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.

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.

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.

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 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.

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.

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.

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.

Phasor analysis is a well-established tool in hyperspectral and lifetime microscopy, providing a powerful, fit-free approach for interpreting complex fluorescence. However, its application has remained largely restricted to imaging-based modalities. Spectral flow cytometry (SFC) enables acquisition of full emission spectra from large numbers of independent single-cell events, offering superior statistical power compared to microscopy, albeit at the expense of spatial and temporal information. Here, we present the first implementation of spectral phasor analysis for SFC (phSFC), establishing a unified analytical framework that preserves interpretative continuity with hyperspectral microscopy while extending phasor-based analysis to high-throughput, single-cell measurements. Using the membrane-sensitive probe LAURDAN as a benchmark, we demonstrate that SFC reproduces phasor signatures of membrane order previously reported by hyperspectral confocal microscopy (HSI). We performed comparative analyses using multilamellar lipid vesicles (MLVs) prepared from known physical order compositions. Both modalities, SFC and HSI, accurately resolved MLVs with fluid, gel and liquid-ordered and liquid-disorder membrane phases, capturing cholesterol-dependent spectral shifts, including trajectories associated with mixtures of the different lipid phase behavior. Although absolute phasor coordinates differed between modalities due to distinct spectral sampling and detector configurations, the relative organization of membrane physical states was preserved. Notably, SFC produced more compact phasor distributions, consistent with larger sample size and enhanced statistical robustness. To further extend phSFC, we first evaluated its capacity to resolve membrane changes in live cultured cells following cholesterol depletion, establishing consistency between HSI and SFC measurements. We then applied phSFC to detect membrane dynamics in primary leukocytes isolated from bronchoalveolar lavage of mice with inflammation-associated lung pathology. LAURDAN fluorescence in the presence of autofluorescence and antibody-derived signals is quantified and discussed with simple solution by n-harmonic phasor analysis unmixing. Together, these results establish SFC as a robust and complementary extension of LAURDAN phasor analysis, bridging HSI and high-throughput flow cytometry measurements.

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.

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.

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.

Membrane binding is thought to trigger early aggregation of alpha-synuclein (aSyn) in neurons. However, live-cell measurements of membrane-proximal aggregation with high specificity remain challenging. We combine three- channel fluorescence lifetime imaging microscopy (FLIM) with Forster resonance energy transfer (FRET) in a model that uses FRET as a proximity filter. A membrane-tethered donor reports membrane-aSyn separation through lifetime changes, while an aSyn-labeled acceptor reports the aggregation state through its lifetime. We estimate per-cell lifetimes and mixture fractions for membrane-bound and membrane-unbound populations using a hierarchical expectation-maximization (EM) algorithm that pools information across pixels. We validate the estimator using Monte Carlo studies. Using experimental neuronal data, this method resolves changes in membrane-proximal aggregation and aggregate-associated lifetimes. This framework provides quantitative per-cell metrics linking membrane proximity and aggregation for comparative live-cell studies. SIGNIFICANCEMany studies map FRET signals pixel by pixel or average signals over whole cells. Neither approach reliably answers key biological questions: how much membrane-proximal aggregation exists in a given cell, and how does it change across conditions? Here, we combine three-channel FLIM-FRET with a hierarchical analysis that estimates per-cell lifetimes and fractions for bound and unbound populations by pooling information across pixels. This method shifts the focus from noisy images to cell-level metrics that support comparisons across treatments, time points, or genotypes. Simulations and neuronal data show improved accuracy under realistic photon budgets and reveal membrane-proximal aggregation effects that were unattainable otherwise. This approach is broadly applicable and extends beyond alpha-synuclein.

