The investigation of cell cycle stage-dependent processes in a population of cells is often performed using flow cytometry. While this approach is high-throughput, it is relatively low in resolution and unable to measure phenotypic changes or processes occurring in subcellular compartments. We integrated automated microscopy with newly developed informatics workflow that enabled the quantitation of multiple fluorescent markers from specific subnuclear regions throughout a population of cells. Telomeres protect chromosome termini and prevent cellular aging. Cancer cells lengthen telomeres by synthesizing new TTAGGG repeats by the enzyme telomerase, while others activate recombination-dependent alternative lengthening of telomeres (ALT). A key feature of the ALT pathway is the specific clustering of promyelocytic leukemia (PML) nuclear bodies at telomeres. These ALT-associated PML bodies (APBs) common in tumors of mesenchymal origin have gained in diagnostic use in the past decade. Here we applied recent improvements in automated microscopy and developed novel informatics workflows for quantitation of multiple fluorescent markers from specific subnuclear regions at the single cell level. Key to this workflow are customized machine learning algorithms within HCS Studio™ Cell Analysis which automatically identify and segment cells into defined regions of interest based on fluorescent markers, measure marker intensities and compute marker colocalizations in specific segmented regions. These multiparametric cellular assays assess cell cycle dynamics as well as the interactome of APBs, are amenable to adherent cells and histological sections, and are adaptable for use with additional markers. In the future we anticipate exploiting these algorithms for a wide range of research questions related to telomere biology with potential to facilitate clinical development of ALT detection assays to benefit patients with these often-poor prognosis tumors. © 2020 International Society for Advancement of Cytometry.

中文翻译：

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开发用于细胞周期分期和病灶定量的自动显微镜辅助高内涵多参数分析。

细胞群中细胞周期阶段依赖性过程的研究通常使用流式细胞术进行。虽然这种方法是高通量的，但它的分辨率相对较低，无法测量亚细胞区室中发生的表型变化或过程。我们将自动显微镜与新开发的信息学工作流程相结合，从而能够对整个细胞群中特定亚核区域的多个荧光标记进行定量。端粒保护染色体末端并防止细胞老化。癌细胞通过端粒酶合成新的 TTAGGG 重复序列来延长端粒，而其他细胞则激活依赖重组的端粒替代延长 (ALT)。ALT 通路的一个关键特征是早幼粒细胞白血病 (PML) 核体在端粒处的特定聚集。在过去十年中，这些在间充质来源肿瘤中常见的 ALT 相关 PML 小体 (APB) 已获得诊断用途。在这里，我们应用了自动化显微镜的最新改进，并开发了新的信息学工作流程，用于在单细胞水平上对来自特定亚核区域的多个荧光标记进行定量。此工作流程的关键是 HCS Studio™ 细胞分析中的自定义机器学习算法，该算法根据荧光标记自动识别细胞并将其分割成定义的感兴趣区域，测量标记强度并计算特定分割区域中的标记共定位。这些多参数细胞测定评估细胞周期动态以及 APB 的相互作用，适合贴壁细胞和组织切片，并适用于其他标记物。未来，我们预计将这些算法用于与端粒生物学相关的广泛研究问题，并有可能促进 ALT 检测分析的临床开发，从而使这些预后不良的肿瘤患者受益。© 2020 国际细胞计量学促进会。