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Sequencing analysis of CD4 positive CAR T cells pre treated with dexamethasone

GSE268123 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2026/05/31 Platform GPL18573Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 14 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
14 samples: 3 × CAR T-1stim vs 3 × dCAR T-1stim vs 3 × CAR T-3stim vs 3 × dCAR T-3stimGroups 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)
In this research, we conducted a preliminary comparison of the in vitro anti-tumor effectiveness and phenotype of CD4+ and CD8+ CAR T cells. Our findings indicate that following multiple rounds of tumor cell stimulation, CD4+ CAR T cells demonstrated robust proliferation capabilities with reduced apoptosis rates, while also maintaining their effector phenotype.
Read the full summary ↓
To further optimize the functionality of CD4+ CAR T cells, we implemented a low-dose decitabine pretreatment strategy for CAR T cells (referred to as dCAR T) during the CAR T cell preparation process. After three rounds of in vitro tumor cell stimulation, dCAR T cells exhibit sustained anti-tumor efficacy and proliferation capacity. Analysis of bulk RNA sequencing data validates the enhanced effector phenotype and proliferation potential of dCAR T cells relative to conventional CAR T cells. These findings indicate that pretreatment with decitabine may modulate the transcriptional profile of CAR T cells.
This dataset
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.

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 GSE268123_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1114714 and SRA study SRP509233. Searching any of these in the dataset finder brings you back here.

Study design
3 × CAR T-1stim vs 3 × dCAR T-1stim vs 3 × CAR T-3stim vs 3 × dCAR T-3stim

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 14 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
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