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

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE314132 | GEO | 2026/02/20

REPOSITORIES: GEO

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Project description:Background: Primary knee osteoarthritis (KOA) is a heterogeneous disease with clinical and molecular contributors. Biofluids contain microRNAs and metabolites that can be measured by omic technologies. Deep learning captures complex non-linear associations within multimodal data but, to date, has not been used for multi-omic-based endotyping of KOA patients. We developed a novel multimodal deep learning framework for clustering of multi-omic data from three subject-matched biofluids to identify distinct KOA endotypes and classify one-year post-total knee arthroplasty (TKA) pain/function responses. Materials and Methods: In 414 KOA patients, subject-matched plasma, synovial fluid and urine were analyzed by microRNA sequencing or metabolomics. Integrating 4 high-dimensional datasets comprising metabolites from plasma (n=151 features), along with microRNAs from plasma (n=421), synovial fluid (n=930), or urine (n=1225), a multimodal deep learning variational autoencoder architecture with K-means clustering was employed. Features influencing cluster assignment were identified and pathway analyses conducted. An integrative machine learning framework combining 4 molecular domains and a clinical domain was then used to classify WOMAC pain/function responses post-TKA within each cluster. Findings: Multimodal deep learning-based clustering of subjects across 4 domains yielded 3 distinct patient clusters. Feature signatures comprising microRNAs and metabolites across biofluids included 30, 16, and 24 features associated with Clusters 1-3, respectively. Pathway analyses revealed distinct pathways associated with each cluster. Integration of 4 multi-omic domains along with clinical data improved response classification performance, with Cluster 3 achieving AUC=0·879 for subject pain response classification and Cluster 2 reaching AUC=0·808 for subject function response, surpassing individual domain classifications by 12% and 15% respectively. Interpretation: We have developed a deep learning-based multimodal clustering model capable of integrating complex multi-fluid, multi-omic data to assist in KOA patient endotyping and test outcome response to TKA surgery.

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

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