Light: Science & 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.

Histopathology based on formalin-fixed and paraffin-embedded tissues remains the gold standard for surgical margin assessment (SMA). However, routine pathological practice is lengthy and laborious, failing to provide immediate feedback to surgeons and pathologists for intraoperative decision-making. In this report, we propose a cost-effective and easy-to-use histological imaging method with speckle illumination microscopy (i.e., HiLo). HiLo can achieve rapid and non-destructive imaging of large and fluorescently-labelled resection tissues at an acquisition speed of 5 cm2/min with 1.3-m lateral resolution and 5.8-m axial resolution, demonstrating a great potential as an intraoperative SMA tool that can be used by surgeons and pathologists to detect residual tumors at surgical margins. It is experimentally validated that HiLo enables rapid diagnosis of different subtypes of human lung adenocarcinoma and hepatocellular carcinoma, producing images with remarkably recognizable cellular features comparable to the gold-standard histology. This work will facilitate the clinical translations of HiLo microscopy to improve the current standard-of-care.

We present a compact pump-and-probe mid-infrared Optothermal Spectrometer (OTHES) equipped with Spatial Probing and Autocorrection (SPAC) optimized for robust intravital application in humans. SPAC-OTHES facilitates alignment stability and spectral comparability across different measurement sessions involving different skin types. Contrary to state-of-the-art, SPAC-OTHES uses camera-based beam detection and an auto-calibration mechanism that enables ca. 73% better spectral reproducibility in intravital measurements in human volunteers than non-calibrated readouts. Moreover, SPAC-OTHES has the potential to lower the glucose quantification error, as demonstrated here in artificial skin phantoms, where an improvement of 52% compared to conventional diode-based detection was observed. The compactness of OTHES, combined with reliable SPAC-readout, has the potential to accelerate commercialization and broad application of biosensors based on mid-infrared spectroscopy.

Accurate characterization of the microscopic point spread function (PSF) is crucial for achieving high-performance localization microscopy (LM). Traditionally, LM assumes a spatially-invariant PSF to simplify the modeling of the imaging system. However, for large fields of view (FOV) imaging, it becomes important to account for the spatially variant nature of the PSF. In this work, we propose an accurate and fast principal component analysis (PCA)-based field-dependent 3D PSF generator (PPG3D) and localizer for LM. Through simulations and experimental 3D single molecule localization microscopy (SMLM), we demonstrate the effectiveness of PPG3D, enabling super-resolution imaging of mitochondria and microtubules with high fidelity over a large FOV. A comparison of PPG3D with three other shift-invariant and shift-variant PSF generators for 3D LM reveals a three-fold improvement in accuracy and an operation speed approximately one hundred times faster. Given its user-friendliness and conciseness, we believe that PPG3D holds great potential for widespread application in SMLM and other imaging modalities.

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.

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.

The large-scale fluorescence microscopy has enabled the observation of dynamic physiological activities at the single cellular level across the mouse cortex, such as distributed neuronal population representations. However, video-rate high-resolution microscopy at sophisticated biological surfaces in nature keeps a challenging task for the tradeoff between the speed, resolution, and field of view. Here we propose Spinning Disk Multifocal Microscopy (SDiM) for arbitrarily shaped surfaces, which enables imaging at centimeter field-of-view, micrometer resolution and up to 30 frames per second across the depth range of 450 {micro}m. We apply this technique in various microscopic systems, including customized macroscopic systems and the Real-time Ultra-large-Scale imaging at High resolution macroscopy (RUSH), in both the reflective mode and the fluorescence mode, and in the study of cortex-wide single-neuron imaging and immune cell tracking. SDiM provides an opportunity for studying the cortex-wide multi-scale cellular interactions in biological tissues.

Deep neural networks have led to significant advancements in microscopy image generation and analysis. In single-molecule localization based super-resolution microscopy, neural networks are capable of predicting fluorophore positions from high-density emitter data, thus reducing acquisition time, and increasing imaging throughput. However, neural network-based solutions in localization microscopy require intensive human intervention and computation expertise to address the compromise between model performance and its generalization. For example, researchers manually tune parameters to generate training images that are similar to their experimental data; thus, for every change in the experimental conditions, a new training set should be manually tuned, and a new model should be trained. Here, we introduce AutoDS and AutoDS3D, two software programs for reconstruction of single-molecule super-resolution microscopy data that are based on Deep-STORM and DeepSTORM3D, that significantly reduce human intervention from the analysis process by automatically extracting the experimental parameters from the imaging raw data. In the 2D case, AutoDS selects the optimal model for the analysis out of a set of pre-trained models, hence, completely removing user supervision from the process. In the 3D case, we improve the computation efficiency of DeepSTORM3D and integrate the lengthy workflow into a graphic user interface that enables image reconstruction with a single click. Ultimately, we demonstrate superior performance of both pipelines compared to Deep-STORM and DeepSTORM3D for single-molecule imaging data of complex biological samples, while significantly reducing the manual labor and computation time.

