Light: Science & Applications

Frequent measurements of blood sugar are essential for the management of diabetes. While finger pricking offers accurate measurements of blood glucose, it is a procedure that causes discomfort and risk of infection. Conversely, minimally invasive biochemical sensors based on micro-needles do not assess glucose in blood but in the interstitial fluid. While most optical sensors also detect in bulk from the interstitial fluid, a depth-gated mid-infrared optoacoustic sensor (DIROS) was recently proposed to non-invasively detect glucose concentrations in blood by means of time-gating. While DIROS was previously demonstrated only in animals, herein we present the first pilot investigation of the sensor in humans, based on a multivariate model fit to measurement data obtained from healthy volunteers (n=5) during an oral glucose tolerance test. By time-gating optoacoustic signals, i.e. selecting time points corresponding to different depths within the skin based on the ultrasound time-of-flight, we confirm in humans an improved measurement accuracy when targeting deeper skin layers, which are rich in vasculature. The results set the first milestone towards depth-dependent in-blood glucose detection in humans and highlight potential for DIROS in clinical application.

Long-term and high-spatiotemporal-resolution 3D imaging of living cells remains an unmet challenge for super-resolution microscopy, owing to the noticeable phototoxicity and limited scanning speed. While emerging light-field microscopy can mitigate this issue through three-dimensionally capturing biological dynamics with merely single snapshot, it suffers from suboptimal resolution insufficient for resolving subcellular structures. Here we propose an Adaptive Learning PHysics-Aware Light-Field Microscopy (Alpha-LFM) with a physics-aware deep learning framework and adaptive-tuning strategies capable for highly-generalizable light-field reconstruction of diverse subcellular dynamics. Alpha-LFM delivers sub-diffraction-limit spatial resolution ([~]120 nm) while maintaining high temporal resolution and low phototoxicity. It enables rapid (at hundreds of volumes per second), long-term (up to 60 hours) 3D super-resolution imaging of diverse intracellular dynamics with exceptional details. Using Alpha-LFM approach, we finely resolve the lysosome-mitochondrial interactions, capture rapid motion of peroxisome and the endoplasmic reticulum, and reveal the variations in mitochondrial fission activity throughout two complete cell cycles.

Evaluating the tissue collagen content in addition to the epithelial morphology has been proven to offer complementary information in histopathology, especially in oncology tumor staging and prediction of survival in cancer patients. One imaging modality widely used for this purpose is second harmonic generation microscopy (SHGM), which reports on the nonlinear susceptibility associated with the collagen fibers. Another method is polarization light microscopy (PLM) combined with picrosirius-red (PSR) tissue staining. However, SHGM requires expensive equipment and provides limited throughput, while PLM and PSR staining are not part of the routine surgical pathology workflow. Here, we utilize phase imaging with computational specificity (PICS) to computationally infer the collagen distribution of unlabeled tissue, with high specificity. PICS utilizes deep learning to translate quantitative phase images (QPI) into corresponding PSR images with high accuracy and inference speed of 200 milisecond per forwardpass through the model once trained. We developed a multimodal imaging instrument that yields both Spatial light Inference Microscopy (SLIM) and polarized light microscopy (PLM) images from the same field of view. Our results indicate that the distributions of collagen fiber orientation, length, and straightness reported by PICS closely match the ones from ground truth as defined by KL-divergence.

