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Cellular and molecular mechanisms determining allogeneic CAR T cell rejection and expansion -- Submission 2 of 2 (scRNA/CITE-seq)

GSE345979 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2026/09/23 Platform GPL30173Platform GPL24676
At a glance

Single-cell RNA-seq · human · 12 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
12 samples: 12 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.
Summary (written by the submitters)
Allogeneic CAR T cells could overcome limitations of autologous therapies but are limited by immune rejection. We evaluated 11 patients with large B-cell lymphoma treated with a single lot of cemacabtagene ansegedleucel (cema-cel), an allogeneic anti-CD19 CAR T product. Despite receiving identical infusion products, patients exhibited heterogeneous cema-cel expansion and clinical outcomes.
Read the full summary ↓
Our integrated analyses using longitudinal TCRβ sequencing, single-cell molecular profiling, and mixed lymphocyte reaction assays revealed that high frequencies of pre-existing, recipient-derived alloreactive CD8+ T cells mediated rapid CAR T rejection in non-expanders. Furthermore, effector-like features, rather than stem/central memory programs, drove robust clonal CAR T expansion consistently across expanders. We confirmed similar expansion patterns in two independent cohorts treated with a separate lot of cema-cel or an allogeneic anti-BCMA CAR T product. These findings highlight distinct cell-extrinsic and cell-intrinsic mechanisms that influence allogeneic CAR T performance and provide insights to optimize donor selection, manufacturing, and product design.
This dataset
Open GSE345979 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 GSE345979_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 PRJNA1522664 and SRA study SRP733167. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42788887) ↗
Study design
12 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 12 sample titles: 12 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.