ACS Photonics

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.

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.

Fluorescence Lifetime Imaging Microscopy (FLIM) enables quantitative mapping of molecular environments in living systems with high biochemical specificity. However, spatial overlap dictated by the diffraction-limited point spread function (PSF) causes a mixing of temporal signals: photons from neighboring emitters collected within the same pixel yield composite decay profiles, generating apparent intermediate lifetimes that can be mistaken for variations in the local molecular environment. We introduce a workflow that applies Mean-Shift Super-Resolution (MSSR) to raw intensity data to generate intensity-derived spatial masks prior to phasor-based lifetime analysis. The method is computationally efficient and preserves decay kinetics because it operates on intensity-derived spatial information rather than modifying temporal data. In U2OS cells labeled with spectrally-overlapping fluorophores, phasor analysis reveals an intermediate lifetime population localized at PSF-overlap interfaces, consistent with optical mixing rather than intrinsic lifetime heterogeneity. MSSR-derived masking suppressed this mixed population while preserving stable phasor cluster centers -i.e. the distribution of similar phasor coordinates in the phasor plane- for each fluorophore. Simulations of strictly monoexponential fluorescence decay emitters further show that blended lifetime decay profiles are present at separations up to 4{sigma} and becomes maximal near [~]1.6{sigma}, indicating that conventional spatial resolution criteria can underestimate lifetime cross-talk. Application of this workflow to three-component FLIM showed also a reduced overlap of pixel distributions in phasor plots while maintaining distinct lifetime signatures. Overall, MSSR-based spatial refinement provides an accessible strategy to improve the spatial resolution while maintaining accuracy of FLIM measurements.

Cellular metabolism is governed by the coordinated organization of macromolecules, including lipids and proteins, together with redox-active cofactors such as NADH and FAD. However, resolving these biochemical features quantitatively and spatially at subcellular resolution remains challenging because no single imaging modality can capture molecular composition, redox state, and tissue architecture simultaneously without labeling. Here, we present MANIFEST (Multi-modAl Nonlinear Imaging with Fluorescence Excitation and Statistical Temporal-resolved spectroscopy), a label-free imaging platform that integrates stimulated Raman scattering (SRS), second harmonic generation (SHG), multiphoton fluorescence (MPF), and fluorescence lifetime imaging microscopy (FLIM). The MANIFEST combines chemical imaging of lipids with autofluorescence- and lifetime-based quantification of NADH and FAD metabolism, enabling spatially resolved analysis of metabolic heterogeneity at organelle and tissue-compartment levels. We apply this framework to four distinct aging or disease models: amyloid-beta-treated tri-cultured brain cells, high-fat diet mouse liver, human non-ischemic cardiomyopathy tissue, and aging mouse retina. Across these systems, MANIFEST reveals disease-associated lipid remodeling, redox imbalance, disrupted metabolic zonation, collagen reorganization, and layer-specific metabolic changes. By integrating complementary nonlinear optical modalities into a single label-free platform, MANIFEST provides a generalizable approach for high-resolution metabolic phenotyping in complex biological systems and offers new opportunities for studying disease mechanisms, aging biology, and metabolism-driven tissue pathology.

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.

The rapid convergence of optical innovation and machine intelligence is reshaping biological imaging by enabling platforms that jointly advance image formation and computational reconstruction for highspeed, high-resolution volumetric microscopy. However, broadly accessible three-dimensional imaging at high spatiotemporal resolution remains limited by the reliance of existing supervised methods on large modality-matched training datasets, the computational burden of conventional iterative reconstruction, and sensitivity to optical mismatch arising from small deviations in the spatial-angular point spread functions. Here, we introduce HYPER-Net, a physics-conditioned self-supervised framework for Fourier light-field microscopy that integrates scan-free volumetric acquisition with fast, robust three-dimensional reconstruction. HYPER-Net incorporates experiment-specific point-spread functions into the learning process in two complementary roles: as the forward operator that enforces measurement consistency and as a conditioning signal that adaptively modulates intermediate feature representations. This design reduces reliance on paired experimental ground-truth volumes, improves robustness to system variation, and enables generalizable reconstruction across diverse biological contexts. Using human colon organoids, embryonic Xenopus laevis hearts, hiPSC-derived cardiac spheroids, and freely moving Caenorhabditis elegans, we demonstrate high-fidelity volumetric imaging of tissue morphology, cardiac function, calcium-contraction coupling, and locomotion-associated neural and muscular dynamics. These results position HYPER-Net as a versatile framework for rapid volumetric imaging and quantitative analysis of dynamic biological systems across basic research and biomedical applications.

