Nature Photonics

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.

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.

Solid-state nanopores offer label-free, real-time single-molecule sensing. However, resolving fast biomolecular transport requires high-bandwidth data acquisition while the intrinsic high-frequency noise limits recovery of informative events. Here we present a hardware-software co-designed nanopore sensing platform that combines wafer-scale low-noise device engineering with deep learning-based signal reconstruction. A low-dielectric SU8 coating on silicon nitride nanopores reduces device capacitance to the pF range and suppresses high-frequency noise by up to 5-fold while maintaining facile, controllable and reproducible fabrication. This extends usable acquisition to 40 MHz and enables capture of fast molecular features. Coupled with a reconstruction model trained on synthetic translocation events embedded in experimentally measured noise, the platform recovers transient sublevels while preserving blockage edges and temporal fidelity. Using engineered DNA molecules carrying dumbbell-like barcodes, we resolve nanometer-scale structural spacings on sub-microsecond timescales, and experimentally quantify translocation dynamics within the sub-10 nanometer regime. Dual-channel measurement on a single nanopore device further demonstrates transferability of the platform by showing robust cross-channel signal reconstruction across distinct baseline noise levels. Our approach provides a general route for reliable recovery of fast event features and may enable more information-rich single-molecule sensing across diverse biomolecular targets.

Molecular information is vital for imaging technology. Optical imaging acquires molecular specificity almost exclusively via labeling strategy, which is fundamentally constrained by limited multiplexing capacity, high running costs, and experimental complexity. Conversely, label-free optical imaging offers substantial technical simplicity but is believed to have little true molecular specificity. Contrary to common belief, here we introduce super-multiplex optical imaging without labeling. By systematically studying paired vibrational spectroscopic imaging and mass spectrometry imaging, we discovered a surprisingly strong (more than 0.9) correlation between their latent space representations, supported by both experiments and theory. This insight prompts us to build supervised learning models to successfully predict spatial distribution of 100 molecular species directly from label-free vibrational images across diverse tissue systems. We developed this technology, named Prediction through Learning with AdvaNced Chemical Kaleidoscope (PLANCK), and demonstrated it with both infrared-based vibrational imaging of organ-scale tissues and Raman-based vibrational imaging of live tissues. Powered by AI, PLANCK decodes the exquisitely rich but otherwise hidden vibrational information into a surprisingly large number of ([≥]100) specific molecular species, providing a cost-effective and scalable solution for basic research and translation, including applications in live imaging.

Label-free tracking of adherent cell migration could enable important insights into biological processes such as tissue repair, inflammatory response, or cancer progression. Nevertheless, visualizing unlabeled animal cells using optical microscopy remains challenging due to low contrast as well as frequent changes in cell shape and number. A promising alternative uses electrical capacitance measurements, which are sensitive to cell adhesion to electrode surfaces. However, prior examples often utilized electrodes with areas larger than single cells, resulting in averaged readouts over multiple cells. Here, we demonstrate label-free, live-cell tracking using a capacitance sensor array with more than 1 million pixels on a 10 micron pitch across an area larger than 1 square centimeter. We show that single cell morphology can be clearly segmented, and then used to reconstruct migration and proliferation dynamics using optical flow. We further track the spreading of multicellular spheroids, revealing fast-moving peripheral regions led by a collective leader cell "front." Finally, we demonstrate label-free imaging of millimeter-scale honeycomb-shaped tissues without the multi-image stitching often required for conventional microscopy. We utilize mutual capacitance measurements with electrically-programmable electrode spacing to reconstruct topographical features of these engineered tissues. Overall, CMOS capacitance imaging arrays enables label-free imaging spanning from single cells to large tissues, in a portable and scalable format for settings where optical microscopy may be difficult to access.

Precise, non-invasive manipulation of individual living cells remains a central challenge in biomedical science, with far-reaching implications for single-cell analysis, tissue engineering, and the study of cell-cell interactions. Here, we report the first demonstration of single-cell control using bulk acoustic standing-wave acoustofluidics with closed-loop feedback. We introduce VeLO (Vector-based Local Optimization), a model-free, reinforcement learning-inspired algorithm that enables programmable two-dimensional manipulation of individual cells using a single piezoelectric transducer. Without prior calibration or physical modeling, VeLO learns system dynamics online from acoustically induced cell displacements and automatically adapts to nonlinear, time-varying conditions. We achieve robust control across multiple cell types (DU-145, Jurkat, K-562) and independent manipulation of multiple cells, including controlled cell-cell contact. By combining simplicity of hardware with autonomous, adaptive control, this approach establishes multimodal acoustofluidics as a versatile tool for label-free, high-precision single-cell manipulation.

