Light: Science & Applications

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.

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.

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.

In vivo infrared thermography is limited by the inherently poor spatial resolution at long wavelengths, low contrast, and the lack of biocompatible contrast agents. Here, we present 3-5 m mid-wave infrared (MWIR) thermography enhanced by an artificial intelligence (AI) network and cold phosphate-buffered saline (PBS) as a thermal contrast agent for noninvasive in vivo imaging with high contrast and resolution. MWIR imaging enabled high thermal sensitivity with microscale spatial resolution, strong relative thermal contrast, and facilitated visualization of the subcutaneous vasculature in the human arm, hand, ankle, the femoral artery and vein in rats, and the femoral vessels in mice, with image contrast further enhanced by AI networks. In a 4T1 tumor-bearing mouse model, AI-enhanced MWIR resolved early-stage tumors of ~2.3 mm and metastases as small as ~1.7 mm. Using cold PBS as a MWIR thermal contrast agent, we achieved precise tumor boundary visualization and real-time imaging-guided tumor resection. AI-enhanced MWIR offers a promising solution for early diagnosis and improved surgical precision.

Label-free scattering imaging is widely used in pathology because it enables sensitive tissue assessment without exogenous contrast agents. Yet its limited optical penetration has prevented scattering-based methods from being applied to whole-organ pathology mapping. Here we present clearing-assisted scattering tomography (CAST), a high-throughput, label-free whole-brain mesoscope enabled by selective lipid clearance for scattering enhancement (SELiC). SELiC modulates endogenous refractive-index heterogeneity in cleared tissue, providing whole-brain optical penetration while retaining strong scattering contrast from amyloid plaques and white-matter fibre bundles. CAST enables volumetric imaging of intact mouse brains and brain-wide mapping of amyloid plaque pathology across anatomical regions. This platform establishes a scalable route for label-free, system-level analysis of amyloid pathology and tissue architecture in Alzheimers disease (AD) models.

High-quality cellular imaging, especially in live cells, remains constrained by the trade-off among signal-to-noise ratio, phototoxicity, and instrumentation complexity. Here, we report a scalable plasmonic metasurface that generates a spatially ordered array of fluorescence-enhancing near-field hotspots and enables self-supervised denoised, cellular imaging with improved feature readability on a conventional wide-field microscope. The registered hotspot lattice serves as a physics-derived functional prior that identifies where fluorescence amplification is physically grounded and steers neural-network training accordingly, reducing reliance on paired ground truth, large external pretrained models, or extensive supervised datasets. We demonstrate two labeling-density-dependent operating regimes: dense labeling for cytoskeleton structural imaging and sparse labeling for multiplexed sensing of plasma-membrane-associated dynamics across the hotspot array. Our work unites scalable nanophotonic hardware and self-supervised computational imaging into a practical platform for structural bioimaging and on-chip live-cell biosensing under simple wide-field imaging conditions.

Cryo-correlative light and electron microscopy methods enable targeted structural analysis of fluorescently labelled features in vitrified specimens. However, correlative workflows on high-pressure frozen samples often remain challenging due to the lack of persistent landmarks for reliable sample tracking and image registration between different microscopes. Standard high-pressure freezing carriers provide little intrinsic reference information, as the exposed sample surface is often smooth and rotationally ambiguous, complicating localisation of regions of interest across imaging platforms. Here, we introduce PinCorr, a 3-mm high-pressure freezing carrier with an integrated coordinate system formed by four asymmetrically arranged pillars with distinct geometries. These built-in landmarks remain visible after freezing and provide a stable, sample-independent reference frame for orientation and correlation between cryo-fluorescence microscopy and electron microscopy. We show that PinCorr supports fluorescence-guided cryo-volume imaging, serial lift-out for cryo-electron tomography and freeze-substitution workflows followed by room-temperature on-section correlation. PinCorr thus provides a hardware-based approach to establishing a persistent spatial reference frame in HPF-based correlative imaging workflows for thick and multicellular specimens.

