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.

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.

Three-dimensional fluorescence microscopy often exhibits anisotropic resolution because axial information is poorly sampled and more blurred than lateral information, which complicates quantitative interpretation of fine 3D structures. Although optical remedies and computational restoration have been explored, many approaches require demanding system calibration or rely on accurate PSF models and assumptions that are difficult to satisfy across all samples and modalities. Here we present DeepIso, a self-supervised isotropy restoration framework that couples supervised pretraining with an internal-learning inference stage to estimate degradation directly from the measured volume. Without explicit PSF specification or enforced lateral-axial structural equivalence, DeepIso recovers axial frequency content and improves the continuity of elongated structures while retaining fine features, with superior performance over existing computational approaches in terms of both visual inspection and quantitative metrics. The method is validated on synthetic benchmarks and experimental datasets, demonstrating isotropy enhancement across confocal, light-sheet, and 3D structured illumination microscopy, thereby supporting downstream volumetric analysis including segmentation and tracking.

Hematoxylin and eosin (H&E) has been the fundamental method for visualising tissue morphology. Recent advances in tissue clearing and microscopy have enabled the observation of tissue morphology in 3D, but incomplete penetration of nucleic acid dyes has remained the bottleneck. To address this, we develop a new staining chemistry called Cyclodextrin and Organic solvent-assisted deep Labelling of ORgans in 3D (COLOR-3D), which attains the best penetration depth and homogeneity among state-of-the-art methods. We also demonstrate the scalability of COLOR-3D and its compatibility with other staining modalities. To bridge the gap between 3D histology and its wider application in biomedical research and histopathology, we develop a computational pipeline to convert 3D fluorescence images into bright-field H&E images, enabling the creation of a 3D atlas of both normal tissues and pathological specimens. Apart from qualitative observation of tissue morphology, COLOR-3D also enables quantitative analysis for studying biological phenomena. In the mouse liver, we discover rare populations of tetranuclear hepatocytes as well as m16n, t4n and t8n hepatocytes. We also propose the first structural model of the liver lobule based on 3D histology. With a more complete penetration, we reveal the following aging-related changes in tissue microarchitecture, including an increase in extreme nuclear polyploidy, and the disruption of vasculature and portal triad.

Light-sheet fluorescence microscopy (LSFM) has revolutionized biological imaging by enabling high spatial and temporal resolution with minimal photodamage. However, conventional LSFM techniques often suffer from striping artifacts in the resulting images due to light scattering and absorption within samples, leading to uneven illumination that negatively impacts the accuracy of subsequent image analyses. To address this limitation, we introduce dodecagon light-sheet fluorescence microscopy (dodecaLSFM), a novel approach that maximizes angular diversity to achieve homogeneous illumination and suppress striping artifacts. dodecaLSFM employs diffraction optics and cylindrical lenses to generate twelve light sheets, providing 360 degree omnidirectional illumination that significantly enhances illumination uniformity compared to traditional mSPIM, mDSLM, and ultramicroscopy systems, which use only one or two illumination planes. We demonstrate the effectiveness of dodecaLSFM by achieving high-resolution imaging of whole mouse brain vasculature following tissue clearing, allowing precise morphometric analysis of vascular networks without striping artifacts. Furthermore, we show that combining dodecaLSFM with expansion microscopy (ExM) enables whole-organ 3D imaging at cellular resolution. This novel approach provides an advanced, scalable solution for large-volume imaging, facilitating detailed structural and functional studies across diverse biological applications.

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.

