Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure.

The ability to evaluate sperm at the microscopic level, at high-throughput, would be useful for assisted reproductive technologies (ARTs), as it can allow specific selection of sperm cells for in vitro fertilization (IVF). The tradeoff between intrinsic imaging and external contrast agents is particularly acute in reproductive medicine. The use of fluorescence labels has enabled new cell-sorting strategies and given new insights into developmental biology. Nevertheless, using extrinsic contrast agents is often too invasive for routine clinical operation. Raising questions about cell viability, especially for single-cell selection, clinicians prefer intrinsic contrast in the form of phase-contrast, differential-interference contrast, or Hoffman modulation contrast. While such instruments are nondestructive, the resulting image suffers from a lack of specificity. In this work, we provide a template to circumvent the tradeoff between cell viability and specificity by combining high-sensitivity phase imaging with deep learning. In order to introduce specificity to label-free images, we trained a deep-convolutional neural network to perform semantic segmentation on quantitative phase maps. This approach, a form of phase imaging with computational specificity (PICS), allowed us to efficiently analyze thousands of sperm cells and identify correlations between dry-mass content and artificial-reproduction outcomes. Specifically, we found that the dry-mass content ratios between the head, midpiece, and tail of the cells can predict the percentages of success for zygote cleavage and embryo blastocyst formation.

Engineering geometrical 3-dimensional untethered in vitro neural tissue mimic.

Formation of tissue models in 3 dimensions is more effective in recapitulating structure and function compared to their 2-dimensional (2D) counterparts. Formation of 3D engineered tissue to control shape and size can have important implications in biomedical research and in engineering applications such as biological soft robotics. While neural spheroids routinely are created during differentiation processes, further geometric control of in vitro neural models has not been demonstrated. Here, we present an approach to form functional in vitro neural tissue mimic (NTM) of different shapes using stem cells, a fibrin matrix, and 3D printed molds. We used murine-derived embryonic stem cells for optimizing cell-seeding protocols, characterization of the resulting internal structure of the construct, and remodeling of the extracellular matrix, as well as validation of electrophysiological activity. Then, we used these findings to biofabricate these constructs using neurons derived from human embryonic stem cells. This method can provide a large degree of design flexibility for development of in vitro functional neural tissue models of varying forms for therapeutic biomedical research, drug discovery, and disease modeling, and engineering applications.

Real-time Jones phase microscopy for studying transparent and birefringent specimens.

Tissue birefringence is an intrinsic marker of potential value for cancer diagnosis. Traditionally, birefringence properties have been studied by using intensity-based formalisms, through the Mueller matrix algebra. On the other hand, the Jones matrix description allows for a direct assessment of the sample's anisotropic response. However, because Jones algebra is based on complex fields, requiring measurements of both phase and amplitude, it is less commonly used. Here we propose a real-time imaging method for measuring Jones matrices by quantitative phase imaging. We combine a broadband phase imaging system with a polarization-sensitive detector to obtain Jones matrices at each point in a megapixel scale image, with near video rate capture speeds. To validate the utility of our approach, we measured standard targets, partially birefringent samples, dynamic specimens, and thinly sliced histopathological tissue.

Harmonically decoupled gradient light interference microscopy (HD-GLIM).

Differential phase sensitive methods, such as Nomarski microscopy, play an important role in quantitative phase imaging due to their compatibility with partially coherent illumination and excellent optical sectioning ability. In this Letter, we propose a new system, to the best of our knowledge, to retrieve differential phase information from transparent samples. It is based on a 4f optical system with an amplitude-type spatial light modulator (SLM), which removes the need for traditional differential interference contrast (DIC) optics and specialized phase-only SLMs. We demonstrate the principle of harmonically decoupled gradient light interference microscopy using standard samples, as well as static and dynamic biospecimens.

Matrix Softness-Mediated 3D Zebrafish Hepatocyte Modulates Response to Endocrine Disrupting Chemicals.

