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.

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.

Regulation of local GTP availability controls RAC1 activity and cell invasion.

Physiological changes in GTP levels in live cells have never been considered a regulatory step of RAC1 activation because intracellular GTP concentration (determined by chromatography or mass spectrometry) was shown to be substantially higher than the in vitro RAC1 GTP dissociation constant (RAC1-GTP Kd). Here, by combining genetically encoded GTP biosensors and a RAC1 activity biosensor, we demonstrated that GTP levels fluctuating around RAC1-GTP Kd correlated with changes in RAC1 activity in live cells. Furthermore, RAC1 co-localized in protrusions of invading cells with several guanylate metabolism enzymes, including rate-limiting inosine monophosphate dehydrogenase 2 (IMPDH2), which was partially due to direct RAC1-IMPDH2 interaction. Substitution of endogenous IMPDH2 with IMPDH2 mutants incapable of binding RAC1 did not affect total intracellular GTP levels but suppressed RAC1 activity. Targeting IMPDH2 away from the plasma membrane did not alter total intracellular GTP pools but decreased GTP levels in cell protrusions, RAC1 activity, and cell invasion. These data provide a mechanism of regulation of RAC1 activity by local GTP pools in live cells.

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.

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.

High-resolution impedance mapping using electrically activated quantitative phase imaging.

Retrieving electrical impedance maps at the nanoscale rapidly via nondestructive inspection with a high signal-to-noise ratio is an unmet need, likely to impact various applications from biomedicine to energy conversion. In this study, we develop a multimodal functional imaging instrument that is characterized by the dual capability of impedance mapping and phase quantitation, high spatial resolution, and low temporal noise. To achieve this, we advance a quantitative phase imaging system, referred to as epi-magnified image spatial spectrum microscopy combined with electrical actuation, to provide complementary maps of the optical path and electrical impedance. We demonstrate our system with high-resolution maps of optical path differences and electrical impedance variations that can distinguish nanosized, semi-transparent, structured coatings involving two materials with relatively similar electrical properties. We map heterogeneous interfaces corresponding to an indium tin oxide layer exposed by holes with diameters as small as ~550 nm in a titanium (dioxide) over-layer deposited on a glass support. We show that electrical modulation during the phase imaging of a macro-electrode is decisive for retrieving electrical impedance distributions with submicron spatial resolution and beyond the limitations of electrode-based technologies (surface or scanning technologies). The findings, which are substantiated by a theoretical model that fits the experimental data very well enable achieving electro-optical maps with high spatial and temporal resolutions. The virtues and limitations of the novel optoelectrochemical method that provides grounds for a wider range of electrically modulated optical methods for measuring the electric field locally are critically discussed.

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.

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.

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy's utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.

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.

Quantifying myelin content in brain tissue using color Spatial Light Interference Microscopy (cSLIM).

Deficient myelination of the brain is associated with neurodevelopmental delays, particularly in high-risk infants, such as those born small in relation to their gestational age (SGA). New methods are needed to further study this condition. Here, we employ Color Spatial Light Interference Microscopy (cSLIM), which uses a brightfield objective and RGB camera to generate pathlength-maps with nanoscale sensitivity in conjunction with a regular brightfield image. Using tissue sections stained with Luxol Fast Blue, the myelin structures were segmented from a brightfield image. Using a binary mask, those portions were quantitatively analyzed in the corresponding phase maps. We first used the CLARITY method to remove tissue lipids and validate the sensitivity of cSLIM to lipid content. We then applied cSLIM to brain histology slices. These specimens are from a previous MRI study, which demonstrated that appropriate for gestational age (AGA) piglets have increased internal capsule myelination (ICM) compared to small for gestational age (SGA) piglets and that a hydrolyzed fat diet improved ICM in both. The identity of samples was blinded until after statistical analyses.

Wolf phase tomography (WPT) of transparent structures using partially coherent illumination.

In 1969, Emil Wolf proposed diffraction tomography using coherent holographic imaging to extract 3D information from transparent, inhomogeneous objects. In the same era, the Wolf equations were first used to describe the propagation correlations associated with partially coherent fields. Combining these two concepts, we present Wolf phase tomography (WPT), which is a method for performing diffraction tomography using partially coherent fields. WPT reconstruction works directly in the space-time domain, without the need for Fourier transformation, and decouples the refractive index (RI) distribution from the thickness of the sample. We demonstrate the WPT principle using the data acquired by a quantitative-phase-imaging method that upgrades an existing phase-contrast microscope by introducing controlled phase shifts between the incident and scattered fields. The illumination field in WPT is partially spatially coherent (emerging from a ring-shaped pupil function) and of low temporal coherence (white light), and as such, it is well suited for the Wolf equations. From three intensity measurements corresponding to different phase-contrast frames, the 3D RI distribution is obtained immediately by computing the Laplacian and second time derivative of the measured complex correlation function. We validate WPT with measurements of standard samples (microbeads), spermatozoa, and live neural cultures. The high throughput and simplicity of this method enables the study of 3D, dynamic events in living cells across the entire multiwell plate, with an RI sensitivity on the order of 10-5.

Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data.

Understanding the mechanisms by which neurons create or suppress connections to enable communication in brain-derived neuronal cultures can inform how learning, cognition and creative behavior emerge. While prior studies have shown that neuronal cultures possess self-organizing criticality properties, we further demonstrate that in vitro brain-derived neuronal cultures exhibit a self-optimization phenomenon. More precisely, we analyze the multiscale neural growth data obtained from label-free quantitative microscopic imaging experiments and reconstruct the in vitro neuronal culture networks (microscale) and neuronal culture cluster networks (mesoscale). We investigate the structure and evolution of neuronal culture networks and neuronal culture cluster networks by estimating the importance of each network node and their information flow. By analyzing the degree-, closeness-, and betweenness-centrality, the node-to-node degree distribution (informing on neuronal interconnection phenomena), the clustering coefficient/transitivity (assessing the "small-world" properties), and the multifractal spectrum, we demonstrate that murine neurons exhibit self-optimizing behavior over time with topological characteristics distinct from existing complex network models. The time-evolving interconnection among murine neurons optimizes the network information flow, network robustness, and self-organization degree. These findings have complex implications for modeling neuronal cultures and potentially on how to design biological inspired artificial intelligence.

High-throughput sperm assay using label-free microscopy: morphometric comparison between different sperm structures of boar and stallion spermatozoa.

The capacity for microscopic evaluation of sperm is useful for assisted reproductive technologies (ART), because this can allow for specific selection of sperm cells for in vitro fertilization (IVF). The objective of this study was to analyze the same sperm samples using two high-resolution methods: spatial light interference microscopy (SLIM) and atomic force microscopy (AFM) to determine if with one method there was more timely and different information obtained than the other. To address this objective, there was evaluation of sperm populations from boars and stallions. To the best of our knowledge, this is the first reported comparison when using AFM and high-sensitivity interferometric microscopy (such as SLIM) to evaluate spermatozoa. Results indicate that with the use of SLIM microscopy there is similar nanoscale sensitivity as with use of AFM while there is approximately 1,000 times greater throughput with use of SLIM. With SLIM, there is also allowace for the measurement of the dry mass (non-aqueous content) of spermatozoa, which may be a new label-free marker for sperm viability. In the second part of this study, there was analysis of two sperm populations. There were interesting correlations between the different compartments of the sperm and the dry mass in both boars and stallions. Furthermore, there was a correlation between the dry mass of the sperm head and the length and width of the acrosome in both boars and stallions. This correlation is positive in boars while it is negative in stallions.

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.

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.

Morphometric analysis of sperm used for IVP by three different separation methods with spatial light interference microscopy.

The goal of this study was to characterize sperm populations resulting from three different methods of sperm selection used for bovine in vitro fertilization. We compared sperm selection with discontinuous Percoll gradients, Swim-Up, and electro-channel. Spatial light interference microscopy (SLIM) was used to evaluate the morphology of the spermatozoa and computer-assisted semen analysis (CASA) was used to evaluate the motility behavior of the sperm. Using these two technologies, we analyzed morphometric parameters and the kinetic (motility) patterns of frozen-thawed Holstein bull spermatozoa after sperm selection. For the first time, we have shown that these methods used to select viable spermatozoa for in vitro fertilization (IVF) result in very different sperm subpopulations. Almost every parameter evaluated resulted in statistical differences between treatment groups. One novel observation was that the dry mass of the sperm head is heavier in spermatozoa selected with the electro-channel than in sperm selected by the other methods. These results show the potential of SLIM microscopy in reproductive biology.Abbreviations: SLIM: spatial light interference microscopy; CASA: computer aided sperm analysis; IVF: in vitro fertilization; BSA: bovine serum albumin; QPI: quantitative phase imaging; IVEP: in vitro embryo production; IACUC: institutional animal care and use committee; CSS: Certified Semen Services; AI: artificial insemination; TALP: Tyrode's Albumin Lactate Pyruvate; MEC: medium for electro-channel; PDMS: polydimethylsiloxane; EC: electro-channel; TM, %: total motility; PM, %: progressive motility; RM, %: percentage of rapid sperm motility; VAP, µm/s: average path velocity; VSL, µm/s: straight-line velocity; VCL, µm/s: curvilinear velocity; ALH, µm: amplitude of lateral head displacement; BCF, Hz: beat cross frequency; STR, %: straightness; LIN, %: and linearity; GLS: generalized least squares; ANOVA: analysis of variance; LSD: Least Significant Difference; SPSS: Statistical Package for the Social Sciences; PCA: principal components analysis.