Label-free Brillouin endo-microscopy for the quantitative 3D imaging of sub-micrometre biology.

This report presents an optical fibre-based endo-microscopic imaging tool that simultaneously measures the topographic profile and 3D viscoelastic properties of biological specimens through the phenomenon of time-resolved Brillouin scattering. This uses the intrinsic viscoelasticity of the specimen as a contrast mechanism without fluorescent tags or photoacoustic contrast mechanisms. We demonstrate 2 µm lateral resolution and 320 nm axial resolution for the 3D imaging of biological cells and Caenorhabditis elegans larvae. This has enabled the first ever 3D stiffness imaging and characterisation of the C. elegans larva cuticle in-situ. A label-free, subcellular resolution, and endoscopic compatible technique that reveals structural biologically-relevant material properties of tissue could pave the way toward in-vivo elasticity-based diagnostics down to the single cell level.

Thermal-tagging photoacoustic remote sensing flowmetry.

Ultrasound coupling is one of the critical challenges for traditional photoacoustic (or optoacoustic) microscopy (PAM) techniques transferred to the clinical examination of chronic wounds and open tissues. A promising alternative potential solution for breaking the limitation of ultrasound coupling in PAM is photoacoustic remote sensing (PARS), which implements all-optical non-interferometric photoacoustic measurements. Functional imaging of PARS microscopy was demonstrated from the aspects of histopathology and oxygen metabolism, while its performance in hemodynamic quantification remains unexplored. In this Letter, we present an all-optical thermal-tagging flowmetry approach for PARS microscopy and demonstrate it with comprehensive mathematical modeling and ex vivo and in vivo experimental validations. Experimental results demonstrated that the detectable range of the blood flow rate was from 0 to 12 mm/s with a high accuracy (measurement error:±1.2%) at 10-kHz laser pulse repetition rate. The proposed all-optical thermal-tagging flowmetry offers an effective alternative approach for PARS microscopy realizing non-contact dye-free hemodynamic imaging.

Molecular tools are crucial for malaria elimination.

The eradication of Plasmodium parasites, responsible for malaria, is a daunting global public health task. It requires a comprehensive approach that addresses symptomatic, asymptomatic, and submicroscopic cases. Overcoming this challenge relies on harnessing the power of molecular diagnostic tools, as traditional methods like microscopy and rapid diagnostic tests fall short in detecting low parasitaemia, contributing to the persistence of malaria transmission. By precisely identifying patients of all types and effectively characterizing malaria parasites, molecular tools may emerge as indispensable allies in the pursuit of malaria elimination. Furthermore, molecular tools can also provide valuable insights into parasite diversity, drug resistance patterns, and transmission dynamics, aiding in the implementation of targeted interventions and surveillance strategies. In this review, we explore the significance of molecular tools in the pursuit of malaria elimination, shedding light on their key contributions and potential impact on public health.

Bichromatic tetraphasic full-field optical coherence microscopy.

Significance: Full-field optical coherence microscopy (FF-OCM) is a prevalent technique for backscattering and phase imaging with epi-detection. Traditional methods have two limitations: suboptimal utilization of functional information about the sample and complicated optical design with several moving parts for phase contrast. Aim: We report an OCM setup capable of generating dynamic intensity, phase, and pseudo-spectroscopic contrast with single-shot full-field video-rate imaging called bichromatic tetraphasic (BiTe) full-field OCM with no moving parts. Approach: BiTe OCM resourcefully uses the phase-shifting properties of anti-reflection (AR) coatings outside the rated bandwidths to create four unique phase shifts, which are detected with two emission filters for spectroscopic contrast. Results: BiTe OCM overcomes the disadvantages of previous FF-OCM setup techniques by capturing both the intensity and phase profiles without any artifacts or speckle noise for imaging scattering samples in three-dimensional (3D). BiTe OCM also utilizes the raw data effectively to generate three complementary contrasts: intensity, phase, and color. We demonstrate BiTe OCM to observe cellular dynamics, image live, and moving micro-animals in 3D, capture the spectroscopic hemodynamics of scattering tissues along with dynamic intensity and phase profiles, and image the microstructure of fall foliage with two different colors. Conclusions: BiTe OCM can maximize the information efficiency of FF-OCM while maintaining overall simplicity in design for quantitative, dynamic, and spectroscopic characterization of biological samples.

Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy.

Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain.

Full-aperture extended-depth oblique plane microscopy through dynamic remote focusing.

