← BioTransfer GEO Dataset Finder
GEO series

A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer

GSE243013 Homo sapiens (human) Single-cell RNA-seq 243 samples Submitted 2025/01/28 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 243 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)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
243 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.
Paper
A single-cell atlas reveals immune heterogeneity in anti-PD-1-treated non-small cell lung cancerCell · 2025
Summary (written by the submitters)
Anti-PD(L)1 with chemotherapy is a standard of care for non-small cell lung cancer (NSCLC), but a varying degree of response to the same regimen is observed in patients. The tumor immune microenvironment (TIME) plays a key role in response to immunotherapy, and the TIME heterogeneity in association with therapeutic outcome is incompletely understood.
Read the full summary ↓
Here we prospectively applied single-cell RNA and TCR sequencing to characterize post-neoadjuvant chemo-immunotherapy treatment tumor samples of 234 NSCLC patients. Our study provides a fine-grained dissection of the TIME heterogeneity underlying response to chemo-immunotherapy in NSCLC, thus representing a valuable resource for improved management of NSCLC.
Published in
A single-cell atlas reveals immune heterogeneity in anti-PD-1-treated non-small cell lung cancer
Liu Z, Yang Z, Wu J et al. · Cell 2025 · PMID 40147443 · doi:10.1016/j.cell.2025.03.018
This dataset
Open GSE243013 in finder → Find more human RNA-seq datasets → Lung cancer landscape →
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 GSE243013_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 PRJNA1015724. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40147443) ↗
Study design
A cohort of 243 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 P1 … P595 and carry no group information.

Samples in this study

+ 203 more — browse all 243 samples with per-sample file links →

More Lung cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.