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CAR design equips diverse immune cells with unique antigen-specific activation identities

GSE333486 Homo sapiens (human) Single-cell RNA-seq 26 samples Submitted 2026/07/27 Platform GPL34281Platform GPL18573
At a glance

Single-cell RNA-seq · human · 26 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)
Samples
26 samples: 26 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)
Haematopoietic stem and progenitor cells (HSPCs) are multipotent cells, capable of generating all haematopoietic lineages, representing a powerful platform for regenerative immunotherapy. In parallel, CAR-engineering has extended beyond T cells, which have achieved success in treating B cell malignancies, to broader immune cell types alongside cell type-specific CAR design.
Read the full summary ↓
However, how CAR architecture modulates activation phenotypes across immune lineages remains poorly understood. Here, we systematically compared previously validated, immune cell-optimised CARs across immune cell types using a human HSPC-derived multilineage CAR-immune cell platform. HSPC-derived T, NK and myeloid cells demonstrated immune activation across different CAR designs whereas cell-specific activation showed CAR design dependency in B cells. Hallmark pathway analysis revealed that immune lineage was the dominant determinant of activation-induced transcriptional states while CAR design modulated signalling programmes within lineage constrained programmes, notably proliferation, inflammation and cytokine responses. Single cell InTraSeq highlights differential activation of signalling pathways depending on CAR design and lineage identity with links to downstream transcriptional programmes across T, B and myeloid cells. Although exploratory and performed with limited sample sizes, this demonstrates the activated phenotypes and pathways mediated by CAR design and lineage identity that may inform the development of next-generation CAR-immune cells with tailored activation-induced characteristics.
This dataset
Open GSE333486 in finder → Find more human 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 GSE333486_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 PRJNA1471166 and SRA study SRP704180. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
26 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 26 sample titles: 26 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.