Endocrine disrupting chemicals (EDC) include synthetic compounds that mimic the structure or function of natural hormones. While most studies utilize live embryos or primary cells from adult fish, these cells rapidly lose functionality when cultured on plastic or glass substrates coated with extracellular matrix proteins. This study hypothesizes that the softness of a matrix with adhered fish cells can regulate the intercellular organization and physiological function of engineered hepatoids during EDC exposure. We scrutinized this hypothesis by culturing zebrafish hepatocytes (ZF-L) on collagen-based hydrogels with controlled elastic moduli by examining morphology, urea production, and intracellular oxidative stress of hepatoids exposed to 17ß-estradiol. Interestingly, the softer gel drove cells to form a cell sheet with a canaliculi-like structure compared to its stiffer gel counterpart. The hepatoids cultured on the softer gel exhibited more active urea production upon exposure to 17ß-estradiol and displayed faster recovery of intracellular reactive oxygen species level confirmed by gradient light interference microscopy (GLIM), a live-cell imaging technique. These results are broadly useful to improve screening and understanding of potential EDC impacts on aquatic organisms and human health.

Three-dimensional mesostructures as high-temperature growth templates, electronic cellular scaffolds, and self-propelled microrobots.

Recent work demonstrates that processes of stress release in prestrained elastomeric substrates can guide the assembly of sophisticated 3D micro/nanostructures in advanced materials. Reported application examples include soft electronic components, tunable electromagnetic and optical devices, vibrational metrology platforms, and other unusual technologies, each enabled by uniquely engineered 3D architectures. A significant disadvantage of these systems is that the elastomeric substrates, while essential to the assembly process, can impose significant engineering constraints in terms of operating temperatures and levels of dimensional stability; they also prevent the realization of 3D structures in freestanding forms. Here, we introduce concepts in interfacial photopolymerization, nonlinear mechanics, and physical transfer that bypass these limitations. The results enable 3D mesostructures in fully or partially freestanding forms, with additional capabilities in integration onto nearly any class of substrate, from planar, hard inorganic materials to textured, soft biological tissues, all via mechanisms quantitatively described by theoretical modeling. Illustrations of these ideas include their use in 3D structures as frameworks for templated growth of organized lamellae from AgCl-KCl eutectics and of atomic layers of WSe2 from vapor-phase precursors, as open-architecture electronic scaffolds for formation of dorsal root ganglion (DRG) neural networks, and as catalyst supports for propulsive systems in 3D microswimmers with geometrically controlled dynamics. Taken together, these methodologies establish a set of enabling options in 3D micro/nanomanufacturing that lie outside of the scope of existing alternatives.

SLIM microscopy allows for visualization of DNA-containing liposomes designed for sperm-mediated gene transfer in cattle.

Naked DNA has been shown to bind naturally to the sperm, a method called sperm-mediated gene transfer (SMGT). Based on these observations, we examined the efficiency of exogenous DNA binding to sperm using liposomes. In this experiment, we analyzed methods to select frozen-thawed bovine sperm, and evaluated the binding of exogenous DNA to those sperm. To determine the optimal selection method, we used Computer-Assisted Sperm Analysis (CASA). Percoll or Swim-Up were used to select sperm, followed by incubation up to 3 h with the liposome-DNA complexes. The samples were collected after 1 h and after 3 h. We used enhanced green fluorescent protein (eGFP) in combination with the liposomes as a marker for exogenous DNA binding. Five treatments per selection method were analyzed: (1) no incubation, no liposomes and no DNA, (2) incubation with no liposomes and no DNA, (3) incubation with liposomes and no DNA, (4) incubation with liposomes and 1 µg of DNA and (5) incubation with liposomes and 10 µg of DNA. The CASA results for total motility and rapid motility were statistically significant (P < 0.01) between the control and the other treatments in the Percoll group as opposed to Swim-Up. Swim-Up was therefore chosen as the optimal selection method. In order to determine if the liposome-DNA complex had bound to sperm, real time PCR was used to detect GFP DNA and images of the sperm were analyzed using the Spatial Light Interference Microscopy (SLIM). SLIM confirmed the presence of liposomes on the sperm head and tail.

Magnified Image Spatial Spectrum (MISS) microscopy for nanometer and millisecond scale label-free imaging.

Label-free imaging of rapidly moving, sub-diffraction sized structures has important applications in both biology and material science, as it removes the limitations associated with fluorescence tagging. However, unlabeled nanoscale particles in suspension are difficult to image due to their transparency and fast Brownian motion. Here we describe a novel interferometric imaging technique referred to as Magnified Image Spatial Spectrum (MISS) microscopy, which overcomes these challenges. The MISS microscope provides quantitative phase information and enables dynamic light scattering investigations with an overall optical path length sensitivity of 0.95 nm at 833 frames per second acquisition rate. Using spatiotemporal filtering, we find that the sensitivity can be further pushed down to 10-3-10-2 nm. We demonstrate the instrument's capability through colloidal nanoparticle sizing down to 20 nm diameter and measurements of live neuron membrane dynamics. MISS microscopy is implemented as an upgrade module to an existing microscope, which converts it into a powerful light scattering instrument. Thus, we anticipate that MISS will be adopted broadly for both material and life sciences applications.

