Detection of bladder cancer cells using quantitative interferometric label-free imaging flow cytometry.

Bladder cancer is one of the most common cancers with a high recurrence rate. Patients undergo mandatory yearly scrutinies, including cystoscopies, which makes bladder cancer highly distressing and costly. Here, we aim to develop a non-invasive, label-free method for the detection of bladder cancer cells in urine samples, which is based on interferometric imaging flow cytometry. Eight urothelial carcinoma and one normal urothelial cell lines, along with red and white blood cells, imaged quantitatively without staining by an interferometric phase microscopy module while flowing in a microfluidic chip, and classified by two machine-learning algorithms, based on deep-learning semantic segmentation convolutional neural network and extreme gradient boosting. Furthermore, urine samples obtained from bladder-cancer patients and healthy volunteers were imaged, and classified by the system. We achieved accuracy and area under the curve (AUC) of 99% and 97% for the cell lines on both machine-learning algorithms. For the real urine samples, the accuracy and AUC were 96% and 96% for the deep-learning algorithm and 95% and 93% for the gradient-boosting algorithm, respectively. By combining label-free interferometric imaging flow cytometry with high-end classification algorithms, we achieved high-performance differentiation between healthy and malignant cells. The proposed technique has the potential to supplant cystoscopy in the bladder cancer surveillance and diagnosis space.

Analyzing Blood Cells of High-Risk Myelodysplastic Syndrome Patients Using Interferometric Phase Microscopy and Fluorescent Flow Cytometry.

Myelodysplastic syndromes (MDSs) are a group of potentially deadly diseases that affect the morphology and function of neutrophils. Rapid diagnosis of MDS is crucial for the initiation of treatment that can vastly improve disease outcome. In this work, we present a new approach for detecting morphological differences between neutrophils isolated from blood samples of high-risk MDS patients and blood bank donors (BBDs). Using fluorescent flow cytometry, neutrophils were stained with 2',7'-dichlorofluorescin diacetate (DCF), which reacts with reactive oxygen species (ROS), and Hoechst, which binds to DNA. We observed that BBDs possessed two cell clusters (designated H and L), whereas MDS patients possessed a single cluster (L). Later, we used FACS to sort the H and the L cells and used interferometric phase microscopy (IPM) to image the cells without utilizing cell staining. IPM images showed that H cells are characterized by low optical path delay (OPD) in the nucleus relative to the cytoplasm, especially in cell vesicles containing ROS, whereas L cells are characterized by low OPD in the cytoplasm relative to the nucleus and no ROS-containing vesicles. Moreover, L cells present a higher average OPD and dry mass compared to H cells. When examining neutrophils from MDS patients and BBDs by IPM during flow, we identified ~20% of cells as H cells in BBDs in contrast to ~4% in MDS patients. These results indicate that IPM can be utilized for the diagnosis of complex hematological pathologies such as MDS.

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.

Sperm-cell DNA fragmentation prediction using label-free quantitative phase imaging and deep learning.

In intracytoplasmic sperm injection (ICSI), a single sperm cell is selected and injected into an egg. The quality of the chosen sperm and specifically its DNA fragmentation have a significant effect on the fertilization success rate. However, there is no method today to measure the DNA fragmentation of live and unstained cells during ICSI. We present a new method to predict the DNA fragmentation of sperm cells using multi-layer stain-free imaging data, including quantitative phase imaging, and lightweight deep learning architectures. The DNA fragmentation ground truth is achieved by staining the cells with acridine orange and imaging them via fluorescence microscopy. Our prediction model is based on the MobileNet convolutional neural network architecture combined with confidence measurement determined by distances between vectors in the latent space. Our results show that the mean absolute error for cells with high prediction confidence is 0.05 and the 90th percentile mean absolute error is 0.1, where the range of DNA fragmentation score is [0,1]. In the future, this model may be applied to improve cell selection by embryologists during ICSI.