The assessment of diabetes severity relies primarily on a count of clinical complications to empirically characterize disease. Disease staging based on clinical complications also employs a scoring system that may not be optimally suited for analysis of earlier stages of diabetes development or for monitoring smaller increments of disease progress with high precision. We propose a novel sensor, which goes beyond the abilities of current state-of-the-art approaches and introduces a new concept in the assessment of biomedical markers by means of ultra-broadband optoacoustic detection. Being insensitive to photon scattering, the new sensor can resolve optical biomarkers in fine detail and as a function of depth and relates epidermal and dermal morphological and micro-vascular density features to diabetes state. We demonstrate basic sensor characteristics in phantoms and examine the novel sensing concept presented in a pilot study using data from 86 participants (20 healthy and 66 diabetic) at an ultra-wide optoacoustic bandwidth of 120 MHz. Machine learning based on ensemble trees was developed and trained in a supervised fashion and subsequently used to examine the relation of sensor data to disease severity, in particular as it associates to diabetes without complications vs. diabetic neuropathy or atherosclerotic cardiovascular disease. We also investigated the sensor performance in relation to HbA1C values. The proposed method achieved statistically significant detection in all different patient groups. The effect of technical parameters, in particular sensor area size and the time window of optoacoustic signals used in data training were also examined in measurements from phantoms and humans. We discuss how optoacoustic sensors fundamentally solve limitations present in optical sensing and, empowered by machine learning, open a new chapter in non-invasive portable sensing for biomedical applications.

Three-dimensional (3D) histopathology involves the microscopic examination of a specimen, which plays a vital role in studying tissues 3D structures and the signs of diseases. However, acquiring high-quality histological images of a whole organ is extremely time-consuming (e.g., several weeks) and laborious, as the organ has to be sectioned into hundreds or thousands of slices for imaging. Besides, the acquired images are required to undergo a complicated image registration process for 3D reconstruction. Here, by incorporating a recently developed vibratome-assisted block-face imaging technique with deep learning, we developed a pipeline termed HistoTRUST that can rapidly and automatically generate subcellular whole organs virtual hematoxylin and eosin (H&E) stained histological images which can be reconstructed into 3D by simple image stacking (i.e., without registration). The performance and robustness of HistoTRUST have been successfully validated by imaging all vital mouse organs (brain, liver, kidney, heart, lung, and spleen) within 1-3 days depending on the size. The generated 3D dataset has the same color tune as the traditional H&E stained histological images. Therefore, the virtual H&E stained images can be directly analyzed by pathologists. HistoTRUST has a high potential to serve as a new standard in providing 3D histology for research or clinical applications.

Fluorescence-based super-resolution optical microscopy (SRM) techniques allow the visualization of biological structures beyond the diffraction limit of conventional microscopes. Despite its successful adoption in cell biology, the integration of SRM into the field of histology has been deferred due to several obstacles. These include limited imaging throughput, high cost, and the need for complex sample preparation. Additionally, the refractive index heterogeneity and high labeling density of commonly available formalin-fixed paraffin-embedded (FFPE) tissue samples pose major challenges to applying existing super-resolution microscopy methods. Here, we demonstrate that photonic chip-based microscopy alleviates several of these challenges and opens avenues for super-resolution imaging of FFPE tissue sections. By illuminating samples through a high refractive-index waveguide material, the photonic chip-based platform enables ultra-thin optical sectioning via evanescent field excitation, which reduces signal scattering and enhances both the signal-to-noise ratio and the contrast. Furthermore, the photonic chip provides decoupled illumination and collection light paths, allowing for total internal reflection fluorescence (TIRF) imaging over large and scalable fields of view. By exploiting the spatiotemporal signal emission via MUSICAL, a fluorescence fluctuation-based super-resolution microscopy (FF-SRM) algorithm, we demonstrate the versatility of this novel microscopy method in achieving superior contrast super-resolution images of diverse FFPE tissue sections derived from human colon, prostate, and placenta. The photonic chip is compatible with routine histological workflows and allows multimodal analysis such as correlative light-electron microscopy (CLEM), offering a promising tool for the adoption of super-resolution imaging of FFPE sections in both research and clinical settings.

