GEO series

Genetic susceptibility to PM2.5 exposure drives transcriptional responses in pediatric asthma: insights from single-cell transcriptomics

GSE300837 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2026/02/25 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 16 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 16 samples: 8 × PBMC high PRS pai vs 8 × PBMC low PRS paiGroups 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.
Paper: Genetic susceptibility to PM(2.5) exposure and transcriptional responses in pediatric asthma: insights from single-cell transcriptomicsFrontiers in immunology · 2026

Summary (written by the submitters)

Background. Exposure to fine particulate matter (PM2.5) increases asthma severity and reduces glucocorticoid responsiveness in children, yet the molecular mechanisms underlying PM2.5 sensitivity remain unclear. We previously identified a PM2.5-sensitive asthma phenotype and developed a PM2.5 sensitivity polygenic risk score (sPRS) correlated with asthma exacerbations and lung function decline.

Read the full summary ↓

This study expands on these results using genetic mapping and single-cell transcriptomics to elucidate the biological pathways mediating PM2.5 sensitivity and generate hypotheses regarding potential therapeutic targets. Research Question. We aimed to determine whether genetic variants associated with PM2.5 sensitivity converge on specific pathways or transcriptional regulators and assess their role in glucocorticoid responsiveness. Methods. We mapped sPRS variants to genes using regulatory annotation tools and performed pathway enrichment analysis. Single-cell RNA sequencing (scRNA-seq) was conducted on peripheral blood mononuclear cells (PBMCs) from patients stratified by sPRS and matched by PM2.5 exposure to identify transcriptional perturbations in response to PM2.5 exposure. In silico analyses predicted regulatory networks and small molecules that could modulate these pathways. Results. sPRS-associated genes were enriched for SMAD2/3 and MAPK signaling, implicating TGF-β1 as a key mediator of PM2.5 sensitivity. scRNA-seq revealed immune cell-specific transcriptional changes linked to inflammation. Notably, HDM2 inhibition downregulated the PM2.5-sensitive transcriptomic signature, suggesting a role in restoring glucocorticoid responsiveness. Interpretation. Our findings suggest a TGF-β1–HDM2–p53 axis may contribute to PM2.5-induced glucocorticoid resistance and identify HDM2 as a potential therapeutic target for future investigation in PM2.5-sensitive children with asthma.

Published in

Genetic susceptibility to PM(2.5) exposure and transcriptional responses in pediatric asthma: insights from single-cell transcriptomics

Kelchtermans J, Qu H, Hou C et al. · Frontiers in immunology 2026 · PMID 41710897 · doi:10.3389/fimmu.2026.1644537

This dataset

Open GSE300837 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 GSE300837_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1282181 and SRA study SRP594660. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41710897) ↗

Study design

8 × PBMC high PRS pai vs 8 × PBMC low PRS pai

Supports a between-group comparison across 16 samples.

How we read this: 2 replicated groups read from 16 sample titles; they account for 16 of them. 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

GSM9069382 PBMC high PRS pair 1 PBMC high PRS pai
GSM9069383 PBMC low PRS pair 1 PBMC low PRS pai
GSM9069384 PBMC high PRS pair 2 PBMC high PRS pai
GSM9069385 PBMC low PRS pair 2 PBMC low PRS pai
GSM9069386 PBMC high PRS pair 3 PBMC high PRS pai
GSM9069387 PBMC low PRS pair 3 PBMC low PRS pai
GSM9069388 PBMC high PRS pair 4 PBMC high PRS pai
GSM9069389 PBMC low PRS pair 4 PBMC low PRS pai
GSM9069390 PBMC high PRS pair 5 PBMC high PRS pai
GSM9069391 PBMC low PRS pair 5 PBMC low PRS pai
GSM9069392 PBMC high PRS pair 6 PBMC high PRS pai
GSM9069393 PBMC low PRS pair 6 PBMC low PRS pai
GSM9069394 PBMC high PRS pair 7 PBMC high PRS pai
GSM9069395 PBMC low PRS pair 7 PBMC low PRS pai
GSM9069396 PBMC high PRS pair 8 PBMC high PRS pai
GSM9069397 PBMC low PRS pair 8 PBMC low PRS pai

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