Conventional Microscopy Research Articles

Extremely high spatiotemporal resolution microscopy for live cell imaging by single photon counting, noise elimination, and a novel restoration algorithm based on probability calculation.

Optical microscopy is essential for direct observation of dynamic phenomena in living cells. According to the classic optical theories, the images obtained through light microscopes are blurred for about half the wavelength of light, and therefore small structures below this "diffraction limit" were thought unresolvable by conventional optical microscopy. In reality, accurately obtained optical images contain complete information about the observed objects. Temporal resolution is also important for the observation of dynamic phenomena. A challenge exists here to overcome the trade-off between the time required for measurement and the accuracy of the measurement. The present paper describes a concrete methodology for reconstructing the structure of an observed object, based on the information contained in the image obtained by optical microscopy. It is realized by accurate single photon counting, complete noise elimination, and a novel restoration algorithm based on probability calculation. This method has been implemented in the Super-resolution Confocal Live Imaging Microscopy (SCLIM) we developed. The new system named SCLIM2M achieves unprecedented high spatiotemporal resolution. We have succeeded in capturing sub-diffraction-limit structures with millisecond-level dynamics of organelles and vesicles in living cells, which were never observed by conventional optical microscopy. Actual examples of the high-speed and high-resolution 4D observation of living cells are presented.

Moiré fringe imaging of heterostructures by scanning transmission electron microscopy

A heterostructured crystalline bilayer specimen is known to produce moiré fringes (MFs) in the conventional transmission electron microscopy (TEM). However, the understanding of how these patterns form in scanning transmission electron microscopy (STEM) remains limited. Here, we extended the double-scattering model to establish the imaging theory of MFs in STEM for a bilayer sample and applied this theory to successfully explain both experimental and simulated STEM images of a perovskite PbZrO3/SrTiO3 system. Our findings demonstrated that the wave vectors of electrons exiting from Layer-1 and their relative positions with the atomic columns of Layer-2 should be taken into account. The atomic column misalignment leads to a faster reduction in the intensity of the secondary scattering beam compared to the single scattering beam as the scattering angle increases. Consequently, the intensity distribution of MFs in the bright field (BF)-STEM can be still described as the product of two single atomic images. However, in high angle annular dark field (HAADF)-STEM, it is approximately described as the superposition of the two images. Our work not only fills a knowledge gap of MFs in incoherent imaging, but also emphasizes the importance of the coherent scattering restricted by the real space when analyzing the HAADF-STEM imaging.

Generating and Analyzing High-Parameter Histology Images with Histoflow Cytometry.

The usage of histology to investigate immune cell diversity in tissue sections such as those derived from the central nervous system (CNS) is critically limited by the number of fluorescent parameters that can be imaged at a single time. Most immune cell subsets have been defined using flow cytometry by using complex combinations of protein markers, often requiring four or more parameters to conclusively identify, which is beyond the capabilities of most conventional microscopes. As flow cytometry dissociates tissues and loses spatial information, there is a need for techniques that can retain spatial information while interrogating the roles of complex cell types. These issues are addressed here by creating a method for expanding the number of fluorescent parameters that can be imaged by collecting the signals of spectrally overlapping fluorophores and using spectral unmixing to separate the signals of each individual fluorophore. These images are then processed using an analysis pipeline to take high-parameter histology images and extract single cells from these images so that the unique fluorescent properties of each cell can be analyzed at a single-cell level. Using flow cytometry-like gating strategies, cells can then be profiled into subsets and mapped back onto the histology sections to not only quantify their abundance, but also establish how they interact with the tissue environment. Overall, the simplicity and potential of using histoflow cytometry to study complex immune populations in histology sections is demonstrated.

