Functional Classification of Cardiomyocytes Derived from Stem Cells

描述(申请人提供)：心脏病是美国每年女性和男性的头号死因。尽管近年来在常规药物和装置治疗方面取得了重大进展，但它们仍不能扭转功能心肌的丧失。有朝一日，随着从人类干细胞中获得功能性心肌细胞的能力，心脏的再生可能成为可能。然而，在这些细胞可以用于心脏修复之前，必须更多地了解它们的电生理学以及它们与天然心脏组织无缝结合的可能性。特别是，建立这些细胞与宿主心肌的电生理兼容性对于将心律失常的风险降至最低至关重要。尽管这一迫切需要，电生理表型的分类在很大程度上是主观的，对于到目前为止已经研究的所有细胞类型，主要依赖于与动作电位形状相关的参数。该项目的总体目标是基于为机器学习开发的技术，开发一种新的、分析的和自动化的方法来对新分化的心肌细胞进行分类。其具体目标是，首先，使用光学标测和微电极记录来生成人胚胎干细胞来源的心肌细胞(hESC-CMS)的功能电生理特征数据集，其次，基于参数描述，使用机器学习技术来对hESC-CMS的表型进行分类。这些技术将涉及线性和非线性降维算法，以及聚类和分类算法。处于不同分化阶段的细胞将在不同的起搏和药物条件下进行评估，这将有助于确定细胞的功能特性。总而言之，这项拟议的研究将使从人类干细胞衍生的心肌细胞分类成为可能。对心肌细胞表型进行分类和鉴定的能力将允许对细胞培养中批次之间的变异性、不同分化过程的影响以及心肌细胞分化和成熟过程中表型的演变进行定量评估。这些都是未来这些细胞用于再生心脏组织的临床应用的关键问题。 与公共卫生相关：干细胞疗法在治疗疾病和衰竭的心脏方面具有巨大的潜力，但仍面临重大挑战。该项目致力于这些细胞的电生理学方面，目标是开发能够分类和识别来自不同种类干细胞的各种心脏细胞的方法。

DESCRIPTION (provided by applicant): Heart disease is the number one cause of death in the United States each year for both women and men. Although significant advances have been made in conventional drug and device therapies in recent years, they have not been able to reverse the loss of functional myocardium. Regeneration of the heart may someday be possible with the ability to derive functional cardiomyocytes from human stem cells. However, before these cells can be used for cardiac repair, more must be known about their electrophysiology and their likelihood to seamlessly integrate with native cardiac tissue. In particular, it is of critical importance to establish the electrophysiological compatibility of these cells with host myocardium to minimize the risk of arrhythmia. Despite this critical need, the classification of the electrophysiological phenotypes has been largely subjective for all cell types that have been studied so far, relying mainly on parameters related to action potential shape. The overall goal of this project is to develop a new, analytical and automated method to classify newly differentiated cardiac cells, based on techniques developed for machine learning. The specific aims are first, to use optical mapping and microelectrode recordings to generate datasets of functional electrophysiological characteristics of human embryonic stem cell-derived cardiomyocytes (hESC-CMs), and second, to use machine learning techniques to classify the phenotypes of the hESC-CMs, based on parametric descriptions. These techniques will involve linear and nonlinear dimensionality reduction algorithms, and clustering and classification algorithms. Cells at different stages of differentiation will be evaluated at different pacing and pharmacological conditions that will help to establish the functional properties of the cells. In summary, the proposed research will enable the classification of cardiomyocytes that are derived from human stem cells. The ability to classify and identify cardiomyocyte phenotypes will permit a quantitative assessment of the batch-to-batch variability in cell cultures, the effect of different differentiation procedures, and the evolution of phenotypes during cardiomyocyte differentiation and maturation. These are critically important issues for the future clinical application of these cells to regenerate cardiac tissue. PUBLIC HEALTH RELEVANCE: Stem cell therapy holds great potential for treating diseased and failing hearts, but still faces significant challenges. This project addresses the electrophysiological aspects these cells, and the goal is to develop methods that can classify and identify the variety of heart cells that are derived from different kinds of stem cells.