GEO series

Blood-based Molecular Endotyping of Fibrotic Hypersensitivity Pneumonitis Identifies Two Immune Subtypes with Divergent Clinical Outcomes

GSE318816 Homo sapiens (human) RNA-seq (gene expression) 126 samples Submitted 2026/06/12 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 126 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: Homo sapiens (human)
Samples: 126 samples: 88 × L-fHP vs 38 × N-fHPGroups 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)

Fibrotic hypersensitivity pneumonitis (fHP) is an antigen driven deadly interstitial lung disease with limited treatment options. Using multiple analysis algorithms, we identified two molecular endotypes with divergent immune signatures in transcriptome of whole blood from a fHP cohort and validated in transcriptomes from whole blood, peripheral blood mononuclear cells, bronchoalveolar lavage, surgical lung biopsy tissue, and in the proteome from blood plasma.

Read the full summary ↓

The lymphocytic endotype (L-fHP) comprising two-thirds of fHP cases showed enrichment of adaptive immune pathways and lymphocyte predominance, while the non-lymphocytic endotype (N-fHP) exhibited enrichment of innate pathways with neutrophil and macrophage predominance. The N-fHP cases had lower baseline pulmonary function, and reduced transplant-free survival compared to L-fHP cases, yet corticosteroid therapy was associated with poorer transplant-free survival in L-fHP and without impact in N-fHP. These results show that blood-based molecular endotyping can uncover prognostic and therapeutic insights and advance precision medicine otherwise inaccessible to traditional clinical phenotyping.

This dataset

Open GSE318816 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 GSE318816_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: 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 PRJNA1420660 and SRA study SRP675664. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

88 × L-fHP vs 38 × N-fHP

Supports a between-group comparison across 126 samples.

How we read this: 2 replicated groups read from 126 sample titles; they account for 126 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

GSM9503748 N-fHP_1 N-fHP
GSM9503749 N-fHP_2 N-fHP
GSM9503750 N-fHP_3 N-fHP
GSM9503751 N-fHP_4 N-fHP
GSM9503752 N-fHP_5 N-fHP
GSM9503753 N-fHP_6 N-fHP
GSM9503754 N-fHP_7 N-fHP
GSM9503755 N-fHP_8 N-fHP
GSM9503756 N-fHP_9 N-fHP
GSM9503757 N-fHP_10 N-fHP
GSM9503758 N-fHP_11 N-fHP
GSM9503759 N-fHP_12 N-fHP
GSM9503760 N-fHP_13 N-fHP
GSM9503761 N-fHP_14 N-fHP
GSM9503762 N-fHP_15 N-fHP
GSM9503763 N-fHP_16 N-fHP
GSM9503764 N-fHP_17 N-fHP
GSM9503765 N-fHP_18 N-fHP
GSM9503766 N-fHP_19 N-fHP
GSM9503767 N-fHP_20 N-fHP
GSM9503768 N-fHP_21 N-fHP
GSM9503769 N-fHP_22 N-fHP
GSM9503770 N-fHP_23 N-fHP
GSM9503771 N-fHP_24 N-fHP
GSM9503772 N-fHP_25 N-fHP
GSM9503773 N-fHP_26 N-fHP
GSM9503774 N-fHP_27 N-fHP
GSM9503775 N-fHP_28 N-fHP
GSM9503776 N-fHP_29 N-fHP
GSM9503777 N-fHP_30 N-fHP
GSM9503778 N-fHP_31 N-fHP
GSM9503779 N-fHP_32 N-fHP
GSM9503780 N-fHP_33 N-fHP
GSM9503781 N-fHP_34 N-fHP
GSM9503782 N-fHP_35 N-fHP
GSM9503783 N-fHP_36 N-fHP
GSM9503784 N-fHP_37 N-fHP
GSM9503785 N-fHP_38 N-fHP
GSM9503786 L-fHP_1 L-fHP
GSM9503787 L-fHP_2 L-fHP

+ 86 more — browse all 126 samples with per-sample file links →

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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.