Advances in Lensless Fluorescence Microscopy Design

Lensless fluorescence microscopy (LLFM) has emerged as a promising approach for biological imaging, offering a simplified, high-throughput, portable, and cost-effective substitute for conventional microscopy techniques by removing lenses in favor of directly recording fluorescent light on a digital sensor. However, there are several obstacles that this novel approach must overcome, such as restrictions on the resolution, field-of-view (FOV), signal-to-noise ratio (SNR), and multicolor-imaging capabilities. This review looks at the most current developments aimed at addressing these challenges and enhancing the performance of LLFM systems. To address these issues, computational techniques, such as deconvolution and compressive sensing, hardware modifications and structured illumination, customized filters, and the utilization of fiber-optic plates, have been implemented. Finally, this review emphasizes the numerous applications of LLFM in tissue analysis, pathogen detection, and cellular imaging, highlighting its adaptability and potential influence in a range of biomedical research and clinical diagnostic areas.

Large-scale microscope with improved resolution using SRGAN

Due to the limited Space-Bandwidth Product (SBP) of optical microscopy system, the Numerical Aperture (NA) and Field of View (FOV)/ Depth of Field (DOF) of conventional optical microscopes are mutually restricted. In this paper, we propose a 20× large-scale (large FOV&DOF) microscope objective lens which is enhanced by deep neural network to achieve the improved resolution close to a 40× Olympus objective lens. Based on training a Super-resolution Generative Adversarial Network (SRGAN) model to transform low-resolution images into super-resolved ones, the resolution of input images captured by low magnification objective is greatly improved without any loss of FOV. And the network output images have better performance on DOF. We also compared SRGAN with two other deep-learning based methods to prove its superiority. Such large-scale microscope with improved resolution could serve to democratize super-resolution imaging.

An enhanced multimode phase imaging method based on the transport of intensity equation.

Label-free biological cell imaging relies on rapid multimode phase imaging of biological samples in natural settings. To improve image contrast, phase is encoded into intensity information using the differential interference contrast (DIC) and Zernike phase contrast (ZPC) techniques. To enable multimode contrast-enhanced observation of unstained specimens, this paper proposes an improved multimode phase imaging method based on the transport of intensity equation (TIE), which combines conventional microscopy with computational imaging. The ZPC imaging module based on adaptive aperture adjustment is applied when the quantitative phase results of biological samples have been obtained by solving the TIE. Simultaneously, a rotationally symmetric shear-based technique is used that can yield isotropic DIC. In this paper, we describe numerical simulation and optical experiments carried out to validate the accuracy and viability of this technology. The calculated Michelson contrast of the ZPC image in the resolution plate experiment increased from 0.196 to 0.394.

Screening of malaria infections in human blood samples with varying parasite densities and anaemic conditions using AI-Powered mid-infrared spectroscopy

BackgroundEffective testing for malaria, including the detection of infections at very low densities, is vital for the successful elimination of the disease. Unfortunately, existing methods are either inexpensive but poorly sensitive or sensitive but costly. Recent studies have shown that mid-infrared spectroscopy coupled with machine learning (MIRs-ML) has potential for rapidly detecting malaria infections but requires further evaluation on diverse samples representative of natural infections in endemic areas. The aim of this study was, therefore, to demonstrate a simple AI-powered, reagent-free, and user-friendly approach that uses mid-infrared spectra from dried blood spots to accurately detect malaria infections across varying parasite densities and anaemic conditions.MethodsPlasmodium falciparum strains NF54 and FCR3 were cultured and mixed with blood from 70 malaria-free individuals to create various malaria parasitaemia and anaemic conditions. Blood dilutions produced three haematocrit ratios (50%, 25%, 12.5%) and five parasitaemia levels (6%, 0.1%, 0.002%, 0.00003%, 0%). Dried blood spots were prepared on Whatman™ filter papers and scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) for machine-learning analysis. Three classifiers were trained on an 80%/20% split of 4655 spectra: (I) high contrast (6% parasitaemia vs. negative), (II) low contrast (0.00003% vs. negative) and (III) all concentrations (all positive levels vs. negative). The classifiers were validated with unseen datasets to detect malaria at various parasitaemia levels and anaemic conditions. Additionally, these classifiers were tested on samples from a population survey in malaria-endemic villages of southeastern Tanzania.ResultsThe AI classifiers attained over 90% accuracy in detecting malaria infections as low as one parasite per microlitre of blood, a sensitivity unattainable by conventional RDTs and microscopy. These laboratory-developed classifiers seamlessly transitioned to field applicability, achieving over 80% accuracy in predicting natural P. falciparum infections in blood samples collected during the field survey. Crucially, the performance remained unaffected by various levels of anaemia, a common complication in malaria patients.ConclusionThese findings suggest that the AI-driven mid-infrared spectroscopy approach holds promise as a simplified, sensitive and cost-effective method for malaria screening, consistently performing well despite variations in parasite densities and anaemic conditions. The technique simply involves scanning dried blood spots with a desktop mid-infrared scanner and analysing the spectra using pre-trained AI classifiers, making it readily adaptable to field conditions in low-resource settings. In this study, the approach was successfully adapted to field use, effectively predicting natural malaria infections in blood samples from a population-level survey in Tanzania. With additional field trials and validation, this technique could significantly enhance malaria surveillance and contribute to accelerating malaria elimination efforts.