Intraoperative pathology remains constrained by ice crystal artifacts in frozen sections and the high cost of emerging slide-free optical methods. Here, we introduce FLASH-Path, a rapid slide-free technique enabling subcellular-resolution imaging of centimeter-scale tissues in 10 minutes. By replacing mechanical thin sectioning or optical thin-layer excitation with thin-layer ([≤]10 {micro}m) fluorescent labeling using commercially available probes, FLASH-Path achieves artifact-free visualization of diverse tissues (e.g., fat, lymph nodes) incompatible with conventional frozen sections. The method integrates with retrofitted clinical fluorescence microscopes or manual observation, ensuring adaptability across resource settings. Fluorescence images are computationally transformed into H&E-like histopathology without ice crystal artifacts or hidden risks from generated images. In colorectal cancer validation, FLASH-Path outperformed frozen sections in speed and imaging area. FLASH-Path enhances clinical accessibility, image traceability, and cost-effectiveness, providing new opportunities for the clinical application and dissemination of slide-free pathology.

Lung cancer is one of the leading causes of cancer death worldwide. The diagnosis of lung cancer based on the analysis of formalin-fixed and paraffin-embedded (FFPE) tissues is laborious and time-consuming, failing to guide surgeons intraoperatively. Here we proposed a rapid histological imaging method, termed microscopy with ultraviolet single-plane illumination (MUSI), to enable rapid ex-or in-vivo imaging of fresh and unprocessed tissues in a label-free and non-destructive manner. The MUSI system allows surgical specimens with large irregular surfaces to be screened at a speed of 0.5 mm2/s with a subcellular resolution, which is sufficient to provide immediate feedback to surgeons and pathologists for intraoperative decision-making. We demonstrate that MUSI can differentiate between different subtypes of human lung adenocarcinomas, revealing diagnostically important features that are comparable to the gold standard FFPE histology. As an assistive imaging platform, MUSI could facilitate the development of precise image-guided surgery and revolutionize the current practice in surgical pathology.

Glaucoma is the leading cause of irreversible blindness, driven by the progressive loss of retinal ganglion cells (RGCs). Stem cell-derived RGC transplantation could revolutionize glaucoma treatment, but the inner limiting membrane (ILM) remains a major obstacle by hindering cell migration into the retina. Interestingly, the ILM represents a double-edged sword for RGC engraftment: on the one hand, it greatly hinders cell migration, whereas on the other hand, its presence during retinal development is necessary for neuronal migration and retinal lamination. As an alternative to current invasive and harmful strategies to disrupt the ILM, we introduce ILM photodisruption, a minimally invasive biophotonic method that can manipulate the integrity of the ILM with unprecedented precision. In this study, we have finetuned the technology in bovine and human organotypic retinal explants to create templated ILM pores, creating entryways for donor RGCs to enter the retina while preserving most of the membrane to confer guidance cues for their engraftment. Applying this technology, we were able to promote donor RGC survival, enhance cell spreading and facilitate integration into the retina. Overall, our findings demonstrate that ILM photodisruption effectively addresses a key barrier in RGC replacement, paving the way for advancing retinal regeneration toward clinical application.

Precision medicine, based on spatial biology, is crucial for accurately diagnosing cancer and predicting drug responses. Here, we introduce the Spectral Imaging-based Single-Step Multiplex Immunofluorescence (SISS-mIF) technique, utilizing hyperspectral imaging to capture fluorescence spectra simultaneously. This approach optimizes tissue autofluorescence spectra for each image automatically, allowing the use of fluorescent direct-labeled antibodies for multicolor staining in a single step. Unlike conventional methods, the images are generated as standardized intensity independent of capture conditions, enabling consistent comparisons under different imaging conditions. This technique allows the detection of CD3, CD5, and CD7 in T-cell lymphoma on a single slide. The use of fluorescent direct-labeled antibodies enables triple staining of CD3, CD5, and CD7 without cross-reactivity, maintaining the same intensity as single stains. Furthermore, we developed a joint Non-Negative Matrix Factorization-based Spatial Clustering Analysis (jNMF-SCA) with a modified spectral unmixing system, highlighting its potential as a supportive diagnostic tool for T-cell lymphoma.

