Induction of morphological changes in death-induced cancer cells monitored by holographic microscopy

Screening for urothelial carcinoma cells in urine based on digital holographic flow cytometry through machine learning and deep learning methods

The incidence of urothelial carcinoma continues to rise annually, particularly among the elderly. Prompt diagnosis and treatment can significantly enhance patient survival and quality of life. Urine cytology remains a widely-used early screening method for urothelial carcinoma, but it still has limitations including sensitivity, labor-intensive procedures, and elevated cost. In recent developments, microfluidic chip technology offers an effective and efficient approach for clinical urine specimen analysis. Digital holographic microscopy, a form of quantitative phase imaging technology, captures extensive data on the refractive index and thickness of cells. The combination of microfluidic chips and digital holographic microscopy facilitates high-throughput imaging of live cells without staining. In this study, digital holographic flow cytometry was employed to rapidly capture images of diverse cell types present in urine and to reconstruct high-precision quantitative phase images for each cell type. Then, various machine learning algorithms and deep learning models were applied to categorize these cell images, and remarkable accuracy in cancer cell identification was achieved. This research suggests that the integration of digital holographic flow cytometry with artificial intelligence algorithms offers a promising, precise, and convenient approach for early screening of urothelial carcinoma.

Automated detection of apoptotic versus nonapoptotic cell death using label-free computational microscopy

Identification of cell death mechanisms, particularly distinguishing between apoptotic versus nonapoptotic pathways, is of paramount importance for a wide range of applications related to cell signaling, interaction with pathogens, therapeutic processes, drug discovery, drug resistance, and even pathogenesis of diseases like cancers and neurogenerative disease among others. Here, we present a novel high-throughput method of identifying apoptotic versus necrotic versus other nonapoptotic cell death processes, based on lensless digital holography. This method relies on identification of the temporal changes in the morphological features of mammalian cells, which are unique to each cell death processes. Different cell death processes were induced by known cytotoxic agents. A deep learning-based approach was used to automatically classify the cell death mechanism (apoptotic vs necrotic vs nonapoptotic) with more than 93% accuracy. This label free approach can provide a low cost (<$250) alternative to some of the currently available high content imaging-based screening tools.

Computational multi-wavelength phase synthesis using convolutional neural networks [Invited]

Multi-wavelength digital holographic microscopy (MWDHM) provides indirect measurements of the refractive index for non-dispersive samples. Successive-shot MWDHM is not appropriate for dynamic samples and single-shot MWDHM significantly increases the complexity of the optical setup due to the need for multiple lasers or a wavelength tunable source. Here we consider deep learning convolutional neural networks for computational phase synthesis to obtain high-speed simultaneous phase estimates on different wavelengths and thus single-shot estimates of the integral refractive index without increased experimental complexity. This novel, to the best of our knowledge, computational concept is validated using cell phantoms consisting of internal refractive index variations representing cytoplasm and membrane-bound organelles, respectively, and a simulation of a realistic holographic recording process. Specifically, in this work we employed data-driven computational techniques to perform accurate dual-wavelength hologram synthesis (hologram-to-hologram prediction), dual-wavelength phase synthesis (unwrapped phase-to-phase prediction), direct phase-to-index prediction using a single wavelength, hologram-to-phase prediction, and 2D phase unwrapping with sharp discontinuities (wrapped-to-unwrapped phase prediction).

Digital holographic microscopy: a noninvasive method to analyze the formation of spheroids

Digital holographic (DH) microscopy is a unique noninvasive method to analyze living cells. With DH microscopy, in vitro cell cultures can be imaged in 2D and pseudo-3D and measurements of size and morphology of the cells are provided. Here, a description of a novel methodology utilizing DH microscopy for the analysis of spheroids is presented. A cell culture protocol is introduced and morphological parameters of cell spheroids as measured by DH microscopy are presented. The study confirms the use of DH microscopy for the analysis of cell spheroids. In the future, organoids can be analyzed with DH microscopy, and it can also be used for drug response and cell death analyses.