This study presents the use of sub-micron nanobubbles (NBs) as contrast agents for ultrasound localization microscopy (ULM), a super-resolution imaging technique that visualizes microvascular structure and flow beyond the acoustic diffraction limit. While ULM has traditionally relied on micron-sized microbubbles (MBs), the reduced dimensions and prolonged circulation times of NBs make them attractive candidates for localization-based imaging. However, their weaker acoustic responses present significant challenges for reliable detection and tracking. To address this challenge, we developed the ULM Master GUI, an interactive framework for optimization of the complete ULM processing pipeline. Using custom ultrasound-compatible wall-less gelatin flow phantoms containing vessel-mimicking channels and bifurcations ranging from 100 to 500 m, we demonstrate that NB-based ULM achieves velocity reconstruction and flow partitioning measurements comparable to conventional MB-based ULM. Across all investigated geometries, NBs faithfully reproduced the underlying flow patterns and hemodynamic behavior despite their substantially reduced acoustic scattering. These findings establish the feasibility of NB-based ULM, expand the range of contrast agents available for localization microscopy, and provide a foundation for future super-resolution ultrasound imaging using nanoscale acoustic contrast agents. The ULM processing GUI is publicly available at https://github.com/grisha1998/ulm-super-resolution-toolbox.

Flow imaging microscopy (FIM) is an important technology for high-throughput characterization of microscopic particles and microorganisms. However, conventional FIM relies on single-plane imaging (SPI), resulting in out-of-focus particles, reduced measurement precision, and incomplete characterization of irregularly shaped objects extending along the z-axis. To address these limitations, a volumetric flow imaging (VFI) framework was developed and implemented on the portable ARTiMiS platform. This approach captures multiple frames along the z-axis and extracts the highest fidelity image for each particle, which can also be used for single image generation with all particles in focus (i.e., all in focus image) and for three-dimensional reconstruction of irregularly shaped objects. Benchmarking VFI with microspheres, live cells (Chlorella vulgaris), and filamentous cyanobacteria demonstrated increased fraction of particles in focus, reduced variability in particle size measurement, and increased resolvability of elongated particles in comparison to conventional SPI on commercially available FIM technologies. For C. vulgaris, VFI-derived size distributions closely matched curated FlowCam measurements without requiring post-processing to exclude out-of-focus particles. All-in-focus image reconstruction enabled simultaneous visualization of particles distributed across multiple depths and consistently resolved a greater proportion of filamentous structures as compared to SPI. For Aphanizomenon sp., Dolichospermum sp., and Planktothrix agardhii, the SPI approach captured only 84%, 61%, and 58%, respectively, of the total filament length resolved by AIF reconstruction. Beyond image-based characterization, VFI enabled estimation of dynamic particle properties such as sinking velocity and mass density. Application of this framework to C. vulgaris cultures revealed distinct mass-density trajectories under nitrogen-replete and nitrogen-deplete conditions, with cell mass density increasing over time under nitrogen-replete conditions and decreasing under nitrogen deprivation. Collectively, these results establish VFI as a next-generation framework for FIM that expands its analytical capabilities beyond conventional morphometric characterization and provides new opportunities for single-cell-enabled environmental monitoring and biomanufacturing.

Hyperspectral imaging (HSI) has gained increasing attention for bone assessment because it captures rich wavelength dependent information associated with mineralised tissue. HSI provides detailed spectral information related to material composition, while 3D geometric information supports the analysis of surface morphology and structural detail. However, integrating spectral and geometric information remains challenging, particularly when conventional reconstruction pipelines depend on external pose estimation. To address this challenge, we propose BoNeRF-HS, a self-calibrated hyperspectral neural radiance field for 3D reconstruction. BoNeRF-HS jointly optimises camera intrinsics, volume density, and hyperspectral radiance, removing the need for COLMAP based poses. To improve spectral modelling, we incorporate a gated spectral adapter head that learns wavelength dependent radiance features for hyperspectral view synthesis. We evaluate BoNeRF-HS on a multi-view hyperspectral dataset containing mouse bone, trabecular bone analogue, and cortical bone analogue samples. Experimental results demonstrate that our framework achieves improved reconstruction quality, and better preservation of bone surface details compared with existing approaches.

