← BioTransfer GEO Dataset Finder
GEO series

Discovery of Key Regulators in Classical Monocyte Phenotypes Linked to COVID-19 Severity using Single-cell Multi-omics Sequencing

GSE297529 Homo sapiens (human) Single-cell RNA-seq 34 samples Submitted 2026/01/22 Platform GPL20795
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 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.
Summary (written by the submitters)
Dysregulated immune responses often accompany severe COVID-19, but the underlying epigenetic mechanisms driving monocyte heterogeneity and disease progression remain poorly understood. Here, we applied single-cell multi-omics profiling to peripheral blood mononuclear cells from individuals across five COVID-19 severity stages. We identified two severity-associated classical monocyte subtypes-IL7R+ and CD163+-characterized by distinct transcriptional and epigenetic landscapes.
Read the full summary ↓
Using constructing of gene regulatory network and in silico TF perturbations, we revealed ETS1 as a key driver of IL7R+ monocytes with T cell-like signaling features, and JDP2 as a repressor maintaining the pro-fibrotic, anti-inflammatory identity of CD163+ monocytes via suppression of AP-1 activity. These subtypes were enriched in moderate-to-critical stages and exhibited signaling pathways associated with tissue remodeling and immune suppression. Our findings define monocyte heterogeneity linked to COVID-19 severity and identify ETS1 and JDP2 as central regulators, offering insight into immune dysregulation and potential therapeutic targets for fibrosis and long-term sequelae.
This dataset
Open GSE297529 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 GSE297529_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 PRJNA1265151. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
34 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 28 samples read as cases, 6 as controls.

How we read this: Read from 34 sample titles: 34 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
Datasets on a similar topic

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.