fold) and remain functional post-expansion. In addition, we report that DN T cells from PWT1D display a phenotype comparable to that of healthy controls, efficiently expand, and are highly functional. As DN T cells are immunoregulatory and can prevent T1D in various mouse models, these observations suggest that autologous DN T cells may be amenable to therapy for the prevention or treatment of T1D.
Published in
Characterization and effective expansion of CD4(-)CD8(-) TCRαβ(+) T cells from individuals living with type 1 diabetes
Fajardo-Despaigne JE, Lombard-Vadnais F, Pelletier AN et al. · Molecular therapy. Methods & clinical development 2025 · PMID 39877593 · doi:10.1016/j.omtm.2024.101400
This dataset
Open GSE285617 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 GSE285617_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 PRJNA1204964 and SRA study SRP554726. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39877593) ↗
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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