The accurate quantification of biological nanoparticles, such as small extracellular vesicles (sEVs), is fundamentally hindered by a resolution-coincidence trade-off in digital assays. While physical confinement can isolate single particles, conventional optical readouts remain diffraction-limited, causing multi-particle occupancy to be miscounted as single events and thereby restricting the analytical dynamic range. Here, we report a nanoplasmonic platform that overcomes this limit by introducing a geometry-defined interface that uniquely unifies nanoscale compartmentalisation and near-field-assisted super-resolution imaging. Utilising a gold plasmonic nanohole array, the strict geometric periodicity of the lattice simultaneously serves as a template for single-vesicle confinement and a deterministic grid that generates an array of localised surface plasmon resonance near-field hotspots. This position-deterministic illumination pattern imposes known geometric priors on the excitation field, shifting high-spatial-frequency information into the detectable bandwidth to achieve sub-100 nm lateral resolution. This dual-purpose geometric determinism enables high-fidelity digital readout of individual vesicles with significantly fewer sub-images than stochastic, speckle-based metasurface structured illumination microscopy approaches. The assay achieves an analytical limit of detection of 143 sEVs/{micro}L, matching the performance of state-of-the-art single-EV counting technologies. It successfully differentiates distinct sEV subpopulations based on surface biomarker expression, establishing a clear pathway for future clinical liquid biopsy applications. By replacing stochastic loading and illumination with geometric design, this work establishes a robust framework for precise vesicle interrogation with broad implications for emerging translational applications and fundamental biology.

Current technologies to monitor membrane potential are either highly invasive and perturb the integrity of the membrane, use labels that compromise biological behavior, or are limited in sensitivity and do not enable simultaneous monitoring of multiple cells and cell populations. Here, we present Mid-IR Assessment of Conformation in Lipids by Ensemble Sensing (MIRACLES) that, by detection of molecular vibration of lipid acyl chains under cell-membranes electric field dynamics, achieves highly sensitive label-free imaging of membrane potential dynamics in living cells. MIRACLES leverages lipid conformational changes within the plasma membrane as intrinsic indicator for cell membrane depolarization and hyperpolarization. As proof-of-concept, we apply MIRACLES to monitor membrane depolarization during glucose stimulated insulin secretion in {beta}-cells at single-cell level and achieve assessment of {beta}-cell functionality in real time. These results highlight the potential of mid-IR optoacoustic as a powerful tool for indirect, label-free potential assessment of cellular metabolic activities.

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.

Light scattering in scintillators is a pervasive problem and a key factor limiting X-ray imaging resolution. Here, we shift scintillator radioluminescence from the traditional visible range into the short-wave infrared (SWIR) or near-infrared II (NIR-II, 1000-3000 nm) window to mitigate light scattering and thereby enhance light penetration and X-ray imaging resolution. We present an NIR II MgGa2O4:Ni2+ scintillator with peak emission at 1340 nm, achieving a threefold improvement in X-ray imaging resolution compared with visible scintillators owing to reduced light scattering. This heavy-metal-free NIR-II scintillator exhibits intense radioluminescence comparable to that of conventional visible-emitting CsI:Tl, achieving a detection limit of 56 nanograys per second, ~100-fold lower than typical doses used in medical imaging. We show that this NIR-II scintillator enables high-resolution X-ray radiography of electronic circuit boards and biological tissues.

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.

Temperature regulation in the brain is essential for maintaining neuronal function and preventing thermally induced damage. Here, we report the development and in vivo application of quantum dots (QDs) to high-resolution thermometry in the mouse brain using two-photon excitation microscopy. These QDs, via the red-to-green photoluminescence (PL) intensity ratios, enabled stable temperature measurements in both normal and chronically hypo-perfused cerebral tissue. Our findings show that localized neuronal activity leads to transient heat generation, which is rapidly dissipated by cerebrovascular responses. In a chronic hypoperfusion model, impaired vascular function resulted in exaggerated and prolonged brain temperature elevations. This thermometry system provides unprecedented insight into the mechanisms of cerebral thermoregulation and highlights the importance of vascular cooling in protecting the brain from heat-induced stress, particularly in pathological conditions such as stroke.

