GEO series

Cellular senescence dysregulates antiviral interferon responses in idiopathic pulmonary fibrosis

GSE334185 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2026/08/01 Platform GPL34281

At a glance

RNA-seq (gene expression) · human · 27 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: 27 samples: 27 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.

Summary (written by the submitters)

Patients with idiopathic pulmonary fibrosis (IPF) are highly vulnerable to respiratory virus infections, but the cellular mechanisms linking fibrotic remodeling to impaired local antiviral defense remain unclear. Here, we investigated how cellular senescence shapes the response of patient-derived healthy and IPF primary lung fibroblasts to influenza A virus (IAV) infection.

Read the full summary ↓

Transcriptomic profiling identified infection as the driver of gene expression in both DNA damage-induced senescent healthy and IPF fibroblasts and revealed induction of canonical antiviral pathways in both cell states. However, senescent IPF fibroblasts adopted a distinct antiviral response state characterized by a broader set of uniquely induced genes and differential coordination of antiviral transcriptional networks. Functionally, senescence increased viral titers in healthy and IPF fibroblasts, while senescent IPF fibroblasts displayed an altered inflammatory response. Network analysis linked viral response- and cell cycle-associated modules specifically to the senescent healthy infected state, whereas these programs were weaker in senescent IPF fibroblasts. Transcription factor inference identified IRF3 and STAT1 as candidate regulators of this altered antiviral state in both senescent healthy and IPF fibroblasts. Consistent with the network and transcription factor analyses, siRNA-mediated depletion of IRF3 or STAT1 significantly reduced IFN-β secretion in senescent healthy fibroblasts, whereas IPF fibroblasts showed only milder effects, indicating a disease-specific dependence on these pathways for antiviral control. Together, these findings show that the combination of cellular senescence and fibrotic fibroblast identity creates a dysfunctional antiviral state that may help explain the high susceptibility of IPF patients to virus-associated acute exacerbations and disease worsening.

This dataset

Open GSE334185 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 GSE334185_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 PRJNA1474104 and SRA study SRP706305. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

27 samples, each with a distinct title — no replicated groups

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

How we read this: Read from 27 sample titles: 27 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

GSM9783093 healthy, IAV, QUI, donor AJAZ, batch2
GSM9783094 healthy, mock, QUI, donor AJAZ, batch2
GSM9783095 healthy, IAV, SEN, donor AJAZ, batch2
GSM9783096 healthy, mock, SEN, donor AJAZ, batch2
GSM9783097 healthy, IAV, QUI, donor HLF250, batch2
GSM9783098 healthy, mock, QUI, donor HLF250, batch2
GSM9783099 healthy, IAV, SEN, donor HLF250, batch2
GSM9783100 healthy, mock, SEN, donor HLF250, batch2
GSM9783101 healthy, IAV, QUI, donor AJDL, batch2
GSM9783102 healthy, mock, QUI, donor AJDL, batch2
GSM9783103 healthy, IAV, SEN, donor AJDL, batch2
GSM9783104 healthy, mock, SEN, donor AJDL, batch2
GSM9783105 IPF, IAV, QUI, donor 86A, batch1
GSM9783106 IPF, mock, QUI, donor 86A, batch1
GSM9783107 IPF, IAV, SEN, donor 86A, batch1
GSM9783108 IPF, mock, SEN, donor 86A, batch1
GSM9783109 IPF, IAV, QUI, donor 155B, batch1
GSM9783110 IPF, mock, QUI, donor 155B, batch1
GSM9783111 IPF, IAV, SEN, donor 155B, batch1
GSM9783112 IPF, mock, SEN, donor 155B, batch1
GSM9783113 IPF, IAV, QUI, donor 83A, batch2
GSM9783114 IPF, mock, QUI, donor 83A, batch2
GSM9783115 IPF, IAV, SEN, donor 83A, batch2
GSM9783116 IPF, mock, SEN, donor 83A, batch2
GSM9783117 IPF, IAV, QUI, donor 110A, batch2
GSM9783118 IPF, IAV, SEN, donor 110A, batch2
GSM9783119 IPF, mock, SEN, donor 110A, batch2

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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.