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High-throughput physical phenotyping of cell differentiation

机译:细胞分化的高通量物理表型

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摘要

In this report, we present multiparameter deformability cytometry (m-DC), in which we explore a large set of parameters describing the physical phenotypes of pluripotent cells and their derivatives. m-DC utilizes microfluidic inertial focusing and hydrodynamic stretching of single cells in conjunction with high-speed video recording to realize high-throughput characterization of over 20 different cell motion and morphology-derived parameters. Parameters extracted from videos include size, deformability, deformation kinetics, and morphology. We train support vector machines that provide evidence that these additional physical measurements improve classification of induced pluripotent stem cells, mesenchymal stem cells, neural stem cells, and their derivatives compared to size and deformability alone. In addition, we utilize visual interactive stochastic neighbor embedding to visually map the high-dimensional physical phenotypic spaces occupied by these stem cells and their progeny and the pathways traversed during differentiation. This report demonstrates the potential of m-DC for improving understanding of physical differences that arise as cells differentiate and identifying cell subpopulations in a label-free manner. Ultimately, such approaches could broaden our understanding of subtle changes in cell phenotypes and their roles in human biology.
机译:在本报告中,我们提出了多参数可变形细胞术(m-DC),其中我们探索了描述多能细胞及其衍生物的物理表型的大量参数。 m-DC利用单细胞的微流体惯性聚焦和流体动力拉伸,结合高速视频记录,可对20多种不同细胞运动和形态学衍生参数进行高通量表征。从视频中提取的参数包括大小,可变形性,变形动力学和形态。我们训练了支持向量机,这些机器提供的证据表明,与单独的大小和可变形性相比,这些附加的物理测量结果可改善诱导性多能干细胞,间充质干细胞,神经干细胞及其衍生物的分类。此外,我们利用视觉交互式随机邻居嵌入来可视化映射这些干细胞及其后代所占据的高维物理表型空间以及分化过程中遍历的途径。该报告证明了m-DC潜在的潜力,可以增进人们对随着细胞分化而产生的物理差异的理解,并以无标记的方式识别细胞亚群。最终,这种方法可以拓宽我们对细胞表型细微变化及其在人类生物学中的作用的理解。

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