Dynamic Tomographic Phase Microscopy by Double Six-Pack Holography.

Three-dimensional (3D) optical imaging of rapidly moving biological cells is difficult to achieve as such samples cannot be scanned over time. Here, we present a dynamic scan-free optical tomography approach for stain-free 3D imaging of biological cells using our new double six-pack tomography technique, whereby 12 off-axis holograms are captured in a single camera exposure without sacrificing resolution or field of view. The proposed system illuminates the sample from 12 angles simultaneously, and 3D refractive index (RI) tomograms are reconstructed from each recorded video frame of the dynamic sample. The technique is verified experimentally by recording flowing silica beads, 3 µm in diameter, with the resulting tomogram RI accuracy being 98.5%. A live swimming sperm cell is also imaged, and dynamic 3D imaging results for both beads and sperm cell are presented. The proposed technique represents a 12-fold increase in dynamic holographic data for tomography.

Sperm Inspection for In Vitro Fertilization via Self-Assembled Microdroplet Formation and Quantitative Phase Microscopy.

We present a new method for the selection of individual sperm cells using a microfluidic device that automatically traps each cell in a separate microdroplet that then individually self-assembles with other microdroplets, permitting the controlled measurement of the cells using quantitative phase microscopy. Following cell trapping and droplet formation, we utilize quantitative phase microscopy integrated with bright-field imaging for individual sperm morphology and motility inspection. We then perform individual sperm selection using a single-cell micromanipulator, which is enhanced by the microdroplet-trapping procedure described above. This method can improve sperm selection for intracytoplasmic sperm injection, a common type of in vitro fertilization procedure.

Limited-angle tomographic phase microscopy utilizing confocal scanning fluorescence microscopy.

We present a multimodal imaging technique, combining tomographic phase microscopy with limited angular projection range and number, and two-channel spinning-disk confocal scanning fluorescence microscopy. This technique allows high-accuracy 3D refractive index (RI) profiling of live cells in spite of the missing projections. The cellular outer shape and its interior organelles measured by the confocal fluorescence imaging not only specify the cell in molecular levels, but also provide the 3D distributions of the whole cell as well as its organelles. We take these additional 3D morphological details as constraints in Gerchberg-Papoulis-based optical diffraction tomography algorithm. We then obtain an accurate 3D RI tomogram, even with a sparse angular range having a small number of perspective projections, otherwise providing low-accuracy RI reconstruction. Then, we obtain both cellular molecular specificity and inner RI values of the cell and its organelles. We compare the reconstructed 3D RI profiles of various samples, demonstrating the superiority of the proposed technique.

Real-Time Stain-Free Classification of Cancer Cells and Blood Cells Using Interferometric Phase Microscopy and Machine Learning.

We present a method for real-time visualization and automatic processing for detection and classification of untreated cancer cells in blood during stain-free imaging flow cytometry using digital holographic microscopy and machine learning in throughput of 15 cells per second. As a preliminary model for circulating tumor cells in the blood, following an initial label-free rapid enrichment stage based on the cell size, we applied our holographic imaging approach, providing the quantitative optical thickness profiles of the cells during flow. We automatically classified primary and metastatic colon cancer cells, where the two types of cancer cells were isolated from the same individual, as well as four types of blood cells. We used low-coherence off-axis interferometric phase microscopy and a microfluidic channel to image cells during flow quantitatively. The acquired images were processed and classified based on their morphology and quantitative phase features during the cell flow. We achieved high accuracy of 92.56% for distinguishing between the cells, enabling further automatic enrichment and cancer-cell grading from blood. © 2020 International Society for Advancement of Cytometry.

Label-free discrimination and selection of cancer cells from blood during flow using holography-induced dielectrophoresis.