Advances in multi-dimensional imaging method and probe developments have brought super-resolution fluorescence microscopy into a functional era. They capture additional single-molecule fluorescence information concurrently with spatial localization, enabling simultaneous identification of molecular species and interrogation of nanoscale environments with rich, high-dimensional imaging information. However, the adoption of multi-dimensional imaging has been hindered by fragmented analysis workflows, complex parameter tuning, and limited integration of advanced computational methods. Here, we introduce an agentic single-molecule multi-dimensional bioimaging AI, referred to as SIMBA, an AI-driven platform that unifies single-molecule localization, spectral processing and deep learning-based denoising within a single agentic and interactive framework. SIMBA incorporates large language model-based agents capable of interpreting user intent, orchestrating analysis pipelines, and dynamically selecting computational tools for automated data processing. We demonstrate that SIMBA enables supports standard single-molecule localization workflow, functional mapping of nanoscale environmental heterogeneity through single-molecule spectral analysis and denoising using developed supervised learning methods. By integrating extensible tool architectures with human language-guided workflows, SIMBA establishes a new paradigm for intelligent microscopy analysis, lowering barriers to multi-dimensional imaging adoption while enabling scalable, reproducible, and adaptive analysis of complex imaging datasets.

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.

High-order multiplexing in single-molecule localisation microscopy (SMLM) is limited by trade-offs between spectral discrimination, imaging speed, and experimental complexity. Here, we show that RGB cameras provide a simple and scalable solution for multi-colour SMLM by exploiting their intrinsic spectral sensitivity for statistical fluorophore discrimination. Using a realistic simulation framework incorporating experimentally derived photon budgets, optical response functions, and camera noise, we achieve simultaneous classification of up to six fluorophores with a mean precision of [~]98%, including perfect discrimination of spectrally overlapping dye pairs, while maintaining an average localisation precision of [~]3.2 nm. Performance remains robust to variations in classification thresholds but degrades with increasing fluorophore number and reduced photon budgets due to spectral overlap and photon noise. These results establish RGB detection as a cost-effective and experimentally straightforward alternative to conventional spectral imaging approaches, enabling accessible, high-throughput multiplexed super-resolution imaging.

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.

Quantitative measurements performed directly in vivo are necessary to understand how forces shape living tissues, yet this remains challenging due to optical scattering and mechanical complexity. Here, we present a method for making absolute force measurements using nanoscopic optical tweezers with a sensitivity of 300 fN in optically turbid biological media. Our approach combines back focal plane interferometry operating within the optical memory effect regime with a global fluctuation-dissipation fitting framework that simultaneously calibrates position detection, trap stiffness, and viscoelastic response. This method overcomes aberration-induced biases by jointly fitting passive fluctuations and driven harmonic responses, enabling robust force reconstruction in thick, scattering tissues within the mechanically relevant frequency range below 300 Hz. We validate our approach using highly scattering Drosophila pupae and embryos, demonstrating reliable in vivo measurements of forces and mechanical properties. Operating at a 1 kHz acquisition bandwidth, the system captures relevant mechanical dynamics without requiring extended high-frequency detection. Using this framework, we quantify the increase in cortical tension during pupal morphogenesis, characterize tissue viscoelasticity, and reveal stage-dependent variations in nuclear membrane tension during embryogenesis, even in the presence of strong ATP-driven fluctuations. Beyond bulk measurements, our method enables the quantitative mechanical characterization of single cells within mechanically coupled tissues.

Light is a modifiable determinant of health, yet real-world exposure assessment is often reduced to illuminance alone, lacks environmental context, or relies on privacy-sensitive sensing. We present SpectraVita, a low-cost, compact multispectral wearable that continuously samples 11 ultraviolet-to-near-infrared bands and, through a privacy-preserving pipeline without cameras or location tracking, produces interpretable digital phenotypes of lighting environment (natural vs. artificial and source type) and vegetation context alongside standard visual and non-visual light metrics. In extensive in-the-wild recordings spanning diverse scenes, times of day, weather conditions, and light sources, we observe distinctive spectral signatures that enable supervised models to achieve a macro-averaged F1 score of 0.988{+/-}0.004 for light-source classification and green-space detection in boundary-free environments. A sensor-derived normalized difference vegetation index (NDVI) emerges as an explainable, physically grounded marker linking natural light exposure and greenness. Robustness is supported by scenario-shift testing, image-segmentation validation, and mixed-environment experiments that demonstrate sensitivity to partial and transient exposures, as well as by longitudinal stationary monitoring and deployment in a cohort of thousands of participants capturing seasonal and behavioral variability. SpectraVita enables individualized, privacy-preserving, longitudinal monitoring of light and greenness exposure at scale, addressing a key measurement gap for precision and population health studies of daily photic environments.

