ABSTRACT
This review examines the biological physics of intracellular transport probed by the coherent optics of dynamic light scattering from optically thick living tissues. Cells and their constituents are in constant motion, composed of a broad range of speeds spanning many orders of magnitude that reflect the wide array of functions and mechanisms that maintain cellular health. From the organelle scale of tens of nanometers and upward in size, the motion inside living tissue is actively driven rather than thermal, propelled by the hydrolysis of bioenergetic molecules and the forces of molecular motors. Active transport can mimic the random walks of thermal Brownian motion, but mean-squared displacements are far from thermal equilibrium and can display anomalous diffusion through Lévy or fractional Brownian walks. Despite the average isotropic three-dimensional environment of cells and tissues, active cellular or intracellular transport of single light-scattering objects is often pseudo-one-dimensional, for instance as organelle displacement persists along cytoskeletal tracks or as membranes displace along the normal to cell surfaces, albeit isotropically oriented in three dimensions. Coherent light scattering is a natural tool to characterize such tissue dynamics because persistent directed transport induces Doppler shifts in the scattered light. The many frequency-shifted partial waves from the complex and dynamic media interfere to produce dynamic speckle that reveals tissue-scale processes through speckle contrast imaging and fluctuation spectroscopy. Low-coherence interferometry, dynamic optical coherence tomography, diffusing-wave spectroscopy, diffuse-correlation spectroscopy, differential dynamic microscopy and digital holography offer coherent detection methods that shed light on intracellular processes. In health-care applications, altered states of cellular health and disease display altered cellular motions that imprint on the statistical fluctuations of the scattered light. For instance, the efficacy of medical therapeutics can be monitored by measuring the changes they induce in the Doppler spectra of livingex vivocancer biopsies.
Subject(s)
Cytoskeleton , Cell Membrane , Cell Movement , Biological Transport , Dynamic Light ScatteringABSTRACT
Lysosomes are the terminal end of catabolic pathways in the cell, as well as signaling centers performing important functions such as the recycling of macromolecules, organelles, and nutrient adaptation. The importance of lysosomes in human health is supported by the fact that the deficiency of most lysosomal genes causes monogenic diseases called as a group Lysosomal Storage Diseases (LSDs). A common phenotypic hallmark of LSDs is the expansion of the lysosomal compartment that can be detected by using conventional imaging methods based on immunofluorescence protocols or overexpression of tagged lysosomal proteins. These methods require the alteration of the cellular architecture (i.e., due to fixation methods), can alter the behavior of cells (i.e., by the overexpression of proteins), and require sample preparation and the accurate selection of compatible fluorescent markers in relation to the type of analysis, therefore limiting the possibility of characterizing cellular status with simplicity. Therefore, a quantitative and label-free methodology, such as Quantitative Phase Imaging through Digital Holographic (QPI-DH), for the microscopic imaging of lysosomes in health and disease conditions may represent an important advance to study and effectively diagnose the presence of lysosomal storage in human disease. Here we proof the effectiveness of the QPI-DH method in accomplishing the detection of the lysosomal compartment using mouse embryonic fibroblasts (MEFs) derived from a Mucopolysaccharidosis type III-A (MSP-IIIA) mouse model, and comparing them with wild-type (WT) MEFs. We found that it is possible to identify label-free biomarkers able to supply a first pre-screening of the two populations, thus showing that QPI-DH can be a suitable candidate to surpass fluorescent drawbacks in the detection of lysosomes dysfunction. An appropriate numerical procedure was developed for detecting and evaluate such cellular substructures from in vitro cells cultures. Results reported in this study are encouraging about the further development of the proposed QPI-DH approach for such type of investigations about LSDs.