We present a method for label-free imaging and sorting of cancer cells in blood, which is based on a dielectrophoretic microfluidic chip and label-free interferometric phase microscopy. The chip used for imaging has been embedded with dielectrophoretic electrodes, and therefore it can be used to sort the cells based on the decisions obtained during the cell flow by the label-free quantitative imaging method. Hence, we obtained a real-time, automatic, label-free imaging flow cytometry with the ability to sort the cells during flow. To validate our model, we combined into the label-free imaging interferometer a fluorescence imaging channel that indicated the correctness of the label-free sorting. We have achieved above 98% classification success and 69% sorting accuracy at flow rates of 4 to 7 µL hr-1 . In the future, this method is expected to help in label-free sorting of circulating tumor cells in blood following an initial state-of-the-art cell enrichment.

Cell and nucleus refractive-index mapping by interferometric phase microscopy and rapid confocal fluorescence microscopy.

We present a multimodal technique for measuring the integral refractive index and the thickness of biological cells and their organelles by integrating interferometric phase microscopy (IPM) and rapid confocal fluorescence microscopy. First, the actual thickness maps of the cellular compartments are reconstructed using the confocal fluorescent sections, and then the optical path difference (OPD) map of the same cell is reconstructed using IPM. Based on the co-registered data, the integral refractive index maps of the cell and its organelles are calculated. This technique enables rapidly measuring refractive index of live, dynamic cells, where IPM provides quantitative imaging capabilities and confocal fluorescence microscopy provides molecular specificity of the cell organelles. We acquire human colorectal adenocarcinoma cells and show that the integral refractive index values are similar for the whole cell, the cytoplasm and the nucleus on the population level, but significantly different on the single cell level.

Holographic virtual staining of individual biological cells.

Many medical and biological protocols for analyzing individual biological cells involve morphological evaluation based on cell staining, designed to enhance imaging contrast and enable clinicians and biologists to differentiate between various cell organelles. However, cell staining is not always allowed in certain medical procedures. In other cases, staining may be time-consuming or expensive to implement. Staining protocols may be operator-sensitive, and hence may lead to varying analytical results, as well as cause artificial imaging artifacts or false heterogeneity. We present a deep-learning approach, called HoloStain, which converts images of isolated biological cells acquired without staining by holographic microscopy to their virtually stained images. We demonstrate this approach for human sperm cells, as there is a well-established protocol and global standardization for characterizing the morphology of stained human sperm cells for fertility evaluation, but, on the other hand, staining might be cytotoxic and thus is not allowed during human in vitro fertilization (IVF). After a training process, the deep neural network can take images of unseen sperm cells retrieved from holograms acquired without staining and convert them to their stainlike images. We obtained a fivefold recall improvement in the analysis results, demonstrating the advantage of using virtual staining for sperm cell analysis. With the introduction of simple holographic imaging methods in clinical settings, the proposed method has a great potential to become a common practice in human IVF procedures, as well as to significantly simplify and radically change other cell analyses and techniques such as imaging flow cytometry.

High-resolution 4-D acquisition of freely swimming human sperm cells without staining.

We present a new acquisition method that enables high-resolution, fine-detail full reconstruction of the three-dimensional movement and structure of individual human sperm cells swimming freely. We achieve both retrieval of the three-dimensional refractive-index profile of the sperm head, revealing its fine internal organelles and time-varying orientation, and the detailed four-dimensional localization of the thin, highly-dynamic flagellum of the sperm cell. Live human sperm cells were acquired during free swim using a high-speed off-axis holographic system that does not require any moving elements or cell staining. The reconstruction is based solely on the natural movement of the sperm cell and a novel set of algorithms, enabling the detailed four-dimensional recovery. Using this refractive-index imaging approach, we believe that we have detected an area in the cell that is attributed to the centriole. This method has great potential for both biological assays and clinical use of intact sperm cells.

Quantitative phase imaging by wide-field interferometry with variable shearing distance uncoupled from the off-axis angle.