Using the Proximity Ligation Assay to Visualize Colocalization of Proteins at the Drosophila Larval Neuromuscular Junction.

In the nearly 50 years since the neuromuscular junction (NMJ) was first established as a model synapse, its molecular composition has been extensively characterized. Early work relied on fluorescent signals to determine whether proteins localized to the pre- and postsynaptic regions. As more synaptic molecules were identified, determining the localization of these proteins relative to each other became important. Conventional microscopy lacks the resolving power to assess whether two proteins are within an appropriate distance to bind directly or be part of a larger complex. Super-resolution and immunoelectron microscopies can improve spatial resolution, but these techniques can be difficult to execute and troubleshoot, and access to these instruments is limiting. However, another approach, proximity labeling, overcomes many of these limitations by using a DNA secondary label that can only be amplified if the two proteins of interest are within 40 nm of each other, which is ∼5× greater than the resolving power of conventional microscopy. In this protocol, we describe the use of the proximity ligation assay, which combines immunohistochemistry with DNA amplification, to reveal protein colocalization in the Drosophila NMJ.

OpenSTED: open-source dynamic intensity minimum system for stimulated emission depletion microscopy.

Stimulated emission depletion (STED) is a powerful super-resolution microscopy technique that can be used for imaging live cells. However, the high STED laser powers can cause significant photobleaching and sample damage in sensitive biological samples. The dynamic intensity minimum (DyMIN) technique turns on the STED laser only in regions of the sample where there is fluorescence signal, thus saving significant sample photobleaching. The reduction in photobleaching allows higher resolution images to be obtained and longer time-lapse imaging of live samples. A stand-alone module to perform DyMIN is not available commercially. In this work, we developed an open-source design to implement three-step DyMIN on a STED microscope and demonstrated reduced photobleaching for timelapse imaging of beads, cells, and tissue. The DyMIN system uses a fast multiplexer circuit and inexpensive field-programmable gate array controlled by Labview software that operates as a stand-alone module for a STED microscope. All software and circuit diagrams are freely available. We compared time-lapse images of bead samples using our custom DyMIN system to conventional STED and recorded a higher signal when using DyMIN after a 50-image sequence. We further demonstrated the DyMIN system for time-lapse STED imaging of live cells and brain tissue slices. Our open-source DyMIN system is an inexpensive add-on to a conventional STED microscope that can reduce photobleaching. The system can significantly improve signal to noise for dynamic time-lapse STED imaging of live samples.

Frequency-Domain Low-Wavenumber Hyperspectral Stimulated Raman Scattering Microscopy.

In recent years, stimulated Raman scattering (SRS) microscopy has experienced rapid technological advancements and has found widespread applications in chemical analysis. Hyperspectral SRS (hSRS) microscopy further enhances the chemical selectivity in imaging by providing a Raman spectrum for each pixel. Time-domain hSRS techniques often require interferometry and ultrashort femtosecond laser pulses. They are especially suited to measuring low-wavenumber Raman transitions but are susceptible to scattering-induced distortions. Frequency-domain hSRS microscopy, on the other hand, offers a simpler optical configuration and demonstrates high tolerance to sample scattering but typically operates within the spectral range of 400-4000 cm-1. Conventional frequency-domain hSRS microscopy is widely employed in biological applications but falls short in detecting chemical bonds with a weaker vibrational energy. In this work, we extend the spectral coverage of picosecond spectral-focusing hSRS microscopy to below 100 cm-1. This frequency-domain low-wavenumber hSRS approach can measure the weaker vibrational energy from the sample and has a strong tolerance to sample scattering. By expanding spectral coverage to 100-4000 cm-1, this development enhances the capability of spectral-domain SRS microscopy for chemical imaging.

