Single-nucleus RNA-sequencing (snRNA-seq) analysis of normal lung tissues, precursor lung lesions and invasive lung adenocarcinomas
Single-nucleus RNA-seq · human · 75 samples
- What it is
- Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
- Organism
- Homo sapiens (human)
- Topic
- Lung cancerThis series is part of our curated briefing on it.
- Samples
- 75 samples: 75 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
- Multimodal spatial-omics reveal co-evolution of alveolar progenitors and proinflammatory niches in progression of lung precursor lesionsCancer cell · 2026
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE308103_RAW.tar (it can run to tens of gigabytes).
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 PRJNA1328749. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 75 sample titles: 75 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.
- GSM9237901 Normal lung tissue of patient 3 [snRNA-seq]
- GSM9237902 Lung tissue with AIS of patient 3 [snRNA-seq]
- GSM9237903 Lung tissue with LUAD of patient 3 [snRNA-seq]
- GSM9237904 Normal lung tissue of patient 4 [snRNA-seq]
- GSM9237905 Second normal lung tissue of patient 4 [snRNA-seq]
- GSM9237906 Lung tissue with AAH of patient 4 [snRNA-seq]
- GSM9237907 Lung tissue with second AAH of patient 4 [snRNA-seq]
- GSM9237908 Lung tissue with LUAD of patient 4 [snRNA-seq]
- GSM9237909 Normal lung tissue of patient 5 [snRNA-seq]
- GSM9237910 Lung tissue with AIS of patient 5 [snRNA-seq]
- GSM9237911 Lung tissue with LUAD of patient 5 [snRNA-seq]
- GSM9237912 Normal lung tissue of patient 6 [snRNA-seq]
- GSM9237913 Lung tissue with AAH of patient 6 [snRNA-seq]
- GSM9237914 Lung tissue with LUAD of patient 6 [snRNA-seq]
- GSM9237915 Normal lung tissue of patient 7 [snRNA-seq]
- GSM9237916 Lung tissue with LUAD of patient 7 [snRNA-seq]
- GSM9237917 Lung tissue with second LUAD of patient 7 [snRNA-seq]
- GSM9237918 Normal lung tissue of patient 8 [snRNA-seq]
- GSM9237919 Lung tissue with AIS of patient 8 [snRNA-seq]
- GSM9237920 Lung tissue with LUAD of patient 8 [snRNA-seq]
- GSM9237921 Normal lung tissue of patient 9 [snRNA-seq]
- GSM9237922 Lung tissue with AAH of patient 9 [snRNA-seq]
- GSM9237923 Lung tissue with AIS of patient 9 [snRNA-seq]
- GSM9237924 Lung tissue with LUAD of patient 9 [snRNA-seq]
- GSM9237925 Normal lung tissue of patient 10 [snRNA-seq]
- GSM9237926 Lung tissue with MIA of patient 10 [snRNA-seq]
- GSM9237927 Lung tissue with LUAD of patient 10 [snRNA-seq]
- GSM9237928 Normal lung tissue of patient 11 [snRNA-seq]
- GSM9237929 Lung tissue with AAH of patient 11 [snRNA-seq]
- GSM9237930 Lung tissue with LUAD of patient 11 [snRNA-seq]
- GSM9237931 Normal lung tissue of patient 12 [snRNA-seq]
- GSM9237932 Lung tissue with AIS of patient 12 [snRNA-seq]
- GSM9237933 Lung tissue with LUAD of patient 12 [snRNA-seq]
- GSM9237934 Normal lung tissue of patient 13 [snRNA-seq]
- GSM9237935 Lung tissue with MIA of patient 13 [snRNA-seq]
- GSM9237936 Lung tissue with LUAD of patient 13 [snRNA-seq]
- GSM9237937 Normal lung tissue of patient 14 [snRNA-seq]
- GSM9237938 Lung tissue with AIS of patient 14 [snRNA-seq]
- GSM9237939 Lung tissue with LUAD of patient 14 [snRNA-seq]
- GSM9237940 Normal lung tissue of patient 15 [snRNA-seq]
+ 35 more — browse all 75 samples with per-sample file links →
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- GSE117570 Reprogramming of Tumor-infiltrating Immune Cells in Early Stage of NSCLC 8 samples same disease briefing
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- GSE17958 Affymetrix SNP array data for 47 lung squamous cell carcinoma and 40 esophageal squamous cell carcinoma samples. 87 samples same disease briefing
- GSE114761 Transforming Growth Factor β-Induced Epithelial-to-Mesenchymal Signature Predicts Metastasis-Free Survival in Non-Small Cell Lung Cancer. 42 samples same disease briefing
- GSE104244 Bone Marrow Derived Macrophages, NSCLC cell lines, and Kras G12D Lung Tumors Treated with Epigenetic Agents 33 samples same disease briefing
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.