Light: Science & Applications

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.

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.

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

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

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.

Dragonfly and cicada wing-inspired titanium nanopillar surfaces show promising bactericidal properties for antibacterial medical implant applications, but the precise mechanisms of bacteria-nanopillar interactions under hydrated conditions remain unclear. Cryo-electron tomography (cryo-ET) enables the visualisation of cellular organelles within their native hydrated cellular environment at molecular resolution. Visualising the bacteria-material interface on nanostructured surfaces by cryo transmission electron microscopy (cryo-TEM) requires the preparation of thin lamellae. Obtaining lamellae of bacteria directly on metal substrates while in a non-fixed and hydrated state requires cryo-focused ion beam (cryo-FIB) milling to isolate the targeted bacteria from the bulk sample. This approach faces additional challenges compared to tissues or cells on TEM grids, as titanium samples require a simultaneous cross-section of soft and hard materials at the same position and require vitrification, which embeds the sample in a thick layer of ice. Nonetheless, we demonstrate how to target a specific bacterium interacting with a titanium nanopillar surface using correlative cryo-fluorescence imaging, and how lamellae can still be prepared from vitrified samples by extracting the targeted bacterium and its surrounding as a small volume and transferring it to a receptor grid for thin lamella preparation, called targeted cryo-lift-out. Here, we outline the workflows and discuss their advantages and limitations for producing lamellae through lift-out techniques under cryogenic conditions, using methods that do not involve gas injection systems (GIS) for the lift-out transfer. These advances enhance cryo-ET applications, enabling in situ investigations of the interface between bacteria and nanopillars to effectively study the bactericidal mechanisms of biomimetic nature-inspired nanotopographies in a hydrated environment.

The ability to detect host cell factors during Staphylococcus aureus infection in vitro by immunofluorescence microscopy is severely hampered by staphylococcal protein A (SpA), a cell wall-anchored protein that binds the fragment crystallizable (Fc) region of immunoglobulins. This interaction generates strong nonspecific fluorescent signals on the bacterial surface, complicating data interpretation and limiting the accuracy of quantitative image analysis. Several measures have been put forward to overcome this obstacle, most importantly the pre-incubation with an anti-SpA antibody (SpA) and the use of human serum (HS) as blocking agent and antibody diluent. To highlight this feature to general fluorescence microscopy users, we here systematically evaluated these two strategies. Using S. aureus coated on coverslips and S. aureus-infected A549 cells, we highlight the efficiencies of both approaches to markedly reduce nonspecific fluorescence, with HS treatment yielding the most profound suppression. Notably, HS, containing high levels of human immunoglobulins, offered a robust, cost-effective and broadly applicable solution for minimizing SpA-driven artifacts, thereby improving immunofluorescence microscopy in S. aureus infection models in vitro.

High-resolution noninvasive imaging of neuronal activity at single-cell resolution deep within brain tissue is essential for understanding brain function and disease. Here we show that an improved 1300-nm three-photon microscope for maximal excitation and collection efficiency enables imaging up to the three-photon depth limit in the intact mouse brain. Our platform achieves structural imaging of brain vasculature at depths up to 2.5 mm and functional imaging of neural activities at depths up to 2 mm, reaching deep brain regions previously inaccessible by multiphoton imaging. These advances extend the frontier of deep-tissue functional imaging and open new possibilities for longitudinal and mechanistic studies in neuroscience and beyond.

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.

Multimode fibers enable minimally invasive, high-resolution imaging through ultrathin probes, thereby enhancing diagnostic precision and facilitating real-time monitoring in delicate anatomical regions. In this work, we introduce HYFEN, a hydrogel-based endomicroscopic imaging platform for flexible, biocompatible, and subcellular-scale fluorescence microscopy. HYFEN leverages the unique properties of hydrogel materials, adaptive optics, and pixel-wise image enhancement to address challenges associated with silica-based fibers, including mode scrambling, limited field of view, and mechanical rigidity. The technique achieves precise mode threading, rapid diffraction-limited focusing at kilohertz speeds, and high-fidelity fluorescence signal acquisition with subcellular resolution. Notably, the approach extends fluorescence imaging under enhanced fiber dimensions and bending conditions that are unachievable with conventional modalities. Together, these advances establish HYFEN as a versatile platform for next-generation biointerfacing and minimally invasive imaging across biomedical and clinical settings.

