Single cell RNAseq on rheumatoid arthritis-related interstitial lung disease
Single-cell RNA-seq · human · 12 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
- 12 samples: 12 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
- Single-Cell Analysis Reveals Peripheral Helper T Cells in Rheumatoid Arthritis-Related Interstitial Lung DiseaseArthritis & rheumatology (Hoboken, N.J.) · 2026
Read the full summary ↓
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 GSE307297_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 PRJNA1315167 and SRA study SRP617556. 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 12 sample titles: 12 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.
- GSM9221423 Lung immune ane epithelial cells from Control_1 patient
- GSM9221424 Lung immune ane epithelial cells from Control_2 patient
- GSM9221425 Lung immune ane epithelial cells from Control_3 patient
- GSM9221426 Lung immune ane epithelial cells from Control_4 patient
- GSM9221427 Lung immune ane epithelial cells from CTD_1 patient
- GSM9221428 Lung immune ane epithelial cells from CTD_2 patient
- GSM9221429 Lung immune ane epithelial cells from CTD_3 patient
- GSM9221430 Lung immune ane epithelial cells from RA_1 patient
- GSM9221431 Lung immune ane epithelial cells from RA_2 patient
- GSM9221432 Lung immune ane epithelial cells from RA_3 patient
- GSM9221433 Lung immune ane epithelial cells from RA_4 patient
- GSM9221434 Lung immune ane epithelial cells from RA_5 patient
- GSE296682 Single-cell transcriptomics of bronchoalveolar lavage reveals divergent macrophage subpopulations and trajectories in interstitial lung disease 9 samples shares interstitial, lung
- GSE310058 Single-nucleus transcriptomic profiling of human lung tissue in chronic obstructive pulmonary disease (COPD) [snRNA-seq] 149 samples shares lung
- GSE319666 Archetype analysis of lung adenocarcinoma premalignancy links heterogeneity in premalignant lesions to diverging features of invasive disease 177 samples shares lung
- GSE223499 Non-small Cell Lung Cancer Brain Metastasis Atlas [single nuclei RNAseq] 42 samples shares lung
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares lung
- GSE271689 Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer 586 samples shares lung
- GSE131907 Single cell RNA sequencing of lung adenocarcinoma 58 samples shares lung
- GSE205335 Single-cell transcriptome profiles of tumor tissues from lung cancer patients receiving immune checkpoint inhibitors. 33 samples shares lung
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.