GEO series

CAR-NKT cells induce low cytokine release syndrome by targeting hyperinflammatory macrophages with mitigation from GM-SCF inhibition.

GSE337190 Mus musculus (mouse) Single-cell RNA-seq 24 samples Submitted 2026/08/07 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 24 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: Mus musculus (mouse)
Samples: 24 samples: 24 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)

Chimeric antigen receptor (CAR)-T cell therapy has demonstrated remarkable efficacy in cancer treatment but is frequently limited by severe toxicities, including cytokine release syndrome (CRS), which is largely driven by host myeloid cell activation. CAR-engineered invariant natural killer T (CAR-NKT) cells have emerged as a promising alternative with comparable antitumor activity and reduced toxicity; however, the mechanisms underlying their improved safety profile remain poorly defined.

Read the full summary ↓

Here, we systematically evaluate CRS induced by human CAR-NKT and CAR-T cells using a heavily tumor-bearing xenograft mouse model that recapitulates clinically relevant CRS. Compared with conventional CAR-T cells, CAR-NKT cells induce significantly lower CRS severity across multiple clinical and biochemical parameters. To elucidate the underlying mechanisms, we perform comprehensive single-cell transcriptomic profiling of mouse immune cells, including myeloid and granulocytic populations, across six anatomical compartments: tumor, blood, liver, lung, spleen, and bone marrow. This integrative analysis reveals dynamic immune remodeling during tumor progression, therapeutic intervention, and CRS development. Mechanistically, CAR-NKT cells preferentially target CD1d⁺ hyperinflammatory macrophages, a key driver of CRS, thereby limiting myeloid-mediated inflammatory responses. Furthermore, pharmacological inhibition of GM-CSF further attenuate CRS, supporting a combinatorial strategy to enhance the safety of CAR-NKT cell therapy. Collectively, these findings provide mechanistic insight into the reduced toxicity of CAR-NKT cells and support their development as a safer and effective platform for cancer immunotherapy.

This dataset

Open GSE337190 in finder → Find more mouse 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 GSE337190_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 PRJNA1484445 and SRA study SRP714241. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

24 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 24 sample titles: 24 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

GSM9850163 Blank Peritoneal Fluid
GSM9850164 Blank Blood
GSM9850165 Blank Liver
GSM9850166 Blank Lung
GSM9850167 Blank Spleen
GSM9850168 Blank Bone Marrow
GSM9850169 Tumor Peritoneal Fluid
GSM9850170 Tumor Blood
GSM9850171 Tumor Liver
GSM9850172 Tumor Lung
GSM9850173 Tumor Spleen
GSM9850174 Tumor Bone Marrow
GSM9850175 T Peritoneal Fluid
GSM9850176 T Blood
GSM9850177 T Liver
GSM9850178 T Lung
GSM9850179 T Spleen
GSM9850180 T Bone Marrow
GSM9850181 NKT Peritoneal Fluid
GSM9850182 NKT Blood
GSM9850183 NKT Liver
GSM9850184 NKT Lung
GSM9850185 NKT Spleen
GSM9850186 NKT Bone Marrow

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