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Two-Stage CAR T-Cell Differentiation in Patients with Large B-Cell Lymphoma

GSE207192 Homo sapiens (human) Single-cell RNA-seq 60 samples Submitted 2025/04/03 Platform GPL21697Platform GPL24676
At a glance

Single-cell RNA-seq · human · 60 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Diffuse large B-cell lymphomaThis series is part of our curated briefing on it.
Samples
60 samples: 11 conditions, mostly in triplicateGroups 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
Two-stage CD8(+) CAR T-cell differentiation in patients with large B-cell lymphomaNature communications · 2025
Summary (written by the submitters)
To investigate the CAR T-cell differentiation in vivo, we performed single-cell, multi-omics analyses of CD28-costimulated CAR T cells from patients with DLBCL who were successfully treated with axicabtagene ciloleucel. Our data shows that CAR T cells undergo two distinct stages of post-infusion clonal expansion.
Published in
Two-stage CD8(+) CAR T-cell differentiation in patients with large B-cell lymphoma
Cao G, Hu Y, Pan T et al. · Nature communications 2025 · PMID 40328775 · doi:10.1038/s41467-025-59298-w
This dataset
Open GSE207192 in finder → Find more human RNA-seq datasets → Diffuse large B-cell lymphoma landscape →
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 GSE207192_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 PRJNA854096 and SRA study SRP384218. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40328775) ↗
Study design
11 conditions, mostly in triplicate
Pre-infusion scRNA and TotalSeq for CAR-T fro… ×4 Pre-infusion scTCR for CAR-T from Patient ×4 Pre_scRNA_P ×3 Pre_TotalSeq_P ×3 Pre_scTCR_P ×3 Post-infusion scRNA and TotalSeq for CAR-T fr… ×3 Post-infusion scRNA and TotalSeq for CAR-T fr… ×3 Post-infusion scTCR for CAR-T from Patient 04… ×3 +30 more

Supports a between-group comparison across 33 samples.

How we read this: 11 replicated groups read from 60 sample titles; they account for 33 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

+ 20 more — browse all 60 samples with per-sample file links →

More Diffuse large B-cell lymphoma datasets

Search all human RNA-seq datasets in GEO →

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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.