Wednesday, May 15, 2024
5/15/2024
PROJECT SUMMARY / ABSTRACT The goal of this proposal is to develop label-free microscopy and computational models to predict the efficiency of generation and the quality of cardiomyocytes (CMs) differentiated from human induced pluripotent stem cells (iPSCs) to improve human cardiovascular health. CMs generated from iPSCs are revolutionizing treatment of heart disease through drug development, disease modeling, cardiac toxicity testing, and regenerative therapy. Since iPSCs can generate autologous or hypoimmunogenic allogeneic, functional CMs, we focus on improving two translational roadblocks facing stem cell manufacturing: predicting the efficiency of iPSC-CM differentiation and assessing the extent of iPSC-CM maturation. Efficient differentiation and maturation are bottlenecks for in vitro and in vivo applications of iPSC-CMs. Single cell heterogeneity within and between batches has impeded the scale-up of CM manufacturing by increasing cost and production times through failed batches. While significant efforts aim to improve iPSC-CMs maturity, compared to adult CMs, iPSC-CMs remain functionally immature, reducing their predictive capacity in vitro and resulting in arrhythmias when used as a cell-based therapy. To realize their research and clinical potential, new single-cell process analytic technologies and models are needed to predict iPSC-CM differentiation efficiency and maturation state. Predictive models provide early identification of failed batches to enable closed loop processes to correct failing batches, resulting in a robust, streamlined process. Current methods to monitor CM biomanufacturing focus on end-stage analytics, are low-throughput, labor-intensive, and destructive. New technologies that can predict differentiation and rapidly identify maturation state at the single cell level are needed to improve iPSC-CM biomanufacturing and advance health care applications of these cells. Changes in cell metabolism provide attractive process analytic assays for iPSC-CM differentiation and maturation. Previous studies, including our own, show that iPSC-CMs undergo dramatic metabolic changes early in differentiation. Given these metabolic changes, we hypothesize that label-free autofluorescence microscopy of metabolic co-enzymes combined with cell morphology can provide real-time early-stage prediction of the efficiency of iPSC-CM differentiation and identify iPSC-CM maturation state during biomanufacturing. Our preliminary data shows that NAD(P)H and FAD fluorescence intensities and lifetimes (optical metabolic imaging, or OMI) can predict on differentiation day 1 the efficiency of iPSC-CM differentiation at day 12, and can monitor changes in CM maturation over 3-months in a touch-free system. Here, we will build and validate this OMI process analytic approach using iPSC-CMs and in vivo benchmarks to create classification models that are robust and developmentally relevant, and seamlessly integrate these tools into the biomanufacturing workflow.
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