Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning

Abstract

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. © 2017 International Society for Advancement of Cytometry.

Evaluation of holographic imaging cytometer holomonitor M4® motility applications

“…Since the HoloMonitor M4 also reports on morphological properties of each cell, further analyses could be conducted to identify variances in morphology associated with differential behavior provided sufficient numbers of the rare population are visualized, as has been previously demonstrated (18,20). When these image analysis packages were compared with standard transwell migration and invasion assays, the tracking of non-directional motility of individual cells over time produced highly correlated relative motilities.…”

“…As a label-free imaging technology that is non-cytotoxic even with prolonged exposure, QPI is a noninvasive method for single cell analysis of adherent cell behavior. More recent reports have used machine learning based analyses of features derived from QPI images to distinguish metastatic potential, drug-response, program activation, and modes of cellular migration (17)(18)(19)(20)(21)(22). (8)).…”

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines

“…). A custom MATLAB code to segment cells from DHM phase maps and record cell morphology, first‐order phase and second‐order phase pixel texture parameters for each cell was modified from a previously described version to measure additional parameters. Seventeen quantitative phase parameters were extracted (defined in the Supporting Information Table S1).…”

“…Optical phase signals imaged across a living cell reveal morphology and subcellular features that relate to aspects of cell physiology. The nucleus , actin cytoskeleton , membrane‐bound organelles , and cytoplasm contribute to the cell phase profile. Events that significantly affect a cell's morphology or subcellular phase texture are often detected longitudinally or by comparing distinct cell populations .…”

“…Elongated and rounded MDA‐MB‐231 cells were classified using a machine learning algorithm with 94% accuracy. However, the prediction of amoeboid or mesenchymal motile behavior from single‐phase images was not assessed . Nevertheless, subtle phase morphology and texture features might further distinguish such subpopulations and predict motile phenotype.…”