GEO series

Interferon-responsive neutrophils and macrophages extricate SARS-CoV-2 Omicron critical patients from the nasty fate of sepsis

GSE263469 Homo sapiens (human) RNA-seq (gene expression) 26 samples Submitted 2024/08/26 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 26 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Samples: 26 samples: 26 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: Interferon-responsive neutrophils and macrophages extricate SARS-CoV-2 Omicron critical patients from the nasty fate of sepsisJournal of medical virology · 2024

Summary (written by the submitters)

Immune characteristics associated with the SARS-CoV-2 Omicron variant of critical-ill patients are currently unclear. Severe disease has been associated with reconfiguration in the immune activity of the lung. Here we investigated the immune cell characteristics including myeloid cells, lymphocytes and alveolar epithelial cells in the bronchoalveolar lavage fluid (BALF) of 26 critical mechanical ventilation-required patients.

Read the full summary ↓

The whole alveolar microenvironment of critical Omicron manifested apparent heterogeneous proinflammatory and interferon-stimulated gene (ISG) signature. CXCL5+ macrophages, VCAN+ macrophages, PI3+ neutrophils and CST7+ neutrophils contributed to the most proinflammatory phenotypes and were positively correlated with disease severity whereas ISG15+ neutrophils and CXCL10+ macrophages contributed to the most ISG phenotype and were negatively correlated with disease severity. Lymphocyte subsets were rare manifesting the lymphopenia in the alveoli and these lymphocyte subsets did not express substantial proinflammatory cytokines. Four different epithelial cell subsets were observed in BALF. The percentages of ISG15+ neutrophils were accompanied by more protective alveolar epithelial cells and may serve as the reshaping of exhaustive phenotype for CD4+ T cells. The CXCL10+ macrophages may promote plasmablast/plasma cell survival and activation. This atlas reveals a potential immune landscape in the bronchoalveolar microenvironment for severe and critical Omicron disease.

Published in

Interferon-responsive neutrophils and macrophages extricate SARS-CoV-2 Omicron critical patients from the nasty fate of sepsis

Wang M, Zhang D, Lei T et al. · Journal of medical virology 2024 · PMID 39206862 · doi:10.1002/jmv.29889

This dataset

Open GSE263469 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 GSE263469_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1097598 and SRA study SRP500408. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39206862) ↗

Study design

26 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 26 sample titles: 26 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

GSM8193163 BALF, C94598, replicate 1, scRNAseq
GSM8193164 BALF, D18348, replicate 1, scRNAseq
GSM8193165 BALF, D18348, replicate 2, scRNAseq
GSM8193166 BALF, D98585, replicate 1, scRNAseq
GSM8193167 BALF, E07554, replicate 1, scRNAseq
GSM8193168 BALF, E07768, replicate 1, scRNAseq
GSM8193169 BALF, E07866, replicate 1, scRNAseq
GSM8193170 BALF, E07928, replicate 1, scRNAseq
GSM8193171 BALF, E07928, replicate 2, scRNAseq
GSM8193172 BALF, E07928, replicate 3, scRNAseq
GSM8193173 BALF, E08111, replicate 1, scRNAseq
GSM8193174 BALF, E08111, replicate 2, scRNAseq
GSM8193175 BALF, E08111, replicate 3, scRNAseq
GSM8193176 BALF, E08262, replicate 1, scRNAseq
GSM8193177 BALF, E08320, replicate 1, scRNAseq
GSM8193178 BALF, E08320, replicate 2, scRNAseq
GSM8193179 BALF, E08322, replicate 1, scRNAseq
GSM8193180 BALF, X519402, replicate 1, scRNAseq
GSM8193181 BALF, X519402, replicate 2, scRNAseq
GSM8193182 BALF, X571559, replicate 1, scRNAseq
GSM8193183 BALF, X571559, replicate 2, scRNAseq
GSM8193184 BALF, X708237, replicate 1, scRNAseq
GSM8193185 BALF, X771100, replicate 1, scRNAseq
GSM8193186 BALF, X771100, replicate 2, scRNAseq
GSM8193187 BALF, X811911, replicate 1, scRNAseq
GSM8193188 BALF, X923241, replicate 2, scRNAseq

Datasets on a similar topic

GSE276544 MVA-based SARS-CoV-2 vaccine candidates encoding different spike protein conformations induce distinct early transcriptional responses which may impact subsequent adaptive immunity 305 samples shares sars-cov-2
GSE288346 Multi-Omics Analysis of the Immunological State at Baseline and Following SARS-CoV-2 mRNA Vaccination in Lung Transplant Recipients [RNA-seq] 106 samples shares sars-cov-2
GSE240766 SARS-CoV-2 infection of human pluripotent stem cell-derived vascular cells reveals smooth muscle cells as key mediators of vascular pathology during infection 101 samples shares sars-cov-2
GSE297329 Evolution of SARS-CoV-2 T cell responses as a function of multiple COVID-19 boosters 98 samples shares sars-cov-2
GSE254699 Transcriptional profiling of SARS-CoV-2-infected Calu-3 cells. 42 samples shares sars-cov-2
GSE303020 Transcriptome analysis of cells after SARS-CoV-2 infection and treatment with MEK1/2-inhibitor Zapnometinib. 36 samples shares sars-cov-2
GSE281237 Widespread gene-environment interactions shape the immune response to SARS-CoV-2 infection in hospitalized COVID-19 patients 29 samples shares sars-cov-2
GSE241292 SARS-CoV-2-infected human nasal epithelial air-liquid interface cultures 16 samples shares sars-cov-2

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.