Significance: The reprojection setup typical of oblique plane microscopy (OPM) limits the effective aperture of the imaging system, and therefore its efficiency and resolution. Large aperture system is only possible through the use of custom specialized optics. A full-aperture OPM made with off the shelf components would both improve the performance of the method and encourage its widespread adoption. Aim: To prove the feasibility of an OPM without a conventional reprojection setup, retaining the full aperture of the primary objective employed. Approach: A deformable lens based remote focusing setup synchronized with the rolling shutter of a complementary metal-oxide semiconductor detector is used instead of a traditional reprojection system. Results: The system was tested on microbeads, prepared slides, and zebrafish embryos. Resolution and pixel throughput were superior to conventional OPM with cropped apertures, and comparable with OPM implementations with custom made optical components. Conclusions: An easily reproducible approach to OPM imaging is presented, eliminating the conventional reprojection setup and exploiting the full aperture of the employed objective.

Photoacoustic microscopy for real-time monitoring of near-infrared optical absorbers inside biological tissue.

Significance: We developed a high-speed optical-resolution photoacoustic microscopy (OR-PAM) system using a high-repetition-rate supercontinuum (SC) light source and a two-axes Galvano scanner. The OR-PAM system enabled real-time imaging of optical absorbers inside biological tissues with excellent excitation wavelength tunability. Aim: In the near-infrared (NIR) wavelength range, high-speed OR-PAM faces limitations due to the lack of wavelength-tunable light sources. Our study aimed to enable high-speed OR-PAM imaging of various optical absorbers, including NIR contrast agents, and validate the performance of high-speed OR-PAM in the detection of circulating tumor cells (CTCs). Approach: A high-repetition nanosecond pulsed SC light source was used for OR-PAM. The excitation wavelength was adjusted by bandpass filtering of broadband light pulses produced by an SC light source. Phantom and in vivo experiments were performed to detect tumor cells stained with an NIR contrast agent within flowing blood samples. Results: The newly developed high-speed OR-PAM successfully detected stained cells both in the phantom and in vivo. The phantom experiment confirmed the correlation between the tumor cell detection rate and tumor cell concentration in the blood sample. Conclusions: The high-speed OR-PAM effectively detected stained tumor cells. Combining high-speed OR-PAM with molecular probes that stain tumor cells in vivo enables in vivo CTC detection.

Infrared nanoimaging of neuronal ultrastructure and nanoparticle interaction with cells.

Here we introduce scattering-type scanning near-field optical microscopy (s-SNOM) as a novel tool for nanoscale chemical-imaging of sub-cellular organelles, nanomaterials and of the interactions between them. Our setup uses a tuneable mid-infrared laser and a sharp scanning probe to image at a resolution substantially surpassing the diffraction limit. The laser can be tuned to excite vibrational modes of functional groups in biomolecules, (e.g. amide moieties), in a way that enables direct chemical mapping without the need for labelling. We, for the first time, chemically image neuronal ultrastructure, identify neuronal organelles and sub-organelle structures as small as 10 nm and validate our findings using transmission electron microscopy (TEM). We produce chemical and morphological maps of neurons treated with gold nanospheres and characterize nanoparticle size and intracellular location, and their interaction with the plasma membrane. Our results show that the label-free nature of s-SNOM means it has a 'true' chemical resolution of up to 20 nm which can be further improved. We argue that it offers significant potential in nanomedicine for nanoscale chemical imaging of cell ultrastructure and the subcellular distribution of nanomaterials within tissues.

Longitudinal intravital imaging of mouse placenta.

Studying placental functions is crucial for understanding pregnancy complications. However, imaging placenta is challenging due to its depth, volume, and motion distortions. In this study, we have developed an implantable placenta window in mice that enables high-resolution photoacoustic and fluorescence imaging of placental development throughout the pregnancy. The placenta window exhibits excellent transparency for light and sound. By combining the placenta window with ultrafast functional photoacoustic microscopy, we were able to investigate the placental development during the entire mouse pregnancy, providing unprecedented spatiotemporal details. Consequently, we examined the acute responses of the placenta to alcohol consumption and cardiac arrest, as well as chronic abnormalities in an inflammation model. We have also observed viral gene delivery at the single-cell level and chemical diffusion through the placenta by using fluorescence imaging. Our results demonstrate that intravital imaging through the placenta window can be a powerful tool for studying placenta functions and understanding the placental origins of adverse pregnancy outcomes.

Open-top Bessel beam two-photon light sheet microscopy for three-dimensional pathology.