Cell density modulates intracellular mass transport in neural networks.

In order to fully understand brain connectivity and elucidate the mechanisms involved in central nervous system disease, the field of neuroscience depends on quantitative studies of neuronal structure and function. Cell morphology and neurite (axonal and dendritic) arborization are typically studied by immunohistochemical and fluorescence techniques. However, dry mass content and intracellular mass transport rates have largely been under-investigated given the inherent difficulties in their measurement. Here, spatial light interference microscopy (SLIM) and dispersion-relation phase spectroscopy (DPS) were used to measure pathlength fluctuations that report on the dry mass and transport within cultured primary neurons across low, medium, and high cell density conditions. It was found that cell density (confluence) affects significantly both the growth rate and mass transport. The analysis method is label-free and does not require neuronal tracing, particle tracking, or neuron reconstruction. Since SLIM can upgrade any existing phase contrast microscope and the imaging and analysis are high-throughput, we anticipate that this approach will be embraced by neuroscientists for broad scale studies. © 2017 International Society for Advancement of Cytometry.

Three-dimensional intracellular transport in neuron bodies and neurites investigated by label-free dispersion-relation phase spectroscopy.

Due to the limitations of fluorescence imaging techniques, the study of intracellular cargo is typically restricted to two-dimensional analyses. To overcome low light levels and the risk of phototoxicity, we employ quantitative phase imaging, a family of full-field imaging techniques that measure the optical path length shift introduced by the specimen. Specifically, we use spatial light interference microscopy (SLIM) to study the transport of mass in whole tomographic volumes and show that a time-correlation technique, dispersion-relation phase spectroscopy (DPS), can be used to simultaneously assay the horizontal and vertical traffic of mass through a cell. To validate our method, we compare the traffic inside cell bodies and neuronal extensions, showing that the vertical transport of mass may prove a more sensitive and interesting metric than similar measurements limited to a 2D, horizontal plane. © 2017 International Society for Advancement of Cytometry.

Refractive index variance of cells and tissues measured by quantitative phase imaging.

The refractive index distribution of cells and tissues governs their interaction with light and can report on morphological modifications associated with disease. Through intensity-based measurements, refractive index information can be extracted only via scattering models that approximate light propagation. As a result, current knowledge of refractive index distributions across various tissues and cell types remains limited. Here we use quantitative phase imaging and the statistical dispersion relation (SDR) to extract information about the refractive index variance in a variety of specimens. Due to the phase-resolved measurement in three-dimensions, our approach yields refractive index results without prior knowledge about the tissue thickness. With the recent progress in quantitative phase imaging systems, we anticipate that using SDR will become routine in assessing tissue optical properties.

White-light diffraction phase microscopy at doubled space-bandwidth product.

White light diffraction microscopy (wDPM) is a quantitative phase imaging method that benefits from both temporal and spatial phase sensitivity, granted, respectively, by the common-path geometry and white light illumination. However, like all off-axis quantitative phase imaging methods, wDPM is characterized by a reduced space-bandwidth product compared to phase shifting approaches. This happens essentially because the ultimate resolution of the image is governed by the period of the interferogram and not just the diffraction limit. As a result, off-axis techniques generates single-shot, i.e., high time-bandwidth, phase measurements, at the expense of either spatial resolution or field of view. Here, we show that combining phase-shifting and off-axis, the original space-bandwidth is preserved. Specifically, we developed phase-shifting diffraction phase microscopy with white light, in which we measure and combine two phase shifted interferograms. Due to the white light illumination, the phase images are characterized by low spatial noise, i.e., <1nm pathlength. We illustrate the operation of the instrument with test samples, blood cells, and unlabeled prostate tissue biopsy.

Artificial confocal microscopy for deep label-free imaging.

