Light: Science & Applications

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.

Fluorescence lifetime imaging ophthalmoscopy permits in vivo assessment of retinal metabolism but has remained limited by insufficient cellular resolution in the human eye. Here we present adaptive optics-enhanced fluorescence lifetime imaging ophthalmoscopy (AOFLIO), a method for single-cell-resolved, in vivo structural and metabolic imaging of the human retinal pigment epithelium (RPE). Through real-time correction of ocular wavefront aberrations, precisely synchronized adaptive optics reflectance and lifetime image acquisition via a phase-locked loop-based timing architecture, and sub-pixel photon registration that localizes individual autofluorescence photons with high spatial precision, AOFLIO directly resolves the RPE cell mosaic and measures autofluorescence decay using the same photons, enabling direct structural-functional correlation at the single-cell level. We demonstrate single-cell RPE lifetime mapping in healthy subjects and reveal altered metabolic signatures and fine characterization of RPE metabolic in age-related macular degeneration. AOFLIO establishes a platform for cellular-scale metabolic imaging in the living human eye.

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.

The crowded intracellular milieu shapes organelle architecture and dynamics, yet nanoscale heterogeneity in its physicochemical properties remains difficult to visualize with conventional fluorescence anisotropy (FA) imaging. Here, we develop fluorescence anisotropy structured illumination microscopy (FA-SIM), which employs orthogonal-polarization structured illumination with dual-angle detection to achieve [~]100-nm resolution and quantitative FA retrieval with 0.56% relative error, representing over 20-fold higher accuracy than conventional FA imaging. With low phototoxicity, FA-SIM enables dual-color, hour-long quantitative super-resolution imaging in cells. Using viscosity standards, defined nanoparticles and small-molecule drug binding assays, we validate FA-SIM as a quantitative reporter of rotational mobility and molecular interactions. In cells, FA-SIM resolves nanoscale crowding heterogeneity, correlates anisotropy landscapes with condensate dynamics, and uncovers a radial crowding gradient across the microtubule network and mitotic spindle. Long-term dual-color imaging further resolves coordinated actin-microtubule remodeling and associated microenvironmental changes. By enabling quantitative, super-resolved mapping of intracellular physical properties in living systems, FA-SIM provides a powerful platform for investigating the physical regulation of cellular organization and dynamics in health and disease.

Multidrug-resistant hypervirulent Klebsiella pneumoniae (MDR-hvKP) poses a severe global health threat. Phage therapy is a promising alternative, but requires precise matching of phage to the bacterial strain. Here, we present a proof-of-concept method that integrates single-cell Raman spectroscopy with deep learning to enable rapid and precise selection of lytic phages against MDR-hvKP. By profiling Raman signatures of strains across multiple KL-types (capsule locus types), we trained three deep learning architectures for phage-host matching. Among them, the CNN_MLP-Transformer achieved the best performance (99.7%), slightly outperforming CNN_MLP (99.2%) and CNN_MLP-Attention (99.5%). Validation using 10 hvKP clinical isolates yielded an average phage selection accuracy of 78.3%. These findings demonstrate the feasibility and clinical potential of AI- augmented Raman spectroscopy as a rapid, label-free, precise strategy for guiding phage therapy against MDR-hvKP infections. TeaserAI-guided single-cell Raman profiling enables rapid precision phage selection against multidrug-resistant K. pneumoniae.

Volumetric fluorescence microscopy is a powerful method for studying complex biological systems because it enables comprehensive observation of structural and physiological dynamics. In particular, light-sheet microscopy (LSM) is a leading option for real-time volumetric fluorescence imaging as it combines high imaging speed, low phototoxicity, minimal photobleaching, high spatiotemporal resolution, and low computational burden. To capture fast biological events, various efforts have been made to improve the imaging speed of volumetric fluorescence microscopy, including LSM. However, existing approaches entail significant trade-offs that make routine volumetric imaging at and beyond video rates challenging under practical conditions. Here, we introduce image-scanning LSM, a method that substantially increases the volumetric imaging speed achievable with LSM while preserving key performance metrics, such as spatial resolution and photon efficiency, as well as accessibility. Our implementation, termed image-scanning oblique plane (ISOP) microscopy, enables volumetric fluorescence imaging at up to 1,000 volumes per second with submicrometer lateral spatial resolution. We demonstrate the broad utility of ISOP microscopy by recording and analyzing the dynamics of behaving and rapidly moving organisms.