Detection noise significantly degrades the quality of structured illumination microscopy (SIM) images, especially under low-light conditions. Although supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts, the requirement of a large amount of high-quality training data severely limits their applications. Here we developed a pixel-realignment-based self-supervised denoising framework for SIM (PRS-SIM) that trains an SIM image denoiser with only noisy data and substantially removes the reconstruction artifacts. We demonstrated that PRS-SIM generates artifact-free images with 10-fold less fluorescence than ordinary imaging conditions while achieving comparable super-resolution capability to the ground truth (GT). Moreover, the proposed method is compatible with multiple SIM modalities such as total internal reflective fluorescence SIM (TIRF-SIM), three-dimensional SIM (3D-SIM), lattice light-sheet SIM (LLS-SIM), and non-linear SIM (NL-SIM). With PRS-SIM, we achieved long-term super-resolution live-cell imaging of various bioprocesses, revealing the clustered distribution of clathrin coated pits and detailed interaction dynamics of multiple organelles and the cytoskeleton.

Communication between cells is largely orchestrated by proteins on the cell surface, which allow information transfer across the cell membrane. Super-resolution and single-molecule visualization of these proteins can be achieved by genetically grafting HTP (HaloTag Protein) into the protein of interest followed by brief incubation of cells with a dye-HTL (dye-linked HaloTag Ligand). This approach allows for use of cutting-edge fluorophores optimized for specific optical techniques or a cell-impermeable dye-HTL to selectively label surface proteins without labeling intracellular copies. However, these two goals often conflict, as many high-performing dyes exhibit membrane permeability. Traditional methods to eliminate cell permeability face synthetic bottlenecks and risk altering photophysical properties. Here we report that dye-HTL reagents can be made cell-impermeable by inserting a charged sulfonate directly into the HTL, leaving the dye moiety unperturbed. This simple, one-step method requires no purification and is compatible with both the original HTL and second-generation HTL.2, the latter offering accelerated labeling. We validate such compounds, termed dye-SHTL ( dye shuttle) conjugates, in live cells via widefield microscopy, demonstrating exclusive membrane staining of extracellular HTP fusion proteins. In transduced primary hippocampal neurons, we label mGluR2, a neuromodulatory G protein-coupled receptor (GPCR), with dyes optimized for stimulated emission by depletion (STED) super-resolution microscopy, allowing unprecedented accuracy in distinguishing surface and receptors from those in internal compartments of the presynaptic terminal, important in neural communication. This approach offers broad utility for surface-specific protein labelling.

Fast live-cell hyperspectral imaging at large field-of-views (FOVs) and high cell confluency remains challenging in vibrational microscopy due to the need for point-by-point focal excitation scanning. Imaging at high cell confluency and large FOVs is important, respectively, for proper cell function and statistical significance of measurements. Here, we introduce phase-shifting mid-infrared optothermal microscopy (PSOM) which interprets molecular-vibrational information as the optical path difference (OPD) induced by mid-infrared absorption and is capable of taking snapshot vibrational images over broad mid-infrared excitation areas at high live-cell confluency. By means of phase-shifting, PSOM suppresses noise to a quarter of current optothermal microscopy modalities to allow capturing live-cell vibrational images at FOVs up to 50 times larger than state-of-the-art. Additionally, it reduces illumination power flux density (PFD) down to 5 orders of magnitude lower than conventional vibrational microscopy--thus, considerably decreasing the possibility of cell photodamage.