Nondestructive pathology based on three-dimensional (3D) optical microscopy holds promise as a complement to traditional destructive hematoxylin and eosin (H&E) stained slide-based pathology by providing cellular information in high throughput manner. However, conventional techniques provided superficial information only due to shallow imaging depths. Herein, we developed open-top two-photon light sheet microscopy (OT-TP-LSM) for intraoperative 3D pathology. An extended depth of field two-photon excitation light sheet was generated by scanning a nondiffractive Bessel beam, and selective planar imaging was conducted with cameras at 400 frames/s max during the lateral translation of tissue specimens. Intrinsic second harmonic generation was collected for additional extracellular matrix (ECM) visualization. OT-TP-LSM was tested in various human cancer specimens including skin, pancreas, and prostate. High imaging depths were achieved owing to long excitation wavelengths and long wavelength fluorophores. 3D visualization of both cells and ECM enhanced the ability of cancer detection. Furthermore, an unsupervised deep learning network was employed for the style transfer of OT-TP-LSM images to virtual H&E images. The virtual H&E images exhibited comparable histological characteristics to real ones. OT-TP-LSM may have the potential for histopathological examination in surgical and biopsy applications by rapidly providing 3D information.

Automatic bright-field smear microscopy for diagnosis of pulmonary tuberculosis.

In recent decades, many studies have been published on the use of automatic smear microscopy for diagnosing pulmonary tuberculosis (TB). Most of them deal with a preliminary step of the diagnosis, the bacilli detection, whereas sputum smear microscopy for diagnosis of pulmonary TB comprises detecting and reporting the number of bacilli found in at least 100 microscopic fields, according to the 5 grading scales (negative, scanty, 1+, 2+ and 3+) endorsed by the World Health Organization (WHO). Pulmonary TB diagnosis in bright-field smear microscopy, however, depends upon the attention of a trained and motivated technician, while the automated TB diagnosis requires little or no interpretation by a technician. As far as we know, this work proposes the first automatic method for pulmonary TB diagnosis in bright-field smear microscopy, according to the WHO recommendations. The proposed method comprises a semantic segmentation step, using a deep neural network, followed by a filtering step aiming to reduce the number of false positives (false bacilli): color and shape filtering. In semantic segmentation, different configurations of encoders are evaluated, using depth-wise separable convolution layers and channel attention mechanism. The proposed method was evaluated with a large, robust, and annotated image dataset designed for this purpose, consisting of 250 testing sets, 50 sets for each of the 5 TB diagnostic classes. The following performance metrics were obtained for automatic pulmonary TB diagnosis by smear microscopy: mean precision of 0.894, mean recall of 0.896, and mean F1-score of 0.895.

On-the-fly Raman microscopy guaranteeing the accuracy of discrimination.

Accelerating the measurement for discrimination of samples, such as classification of cell phenotype, is crucial when faced with significant time and cost constraints. Spontaneous Raman microscopy offers label-free, rich chemical information but suffers from long acquisition time due to extremely small scattering cross-sections. One possible approach to accelerate the measurement is by measuring necessary parts with a suitable number of illumination points. However, how to design these points during measurement remains a challenge. To address this, we developed an imaging technique based on a reinforcement learning in machine learning (ML). This ML approach adaptively feeds back "optimal" illumination pattern during the measurement to detect the existence of specific characteristics of interest, allowing faster measurements while guaranteeing discrimination accuracy. Using a set of Raman images of human follicular thyroid and follicular thyroid carcinoma cells, we showed that our technique requires 3,333 to 31,683 times smaller number of illuminations for discriminating the phenotypes than raster scanning. To quantitatively evaluate the number of illuminations depending on the requisite discrimination accuracy, we prepared a set of polymer bead mixture samples to model anomalous and normal tissues. We then applied a home-built programmable-illumination microscope equipped with our algorithm, and confirmed that the system can discriminate the sample conditions with 104 to 4,350 times smaller number of illuminations compared to standard point illumination Raman microscopy. The proposed algorithm can be applied to other types of microscopy that can control measurement condition on the fly, offering an approach for the acceleration of accurate measurements in various applications including medical diagnosis.

High-density volumetric super-resolution microscopy.