High-resolution imaging under physiological conditions is essential for studying biological mechanisms and disease processes. However, achieving this goal remains challenging due to optical aberrations and scattering from heterogeneous tissue structures, compounded by motion artifacts from awake animals. In this study, we developed a rapid and accurate adaptive optics system called multiplexing digital focus sensing and shaping (MD-FSS) for deep-tissue multiphoton microscopy. Under two-photon excitation, MD-FSS precisely measures the aberrated point spread function in approximately 0.1 s per measurement, effectively compensating for both aberrations and scattering to achieve subcellular resolution in deep tissue. Using MD-FSS integrated with two-photon microscopy, we achieved high-resolution brain imaging through thinned or optically cleared skull windows, two near noninvasive methods to access mouse brain, reaching depths up to 600 m below the pia in awake behaving mice. Our findings revealed significant differences in microglial functional states and microvascular circulation dynamics between awake and anesthetized conditions, highlighting the importance of studying brain function in awake mice through noninvasive methods. We captured functional imaging of fine neuronal structures at subcellular level in both somatosensory and visual cortices. Additionally, we demonstrated high-resolution imaging of microvascular structures and neurovascular coupling across multiple cortical regions and depths in the awake brain. Our work shows that MD-FSS robustly corrects tissue-induced aberrations and scattering through rapid PSF measurements, enabling near-noninvasive, high-resolution imaging in awake, behaving mice.

Histologically stained tissue sections are considered the gold standard for studying microscopic anatomy and diagnosing disease in clinical practice. However, the processes of sectioning and staining are laborious, and the overall method relies on two-dimensional (2D) analysis. In contrast, X-ray-based virtual histology offers the advantage of virtual sectioning while retaining the full three-dimensional (3D) volumetric representation of the tissue. Nevertheless, its grayscale nature has prevented it to be readily utilized by pathologists who are accustomed to conventional histological stains. In this work, we present a histology-guided enhancement platform that can integrate the 3D information provided by synchrotron radiation phase-contrast microCT with the rich visual features characteristic of histological stains. We introduce a multi-stage microCT-histology co-registration method combined with a virtual staining deep neural network and demonstrate successful virtual histological staining of microCT human and mouse lung tissue that closely resembles standard histology. We evaluate our strategy on multiple histological stains and apply it to identify 3D collagen-based remodeling of pulmonary arteries in patients with pulmonary hypertension. Overall, this innovative enhancement pipeline has the potential to aid in the incorporation of microCT into clinical practice, and advance non-destructive 3D pathology for improved diagnostic efficiency and accuracy.

Multimodal microscopy combining various imaging approaches can provide complementary information about tissue in a single imaging session. Here, we demonstrate a multimodal approach combining three-photon microscopy (3PM) and spectral-domain optical coherence microscopy (SD-OCM). We show that an optical parametric chirped-pulse amplification (OPCPA) laser source, which is the standard source for three-photon fluorescence excitation and third harmonic generation (THG), can be used for simultaneous OCM, 3-photon (3P) fluorescence and THG imaging. We validated the system performance in deep mouse brains in vivo with an OPCPA source operating at 1620 nm center wavelength. We visualized small structures such as myelinated axons, neurons, and large fiber tracts in white matter with high spatial resolution non-invasively using linear and nonlinear contrast at >1 mm depth in intact adult mouse brain. Our results showed that simultaneous OCM and 3PM at the long wavelength window can be conveniently combined for deep tissue imaging in vivo.

Super-resolution microscopy allows for stunning images with a resolution well beyond the optical diffraction limit, but the imaging techniques are demanding in terms of instrumentation and software. Using scientific-grade cameras, solid-state lasers and top-shelf microscopy objective lenses drives the price and complexity of the system, limiting its use to well-funded institutions. However, by harnessing recent developments in CMOS image sensor technology and low-cost illumination strategies, super-resolution microscopy can be made available to the mass-markets for a fraction of the price. Here, we present a 3D printed, self-contained super-resolution microscope with a price tag below 1000 $ including the objective and a cellphone. The system relies on a cellphone to both acquire and process images as well as control the hardware, and a photonic-chip enabled illumination. The system exhibits 100nm optical resolution using single-molecule localization microscopy and can provide live super-resolution imaging using light intensity fluctuation methods. Furthermore, due to its compactness, we demonstrate its potential use inside bench-top incubators and high biological safety level environments imaging SARS-CoV-2 viroids. By the development of low-cost instrumentation and by sharing the designs and manuals, the stage for democratizing super-resolution imaging is set.

