Functional Classification of Cardiomyocytes Derived from Stem Cells

干细胞来源的心肌细胞的功能分类

基本信息

批准号：
8259042
负责人：
LESLIE TUNG
金额：
$ 20.5万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-04-20 至 2014-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8259042
关键词：
Action Potentials Address Adipocytes Adrenergic Agents Algorithms Arrhythmia Blood Cells Bone Marrow Cardiac Cardiac Myocytes Cause of Death Cell Culture Techniques Cells Characteristics Classification Data Data Set Descriptor Development Devices Discriminant Analysis Electrophysiology (science)Embryo Evaluation Evolution Face Frequencies Future Goals Heart Heart Atrium Heart Diseases Isometric Exercise Machine Learning Maps Measurement Methods Microelectrodes Morphology Myocardium Natural regeneration Nodal Optics Pharmaceutical Preparations Phenotype Principal Component Analysis Procedures Property Regenerative Medicine Research Research Personnel Resources Rest Risk Series Shapes Staging Stem cells Techniques Testing Time Tissues Training Transplantation Umbilical Cord Blood United States Variant Ventricular Woman adrenergic adult stem cell base cardiac repair cell type cholinergic clinical application functional loss heart cell human embryonic stem cell human stem cells induced pluripotent stem cell innovation interest men molecular marker novel public health relevance research study response stem cell therapy tissue repair

项目摘要

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.

描述（由申请人提供）：心脏病是美国和男性每年在美国的第一名死亡原因。尽管近年来在常规药物和装置疗法方面取得了重大进展，但它们无法扭转功能性心肌的丧失。有一天可以从人类干细胞中获得功能性心肌细胞的能力。但是，在这些细胞可用于心脏修复之前，必须了解其电生理学以及与天然心脏组织无缝集成的可能性。特别是，建立这些细胞与宿主心肌的电生理兼容性以最大程度地减少心律不齐的风险至关重要。尽管有这种急需的需求，但对于迄今为止研究的所有细胞类型，电生理表型的分类主要是主观的，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用于机器学习开发的技术，开发一种新的，分析和自动化的方法来对新分化的心脏细胞进行分类。首先，使用光学映射和微电极记录来生成人类胚胎干细胞衍生的心肌细胞（HESC-CMS）功能电生理特征的数据集，其次，使用机器学习技术来根据参数的访问量表来对HESC-CMS的表型进行分类。这些技术将涉及线性和非线性维度降低算法以及聚类和分类算法。分化不同阶段的细胞将在不同的起搏和药理条件下进行评估，这将有助于建立细胞的功能特性。总而言之，拟议的研究将使从人类干细胞中得出的心肌细胞分类。分类和识别心肌细胞表型的能力将允许对细胞培养物中批次之间变异性，不同分化程序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。对于这些细胞再生心脏组织的未来临床应用是至关重要的问题。公共卫生相关性：干细胞疗法具有治疗患病和失败心脏的巨大潜力，但仍面临重大挑战。该项目解决了这些细胞的电生理方面，目的是开发可以分类和识别从不同种类干细胞中的心脏细胞进行分类和识别的方法。