GEO series

Human airway submucosal gland organoids to study respiratory inflammation and infection [bulk RNA-seq]

GSE271148 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2025/06/12 Platform GPL30173

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. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 16 samples: 8 conditions, mostly in duplicateGroups 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: Human airway submucosal gland organoids to study respiratory inflammation and infectionCell stem cell · 2025

Summary (written by the submitters)

The human airway lining consists of two physiologically distinct compartments: the surface airway epithelium (SAE) and the submucosal glands (SMGs). Despite their critical role, SMGs have remained largely overlooked in airway in vitro modeling of respiratory inflammation and infection. In this study, we leverage long-term cultured organoids derived separately from SAE and SMG to investigate their unique physiological characteristics.

Read the full summary ↓

Single-cell RNA sequencing (scRNA-seq) analysis confirms that these organoid models accurately replicate the cellular heterogeneity inherent to each tissue type. Specifically, SMG organoids are enriched in MUC5B-producing mucous cells and generate alpha-Smooth Muscle Actin (αSMA)-expressing myoepithelial cells. We identify ANPEP/CD13 as a specific cell surface marker for SMG secretory cells. Exposure to cytokines elicits distinct transcriptomic responses in SMG secretory cells, providing insights into the cellular mechanisms underpinning inflammatory pathogenesis. Infection assays with human alpha-coronavirus 229E (HCoV-229E) reveal a selective vulnerability of CD13-positive secretory cells within SMG organoids, triggering a specific unfolded protein response associated with endoplasmic reticulum (ER) stress. These findings broaden the utility of airway organoids for precision modeling of respiratory (patho-)physiology

Published in

Human airway submucosal gland organoids to study respiratory inflammation and infection

Lin L, Pou Casellas C, Dost AFM et al. · Cell stem cell 2025 · PMID 40513559 · doi:10.1016/j.stem.2025.05.013

This dataset

Open GSE271148 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 GSE271148_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 PRJNA1130065 and SRA study SRP517128. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40513559) ↗

Study design

8 conditions, mostly in duplicate

CD13-, Control, ×2 CD13+, Control, ×2 CD13-, IL-1B, ×2 CD13+, IL-1B, ×2 CD13-, IL-13, ×2 CD13+, IL-13, ×2 CD13-, TNFa, ×2 CD13+, TNFa, ×2

Supports a between-group comparison across 16 samples.

How we read this: 8 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

GSM8370791 CD13-, Control, rep1 CD13-, Control,
GSM8370792 CD13-, Control, rep2 CD13-, Control,
GSM8370793 CD13+, Control, rep1 CD13+, Control,
GSM8370794 CD13+, Control, rep2 CD13+, Control,
GSM8370795 CD13-, IL-1B, rep1 CD13-, IL-1B,
GSM8370796 CD13-, IL-1B, rep2 CD13-, IL-1B,
GSM8370797 CD13+, IL-1B, rep1 CD13+, IL-1B,
GSM8370798 CD13+, IL-1B, rep2 CD13+, IL-1B,
GSM8370799 CD13-, IL-13, rep1 CD13-, IL-13,
GSM8370800 CD13-, IL-13, rep2 CD13-, IL-13,
GSM8370801 CD13+, IL-13, rep1 CD13+, IL-13,
GSM8370802 CD13+, IL-13, rep2 CD13+, IL-13,
GSM8370803 CD13-, TNFa, rep1 CD13-, TNFa,
GSM8370804 CD13-, TNFa, rep2 CD13-, TNFa,
GSM8370805 CD13+, TNFa, rep1 CD13+, TNFa,
GSM8370806 CD13+, TNFa, rep2 CD13+, TNFa,

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