Histological haematoxylin and eosin-stained (H&E) tissue sections are used as the gold standard for pathologic detection of cancer, tumour margin detection, and disease diagnosis1. Producing H&E sections, however, is invasive and time-consuming. Non-invasive optical imaging modalities, such as optical coherence tomography (OCT), permit label-free, micron-scale 3D imaging of biological tissue microstructure with significant depth (up to 1mm) and large fields-of-view2, but are difficult to interpret and correlate with clinical ground truth without specialized training3. Here we introduce the concept of a virtual biopsy, using generative neural networks to synthesize virtual H&E sections from OCT images. To do so we have developed a novel technique, "optical barcoding", which has allowed us to repeatedly extract the 2D OCT slice from a 3D OCT volume that corresponds to a given H&E tissue section, with very high alignment precision down to 25 microns. Using 1,005 prospectively collected human skin sections from Mohs surgery operations of 71 patients, we constructed the largest dataset of H&E images and their corresponding precisely aligned OCT images, and trained a conditional generative adversarial network4 on these image pairs. Our results demonstrate the ability to use OCT images to generate high-fidelity virtual H&E sections and entire 3D H&E volumes. Applying this trained neural network to in vivo OCT images should enable physicians to readily incorporate OCT imaging into their clinical practice, reducing the number of unnecessary biopsy procedures.

Wide-field fluorescence microscopy through axial scanning provides a simple way for volumetric imaging of cellular and intracellular activities, but the optical transfer function (OTF) of wide-field microscopy suffers from axial frequency deficiencies, leading to strong interference from out-of-focus fluorescence signals and reduced imaging quality. Richardson-Lucy (RL) deconvolution and its variants are commonly employed to reduce inter-plane signal interference of wide-field microscopy. However, these methods are still affected by the "missing cone" issue inherent in the OTF, compromising both the axial resolution and optical sectioning capability. Existing deep learning methods could realize high-fidelity 3D image stack restoration, but relying on high-quality paired datasets or specific assumptions about sample distributions. Here, we propose a novel method named physics-informed ellipsoidal coordinate encoding implicit neural representation (PIECE-INR), to tackle the challenges of background signal interference and resolution loss in axial scanning image stacks using the wide-field microscopy. In PIECE-INR, we integrate the wide-field fluorescence imaging model with the self-supervised INR network for high-fidelity reconstruction of 3D fluorescence data without the need of additional ground truth data for training. We further design a novel ellipsoidal coordinate encoding based on the systems OTF constraints and incorporate implicit priors derived from the physical model as the loss function into the reconstruction process. Our approach enables block-wise reconstruction of large-scale images by using localized physical information. We demonstrate state-of-the-art performance of our PIECE-INR method in volumetric imaging of live HeLa cells, large-volume C. elegans whole-embryo, and mitochondrial dynamics.

Despite the easier use of multiple tissue clearing techniques in recent years, poor access to adequate light-sheet fluorescence microscopy remains a major obstacle for biomedical end users. Here, we propose a solution by developing descSPIM (desktop-equipped SPIM for cleared specimens) as a low-cost ($20,000-50,000), low-expertise (one-day installation by a non-expert), yet practically substantial do-it-yourself light-sheet microscopy. Academically open-sourced (https://github.com/dbsb-juntendo/descSPIM), descSPIM allows routine three-dimensional imaging of cleared samples in minutes.

High-resolution optical imaging of deep tissue in-situ such as the living brain is fundamentally challenging because of the aberration and scattering of light. In this work, we develop an innovative adaptive optics three-photon microscope based on direct focus sensing and shaping that can accurately measure and effectively compensate for both low- and high-order specimen-induced aberrations and recover near-diffraction-limited performance at depth. A conjugate adaptive optics configuration with remote focusing enables in vivo imaging of fine neuronal structures in the mouse cortex through the intact skull up to a depth of 750 {micro}m below pia, making high-resolution microscopy in cortex near non-invasive. Functional calcium imaging with high sensitivity and accuracy, and high-precision laser-mediated microsurgery through the intact skull were demonstrated. Moreover, we also achieved in vivo high-resolution imaging of the deep cortex and subcortical hippocampus up to 1.1 mm below pia within the intact brain.

In vivo microscopy (IVM) has shown great promise to improve early detection of epithelial precancer, but it suffers from fundamental trade-offs that limit the resolution, field-of-view (FOV) and depth-of-field (DOF). Here, we present PrecisionView, a compact, deep-learning-enabled endomicroscope that breaks these constrains and achieves 20 mm2 FOV and 500 {micro}m DOF with 4 {micro}m resolution, representing approximately 5x increase in FOV and 8x larger DOF compared to conventional IVM with similar resolution. PrecisionView integrates a deep-learning optimized phase mask and real-time reconstruction, enabling rapid in vivo assessment of two key hallmarks of cancer: epithelial cell nuclear morphology and subsurface microvasculature through fluorescence and reflectance imaging. By imaging oral cavity of healthy volunteers and cervical specimens with precancerous lesions, PrecisionView generates large-scale (1-3 cm2) co-registered maps of cellular and vascular structures, revealing distinct microscopic patterns associated with anatomic structures and precancerous lesions. Our results suggest the potential of this computational endomicroscope to address the unmet need for early cancer detection at the point-of-care.

