GEO series

Single-cell analysis reveals a novel regulatory mechanism of endothelial-to-mesenchymal transition

GSE237895 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2024/06/27 Platform GPL30209

At a glance

Single-cell RNA-seq · human · 12 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: 12 samples: 12 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: Integrated single-cell RNA-seq analysis reveals mitochondrial calcium signaling as a modulator of endothelial-to-mesenchymal transitionScience advances · 2024

Summary (written by the submitters)

The transdifferentiation of endothelial cells (ECs) towards a mesenchymal-like phenotype, referred to as endothelial-to-mesenchymal transition (EndMT), is critical for embryonic development, and in adults it is one of the major contributors to the onset of diseases, including cancer, fibrosis and a number of cardiovascular disorders. Here we identified mitochondrial calcium signaling as a key regulator of EndMT in three in vitro EndMT models, as well as in physiopathological conditions in vivo.

Read the full summary ↓

Pharmacological inhibition of the mitochondrial calcium uniporter (MCU) prevents EndMT. Deletion of MCU in ECs confirms loss of EndMT during cardiac embryo development as well as in a hind limb ischemia mouse model. Together, our data provide evidence of a novel regulatory mechanism of endothelial transdifferentation, thus potentially allowing for the development of new therapeutic interventions for EndMT-related diseases.

Published in

Integrated single-cell RNA-seq analysis reveals mitochondrial calcium signaling as a modulator of endothelial-to-mesenchymal transition

Lebas M, Chinigò G, Courmont E et al. · Science advances 2024 · PMID 39121218 · doi:10.1126/sciadv.adp6182

This dataset

Open GSE237895 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 GSE237895_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 PRJNA997064 and SRA study SRP450661. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39121218) ↗

Study design

12 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 12 sample titles: 12 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

GSM7656140 1 HUVEC PLKO-empty
GSM7656141 2 HUVEC shERG
GSM7656142 3 HUVEC pRRL-empty
GSM7656143 4 HUVEC SNAIOE
GSM7656144 5 HUVEC PLKO-empty
GSM7656145 6 HUVEC shERG
GSM7656146 7 HUVEC pRRL-empty
GSM7656147 8 HUVEC SNAIOE
GSM7656148 9 HUVEC CTL DAY7
GSM7656149 10 HUVEC TGF DAY7
GSM7656150 11 HUVEC CTL DAY7
GSM7656151 12 HUVEC TGF DAY7

Datasets on a similar topic

GSE327101 Single-Cell Multimodal Atlas Reveals Regulatory Programs in the Human Brain 220 samples shares single-cell, regulatory
GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares single-cell
GSE254315 Single-cell transcriptome analysis reveals the molecular mechanisms underlying age-related changes in human testis 25 samples shares single-cell
GSE328048 Single-cell transcriptome reveals keratinocyte subclusters that contribute to altered differentiation and inflammatory responses in atopic dermatitis 116 samples shares single-cell
GSE324131 A single-cell and spatial atlas of prostate cancer reveals the combinatorial nature of gene modules underlying lineage plasticity and metastasis 38 samples shares single-cell
GSE269877 Single-cell RNA-sequencing of BCG naïve and recurrent non-muscle invasive bladder cancer reveals a CD6/ALCAM-mediated immune-suppressive pathway 38 samples shares single-cell
GSE335480 Single-cell tracking reveals tumor-reactive T cell plasticity during melanoma TIL therapy 38 samples shares single-cell

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.