Digital Holographic Microscopy to Assess Cell Behavior.

Digital holographic microscopy is an imaging technique particularly well suited to the study of living cells in culture, as no labeling is required and computed phase maps produce high contrast, quantitative pixel information. A full experiment involves instrument calibration, cell culture quality checks, selection and setup of imaging chambers, a sampling plan, image acquisition, phase and amplitude map reconstruction, and parameter map post-processing to extract information about cell morphology and/or motility. Each step is described below, focusing on results from imaging four human cell lines. Several post-processing approaches are detailed, with an aim of tracking individual cells and dynamics of cell populations.

Dual-modality digital holographic and polarization microscope to quantify phase and birefringence signals in biospecimens with a complex microstructure.

Optical phase and birefringence signals occur in cells and thin, semi-transparent biomaterials. A dual-modality quantitative phase and polarization microscope was designed to study the interaction of cells with extracellular matrix networks and to relate optical pathlength and birefringence signals within structurally anisotropic biomaterial constructs. The design was based on an existing, custom-built digital holographic microscope, to which was added a polarization microscope utilizing liquid crystal variable retarders. Phase and birefringence channels were calibrated, and data was acquired sequentially from cell-seeded collagen hydrogels and electrofabricated chitosan membranes. Computed phase height and retardance from standard targets were accurate within 99.7% and 99.8%, respectively. Phase height and retardance channel background standard deviations were 35 nm and 0.6 nm, respectively. Human fibroblasts, visible in the phase channel, aligned with collagen network microstructure, with retardance and azimuth visible in the polarization channel. Electrofabricated chitosan membranes formed in 40 µm tall microfluidic channels possessed optical retardance ranging from 7 to 11 nm, and phase height from 37 to 39 µm. These results demonstrate co-registered dual-channel acquisition of phase and birefringence parameter maps from microstructurally-complex biospecimens using a novel imaging system combining digital holographic microscopy with voltage-controlled polarization microscopy.

Tracking Single Cells Motility on Different Substrates.

Motility is a key property of a cell, required for several physiological processes, including embryonic development, axon guidance, tissue regeneration, gastrulation, immune response, and cancer metastasis. Therefore, the ability to examine cell motility, especially at a single cell level, is important for understanding various biological processes. Several different assays are currently available to examine cell motility. However, studying cell motility at a single cell level can be costly and/or challenging. Here, we describe a method of tracking random cell motility on different substrates such as glass, tissue-culture polystyrene, and type I collagen hydrogels, which can be modified to generate different collagen network microstructures. In this study we tracked MDA-MB-231 breast cancer cells using The CytoSMARTTM System (Lonza Group, Basel, Switzerland) for live cell imaging and assessed the average cell migration speed using ImageJ and wrMTrck plugin. Our cost-effective and easy-to-use method allows studying cell motility at a single cell level on different substrates with varying degrees of stiffness and varied compositions. This procedure can be successfully performed in a highly accessible manner with a simple setup.

Morphology, Motility, and Cytoskeletal Architecture of Breast Cancer Cells Depend on Keratin 19 and Substrate.

Cancer cells gain motility through events that accompany modulation of cell shape and include altered expression of keratins. However, the role of keratins in change of cancer cell architecture is not well understood. Therefore, we ablated the expression of keratin 19 (K19) in breast cancer cells of the MDA-MB-231 cell line and found that cells lacking K19 become more elongated in culture, with morphological reversion toward the parental phenotype upon transduction of KRT19. Also, the number of actin stress fibers and focal adhesions were significantly reduced in KRT19 knockout (KO) cells. The altered morphology of KRT19 KO cells was then characterized quantitatively using digital holographic microscopy (DHM), which not only confirmed the phenotypic change of KRT19 KO cells but also identified that the K19-dependent morphological change is dependent on the substrate type. A new quantitative method of single cell analysis from DHM, via average phase difference maps, facilitated evaluation of K19-substrate interactive effects on cell morphology. When plated on collagen substrate, KRT19 KO cells were less elongated and resembled parental cells. Assessing single cell motility further showed that while KRT19 KO cells moved faster than parental cells on a rigid surface, this increase in motility became abrogated when cells were plated on collagen. Overall, our study suggests that K19 inhibits cell motility by regulating cell shape in a substrate-dependent manner. Thus, this study provides a potential basis for the altered expression of keratins associated with change in cell shape and motility of cancer cells. © 2020 International Society for Advancement of Cytometry.

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

Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and reproducible optical phase signatures of cell lines for classification with machine learning and to correlate optical phase parameters to motile behavior. Digital holographic microscopy (DHM) reconstructed phase maps of cells from two pairs of cancer and non-cancer cell lines. Seventeen image parameters were extracted from each cell's phase map, used for linear support vector machine learning, and correlated to scratch wound closure and Boyden chamber chemotaxis. The classification accuracy was between 90% and 100% for the six pairwise cell line comparisons. Several phase parameters correlated with wound closure rate and chemotaxis across the four cell lines. The level of cell confluence in culture affected phase parameters in all cell lines tested. Results indicate that optical phase features of cell lines are a robust set of quantitative data of potential utility for phenotypic screening and prediction of motile behavior. © 2019 International Society for Advancement of Cytometry.

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

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.