The muscle and neural architecture of Taenia crassiceps cysticerci revisited; implications on head-tail polarization of the larvae.

Taenia crassiceps has been used for decades as an experimental model for the study of human and porcine cysticercosis. Even though, its life cycle, tissue organization, ultrastructure and immune response elicited in the host, have been extensively described, there are many other biological questions remaining to be addressed. In the present study we revisited the muscle and neural architecture of cysticerci in two of the most frequently used strains (WFU and ORF), using conventional staining and confocal microscopy imaging, aiming to assemble an updated anatomy. Differences between both strains, including polarization processes during development of the young budding larvae, are emphasized. We also performed a search for genes that have been related to peptidergic neural processes in other related flatworms. These findings can help to understand the anatomical and molecular consequences of the scolex presence or absence in both strains.

Zero-shot denoising of microscopy images recorded at high-resolution limits.

Conventional and electron microscopy visualize structures in the micrometer to nanometer range, and such visualizations contribute decisively to our understanding of biological processes. Due to different factors in recording processes, microscopy images are subject to noise. Especially at their respective resolution limits, a high degree of noise can negatively effect both image interpretation by experts and further automated processing. However, the deteriorating effects of strong noise can be alleviated to a large extend by image enhancement algorithms. Because of the inherent high noise, a requirement for such algorithms is their applicability directly to noisy images or, in the extreme case, to just a single noisy image without a priori noise level information (referred to as blind zero-shot setting). This work investigates blind zero-shot algorithms for microscopy image denoising. The strategies for denoising applied by the investigated approaches include: filtering methods, recent feed-forward neural networks which were amended to be trainable on noisy images, and recent probabilistic generative models. As datasets we consider transmission electron microscopy images including images of SARS-CoV-2 viruses and fluorescence microscopy images. A natural goal of denoising algorithms is to simultaneously reduce noise while preserving the original image features, e.g., the sharpness of structures. However, in practice, a tradeoff between both aspects often has to be found. Our performance evaluations, therefore, focus not only on noise removal but set noise removal in relation to a metric which is instructive about sharpness. For all considered approaches, we numerically investigate their performance, report their denoising/sharpness tradeoff on different images, and discuss future developments. We observe that, depending on the data, the different algorithms can provide significant advantages or disadvantages in terms of their noise removal vs. sharpness preservation capabilities, which may be very relevant for different virological applications, e.g., virological analysis or image segmentation.

Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques – a short review including new research

Being able to image the microstructure of growth cartilage is important for understanding the onset and progression of diseases such as osteochondrosis and osteoarthritis, as well as for developing new treatments and implants. Studies of cartilage using conventional optical brightfield microscopy rely heavily on histological staining, where the added chemicals provide tissue-specific colours. Other microscopy contrast mechanisms include polarization, phase- and scattering contrast, enabling non-stained or ‘label-free’ imaging that significantly simplifies the sample preparation, thereby also reducing the risk of artefacts. Traditional high-performance microscopes tend to be both bulky and expensive. Computational imaging denotes a range of techniques where computers with dedicated algorithms are used as an integral part of the image formation process. Computational imaging offers many advantages like 3D measurements, aberration correction and quantitative phase contrast, often combined with comparably cheap and compact hardware. X-ray microscopy is also progressing rapidly, in certain ways trailing the development of optical microscopy. In this study, we first briefly review the structures of growth cartilage and relevant microscopy characterization techniques, with an emphasis on Fourier ptychographic microscopy (FPM) and advanced x-ray microscopies. We next demonstrate with our own results computational imaging through FPM and compare the images with hematoxylin eosin and saffron (HES)-stained histology. Zernike phase contrast, and the nonlinear optical microscopy techniques of second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) are explored. Furthermore, X-ray attenuation-, phase- and diffraction-contrast computed tomography (CT) images of the very same sample are presented for comparisons. Future perspectives on the links to artificial intelligence, dynamic studies and in vivo possibilities conclude the article.