The coronavirus disease 2019 (COVID-19) has impacted health globally. Cumulative evidence points to long-term effects of COVID-19 such as cardiovascular and cognitive disorders diagnosed in patients even after the recovery period. In particular, micrometer-sized blood clots and hyperactivated platelets have been identified as potential indicators of long COVID. Here we resolve individual microclot structures in platelet-rich plasma of donors with different subphenotypes of COVID-19 in a label-free manner, using 3D digital holo-tomographic microscopy (DHTM). Based on 3D refractive index (RI) tomograms, the size, dry mass, and prevalence of microclot composites were quantified and then parametrically differentiated from fibrin-rich microclots and platelet aggregates in the plasma of COVID-19 donors. Importantly, fewer microclots and platelet aggregates were detected in the plasma of healthy controls when compared to COVID-19 donors. Our work highlights the utility of integrating DHTM in clinical settings that may allow the detection of individuals at risk of developing microvascular thrombotic disorders and for monitoring the efficiency of prescribed treatments by screening plasma samples.

Adipose tissue plasticity and functional heterogeneity play a central role in maintaining energy homeostasis, and their malfunction leads to metabolic disorders such as obesity, diabetes, and cardiometabolic disease. Rapid, single-cell metabolic imaging of intact fat tissue not only extends our understanding of metabolic dynamics and heterogeneity but also holds great potential as a tool for clinical diagnosis. However, the use of exogenous labels and dyes in conventional optical microscopy results in tissue deformation and requires time-consuming tissue preparation. Here, we demonstrated single-cell imaging of metabolic changes and heterogeneity in freshly excised adipose tissues that can distinguish tissue types without the need for exogenous labels using bond-specific, non-destructive, mid-infrared optoacoustic microscopy (MiROM) that allows preserving the native tissue architecture with minimal sample preparation time. Further leveraging MiROM, we monitored intracellular molecular and morphological changes during postnatal remodeling of adipose tissue when metabolic characteristics of adipocytes undergo a transient drastic change. Additionally, we developed an AI-based quantitative spatial tissue analysis tool (Q-SAT) to predict the spatial distribution of white fat- and brown fat-like features, providing a robust digital scoring method for adipose tissue phenotypic assessment. Collectively, we implemented MiROM as an enabling technology to provide fast, label-free metabolic imaging of unprocessed adipose tissue, opening a new perspective for understanding and characterizing the morpho-functional dynamics of adipose tissue remodeling.

Single Molecule Orientation and Localization Microscopy (SMOLM) aims at simultaneously measuring the position and orientation of single molecules, generating orientation-encoded super-resolved images by estimating both their 3D mean orientation and the extent of their angular fluctuations (wobble). Most existing SMOLM approaches rely on the engineering of single molecules point spread functions, which requires complex optical setups and long computational times that can be an obstacle in dense cellular environments with high detection density and challenging imaging conditions. In this work, we propose a simpler and effective method named 4polar3D, based on the estimation of single molecule intensities projected onto four polarized channels with controlled numerical apertures. This strategy enables 3D orientation measurements of single molecules in addition to their 2D localization in a fast processing step. We demonstrate that 4polar3D can resolve nanoscale molecular organization in whole cells crowded structures, uncovering 3D-oriented actin filament networks in densely packed lamellipodia and podosomes.

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Current clinical imaging modalities provide resolution adequate for diagnosis but are unable to provide detail of structural changes in the heart, across length-scales, necessary for understanding underlying pathophysiology of disease. Hierarchical Phase-Contrast Tomography (HiP-CT), using new (4th) generation synchrotron sources, potentially overcomes this limitation, allowing micron resolution imaging of intact adult organs with unprecedented detail. In this proof of principle study (n=2), we show the utility of HiP-CT to image whole adult human hearts ex-vivo: one control without known cardiac disease and one with multiple known cardiopulmonary pathologies. The resulting multiscale imaging was able to demonstrate exemplars of anatomy in each cardiac segment along with novel findings in the cardiac conduction system, from gross (20 um/voxel) to cellular scale (2.2 um/voxel), non-destructively, thereby bridging the gap between macroscopic and microscopic investigations. We propose that the technique represents a significant step in virtual autopsy methods for studying structural heart disease, facilitating research into abnormalities across scales and age-groups. It opens up possibilities for understanding and treating disease; and provides a cardiac blueprint with potential for in-silico simulation, device design, virtual surgical training, and bioengineered heart in the future.