Volumetric super-resolution microscopy typically encodes the 3D position of single-molecule fluorescence into a 2D image by changing the shape of the point spread function (PSF) as a function of depth. However, the resulting large and complex PSF spatial footprints reduce biological throughput and applicability by requiring lower labeling densities to avoid overlapping fluorescent signals. We quantitatively compare the density dependence of single-molecule light field microscopy (SMLFM) to other 3D PSFs (astigmatism, double helix and tetrapod) showing that SMLFM enables an order-of-magnitude speed improvement compared to the double helix PSF by resolving overlapping emitters through parallax. We demonstrate this optical robustness experimentally with high accuracy ( > 99.2 ± 0.1%, 0.1 locs µm-2) and sensitivity ( > 86.6 ± 0.9%, 0.1 locs µm-2) through whole-cell (scan-free) imaging and tracking of single membrane proteins in live primary B cells. We also exemplify high-density volumetric imaging (0.15 locs µm-2) in dense cytosolic tubulin datasets.

Non-invasive methods of monitoring Fe3O4 magnetic nanoparticle toxicity in human liver HepaRG cells using impedance biosensing and Coherent anti-Stokes Raman spectroscopic (CARS) microscopy.

Functionalized nanoparticles have been developed for use in nanomedicines for treating life threatening diseases including various cancers. To ensure safe use of these new nanoscale reagents, various assays for biocompatibility or cytotoxicity in vitro using cell lines often serve as preliminary assessments prior to in vivo animal testing. However, many of these assays were designed for soluble, colourless materials and may not be suitable for coloured, non-transparent nanoparticles. Moreover, cell lines are not always representative of mammalian organs in vivo. In this work, we use non-invasive impedance sensing methods with organotypic human liver HepaRG cells as a model to test the toxicity of PEG-Fe3O4 magnetic nanoparticles. We also use Coherent anti-Stokes Raman Spectroscopic (CARS) microscopy to monitor the formation of lipid droplets as a parameter to the adverse effect on the HepaRG cell model. The results were also compared with two commercial testing kits (PrestoBlue and ATP) for cytotoxicity. The results suggested that the HepaRG cell model can be a more realistic model than commercial cell lines while use of impedance monitoring of Fe3O4 nanoparticles circumventing the uncertainties due to colour assays. These methods can play important roles for scientists driving towards the 3Rs principle - Replacement, Reduction and Refinement.

Benchtop mesoSPIM: a next-generation open-source light-sheet microscope for cleared samples.

In 2015, we launched the mesoSPIM initiative, an open-source project for making light-sheet microscopy of large cleared tissues more accessible. Meanwhile, the demand for imaging larger samples at higher speed and resolution has increased, requiring major improvements in the capabilities of such microscopes. Here, we introduce the next-generation mesoSPIM ("Benchtop") with a significantly increased field of view, improved resolution, higher throughput, more affordable cost, and simpler assembly compared to the original version. We develop an optical method for testing detection objectives that enables us to select objectives optimal for light-sheet imaging with large-sensor cameras. The improved mesoSPIM achieves high spatial resolution (1.5 µm laterally, 3.3 µm axially) across the entire field of view, magnification up to 20×, and supports sample sizes ranging from sub-mm up to several centimeters while being compatible with multiple clearing techniques. The microscope serves a broad range of applications in neuroscience, developmental biology, pathology, and even physics.

Sounding out the dynamics: a concise review of high-speed photoacoustic microscopy.

Significance: Photoacoustic microscopy (PAM) offers advantages in high-resolution and high-contrast imaging of biomedical chromophores. The speed of imaging is critical for leveraging these benefits in both preclinical and clinical settings. Ongoing technological innovations have substantially boosted PAM's imaging speed, enabling real-time monitoring of dynamic biological processes. Aim: This concise review synthesizes historical context and current advancements in high-speed PAM, with an emphasis on developments enabled by ultrafast lasers, scanning mechanisms, and advanced imaging processing methods. Approach: We examine cutting-edge innovations across multiple facets of PAM, including light sources, scanning and detection systems, and computational techniques and explore their representative applications in biomedical research. Results: This work delineates the challenges that persist in achieving optimal high-speed PAM performance and forecasts its prospective impact on biomedical imaging. Conclusions: Recognizing the current limitations, breaking through the drawbacks, and adopting the optimal combination of each technology will lead to the realization of ultimate high-speed PAM for both fundamental research and clinical translation.

Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon.