High-resolution optical microscopy enables nanoscale investigation of molecular structures but is challenged by sample drift during long acquisitions, particularly in thick biological tissues where trans-illumination is unpractical. Precise stabilization at the nanoscale is critical for high-resolution imaging techniques like localization microscopy and single particle tracking. Here we introduce a method combining homogenized differential phase contrast imaging with cross-correlation-based analysis to achieve automated, precise 3D drift correction applicable under oblique back-illumination. We demonstrate its effectiveness in fixed and live organotypic brain slices, maintaining focus within tens of nanometers and enabling high-quality nanoscale mapping of extracellular structures based on single particle tracking. Furthermore, we illustrate its application to opaque liver tissues combined with near-infrared single particle tracking. Our label-free approach provides a versatile solution for stabilizing optical microscopes in thick non-transparent tissues, facilitating extended high-resolution imaging across increasingly complex biological samples.

High-plex spectral imaging has the potential to transform the analysis of spatial organization in cells and tissues, yet its practical implementation remains limited by challenges in panel design, sample preparation, signal balancing, and experimental validation. While cyclic imaging approaches are widely used in spatial omics, spectral imaging across the full fluorescence spectrum and computational unmixing remain underutilized due to these challenges. Here, we present a generalizable framework for high-plex spectral imaging that leverages DNA-barcoded labeling and programmable signal amplification to provide precise control over fluorescence signal composition. Orthogonal DNA barcodes decouple target labeling from fluorophore detection, enabling reversible fluorophore application and systematic panel optimization directly on the same sample. Programmable DNA-based amplification further enables independent and quantitative tuning of fluorescence intensities across targets, overcoming a key limitation of spectral unmixing, namely imbalanced signal contributions in overlapping channels, and thereby improving accuracy and robustness. The framework also supports the generation of experiment-specific ground truth datasets and systematic evaluation of unmixing algorithms, providing a quantitative basis for panel validation and performance assessment. We demonstrate the practical implementation of this framework by developing a panel for simultaneous imaging of 15 subcellular structures without fluidic cycling and using the optimized panel to profile the effects of chemical perturbations on subcellular organization. We quantitatively evaluate panel compilation and provide a rigorous assessment unmixing performance using both linear and reference-free unmixing methods. Importantly, we leverage foundation models trained on standard fluorescence data, for segmentation-free, high-dimensional analysis of spectrally unmixed images without needing large datasets or model retraining. Together, we establish a practical and tunable framework for high-plex spectral imaging that lowers experimental barriers and enables broader adoption of spectral unmixing for biological and biomedical applications.

Investigating the movements and conformational changes of proteins in living cells is essential for understanding their function. The recently introduced fluorescence nanoscopy method called MINSTED has successfully tracked single fluorophore-labelled proteins, albeit in fixed cells and two dimensions only. Here we introduce a MINSTED setup for live-cell tracking of individual proteins in three dimensions (3D) with a localization precision {sigma} down to < 1 nm. Applied to the motor protein kinesin-1, our MINSTED nanoscope follows single proteins in 3D as they process along microtubules in 16 nm steps. Individual steps are resolved amid substantial intracellular background. The unique capability of the 3D stimulated emission depletion (STED) beam to carve out the signal of the protein label of interest enables efficient single-molecule investigations at hitherto unpractically high concentrations of labelled proteins and thus in crowded live-cell environments.

Multicolour and spectrally resolved single-molecule microscopy can reveal molecular interactions, nanoscale environments and dynamics, but usually depends on experimentally complex detection architectures based on beam splitting, spectral dispersion or engineered point spread functions. Here we show that spatially patterned detectors offer a conceptually simpler route to spectral single-molecule imaging. By replacing a conventional monochrome camera with a commercially available colour CMOS detector and fitting the raw detector response directly, we recover both molecular position and spectral fingerprint from a single image without optical splitting, channel registration or demosaicing. We term this approach spatial spectral single-molecule microscopy, or S3M. We show that S3M retains single-molecule sensitivity across the visible spectrum, enables robust spectral multiplexing, and supports applications spanning multicolour single-molecule tracking, single-molecule Forster resonance energy transfer, multicolour localisation microscopy and spectral PAINT. Although spatial patterning necessarily trades photon efficiency for spectral information, current low noise detectors already provide sufficient performance for a broad range of experiments. Spatially patterned detection therefore establishes a widely accessible strategy for simplified spectral microscopy and single-molecule spectroscopy, and points towards a new class of detector informed photonic measurement schemes for nanoscale imaging.