Label-free single-cell imaging for quality control of cardiomyocyte biomanufacturing

项目总结/摘要 这项建议的目标是开发无标记显微镜和计算模型来预测效率 人诱导多能干细胞分化为心肌细胞（CMs）的生成和质量 （iPSCs）来改善人类心血管健康。从iPSC产生的CM正在彻底改变治疗 通过药物开发、疾病建模、心脏毒性测试和再生疗法治疗心脏病。 由于iPSC可以产生自体或低免疫原性的同种异体功能性CM，我们专注于改善 干细胞制造面临的两个转化障碍：预测iPSC-CM分化的效率 以及评估iPSC-CM成熟的程度。 有效的分化和成熟是iPSC-CM体外和体内应用的瓶颈。单个 批次内和批次间的细胞异质性阻碍了CM生产的规模化， 成本和生产时间。虽然大量的努力旨在提高iPSC-CM的成熟度， 与成年CM相比，iPSC-CM在功能上仍然不成熟，降低了它们在体外的预测能力， 当用作基于细胞的治疗时导致心律失常。为了实现其研究和临床潜力，新 需要单细胞过程分析技术和模型来预测iPSC-CM分化效率 成熟状态。预测模型可及早识别不合格批次，以实现闭环 流程，以纠正不合格批次，从而形成一个强大的，精简的流程。目前监测CM的方法 生物制造专注于最终阶段的分析，是低通量，劳动密集型和破坏性的。新 需要能够在单细胞水平上预测分化并快速鉴定成熟状态的技术 改善iPSC-CM生物制造并推进这些细胞的医疗保健应用。 细胞代谢的变化提供了用于iPSC-CM分化的有吸引力的过程分析测定， 成熟以前的研究，包括我们自己的研究，表明iPSC-CM在早期经历了巨大的代谢变化， 在分化中。考虑到这些代谢变化，我们假设无标记自体荧光显微术 结合细胞形态学的代谢辅酶可以提供实时的早期预测， iPSC-CM分化的效率和鉴定iPSC-CM在生物制造期间的成熟状态。我们 初步数据显示NAD（P）H和FAD荧光强度和寿命（光学代谢成像， 或OMI）可以在分化第1天预测iPSC-CM在第12天分化的效率，并且可以监测iPSC-CM在第12天分化的效率。 在无接触系统中3个月内CM成熟度的变化。在这里，我们将构建并验证这个OMI 使用iPSC-CM和体内基准的过程分析方法来创建分类模型， 强大且与开发相关，并将这些工具无缝集成到生物制造工作流程中。

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.