Monobutyl phthalate can induce autophagy and metabolic disorders by activating the ire1a-xbp1 pathway in zebrafish liver

Monobutyl phthalate (MBP) can exist in biological organisms for a long time because of its excellent fat solubility, and it has been found to have certain toxic effects. In this study, the acute effects of MBP on endoplasmic reticulum (ER) stress and metabolism in the zebrafish liver were studied. After continuous exposure to MBP (5 and 10 mg / L) for 96 h, ER damage and the appearance of apoptotic bodies and autophagosomes were found in liver. This is because MBP stimulated the ire-xbp1 pathway of ER stress, thus leading to apoptosis and autophagy. Also, through analysis of metabolic enzymes and genes, it was found that the activated ire-xbp1 pathway could promote lipid synthesis and cause the accumulation of lipid droplets. The gene pparγ related to lipid storage affected the level of insulin, which can also further affect the glucose metabolism process, that is, glycolysis and aerobic respiration were inhibited. And the pentose phosphate pathway (PPP) was activated as a compensation mechanism to alleviate glycogen accumulation. The abnormal supply of energy and the death of excessive cells will eventually severely damage the zebrafish liver. This study will enrich the knowledge about the toxic effects of MBP.

Digital Holographic Imaging as a Method for Quantitative, Live Cell Imaging of Drug Response to Novel Targeted Cancer Therapies

Digital holographic imaging (DHI) is a noninvasive, live cell imaging technique that enables long-term quantitative visualization of cells in culture. DHI uses phase-shift imaging to monitor and quantify cellular events such as cell division, cell death, cell migration, and drug responses. In recent years, the application of DHI has expanded from its use in the laboratory to the clinical setting, and currently it is being developed for use in theranostics. Here, we describe the use of the DHI platform HoloMonitorM4 to evaluate the effects of novel, targeted cancer therapies on cell viability and proliferation using the HeLa cancer cell line as a model. We present single cell tracking and population-wide analysis of multiple cell morphology parameters.

Quantitative Phase Dynamics of Cancer Cell Populations Affected by Blue Light

Increased exposition to blue light may induce many changes in cell behavior and significantly affect the critical characteristics of cells. Here we show that multimodal holographic microscopy (MHM) within advanced image analysis is capable of correctly distinguishing between changes in cell motility, cell dry mass, cell density, and cell death induced by blue light. We focused on the effect of blue light with a wavelength of 485 nm on morphological and dynamical parameters of four cell lines, malignant PC-3, A2780, G361 cell lines, and the benign PNT1A cell line. We used MHM with blue light doses 24 mJ/cm2, 208 mJ/cm2 and two kinds of expositions (500 and 1000 ms) to acquire real-time quantitative phase information about cellular parameters. It has been shown that specific doses of the blue light significantly influence cell motility, cell dry mass and cell density. These changes were often specific for the malignant status of tested cells. Blue light dose 208 mJ/cm2 × 1000 ms affected malignant cell motility but did not change the motility of benign cell line PNT1A. This light dose also significantly decreased proliferation activity in all tested cell lines but was not so deleterious for benign cell line PNT1A as for malignant cells. Light dose 208 mJ/cm2 × 1000 ms oppositely affected cell mass in A2780 and PC-3 cells and induced different types of cell death in A2780 and G361 cell lines. Cells obtained the least damage on lower doses of light with shorter time of exposition.

Evaluation of the Impact of Imprinted Polymer Particles on Morphology and Motility of Breast Cancer Cells by Using Digital Holographic Cytometry

Breast cancer is the second most common cancer type worldwide and breast cancer metastasis accounts for the majority of breast cancer-related deaths. Tumour cells produce increased levels of sialic acid (SA) that terminates the monosaccharide on glycan chains of the glycosylated proteins. SA can contribute to cellular recognition, cancer invasiveness and increase the metastatic potential of cancer cells. SA-templated molecularly imprinted polymers (MIPs) have been proposed as promising reporters for specific targeting of cancer cells when deployed in nanoparticle format. The sialic acid-molecularly imprinted polymers (SA-MIPs), which use SA for the generation of binding sites through which the nanoparticles can target and stain breast cancer cells, opens new strategies for efficient diagnostic tools. This study aims at monitoring the effects of SA-MIPs on morphology and motility of the epithelial type MCF-7 and the highly metastatic MDAMB231 breast cancer cell lines, using digital holographic cytometry (DHC). DHC is a label-free technique that is used in cell morphology studies of e.g., cell volume, area and thickness as well as in motility studies. Here, we show that MCF-7 cells move slower than MDAMB231 cells. We also show that SA-MIPs have an effect on cell morphology, motility and viability of both cell lines. In conclusion, by using DH microscopy, we could detect SA-MIPs impact on different breast cancer cells regarding morphology and motility.

