GEO series

Heterogeneity and Clinical Relevance of Group 2 Innate Lymphoid Cells Subsets in Nasal Polyps

GSE324445 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2026/05/08 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 10 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: 10 samples: 10 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: Heterogeneity and clinical relevance of group 2 innate lymphoid cells subsets in nasal polypsThe Journal of allergy and clinical immunology · 2026

Summary (written by the submitters)

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is characterized by type 2 (T2) inflammation with elevated IL-5 and IL-13. Although group 2 innate lymphoid cells (ILC2s) drive T2 inflammation, their subset diversity and clinical relevance in nasal polyps (NPs) remain unclear. Objective: To investigate cellular sources of T2 cytokines, subset heterogeneity of ILC2s and relationship between ILC2 subsets and clinical severity in CRSwNP.

Read the full summary ↓

Methods: ILC2s and CD4⁺ T cells were isolated from NP tissue and analyzed for cytokine production. ILC2s from six NP and four peripheral blood (PB) samples underwent single-cell RNA sequencing. Differential gene expression, subset heterogeneity, and pseudotime trajectory analyses were performed. Flow cytometry validated ILC2 subsets and associations with clinical parameters. Results: Under ex vivo conditions, NP-derived ILC2s produced higher IL-5 and IL-13 levels than Th2 cells . NP-derived ILC2s showed downregulation of early developmental and trafficking genes (e.g. CD48, S1PR1) and upregulation of T2-associated cytokines (e.g. IL5, IL13), chemokines (e.g. XCL1, CXCL8), remodeling factors (e.g. AREG, TNFSF14), and METRNL. Four ILC2 subsets reflecting activation states were identified: migratory (tissue-homing with less activation), transitional (intermediate activation), inflammatory (high activation and T2 cytokines), and exhausted-like (expression of inhibitory receptors: e.g. TIGIT). The combined number of T2 cytokine-enriched subsets (transitional, inflammatory, and exhausted-like ILC2s) correlated with Lund-Mackay CT scores. Inflammatory and exhausted-like ILC2s were associated with clinical symptom severity. Conclusion: ILC2s are one of the important effector populations driving local T2 inflammation in NPs and include four subsets with specific clinical associations. Subset-level ILC2 profiling may clarify CRSwNP pathophysiology and inform clinical stratification.

Published in

Heterogeneity and clinical relevance of group 2 innate lymphoid cells subsets in nasal polyps

Kidoguchi M, Iwasaki N, Poposki JA et al. · The Journal of allergy and clinical immunology 2026 · PMID 41956382 · doi:10.1016/j.jaci.2026.03.021

This dataset

Open GSE324445 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 GSE324445_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 PRJNA1435148. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41956382) ↗

Study design

10 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 10 sample titles: 10 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

GSM9576383 NP1 scRNA-seq
GSM9576384 NP2 scRNA-seq
GSM9576385 NP3 scRNA-seq
GSM9576386 NP4 scRNA-seq
GSM9576387 NP5 scRNA-seq
GSM9576388 NP6 scRNA-seq
GSM9576389 PB1 scRNA-seq
GSM9576390 PB2 scRNA-seq
GSM9576391 PB3 scRNA-seq
GSM9576392 PB4 scRNA-seq

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