Widefield microscopy of optically thick specimens typically features reduced contrast due to "spatial crosstalk", in which the signal at each point in the field of view is the result of a superposition from neighbouring points that are simultaneously illuminated. In 1955, Marvin Minsky proposed confocal microscopy as a solution to this problem. Today, laser scanning confocal fluorescence microscopy is broadly used due to its high depth resolution and sensitivity, but comes at the price of photobleaching, chemical, and photo-toxicity. Here, we present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity, on unlabeled specimens, nondestructively. We equipped a commercial laser scanning confocal instrument with a quantitative phase imaging module, which provides optical path-length maps of the specimen in the same field of view as the fluorescence channel. Using pairs of phase and fluorescence images, we trained a convolution neural network to translate the former into the latter. The training to infer a new tag is very practical as the input and ground truth data are intrinsically registered, and the data acquisition is automated. The ACM images present significantly stronger depth sectioning than the input (phase) images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and 3D liver cancer spheroids. By training on nucleus-specific tags, ACM allows for segmenting individual nuclei within dense spheroids for both cell counting and volume measurements. In summary, ACM can provide quantitative, dynamic data, nondestructively from thick samples, while chemical specificity is recovered computationally.

Circadian Volume Changes in Hippocampal Glia Studied by Label-Free Interferometric Imaging.

Complex brain functions, including learning and memory, arise in part from the modulatory role of astrocytes on neuronal circuits. Functionally, the dentate gyrus (DG) exhibits differences in the acquisition of long-term potentiation (LTP) between day and night. We hypothesize that the dynamic nature of astrocyte morphology plays an important role in the functional circuitry of hippocampal learning and memory, specifically in the DG. Standard microscopy techniques, such as differential interference contrast (DIC), present insufficient contrast for detecting changes in astrocyte structure and function and are unable to inform on the intrinsic structure of the sample in a quantitative manner. Recently, gradient light interference microscopy (GLIM) has been developed to upgrade a DIC microscope with quantitative capabilities such as single-cell dry mass and volume characterization. Here, we present a methodology for combining GLIM and electrophysiology to quantify the astrocyte morphological behavior over the day-night cycle. Colocalized measurements of GLIM and fluorescence allowed us to quantify the dry masses and volumes of hundreds of astrocytes. Our results indicate that, on average, there is a 25% cell volume reduction during the nocturnal cycle. Remarkably, this cell volume change takes place at constant dry mass, which suggests that the volume regulation occurs primarily through aqueous medium exchange with the environment.

Cell Cycle Stage Classification Using Phase Imaging with Computational Specificity.

Traditional methods for cell cycle stage classification rely heavily on fluorescence microscopy to monitor nuclear dynamics. These methods inevitably face the typical phototoxicity and photobleaching limitations of fluorescence imaging. Here, we present a cell cycle detection workflow using the principle of phase imaging with computational specificity (PICS). The proposed method uses neural networks to extract cell cycle-dependent features from quantitative phase imaging (QPI) measurements directly. Our results indicate that this approach attains very good accuracy in classifying live cells into G1, S, and G2/M stages, respectively. We also demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as cell population distribution across different stages of the cell cycle. We envision that the proposed method can become a nondestructive tool to analyze cell cycle progression in fields ranging from cell biology to biopharma applications.

Spatial light interference microscopy: principle and applications to biomedicine.

In this paper, we review spatial light interference microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white-light illumination. As one of the most sensitive quantitative phase imaging (QPI) methods, SLIM allows for speckle-free phase reconstruction with sub-nanometer path-length stability. We first review image formation in QPI, scattering, and full-field methods. Then, we outline SLIM imaging from theory and instrumentation to diffraction tomography. Zernike's phase-contrast microscopy, phase retrieval in SLIM, and halo removal algorithms are discussed. Next, we discuss the requirements for operation, with a focus on software developed in-house for SLIM that enables high-throughput acquisition, whole slide scanning, mosaic tile registration, and imaging with a color camera. We introduce two methods for solving the inverse problem using SLIM, white-light tomography, and Wolf phase tomography. Lastly, we review the applications of SLIM in basic science and clinical studies. SLIM can study cell dynamics, cell growth and proliferation, cell migration, mass transport, etc. In clinical settings, SLIM can assist with cancer studies, reproductive technology, blood testing, etc. Finally, we review an emerging trend, where SLIM imaging in conjunction with artificial intelligence brings computational specificity and, in turn, offers new solutions to outstanding challenges in cell biology and pathology.

Synthetic aperture interference light (SAIL) microscopy for high-throughput label-free imaging.

