GEO series

Macrophage-exacerbated viral lung injury and mechanical overdistension-driven bacterial superinfection replicated in a human lung chip

GSE322591 Homo sapiens (human) RNA-seq (gene expression) 1 samples Submitted 2026/06/28 Platform GPL18573

At a glance

RNA-seq (gene expression) · human · 1 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: 1 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.

Summary (written by the submitters)

Patients with severe influenza-induced lung injury exhibit disrupted alveolar barrier and persistent viral load, accompanied by hyperinflammation, immune cell recruitment, and risk of bacterial superinfection. These complications are often exacerbated in patients undergoing mechanical ventilation.

Read the full summary ↓

Leveraging an immune-competent human lung alveolus-on-a-chip (Lung Chip) lined by human pulmonary alveolar epithelium co-cultured with monocyte-derived macrophages and interfaced with lung microvascular endothelium, we show that monocyte-derived macrophages play a central role in driving key hallmarks of severe influenza, including activated cell death programs. Physiological strain mimicking breathing motions supported alveolar homeostasis, whereas pathological strain analogous to mechanical ventilator-induced lung overdistension promoted secondary Pseudomonas aeruginosa infection. Computational gene network analysis combined with experimental validation identified SIRT1 as a key modulator of inflammation and susceptibility to bacterial superinfection, which can be mitigated using pharmacological SIRT1 activators. Together, these findings underscore the importance of incorporating macrophages into preclinical models of viral lung injury and establish an integrated workflow combining human Organ Chips and computational approaches for gaining insights into biophysical and molecular disease drivers while facilitating the identification of therapeutic targets and candidate countermeasures.

This dataset

Open GSE322591 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 GSE322591_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 PRJNA1430643 and SRA study SRP680271. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 1 more — browse all 1 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.