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用于辐照后细胞集落生长的自动时分辨析的框架。

A framework for automated time-resolved analysis of cell colony growth after irradiation.

机构信息

Department of Radiation Oncology and Radiation Therapy, University Hospital Heidelberg, Germany.

出版信息

Phys Med Biol. 2021 Jan 29;66(3):035017. doi: 10.1088/1361-6560/abd00d.

DOI:10.1088/1361-6560/abd00d
PMID:33264763
Abstract

Understanding dose-dependent survival of irradiated cells is a pivotal goal in radiotherapy and radiobiology. To this end, the clonogenic assay is the standard in vitro method, classifying colonies into either clonogenic or non-clonogenic based on a size threshold at a fixed time. Here we developed a methodological framework for the automated analysis of time course live-cell image data to examine in detail the growth dynamics of large numbers of colonies that occur during such an experiment. We developed a segmentation procedure that exploits the characteristic composition of phase-contrast images to identify individual colonies. Colony tracking allowed us to characterize colony growth dynamics as a function of dose by extracting essential information: (a) colony size distributions across time; (b) fractions of differential growth behavior; and (c) distributions of colony growth rates across all tested doses. We analyzed three data sets from two cell lines (H3122 and RENCA) and made consistent observations in line with already published results: (i) colony growth rates are normally distributed with a large variance; (ii) with increasing dose, the fraction of exponentially growing colonies decreases, whereas the fraction of delayed abortive colonies increases; as a novel finding, we observed that (iii) mean exponential growth rates decrease linearly with increasing dose across the tested range (0-10 Gy). The presented method is a powerful tool to examine live colony growth on a large scale and will help to deepen our understanding of the dynamic, stochastic processes underlying the radiation response in vitro.

摘要

理解受照射细胞的剂量依赖性存活是放射治疗学和放射生物学的一个关键目标。为此,集落形成测定法是体外标准方法,根据固定时间的大小阈值将集落分类为集落形成或非集落形成。在这里,我们开发了一种方法框架,用于自动分析时程活细胞图像数据,以详细研究此类实验中发生的大量集落的生长动态。我们开发了一种分割程序,利用相差图像的特征组成来识别单个集落。集落跟踪使我们能够通过提取基本信息来描述剂量依赖性的集落生长动态:(a) 随时间变化的集落大小分布;(b) 不同生长行为的分数;和 (c) 所有测试剂量的集落生长速率分布。我们分析了来自两个细胞系(H3122 和 RENCA)的三个数据集,并得出了与已发表结果一致的一致观察结果:(i) 集落生长速率呈正态分布,方差较大;(ii) 随着剂量增加,指数生长集落的分数减少,而延迟性流产集落的分数增加;作为一个新的发现,我们观察到 (iii) 在测试范围内(0-10 Gy),平均指数生长速率随剂量线性降低。所提出的方法是大规模检查活集落生长的有力工具,将有助于加深我们对体外放射反应中动态、随机过程的理解。

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