Robinson Research Institute, School of Medicine, The University of Adelaide, South Australia, Australia.
Cytometry A. 2020 Apr;97(4):378-393. doi: 10.1002/cyto.a.23988. Epub 2020 Feb 21.
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.
使用流式细胞术通常可以对细胞群体中的细胞周期阶段依赖性过程进行研究。虽然这种方法具有高通量的特点，但分辨率相对较低，无法测量细胞亚区室中发生的表型变化或过程。我们将自动化显微镜与新开发的信息学工作流程相结合，使我们能够对细胞群体中特定亚核区域的多个荧光标记物进行定量。端粒保护染色体末端，防止细胞衰老。癌细胞通过酶端粒酶合成新的 TTAGGG 重复序列来延长端粒，而其他细胞则激活依赖于重组的端粒的替代性延长（ALT）。ALT 途径的一个关键特征是早幼粒细胞白血病（PML）核体在端粒处的特异性聚集。这些与 ALT 相关的 PML 体（APB）在过去十年中在源自间充质的肿瘤的诊断中得到了广泛应用。在这里，我们应用了自动化显微镜的最新进展，并开发了新的信息学工作流程，用于在单细胞水平上对特定亚核区域的多个荧光标记物进行定量。该工作流程的关键是 HCS Studio™细胞分析中的定制机器学习算法，该算法可以根据荧光标记物自动识别和分割细胞到定义的感兴趣区域，测量标记物的强度，并计算特定分割区域中的标记物共定位。这些多参数细胞检测评估细胞周期动力学以及 APB 的相互作用组，适用于贴壁细胞和组织切片，并且可以适应额外标记物的使用。在未来，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，以促进 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 国际细胞分析协会。