Understanding processes such as blood-brain barrier (BBB) disruption and tumor progression can greatly benefit from simultaneous molecular, functional, and hemodynamic imaging in deep tissue, yet few existing imaging modalities can provide all three in a single system. Here, we present an integrated imaging platform that combines 3D photoacoustic tomography with ultrasound localization microscopy (3D-PAULM) to enable intrinsically co-registered, multiparametric imaging. 3D-PAULM unifies multispectral photoacoustic molecular imaging, ultrasound B-mode imaging, microbubble-enhanced power Doppler, and ultrasound localization microscopy, and concurrently measures blood oxygenation, blood perfusion, microvascular flow dynamics, and molecular probes from near-infrared dyes and photoswitchable phytochromes. We apply 3D-PAULM to quantify BBB leakage in focal ischemia and systemic inflammation, and to perform high-sensitivity molecular imaging of solid tumors alongside functional mapping of tumor hypoxia and super-resolved vascular remodeling. Together, these results establish 3D-PAULM as a versatile platform for integrated functional and molecular imaging in deep tissue.

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.

Fluorescence lifetime imaging microscopy (FLIM) provides molecular contrast that is largely independent of fluorophore concentration, yet it remains constrained by a persistent trade-off among acquisition speed, photon dose, and detector complexity. To address this challenge, we developed image-projection fluorescence lifetime imaging microscopy (IP-FLIM), an integrated optical and computational platform that enables high-resolution, component-resolved lifetime imaging using only a linear single-photon avalanche diode array. We validate IP-FLIM using fluorescent microbeads and bovine pulmonary artery endothelial cells, demonstrating up to 22.3x improvement in contrast-to-noise ratio and 72.3% reduction in background noise over conventional filtered back-projection reconstruction. By combining wide-field projection acquisition with computational k-space reconstruction, IP-FLIM provides a scalable route to fast, high-resolution multiplex lifetime imaging.

Inverse-scattering methods enable label-free, quantitative visualization of a samples three-dimensional (3D) refractive index (RI), providing intrinsic and volumetric morphological contrast without exogenous labels. This is achieved by developing computational frameworks that reconstruct the samples 3D RI from a series of scattering measurements acquired under different data-capture conditions. Recent advances have demonstrated successful 3D RI reconstructions in multiple-scattering samples using angle-varying illuminations; however, these studies have primarily focused on non-absorptive samples. Here, we extend the multi-slice beam propagation (MSBP) inverse-scattering framework to reconstruct complex-valued RI, encompassing both the samples conventional RI (real part) and absorptivity (imaginary part). We show that reconstructing complex-valued RI makes the inverse problem ill-posed under angle-varying illumination alone, and that incorporating measurement diversity from both angle-varying illumination and sample defocus is necessary to ensure stable and accurate convergence. Experimental demonstrations were conducted on 1) dyed microsphere samples to characterize accuracy of reconstructed RI and absorptivity; and 2) diverse absorptive scattering samples to demonstrate biological utility. These results represent an important step for label-free volumetric imaging in biological tissue, which typically exhibits both scattering and absorption.

Soft X-ray tomography (SXT) is an emerging modality for whole-cell 3D imaging in near-native states. However, the effective spatial resolution is limited by optical artifacts characterized by the point spread function (PSF). To achieve optimal resolution via PSF inversion, we propose a measurement-supervised deep learning framework. Bypassing purely data-driven neural networks that are prone to hallucinations, we employ a measurement-supervised, instance-specific optimization strategy strictly constrained by a differentiable SXT formation forward model. The structural fidelity was validated using split-tilt Fourier ring correlation (FRC), ensuring the recovered high-frequency features reflect genuine specimen features rather than random artifacts. Our results demonstrate that this optimization consistently increases FRC resolution and enhances visual ultrastructural details across diverse biological structures. Furthermore, by recovering high-frequency features from sparse-angular projections, we show that spatial resolution can be maintained using only half the radiation exposure. This approach effectively compensates for the degradations caused by angular sparsity, providing a hardware-free computational solution to minimize radiation damage, maximize imaging speed, and overcome the optical and dosimetric limits of SXT.

