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Single cell RNA sequencing to study the in vivo behaviour of allogeneic HSC-derived MCAR-engineered NKT cells

GSE270820 Homo sapiens (human) Single-cell RNA-seq 30 samples Submitted 2025/07/29 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 30 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
30 samples: 30 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)
Ovarian cancer (OC) poses a significant challenge due to frequent recurrence linked to tumor heterogeneity and an immunosuppressive tumor microenvironment (TME). Here, we generated allogeneic MSLN-targeting CAR (MCAR)-engineered invariant natural killer T (MCAR-NKT) cells, demonstrating high-yield and high-purity production, potent antitumor efficacy, multiple tumor targeting mechanism, and significant in vivo expansion and persistence.
Read the full summary ↓
Two therapeutic cell products (i.e., CAR-T and CAR-NKT cells) were analyzed at five time points: day 0 (pre-infusion), and days 14, 28, 42, and 56 post-infusion. Day 0 samples were collected prior to cell administration, whereas post-infusion samples were isolated from tumor sites, peripheral blood, spleen, and liver. These findings underscore the feasibility and therapeutic potential of allogeneic MCAR-NKT cell products for OC, laying a foundation for further translation and clinical development.
This dataset
Open GSE270820 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 GSE270820_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 PRJNA1128571 and SRA study SRP516256. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
30 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 30 sample titles: 30 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.