Subject(s)
Lysosomes , Lysosomes/metabolism , Animals , Mice , Fibroblasts/metabolism , Fibroblasts/pathology , Humans , Lysosomal Storage Diseases/metabolism , Lysosomal Storage Diseases/pathology , Lysosomal Storage Diseases/genetics , Lysosomal Storage Diseases/diagnosis , Mucopolysaccharidosis III/metabolism , Mucopolysaccharidosis III/pathology , Mucopolysaccharidosis III/genetics , Quantitative Phase ImagingABSTRACT
Digital holography (DH) is an important method for three-dimensional (3D) imaging since it allows for the recording and reconstruction of an object's amplitude and phase information. However, the field of view (FOV) of a DH system is typically restricted by the finite size of the pixel pitch of the digital image sensor. We proposed a new configuration of the DH system based on Fresnel's bi-mirror to achieve doubling the camera FOV of the existing off-axis DH system which leveraged single-shot acquisition and a common-path optical framework. The dual FOV was obtained by spatial frequency multiplexing corresponding to two different information-carrying beams from an object. Experimental evidence of the proposed dual FOV-DH system's viability was provided by imaging two different areas of the test object and an application to surface profilometry by measuring the step height of the resolution chart which showed excellent agreement with an optical profiler. Due to the simple configuration, the proposed system could find a wide range of applications, including in microscopy and optical metrology.
ABSTRACT
Quantitative phase imaging (QPI) enables nondestructive, real-time, label-free imaging of transparent specimens and can reveal information about their fundamental properties such as cell size and morphology, mass density, particle dynamics, and cellular fluctuations. Development of high-performance and low-cost quantitative phase imaging systems is thus required in many fields, including on-site biomedical imaging and industrial inspection. Here, we propose an ultracompact, highly stable interferometer based on a single-layer dielectric metasurface for common path off-axis digital holography and experimentally demonstrate quantitative phase imaging. The interferometric imaging system leveraging an ultrathin multifunctional metasurface captures image plane holograms in a single shot and provides quantitative phase information on the test samples for extraction of its physical properties. With the benefits of planar engineering and high integrability, the proposed metasurface-based method establishes a stable miniaturized QPI system for reliable and cost-effective point-of-care devices, live cell imaging, 3D topography, and edge detection for optical computing.
ABSTRACT
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.
Subject(s)
Holography/methods , Microscopy/methods , Staining and Labeling/methods , Algorithms , Deep Learning , Flow Cytometry , Humans , Image Processing, Computer-Assisted/methods , Male , Neural Networks, Computer , Spermatozoa/metabolismABSTRACT
The vacuum degree is the key parameter reflecting the quality and performance of vacuum glass. This investigation proposed a novel method, based on digital holography, to detect the vacuum degree of vacuum glass. The detection system was composed of an optical pressure sensor, a Mach-Zehnder interferometer and software. The results showed that the deformation of monocrystalline silicon film in an optical pressure sensor could respond to the attenuation of the vacuum degree of vacuum glass. Using 239 groups of experimental data, pressure differences were shown to have a good linear relationship with the optical pressure sensor's deformations; pressure differences were linearly fitted to obtain the numerical relationship between pressure difference and deformation and to calculate the vacuum degree of the vacuum glass. Measuring the vacuum degree of vacuum glass under three different conditions proved that the digital holographic detection system could measure the vacuum degree of vacuum glass quickly and accurately. The optical pressure sensor's deformation measuring range was less than 4.5 µm, the measuring range of the corresponding pressure difference was less than 2600 pa, and the measuring accuracy's order of magnitude was 10 pa. This method has potential market applications.
