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Single-cell transcriptomics reveals distinct molecular alterations of human renal cell carcinoma-derived endothelial cells that are stable in ex vivo primary culture [RNA-Seq]

GSE237427 Homo sapiens (human) Single-cell RNA-seq 11 samples Submitted 2024/07/27 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 11 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)
Topic
Kidney cancerThis series is part of our curated briefing on it.
Samples
11 samples: 11 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)
Even though tumor endothelial cells are a key component of the tumor microenvironment and represent the main interface between tumor cells and the host’s circulation and immune system, their phenotypes and functional properties are relatively poorly understood. To address this deficit, we purified tumor and matched normal endothelial cells from highly vascularized kidney cancer specimens and performed single cell RNA-seq to characterize endothelial cell gene expression patterns and heterogeneity.
Read the full summary ↓
We found that tumor endothelial cells from multiple donors shared a common phenotype with increased expression of pathways related to extracellular matrix regulation, cell-cell communication and insulin growth factor signaling. We also found that while normal endothelial cells from kidney and liver were divergent, most tumor endothelial cells from kidney and liver cancer shared common gene expression signatures suggesting a congruence of tumor endothelial cell phenotypes. Using RNA-seq, we determined that many of the differentially regulated genes between tumor and normal endothelial cells were maintained in primary culture, which in turn permitted study of their functional phenotypes in vitro. Cultured tumor endothelial cells were resistant to apoptosis after trophic factor removal and displayed increased adhesiveness for CD+ CD25+ T regulatory cells. Our studies delineate the unique functional and immunophenotypic properties of kidney cancer tumor endothelial cells, which will serve as the basis to develop new therapies.
This dataset
Open GSE237427 in finder → Find more human RNA-seq datasets → Kidney cancer landscape →
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 GSE237427_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 PRJNA994933 and SRA study SRP449603. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
11 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 11 sample titles: 11 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
More Kidney cancer datasets

Search all human RNA-seq datasets in GEO →

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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.