Investigating cardiac dynamics, including contractile function and intracardiac flow, requires volumetric imaging capable of resolving whole-organ events at micrometer resolution and millisecond timescales. However, the limited readout bandwidth of detectors imposes fundamental trade-offs among spatial sampling, field of view, and achievable volume rates. Here we introduce compressive axial-integrated planar scanning (CAPS) microscopy, a computational imaging framework that combines rapid light-sheet scanning, detection-side axial multiplexing with model-based reconstruction to enhance detector bandwidth utilization for high-speed volumetric imaging. Using widely accessible optical sensors and components, CAPS achieves cellular-scale resolving power across heart chambers at 200 volumes per second with an effective detector pixel rate of 5.82 GHz, representing a [~]15-fold increase in spatiotemporal throughput relative to uncompressed volumetric acquisition. Coordinated high-speed encoding and computational reconstruction further mitigate rolling-shutter distortions in CMOS sensors while preserving frame rate and intrinsic optical sectioning. We demonstrate that CAPS enables beat-resolved imaging of single-cell cardiomyocyte kinematics, chamber-scale contractile dynamics, and intracardiac hemodynamics in zebrafish larvae under both healthy and pharmacologically perturbed conditions. Collectively, these advances establish CAPS as a powerful framework for quantitative, in vivo characterization of coordinated and disrupted cardiac dynamics at cellular resolution, supporting high-speed volumetric interrogation of organ-level function and disease progression.

Label-free optical redox imaging utilizes endogenous NAD(P)H and FAD autofluorescence to evaluate metabolism in living specimens. The conventional optical redox ratio collapses these two channels into a single value; however, it does not indicate whether a pixel has sufficient photon support or the cellular context necessary for quantitative aggregation. To address this limitation, we introduce FPhaS, a fixed-calibration phase- autofluorescence framework that integrates quantitative phase imaging (QPI) with simultaneous label-free autofluorescence multi-harmonic microscopy (SLAM), using fluorescence lifetime imaging (FLIM) solely for validation. Because QPI and SLAM are acquired with the same objective, a unified non-biological calibration aligns phase-derived structural data with the autofluorescence frame, yielding a residual error of 0.39 pixels. This calibration is maintained across all biological specimens. This shared geometric reference enables local evaluation of structural and metabolic information, rather than comparing approximately aligned images. FPhaS decomposes the data into cell presence, ratio credibility, and confidence-supported pooling. We validated FPhaS on A549 cells under high and low-photon conditions; the framework is designed to generalize to other cell and tissue types. Confidence-weighted intensity redox estimates were compared with lifetime-derived measurements within mask-locked cellular regions. Concordance improved exclusively when both the denominator photon support and an independent structural criterion were satisfied. The same reference layer generated cell-level descriptors of metabolic content, metabolic-structural organization, and measurement reliability, while also constraining the CombinedWLS reconstruction under diminished fluorescence acquisition. FPhaS redefines label-free metabolic imaging from producing comprehensive ratio maps to identifying regions where optical evidence substantiates quantitative inference.

Single-molecule localization microscopy (SMLM) enables nanoscale imaging but remains limited in three-dimensional (3D), high-speed, and high-density applications due to background fluorescence, photon inefficiency, and large point-spread function (PSF) footprints. Here, we present single-objective light-sheet microscopy with PSF-engineering enhanced biplane detection (SoLiD-3D), a versatile imaging platform that integrates dual-wavelength light-sheet illumination with dual-color, multi-configuration biplane imaging for parallel acquisition with PSF engineered detection for high-speed volumetric SMLM. Parallelized single-objective light-sheet excitation combined with PSF engineering overcomes key limitations of conventional wide-field and biplane approaches. Independent control of two excitation wavelengths for optical sectioning enables simultaneous dual-target imaging and single-target dual-color imaging with improved contrast and temporal resolution utilizing dynamically displaced light sheets for volumetric coverage. Using SoLiD-3D, we demonstrate high-speed single- and dual-target dual-color imaging that doubles localization density without sacrificing photon efficiency and continuous volumetric imaging via PSF-engineering enhanced biplane detection for whole-cell 3D imaging with improved axial localization performance over extended depth ranges. We further demonstrate improved speed by utilizing the Hummus PSF, a compact engineered PSF that enables high-precision 3D localization with a substantially reduced spatial footprint, for the first time for super-resolution imaging applications. Taken together, SoLiD-3D mitigates the trade-off between axial range and localization precision and offers improved speed compared to conventional 3D SMLM approaches.