Serial-section immunohistochemistry (IHC) is the largest available source of paired hematoxylin and eosin (HE) and IHC whole slide images, yet it remains underexploited for cell-level supervision: adjacent sections sample non-identical cells, and residual registration error prevents direct assignment of IHC labels to individual HE cells. We present CellDF (Cell Displacement Field), which turns registered serial-section data into pairs of HE cells and their IHC labels by solving cell matching at whole-slide scale and assessing its reliability without ground-truth correspondences. CellDF estimates a locally adaptive residual displacement field through iterated kernel regression over each HE cells K nearest IHC candidates; a sparse-kernel variant keeps it tractable at the cell counts of a whole slide, where pairwise matchers are not. The within-tile distribution of the estimated displacements yields two ground-truth-free statistics, the directional scatter{sigma}{theta} and the between-tile angular deviation |{Delta}{theta}|, that localize matching quality more finely than landmark-based target registration error and drive a two-stage outlier filter that withholds labels where matching is unreliable. On 54 same-section HyReCo pairs,{sigma}{theta} correlates only moderately with landmark error and flags localized restaining damage that global error misses; on 30 four-marker Acrobat serial-section cases, the same statistic flags which IHC marker, if any, lies physically close enough to HE to support cell-level transfer. As a proof of concept, IHC labels transferred through CellDF trained a cell classifier on HE embeddings that generalized to held-out cells within the sample (F1 0.85, AUROC 0.88), establishing serial-section IHC as a usable cell-level labeling resource. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/733058v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@a9b3dcorg.highwire.dtl.DTLVardef@15f652corg.highwire.dtl.DTLVardef@1eb3396org.highwire.dtl.DTLVardef@87dda2_HPS_FORMAT_FIGEXP M_FIG C_FIG

Designing transcutaneous skeletal muscle oxygenation (SmO2) sensors requires jointly optimizing source--detector geometry and wavelength selection while guaranteeing performance across populations that vary in subcutaneous fat thickness and skin pigmentation. We present a multi-fidelity Bayesian optimization (MFBO) framework that couples Monte Carlo light-transport simulations at two photon-count fidelities to a distributionally robust design objective. An autoregressive Gaussian-process surrogate learns the correlation between inexpensive low-photon-count and accurate high-photon-count simulations, and a cost-aware acquisition function decides both where and at what fidelity to sample. Robustness across the population is enforced with Conditional Value-at-Risk (CVaR) and entropic-risk (ERM) objectives that target worst-case subjects rather than the population average. On a five-layer forearm tissue model with anthropometric variability we find (i) a fidelity regime that is favorable for MFBO where the low-fidelity surrogate is rank-informative (Spearman {rho} = 0.84) but biased, at 100x lower cost; (ii) MFBO attains 23% higher robust sensitivity than a strong high-fidelity single-fidelity baseline at equal budget (p = 0.035), and avoids the optimistic bias that causes low-fidelity-only optimization to collapse when its designs are validated at high fidelity; (iii) CVaR/ERM objectives improve worst-case tail performance by {approx}23% relative to a mean objective without sacrificing average sensitivity; and (iv) discovered designs improve robust tail sensitivity by roughly 3--6x over commercial and heuristic optode layouts, with the largest gains in the high-fat and high-melanin subpopulations. The methodology bridges stochastic light-transport physics with sample-efficient machine-learning optimization and generalizes to cerebral oximetry, photodynamic therapy planning, and wearable physiological monitors.