Optical scattering in biological tissues fundamentally limits fluorescence imaging by disrupting spatial and angular information, thereby restricting volumetric visualization. Although hardware-intensive and computational approaches have advanced scattering microscopy, practical three-dimensional imaging through tissue remains constrained by instrumental complexity and axial ambiguity. Here, we present volumetric scattering microscopy (VSM), a scan-free, optical-computational framework for three-dimensional fluorescence imaging in scattering biological media. VSM captures angularly resolved speckle-encoded fluorescence using an aperture-segmented Fourier light-field configuration and reconstructs volumetric structure through adaptive feature-based descattering and joint sub-pupil alignment. This hybrid strategy preserves angular information embedded in scattered light without wavefront measurement or mechanical scanning, while maintaining the simplicity of a standard epi-fluorescence architecture. We demonstrate high-fidelity volumetric reconstruction across phantoms, engineered cellular systems, ex vivo tissues with volumetric muscle loss, and intact Xenopus embryos, achieving preserved spatial resolution, enhanced optical sectioning, and quantitative accuracy under strong scattering conditions. VSM supports large-field, robust volumetric imaging in both layered and fully embedded scattering environments. By transforming scattered light into a structured encoding resource, VSM establishes a scalable pathway toward routine three-dimensional fluorescence imaging in complex biological systems.

Imaging subcellular structures deep within thick, turbid biological tissues remains fundamentally limited by light scattering, which distorts optical wavefronts and degrades contrast, resolution, and sensitivity. These limitations hinder quantitative interrogation of complex biological systems where resolving dynamic microenvironments at subcellular resolution is critical. Here, we introduce scattering-enabled epi-quantitative phase imaging (SEEQPI), a label-free method that leverages tissue scattering and provides subcellular spatial resolution, nanometer-scale spatiotemporal phase sensitivity, and millimeter-scale imaging depth in murine brains. SEEQPI is enabled by common-path phase-shifting confocal epi-interferometry with near-infrared illumination and the scattering-enabled phase reconstruction algorithm. SEEQPI requires low illumination power, minimizing tissue damage while enabling high-speed imaging of biological dynamics. We demonstrate simultaneous, colocalized imaging of subcellular structures with SEEQPI, third-harmonic generation, and three-photon fluorescence microscopy in liver cancer spheroids and in vivo mouse brains. SEEQPI enables quantitative, longitudinal studies of dry mass dynamics in intact, living biological systems.

Artificial vision systems hold transformative potential for biomedical imaging, diagnostics, and translational research by emulating and extending the capabilities of biological eyes. However, current techniques often face intrinsic trade-offs between spatial resolution, field of view, and depth perception, particularly in compact, biologically relevant settings. Here, we introduce FOLIC, a foveated light-field compound imaging system, which integrates compound-eye-inspired wide angular coverage and chambered-eye-inspired spatial acuity within a unified multi-aperture concave architecture. FOLIC naturally generates peripheral, blend, and foveated zones from a single capture, enabling seamless, depth-extended, multiscale visualization from wide-field context down to single-cell lateral resolution. We validate FOLIC across diverse fluorescent and non-fluorescent specimens, including cellular phantoms, tissue sections, and small organisms, demonstrating its versatility and scalability for biomedical research and related translational applications. We anticipate FOLIC to offer a biologically informed design blueprint for future artificial vision systems. TeaserA bioinspired system unifies compound and chambered eye principles to achieve wide-field volumetric microscopy.

Accurate monitoring of microalgae is essential for assessing marine ecological health and preventing harmful algal blooms in ocean engineering. Current in situ identification methods often suffer from limited discriminative feature extraction and inadequate adaptation to complex underwater imaging conditions. This study introduces a lightweight dual-attention neural network, termed ANMM, designed for real-time, in situ hyperspectral classification of microalgae within integrated underwater monitoring systems. The model strengthens a deepened AlexNet backbone with multi-head latent attention (MLA) and multi-head self-attention (MSA) mechanisms, which jointly enhance local feature refinement and global spectral dependency modeling. An early-stopping strategy is further incorporated to prevent overfitting and ensure robust generalization. Evaluated on a custom dataset of field-collected fluorescence spectra, the model achieves a classification accuracy of 98.91%, outperforming several state-of-the-art deep-learning counterparts. With a compact parameter size of 16.34 M and low-latency inference on edge hardware, the system demonstrates strong potential for deployment on embedded underwater sensing platforms. This work provides a practical and efficient AI-driven solution for continuous marine microalgae monitoring, supporting advances in ocean observation technology and ecological engineering.

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.

