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Tumor Antigen–Independent Targeting of Solid Tumors by Armored 1 Macrophage-Directed anti-TREM2-CAR T Cells [human scRNA-seq]

GSE313197 Homo sapiens (human) Single-cell RNA-seq 23 samples Submitted 2026/07/29 Platform GPL34281Platform GPL34295
At a glance

Single-cell RNA-seq · human · 23 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
23 samples
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)
Chimeric antigen receptor (CAR) T cell therapy has shown remarkable clinical efficacy in hematological malignancies and, more recently, in autoimmune diseases. However, its application in solid tumors has been limited by tumor antigen heterogeneity, antigen escape, and the immunosuppressive tumor microenvironment (TME), which is particularly shaped by tumor associated macrophages (TAMs).
Read the full summary ↓
To overcome these barriers, we developed a novel CAR T cell strategy targeting human TREM2⁺ immunosuppressive TAMs. Macrophage-directed anti- TREM2-CAR T cells demonstrated potent anti-TREM2 activity in both in vitro assays and in solid tumor models in vivo. To further enhance intratumoral T cell function, we incorporated a CAR T cell responsive synthetic DNA biosensor comprising NFAT motifs that enable localized secretion of interleukin-12 upon CAR activation. In murine tumor models, this “armored” TREM2-targeted CAR-T cell approach led to robust remodeling of the TME and tumor-draining lymph nodes, characterized by TREM2⁺ TAM depletion, enhanced T and NK cell infiltration and activation, and complete tumor eradication, independent of tumor antigen expression.
This dataset
Open GSE313197 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 GSE313197_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 PRJNA1378778 and SRA study SRP654029. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
A cohort of 23 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run AB20835 … AB8265 and carry no group information.

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.