ABSTRACT
Quantitative phase imaging and measurement of surface topography and fluid dynamics for objects, especially for moving objects, is critical in various fields. Although effective, existing synchronous phase-shifting methods may introduce additional phase changes in the light field due to differences in optical paths or need specific optics to implement synchronous phase-shifting, such as the beamsplitter with additional anti-reflective coating and a micro-polarizer array. Therefore, we propose a synchronous phase-shifting method based on the Mach-Zehnder interferometer to tackle these issues in existing methods. The proposed method uses common optics to simultaneously acquire four phase-shifted digital holograms with equal optical paths for object and reference waves. Therefore, it can be used to reconstruct the phase distribution of static and dynamic objects with high precision and high resolution. In the experiment, the theoretical resolution of the proposed system was 1.064 µm while the actual resolution could achieve 1.381 µm, which was confirmed by measuring a phase-only resolution chart. Besides, the dynamic phase imaging of a moving standard object was completed to verify the proposed system's effectiveness. The experimental results show that our proposed method is suitable and promising in dynamic phase imaging and measurement of moving objects using phase-shifting digital holography.
ABSTRACT
Lensless holographic microscopy (LHM) comes out as a promising label-free technique since it supplies high-quality imaging and adaptive magnification in a lens-free, compact and cost-effective way. Compact sizes and reduced prices of LHMs make them a perfect instrument for point-of-care diagnosis and increase their usability in limited-resource laboratories, remote areas, and poor countries. LHM can provide excellent intensity and phase imaging when the twin image is removed. In that sense, multi-illumination single-holographic-exposure lensless Fresnel (MISHELF) microscopy appears as a single-shot and phase-retrieved imaging technique employing multiple illumination/detection channels and a fast-iterative phase-retrieval algorithm. In this contribution, we review MISHELF microscopy through the description of the principles, the analysis of the performance, the presentation of the microscope prototypes and the inclusion of the main biomedical applications reported so far.
Subject(s)
Holography , Lenses , Microscopy/methods , Lighting , Holography/methods , AlgorithmsABSTRACT
Digital holographically sensed 3D data processing, which is useful for AI-based vision, is demonstrated. Three prominent methods of learning from datasets such as sensed holograms, computationally retrieved intensity and phase from holograms forming concatenated intensity-phase (whole information) images, and phase-only images (depth information) were utilized for the proposed multi-class classification and multi-output regression tasks of the chosen 3D objects in supervised learning. Each dataset comprised 2268 images obtained from the chosen eighteen 3D objects. The efficacy of our approaches was validated on experimentally generated digital holographic data then further quantified and compared using specific evaluation matrices. The machine learning classifiers had better AUC values for different classes on the holograms and whole information datasets compared to the CNN, whereas the CNN had a better performance on the phase-only image dataset compared to these classifiers. The MLP regressor was found to have a stable prediction in the test and validation sets with a fixed EV regression score of 0.00 compared to the CNN, the other regressors for holograms, and the phase-only image datasets, whereas the RF regressor showed a better performance in the validation set for the whole information dataset with a fixed EV regression score of 0.01 compared to the CNN and other regressors.
ABSTRACT
Metallic zinc is regarded as an ideal anode material for high-energy aqueous zinc ion batteries owing to its high theoretical capacity, low cost, and abundant resource. However, the undesirable dendrite formation and side reactions occurring on Zn anode during the long-term cycling process seriously restrict the electrochemical performance of the device. Herein, 1-hydroxy ethylidene-1,1-diphosphonic acid (HEDP) is used as electrolyte additive to release the chemical corrosion and hydrogen evolution occurring on Zn anode based on the absorption of HEDP on the Zn foil. Moreover, the strong coordination of HEDP with Zn2+ can balance ion flux at the electrode/electrolyte interface, thus inducing uniform Zn deposition. Thereby, Zn anode exhibits a prolonged cycle life of reversible Zn plating/stripping under different current densities (2800 h at 2 mA cm-2 , 1 mAh cm-2 , and more than 1772 h at 4 mA cm-2 , 1 mAh cm-2 ). Moreover, the cell shows a high average coulombic efficiency of ≈99.6% for ≈600 cycles at 1 mA cm-2 with a cycling capacity of 1 mAh cm-2 . This work provides a facile yet effective method for developing reversible aqueous zinc metal batteries.