The advancement in deep learning techniques has made Convolutional Neural Networks (CNNs) a powerful tool for the fully automated classification of zooplankton images. In this study, we systematically investigate how network selection, colour information and differences in imaging instruments affect the classification of zooplankton images by comparing multiple state-of-the-art CNNs on images of zooplankton and marine snow from the in situ Continuous Particle Imaging and Classification Sensor (CPICS), Video Plankton Recorder (VPR), In Situ Ichtyoplankton Imaging System (ISIIS), and the on-board Plankton Imager (Pi-10). With differences between models of 7.8 to 19% in F1-score, we find that model selection strongly affects the classification performance, with EfficientNetV2S showing the most reliable overall performance. Moreover, differences between model architectures are largest for the least abundant classes (<100 labeled images), which implies that when these are present, careful model selection is most beneficial. The high image quality of the Pi-10 strongly increases the performance for the least abundant classes compared to the other instruments. In addition, we find a significant correlation (r = 0.597) between ImageNet the performance and F1-score on zooplankton images, which implies that more generally, a model that performs well on ImageNet will perform well for zooplankton classification. Colour information increases the F1-score of the best performing classifier with 2.8%, but provides a stronger benefit (25% F1-score) for classes with <100 images. The overall performance increase of colour information is less than expected and questions the advantage of recording colour information for zooplankton.

With the rapid expansion of global food demand, aquaculture has become a critical pillar for future food security. However, aquaculture systems remain highly vulnerable to pathogenic bacteria, and rapid identification of antagonistic microbes is essential for sustainable disease control. Conventional evaluation approaches rely on fluorescence labeling or post-culture assays, limiting the ability to quantify dynamic interactions in mixed microbial populations in a real-time and label-free manner. Here, we propose a computational framework for classifying the mixing ratio of Vibrio harveyi and environmental bacteria using time-series motion features extracted from microscopy videos. We defined 24 interpretable motility descriptors and employed a Temporal Convolutional Network (TCN) to learn their temporal structure. The proposed method achieved a classification accuracy of 93.3%, outperforming conventional static statistical approaches and alternative machine learning models. These findings indicate that mixture discrimination in microbial communities is governed not by absolute motility magnitude, but by collective alignment and its temporal stability. Our study establishes a time-resolved computational framework for quantifying dynamic collective order in mixed microbial populations and highlights its potential for label-free automated screening and robotic microbiological applications.

Cryo-volume electron microscopy (cryo-vEM) enables near-native visualization of cellular ultrastructure, but its broad use is limited by low image contrast and the high cost of dense voxel-level annotation. Existing automated segmentation methods often generalize poorly across cell types, organelles, and imaging conditions. Here, we introduce SparseSeg, a target-conditioned, sparsity-driven segmentation framework that treats organelle segmentation as a discovery process rather than a closed-set classification task. SparseSeg uses a small number of context-specific exemplars to iteratively propagate reliable supervision through the volume. It combines sparse patch-based sampling, a multi-kernel U-Net, and geometry-consistent refinement to expand accurate segmentation while suppressing context-dependent false positives. Across serial cryo-FIB-SEM and conventional vEM datasets, SparseSeg achieves robust segmentation under extreme sparse annotation, including settings with less than 1% labeled slices. This framework reduces annotation burden while preserving morphological fidelity for quantitative cryo-vEM analysis.

Single-particle tracking (SPT) over time enables direct observation of molecular transport and interactions in living cells. Fluorescence-based SPT has provided insights into intracellular processes such as endocytosis, receptor signaling, and drug delivery. Extending the observation window to several hours and beyond is critical for capturing slow intracellular dynamics, including the full course of endosomal trafficking, the long-term accumulation of particles within subcellular compartments, and transitions between transport modes that occur on hour-scale timescales. However, long-term intracellular SPT under visible-wavelength excitation remains challenging because fluorescence probes generally suffer from photobleaching and phototoxicity. While near-infrared (NIR) excitation can simultaneously mitigate these issues, generally weak emission of NIR-emitting dyes has hindered its wide application in long-term SPT. Here, we demonstrate long-term NIR SPT using atomically precise gold quantum needles, Au42(PET)32 (PET = 2-phenylethanethiolate). Continuous tracking of intracellular particles in living HEK293 cells was achieved for up to 12 h. Trajectory analysis revealed temporal transitions between directional and diffusive transport, as well as the accumulation of multiple particles within localized intracellular domains over several-hour timescales. The high photostability of Au42, combined with low phototoxicity of NIR excitation, enables visualization of intracellular transport dynamics over timescales difficult to access using conventional visible fluorescent probes. These results establish Au42-based NIR imaging as a platform for long-term, low-phototoxicity intracellular SPT and provide a framework for investigating slow intracellular dynamics in living systems.