An optical study of drug resistance detection in endometrial cancer cells by dynamic and quantitative phase imaging

Platinum chemosensitivity detection plays a vital role during endometrial cancer treatment because chemotherapy responses have profound influences on patient's prognosis. Although several methods can be used to detect drug resistance characteristics, studies on detecting drug sensitivity based on dynamic and quantitative phase imaging of cancer cells are rare. In this study, digital holographic microscopy was applied to distinguish drug-resistant and nondrug-resistant endometrial cancer cells. Based on the reconstructed phase images, temporal evolutions of cell height (CH), cell projected area (CPA) and cell volume were quantitatively measured. The results show that change rates of CH and CPA were significantly different between drug-resistant and nondrug-resistant endometrial cancer cells. Furthermore, the results demonstrate that morphological characteristics have the potential to be utilized to distinguish the drug sensitivity of endometrial cancer cells, and it may provide new perspectives to establish optical methods to detect drug sensitivity and guide chemotherapy in endometrial cancer.

Whole-Cell Multiparameter Assay for Ricin and Abrin Activity-Based Digital Holographic Microscopy

Ricin and abrin are ribosome-inactivating proteins leading to inhibition of protein synthesis and cell death. These toxins are considered some of the most potent and lethal toxins against which there is no available antidote. Digital holographic microscopy (DHM) is a time-lapse, label-free, and noninvasive imaging technique that can provide phase information on morphological features of cells. In this study, we employed DHM to evaluate the morphological changes of cell lines during ricin and abrin intoxication. We showed that the effect of these toxins is characterized by a decrease in cell confluence and changes in morphological parameters such as cell area, perimeter, irregularity, and roughness. In addition, changes in optical parameters such as phase-shift, optical thickness, and effective-calculated volume were observed. These effects were completely inhibited by specific neutralizing antibodies. An enhanced intoxication effect was observed for preadherent compared to adherent cells, as was detected in early morphology changes and confirmed by annexin V/propidium iodide (PI) apoptosis assay. Detection of the dynamic changes in cell morphology at initial stages of cell intoxication by DHM emphasizes the highly sensitive and rapid nature of this method, allowing the early detection of active toxins.

In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy

Among all environmental pollutants, the toxic heavy metal cadmium is considered as a human carcinogen. Cadmium may induce cell death by apoptosis in various cell types, although the underlying mechanisms are still unclear. In this paper we show how a label-free digital holography (DH)-based technique is able to quantify the evolution of key biophysical parameters of cells during the exposure to cadmium for the first time. Murine embryonic fibroblasts NIH 3T3 are chosen here as cellular model for studying the cadmium effects. The results demonstrate that DH is able to retrieve the temporal evolution of different key parameters such as cell volume, projected area, cell thickness and dry mass, thus providing a full quantitative characterization of the cell physical behaviour during cadmium exposure. Our results show that the label-free character of the technique would allow biologists to perform systematic and reliable studies on cell death process induced by cadmium and we believe that more in general this can be easily extended to others heavy metals, thus avoiding the time-consuming, expensive and invasive label-based procedures used nowadays in the field. In fact, pollution by heavy metals is severe issue that needs rapid and reliable methods to be settled.

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.

High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells

Digital holographic cytometry (DHC) permits label-free visualization of adherent cells. Dozens of cellular features can be derived from segmentation of hologram-derived images. However, the accuracy of single cell classification by these features remains limited for most applications, and lack of standardization metrics has hindered independent experimental comparison and validation. Here we identify twenty-six DHC-derived features that provide biologically independent information across a variety of mammalian cell state transitions. When trained on these features, machine-learning algorithms achieve blind single cell classification with up to 95% accuracy. Using classification accuracy to guide platform optimization, we develop methods to standardize holograms for the purpose of kinetic single cell cytometry. Applying our approach to human melanoma cells treated with a panel of cancer therapeutics, we track dynamic changes in cellular behavior and cell state over time. We provide the methods and computational tools for optimizing DHC for kinetic single adherent cell classification.

Automated classification of cell morphology by coherence-controlled holographic microscopy

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.

Investigating fibroblast cells under "safe" and "injurious" blue-light exposure by holographic microscopy

The exposure to visible light has been shown to exert various biological effects, such as erythema and retinal degeneration. However, the phototoxicity mechanisms in living cells are still not well understood. Here we report a study on the temporal evolution of cell morphology and volume during blue light exposure. Blue laser irradiation is switched during the operation of a digital holography (DH) microscope between what we call here "safe" and "injurious" exposure (SE & IE). The results reveal a behaviour that is typical of necrotic cells, with early swelling and successive leakage of the intracellular liquids when the laser is set in the "injurious" operation. In the phototoxicity investigation reported here the light dose modulation is performed through the very same laser light source adopted for monitoring the cell's behaviour by digital holographic microscope. We believe the approach may open the route to a deep investigation of light-cell interactions, with information about death pathways and threshold conditions between healthy and damaged cells when subjected to light-exposure. 3D Morphology and quantitative phase information from late stage of necrosis cell death.

