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T-cell Multiomic Analysis Identifies Subsets and Mechanisms of Interaction with Epithelial Cells in Idiopathic Pulmonary Fibrosis

GSE326573 Homo sapiens (human) Single-cell RNA-seq 59 samples Submitted 2026/07/14 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 59 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)
Samples
59 samples: 56 distinct titles, 1 of them repeatedGroups 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)
While T-cell numbers are elevated in IPF lungs, including in fibrotic regions, their contributions to fibrosis, especially beyond inflammation, remain unclear. We used single-cell RNA and protein profiling (CITE-seq) of CD3⁺ T cells from control and fibrotic lungs to investigate molecular pathways involved in T-cell functions in pulmonary fibrosis.
Read the full summary ↓
This study reveals a previously unrecognized role for MIF/CXCR4/EGFR signaling in T-cell activation. Targeting the epithelial–T-cell axis may provide novel therapeutic strategies to mitigate T-cell-mediated epithelial damage and slow IPF progression.
This dataset
Open GSE326573 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 GSE326573_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 PRJNA1445938 and SRA study SRP688219. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
56 distinct titles, 1 of them repeated
non-diseased control ×4

How we read this: Read from 59 sample titles: 56 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

+ 19 more — browse all 59 samples with per-sample file links →

Datasets on a similar topic

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.