We introduce a new shearing interferometry module for digital holographic microscopy, in which the off-axis angle, which defines the interference fringe frequency, is not coupled to the shearing distance, as is the case in most shearing interferometers. Thus, it enables the selection of shearing distance based on the spatial density of the sample, without losing spatial frequency content due to overlapping of the complex wave fronts in the spatial frequency domain. Our module is based on a 4f imaging unit and a diffraction grating, in which the hologram is generated from two mutually coherent, partially overlapping sample beams, with adjustable shearing distance, as defined by the position of the grating, but with a constant off-axis angle, as defined by the grating period. The module is simple, easy to align, and presents a nearly common-path geometry. By placing this module as an add-on unit at the exit port of an inverted microscope, quantitative phase imaging can easily be performed. The system is characterized by a 2.5 nm temporal stability and a 3.4 nm spatial stability, without using anti-vibration techniques. We provide quantitative phase imaging experiments of silica beads with different shearing distances, red blood cell fluctuations, and cancer cells flowing in a micro-channel, which demonstrate the capability and versatility of our approach in different imaging scenarios.

TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set.

We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cell lines acquired by quantitative phase imaging. The proposed method, called transferring of pre-trained generative adversarial network (TOP-GAN), is hybridization between transfer learning and generative adversarial networks (GANs). Healthy cells and cancer cells of different metastatic potential have been imaged by low-coherence off-axis holography. After the acquisition, the optical path delay maps of the cells are extracted and directly used as inputs to the networks. In order to cope with the small number of classified images, we use GANs to train a large number of unclassified images from another cell type (sperm cells). After this preliminary training, we change the last layers of the network and design automatic classifiers for the correct cell type (healthy/primary cancer/metastatic cancer) with 90-99% accuracies, although small training sets of down to several images are used. These results are better in comparison to other classic methods that aim at coping with the same problem of a small training set. We believe that our approach makes the combination of holographic microscopy and deep learning networks more accessible to the medical field by enabling a rapid, automatic and accurate classification in stain-free imaging flow cytometry. Furthermore, our approach is expected to be applicable to many other medical image classification tasks, suffering from a small training set.

Simultaneous off-axis multiplexed holography and regular fluorescence microscopy of biological cells.

We present a new technique for obtaining simultaneous multimodal quantitative phase and fluorescence microscopy of biological cells, providing both quantitative phase imaging and molecular specificity using a single camera. Our system is based on an interferometric multiplexing module, externally positioned at the exit of an optical microscope. In contrast to previous approaches, the presented technique allows conventional fluorescence imaging, rather than interferometric off-axis fluorescence imaging. We demonstrate the presented technique for imaging fluorescent beads and live biological cells.

Stain-free interferometric phase microscopy correlation with DNA fragmentation stain in human spermatozoa.

Acridine orange (AO) staining is used to diagnose DNA fragmentation status in sperm cells. Interferometric phase microscopy (IPM) is an optical imaging method based on digital holographic microscopy that provides quantitative morphological and refractive index imaging of cells in vitro without the need for staining. We have imaged sperm cells using stain-free IPM in order to estimate different cellular parameters, such as acrosome dry mass and size, in addition to an embryologist evaluation according to the World Health Organization (WHO)-2010 criteria. Following this, the same sperm cells were stained by AO, imaged using a fluorescence confocal microscope and assessed by the AO-emitted color, forming five DNA fragmentation groups. These DNA fragmentation groups were correlated with the embryologist-based classification and the IPM-based morphological parameters. Our results indicate on significant differences in the IPM-based parameters between groups with different fragmentation levels. Based on the validation with AO, we conclude that stain-free IPM images analyzed digitally may assist in selecting sperm cells with intact DNA prior to intracytoplasmic sperm injection. This information may potentially increase percentage of successful pregnancies.

Simultaneous three-wavelength unwrapping using external digital holographic multiplexing module.

