Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues
Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues
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DOI：
10.1016/j.biomaterials.2016.06.040
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发表时间：
2016-10-01
期刊：
影响因子：
14
通讯作者：
Losert, Wolfgang
中科院分区：
文献类型：
--
作者：
Chen, Desu;Sarkar, Sumona;Losert, Wolfgang
细胞形态学已被确定为干细胞对生物材料反应的潜在指标。然而，生物材料中细胞形状表型的确定由于细胞群体的异质性、微环境的异质性和细胞形态的多参数定义而变得复杂。为了将细胞形态与细胞-材料相互作用联系起来，我们开发了一个基于支持向量机的形状表型框架。实施特征选择程序以选择细胞形状度量的最显著组合来构建具有准确性和稳定性的分类器，以识别和预测异质细胞群体中微环境驱动的形态学差异。该分析在多细胞水平上进行，其中“超级细胞”方法使用小群体单细胞的平均形状测量来解释异质群体和微环境。子采样验证算法揭示了分类器稳定性和泛化能力所需的超胞大小和样本大小的范围。例如，在第1天比较人骨髓基质细胞（hBMSC）对纤维微环境与平坦微环境的反应。我们的分析表明，57个细胞（分为大小为4的超级细胞）是表型分型所需的最小值。该分析确定，短轴长度、坚固性和平均负曲率的组合是hBMSCs对纤维微环境响应的最强的基于早期形状的指标。(C)2016由Elsevier Ltd.出版
Cell morphology has been identified as a potential indicator of stem cell response to biomaterials. However, determination of cell shape phenotype in biomaterials is complicated by heterogeneous cell populations, microenvironment heterogeneity, and multi-parametric definitions of cell morphology. To associate cell morphology with cell-material interactions, we developed a shape phenotyping framework based on support vector machines. A feature selection procedure was implemented to select the most significant combination of cell shape metrics to build classifiers with both accuracy and stability to identify and predict microenvironment-driven morphological differences in heterogeneous cell populations. The analysis was conducted at a multi-cell level, where a "supercell" method used average shape measurements of small groups of single cells to account for heterogeneous populations and microenvironment. A subsampling validation algorithm revealed the range of supercell sizes and sample sizes needed for classifier stability and generalization capability. As an example, the responses of human bone marrow stromal cells (hBMSCs) to fibrous vs flat microenvironments were compared on day 1. Our analysis showed that 57 cells (grouped into supercells of size 4) are the minimum needed for phenotyping. The analysis identified that a combination of minor axis length, solidity, and mean negative curvature were the strongest early shape-based indicator of hBMSCs response to fibrous microenvironment. (C) 2016 Published by Elsevier Ltd.