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Locus-specific and genome-wide analysis of adaptations to hypoxia in iPSC-derived endothelium from a population adapted to high altitude [RNA-Seq]

GSE271456 Homo sapiens (human) RNA-seq (gene expression) 20 samples Submitted 2024/12/01 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 20 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
20 samples: 10 × iPSC-EC, Han Chinese, hypoxia, ind vs 10 × iPSC-EC, Han Chinese, normoxia, indGroups 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
Transcriptomic analysis of iPSC-derived endothelium reveals adaptations to high altitude hypoxia in energy metabolism and inflammationPLoS genetics · 2025
Summary (written by the submitters)
Tibetan adaptation to high-altitude hypoxia remains a classic example of Darwinian selection in humans. Amongst Tibetans, alleles in the EPAS1 gene - whose protein product, Hif-2α, is a central regulator of the hypoxia response - have repeatedly been shown to carry some of the strongest signals of positive selection in humans and to influence several adaptive phenotypes.
Read the full summary ↓
We recently showed that the selected haplotype at this locus spans a hypoxia-dependent enhancer (ENH5) that contributes to the regulation of EPAS1 in a variety of cell types. However, selective sweep signals alone may account for only part of the phenotypes that differentiate Tibetans from closely related lowlanders. Therefore, there is a pressing need to functionally probe adaptive alleles and their impact at the genome-wide level and across cell types to uncover the full range of beneficial traits. To cast a wider net, we established a library of induced pluripotent stem cells (iPSCs) derived from Tibetan and Han Chinese individuals, a robust model system allowing precise exploration of both locus-specific and genome-wide effects on transcriptional responses. We harness this system by differentiating the iPSC library into vascular endothelium and investigating the locus-specific effects of the ENH5 enhancer in this cellular context. In addition, we use it to explore Tibetan-specific transcriptome-wide responses and find evidence that energy metabolism and immune pathways have been shaped by natural selection in Tibetans. Finally, to aid with the interpretation of the transcriptional differences between populations, we test for polygenic adaptations as a complementary in silico approach for the identification of beneficial Tibetan phenotypes.
Published in
Transcriptomic analysis of iPSC-derived endothelium reveals adaptations to high altitude hypoxia in energy metabolism and inflammation
Gray OA, Witonsky DB, Jousma J et al. · PLoS genetics 2025 · PMID 39928692 · doi:10.1371/journal.pgen.1011570
This dataset
Open GSE271456 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 GSE271456_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1131465 and SRA study SRP517844. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39928692) ↗
Study design
10 × iPSC-EC, Han Chinese, hypoxia, ind vs 10 × iPSC-EC, Han Chinese, normoxia, ind

Supports a between-group comparison across 20 samples.

How we read this: 2 replicated groups read from 20 sample titles; they account for 20 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
Datasets on a similar topic

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