Tracking immune cells deep within living tissue remains a fundamental challenge due to the diffraction-limited resolution of ultrasound imaging and the inability to resolve dense cellular populations. Here, we introduce an intracellular super-resolution ultrasound imaging framework based on stochastic phase-changing nanodroplets (NDs) and ultrasound localization microscopy (ULM). We engineer [~]170 nm perfluorocarbon NDs that undergo reversible, stochastic liquid-gas transitions under acoustic excitation, generating temporally sparse "blinking" signals. Leveraging the intrinsic endocytic activity of macrophages, these NDs are internalized, enabling intracellular contrast generation independent of vascular flow. We validate this approach across imaging scales, from controlled phantoms and in vitro systems to in vivo tumor models, demonstrating robust intracellular blinking, high cell viability, and consistent super-resolution reconstruction in dense cellular environments. The stochastic blinking of internalized NDs provides the temporal separation required to localize individual sources, overcoming a central limitation of conventional ULM. Following systemic administration, ND-labeled macrophages are tracked in vivo after homing to the liver, where super-resolution ULM resolves cellular distributions with a spatial resolution of 26.3 {+/-} 3.2 {micro}m, corresponding to a 6.1-fold improvement over diffraction-limited imaging. This work establishes a previously unexplored paradigm for ultrasound-based intracellular super-resolution imaging, enabling non-invasive visualization of immune cell organization in deep tissue. By introducing spatiotemporally programmable intracellular contrast, this approach expands ultrasound beyond vascular imaging toward functional cellular imaging, with broad implications for immunology, diagnostics, and cell-based therapies.

Cellular metabolism is highly dynamic, intertwined and spatially heterogenous, yet methods that can simultaneously visualize multiple metabolic pathways in living systems remain largely limited. Here, we present MetaboRamics: highly multiplexed metabolic imaging by stimulated Raman for spatial metabolomics in live cells. Through rational probe selection and careful optimization, isotope editing, and robust spectral unmixing, we establish a metabolic palette of 16-colors spanning glucose uptake and utilization, lipid uptake and synthesis, choline metabolism, DNA synthesis, and amino acid incorporation in addition to endogenous proteins, lipids and redox signals. Incorporation of organelle-targeted Raman probes further enables spatial interactomics and assessment of organelle activities. Applying MetaboRamics to epithelial-mesenchymal transition (EMT), we observe metabolic rewiring in mesenchymal cells, reflected by reduced glucose-derived biomass, lipid turnover, protein synthesis, and altered redox balance. Finally, we perform optical phenotyping of cellular states under metabolic stress of serum deprivation, nutrient overload (fructose, and saturated fatty acid), inflammation, and pharmacological perturbations to reveal subcellular metabolic changes. This work fully realizes the potential of stimulated Raman scattering (SRS) microscopy for super-multiplexed metabolic imaging by establishing, for the first time, a 16-plex platform for live-cell spatial metabolomics.

Coherent structured illumination microscopy (c-SIM) is a synthetic aperture optical technique for sub-diffraction limit imaging that extends the utility of traditional SIM to non-fluorescent samples. Here, we present a complementary 5-beam implementation of c-SIM that provides enhanced optical sectioning compared to conventional quadrupolar illumination. Since our approach detects intensity images due to coherent light scattering, it avoids the complications associated with detecting complex fields. Through comparative measurements on calibration samples and live microalgae, we show that 5-beam c-SIM effectively suppresses coherent defocus effects, improving image quality while simultaneously providing a 2-fold lateral resolution improvement.