ABSTRACT
A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed in different depths by using standard techniques of digital holography. We claim that a 2D convolutional network (CNN) cannot be efficient in decoding this volumetric information spread across the whole image as it inherently operates on local spatial features. Therefore, we propose a method, where we extract the volumetric information of the hologram by mapping it to a volume-using a standard wavefield propagation algorithm-and then feed it to a 3D-CNN-based architecture. We apply this method to a challenging real-life classification problem and compare its performance with an equivalent 2D-CNN counterpart. Furthermore, we inspect the robustness of the methods to slightly defocused inputs and find that the 3D method is inherently more robust in such cases. Additionally, we introduce a hologram-specific augmentation technique, called hologram defocus augmentation, that improves the performance of both methods for slightly defocused inputs. The proposed 3D-model outperforms the standard 2D method in classification accuracy both for in-focus and defocused input samples. Our results confirm and support our fundamental hypothesis that a 2D-CNN-based architecture is limited in the extraction of volumetric information globally encoded in the reconstructed hologram image.
Subject(s)
Holography , Imaging, Three-Dimensional , Neural Networks, Computer , Holography/classificationABSTRACT
Lensless holographic microscope (LHM) is an emerging very promising technology that provides high-quality imaging and analysis of biological samples without utilizing any lens for imaging. Due to its small size and reduced price, LHM can be a very useful tool for the point-of-care diagnosis of diseases, sperm assessment, or microfluidics, among others, not only employed in advanced laboratories but also in poor and/or remote areas. Recently, several LHMs have been reported in the literature. However, complete characterization of their optical parameters remains not much presented yet. Hence, we present a complete analysis of the performance of a compact, reduced cost, and high-resolution LHM. In particular, optical parameters such as lateral and axial resolutions, lateral magnification, and field of view are discussed into detail, comparing the experimental results with the expected theoretical values for different layout configurations. We use high-resolution amplitude and phase test targets and several microbeads to characterize the proposed microscope. This characterization is used to define a balanced and matched setup showing a good compromise between the involved parameters. Finally, such a microscope is utilized for visualization of static, as well as dynamic biosamples.
Subject(s)
Holography , Lenses , Calibration , Cost-Benefit Analysis , MicroscopyABSTRACT
We propose a compressive self-interference incoherent digital holography (SIDH) with a geometric phase metalens for section-wise holographic object reconstruction. We specify the details of the SIDH with a geometric phase metalens design that covers the visible wavelength band, analyze a spatial distortion problem in the SIDH and address a process of a compressive holographic section-wise reconstruction with analytic spatial calibration. The metalens allows us to realize a compressive SIDH system in the visible wavelength band using an image sensor with relatively low bandwidth. The operation of the proposed compressive SIDH is verified through numerical simulations.
ABSTRACT
BACKGROUND: Quantitative phase imaging (QPI) is an established tool for the marker-free classification and quantitative characterization of biological samples. For spherical objects, such as cells in suspension, microgel beads, or liquid droplets, a single QPI image is sufficient to extract the radius and the average refractive index. This technique is invaluable, as it allows the characterization of large sample populations at high measurement rates. However, until now, no universal software existed that could perform this type of analysis. Besides the choice of imaging modality and the variety in imaging software, the main difficulty has been to automate the entire analysis pipeline from raw data to ensemble statistics. RESULTS: We present DryMass, a powerful tool for QPI that covers all relevant steps from loading experimental data (multiple file formats supported), computing the phase data (built-in, automated hologram analysis), performing phase background corrections (offset, tilt, second order polynomial) to fitting scattering models (light projection, Rytov approximation, Mie simulations) to spherical phase objects for the extraction of dry mass, radius, and average refractive index. The major contribution of DryMass is a user-convenient, reliable, reproducible, and automated analysis pipeline for an arbitrary number of QPI datasets of arbitrary sizes. CONCLUSION: DryMass is a leap forward for data analysis in QPI, as it not only makes it easier to visualize raw QPI data and reproduce previous results in the field, but it also opens up QPI analysis to users without a background in programming or phase imaging.
