Translational Medical Research Center, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo, Japan.
Department of Interdisciplinary Informatics, Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.
PLoS One. 2020 Apr 17;15(4):e0231774. doi: 10.1371/journal.pone.0231774. eCollection 2020.
Cancer is a complex disease caused by multiple types of interactions. To simplify and normalize the assessment of drug effects, spheroid microenvironments have been utilized. Research models that involve agent measurement with the examination of clonogenic survival by monitoring culture process with image analysis have been developed for spheroid-based screening. Meanwhile, computer simulations using various models have enabled better predictions for phenomena in cancer. However, user-based parameters that are specific to a researcher's own experimental conditions must be inputted. In order to bridge the gap between experimental and simulated conditions, we have developed an in silico analysis method with virtual three-dimensional embodiment computed using the researcher's own samples. The present work focused on HeLa spheroid growth in soft agar culture, with spheroids being modeled in silico based on time-lapse images capturing spheroid growth. The spheroids in silico were optimized by adjusting the growth curves to those obtained from time-lapse images of spheroids and were then assigned virtual inner proliferative activity by using generations assigned to each cellular particle. The ratio and distribution of the virtual inner proliferative activities were confirmed to be similar to the proliferation zone ratio and histochemical profiles of HeLa spheroids, which were also consistent with those identified in an earlier study. We validated that time-lapse images of HeLa spheroids provided virtual inner proliferative activity for spheroids in vitro. The present work has achieved the first step toward an in silico analysis method using computational simulation based on a researcher's own samples, helping to bridge the gap between experiment and simulation.
癌症是一种由多种类型相互作用引起的复杂疾病。为了简化和规范药物效果的评估,人们利用了球体微环境。已经开发了基于球体的筛选研究模型,该模型涉及通过监测图像分析的培养过程来测量药剂,并通过监测克隆存活来检查。同时,使用各种模型的计算机模拟能够更好地预测癌症中的现象。然而,用户基于参数必须输入特定于研究人员自己的实验条件。为了弥合实验和模拟条件之间的差距,我们开发了一种基于虚拟三维体现的计算方法,该方法使用研究人员自己的样本进行计算。本工作集中在软琼脂培养中的 HeLa 球体生长上,通过基于延时图像捕获球体生长的计算机模拟球体生长。通过调整生长曲线来模拟球体的延时图像,使球体在计算机中得到优化,然后通过分配给每个细胞粒子的世代来赋予虚拟内部增殖活性。虚拟内部增殖活性的比率和分布被证实与 HeLa 球体的增殖区比率和组织化学特征相似,这也与早期研究中的结果一致。我们验证了 HeLa 球体的延时图像为体外球体提供了虚拟内部增殖活性。本工作已经实现了使用基于研究人员自己样本的计算模拟进行计算机模拟分析方法的第一步,有助于弥合实验和模拟之间的差距。