Super-resolution microscopy with DNA-PAINT enables molecular-scale, multiplexed, and quantitative imaging, but its throughput is limited by slow binding kinetics and elevated background at high probe concentrations. Recent speed-optimized and fluorogenic probes improve performance but impose strong constraints on sequence design, revealing a fundamental tradeoff between fast binding and efficient quenching. Here, we introduce a modular probe architecture that spatially decouples binding kinetics from fluorophore-quencher interactions by integrating speed-optimized sequence motifs with PEG spacers. Using DNA origami nanostructures, we demonstrate enhanced localization rates, signal-to-background ratios, and imaging efficiency compared to state-of-the-art probes. We validate our approach in cells, demonstrating its capability to image nuclear targets and enabling three-dimensional imaging of the endoplasmic reticulum using standard widefield illumination. Our work establishes a general framework for fast, multiplexed, and low-background super-resolution imaging.

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.

In both neonates and adults, the presence of malignancy-associated mutations in peripheral blood (PB) correlates with an elevated risk of future neoplastic transformation, with certain mutations, such as KMT2A rearrangements, exhibiting near-complete penetrance. If feasible, pre-malignant screening could enable early intervention and even disease prevention. However, nucleic acid sequencing- and hybridization-based mutation detection have limited cost-efficiency, constraining their use in screening. Here, we introduce a computer vision platform that can identify mutant cells in fresh PB samples that carry KMT2A-MLLT3 (a frequent mutation in pediatric and adult leukemias and detectable in newborn blood samples) or JAK2-V617F (a frequent mutation in myeloproliferative neoplasms and clonal hematopoiesis). This is achieved by high-throughput single-cell imaging and mutation detection by machine learning (ML)-powered morphology recognition. The ML models were developed by cross-species learning of conserved features between mutant cells from mouse genetic models and from human samples, enabling a cost-effective approach for detecting mutations in live blood cells. This platform holds promise for pre-malignant screening in asymptomatic neonates and adults with KMT2A-MLLT3 or JAK2-V617F mutation and is potentially generalizable to the detection other malignancy-associate mutations. Our platform provides a novel single-cell morphological data modality that complements existing single-cell genomics.

The neurosensory retina is one of the most metabolically active tissues in the body and a uniquely accessible extension of the central nervous system, where neuronal and vascular structures can be visualized non-invasively. Its accessibility and highly organized laminar architecture make it a powerful model for studying vascular development and a window into systemic health. Although computational analyses of retinal images have enabled risk assessment for ocular and systemic diseases, most vascular studies rely on two-dimensional frameworks with limited resolution of capillary structure and layer-specific organization. Here, we present a high-resolution three-dimensional (3D) imaging and analysis pipeline enabling quantification of retinal microvasculature and extraction of structural and network metrics across vascular layers. We apply this approach to two mouse models of aberrant retinal vascular development: one with spontaneous postnatal chorioretinal neovascularization and another with disrupted neurovascular lattice formation and layered organization in early life. Across both pathologic contexts, 3D analysis provides detailed characterization of vascular architecture and identifies early vulnerability of the intermediate layer plexus (IMP) as a sensitive indicator of abnormal remodeling and neovascularization. This framework enables precise characterization of retinal vasculature and establishes a foundation for identifying new retinal biomarkers with potential relevance to neurovascular and systemic disease.

Deep learning can extract quantitative measurements from microscopy images that are inaccessible to classical analysis, but developing these models requires machine learning expertise that most imaging scientists do not have. Here we present a framework in which a researcher describes their microscopy problem to a large language model (LLM) agent in under ten minutes of conversation--specifying what they image, what they want to measure, and what success looks like--and the agent autonomously handles the rest: designing physics-based training data, implementing a neural network, training, diagnosing failures, and iterating without human intervention. A researcher can start the agent before leaving the lab; overnight, it tests tens to a hundred model variations, each one an experiment that would otherwise demand active attention. We validate the framework across six microscopy modalities and four problem types. On the BBBC039 nuclear segmentation benchmark, the agent autonomously trains a U-Net with 3-class semantic segmentation and morphological post-processing, achieving pixel-level Dice of 0.97 and object-level F1 of 0.84--within 7% of the published baseline--while diagnosing a data pipeline bug that no amount of hyperparameter tuning could resolve. On single-protein holographic microscopy, the agent reads a published paper, designs a simulator, and develops an optimized model in a single session. On PatchCamelyon histopathology classification, the agent autonomously evolves through four optimization phases--from scratch training through transfer learning and regularization to inference-time ensembling--completing 97 iterations on 262,144 images to reach 89.3% test accuracy and 96.3% AUC, nearly matching the published rotation-equivariant baseline. This framework enables microscopy researchers to use deep learning-based image analysis without machine learning domain knowledge.

