Evaluation of women's aging influence on sperm passage inside the fallopian tube using 3D dynamic mechanical modeling.

The fallopian tubes play an important role in human fertility by facilitating the spermatozoa passage to the oocyte as well as later actively facilitating the fertilized oocyte transportation to the uterus cavity. The fallopian tubes undergo changes involving biological, physical, and morphological processes due to women aging, which may impair fertility. Here, we have modelled fallopian tubes of women at different ages and evaluated the chances of normal and pathological sperm cells reaching the fertilization site, the ampulla. By utilizing a unique combination of simulative tools, we implemented dynamic three-dimensional (3D) detailed geometrical models of many normal and pathological sperm cells swimming together in 3D geometrical models of three fallopian tubes associated with different women's age groups. By tracking the sperm cell swim, we found that for all age groups, the number of normal sperm cells in the ampulla is the largest, compared with the pathological sperm cells. On the other hand, the number of normal sperm cells in the fertilization site decreases due to the morphological and mechanical changes that occur in the fallopian tube with age. Moreover, in older ages, the normal sperm cells swim with lower velocities and for shorter distances inside the ampulla toward the ovary. Thus, the changes that the human fallopian tube undergoes due to women's aging have a significant influence on the human sperm cell motility. Our model of sperm cell motility through the fallopian tube in relation to the woman's age morphological changes provides a new scope for the investigation and treatment of diseases and infertility cases associated with aging, as well as a potential personalized medicine tool for evaluating the chances of a natural fertilization per specific features of a man's sperm and a woman's reproductive system.

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.

High Quantum Yield Amino Acid Carbon Quantum Dots with Unparalleled Refractive Index.

Carbon quantum dots (CQDs) are one of the most promising types of fluorescent nanomaterials due to their exceptional water solubility, excellent optical properties, biocompatibility, chemical inertness, excellent refractive index, and photostability. Nitrogen-containing CQDs, which include amino acid based CQDs, are especially attractive due to their high quantum yield, thermal stability, and potential biomedical applications. Recent studies have attempted to improve the preparation of amino acid based CQDs. However, the highest quantum yield obtained for these dots was only 44%. Furthermore, the refractive indices of amino acid derived CQDs were not determined. Here, we systematically explored the performance of CQDs prepared from all 20 coded amino acids using modified hydrothermal techniques allowing more passivation layers on the surface of the dots to optimize their performance. Intriguingly, we obtained the highest refractive indices ever reported for any CQDs. The values differed among the amino acids, with the highest refractive indices found for positively charged amino acids including arginine-CQDs (∼2.1), histidine-CQDs (∼2.0), and lysine-CQDs (∼1.8). Furthermore, the arginine-CQDs reported here showed a nearly 2-fold increase in the quantum yield (∼86%) and a longer decay time (∼8.0 ns) compared to previous reports. In addition, we also demonstrated that all amino acid based CQD materials displayed excitation-dependent emission profiles (from UV to visible) and were photostable, water-soluble, noncytotoxic, and excellent for high contrast live cell imaging or bioimaging. These results indicate that amino acid based CQD materials are high-refractive-index materials applicable for optoelectronic devices, bioimaging, biosensing, and studying cellular organelles in vivo. This extraordinary RI may be highly useful for exploring cellular elements with different densities.

Six-pack holography for dynamic profiling of thick and extended objects by simultaneous three-wavelength phase unwrapping with doubled field of view.

Dynamic holographic profiling of thick samples is limited due to the reduced field of view (FOV) of off-axis holography. We present an improved six-pack holography system for the simultaneous acquisition of six complex wavefronts in a single camera exposure from two fields of view (FOVs) and three wavelengths, for quantitative phase unwrapping of thick and extended transparent objects. By dynamically generating three synthetic wavelength quantitative phase maps for each of the two FOVs, with the longest wavelength being 6207 nm, hierarchical phase unwrapping can be used to reduce noise while maintaining the improvements in the 2π phase ambiguity due to the longer synthetic wavelength. The system was tested on a 7 µm tall PDMS microchannel and is shown to produce quantitative phase maps with 96% accuracy, while the hierarchical unwrapping reduces noise by 93%. A monolayer of live onion epidermal tissue was also successfully scanned, demonstrating the potential of the system to dynamically decrease scanning time of optically thick and extended samples.

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.

Dynamic 3D Modeling for Human Sperm Motility through the Female Cervical Canal and Uterine Cavity to Predict Sperm Chance of Reaching the Oocyte.

Sperm motility in the female genital tract is a key factor in the natural selection of competent cells that will produce a healthy offspring. We created a dynamic three-dimensional (3D) mechanical model of human sperm cells swimming inside cervical canal and uterine cavity dynamic 3D models, all generated based on experimental studies. Using these simulations, we described the sperm cells' behaviors during swimming inside the 3D tract model as a function of 3D displacement and time. We evaluated normal- and abnormal-morphology sperm cells according to their chances of reaching the oocyte site. As expected, we verified that the number of normal sperm cells that succeeded in reaching the fallopian tube sites is greater than the number of abnormal sperm cells. However, interestingly, after inspecting various abnormal sperm cells, we found out that their scores changed compared to swimming in an infinite medium, as is the case with in vitro fertilization. Thus, the interactions of abnormal sperm cells and the complicated geometry and dynamics of the uterus are significant factors in the filtering of abnormal sperm cells until they reach the oocyte site. Our study provides an advanced tool for sperm analysis and selection criteria for fertility treatments.

