Evaluation of holographic imaging cytometer holomonitor M4® motility applications

Abstract

Digital holographic cytometry (DHC) and other methods of quantitative phase imaging permit extended time-lapse imaging of mammalian cells in the absence of induced cellular toxicity. Manufactured DHC platforms equipped with semi-automated image acquisition, segmentation, and analysis software packages (or modules) for assessing cell behavior are now commercially available. When housed in mammalian cell incubators these cytometers offer the potential to monitor and quantify a range of cellular behaviors without disrupting routine culture. Realization of this potential requires validation against established standards. Two proprietary software modules for assessing cellular motility available using the HoloMonitor M4 DHC platform were evaluated on human melanoma cells lines with known relative motility and metastatic potential. One such software package, the Track Cells module, was run during routine culture. In addition, the Wound Healing module was conducted in parallel with established transwell migration and invasion assays. Each module was evaluated for reproducibility and correlation to established assays. Both software modules reliably recorded increased cellular motility in the metastatic 1205Lu line as compared with the non-metastatic WM793 line. In a direct comparison of the two propriety DHC software modules and two established transwell assays, the relative cell motilities were well correlated. The granularity of data provided by the Track Cells module permitted the additional identification of rare hyper-motile cells in the metastatic population and the distinction of motility from division associated displacement. The two HoloMonitor M4 DHC proprietary software modules for assessing cellular motility yielded reproducible results that were well-correlated with established standards.

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

“…A leaveone-out classification analysis (Supporting Information Table S2) suggested predictive power derived from most of the 17 parameters, which could be reduced to five to six principal components, in accordance with other studies (2,8,49). Taken together, these results indicate that phase parameters from the pixel histogram and gray-level co-occurrence matrix, as well as cell outline-based morphology features (8,32,53), help to classify adherent cell lines. Combining "two-dimensional" and "three-dimensional" phase parameters into signatures input to machine learning makes the algorithm more flexible, as geometric/two-dimensional parameters are likely more important for classifying cell lines of dissimilar shape, and higher-order parameters more important for classifying cell lines of similar shape.…”

“…Similarly, DHM phase values have been used to evaluate global changes to epithelial cell layer surface roughness, which correlated to altered cell motility after exposure to a drug delivery system . Another study recently correlated cell morphometric parameters from in‐incubator time‐lapse DHM with transwell migration and invasion assays . This study compared two melanoma cell lines, metastatic 1205Lu and non‐metastatic WM793, and found a correlation between DHM motility parameters and invasive capacity of the cells.…”

“…Quantitative, time‐lapse QPI image series acquired during the scratch wound assay could clarify if different processes were happening along the scratch wound border. Rates of cell division , collective migration , and single‐cell migration could be determined from phase maps of the wound border. Further investigation would also yield mechanistic insight into phase parameter sensitivity to chemotaxis by tracking multiple cells in time‐series phase maps.…”

“…A recent effort to establish guidelines for live cell imaging reported an improved detection of motile phenotypes by lowering background variation due to technical artifacts (31). Similar studies addressing reproducibility of QPI measurements of cells (15,32) allow for easier adoption of optical phase-based approaches for research and eventually in the clinic.…”

Discrimination between Breast Cancer Cells and White Blood Cells by Non-Invasive Measurements: Implications for a Novel In Vitro-Based Circulating Tumor Cell Model Using Digital Holographic Cytometry

“…A more cell-friendly and speedy method of investigating morphological differences would facilitate CTC research. Digital holographic cytometry (DHC) is a powerful tool for label-free cell observations and the evaluation of cell morphological and dynamical parameters in vitro [20][21][22][23][24][25][26]. Studies using DHC include different cell types, from protozoa, bacteria and plant cells to mammalian cells such as nerve cells, stem cells and tumor cells [23].…”