Using Deep Learning to Predict Induced Pluripotent Stem Cell-Derived Cardiomyocyte (iPSC-CM) Differentiation Outcomes

摘要 人诱导多能干细胞来源的心肌细胞(IPSC-CMS)提供了转化的新途径 来对抗心脏病。它们已被广泛用于疾病机制建模和药物预测。 回应。尽管在IPSC-CMS分化方面取得了显著进展，但分化结果仍然不同 跨批次、细胞系和方案，导致显著的实验变异性。结果, 确定差异化结果是至关重要的。IPSC-CM的现行表征标准 分化结果包括监测心肌细胞的功能或遗传属性 差异化。然而，这些过程非常耗时、不精确或昂贵。发现 更早的时间点和可以确定差异化结果的可扩展、准确的标记对于 消除心肌细胞分化的严重瓶颈，创造更好的IPSC-CM材料。vt.给出 IPSC-CMS在心血管研究中的重要性与日俱增，IPSC-CM分化的研究进展将会广泛 加快寻找治疗方法。 在这里，我建议利用人工智能(AI)、深度学习和计算机视觉来开发可扩展的、 准确描述和预测IPSC-CM分化结果的方法。要做到这一点，我将首先 使用深度学习模型识别可用于预测和确定的标记和时间点 差异化结果。我已经区分了IPSC-CMS，并获得了每天的图像数据集 用它们的最终分化结果标记的分化。我将使用深度分析数据集 学习模型-图像分类器-确定可用于预测的最早时间点 差异化结果。然后，我将把结果与转录数据相关联，以获得机械性的洞察。至 评估深度学习方法是否是可扩展和准确的差异化预测模型 结果，我将区分遗传上不同的IPSCs系，以创建额外的数据集来微调模型 然后评估模型的可伸缩性潜力。为了验证模型的准确性，我将首先验证 该模型的预测与分化的心肌细胞的传统功能和遗传标记相一致。 然后，我将从功能上、形态上和基因上将模型所做的预测与现有的预测进行比较 评估分化结果的方法。 这项提议的完成将消除IPSC-CM差异化的一个主要瓶颈；创建一个广泛的IPSC- 并加快了人工智能和深度学习在IPSC-CMS中的应用。 此外，概述的培训将为我提供计算和再生医学专业知识 后来成为一名独立的调查员和内科科学家。

ABSTRACT Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) provide transformative new avenues to combat heart diseases. They have been used extensively to model disease mechanisms and predict drug responses. Despite significant advancements in iPSC-CMs differentiation, differentiation outcomes still vary across batches, cell lines, and protocols, resulting in significant experimental variability. As a result, characterizing differentiation outcomes is essential. The current standards to characterize iPSC-CM differentiation outcomes involve monitoring for functional or genetic attributes of cardiomyocytes during the differentiation. However these processes are prohibitively time consuming, imprecise, or expensive. Discovering earlier time points and scalable, accurate markers that can determine differentiation outcomes is critical for eliminating a severe bottleneck in cardiomyocyte differentiation and creating better iPSC-CM materials. Given the rising importance of iPSC-CMs in cardiovascular research, advances in iPSC-CM differentiation would widely accelerate the search for cures. Here, I propose leveraging Artificial Intelligence (AI), deep learning and computer vision to develop scalable, accurate methods for characterizing and predicting iPSC-CM differentiation outcomes. To achieve this, I will first use deep learning models to identify markers and time points that can be used to predict and determine differentiation outcomes. I have differentiated iPSC-CMs and obtained a dataset of images at each day of differentiation that are labeled with their final differentiation outcome. I will analyze the dataset using a deep learning model—an image classifier—to determine the earliest time point that can be used to predict differentiation outcomes. I will then correlate results with transcriptomic data to gain mechanistic insight. To evaluate whether deep learning methods are scalable and accurate models for predicting differentiation outcomes, I will differentiate genetically diverse iPSCs lines to create an additional dataset to fine tune the model and then evaluate the model’s potential for scalability. To validate the accuracy of the model, I will first verify that the model’s predictions align with conventional functional and genetic markers of differentiated cardiomyocytes. Then, I will functionally, morphologically, and genetically compare predictions made by the model against existing methods for evaluating differentiating outcomes. Completion of this proposal will eliminate a main bottleneck in iPSC-CM differentiation; create an extensive iPSC- CM differentiation ‘morphology atlas’; and accelerate the application of AI and deep learning to iPSC-CMs. Additionally, the outlined training will provide me with the computational and regenerative medicine expertise required to later succeed as an independent investigator and physician scientist.