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

On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning

We present a rapid label-free imaging flow cytometry and cell classification approach based directly on raw digital holograms. Off-axis holography enables real-time acquisition of cells during rapid flow. However, classification of the cells typically requires reconstruction of their quantitative phase profiles, which is time-consuming. Here, we present a new approach for label-free classification of individual cells based directly on the raw off-axis holographic images, each of which contains the complete complex wavefront (amplitude and quantitative phase profiles) of the cell. To obtain this, we built a convolutional neural network, which is invariant to the spatial frequencies and directions of the interference fringes of the off-axis holograms. We demonstrate the effectiveness of this approach using four types of cancer cells. This approach has the potential to significantly improve both speed and robustness of imaging flow cytometry, enabling real-time label-free classification of individual cells.

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning

About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. Research studies showed that the cell morphology-based method is promising to be a new route for chemotherapeutic sensitivity evaluation. Here, we offer how the drug resistance of EOC cells can be assessed through a label-free and high-throughput microfluidic flow cytometer equipped with a digital holographic microscope reinforced by machine learning. It is the first time that such type of assessment is performed to the best of our knowledge. Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. The result shows that the SVM classifier achieves the optimal performance with an accuracy of 92.2% and an area under the curve of 0.96. This study demonstrates that the proposed method achieves high-accuracy, high-throughput, and label-free assessment of the drug resistance of EOC cells. Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.

Exploiting the potential of commercial digital holographic microscopy by combining it with 3D matrix cell culture assays

3D cell culture assays are becoming increasingly popular due to their higher resemblance to tissue environment. These provide an increased complexity compared to the growth on 2D surface and therefore allow studies of advanced cellular properties such as invasion. We report here on the use of 3D Matrigel cell preparations combined with a particular gentle and informative type of live-cell microscopy: quantitative digital holographic microscopy (DHM), here performed by a commercial software-integrated system, currently mostly used for 2D cell culture preparations. By demonstrating this compatibility, we highlight the possible time-efficient quantitative analysis obtained by using a commercial software-integrated DHM system, also for cells in a more advanced 3D culture environment. Further, we demonstrate two very different examples making use of this advantage by performing quantitative DHM analysis of: (1) wound closure cell monolayer Matrigel invasion assay and (2) Matrigel-trapped single and clumps of suspension cells. For both these, we benefited from the autofocus functionality of digital phase holographic imaging to obtain 3D information for cells migrating in a 3D environment. For the latter, we demonstrate that it is possible to quantitatively measure tumourigenic properties like growth of cell clump (or spheroid) over time, as well as single-cell invasion out of cell clump and into the surrounding extracellular matrix. Overall, our findings highlight several possibilities for 3D digital holographic microscopy applications combined with 3D cell preparations, therein studies of drug response or genetic alterations on invasion capacity as well as on tumour growth and metastasis.

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.

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.

Evaluation of holographic imaging cytometer holomonitor M4® motility applications

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. © 2018 International Society for Advancement of Cytometry.

Oxidative Stress Modulation and ROS-Mediated Toxicity in Cancer: A Review on In Vitro Models for Plant-Derived Compounds

Medicinal and aromatic plants (MAPs) are known and have been long in use for a variety of health and cosmetics applications. Potential pharmacological usages that take advantage of bioactive plant-derived compounds' antimicrobial, antifungal, anti-inflammatory, and antioxidant properties are being developed and many new ones explored. Some phytochemicals could trigger ROS-mediated cytotoxicity and apoptosis in cancer cells. A lot of effort has been put into investigating novel active constituents for cancer therapeutics. While other plant-derived compounds might enhance antioxidant defenses by either radical scavenging or stimulation of intracellular antioxidant enzymes, the generation of reactive oxygen species (ROS) leading to oxidative stress is one of the strategies that may show effective in damaging cancer cells. The biochemical pathways involved in plant-derived bioactive compounds' properties are complex, and in vitro platforms have been useful for a comprehensive understanding of the mechanism of action of these potential anticancer drugs. The present review aims at compiling the findings of particularly interesting studies that use cancer cell line models for assessment of antioxidant and oxidative stress modulation properties of plant-derived bioactive compounds.

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.