GEO series

Lin-CD117+CD34+FceRI+ progenitor cells are increased in chronic spontaneous urticaria and predict clinical responsiveness to anti-IgE therapy

GSE261424 Homo sapiens (human) Single-cell RNA-seq 1 samples Submitted 2024/04/30 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 1 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: 1 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: Lin(-)CD117(+)CD34(+)FcεRI(+) progenitor cells are increased in chronic spontaneous urticaria and predict clinical responsiveness to anti-IgE therapyAllergy · 2024

Summary (written by the submitters)

Background: Chronic spontaneous urticaria (CSU) is a common, debilitating skin disorder characterised by recurring episodes of raised, itchy and sometimes painful wheals lasting longer than 6 weeks. CSU is mediated by mast cells which are absent from peripheral blood. However, lineage-CD34hiCD117int/hiFcεRI+ cells in blood have previously been shown to represent a mast cell precursor.

Read the full summary ↓

Methods: We enumerated FcεRI-, FcεRI+ and FcεRIhi lineage-CD34+CD117+ cells using flow cytometry in blood of patients with CSU (n=55), including 12 patients receiving omalizumab and 43 not receiving omalizumab (n=43). Twenty-two control samples were studied. Disease control and patient response to omalizumab was evaluated using the Urticaria Control Test. We performed single cell RNA sequencing (scRNA-seq) on lineage-CD34hiCD117hi blood cells from a subset of patients with CSU (n=8) and healthy controls (n=4). Results: CSU patients had more Lineage-CD34+CD117+FcεRI+ blood cells than controls. Lineage-CD34+CD117+FcεRI+ cells were significantly higher in patients with CSU who had an objective clinical response to omalizumab when compared to patients who had poor disease control 90 days after initiation of omalizumab. scRNA-seq revealed that lineage-CD34+CD117+FcεRI+ cells contained both lymphoid and myeloid progenitor lineages, with omalizumab responsive patients having proportionally more myeloid progenitors. The myeloid progenitor lineage contained small numbers of true mast cell precursors along with more immature FcεRI- and FcεRI+ myeloid progenitors. Conclusion: Increased blood CD34+CD117+FcεRI+ cells may reflect enhanced bone marrow egress in the setting of CSU. High expression of these cells strongly predicts better clinical responses to the anti-IgE therapy, omalizumab.

Published in

Lin(-)CD117(+)CD34(+)FcεRI(+) progenitor cells are increased in chronic spontaneous urticaria and predict clinical responsiveness to anti-IgE therapy

Ridge K, Moran B, Alvarado-Vazquez PA et al. · Allergy 2024 · PMID 38634175 · doi:10.1111/all.16127

This dataset

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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 GSE261424_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 PRJNA1086921 and SRA study SRP494825. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38634175) ↗

Samples in this study

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