Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning.
Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning.
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DOI：
10.1002/cyto.a.23316
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发表时间：
2018-03
期刊：
影响因子：
--
通讯作者：
Raub CB
中科院分区：
文献类型：
--
作者：
Lam VK;Nguyen TC;Chung BM;Nehmetallah G;Raub CB
使用数字全息显微镜（DHM）的非侵入性，快速采集定量相位图，允许在透明基底上跟踪快速细胞运动。在体外二维表面上，MDA-MB-231癌细胞呈现与迁移模式和基底硬度相关的几种形态，与体内癌症侵袭机制相关。从DHM的定量相位信息可以准确地分类粘附癌细胞亚群与临床相关性。为了测试这一点，将来自侵袭性乳腺癌MDA-MB-231细胞系的细胞在玻璃、组织培养物处理的聚苯乙烯和胶原水凝胶上培养，并用DHM成像，然后在对F-肌动蛋白和细胞核染色后进行落射荧光显微镜检查。在不同的基板上，在细胞分裂过程中，并在基质粘附，将它们与F-肌动蛋白功能的细胞相参数的趋势进行了跟踪。使用来自全息相位重建的参数和来自常规相位图像的细胞几何特征来训练和测试支持向量机学习算法，并用于区分细长和圆形细胞形态。DHM能够以94%的准确度区分MDA-MB-231细胞的细长和圆形形态，而使用常规明视野显微镜的细胞几何特征的准确度为83%。这一发现表明DHM检测和监测与细胞周期阶段状态、底物粘附和运动性相关的癌细胞形态的潜力。
The noninvasive, fast acquisition of quantitative phase maps using digital holographic microscopy (DHM) allows tracking of rapid cellular motility on transparent substrates. On two-dimensional surfaces in vitro, MDA-MB-231 cancer cells assume several morphologies related to the mode of migration and substrate stiffness, relevant to mechanisms of cancer invasiveness in vivo. The quantitative phase information from DHM may accurately classify adhesive cancer cell subpopulations with clinical relevance. To test this, cells from the invasive breast cancer MDA-MB-231 cell line were cultured on glass, tissue-culture treated polystyrene, and collagen hydrogels, and imaged with DHM followed by epifluorescence microscopy after staining F-actin and nuclei. Trends in cell phase parameters were tracked on the different substrates, during cell division, and during matrix adhesion, relating them to F-actin features. Support vector machine learning algorithms were trained and tested using parameters from holographic phase reconstructions and cell geometric features from conventional phase images, and used to distinguish between elongated and rounded cell morphologies. DHM was able to distinguish between elongated and rounded morphologies of MDA-MB-231 cells with 94% accuracy, compared to 83% accuracy using cell geometric features from conventional brightfield microscopy. This finding indicates the potential of DHM to detect and monitor cancer cell morphologies relevant to cell cycle phase status, substrate adhesion, and motility.
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