Upon T cell receptor (TCR) engagement, a T cell forms an immunological synapse (IS) with an antigen-presenting cell (APC), which can be mimicked by purified ligands on supported lipid bilayers (SLBs)1,2. Microvilli actively scan the surface; upon initial engagement, F-actin-dependent TCR microclusters form, and the central supramolecular activation cluster (cSMAC) sustains TCR signaling in CD8 T cells3,4. Although signaling activities within the IS have been observed qualitatively through total internal reflection immunofluorescence microscopy5-7, the stoichiometric relationships among the components of the TCR signalosome remain unknown. In this study, we employed a two-step approach to quantify the components of the TCR signalosome. First, Jurkat cell lines expressing GFP-tagged proteins on a knockout background were used to calibrate fluorescence intensity (IF) signals against molecular copy numbers, based on measurements of single-tag signals and multiple corrections. In the second step, this calibration was applied to determine the stoichiometries of key TCR signalosome components, including TCR, CD8, CD28, CD45, PD-1, Lck, ZAP-70, LAT, and PLC{gamma}1, across scanning, early activation, and sustained activation states in human primary T cells. We refer to the method as quantitative extrapolation from single-tags (QuEST) immunofluorescence microscopy. Applying the QuEST, we were surprised to find that the ZAP-70:TCR ratio in microclusters and the cSMAC was 1:1, far from the potential 10:1 ratio. Nanoscale structures of the TCR signalosome were further captured using direct stochastic optical reconstruction microscopy (dSTORM), confirming that ZAP-70 was strongly co-localized with the TCR. Moreover, we applied QuEST to confirm the presence of T cell intrinsic CD28 recruitment, independent of CD80 or CD86 on SLBs, during TCR activation. This T cell intrinsic CD28 recruitment could be disrupted through engagement of PD-1 with PD-L1 on SLBs. This shows that PD-1 engagement can disrupt T cell intrinsic CD28 costimulation. QuEST provides a broadly applicable pipeline for quantitative analysis of TCR signalosomes in human primary cells, enabling a quantitative basis for the rational manipulation and engineering of the TCR signalosome in immunotherapies.

We present a live-to-cryo correlative imaging workflow for multiscale structural and chemical analysis of biological tissues in their near-native state. The method integrates live super-resolution fluorescence microscopy, live and cryogenic Raman spectroscopy, and targeted cryogenic focused ion beam/scanning electron microscopy, transmission electron microscopy, electron tomography, energy dispersive X-ray spectroscopy, and electron diffraction. This approach enables precise 3D targeting and nanoscale imaging of selected regions across four orders of magnitude in spatial resolution, while preserving ultrastructure and chemical composition. Using regenerating zebrafish scales as a benchmark, we visualize collagen fibril orientation, local matrix density, and mineral composition within the extracellular matrix. We identify a plywood-like architecture of unmineralized collagen with orientation-independent density variation, and reveal curved, acidic phosphate-rich mineral platelets aligned with collagen fibrils. This workflow establishes a generalizable strategy for comprehensive 3D correlative analysis of hybrid tissues, and opens new opportunities for studying native structure-function relationships at the interface of biology and materials science.

Fourier ptychographic microscopy (FPM) uses sequential multi-angle illumination and iterative phase retrieval to recover a high-resolution complex image from a series of low-resolution brightfield and darkfield images. We present OpenFPM, an open-source FPM platform in which conventional and optomechanical hardware is replaced with compact, low-cost 3D printed components. Illumination, sample and objective positioning, and camera triggering are controlled using a Python-based interface on a Raspberry Pi microcomputer. With a 10 x /0.25 NA objective lens and 636 nm illumination, OpenFPM experimentally achieves amplitude and phase reconstructions with an effective synthetic NA of 0.90 over a 1 mm field-of-view. This platform gives researchers accessible and affordable hardware for developing and testing LED-array microscopy techniques for a range of biomedical imaging applications.

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.

Three-dimensional (3D) handheld photoacoustic tomography typically relies on bulky and expensive external positioning trackers to correct motion artifacts, which severely limits its clinical flexibility and accessibility. To address this challenge, we present PA-SfM, a tracker-free framework that leverages exclusively single-modality photoacoustic data for both sensor pose recovery and high-fidelity 3D reconstruction via differentiable acoustic radiation modeling. Unlike traditional Structure-from-Motion (SfM) methods that formulate pose estimation as a geometry-driven optimization over visual features, PA-SfM integrates the acoustic wave equation into a differentiable programming pipeline. By leveraging a high-performance, GPU-accelerated acoustic radiation kernel, the framework simultaneously optimizes the 3D photoacoustic source distribution and the sensor array pose via gradient descent. To ensure robust convergence in freehand scenarios, we introduce a coarse-to-fine optimization strategy that incorporates geometric consistency checks and rigid-body constraints to eliminate motion outliers. We validated the proposed method through both numerical simulations and in-vivo rat experiments. The results demonstrate that PA-SfM achieves sub-millimeter positioning accuracy and restores high-resolution 3D vascular structures comparable to ground-truth benchmarks, offering a low-cost, softwaredefined solution for clinical freehand photoacoustic imaging. The source code is publicly available at https://github.com/JaegerCQ/PA-SfM.