Subject(s)
Algorithms , Cell Size , Image Processing, Computer-Assisted , Microscopy/methods , Cell Nucleus/metabolism , HL-60 Cells , Humans , RefractometryABSTRACT
BACKGROUND AND OBJECTIVES: Photodynamic therapy (PDT) has gained widespread popularity in the last decades because of its distinctive advantages over the other commonly used cancer treatments. PDT dosimetry is a crucial factor in achieving a good optimization of PDT treatment planning. PDT dosimetry is a complex task since light dose as well as photosensitizer and oxygen concentrations in tissue need to be measured (ideally continuously) to be able to fully characterize the biological response. Light dose in PDT is routinely measured by the optical fibers that provide dose data at a limited number of discrete points and are not able to capture spatial dose profiles. The objective of this study is to propose and develop a new optical method for online monitoring of the dose profile data for PDT. STUDY DESIGN/MATERIALS AND METHODS: Using the digital holography technique, first, the general sketch of an experimental setup for PDT light dosimetry is provided. The theory behind the proposed method for using the experimental setup in PDT light dosimetry is fully described, and its limits of validity are determined. In a proof of principle study, the ability of the method for online monitoring of the absorbed light dose profile in PDT is evaluated by a simple experimental setup. RESULTS: The experimental results confirm the usefulness of the proposed method in providing continuous online dose profiles. The absorbed light dose profiles from an infrared light source in a quartz cell containing water are measured and shown. The depth-dose curves are extracted and it is shown that how these dosimetric data can be used for assisting the physicians in determining the appropriate spatiotemporal characteristics for treating the infected tissues and solid tumors with the required light dose amounts. A conversion relation is also derived for transforming the measured light dose with the proposed method to the most frequently used dose values by PDT practitioners, in terms of light power per square area. CONCLUSIONS: There is no restriction in using the method with other commonly used light sources in PDT, like light-emitting diodes and filtered lamps, with different wavelengths in visible or infrared regions of the spectrum. More complex experimental setups can be used in future studies to study the role of accumulated photosensitizers in malignant tissues. The proposed method in this study can also be used for light dose monitoring in other biomedical applications, where light is used for treating special diseases, and patients must receive sufficient amounts of light dose. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.
Subject(s)
Neoplasms , Photochemotherapy , Humans , Infrared Rays , Neoplasms/drug therapy , Optical Fibers , Photosensitizing Agents/therapeutic useABSTRACT
We present a dual wavelength digital holographic technique for three-dimensional microscopic imaging of layered structures, where layers are separated from one another by the axial distances exceeding the wavelength of imaging light. Our methodology not only provides the three-dimensional structure of each layer, but also allows the height differentiation of distinct layers. We have also implemented a technique suppressing low intensity signal when no reliable phase information can be extracted, based on the quality of the interference fringe pattern. We utilize a dual wavelength setup, where the combination of two overlapping interferometers enables simultaneous acquisition of two phase profiles. We demonstrate that this imaging modality is particularly well-suited for imaging of multilayered electrode structures embedded in glass.
ABSTRACT
Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted by marine biologists on the basis of their own experience. On the other hand, deep learning is recognized as the elective technique for solving image classification problems. However, a large amount of training data is usually needed, thus requiring the synthetic enlargement of the dataset through data augmentation. In the case of microalgae, the large variety of species that populate the marine environments makes it arduous to perform an exhaustive training that considers all the possible classes. However, commercial test slides containing one diatom element per class fixed in between two glasses are available on the market. These are usually prepared by expert diatomists for taxonomy purposes, thus constituting libraries of the populations that can be found in oceans. Here we show that such test slides are very useful for training accurate deep Convolutional Neural Networks (CNNs). We demonstrate the successful classification of diatoms based on a proper CNNs ensemble and a fully augmented dataset, i.e., creation starting from one single image per class available from a commercial glass slide containing 50 fixed species in a dry setting. This approach avoids the time-consuming steps of water sampling and labeling by skilled marine biologists. To accomplish this goal, we exploit the holographic imaging modality, which permits the accessing of a quantitative phase-contrast maps and a posteriori flexible refocusing due to its intrinsic 3D imaging capability. The network model is then validated by using holographic recordings of live diatoms imaged in water samples i.e., in their natural wet environmental condition.
