GEO series

TET2 Mutant Clonal Hematopoiesis Enhances Severity of COVID-19 Related Cytokine Release Syndrome and is Associated with Increased Mortality [scRNA-seq]

GSE210433 Homo sapiens (human) Single-cell RNA-seq 34 samples Submitted 2024/08/14 Platform GPL20301

At a glance

Single-cell RNA-seq · human · 34 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: 34 samples: 34 × scRNA-seq - PatientGroups 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: RNA Shielding of p65 Is Required to Potentiate Oncogenic Inflammation in TET2-Mutated Clonal HematopoiesisCancer discovery · 2024

Summary (written by the submitters)

Coronavirus disease 2019 (COVID-19) is associated with significant morbidity and mortality, albeit with considerable heterogeneity among affected individuals. Emerging evidence points towards an important role of preexisting host factors, such as a deregulated inflammatory response at the time of infection. Here, we demonstrate the negative impact of clonal hematopoiesis, a prevalent clonal disorder of ageing individuals, on COVID-19-related cytokine release severity and mortality.

Read the full summary ↓

In this study we perform single cell RNA sequencing of PBMCs from COVID19 patients and patients with clonal hematopoiesis.

Published in

RNA Shielding of p65 Is Required to Potentiate Oncogenic Inflammation in TET2-Mutated Clonal Hematopoiesis

Ben-Crentsil NA, Mohammed Ismail W, Balasis ME et al. · Cancer discovery 2024 · PMID 39189614 · doi:10.1158/2159-8290.CD-24-0093

This dataset

Open GSE210433 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 GSE210433_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 PRJNA865721 and SRA study SRP389703. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39189614) ↗

Study design

34 × scRNA-seq - Patient

Supports a between-group comparison across 34 samples.

How we read this: 1 replicated groups read from 34 sample titles; they account for 34 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

GSM6430179 scRNA-seq - Patient 1 scRNA-seq - Patient
GSM6430180 scRNA-seq - Patient 4 scRNA-seq - Patient
GSM6430181 scRNA-seq - Patient 5 scRNA-seq - Patient
GSM6430182 scRNA-seq - Patient 6 scRNA-seq - Patient
GSM6430183 scRNA-seq - Patient 7 scRNA-seq - Patient
GSM6430184 scRNA-seq - Patient 8 scRNA-seq - Patient
GSM6430185 scRNA-seq - Patient 9 scRNA-seq - Patient
GSM6430186 scRNA-seq - Patient 12 scRNA-seq - Patient
GSM6430187 scRNA-seq - Patient 13 scRNA-seq - Patient
GSM6430188 scRNA-seq - Patient 14 scRNA-seq - Patient
GSM6430189 scRNA-seq - Patient 15 scRNA-seq - Patient
GSM6430190 scRNA-seq - Patient 16 scRNA-seq - Patient
GSM6430191 scRNA-seq - Patient 18 scRNA-seq - Patient
GSM6430192 scRNA-seq - Patient 19 scRNA-seq - Patient
GSM6430193 scRNA-seq - Patient 20 scRNA-seq - Patient
GSM6430194 scRNA-seq - Patient 21 scRNA-seq - Patient
GSM6430195 scRNA-seq - Patient 23 scRNA-seq - Patient
GSM6430196 scRNA-seq - Patient 24 scRNA-seq - Patient
GSM6430197 scRNA-seq - Patient 25 scRNA-seq - Patient
GSM6430198 scRNA-seq - Patient 27 scRNA-seq - Patient
GSM6430199 scRNA-seq - Patient 28 scRNA-seq - Patient
GSM6430200 scRNA-seq - Patient 29 scRNA-seq - Patient
GSM6430201 scRNA-seq - Patient 30 scRNA-seq - Patient
GSM6430202 scRNA-seq - Patient 34 scRNA-seq - Patient
GSM6430203 scRNA-seq - Patient 35 scRNA-seq - Patient
GSM6430204 scRNA-seq - Patient 36 scRNA-seq - Patient
GSM6430205 scRNA-seq - Patient 37 scRNA-seq - Patient
GSM6430206 scRNA-seq - Patient 38 scRNA-seq - Patient
GSM6430207 scRNA-seq - Patient 40 scRNA-seq - Patient
GSM6430208 scRNA-seq - Patient 41 scRNA-seq - Patient
GSM6430209 scRNA-seq - Patient 42 scRNA-seq - Patient
GSM6430210 scRNA-seq - Patient 43 scRNA-seq - Patient
GSM6430211 scRNA-seq - Patient 45 scRNA-seq - Patient
GSM6430212 scRNA-seq - Patient 46 scRNA-seq - Patient

Datasets on a similar topic

GSE210435 TET2 Mutant Clonal Hematopoiesis Enhances Severity of COVID-19 Related Cytokine Release Syndrome and is Associated with Increased Mortality. 363 samples shares covid-19, tet2, hematopoiesis, increased, mortality, enhances, severity
GSE210432 TET2 Mutant Clonal Hematopoiesis Enhances Severity of COVID-19 Related Cytokine Release Syndrome and is Associated with Increased Mortality [scMULT] 36 samples shares covid-19, tet2, hematopoiesis, increased, mortality, enhances, severity
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GSE264443 Dynamic gene expression analysis reveals distinct severity phases of immune and cellular dysregulation in COVID-19 189 samples shares covid-19, severity
GSE208342 Transcriptome and microbiome profile of nasopharyngeal samples among COVID-19 patients with different levels of severity revealed through RNA-seq analysis 20 samples shares covid-19, severity
GSE196198 Longitudinal single-cell multiomics data for different severity of COVID-19 100 samples shares covid-19, severity
GSE297529 Discovery of Key Regulators in Classical Monocyte Phenotypes Linked to COVID-19 Severity using Single-cell Multi-omics Sequencing 34 samples shares covid-19, severity

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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.