Multimodal 3D imaging has emerged as a powerful approach for investigating complex tissue architecture in pathological specimens. Techniques such as propagation-based phase-contrast computed tomography (PCT), light-sheet microscopy (LSM), and three-photon microscopy (3PM) provide complementary information on unlabeled tissue morphology based on distinct intrinsic contrast mechanisms. However, integrating these heterogeneous datasets into a unified spatial framework remains challenging due to differences in imaging geometry, spatial resolution, and modality-specific distortions. In this study, we present a registration pipeline for spatially aligning volumetric datasets acquired with PCT, LSM, and 3PM from formalin-fixed paraffin-embedded (FFPE) human colon cancer specimens. Biopsies from theses specimens were optically cleared and imaged sequentially using the three high-resolution modalities. To compensate for large positional differences between acquisitions, a three-stage cascade registration strategy was developed, consisting of coarse global alignment on down-sampled data, followed by rigid refinement at intermediate resolution. Mutual information was used as the similarity metric to ensure robust multimodal registration. The resulting framework enables the generation of spatially aligned multi-channel 3D datasets that combine structural information from X-ray phase-contrast imaging with complementary optical contrast signals. Beyond registration, we demonstrate that the fused six-dimensional feature space can be further exploited for unsupervised tissue characterization using a Gaussian Mixture Model (GMM), enabling data-driven identification of spatially coherent tissue regions without manual annotation. Qualitative evaluation confirms consistent alignment of major anatomical structures across modalities, while the unsupervised clustering reveals biologically meaningful patterns despite modality-specific noise and resolution differences. While further optimization and validation across larger datasets will enhance its computational efficiency and breadth of application, the approach already demonstrates strong potential for comprehensive tissue analysis and enables scalable, label-free 3D characterization of colon cancer tissue architecture.

Bessel Beams (BBs) and BB lattices are structured-light excitation profiles frequently applied in material processing, nonlinear spectroscopy and in many fluorescence microscopy methods such as Light Sheet Microscopy (LSM). In LSM, BBs and BB-lattices offer wider excitation profiles, higher acquisition rate, enhanced resolution, and improved signal-to-noise ratio, while reducing the overall phototoxicity. However, this performance improvement typically comes at the cost of layout complexity and spatial constraints, originating from the optical arrangement required for obtaining BB features and for multiplexing the BB in a lattice of beamlets. Here, we introduce a novel method for encoding in a single flat element all the optical operations required to generate a BB lattice, including those of the excitation objective. We assessed the effective capabilities of this approach, using Meta-Surface (MS) technology to fabricate the corresponding flat optical element and to characterize its optical figures. Finally, we demonstrated its actual application in LSM, recording neuronal activity at cellular resolution in the zebrafish larval brain using fluorescence based neuronal activity reporters. In perspective, this approach, applied here for LSM, prompts a step forward in the BB versatility and in the BB application scenarios.

AbstractSuper-resolution structured illumination microscope (SR-SIM) has been established as a powerful tool for visualizing subcellular dynamics and studying organelle interactions in live cells. However, the interfering Gaussian beams result in a limited and nonuniform field of view (FOV) which hinders its application for large whole-cell dynamics and pathological sample imaging. Here, we proposed a joint spatial-temporal light modulation (JSTLM) method to reshape the excitation light field into flat-field structured illumination without disturbing the interfering fringes. Our flat-field structured illumination microscopy (flat-field SIM) improves the uniformity across the whole FOV significantly, hence enabling SR image stitching. Skeleton dynamics and vesicle transportation in and between whole cells were visualized by flat-field SIM. With the stitching of multi-FOV flat-field SIM images, millimeter-sized SR images can be obtained which provides the possibility for cell heterogeneity studies and pathological diagnoses. The JSTLM method can be further incorporated with regions of interest to reduce unnecessary photodamage to live cells during multicolor imaging. ContributionsY.L. and X.H.C. conceived and designed the idea. Y.L., S.M.L., X.J., and G.W. built the SIM setup. Y.L. performed the data acquisitions. Y.L. and X.H.C. conducted the optical wave simulation. J.Z. prepared the cell samples. Y.T. and L.B.W. performed the image analyses. Y.L. prepared the illustrations. X.H.C. and J.G. supervised the project. Y.L. and H.L. wrote the manuscript.