自生持续随机力驱动生长肿瘤中的相分离
Self-generated persistent random forces drive phase separation in growing tumors
摘要：
单个实体瘤由几乎相同的细胞组成，表现出异质性动力学。核心中的细胞动力学是玻璃状的，而外围中的细胞经历扩散或超扩散行为。使用均方位移或自中间散射函数来量化异质性，这涉及对细胞群体进行平均，隐藏了集体运动的复杂性。使用t-分布随机邻居嵌入（t-SNE），一种流行的无监督机器学习降维技术，我们表明，相空间结构的不断发展的细胞群体，由细胞分裂和凋亡驱动，分区成几乎不相交的集，主要由核心和外围细胞。非平衡相分离是由细胞分裂引起的自生主动力的持久性差异驱动的。t-SNE揭示的广泛异质性为使用实验成像数据理解肿瘤内异质性的起源铺平了道路。
英文摘要：
A single solid tumor, composed of nearly identical cells, exhibits heterogeneous dynamics. Cells dynamics in the core is glass-like whereas those in the periphery undergo diffusive or super-diffusive behavior. Quantification of heterogeneity using the mean square displacement or the self-intermediate scattering function, which involves averaging over the cell population, hides the complexity of the collective movement. Using the t-distributed stochastic neighbor embedding (t-SNE), a popular unsupervised machine learning dimensionality reduction technique, we show that the phase space structure of an evolving colony of cells, driven by cell division and apoptosis, partitions into nearly disjoint sets composed principally of core and periphery cells. The non-equilibrium phase separation is driven by the differences in the persistence of self-generated active forces induced by cell division. Extensive heterogeneity revealed by t-SNE paves way towards understanding the origins of intratumor heterogeneity using experimental imaging data.
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