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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.

In this work we seek to demonstrate acceptable classification performance for classifiers trained using augmented training data of synthetic cells imaged by simulated holography and evaluated using experimentally collected holographic data. In particular, we utilize experimentally collected DHM phase maps derived from the MDA-MB-231 breast cancer cell and immortalized human gingival fibroblast (GIE) cell lines. Automated segmentation was performed using a floodfill clustering approach and experimental feature distributions were used to generate statistically random synthetic cell realizations for each class.

A cytoskeletal protein keratin 19 (K19) is highly expressed in breast cancer but its effects on breast cancer cell mechanics are unclear. In MCF7 cells where K19 expression is ablated,we found that K19 is required to maintain rounded epithelial-like shape and tight cell-cell adhesion. A loss of K19 also lowered cell surface E-cadherin levels. Inhibiting internalization restored cell-cell adhesion of KRT19 knockout cells, suggesting that E-cadherin internalization contributed to defective adhesion. Ultimately, while K19 inhibited cell migration and invasion, it was required for cells to form colonies in suspension. Our results suggest that K19 stabilizes E-cadherin complexes at the cell membrane to maintain cell-cell adhesion which inhibits cell invasiveness but provides growth and survival advantages for circulating tumor cells.

A cytoskeletal protein keratin 19 (K19) is highly expressed in breast cancer but its effects on breast cancer cell mechanics are unclear. Using KRT19 knockout (KO) cells and cells where K19 expression was rescued, we found that K19 is required to maintain rounded epithelial-like shape and tight cell-cell adhesion of MCF7 cells. A loss of K19 resulted in a lower level of plakoglobin and internalization of E-cadherin in early and recycling endosomes. Inhibiting internalization restored cell-cell adhesion of KRT19 KO cells, suggesting E-cadherin internalization contributes to defective adhesion. Ultimately, while K19 inhibited cell migration, it was required for cells to form colonies in suspension. Our results suggest that K19 stabilizes E-cadherin complexes at the cell membrane to maintain cell-cell adhesion which inhibits cell migration but provides growth and survival advantages for circulating tumor cells. These findings provide context-dependent roles of K19 during metastasis.

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.

Significance: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells’ morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies. Aim: Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. Approach: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning. Results: Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line. Conclusions: The proposed epithelial–mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.

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.

Fluorescence microscopy (FM) is costly, time consuming, and requires considerable sample preparation. Here we present a virtual fluorescence staining method based on DL to transform fluorescence images of molecular labels into other molecular fluorescence labels.

ML is useful for classifying cells based on features derived from quantitative phase images. However, classifying cells with graded phenotype remains challenging. To address this, several ML algorithms were trained and validated on reconstructed phase images.

Fluorescence microscopy plays a vital role in understanding the subcellular structures of living cells. However, it requires considerable effort in sample preparation related to chemical fixation, staining, cost, and time. To reduce those factors, we present a virtual fluorescence staining method based on deep neural networks (VirFluoNet) to transform fluorescence images of molecular labels into other molecular fluorescence labels in the same field-of-view. To achieve this goal, we develop and train a conditional generative adversarial network (cGAN) to perform digital fluorescence imaging demonstrated on human osteosarcoma U2OS cell fluorescence images captured under Cell Painting staining protocol. A detailed comparative analysis is also conducted on the performance of the cGAN network between predicting fluorescence channels based on phase contrast or based on another fluorescence channel using human breast cancer MDA-MB-231 cell line as a test case. In addition, we implement a deep learning model to perform autofocusing on another human U2OS fluorescence dataset as a preprocessing step to defocus an out-focus channel in U2OS dataset. A quantitative index of image prediction error is introduced based on signal pixel-wise spatial and intensity differences with ground truth to evaluate the performance of prediction to high-complex and throughput fluorescence. This index provides a rational way to perform image segmentation on error signals and to understand the likelihood of mis-interpreting biology from the predicted image. In total, these findings contribute to the utility of deep learning image regression for fluorescence microscopy datasets of biological cells, balanced against savings of cost, time, and experimental effort. Furthermore, the approach introduced here holds promise for modeling the internal relationships between organelles and biomolecules within living cells., 20 pages, 9 figures

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.

Optical phase signatures obtained from a telecentric DHM system input to machine learning algorithms classify cell lines of mesenchymal and epithelial morphologies with high accuracy, leading to sensitive and reproducible phenotypic profiling of cell lines.

A telecentric DHM system and a support vector machine-based classifier were developed to investigate the effects of cytokeratin 19 knockout on the morphology and phase features of MDA-MB-231 breast cancer cells.

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.

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

Digital holographic microscopy (DHM) provides label-free and real-time quantitative phase information relevant to the analysis of dynamic biological systems. A DHM based on telecentric configuration optically mitigates phase aberrations due to the microscope objective and linear high frequency fringes due to the reference beam thus minimizing digital aberration correction needed for distortion free 3D reconstruction. The purpose of this work is to quantitatively assess growth and migratory behavior of invasive cancer cells using a telecentric DHM system. Together, the height and lateral shape features of individual cells, determined from time-lapse series of phase reconstructions, should reveal aspects of cell migration, cell-matrix adhesion, and cell cycle phase transitions. To test this, MDA-MB-231 breast cancer cells were cultured on collagen-coated or un-coated glass, and 3D holograms were reconstructed over 2 hours. Cells on collagencoated glass had an average 14% larger spread area than cells on uncoated glass (n=18-22 cells/group). The spread area of cells on uncoated glass were 15-21% larger than cells seeded on collagen hydrogels (n=18-22 cells/group). Premitotic cell rounding was observed with average phase height increasing 57% over 10 minutes. Following cell division phase height decreased linearly (R2=0.94) to 58% of the original height pre-division. Phase objects consistent with lamellipodia were apparent from the reconstructions at the leading edge of migrating cells. These data demonstrate the ability to track quantitative phase parameters and relate them to cell morphology during cell migration and division on adherent substrates, using telecentric DHM. The technique enables future studies of cell-matrix interactions relevant to cancer.