Metasurface‐Based Mueller Matrix Microscope

AbstractIn conventional optical microscopes, image contrast of objects mainly results from the differences in light intensity and/or color. Muller matrix optical microscopes (MMMs), on the other hand, can provide significantly enhanced image contrast and rich information about objects by analyzing their interactions with polarized light. However, state‐of‐the‐art MMMs are fundamentally limited by bulky and slow polarization state generators and analyzers. Here, the study demonstrates a metasurface‐based MMM, i.e., Meta‐MMM, which is equipped with a chip‐integrated, single‐shot metasurface polarization state analyzer (Meta‐PSA). The Meta‐MMM is featured with high‐speed measurement (≈2s per Muller matrix (MM) image), superior operation stability, dual‐color operation, and high measurement accuracy (measurement error 1–2%) for MM imaging. The Meta‐MMM is applied to nanostructure characterization, surface morphology analysis, and discovering birefringent structures in honeybee wings. The Meta‐MMMs hold the promise to revolutionize various applications from biological imaging, medical diagnosis, and material characterization to industry inspection and space exploration.

A Minimal Approach to Demonstrate Concordance of Digital and Conventional Microscopy in Toxicologic Pathology.

Digitalization of pathology workflows has undergone a rapid evolution and has been widely established in the diagnostic field but remains a challenge in the nonclinical safety context due to lack of regulatory guidance and validation experience for good laboratory practice (GLP) use. One means to demonstrate that digital slides are fit for purpose, that is, provide sufficient quality for pathologists to reach a diagnosis, is conduction of comparison studies, which have been published both, for veterinary and human diagnostic pathology, but not for toxicologic pathology. Here, we present an approach that uses study material from nonclinical safety studies and that allows for the statistical comparison of concordance rates for glass and digital slide evaluation while minimizing time and effort for the involved personnel. Using a benchmark study design, we demonstrate that evaluation of digital slides fits the purpose of nonclinical safety evaluation. These results add to reports of successful workflow validations and support the full adaptation of digital pathology in the regulatory field.

In Situ Structural Characterization of Cardiomyocyte Microenvironment by Multimodal STED Microscopy.

Within the myocardium, cardiomyocytes reside in a complex and dynamic extracellular matrix (ECM) consisting of a basement membrane (BM) and interstitial matrix. The interactions between cardiomyocytes and the myocardial ECM play a critical role in maintaining cardiac geometry and function throughout cardiac development and in adult hearts. Understanding how the structural changes of the myocardial ECM affect cardiomyocyte function requires knowledge of pericellular structures. These structures are of a size beyond the resolution of conventional optical microscopy. Here, we demonstrated multi-scale and multi-aspect characterization of the cardiomyocyte microenvironment in myocardial tissue sections using multimodal stimulated emission depletion (STED) microscopy. Second harmonic generation and autofluorescence facilitated multiplexed imaging, enabling the interpretation of protein distribution in 3D. STED imaging modality revealed BM structures of cardiomyocytes and myocardial capillaries at the subdiffractional level. Moreover, meaningful measurements retrieved from acquired images, such as sarcomere length and capillary density, enabled quantitative assessment of myocardial structures.

High-resolution, large field-of-view label-free imaging via aberration-corrected, closed-form complex field reconstruction

Computational imaging methods empower modern microscopes to produce high-resolution, large field-of-view, aberration-free images. Fourier ptychographic microscopy can increase the space-bandwidth product of conventional microscopy, but its iterative reconstruction methods are prone to parameter selection and tend to fail under excessive aberrations. Spatial Kramers–Kronig methods can analytically reconstruct complex fields, but is limited by aberration or providing extended resolution enhancement. Here, we present APIC, a closed-form method that weds the strengths of both methods while using only NA-matching and darkfield measurements. We establish an analytical phase retrieval framework which demonstrates the feasibility of analytically reconstructing the complex field associated with darkfield measurements. APIC can retrieve complex aberrations of an imaging system with no additional hardware and avoids iterative algorithms, requiring no human-designed convergence metrics while always obtaining a closed-form complex field solution. We experimentally demonstrate that APIC gives correct reconstruction results where Fourier ptychographic microscopy fails when constrained to the same number of measurements. APIC achieves 2.8 times faster computation using image tile size of 256 (length-wise), is robust against aberrations compared to Fourier ptychographic microscopy, and capable of addressing aberrations whose maximal phase difference exceeds 3.8π when using a NA 0.25 objective in experiment.

