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Novel Long Noncoding RNA HEAT4 Affects Monocyte Subtypes, Reducing Inflammation and Promoting Vascular Healing [RNA-seq_KD]

GSE267412 Homo sapiens (human) Single-cell RNA-seq 14 samples Submitted 2024/07/15 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 14 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
14 samples: 4 × Monocytes, siSCR + LPS, vs 4 × Monocytes, siHeat4 + LPS, vs 3 × Monocytes, siSCR, vs 3 × Monocytes, siHeat4,Groups 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
Novel Long Noncoding RNA HEAT4 Affects Monocyte Subtypes, Reducing Inflammation and Promoting Vascular HealingCirculation · 2024
Summary (written by the submitters)
BACKGROUND: Activation of the immune system contributes to cardiovascular diseases. The role of human-specific long noncoding RNAs in cardioimmunology is poorly understood. METHODS: Single-cell sequencing in peripheral blood mononuclear cells revealed a novel human-specific long noncoding RNA called HEAT4 (heart failure–associated transcript 4).
Read the full summary ↓
HEAT4 expression was assessed in several in vitro and ex vivo models of immune cell activation, as well as in the blood of patients with heart failure (HF), acute myocardial infarction, or cardiogenic shock. The transcriptional regulation of HEAT4 was verified through cytokine treatment and single-cell sequencing. Loss-of-function and gain-of-function studies and multiple RNA–protein interaction assays uncovered a mechanistic role of HEAT4 in the monocyte anti-inflammatory gene program. HEAT4 expression and function was characterized in a vascular injury model in NOD.CB17-Prkdc scid/Rj mice. RESULTS: HEAT4 expression was increased in the blood of patients with HF, acute myocardial infarction, or cardiogenic shock. HEAT4 levels distinguished patients with HF from people without HF and predicted all-cause mortality in a cohort of patients with HF over 7 years of follow-up. Monocytes, particularly anti-inflammatory CD16+ monocytes, which are increased in patients with HF, are the primary source of HEAT4 expression in the blood. HEAT4 is transcriptionally activated by treatment with anti-inflammatory interleukin-10. HEAT4 activates anti-inflammatory and inhibits proinflammatory gene expression. Increased HEAT4 levels result in a shift toward more CD16+ monocytes. HEAT4 binds to S100A9, causing a monocyte subtype switch, thereby reducing inflammation. As a result, HEAT4 improves endothelial barrier integrity during inflammation and promotes vascular healing after injury in mice. CONCLUSIONS: These results characterize a novel endogenous anti-inflammatory pathway that involves the conversion of monocyte subtypes into anti-inflammatory CD16+ monocytes. The data identify a novel function for the class of long noncoding RNAs by preventing protein secretion and suggest long noncoding RNAs as potential targets for interventions in the field of cardioimmunology.
Published in
Novel Long Noncoding RNA HEAT4 Affects Monocyte Subtypes, Reducing Inflammation and Promoting Vascular Healing
Kneuer JM, Grajek IA, Winkler M et al. · Circulation 2024 · PMID 39005211 · doi:10.1161/CIRCULATIONAHA.124.069315
This dataset
Open GSE267412 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 GSE267412_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 PRJNA1111453 and SRA study SRP507572. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39005211) ↗
Study design
4 × Monocytes, siSCR + LPS, vs 4 × Monocytes, siHeat4 + LPS, vs 3 × Monocytes, siSCR, vs 3 × Monocytes, siHeat4,

Supports a between-group comparison across 14 samples.

How we read this: 4 replicated groups read from 14 sample titles; they account for 14 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
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