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.

Label-free biomedical optical imaging.

Label-free optical imaging employs natural and nondestructive approaches for the visualisation of biomedical samples for both biological assays and clinical diagnosis. Currently, this field revolves around multiple broad technology-oriented communities, each with a specific focus on a particular modality despite the existence of shared challenges and applications. As a result, biologists or clinical researchers who require label-free imaging are often not aware of the most appropriate modality to use. This manuscript presents a comprehensive review of and comparison among different label-free imaging modalities and discusses common challenges and applications. We expect this review to facilitate collaborative interactions between imaging communities, push the field forward and foster technological advancements, biophysical discoveries, as well as clinical detection, diagnosis, and monitoring of disease.

Prediction of Sperm Progression in Three Dimensions Using Rapid Optical Imaging and Dynamic Mechanical Modeling.

We present a multidisciplinary approach for predicting how sperm cells with various morphologies swim in three-dimensions (3D), from milliseconds to much longer time scales at spatial resolutions of less than half a micron. We created the sperm 3D geometry and built a numerical mechanical model using the experimentally acquired dynamic 3D refractive-index profiles of sperm cells swimming in vitro as imaged by high-resolution optical diffraction tomography. By controlling parameters in the model, such as the size and shape of the sperm head and tail, we can then predict how different sperm cells, normal or abnormal, would swim in 3D, in the short or long term. We quantified various 3D structural factor effects on the sperm long-term motility. We found that some abnormal sperm cells swim faster than normal sperm cells, in contrast to the commonly used sperm selection assumption during in vitro fertilization (IVF), according to which sperm cells should mainly be chosen based on their progressive motion. We thus establish a new tool for sperm analysis and male-infertility diagnosis, as well as sperm selection criteria for fertility treatments.

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.

Classification of tissue biopsies by Raman spectroscopy guided by quantitative phase imaging and its application to bladder cancer.

We present a multimodal label-free optical measurement approach for analyzing sliced tissue biopsies by a unique combination of quantitative phase imaging and localized Raman spectroscopy. First, label-free quantitative phase imaging of the entire unstained tissue slice is performed using automated scanning. Then, pixel-wise segmentation of the tissue layers is performed by a kernelled structural support vector machine based on Haralick texture features, which are extracted from the quantitative phase profile, and used to find the best locations for performing the label-free localized Raman measurements. We use this multimodal label-free measurement approach for segmenting the urothelium in benign and malignant bladder cancer tissues by quantitative phase imaging, followed by location-guided Raman spectroscopy measurements. We then use sparse multinomial logistic regression (SMLR) on the Raman spectroscopy measurements to classify the tissue types, demonstrating that the prior segmentation of the urothelium done by label-free quantitative phase imaging improves the Raman spectra classification accuracy from 85.7% to 94.7%.

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.

Cancer-Cell Deep-Learning Classification by Integrating Quantitative-Phase Spatial and Temporal Fluctuations.

We present a new classification approach for live cells, integrating together the spatial and temporal fluctuation maps and the quantitative optical thickness map of the cell, as acquired by common-path quantitative-phase dynamic imaging and processed with a deep-learning framework. We demonstrate this approach by classifying between two types of cancer cell lines of different metastatic potential originating from the same patient. It is based on the fact that both the cancer-cell morphology and its mechanical properties, as indicated by the cell temporal and spatial fluctuations, change over the disease progression. We tested different fusion methods for inputting both the morphological optical thickness maps and the coinciding spatio-temporal fluctuation maps of the cells to the classifying network framework. We show that the proposed integrated triple-path deep-learning architecture improves over deep-learning classification that is based only on the cell morphological evaluation via its quantitative optical thickness map, demonstrating the benefit in the acquisition of the cells over time and in extracting their spatio-temporal fluctuation maps, to be used as an input to the classifying deep neural network.

Roadmap on digital holography [Invited].

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.

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.

Six-pack holographic imaging for dynamic rejection of out-of-focus objects.

Six-pack holography is adapted to reject out-of-focus objects in dynamic samples, using a single camera exposure and without any scanning. By illuminating the sample from six different angles in parallel using a low-coherence source, out-of-focus objects are laterally shifted in six different directions when projected onto the focal plane. Then pixel-wise averaging of the six reconstructed images creates a significantly clearer image, with rejection of out-of-focus objects. Dynamic imaging results are shown for swimming microalgae and flowing microbeads, including numerical refocusing by Fresnel propagation. The averaged images reduced the contribution of out-of-focus objects by up to 83% in comparison to standard holograms captured using the same light source, further improving the system sectioning capabilities. Both simulation and experimental results are presented.

Polarization-independent differential interference contrast and off-axis holography combined module.

We present an external portable module for transforming bright-field microscopy to differential interference contrast (DIC) microscopy and digital holographic microscopy together. The module is composed of simple optical elements, positioned between the microscope output plane and the digital camera plane; thus, it can be integrated externally with existing microscopes. The proposed module enables polarization DIC imaging, without special polarization elements, under either white-light or coherent illumination, providing label-free imaging of biological samples, as recorded directly by the digital camera. In addition, by rotating one element inside the module, an off-axis hologram is created on the camera under coherent illumination, thus providing the possibility for reconstruction of the quantitative phase profile of the same sample. The method is demonstrated for imaging silica microspheres and biological cells.

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.