论文标题
自我生成的持续随机力在生长肿瘤中驱动阶段分离
Self-generated persistent random forces drive phase separation in growing tumors
论文作者
论文摘要
由几乎相同细胞组成的单个实体瘤表现出异质动力学。核心中的细胞动力学类似于玻璃，而周围的细胞会经历扩散或超扩散行为。使用均方根位移或自相隔散射函数对异质性进行定量，涉及在细胞种群上平均，这隐藏了集体运动的复杂性。使用T分布的随机邻居嵌入（T-SNE），这是一种流行的无监督的机器学习维度降低技术，我们表明，由细胞分裂和凋亡驱动的不断发展的细胞菌落的相空间结构，将几乎不相同的核心和外围细胞组成。非平衡相的分离是由细胞分裂引起的自我产生的活性力持续性的差异驱动的。 T-SNE Paves揭示了广泛的异质性，可以使用实验成像数据来理解肿瘤内异质性的起源。
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.
