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Biomimetic model of human small airways reveals functional impacts of the cystic fibrosis microenvironment on monocytes and macrophages

GSE311629 Homo sapiens (human) RNA-seq (gene expression) 35 samples Submitted 2025/12/01 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 35 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
35 samples: 35 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)
Several muco-obstructive lung diseases in humans, including cystic fibrosis (CF), feature the chronic recruitment of blood leukocytes and their reprogramming within the lumen of small airways to drive pathological adaptations such as altered immune mediator secretion and defective killing of bacteria. To investigate these mechanisms, we engineered a biomimetic model in which primary human blood monocytes migrate through a human small airway epithelium, andepithelium and differentiate into macrophages upon 4-day exposure to macrophage colony-stimulating factor.
Read the full summary ↓
We observed that airway-recruited monocytes (ArMos) obtained with the control chemoattractant C-C motif ligand 2 (CCL2) altered their transcriptome and secretome upon transmigration, and further upon differentiation into airway-recruited macrophages (ArMas), as compared to circulating monocytes. We observed specific additional alterations in transcriptomic and secretomic properties of ArMos and ArMas when they were conditioned in CF airway supernatant (derived from cell- and bacteria-free sputum) [CFASN], compared CCL2 conditions. Critically, CF ArMas (but not CF ArMos), showed a much reduced ability to kill P. aeruginosa, a common CF pathogen. These findings extend prior studies on small airway-recruited neutrophils, and identify immune tolerance by macrophages as one underlying cause of bacterial colonization in CF. This biomimetic model support further mechanistic and preclinical investigations of small airway-recruited monocytes and macrophages in CF and other diseases featuring chronic inflammation and infection, like non-CF bronchiectasis, and chronic obstructive pulmonary disease.
This dataset
Open GSE311629 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 GSE311629_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 PRJNA1370046 and SRA study SRP649382. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
35 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 35 sample titles: 35 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
Datasets on a similar topic

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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.