Label-free high temporal resolution assessment of cell proliferation using digital holographic microscopy

Cell proliferation assays are widely applied in biological sciences to understand the effect of drugs over time. However, current methods often assess cell population growth indirectly, that is, the cells are not actually counted. Instead other parameters, for example, the amount of protein, are determined. These methods often also demand phototoxic labels, have low temporal resolution, or employ end-point assays, and frequently are labor intensive. We have developed a robust and label-free kinetic cell proliferation assay with high temporal resolution for adherent cells using digital holographic microscopy (DHM), one of many quantitative phase microscopy techniques. As no labels or stains are required, and only very low intensity illumination is necessary, the technique allows for noninvasive continuous cell counting. Only two image processing settings were adjusted between cell lines, making the assay practical, user friendly, and free of user bias. The developed direct assay was validated by analyzing cell cultures treated with various concentrations of the anti-cancer drug etoposide, a well-established topoisomerase inhibitor that causes DNA damage and leads to programmed cell death. After treatment, the unstained adherent cells were nondestructively imaged every 30 min for 36 h inside a cell incubator. In the recorded time-lapse image sequences, individual cells were automatically identified to provide detailed growth curves and growth rate data of cell number, confluence, and average cell volume. Our results demonstrate how these parameters facilitate a deeper understanding of cell processes than what is achievable with current single-parameter and end-point methods. © 2017 International Society for Advancement of Cytometry.

Supervised classification of etoposide-treated in vitro adherent cells based on noninvasive imaging morphology

Single-cell studies using noninvasive imaging is a challenging, yet appealing way to study cellular characteristics over extended periods of time, for instance to follow cell interactions and the behavior of different cell types within the same sample. In some cases, e.g., transplantation culturing, real-time cellular monitoring, stem cell studies, in vivo studies, and embryo growth studies, it is also crucial to keep the sample intact and invasive imaging using fluorophores or dyes is not an option. Computerized methods are needed to improve throughput of image-based analysis and for use with noninvasive microscopy such methods are poorly developed. By combining a set of well-documented image analysis and classification tools with noninvasive microscopy, we demonstrate the ability for long-term image-based analysis of morphological changes in single cells as induced by a toxin, and show how these changes can be used to indicate changes in biological function. In this study, adherent cell cultures of DU-145 treated with low-concentration (LC) etoposide were imaged during 3 days. Single cells were identified by image segmentation and subsequently classified on image features, extracted for each cell. In parallel with image analysis, an MTS assay was performed to allow comparison between metabolic activity and morphological changes after long-term low-level drug response. Results show a decrease in proliferation rate for LC etoposide, accompanied by changes in cell morphology, primarily leading to an increase in cell area and textural changes. It is shown that changes detected by image analysis are already visible on day 1 for [Formula: see text] etoposide, whereas effects on MTS and viability are detected only on day 3 for [Formula: see text] etoposide concentration, leading to the conclusion that the morphological changes observed occur before and at lower concentrations than a reduction in cell metabolic activity or viability. Three classifiers are compared and we report a best case sensitivity of 88% and specificity of 94% for classification of cells as treated/untreated.

Quantitative phase imaging for cell culture quality control

The potential of quantitative phase imaging (QPI) with digital holographic microscopy (DHM) for quantification of cell culture quality was explored. Label-free QPI of detached single cells in suspension was performed by Michelson interferometer-based self-interference DHM. Two pancreatic tumor cell lines were chosen as cellular model and analyzed for refractive index, volume, and dry mass under varying culture conditions. Firstly, adequate cell numbers for reliable statistics were identified. Then, to characterize the performance and reproducibility of the method, we compared results from independently repeated measurements and quantified the cellular response to osmolality changes of the cell culture medium. Finally, it was demonstrated that the evaluation of QPI images allows the extraction of absolute cell parameters which are related to cell layer confluence states. In summary, the results show that QPI enables label-free imaging cytometry, which provides novel complementary integral biophysical data sets for sophisticated quantification of cell culture quality with minimized sample preparation. © 2017 International Society for Advancement of Cytometry.