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Transcriptional adaptation of human airway epithelium upon cigarette smoking

GSE237252 Homo sapiens (human) RNA-seq (gene expression) 77 samples Submitted 2025/08/10 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 77 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
77 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
Differential DNA methylation in bronchial biopsies between persistent asthma and asthma in remissionThe European respiratory journal · 2020
Summary (written by the submitters)
Despite the well-known adverse health effects, smoking is still one of the leading causes of preventable death worldwide. Smoking has shown to significantly alter gene transcription in blood, but limited studies have assessed the effects of smoking on structural lung cells. Here, we investigated how the airway wall responds and adapts to chronic smoke exposure by changing airway epithelial transcriptional profiles in response to cigarette smoke exposure, using RNA-Seq.
Read the full summary ↓
This study identifies panels of genes, related to the response of airway epithelial cells to cigarette smoke. Furthermore, our study shows especially luminal epithelial cells are affected by smoke and that the airways are epigenetically regulated to promote the expression of oxidative stress-response genes, which dampens the effects of chronic smoke exposure.
Published in
Differential DNA methylation in bronchial biopsies between persistent asthma and asthma in remission
Vermeulen CJ, Xu CJ, Vonk JM et al. · The European respiratory journal 2020 · PMID 31699840 · doi:10.1183/13993003.01280-2019
This dataset
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.

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 GSE237252_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA994479. Searching any of these in the dataset finder brings you back here.

Samples in this study

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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.