← BioTransfer GEO Dataset Finder
GEO series

Single-Cell Transcriptomic Analysis of Kaposi Sarcoma

GSE337872 Homo sapiens (human) Single-cell RNA-seq 23 samples Submitted 2026/07/07 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 23 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
23 samples: 23 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
Single-Cell Transcriptomic Analysis of Kaposi SarcomaPLoS pathogens · 2025
Summary (written by the submitters)
Kaposi Sarcoma (KS) is a complex tumor caused by KS-associated herpesvirus 8 (KSHV). Histological analysis reveals a mixture of “spindle cells”, vascular-like spaces, extravasated erythrocytes, and immune cells. In order to elucidate the infected and uninfected cell types in KS tumors, we examined twenty-five skin and blood samples from sixteen subjects by single cell RNA sequence analyses.
Read the full summary ↓
Two populations of KSHV-infected cells were identified, one of which represented a CD34-negative proliferative fraction of endothelial cells, and the second representing CD34-positive cells expressing endothelial genes found in a variety of cell types including high endothelial venules, fenestrated capillaries, and endothelial tip cells. Although both infected clusters contained cells expressing lytic and latent KSHV genes, the CD34+ cells expressed more K5 and less K12. Novel cellular biomarkers were identified in the KSHV infected cells, including the sodium channel SCN9A. The number of KSHV positive cells was found to be less than 10% of total tumor cells in all samples and correlated inversely with tumor-infiltrating immune cells. T-cell receptor clones were expanded in KS tumors and blood, although in differing magnitudes. Changes in cellular composition in KS tumors after treatment with antiretroviral therapy alone, or immunotherapy were noted. These studies demonstrate the feasibility of single cell analyses to identify prognostic and predictive biomarkers.
Published in
Single-Cell Transcriptomic Analysis of Kaposi Sarcoma
Rauch DA, Ramos PV, Khanfar M et al. · PLoS pathogens 2025 · PMID 40168402 · doi:10.1371/journal.ppat.1012233
This dataset
Open GSE337872 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 GSE337872_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 PRJNA1168592 and SRA study SRP543512. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40168402) ↗
Study design
23 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 23 sample titles: 23 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.