Kim Joseph J, Vega Sebastián L, Moghe Prabhas V
Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA. P41 EB001046
Methods Mol Biol. 2013;1052:41-8. doi: 10.1007/7651_2013_29.
Current methods to characterize cell-biomaterial interactions are population-based and rely on imaging or biochemical analysis of end-point biological markers. The analysis of stem cells in cultures is further challenged by the heterogeneous nature and divergent fates of stem cells, especially in complex, engineered microenvironments. Here, we describe a high content imaging-based platform capable of identifying cell subpopulations based on cell phenotype-specific morphological descriptors. This method can be utilized to identify microenvironment-responsive morphological descriptors, which can be used to parse cells from a heterogeneous cell population based on emergent phenotypes at the single-cell level and has been successfully deployed to forecast long-term cell lineage fates and screen regenerative phenotype-prescriptive biomaterials.
目前用于表征细胞与生物材料相互作用的方法是基于群体的，并且依赖于对终点生物标志物的成像或生化分析。培养物中干细胞的分析因干细胞的异质性和不同命运而面临进一步挑战，尤其是在复杂的工程微环境中。在这里，我们描述了一个基于高内涵成像的平台，该平台能够根据细胞表型特异性形态学描述符识别细胞亚群。该方法可用于识别微环境响应性形态学描述符，这些描述符可用于基于单细胞水平的新兴表型从异质细胞群体中解析细胞，并且已成功用于预测长期细胞谱系命运和筛选具有再生表型规定性的生物材料。
