Gorospe Giann, Zhu Renjun, Millrod Michal A, Zambidis Elias T, Tung Leslie, Vidal Rene
IEEE Trans Biomed Eng. 2014 Sep;61(9):2389-95. doi: 10.1109/TBME.2014.2311387.
Methods for obtaining cardiomyocytes from human embryonic stem cells (hESCs) are improving at a significant rate. However, the characterization of these cardiomyocytes (CMs) is evolving at a relatively slower rate. In particular, there is still uncertainty in classifying the phenotype (ventricular-like, atrial-like, nodal-like, etc.) of an hESC-derived cardiomyocyte (hESC-CM). While previous studies identified the phenotype of a CM based on electrophysiological features of its action potential, the criteria for classification were typically subjective and differed across studies. In this paper, we use techniques from signal processing and machine learning to develop an automated approach to discriminate the electrophysiological differences between hESC-CMs. Specifically, we propose a spectral grouping-based algorithm to separate a population of CMs into distinct groups based on the similarity of their action potential shapes. We applied this method to a dataset of optical maps of cardiac cell clusters dissected from human embryoid bodies. While some of the nine cell clusters in the dataset are presented with just one phenotype, the majority of the cell clusters are presented with multiple phenotypes. The proposed algorithm is generally applicable to other action potential datasets and could prove useful in investigating the purification of specific types of CMs from an electrophysiological perspective.
从人类胚胎干细胞（hESCs）获取心肌细胞的方法正在以显著的速度改进。然而，这些心肌细胞（CMs）的表征发展速度相对较慢。特别是，在对hESC衍生的心肌细胞（hESC-CM）的表型（心室样、心房样、节点样等）进行分类时仍存在不确定性。虽然先前的研究根据动作电位的电生理特征确定了CM的表型，但分类标准通常是主观的，且不同研究之间存在差异。在本文中，我们使用信号处理和机器学习技术开发了一种自动方法，以区分hESC-CM之间的电生理差异。具体而言，我们提出了一种基于频谱分组的算法，根据动作电位形状的相似性将一群CMs分成不同的组。我们将此方法应用于从人类胚状体解剖得到的心脏细胞簇的光学图谱数据集。虽然数据集中九个细胞簇中的一些仅呈现一种表型，但大多数细胞簇呈现多种表型。所提出的算法通常适用于其他动作电位数据集，并可能从电生理角度证明对研究特定类型CMs的纯化有用。
