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Enhancing Non-Small Cell Lung Cancer Treatment Utilizing Natural Killer Cell-Derived Extracellular Vesicles (NKEVS) As A Novel Adoptive Cellular Therapeutic [PBMC]

GSE274588 Homo sapiens (human) Single-cell RNA-seq 57 samples Submitted 2025/07/09 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 57 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)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
57 samples: 57 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.
Paper
NK cell-derived extracellular vesicles enhance cytotoxicity and immune cell recruitment in non-small cell lung cancerFrontiers in immunology · 2025
Summary (written by the submitters)
An established mechanism contributing to Immune Checkpoint Inhibitor (ICI) therapy failure is tumor evasion of T-cell responses via downregulation of human leukocyte antigen (HLA). Conversely, the effector function of Natural Killer (NK) cells is enhanced in the absence of HLA expression, making NK-based cellular therapies an attractive option for tumors that are resistant to ICIs.
Read the full summary ↓
We investigated the preclinical efficacy of using selective isolation of NK cells and harvesting of NKEVs in NSCLC. Single-cell RNAseq (scRNAseq) was used to examine the cellular landscape in PBMCs and tumor tissue. NK cells were isolated from PBMCs, expanded in vitro, and NK-derived EVs (NKEV) were collected and the EV RNA and protein cargo was characterized using proteomics and transcriptomics. The functional capabilities of patient derived NKEVs were assayed with 3-dimensional organoid-like structures derived from patient tumor cells. This work described the cellular landscape in NSCLC circulating and tumor infiltrating immune cells. Additionally, it demonstrated that NKEVs can be successfully harvested from patient derived, expanded NK cells, and highlighted their anti-tumor properties in combination with standard-of-care therapies.
Published in
NK cell-derived extracellular vesicles enhance cytotoxicity and immune cell recruitment in non-small cell lung cancer
Palade J, Alsop E, Tang N et al. · Frontiers in immunology 2025 · PMID 40821787 · doi:10.3389/fimmu.2025.1633010
This dataset
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.

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 GSE274588_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1147249 and SRA study SRP526010. Searching any of these in the dataset finder brings you back here.

Study design
57 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 57 sample titles: 57 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

+ 17 more — browse all 57 samples with per-sample file links →

More Lung cancer datasets

Search all human RNA-seq datasets in GEO →

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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.