GEO series

Single-cell multimodal profiling of monocytes reveals diverse phenotypes and alterations linked to cardiovascular disease risks [CITE-seq]

GSE269478 Homo sapiens (human) Single-cell RNA-seq 30 samples Submitted 2024/07/29 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 30 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: 30 samples: 30 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: Monocyte Single-Cell Multimodal Profiling in Cardiovascular Disease Risk StatesCirculation research · 2024

Summary (written by the submitters)

Monocytes are a critical innate immune system cell type that serves homeostatic and immunoregulatory functions. They have been identified historically by the cell surface expression of CD14 and CD16. However, recent single-cell studies have revealed that they are much more heterogeneous than previously realized. We utilized cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-cell RNA sequencing (scRNA-seq) to describe the comprehensive transcriptional and phenotypic landscape of 437,126 monocytes.

Read the full summary ↓

This high-dimensional multimodal approach identified vast phenotypic diversity and functionally distinct subsets, including IFN-responsive, MHCIIhi, monocyte-platelet aggregates, as well as non-classical, sand several subpopulations of classical monocytes. Using flow cytometry, we validated the existence of MHCII+CD275+ MHCIIhi, CD42b+ monocyte-platelet aggregates, CD16+CD99- non-classical monocytes, and CD99+ classical monocytes. Each subpopulation exhibited unique characteristics, developmental trajectories, transcriptional regulation, and tissue distribution. In addition, alterations associated with cardiovascular disease (CVD) risk factors, including race, smoking, and hyperlipidemia were identified. Moreover, the effect of hyperlipidemia was recapitulated in mouse models of elevated cholesterol. This integrative and cross-species comparative analysis provides a new perspective on comparison of alterations in monocytes in pathological conditions and offers insights into monocyte-driven mechanisms in CVD and the potential for monocyte subpopulation targeted therapies.

Published in

Monocyte Single-Cell Multimodal Profiling in Cardiovascular Disease Risk States

Bashore AC, Xue C, Kim E et al. · Circulation research 2024 · PMID 39105287 · doi:10.1161/CIRCRESAHA.124.324457

This dataset

Open GSE269478 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 GSE269478_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 PRJNA1122114 and SRA study SRP512885. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39105287) ↗

Study design

30 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 30 sample titles: 30 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

GSM8316259 monocyte_cite_1A, GEX
GSM8316260 monocyte_cite_1A, ADT
GSM8316261 monocyte_cite_1B, GEX
GSM8316262 monocyte_cite_1B, ADT
GSM8316263 monocyte_cite_2A, GEX
GSM8316264 monocyte_cite_2A, ADT
GSM8316265 monocyte_cite_2B, GEX
GSM8316266 monocyte_cite_2B, ADT
GSM8316267 monocyte_cite_3A, GEX
GSM8316268 monocyte_cite_3A, ADT
GSM8316269 monocyte_cite_3B, GEX
GSM8316270 monocyte_cite_3B, ADT
GSM8316271 monocyte_cite_4A, GEX
GSM8316272 monocyte_cite_4A, ADT
GSM8316273 monocyte_cite_4B, GEX
GSM8316274 monocyte_cite_4B, ADT
GSM8316275 monocyte_cite_5A, GEX
GSM8316276 monocyte_cite_5A, ADT
GSM8316277 monocyte_cite_5B, GEX
GSM8316278 monocyte_cite_5B, ADT
GSM8316279 monocyte_cite_6, GEX
GSM8316280 monocyte_cite_6, ADT
GSM8316281 monocyte_cite_7, GEX
GSM8316282 monocyte_cite_7, ADT
GSM8316283 monocyte_cite_8, GEX
GSM8316284 monocyte_cite_8, ADT
GSM8316285 monocyte_cite_9, GEX
GSM8316286 monocyte_cite_9, ADT
GSM8316287 monocyte_cite_10, GEX
GSM8316288 monocyte_cite_10, ADT

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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.