ArticlebioRxiv : the preprint server for biology2026
Early multi-omic signatures and machine learning models predict cardiomyocyte differentiation efficiency and enable robust hPSC differentiation to cardiomyocytes.
Austin K Feeney, Aaron D Simmons, Elizabeth F Bayne, Yanlong Zhu, Mason R Pentes, Paulo F Cobra, Jianhua Zhang, Timothy J Kamp, Ying Ge, Sean P Palecek
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
10 authors.
Austin K FeeneyDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
Aaron D SimmonsDepartment of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
Elizabeth F BayneDepartment of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.
Yanlong ZhuDepartment of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI 53705, USA.
Mason R PentesDepartment of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
Paulo F CobraNational Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI 53706, USA.
Jianhua ZhangDepartment of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI 53705, USA.
Timothy J KampDepartment of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI 53705, USA.ORCID 0000-0003-2103-7876
Ying GeDepartment of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.
Sean P PalecekDepartment of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.ORCID 0000-0003-4917-5584
Funding
UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Justine Yang Bruce · 1985 to 2026
$142.6M
Biology of Aging and Age-Related Diseases Training GrantT32AG000213 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Rozalyn M. Anderson, Sanjay Asthana · 1991 to 2026
$9.9M
Biotechnology Training ProgramT32GM135066 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI SCOTT M. COYLE, BRIAN G FOX · 2020 to 2026
$7.0M
Integrated Training For Physician-ScientistsT32GM140935 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI Anna Huttenlocher, Jeniel E Nett · 2021 to 2026
$6.5M
NMR User Program at NMRFAMR24GM141526 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI Katherine Anne Henzler-Wildman · 2021 to 2026
$6.4M
Ultra High Resolution Mass Spectrometer for Biomedical ResearchS10OD018475 · OD · UNIVERSITY OF WISCONSIN-MADISON · PI GE, YING · 2015 to 2015
$2.0M
A Multi-Omics Approach to Discover Metabolic Critical Quality Attributes for Cardiomyocyte BiomanufacturingR01HL148059 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI PALECEK, SEAN P · 2019 to 2022
$1.5M
Technologies enabling robust closed-loop manufacturing of human pluripotent stem cell-derived cardiomyocytesR01HL178095 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI Sean P Palecek · 2025 to 2026
$1.2M
Enhancing robustness of human pluripotent stem cell differentiation to cardiomyocytes by uncovering on- and off-target differentiation trajectories via single nucleus multi-omicsF30HL173988 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI Austin K. Feeney · 2024 to 2026
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. 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.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Early multi-omic signatures and machine learning models predict cardiomyocyte differentiation efficiency and enable robust hPSC differentiation to cardiomyocytes. · full record | OpenQuestion