Quantitative phase imaging (QPI) is a valuable label-free modality that has gained significant interest due to its wide potentials, from basic biology to clinical applications. Most existing QPI systems measure microscopic objects via interferometry or nonlinear iterative phase reconstructions from intensity measurements. However, all imaging systems compromise spatial resolution for the field of view and vice versa, i.e., suffer from a limited space bandwidth product. Current solutions to this problem involve computational phase retrieval algorithms, which are time-consuming and often suffer from convergence problems. In this article, we presented synthetic aperture interference light (SAIL) microscopy as a solution for high-resolution, wide field of view QPI. The proposed approach employs low-coherence interferometry to directly measure the optical phase delay under different illumination angles and produces large space-bandwidth product label-free imaging. We validate the performance of SAIL on standard samples and illustrate the biomedical applications on various specimens: pathology slides, entire insects, and dynamic live cells in large cultures. The reconstructed images have a synthetic numeric aperture of 0.45 and a field of view of 2.6 × 2.6 mm2. Due to its direct measurement of the phase information, SAIL microscopy does not require long computational time, eliminates data redundancy, and always converges.

Computational interference microscopy enabled by deep learning.

Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light interference microscopy (SLIM) is a highly sensitive QPI method due to its partially coherent illumination and common path interferometry geometry. However, SLIM's acquisition rate is limited because of the four-frame phase-shifting scheme. On the other hand, off-axis methods such as diffraction phase microscopy (DPM) allow for single-shot QPI. However, the laser-based DPM system is plagued by spatial noise due to speckles and multiple reflections. In a parallel development, deep learning was proven valuable in the field of bioimaging, especially due to its ability to translate one form of contrast into another. Here, we propose using deep learning to produce synthetic, SLIM-quality, and high-sensitivity phase maps from DPM using single-shot images as the input. We used an inverted microscope with its two ports connected to the DPM and SLIM modules such that we have access to the two types of images on the same field of view. We constructed a deep learning model based on U-net and trained on over 1000 pairs of DPM and SLIM images. The model learned to remove the speckles in laser DPM and overcame the background phase noise in both the test set and new data. The average peak signal-to-noise ratio, Pearson correlation coefficient, and structural similarity index measure were 29.97, 0.79, and 0.82 for the test dataset. Furthermore, we implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image. We demonstrated this principle of computational interference microscopy imaging using blood smears, as they contain both erythrocytes and leukocytes, under static and dynamic conditions.

Multiscale Assay of Unlabeled Neurite Dynamics Using Phase Imaging with Computational Specificity.

Primary neuronal cultures have been widely used to study neuronal morphology, neurophysiology, neurodegenerative processes, and molecular mechanism of synaptic plasticity underlying learning and memory. However, the unique behavioral properties of neurons make them challenging to study, with phenotypic differences expressed as subtle changes in neuronal arborization rather than easy-to-assay features such as cell count. The need to analyze morphology, growth, and intracellular transport has motivated the development of increasingly sophisticated microscopes and image analysis techniques. Due to its high-contrast, high-specificity output, many assays rely on confocal fluorescence microscopy, genetic methods, or antibody staining techniques. These approaches often limit the ability to measure quantitatively dynamic activity such as intracellular transport and growth. In this work, we describe a method for label-free live-cell cell imaging with antibody staining specificity by estimating the associated fluorescence signals via quantitative phase imaging and deep convolutional neural networks. This computationally inferred fluorescence image is then used to generate a semantic segmentation map, annotating subcellular compartments of live unlabeled neural cultures. These synthetic fluorescence maps were further applied to study the time-lapse development of hippocampal neurons, highlighting the relationships between the cellular dry mass production and the dynamic transport activity within the nucleus and neurites. Our implementation provides a high-throughput strategy to analyze neural network arborization dynamically, with high specificity and without the typical phototoxicity and photobleaching limitations associated with fluorescent markers.

Monitoring reactivation of latent HIV by label-free gradient light interference microscopy.

Human immunodeficiency virus (HIV) can infect cells and take a quiescent and nonexpressive state called latency. In this study, we report insights provided by label-free, gradient light interference microscopy (GLIM) about the changes in dry mass, diameter, and dry mass density associated with infected cells that occur upon reactivation. We discovered that the mean cell dry mass and mean diameter of latently infected cells treated with reactivating drug, TNF-α, are higher for latent cells that reactivate than those of the cells that did not reactivate. Cells with mean dry mass and diameter less than approximately 10 pg and 8 µm, respectively, remain exclusively in the latent state. Also, cells with mean dry mass greater than approximately 28-30 pg and mean diameter greater than 11-12 µm have a higher probability of reactivating. This study is significant as it presents a new label-free approach to quantify latent reactivation of a virus in single cells.