Subject(s)
Diatoms/classification , Holography , Machine Learning , Microscopy , Neural Networks, ComputerABSTRACT
The digital in-line holographic microscope (DIHM) was developed for a 2D imaging technology and has recently been adapted to 3D imaging methods, providing new approaches to obtaining volumetric images with both a high resolution and wide field-of-view (FOV), which allows the physical limitations to be overcome. However, during the sectioning process of 3D image generation, the out-of-focus image of the object becomes a significant impediment to obtaining evident 3D features in the 2D sectioning plane of a thick biological sample. Based on phase retrieved high-resolution holographic imaging and a 3D deconvolution technique, we demonstrate that a high-resolution 3D volumetric image, which significantly reduces wave-front reconstruction and out-of-focus artifacts, can be achieved. The results show a 3D volumetric image that is more finely focused compared to a conventional 3D stacked image from 2D reconstructed images in relation to micron-size polystyrene beads, a whole blood smear, and a kidney tissue sample. We believe that this technology can be applicable for medical-grade images of smeared whole blood or an optically cleared tissue sample for mobile phytological microscopy and laser sectioning microscopy.
ABSTRACT
Standard computer vision methods are usually based on powerful contact-less measurement approaches but applications, especially at the micro-scale, are restricted by finite depth-of-field and fixed working distance of imaging devices. Digital holography is a lensless, indirect imaging method recording the optical wave diffracted by the object onto the image sensor. The object is reconstructed numerically by propagating the recorded wavefront backward. The object distance becomes a computation parameter that can be chosen arbitrarily and adjusted to match the object position. No refractive lens is used and usual depth-of-field and working distance limitations are replaced by less restrictive ones tied to the laser-source coherence-length and to the size and resolution of the camera sensor. This paper applies digital holography to artificial visual in-plane position sensing with an extra-large range-to-resolution ratio. The object is made of a pseudoperiodic pattern allowing a subpixel resolution as well as a supra field-of-observation displacement range. We demonstrate an in-plane resolution of 50 nm and 0.002deg. in X, Y and θ respectively, over a working distance range of more than 15 cm. The allowed workspace extends over 12×10×150mm3. Digital holography extends the field of application of computer vision by allowing an extra-large range of working distances inaccessible to refractive imaging systems.
ABSTRACT
Label-free detection, analysis, and rapid tracking of nanoparticles is crucial for future ultrasensitive sensing applications, ranging from understanding of biological interactions to the study of size-dependent classical-quantum transitions. Yet optical techniques to distinguish nanoparticles directly among their background remain challenging. Here we present amplified interferometric scattering microscopy (a-iSCAT) as a new all-optical method capable of detecting individual nanoparticles as small as 15 kDa proteins that is equivalent to half a GFP. By balancing scattering and reflection amplitudes the interference contrast of the nanoparticle signal is amplified 1 to 2 orders of magnitude. Beyond high sensitivity, a-iSCAT allows high-speed image acquisition exceeding several hundreds of frames-per-second. We showcase the performance of our approach by detecting single Streptavidin binding events and by tracking single Ferritin proteins at 400 frames-per-second with 12 nm localization precision over seconds. Moreover, due to its extremely simple experimental realization, this advancement finally enables a cheap and routine implementation of label-free all-optical single nanoparticle detection platforms with sensitivity operating at the single protein level.