GEO series

Immune-Epithelial Interactions and Tissue Remodeling in the Inflammatory Respiratory Nasal Tract [scRNA-seq]

GSE235711 Homo sapiens (human) Single-cell RNA-seq 20 samples Submitted 2025/11/13 Platform GPL20301

At a glance

Single-cell RNA-seq · human · 20 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: 20 samples: 20 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.
Paper: Multi-scaled transcriptomics of chronically inflamed nasal epithelium reveals immune-epithelial dynamics and tissue remodeling in nasal polyp formationImmunity · 2025

Summary (written by the submitters)

Chronic rhinosinusitis (CRS) is a common inflammatory disease of the nasal cavity and sinuses that affects millions of individuals worldwide. The complex pathophysiology of CRS remains poorly understood, with emerging evidence implicating diverse immune and epithelial cell types in the disease process. We employed single-cell RNA sequencing (scRNA-seq) in dissociated tissues to investigate the cellular and molecular heterogeneity of CRS with and without nasal polyps.

Published in

Multi-scaled transcriptomics of chronically inflamed nasal epithelium reveals immune-epithelial dynamics and tissue remodeling in nasal polyp formation

Liao G, Nakayama T, Zhu B et al. · Immunity 2025 · PMID 40945518 · doi:10.1016/j.immuni.2025.08.009

This dataset

Open GSE235711 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 GSE235711_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 PRJNA986941 and SRA study SRP445589. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40945518) ↗

Study design

20 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 20 sample titles: 20 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

GSM7508289 Control_1, Ethmoid sinus, scRNAseq
GSM7508290 Control_2, Ethmoid sinus, scRNAseq
GSM7508291 Control_3, Ethmoid sinus, scRNAseq
GSM7508292 Control_4, Ethmoid sinus, scRNAseq
GSM7508293 Control_5, Ethmoid sinus, scRNAseq
GSM7508294 CRSsNP_Eth_1, Ethmoid sinus, scRNAseq
GSM7508295 CRSsNP_Eth_2, Ethmoid sinus, scRNAseq
GSM7508296 CRSsNP_Eth_3, Ethmoid sinus, scRNAseq
GSM7508297 CRSsNP_Eth_4, Ethmoid sinus, scRNAseq
GSM7508298 CRSsNP_Eth_5, Ethmoid sinus, scRNAseq
GSM7508299 CRSwNP_Eth_1, Ethmoid sinus, scRNAseq
GSM7508300 CRSwNP_Eth_2, Ethmoid sinus, scRNAseq
GSM7508301 CRSwNP_Eth_3, Ethmoid sinus, scRNAseq
GSM7508302 CRSwNP_Eth_6, Ethmoid sinus, scRNAseq
GSM7508303 CRSwNP_NP_1, Nasal polyps, scRNAseq
GSM7508304 CRSwNP_NP_2, Nasal polyps, scRNAseq
GSM7508305 CRSwNP_NP_3, Nasal polyps, scRNAseq
GSM7508306 CRSwNP_Eth_4, Ethmoid sinus, scRNAseq
GSM7508307 CRSwNP_Eth_5, Ethmoid sinus, scRNAseq
GSM7508308 CRSwNP_NP_6, Nasal polyps, scRNAseq

Datasets on a similar topic

GSE235714 Immune-Epithelial Interactions and Tissue Remodeling in the Inflammatory Respiratory Nasal Tract [NanoString GeoMx spatial transcriptomics] 40 samples shares immune-epithelial, interactions, respiratory, remodeling, nasal, tract
GSE276503 Myeloid-derived IL1B drives epithelial and stromal cell remodeling during chronic rhinosinusitis and nasal polyps [scRNA-seq] 33 samples shares remodeling, scrna-seq, nasal
GSE235715 Multi-scaled transcriptomics of chronically inflamed nasal epithelium reveals immune-epithelial dynamics and tissue remodeling in nasal polyp formation 60 samples shares immune-epithelial, remodeling, nasal
GSE309392 Cancer-associated fibroblasts promote esophageal carcinogenesis and lung metastasis by CDH11-mediated extracellular matrix environment remodeling [scRNA-Seq] 23 samples shares remodeling, scrna-seq
GSE318564 Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions [scRNA-seq] 13 samples shares interactions, scrna-seq
GSE310006 The NuRD chromatin remodeling complex regulates tRNA genes through direct interactions with RNA polymerase III 54 samples shares interactions, remodeling
GSE341495 Whole-Transcriptome Profiling Reveals RNA Expression Dynamics and ceRNA Network Remodeling across Health, Sepsis, and Sepsis-Associated Acute Respiratory Distress Syndrome 24 samples shares respiratory, remodeling
GSE341493 Whole-Transcriptome Profiling Reveals RNA Expression Dynamics and ceRNA Network Remodeling across Health, Sepsis, and Sepsis-Associated Acute Respiratory Distress Syndrome [RNA-Seq] 12 samples shares respiratory, remodeling

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