We present an external interferometric setup that is able to simultaneously acquire three wavelengths of the same sample instance without scanning or multiple exposures. This setup projects onto the monochrome digital camera three off-axis holograms with rotated fringe orientations, each from a different wavelength channel, without overlap in the spatial-frequency domain, and thus allows the full reconstruction of the three complex wavefronts from the three wavelength channels. We use this new setup for three-wavelength phase unwrapping, allowing phase imaging of thicker objects than possible with a single wavelength, but without the increased level of noise. We demonstrate the proposed technique for micro-channel profiling and label-free cell imaging.

Integral refractive index imaging of flowing cell nuclei using quantitative phase microscopy combined with fluorescence microscopy.

We suggest a new multimodal imaging technique for quantitatively measuring the integral (thickness-average) refractive index of the nuclei of live biological cells in suspension. For this aim, we combined quantitative phase microscopy with simultaneous 2-D fluorescence microscopy. We used 2-D fluorescence microscopy to localize the nucleus inside the quantitative phase map of the cell, as well as for measuring the nucleus radii. As verified offline by both 3-D confocal fluorescence microscopy and 2-D fluorescence microscopy while rotating the cells during flow, the nucleus of cells in suspension that are not during division can be assumed to be an ellipsoid. The entire shape of a cell in suspension can be assumed to be a sphere. Then, the cell and nucleus 3-D shapes can be evaluated based on their in-plain radii available from the 2-D phase and fluorescent measurements, respectively. Finally, the nucleus integral refractive index profile is calculated. We demonstrate the new technique on cancer cells, obtaining nucleus refractive index values that are lower than those of the cytoplasm, coinciding with recent findings. We believe that the proposed technique has the potential to be used for flow cytometry, where full 3-D refractive index tomography is too slow to be implemented during flow.

Individual sperm selection by microfluidics integrated with interferometric phase microscopy.

The selection of sperm cells possessing normal morphology and motility is crucial for many assisted reproductive technologies (ART), especially for intracytoplasmic sperm injection (ICSI), as sperm quality directly affects the probability of inducing healthy pregnancy. We present a novel platform for real-time quantitative analysis and selection of individual sperm cells without staining. Towards this end, we developed an integrated approach, combining interferometric phase microscopy (IPM), for stain-free sperm imaging and real-time automatic analysis based on the sperm cell 3D morphology and contents, with a disposable microfluidic device, for sperm selection and enrichment. On testing the capabilities of the microfluidic device, we obtained successful selection of sperm cells with a selectivity of 89.5±3.5%, with no negative-decision sperm cells being inadvertently selected. In addition, we demonstrate the accuracy of sperm cell analysis using IPM by comparing the quantitative analysis produced by our IPM-based algorithm to the qualitative visual analysis performed independently by an experienced embryologist, which resulted in precision and specificity of 100%. We believe that the presented integrated approach has the potential to dramatically change the way sperm cells are selected for ICSI and other ART procedures, making the selection process more objective, quantitative and automatic, and thereby increasing success rates.

Automated analysis of individual sperm cells using stain-free interferometric phase microscopy and machine learning.

Currently, the delicate process of selecting sperm cells to be used for in vitro fertilization (IVF) is still based on the subjective, qualitative analysis of experienced clinicians using non-quantitative optical microscopy techniques. In this work, a method was developed for the automated analysis of sperm cells based on the quantitative phase maps acquired through use of interferometric phase microscopy (IPM). Over 1,400 human sperm cells from 8 donors were imaged using IPM, and an algorithm was designed to digitally isolate sperm cell heads from the quantitative phase maps while taking into consideration both the cell 3D morphology and contents, as well as acquire features describing sperm head morphology. A subset of these features was used to train a support vector machine (SVM) classifier to automatically classify sperm of good and bad morphology. The SVM achieves an area under the receiver operating characteristic curve of 88.59% and an area under the precision-recall curve of 88.67%, as well as precisions of 90% or higher. We believe that our automatic analysis can become the basis for objective and automatic sperm cell selection in IVF. © 2017 International Society for Advancement of Cytometry.