Department of Computational Medicine, University of California, Los Angeles, CA, United States of America; Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America.
Institute for Systems Biology, Seattle, WA, United States of America.
J Theor Biol. 2023 Nov 7;575:111645. doi: 10.1016/j.jtbi.2023.111645. Epub 2023 Oct 18.
Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized uniform exponential growth of the cell population. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture a departure from the uniform exponential growth model for the initial growth ("take-off"). Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth dynamics, which could be explained by the presence of inter-converting subpopulations with different growth rates, and which could last for many generations. Based on the hypothesis of existence of multiple subpopulations, we developed a branching process model that was consistent with the experimental observations.
最近在单细胞分辨率水平的研究揭示了名义上克隆的肿瘤细胞群体中的表型异质性。这种异质性影响细胞的生长行为，可能导致偏离细胞群体理想化的均一指数增长。在这里，我们测量了起始细胞数不同的 HL60 白血病细胞群体在培养物中的随机时间进程，以捕捉初始生长的均一指数增长模型的偏离（“起飞”）。尽管这些细胞源自同一细胞克隆，但我们观察到个别培养物的早期生长模式存在显著差异，其生长动力学存在统计学上的显著差异，这可以用具有不同生长速率的相互转化亚群的存在来解释，并且这种差异可以持续多代。基于存在多个亚群的假设，我们开发了一个分支过程模型，该模型与实验观察结果一致。
