← BioTransfer GEO Dataset Finder
GEO series

Transcriptomic Analysis Identifies Transient Mesendodermal State and Lineage Divergence in Human Pluripotent Stem Cell Differentiation

GSE338003 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2026/07/16 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 21 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
21 samples: 7 conditions, mostly in triplicateGroups 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)
Human pluripotent stem cells serve as a vital model for studying early human lineage specification, yet conventional assessments relying on canonical markers may overlook transient intermediate states and broader cellular programs. Here we combined directed differentiation of human induced pluripotent stem cells toward neuroectodermal, cardiac mesodermal, and hepatic endodermal lineages with comparative transcriptomic profiling across timepoints.
Read the full summary ↓
Our analyses revealed a transient primitive streak-like mesendodermal state shared by mesodermal and endodermal trajectories, followed by lineage-specific divergence characterized by distinct transcriptional, metabolic, proliferative, and chromatin remodeling dynamics. Notably, endodermal differentiation exhibited rapid definitive endoderm commitment with enriched oxidative metabolism, whereas cardiac mesoderm differentiation showed progressive transcriptional remodeling and cardiac progenitor activation. These findings demonstrate that comparative transcriptomics can resolve developmental intermediates and cellular-state dynamics during human germ layer specification, providing a framework for evaluating lineage commitment beyond canonical marker expression.
This dataset
Open GSE338003 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 GSE338003_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 PRJNA1492089 and SRA study SRP716347. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
7 conditions, mostly in triplicate
2D_D0 ×3 2D_D1 ×3 2D_D3 ×3 2D_D6 ×3 2D_D9 ×3 2D_D12 ×3 2D_D16 ×3

Supports a between-group comparison across 21 samples.

How we read this: 7 replicated groups read from 21 sample titles; they account for 21 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

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.