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Immunopeptidome profiling in pulmonary fibrosis provides a new platform for identifying therapeutic targets

GSE315637 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2026/05/07 Platform GPL24247Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 16 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
16 samples: 3 × TCR_NC vs 3 × TCR_VC vs 2 × BLM_Maf vs 2 × MafGroups 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
Immunopeptidome profiling in pulmonary fibrosis provides a platform for identifying therapeutic targetsNature immunology · 2026
Summary (written by the submitters)
Fibrosis is the common and final pathological outcome of many chronic diseases and is a significant unmet medical need. Changes in the peptide profile presented by major histocompatibility complex class I molecules (MHC-I) are important pathological signals in diseases. The identification of "new antigens" in fibrosis potentially helps understand the pathogenesis and the therapeutic target.
Read the full summary ↓
Here, we show that characterizing the MHC-I immunopeptidome of pulmonary fibrosis tissues identifies a series of pathological MHC-I ligands, leading to the discovery of multiple "fibrosis-associated antigens (FAAs)". Notably, three MHC-I peptides from Tns3, Apbb2, and Maf effectively block pulmonary fibrosis progression when used as a therapeutic vaccine. This study shows that profiling the immunopeptidome in fibrotic disease provides a promising platform for identifying therapeutic targets.
Published in
Immunopeptidome profiling in pulmonary fibrosis provides a platform for identifying therapeutic targets
Bai Z, Lan T, Hong W et al. · Nature immunology 2026 · PMID 42010059 · doi:10.1038/s41590-026-02501-x
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 GSE315637_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1398364 and SRA study SRP659815. Searching any of these in the dataset finder brings you back here.

Study design
3 × TCR_NC vs 3 × TCR_VC vs 2 × BLM_Maf vs 2 × Maf

Supports a between-group comparison across 10 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 10 of them. 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
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