Cryo-volume electron microscopy (CVEM) enables three-dimensional imaging of biological ultrastructure in a near-native state but has been limited by low image contrast and charging artifacts that hinder data interpretation and complicate automation of data acquisition. Here we present an experimental and computational workflow that combines orthogonal cryo-SEM imaging, spot-geometry optimized O+ plasma-FIB milling, dedicated acquisition-control routines, and dedicated image alignment procedure. The workflow enables autonomous acquisition of volumetric datasets from vitrified cells and tissues at [~]15-20 nm isotropic resolution. In addition, sub-volume averaging of 113 nuclear pore complexes extracted from CVEM dataset of Cos-7 cell yielded its reconstruction at 9.4 nm resolution. Together, these results establish CVEM as a robust platform for autonomous high-resolution volumetric imaging and structural analysis of vitrified biological specimens.

Genomic DNA is wrapped around core histones to form nucleosomes, which are organized in cells from euchromatin to heterochromatin with distinct genome functions. Although transcription is known to shape chromatin behavior in live cells, it remains unclear how different transcription systems shape chromatin classes and nuclear subcompartments. We developed machine learning-assisted Repli-Histo labeling to classify euchromatin and heterochromatin classes (Classes IA, IB, II, and III) and combined it with single-nucleosome imaging in live cells. Nucleosome motion was progressively constrained from euchromatin to heterochromatin. RNA polymerase II inhibition by THZ1, DRB, or -amanitin increased nucleosome motion in euchromatic Classes IA and IB and in heterochromatin around nucleoli, but not at the nuclear periphery. In contrast, RNA polymerase I inhibition by CX-5461 selectively increased nucleosome motion in Class III heterochromatin around nucleoli. Our study reveals that Pol II and Pol I transcription shape chromatin behavior in distinct chromatin classes and nuclear subcompartments. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/736477v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@127137dorg.highwire.dtl.DTLVardef@709a16org.highwire.dtl.DTLVardef@94550corg.highwire.dtl.DTLVardef@5ba6ec_HPS_FORMAT_FIGEXP M_FIG C_FIG

Classical immunolabeling approaches can achieve homogeneous and continuous labeling of cellular membranes and organelles at wide-field and confocal resolution. In super-resolution and expansion microscopy, however, the lack of high-density labels hampers the localization of membrane proteins and protein complexes within their membrane context. Here we show that secondary antibodies coupled to the lipophilic dyes ATTO 647N or ATTO 550 brightly label the nuclear envelope, mitochondria, and endoplasmic reticulum of fixed, permeabilized cells, and that graded labelling intensities allow selective visualization of organelles and precise segmentation of mitochondria. Using state-of-the-art super-resolution and expansion microscopy, we achieve high-density labelling of nuclear and mitochondrial membranes, with targeting and density comparable to existing membrane-labelling approaches and a signal that can be further amplified with additional secondary antibodies. Finally, we show that these dye-conjugated IgG allow to resolve mitochondria-ER contacts and mitochondrial ultrastructure as well as precise visualization of the nuclear envelope and its invaginations. This study demonstrates that secondary antibodies conjugated to lipophilic fluorophores represent stable, convenient and affordable tools for organelle visualization in conventional microscopy and for high-density labeling of membranes in super-resolution and expansion microscopy.