Light: Science & Applications

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.

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.

Optical fluorescence microscopy enables visualization of biological structures and dynamics. However, the intrinsic diffraction limit, especially axially, and depth-related scattering noise compromise the image resolution and fidelity. Computational 3D deconvolution is a promising approach for mitigating these issues, yet its execution is hindered by inaccurate and cumbersome theoretical modeling or experimental measurement of 3D point spread function (PSF), as well as ineffective 3D noise regularization. Furthermore, in the 3D super-resolution regime, there remains a lack of standardized tools for evaluating 3D super-resolution fidelity. Here, we present the 3D adaptive deconvolution and evaluation (3D-ADE) toolkit, which comprises 3D-Ada deconvolution with physics-oriented automatic 3D-PSF calibration, and 3D-SQUIRREL for 3D super-resolution quality assessment. It effectively resolves noise instability, eliminates the need for 3D-PSF calibration, and reliably assesses the fidelity of 3D resolution extension via deconvolution, physical, and deep-learning-based methods. Accessible via multiple software platforms, 3D-ADE enhances the versatility of 3D deconvolution and fills the gap in 3D super-resolution evaluation tools, and thereby advances volumetric fluorescence imaging applications.

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.

With increasing interest in studying biological systems across spatial scales--from centimetres down to nanometres--histology continues to be the gold standard for tissue imaging at cellular resolution, providing an essential bridge between macroscopic and nanoscopic analysis. However, its inherently destructive and two-dimensional nature limits its ability to capture the full three-dimensional complexity of tissue architecture. Here we show that phase-contrast X-ray microscopy can enable three-dimensional virtual histology with subcellular resolution. This technique provides direct quantification of electron density without restrictive assumptions, allowing for direct characterisation of cellular nuclei in a standard laboratory setting. By combining high spatial resolution and soft tissue contrast, with automated segmentation of cell nuclei, we demonstrated virtual H&E staining using machine learning-based style transfer, yielding volumetric datasets compatible with existing histopathological analysis tools. Furthermore, by integrating electron density and the sensitivity to nanometric features of the dark field contrast channel, we achieve stain-free, high-content imaging capable of distinguishing nuclei and extracellular matrix.

Obtaining structural information from the enteric nervous system (ENS) within intact intestinal tissue requires microscopy systems capable of imaging through multiple tissue layers and during ongoing physiological motion. Tissue opacity, three-dimensional geometry, and spontaneous contractions strongly constrain volumetric imaging, limiting the applicability of most conventional linear optical techniques to imaging in either dissected, stretched or pharmacologically suppressed tissues. We apply Spectro-temporal Laser Imaging by Diffracted Excitation (SLIDE) microscopy, a diffraction-based scanning approach enabling fast volumetric two-photon imaging, to record the ENS in an intact ex vivo intestinal preparation from a transgenic mouse line expressing the red fluorescent protein TdTomato in peripheral and enteric neurons and glia. We achieved fast volumetric imaging during spontaneous contractions, capable of resolving micrometer-scale displacements in three dimensions, without inducing observable photodamage or compromising tissue viability over the experimental timescale. This work establishes 4D-SLIDE microscopy as a robust experimental framework for visualizing enteric neural structures within their native three-dimensional context during physiological motion, with direct relevance for conditions involving altered intestinal mechanics.

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.

Quantitative phase microscopy (QPM) enables label-free imaging of structure and dynamics in biological and physical systems, yet achieving high-speed three-dimensional (3D) QPM with strong optical sectioning remains a central challenge. Here, we introduce a single-shot reflection-mode temporal focusing QPM (TF-QPM) that provides sub-micron optical sectioning without needs of any mechanical scanning or multiplexed acquisitions. By extending temporal focusing beyond its conventional use in multiphoton fluorescence microscopy, TF-QPM enables diffraction-limited label-free phase-sensitive volumetric imaging with 402 nm lateral and 920 nm axial resolution, markedly reduced speckle noise, and depth-resolved imaging at 3,709 Hz frame rate --an order of magnitude faster than most existing techniques and currently only limited by the camera speed. The resulting spatiotemporal phase sensitivity enables precise 3D tracking of particle motion and quantitative characterization of fast dynamics in complex and anisotropic media. For tissue imaging applications, TF-QPM achieves histology-level resolution in intact samples and supports pixel-level virtual staining, providing a rapid, label-free alternative to conventional sectioning-based workflows. Together, these results establish TF-QPM as a scanless, high-speed platform for rapid, label-free volumetric imaging across both basic research and translational applications.

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.

The resolving power of optical microscopy is fundamentally constrained by the diffraction of light, limiting our ability to visualize subcellular structures. Computational methods, particularly deconvolution, can restore blurred images but critically depend on an accurate point spread function (PSF), whose estimation is often impractical and error-prone, leading to artifacts. Here, we introduce Nonlinear Fourier Re-weighting (NFR), a rapid algorithm that operates without any prior knowledge of the imaging system, achieving deconvolution-like effects through a single logarithmic mapping of the images Fourier spectrum. This non-iterative process re-balances spatial frequency components to computationally reverse the effects of optical blurring. We demonstrate that NFR robustly enhances resolution beyond the Sparrow limit and recovers authentic structural details. NFR excels where traditional methods fail, remaining effective in the presence of severe optical aberrations and high noise. Furthermore, NFR synergistically improves the output of super-resolution modalities like structured illumination microscopy (SIM), and its near-instantaneous processing enables real-time enhancement of dynamic biological processes, such as in vivo multi-photon microscopic imaging deep within scattering tissue. By decoupling high-fidelity image restoration from system modeling, NFR offers a powerful, accessible, and universally applicable tool for improving image quality across diverse microscopic techniques, facilitating the analysis of large datasets and the discovery of previously obscured biological phenomena.

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.