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BALF gene expression profiling reveals innate immune activation bias in persistent post-COVID-19 ILD

GSE291409 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2026/02/25 Platform GPL28038
At a glance

RNA-seq (gene expression) · human · 21 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
21 samples
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
BALF gene expression profiling reveals a fibrosis associated innate immune activation bias in persistent post-COVID-19 ILDAmerican journal of physiology. Lung cellular and molecular physiology · 2026
Summary (written by the submitters)
Survivors of COVID-19 can experience long-term lung complications (pulmonary sequelae), but the underlying mechanisms remain unclear. While most patients with major COVID-19 lung injury eventually recover essentially completely, some experience significant residual damage. To investigate the underlying differences, we analyzed, using bronchoalveolar lavage fluid (BALF), the alveolar immune cell compartments of a group of patients with post-COVID-19 interstitial lung disease (ILD) six months after acute COVID-19.
Read the full summary ↓
Patients were categorized into two groups, based on High-Resolution Computed Tomography (HRCT) evaluation a year later: those with persistent HRCT abnormalities compatible with fibrosis (persistent post-COVID-19 ILD, n=6) and those with resolved lung lesions (resolved post-COVID-19 ILD, n=13). Additionally, 6 patients with pre-existing ILD were included in the study, after recovery from COVID-19. Bulk RNA transcriptomics analyses of BALF cells revealed pathways of neutrophil and monocyte chemotaxis to be enriched in patients with persistent HRCT abnormalities, consistent with increased monocyte-like cell recruitment in the lungs. Furthermore, increased gene expression of markers of pro-fibrotic macrophages/monocytes, such as SPP1 and pro-inflammatory cytokines, such as Il-1b, was also observed. Conversely, patients with resolved post-COVID-19 ILD displayed BALF cell gene expression signatures indicative of T-cell activation. Additionally, BALF gene expression patterns associated with pro-fibrotic macrophage activation, neutrophil chemotaxis and downregulation of T-cell activation were also observed in patients with pre-existing fibrotic ILD following COVID-19. These findings suggest that immune response imbalance leading to prolonged activation of innate immunity and subdued adaptive immune responses may be associated with persistent post-COVID-19 ILD.
Published in
BALF gene expression profiling reveals a fibrosis associated innate immune activation bias in persistent post-COVID-19 ILD
Antoniou KM, Vasarmidi E, Galaras A et al. · American journal of physiology. Lung cellular and molecular physiology 2026 · PMID 41236898 · doi:10.1152/ajplung.00243.2025
This dataset
Open GSE291409 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 GSE291409_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 PRJNA1233318 and SRA study SRP568738. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41236898) ↗
Study design
A cohort of 21 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run TSq1 … TSq7 and carry no group information.

Samples in this study
Datasets on a similar topic

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