INTRODUCTION: Schistosomiasis is a significant public health concern, especially in Sub-Saharan Africa. Conventional microscopy is the standard diagnostic method in resource-limited settings, but with limitations, such as the need for expert microscopists. An automated digital microscope with artificial intelligence (Schistoscope), offers a potential solution. This field study aimed to validate the diagnostic performance of the Schistoscope for detecting and quantifying Schistosoma haematobium eggs in urine compared to conventional microscopy and to a composite reference standard (CRS) consisting of real-time PCR and the up-converting particle (UCP) lateral flow (LF) test for the detection of schistosome circulating anodic antigen (CAA). METHODS: Based on a non-inferiority concept, the Schistoscope was evaluated in two parts: study A, consisting of 339 freshly collected urine samples and study B, consisting of 798 fresh urine samples that were also banked as slides for analysis with the Schistoscope. In both studies, the Schistoscope, conventional microscopy, real-time PCR and UCP-LF CAA were performed and samples with all the diagnostic test results were included in the analysis. All diagnostic procedures were performed in a laboratory located in a rural area of Gabon, endemic for S. haematobium. RESULTS: In study A and B, the Schistoscope demonstrated a sensitivity of 83.1% and 96.3% compared to conventional microscopy, and 62.9% and 78.0% compared to the CRS. The sensitivity of conventional microscopy in study A and B compared to the CRS was 61.9% and 75.2%, respectively, comparable to the Schistoscope. The specificity of the Schistoscope in study A (78.8%) was significantly lower than that of conventional microscopy (96.4%) based on the CRS but comparable in study B (90.9% and 98.0%, respectively). CONCLUSION: Overall, the performance of the Schistoscope was non-inferior to conventional microscopy with a comparable sensitivity, although the specificity varied. The Schistoscope shows promising diagnostic accuracy, particularly for samples with moderate to higher infection intensities as well as for banked sample slides, highlighting the potential for retrospective analysis in resource-limited settings. TRIAL REGISTRATION: NCT04505046 ClinicalTrials.gov.

Implementation of a portable diffraction phase microscope for digital histopathology.

Quantitative phase imaging (QPI) has a significant advantage in histopathology as it helps in differentiating biological tissue structures and cells without the need for staining. To make this capability more accessible, it is crucial to develop compact and portable systems. In this study, we introduce a portable diffraction phase microscopy (DPM) system that allows the acquisition of phase map images from various organs in mice using a low-NA objective lens. Our findings indicate that the cell and tissue structures observed in portable DPM images are similar to those seen in conventional histology microscope images. We confirmed that the developed system's performance is comparable to the benchtop DPM system. Additionally, we investigate the potential utility of digital histopathology by applying deep learning technology to create virtual staining of DPM images.

Bimodal optical and optoacoustic multiview microscope in the frequency-domain.

Hybrid fluorescence and optoacoustic microscopy systems have recently emerged as powerful imaging modalities concurrently capturing both radiative and non-radiative molecular relaxations in biological tissues. Nevertheless, such approaches provide limited information as specimens are imaged exclusively from one side, not permitting the acquisition of their full anatomical, structural, or functional features in multiple views of interest. Herein we present a bimodal optical and optoacoustic multiview (BOOM) cost-efficient microscope operating in the frequency-domain for the comprehensive label-free imaging of established and emerging model organisms. Thus, the capabilities of BOOM microscopy have been proven suitable for highly demanding observations in developmental biology and embryology.

Fast cancer imaging in pancreatic biopsies using infrared imaging.

Pancreatic cancer, particularly Pancreatic ductal adenocarcinoma, remains a highly lethal form of cancer with limited early diagnosis and treatment options. Infrared (IR) spectroscopy, combined with machine learning, has demonstrated great potential in detecting various cancers. This study explores the translation of a diagnostic model from Fourier Transform Infrared to Quantum Cascade Laser (QCL) microscopy for pancreatic cancer classification. Furthermore, QCL microscopy offers faster measurements with selected frequencies, improving clinical feasibility. Thus, the goals of the study include establishing a QCL-based model for pancreatic cancer classification and creating a fast surgical margin detection model using reduced spectral information. The research involves preprocessing QCL data, training Random Forest (RF) classifiers, and optimizing the selection of spectral features for the models. Results demonstrate successful translation of the diagnostic model to QCL microscopy, achieving high predictive power (AUC = 98%) in detecting cancerous tissues. Moreover, a model for rapid surgical margin recognition, based on only a few spectral frequencies, is developed with promising differentiation between benign and cancerous regions. The findings highlight the potential of QCL microscopy for efficient pancreatic cancer diagnosis and surgical margin detection within clinical timeframes of minutes per surgical resection tissue.