Measure for optical robustness of directly bonded glass-to-glass joint using its interaction with damage induced by fs laser.

To produce more powerful compact ultrafast lasers, research aims at improving the quality of bonds between components inside the laser cavity. Increasing bond robustness under optical irradiation helps the bonds to survive the high energy pulses that these lasers are designed to produce. A measure for such robustness is reported here to support work toward improved bonding processes for such lasers. We produced bonds between pairs of optical grade fused silica glass cylinders using a wet direct bonding procedure. We evaluated these bonds using conventional microscopy, including scanning electron microscopy (SEM) and optical microscopy, without quantifiable results. The bond interface was not discernible through conventional SEM imaging, even after cross sectioning and polishing. The majority of the interface was also undetectable in optical micrographs, except for some limited areas of interfacial disturbance. To obtain quantifiable results for optical robustness, we used an 800nm femtosecond laser to produce filament-shaped damage from a focal spot moving across the interface. Microscopy of the damage showed its interaction with the interface, the presence of which caused a ≈0.130 to ≈0.230mm long interruption in the damage line. The exact value depended not only on laser power but also interface quality, and thereby quantified the optical robustness. The reported method proved more sensitive in detecting bonds of fused silica samples compared to other visualization techniques used. Our results suggest a nuanced understanding of bonded glass joints-mechanically sound, yet with limited optical robustness under specific laser conditions.

Far-Field Super-Resolution Microscopy Using Evanescent Illumination: A Review

The resolution of conventional optical microscopy is restricted by the diffraction limit. Light waves containing higher-frequency information about the sample are bound to the sample surface and cannot be collected by far-field optical microscopy. To break the resolution limit, researchers have proposed various far-field super-resolution (SR) microscopy imaging methods using evanescent waves to transfer the high-frequency information of samples to the low-frequency passband of optical microscopy. Optimization algorithms are developed to reconstruct a SR image of the sample by utilizing the high-frequency information. These techniques can be collectively referred to as spatial-frequency-shift (SFS) SR microscopy. This review aims to summarize the basic principle of SR microscopy using evanescent illumination and introduce the advances in this research area. Some current challenges and possible directions are also discussed.

Quantitative phase imaging techniques for measuring scattering properties of cells and tissues: a review-part II.

Quantitative phase imaging (QPI) is a non-invasive, label-free technique that provides intrinsic information about the sample under study. Such information includes the structure, function, and dynamics of the sample. QPI overcomes the limitations of conventional fluorescence microscopy in terms of phototoxicity to the sample and photobleaching of the fluorophore. As such, the application of QPI in estimating the three-dimensional (3D) structure and dynamics is well-suited for a range of samples from intracellular organelles to highly scattering multicellular samples while allowing for longer observation windows. We aim to provide a comprehensive review of 3D QPI and related phase-based measurement techniques along with a discussion of methods for the estimation of sample dynamics. We present information collected from 106 publications that cover the theoretical description of 3D light scattering and the implementation of related measurement techniques for the study of the structure and dynamics of the sample. We conclude with a discussion of the applications of the reviewed techniques in the biomedical field. QPI has been successfully applied to 3D sample imaging. The scattering-based contrast provides measurements of intrinsic quantities of the sample that are indicative of disease state, stage of growth, or overall dynamics. We reviewed state-of-the-art QPI techniques for 3D imaging and dynamics estimation of biological samples. Both theoretical and experimental aspects of various techniques were discussed. We also presented the applications of the discussed techniques as applied to biomedicine and biology research.