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Early multi-omic signatures and machine learning models predict cardiomyocyte differentiation efficiency and enable robust hPSC differentiation to cardiomyocytes

GSE314132 Homo sapiens (human) RNA-seq (gene expression) 80 samples Submitted 2026/02/20 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 80 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
80 samples: 21 conditions, mostly with about 4 replicates eachGroups 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
Early multi-omic signatures and machine learning models predict cardiomyocyte differentiation efficiency and enable robust hPSC differentiation to cardiomyocytesbioRxiv : the preprint server for biology · 2026
Summary (written by the submitters)
Protocols for generating cardiomyocytes (CMs) from human pluripotent stem cells (hPSCs) have existed for nearly two decades, yet manufacturing variability in terminal cell identity continues to limit clinical translation. To uncover the origin of fate divergence during hPSC-CM differentiation, we performed temporal transcriptomics, proteomics, and metabolomics of high and low efficiency differentiations.
Read the full summary ↓
We identified significant early multi-omic divergence between differentiation batches and key pathways underlying fate divergence at critical differentiation stages included Wnt, MAPK, and glucose metabolism. Machine learning models trained on early candidate gene markers predicted hPSC-CM purity better than models using canonical cardiac development markers. Lastly, multi-omic insights informed perturbations, including Wnt and MAPK inhibition, which produced higher CM purities and yields. Our results showcase multi-omic analysis coupled with machine learning models as a powerful tool to identify cell fate determinants and enable robust manufacturing of complex cell products such as hPSC-derived cell therapies.
Published in
Early multi-omic signatures and machine learning models predict cardiomyocyte differentiation efficiency and enable robust hPSC differentiation to cardiomyocytes
Feeney AK, Simmons AD, Bayne EF et al. · bioRxiv : the preprint server for biology 2026 · PMID 41542644 · doi:10.64898/2026.01.08.698025
This dataset
Open GSE314132 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 GSE314132_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 PRJNA1390140 and SRA study SRP656011. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41542644) ↗
Study design
21 conditions, mostly with about 4 replicates each
WTC11 hiPSC, Passage 52, high purity hPSM-CM … ×4 WTC11 hiPSC, Passage 52, high purity hPSM-CM … ×4 WTC11 hiPSC, Passage 52, high purity hPSM-CM … ×4 WTC11 hiPSC, Passage 52, high purity hPSM-CM … ×4 WTC11 hiPSC, Passage 69, low purity hPSM-CM d… ×4 WTC11 hiPSC, Passage 69, low purity hPSM-CM d… ×4 WTC11 hiPSC, Passage 69, low purity hPSM-CM d… ×4 WTC11 hiPSC, Passage 69, low purity hPSM-CM d… ×4 +14 more

Supports a between-group comparison across 79 samples.

How we read this: 21 replicated groups read from 80 sample titles; they account for 79 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

+ 40 more — browse all 80 samples with per-sample file links →

Datasets on a similar topic

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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.