Protein-protein interactions (PPIs) mediate diverse cellular processes, but PPIs are typically characterized using reconstituted in vitro biochemical and biophysical approaches. Current approaches for PPI detection in living cells are limited in the scope of interactions they can capture and often require prior knowledge of the interacting partners. To close this gap, we developed triplet tumbling microscopy (TTM), which reveals the interactions of a tagged protein of interest in cells in real time. TTM reports protein complex size from rotational diffusion ("tumbling") by leveraging infrared-triggerable emission from triplet states to track tumbling over nanoseconds to hundreds of microseconds. These long-lived triplets overcome the size limitations of existing rotational diffusion-based approaches, enabling TTM to measure species from small protein complexes to organelle-scale beads. In living cells, we apply TTM to detect PPIs, quantify fraction bound, and distinguish protein complexes by size. We measure diverse types of interactions, including rapamycin-induced dimerization, p53 homo-oligomerization, and binding of the E3-ligase E6AP to the human papilloma virus 16 E6 protein. The required hardware is compatible with most fluorescent microscopes, making TTM a versatile way to extract molecular insights from the complex context of living cells. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/723557v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1e70768org.highwire.dtl.DTLVardef@974813org.highwire.dtl.DTLVardef@1fd122borg.highwire.dtl.DTLVardef@1b3da96_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

Single-molecule localization microscopy (SMLM) methods enable fluorescence imaging of biological specimens with nanometer-scale resolution. Although fluorophore localization precision is theoretically limited only by photon statistics, in practice the resolution of SMLM images is often degraded by physical drift of the sample and/or the microscope during data acquisition. At present, correcting this effect requires either specialized stabilization systems or computationally intensive post-processing, and established drift correction algorithms based on image cross-correlation suffer from limited temporal resolution. In this study we introduce COMET, a new method for SMLM drift estimation which achieves a substantially higher precision, accuracy, and temporal resolution compared with existing algorithmic approaches. We demonstrate that improved drift estimation translates directly into higher SMLM image resolution, limited by localization precision rather than drift artifacts. COMET is applicable to all types of SMLM data, operating directly on 2D or 3D localization datasets, and is readily integrated into analysis workflows. We benchmark its performance using both simulations and experiments, including STORM, MINFLUX, and Sequential OligoSTORM measurements, where long acquisition times make drift correction particularly challenging. COMET is published as an open-source, Python-based software project and is also available on open cloud-computing platforms.

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.

Light-sheet microscopy (LSM) has revolutionized bioimaging by delivering high-contrast volumetric resolution with minimal photodamage. Spatial wavefront shaping, used to gen{-}erate lattice and Airy light-sheets, has been particularly effective in advancing LSM be{-}yond the Rayleigh limit. Despite its broad adoption, most LSM implementations rely on rigid dual-objective geometries that complicate sample handling and impose a trade-off between imaging field of view (FoV) and axial resolution. Here, we introduce space-time light-sheet microscopy (ST-LSM), a single-objective strategy that exploits space-time (ST) correlations for the first time. ST-LSM goes beyond separate spatial or temporal modulation to jointly modulate the spatiotemporal spectral structure of a pulse. This uniquely enabled light-sheets with wavelength-scale thickness over millimeter-scale dis{-}tances. When compared to state-of-the-art approaches, ST-LSM eliminates the dual-objective constraint, expands the sample-accessible volume by 25x, and increases the FoV by 10x without sacrificing sectioning resolution. We demonstrate the versatility of ST-LSM by using a single setup to image specimens across four orders of magnitude in size, from whole roots and developing embryos, down to mammalian cells with sub-cellular axial resolution. These results position ST-LSM as an accessible and high-performance optical microscopy platform at a variety of biological scales, by translating space-time wave-packet physics into a practical imaging modality.

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.

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.

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.