Rapid and accurate pathogen identification is crucial for the clinical management of infectious diseases, particularly sepsis and severe respiratory infections, yet standard clinical workflows remain slow and resource-intensive. Here, we developed an automated, high-throughput imaging platform built on standard, clinically accessible bright-field microscopy, and generated a large dataset comprising 24.9 million label-free bacterial cells across six focal pathogens. Leveraging this resource, we trained a neural network (ESKAPe-ResNet) to identify ESKAPe species at the single-bacterium level. The model achieved >92% accuracy in species-level classification and >82% accuracy in quantifying ESKAPe abundance in mock mixtures, with high specificity against non-ESKAPe bacteria. In clinical validation using sputum, bronchoalveolar lavage fluid and blood samples from patients with respiratory infections and sepsis, the approach correctly identified the dominant ESKAPe pathogen in >78% of samples after minimum broth culture enrichment. The imaging-to-identification pipeline was completed in under 10 minutes, and coupled with brief cultivation, the median time to accurate identification was reduced to 5-6 hours, compared with days for conventional blood culture-based workflows. This work establishes the proof-of-principle for label-free, hardware-minimal rapid pathogen identification, providing a clinically deployable workflow to expedite diagnosis and reduce mortality in severe bacterial infections.

Multi-organelle segmentation in volumetric electron microscopy (vEM) faces several challenges, including severe class imbalance, the presence of small, rare classes, and inconsistent class coverage across crops. While recent work has focused primarily on architectural design, the impact of sampling, loss functions, and masking strategies on training effectiveness remains comparatively underexplored in vEM organelle segmentation. Here, we systematically evaluate sampling strategies, loss configurations, masking approaches, and model families (CNNs and vision transformers) on the CellMap benchmark. Using 289 annotated 3D FIB-SEM crops, we establish a 32-class segmentation benchmark with stratified train, validation, and test splits, and evaluate all the methods under the same training and inference settings. Across controlled ablations, the proposed combination of repeat-factor sampling, Tversky-BCE loss, and masking achieved the strongest rare-class performance, increasing rare-class mean Dice (mDice) from 0.3244 under uniform sampling to 0.3409. This corresponds to an absolute gain of +0.0165 mDice and a 5.1% relative improvement, while preserving comparable performance on common classes. Overall, we find that sampling, loss design, and masking contribute as much to performance variation as the choice of architecture, highlighting the importance of training-recipe design alongside model architecture in vEM organelle segmentation.

Whole-tissue spatial proteomics level provides critical insights into region-specific biological regulations but remains challenging. Previously, we introduced the Micro-scaffold Assisted Spatial Proteomics (MASP) concept for whole-tissue mapping. However, this prototype required substantial development in spatial resolution, practicality, and throughput for practical application. Here we present a next-generation MASP technique (hex-MASP) featuring i) a new design of hexagonal-micro-wells fabricated with optimized Projection Micro-Stereolithography (P{micro}SL) 3D-printing, achieving high spatial resolution, sampling robustness and mechanical strength for reproducibly compartmentalizing even tough tissues; ii) enhanced throughput/effectiveness in sample preparation and LC-MS analysis with high quantitative quality. Applied to mouse brain, hex-MASP for the first time achieved in-depth, whole-tissue mapping for >6,000 proteins in mouse brains, with high spatial accuracy and excellent data quality. The substantially improved resolution revealed critical regional details across the entire brain, that were not previously captured, enabling precise depiction of protein distribution heterogeneity. This technique enabled the discovery of many unreported regionally-enriched proteins across brain structures. We further applied hex-MASP to investigate the intra-brain distribution of intracerebroventricularly-dosed antibody therapeutics and related proteins, which to our knowledge, enabled whole tissue mapping of protein drugs for the first time and revealed novel mechanistic insights into antibody distribution and localized treatment effects. Hex-MASP represent a robust, scalable platform for whole-tissue spatial proteomics.