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.

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.

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.

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.

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.

PhUn-Net: ready-to-use neural network for unwrapping quantitative phase images of biological cells.

We present a deep-learning approach for solving the problem of 2π phase ambiguities in two-dimensional quantitative phase maps of biological cells, using a multi-layer encoder-decoder residual convolutional neural network. We test the trained network, PhUn-Net, on various types of biological cells, captured with various interferometric setups, as well as on simulated phantoms. These tests demonstrate the robustness and generality of the network, even for cells of different morphologies or different illumination conditions than PhUn-Net has been trained on. In this paper, for the first time, we make the trained network publicly available in a global format, such that it can be easily deployed on every platform, to yield fast and robust phase unwrapping, not requiring prior knowledge or complex implementation. By this, we expect our phase unwrapping approach to be widely used, substituting conventional and more time-consuming phase unwrapping algorithms.

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.

First experimental realization of six-pack holography and its application to dynamic synthetic aperture superresolution.

It has long been assumed that off-axis holography is less spatial bandwidth efficient than on-axis holography. Six-pack holography (6PH) is the first off-axis configuration that changes this paradigm. We present the first experimental realization of 6PH, an off-axis interferometric system capable of spatially multiplexing six complex wavefronts while using the same number of camera pixels needed for a single off-axis hologram. Each of the six parallel complex wavefronts is encoded using a different fringe orientation and can be fully reconstructed. This technique is especially useful for dynamic samples, as it allows the acquisition of six complex wavefronts simultaneously. There are many applications for the data that can be compressed into the six channels. Here, we utilize 6PH to increase resolution in dynamic synthetic aperture imaging, where each of the six optically compressed off-axis holograms encodes a different spatial frequency range of the imaged sample, yielding 1.62 × resolution enhancement.

Optimal spatial bandwidth capacity in multiplexed off-axis holography for rapid quantitative phase reconstruction and visualization: erratum.

We correct a typo that repeated itself in several equations. Our previous results and conclusions are unchanged.

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.

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.

Six-pack off-axis holography.

We present a new holographic concept, named six-pack holography (6PH), in which we compress six off-axis holograms into a single multiplexed off-axis hologram without loss of magnification or resolution. The multiplexed hologram contains straight off-axis fringes with six different orientations, and can be generated optically or digitally. We show that since the six different complex wavefronts do not overlap in the spatial frequency domain, they can be fully reconstructed. 6PH allows more than 50% improvement in the spatial bandwidth consumption when compared to the best multiplexing method proposed so far. We expect the 6PH concept to be useful for a variety of applications, such as field-of-view multiplexing, wavelength multiplexing, temporal multiplexing, multiplexing for super-resolution imaging, and others.

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.

Angular phase unwrapping of optically thick objects with a thin dimension.

We present a new phase unwrapping approach, which allows reconstruction of optically thick objects that are optically thin from at least one viewing angle, by considering the information stored in the object phase maps captured from consecutive angles. Our algorithm combines 1-D phase unwrapping in the angular dimension with conventional 2-D phase unwrapping, to achieve unwrapping of the object from the optically thick perspective. We thus obtain quantitative phase imaging of objects that were previously impossible to image in certain viewing angles. To demonstrate our approach, we present both numerical simulation and experimental results for quantitative phase imaging of biological cells.

Localized measurements of physical parameters within human sperm cells obtained with wide-field interferometry.

We developed a new method to identify the separate cellular compartments in the optical path delay (OPD) maps of un-labeled spermatozoa. This was conducted by comparing OPD maps of fixed, un-labeled spermatozoa to bright field images of the same cells following labeling. The labeling enabled us to identify the acrosomal and nuclear compartments in the corresponding OPD maps of the cells. We then extracted the refractive index maps of fixed cells by dividing the OPD maps of spermatozoa by the corresponding thickness maps of the same cells, obtained with AFM. Finally, the dry mass of the head, nucleus and acrosome of un-labeled immobile spermatozoa, was measured. This method provides the ability to quantitatively measure the dry mass of cellular compartments within human spermatozoa. We expect that these measurements will assist label-free selection of sperm cells for fertilization.