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Transcriptional responses of T cells activated via CD22-specific T cell receptor compared to CD22-specific chimeric antigen receptor

GSE255323 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2025/04/16 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 36 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
36 samples: 36 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
CD22 TCR-engineered T cells exert antileukemia cytotoxicity without causing inflammatory responsesScience advances · 2025
Summary (written by the submitters)
Chimeric antigen receptor (CAR) T cells are effective against B-cell malignancies but are associated with cytokine-driven inflammatory toxicities such as cytokine release syndrome (CRS). Historically, T cell receptor (TCR) engineered T cell therapies are rarely associated with CRS. However, it is challenging to compare diverse cell products utilized in different clinical contexts and against different antigens.
Read the full summary ↓
In this study, TCR and CAR were made to target the same source of antigen, CD22, expressed by B-cell malignancies: the TCR recognizes CD22-derived peptide processed and presented in the context of HLA-A*02:01, and the CAR recognizes CD22 protein expressed on the cell surface. In vivo studies comparing the CD22 TCR-T cells to CD22 CAR-T cells demonstrated that the TCR-T cells could clear leukemia without inducing systemic proinflammatory cytokine elevation, whereas CD22 CAR-T cells induced high levels of circulating proinflammatory cytokine reminiscent of CRS. T cells activated through either the TCR or CAR by an identical leukemia cell line demonstrated differential transcriptional responses. T cells activated via the CAR had disproportionate and significant upregulation of inflammatory gene sets compared to T-cells activated via the TCR.
Published in
CD22 TCR-engineered T cells exert antileukemia cytotoxicity without causing inflammatory responses
Nguyen KA, Liu Z, Davies JS et al. · Science advances 2025 · PMID 40203088 · doi:10.1126/sciadv.adq4297
This dataset
Open GSE255323 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 GSE255323_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 PRJNA1074674 and SRA study SRP488875. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40203088) ↗
Study design
36 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 36 sample titles: 36 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.