Laser Speckle Contrast Imaging (LSCI) is a non-contact, label-free optical technique widely used in biomedical research and clinical applications. It enables real-time visualization and quantification of microvascular blood flow by analyzing the temporal fluctuations of laser speckles induced by moving red blood cells. However, conventional LSCI uses visible or near-infrared illumination, which--during prolonged exposure (e.g., >1{square}hr)--can induce sublethal neural stress and cause signal drift, compromising physiological relevance and raising ethical concerns. To mitigate these limitations, we introduce TunLSCI--a TransUNet-based recovery network designed to reconstruct high-fidelity mouse cerebral blood flow (CBF) indices from ultra-low-illumination LSCI. We train our network on paired ultra-low-illumination (1.27 {micro}W/mm2) and conventional LSCI data ([~]200 {micro}W/mm2 illumination, the latter as reference), and demonstrate that it outperforms the conventional standard analytical LSCI processing pipeline based on stLASCA, particularly in reconstructing fine vasculature from few frames, suppressing speckle noise, and maintaining robustness against exposure variations. We validate that the proposed TunLSCI reduces illumination power density by [~]157-fold compared with conventional stLASCA, well below the safety threshold for cortical exposure in mice and markedly improves stability during a 2-hour continuous mouse CBF monitoring. Our method significantly minimizes the phototoxic burden of LSCI while preserving spatiotemporal fidelity and quantitative accuracy, thus enabling longitudinal, high-biosafety cerebral perfusion tracking in vivo over multi-hours.

The eye offers a unique non-invasive window for accessing single-cell level structures and functions of the central nervous system (CNS) throughout the retina. However, strong and space-varying ocular aberrations, along with limited volume rates, challenge large-scale cellular imaging in living eyes and stymie the full potential of possible biological and pathological studies in retina. Here, we present plenoptic illumination scanning laser ophthalmoscopy (PI-SLO), a 3D fluorescent retinal imaging modality that enables high-speed, widefield, volumetric single-cell imaging with low phototoxicity. By capturing multiple angular images of fluorescence signals from the entire volume, PI-SLO enables digital aberration correction and 3D imaging across a >20{o} FOV with >23 Hz volume rate. We leverage this structural and functional imaging modality to investigate three key aspects of CNS physiology through the living mouse retina, including: microglial process dynamics, vascular perfusion, and light evoked calcium fluxes in inner retinal neurons. PI-SLO is a versatile non-invasive platform for in vivo investigation of retinal and CNS physiology at the cellular level.

Cancer heterogeneity and its transformation with time propels treatment resistance and confounds patient outcomes. The inability to monitor in vivo the low abundance, heterocellular phenotypes that resist treatment and ultimately lead to patient death limits the ability to design precision therapies. Here we overcome limitations in multiplexed fluorescence phenotyping to introduce real-time, cellular resolution visualization of tumor heterogeneity in vivo. This method was performed to simultaneously map for the first time 5 individual biomarkers of stemness, proliferation, metabolism, leukocytes and angiogenesis deep within the peritoneal cavities of micrometastatic cancer mouse models at 17 frames per second (fps). The newly developed imaging system revealed distinct cancer cell phenotype-immune cell spatial correlations and clearly visualized the dynamic spatial response of resistant cancer cell niches following treatment. Furthermore, wide-field datasets were generated to facilitate derivation of a mathematical framework for quantifying biomarker spatial variation and thereby overcoming the area restrictions of conventional tumor biopsy. These results pave the way for real-time identification of cancer cell phenotypes in a clinical setting, on which optimized treatment regimens can be based for personalized treatment and precision therapy e.g., tumor margin determination during surgical resection. Additionally, this modality can be used to obtain more fundamental insights into tumor heterogeneity and how treatments affect the molecular and cellular responses of patient-specific disease.