Non-invasive, high-resolution visualization of mouse brain vasculature remains challenging due to significant light scattering and absorption by mammalian tissues, hence many optical imaging protocols require scalp and/or skull excision. Here we present a fully reversible tartrazine-based optical clearing strategy that enables cortex-wide optical coherence tomography angiography (OCTA) through intact scalp and skull. We characterized tartrazine properties in the near infrared (NIR)-II band of the 1.3 {micro}m swept-source OCTA system, confirming minimal absorption across 1.25-1.35 {micro}m wavelength range and an effectively constant refractive index, suggesting negligible OCTA distortions. Spatially selective agent application showed that intracranial vessels emerge selectively within the treated region of interest (ROI), whereas untreated regions retain strong interference by the scalp vascular features. Depth-encoded projections and cross-sectional OCTA demonstrated an increased signal recovery at depth and an extended vessel-detection range after clearing. Vessel-map changes were quantified using intersection-over-union and Dice coefficients, yielding high similarity outside the ROI and reduced similarity within the ROI, consistent with a transition from scalp to brain vasculature. Reproducibility was confirmed in three independent 11-week-old mice and validated against scalp-removed reference OCTA. Screening tartrazine in the 0.3-0.8 Molar concentration range (7-min application) identified 0.6 M as optimal for whole-cortex scanning, balancing clearing efficacy and solution handling. Finally, the protocol generalized across mice aged 5-18 weeks. This approach provides a practical route to non-invasive structural cerebrovascular mapping with OCTA.

Tissue-induced optical aberrations fundamentally constrain intravital microscopy in deep or complex biological specimens. While adaptive optics (AO) can compensate for these aberrations, conventional AO methods are limited by either guide-star dependency or slow correction speeds. Here, we develop MeNet-AO, a multi-encoder network-based AO method that enables rapid, guide-star-free aberration correction. By integrating a noise-resilient, structure-independent feature extraction model with a physics-informed multi-encoder architecture, MeNet-AO jointly decodes multiple large-amplitude aberration modes from wavefront-modulated image pairs, achieving an effective balance between prediction accuracy and temporal efficiency. Validated in living organisms, MeNet-AO improves fluorescence imaging in zebrafish brain and eye, enhances neuronal calcium transients and direction selectivity in mouse visual cortex, and enables subcellular-resolution microglial calcium imaging through thinned-skull windows - revealing spatiotemporally heterogeneous signaling patterns previously obscured by skull aberration. The speed and robustness of MeNet-AO in low-signal and scattering conditions establish it as a versatile platform for dynamic subcellular imaging deep within native tissue environments.

Clearing Assisted Scattering Tomography (CAST) extends coherent scattering tomography to whole-brain imaging, enabling visualization of fine-scale brain-wide connectivity. As a coherent optical tomography modality, CAST is inherently affected by speckle noise, which degrades image quality and limits quantitative analysis. However, existing speckle reduction methods developed for optical coherence tomography (OCT) are not directly transferable to CAST images due to differences in sample and noise statistics. Here, we present a learning-based cleared-sample speckle reduction network, termed CLEAR Net, specifically designed for CAST imaging, which effectively suppresses speckle noise in whole-brain white matter images while preserving fine structural details. We quantitatively benchmarked CLEAR Net against representative speckle reduction algorithms on CAST datasets and further evaluated its generalizability using publicly available ophthalmic datasets.

A critical challenge in endocrine neurosurgery is intraoperative discrimination between normal pituitary tissue and pituitary neuroendocrine tumors (PitNETs). Suggesting the universal persistence of near-infrared autofluorescence (NIRAF) in endocrine organs and inspired by routine clinical use of NIRAF for parathyroid gland identification, we discovered that pituitary NIRAF can be employed for label-free transsphenoidal surgery guidance. Ex vivo confocal spectral imaging of 33 specimens identified secretory granules as the dominant long-wavelength fluorescence source and showed that normal pituitary had higher granule content than PitNETs. For the first time, we made use of the pituitary NIRAF during surgery and assessed its performance for pituitary/adenoma separation in vivo for 27 surgeries and showed near-perfect separability between pituitary and non-pituitary measurement sites with ROC-AUC of 0.98. The obtained results clearly demonstrate that the suggested method, based on the solid microscopic background, has the potential for clinical translation and paves the way for enhanced gland preservation during resection.

Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains challenging in densely packed tissues, irregular nuclear morphologies, and across heterogeneous imaging modalities. Here we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation that combines a residual attention 3D U-Net architecture with a reversible gradient-field representation for robust centroid-aware instance reconstruction. NucVerse3D is trained end to end in 3D using modality-agnostic preprocessing and isotropic scale normalization, enabling deployment across confocal microscopy, two-photon microscopy, light-sheet microscopy, micro-computed tomography, and scanning electron microscopy volumes. We benchmarked NucVerse3D on seven volumetric datasets spanning multiple species and tissues, comprising more than forty thousand manually annotated nuclei, including newly released ground-truth datasets of mouse liver tissue (control and hepatocellular carcinoma) and Drosophila brain glial nuclei. Across datasets, NucVerse3D achieved consistently high precision, recall, F1-score, and average precision, and outperformed the state-of-the-art methods particularly in dense and irregular settings, while remaining competitive on simpler cases. A single generalized model trained on pooled data matched the performance of dataset-specific models, and ablation experiments demonstrated that preprocessing and scale normalization substantially contribute to performance under strict intersection-over-union criteria. To demonstrate the biomedical utility of NucVerse3D, we applied it to three-dimensional liver images from a mouse model of hepatocellular carcinoma (HCC). High-fidelity, nucleus-by-nucleus segmentation enabled the quantification of the Nuclear Decoupling Score (NDS), which captures deviations in nuclear DNA-volume coupling at the single-nucleus level. NDS analysis revealed a progressive increase in nuclear abnormalities within tumor regions, forming spatially coherent domains of dysregulated nuclei and highlighting NDS as a potential quantitative biomarker of dysplastic and tumor tissue. Together, NucVerse3D provides a robust and generalizable solution for 3D nuclear instance segmentation and enables quantitative nuclear phenotyping across imaging modalities. Highlights- NucVerse3D provides accurate 3D nuclear instance segmentation across modalities - Residual attention and gradient fields enable robust separation of dense nuclei - New 3D annotated datasets of mouse liver and Drosophila brain are released - A generalized model achieves performance comparable to dataset-specific training - 3D nuclear phenotyping reveals spatially organized nuclear abnormalities in HCC

Fluorescence microscopy is a cornerstone of biological research. However, fluorescent labeling is challenging in live cells and is constrained by photobleaching and phototoxicity. Label-free methods allow cells to be studied in their native state, but most techniques have poor contrast, lack 3D capability, rely on complex optics, and fail to provide structural information. We present broadband backscattering confocal microscopy (BBCM), which employs a broadband supercontinuum laser and collects backscattered light in confocal geometry using a photomultiplier tube. Broadband illumination averages out size-dependent oscillations that confound monochromatic backscattering. This eliminates blind spots and intensity ambiguities, allowing all scatterers to be visible, with the signal increasing approximately linearly with scatterer size. BBCM is easy to retrofit to standard confocal microscopes, requires no specialized optics, and is straightforward for nonspecialists. It enables high-contrast, label-free 3D imaging of live cells with size sensitivity to subcellular structures without employing custom optics or complex data processing.

Fluorescence imaging with spectrally variant fluorophores allows the spatial mapping of biological structures with exquisite cellular and molecular specificity. However, the ability to robustly discriminate multiple fluorophores in any single imaging experiment is greatly hindered by the broad emission spectra of bio-compatible fluorophores and the large contribution of noise in low-energy regime fluorescence microscopy. In this study, we propose a novel machine learning framework, Bleaching-Excitation-Emission Photodynamics (BEEP) learning, that exploits multiple discriminatory features of fluorescent dyes to greatly expand the number of distinguishable objects in an image by integrating emission spectra, excitation variability, and bleaching dynamics into a unified multi-view, fluorescence unmixing approach. Our method is built upon a rank-one-tensor-based generalized linear model and leverages two biophysically grounded assumptions: consistent spectral and bleaching behaviors under fixed excitation, and invariant fluorophore abundances across excitations. We first extract excitation-specific spectral and bleaching signatures from reference images, and then use them to estimate abundances in complex mixtures. Experimental results on both simulated and real images of microbial populations demonstrate that our approach significantly outperforms conventional and partially multi-view methods, offering improved robustness and accuracy in highly multiplexed fluorescence imaging.

Expansion microscopy (ExM) enables nanoscale imaging for disease characterization. However, whole-organ analyses remain limited by several challenges. Current super-resolution methods either require high-resolution ground-truth data or assume spatially uniform point spread functions--assumptions that rarely hold in whole-organ imaging with depth-varying aberrations and illumination drift. Existing methods also worsen storage demands by inflating already multi-terabyte datasets without using neural compression. We propose a single-stage, self-supervised framework that addresses both resolution anisotropy and storage constraints through compression-aware isotropic super-resolution. Our approach combines a 2D lateral encoder that operates directly on raw slices to avoid memory limits with a lightweight volumetric decoder that preserves cross-slice continuity. A vector-quantized variational autoencoder (VQ-VAE) provides an information-sufficient bottleneck, achieving up to 128x slice compression and up to 8x axial resolution enhancement. This latent-centric design yields approximately 1000x reduction in storage compared with storing fully isotropic volumes. The framework achieves higher GPU throughput, lower memory usage, and stronger multi-GPU scalability than prior methods. By designating compressed latent space as the native storage format, it enables efficient on-demand isotropic reconstruction directly from compact representations. This combination of isotropic enhancement and neural compression framework therefore makes large-scale, whole-organ ExM analysis practical while maintaining analysis-ready accessibility, addressing a bottleneck in translating ExM to clinical biomarker discovery.