高通量自动化单细胞成像分析揭示胶质母细胞瘤干细胞群体在状态转变过程中的动态变化。
High-Throughput Automated Single-Cell Imaging Analysis Reveals Dynamics of Glioblastoma Stem Cell Population During State Transition.
机构信息
Department of Cellular and Molecular Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Molecular Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, USA.
出版信息
Cytometry A. 2019 Mar;95(3):290-301. doi: 10.1002/cyto.a.23728. Epub 2019 Feb 6.
Cancer stem cells (CSCs) are a heterogeneous and dynamic self-renewing population that stands at the top of tumor cellular hierarchy and contribute to tumor recurrence and therapeutic resistance. As methods of CSC isolation and functional interrogation advance, there is a need for a reliable and accessible quantitative approach to assess heterogeneity and state transition dynamics in CSCs. We developed a high-throughput automated single cell imaging analysis (HASCIA) approach for the quantitative assessment of protein expression with single-cell resolution and applied the method to investigate spatiotemporal factors that influence CSC state transition using glioblastoma (GBM) CSCs (GSCs) as a model system. We were able to validate the quantitative nature of this approach through comparison of the protein expression levels determined by HASCIA to those determined by immunoblotting. A virtue of HASCIA was exemplified by detection of a subpopulation of SOX2-low cells, which expanded in fraction size during state transition. HASCIA also revealed that GSCs were committed to loose stem cell state at an earlier time point than the average SOX2 level decreased. Functional assessment of stem cell frequency in combination with the quantification of SOX2 expression by HASCIA defined a stable cutoff of SOX2 expression level for stem cell state. We also developed an approach to assess local cell density and found that denser monolayer areas possess higher average levels of SOX2, higher cell diversity, and a presence of a sub-population of slowly proliferating SOX2-low GSCs. HASCIA is an open source software that facilitates understanding the dynamics of heterogeneous cell population such as that of GSCs and their progeny. It is a powerful and easy-to-use image analysis and statistical analysis tool available at https://hascia.lerner.ccf.org. © 2019 International Society for Advancement of Cytometry.
癌症干细胞 (CSCs) 是一种异质性和动态的自我更新群体，位于肿瘤细胞层次结构的顶端，有助于肿瘤复发和治疗抵抗。随着 CSC 分离和功能检测方法的进步，需要有一种可靠且易于使用的定量方法来评估 CSCs 的异质性和状态转变动力学。我们开发了一种高通量自动化单细胞成像分析 (HASCIA) 方法，用于以单细胞分辨率定量评估蛋白质表达，并应用该方法研究影响 CSC 状态转变的时空因素，使用胶质母细胞瘤 (GBM) CSCs (GSCs) 作为模型系统。我们通过将 HASCIA 确定的蛋白质表达水平与免疫印迹确定的蛋白质表达水平进行比较，验证了这种方法的定量性质。HASCIA 的一个优点是能够检测到 SOX2 低细胞的亚群，该亚群在状态转变过程中细胞分数大小增加。HASCIA 还表明，GSCs 比平均 SOX2 水平降低更早地向松散的干细胞状态转变。结合 HASCIA 对干细胞频率的功能评估和 SOX2 表达的定量，确定了干细胞状态的 SOX2 表达水平稳定截止值。我们还开发了一种评估局部细胞密度的方法，发现更密集的单层区域具有更高的平均 SOX2 水平、更高的细胞多样性以及存在亚群缓慢增殖的 SOX2 低 GSCs。HASCIA 是一个开源软件，有助于理解异质性细胞群体（如 GSCs 及其后代）的动力学。它是一个强大且易于使用的图像分析和统计分析工具，可在 https://hascia.lerner.ccf.org 获得。©2019 国际细胞分析协会。
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