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.

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

Scaling neural interfaces to ever-higher channel counts has accelerated rapidly with advances in thin-film fabrication, lithography, and connectorization, enabling passive arrays to reach thousands of channels and chart credible pathways to much larger formats. Integrating active electronics directly at the sensing sites offers a complementary route to higher channel density by reducing the number of interconnects required to access large arrays. Here we introduce a monolithic flexible thin-film integrated circuit platform for active neural sensing, inspired by active-matrix display technology. The system integrates dual-gate amorphous indium gallium zinc oxide transistors on polyimide substrates to implement in-pixel transconductance amplification and row-column time-division multiplexing, improving scability for high-channel-count applications. Co-optimization of device architecture, contact engineering, and a hybrid ceramic-polymer thin-film encapsulation yields stable operation with projected lifetimes exceeding 38 years under accelerated aging. In acute and chronic in vivo rat studies, the platform exhibits negligible thermal burden, robust sensory-evoked recordings, and stable functionality over 30 days despite tissue encapsulation. These results establish display-inspired flexible thin-film electronics as a scalable building block for next-generation neural interfaces.

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

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.

Coherent Raman spectroscopy enables label-free biochemical fingerprinting of live cells with subcellular resolution. We previously developed a machine learning framework capable of classifying glioma FFPE tissues using Raman spectral signatures. To accelerate live cell acquisition, we previously developed RADAR (Raman Spectral Analysis Using Deep Learning for Artifact Removal), a method that increases imaging speed by an order of magnitude while preserving spectral integrity. By integrating high-speed Raman imaging with supervised machine learning, we aimed to define unique biochemical fingerprints specific to cell type. We hypothesized that intrinsic biochemical composition alone is sufficient to distinguish cellular identity and tumor subtype. To test this, we generated metabolic maps of diverse brain-derived cell types--including astrocytoma, oligodendroglioma, and glioblastoma cells--using coherent Raman spectroscopy at single-cell resolution. Patient-derived brain tumor cell lines representing genetically heterogeneous backgrounds were analyzed. Samples were stratified by IDH1 mutation status (IDH1-mutant and IDH1-wild-type) and histologically classified as oligodendroglioma or astrocytoma. Raman spectral data were acquired from 286 live single cells across the two principal molecular classes, with further subdivision into two histologic subtypes within the IDH1-mutant group. Classification was performed using an XGBoost model with shallow tree depth (1-3), a 20% held-out test set, and grouped, stratified 5-fold cross-validation to control for sample-level bias. The machine learning framework distinguished IDH1-mutant from IDH1-wild-type cells with a ROC-AUC of 0.78 and further discriminated IDH1-mutant astrocytoma from oligodendroglioma cells with a ROC-AUC of 0.81. Feature importance analysis demonstrated that separation between IDH1-mutant and IDH1-wild-type cells was driven primarily by Raman peaks associated with protein amide bands, total NADH, unsaturated fatty acids, and heme-related vibrational modes. Within the IDH1-mutant class, discrimination between oligodendroglioma and astrocytoma was driven by lipid-rich vesicle signatures, protein/polyamide amide bands, and lipid-associated spectral features. Together, these findings support the feasibility of label-free, machine learning-assisted Raman profiling to resolve clinically relevant glioma subtypes at single-cell resolution. This scalable analytical framework provides a translational platform for investigating metabolic heterogeneity, therapeutic response, co-culture systems, and patient-derived organoid models.

Quantitative structural analysis by Photoactivated Localization Microscopy (PALM) on the nanoscale is often restricted to fixed cells because motion during prolonged data acquisition distorts image reconstruction. Here, we develop motion-corrected PALM (mcPALM), a live-cell super-resolution approach combining a conventional fluorescence channel with PALM to correct motion-induced spreading of localizations. We further introduce a photoactivation-based correction to estimate molecule numbers from incomplete trajectories. Using PI3P-marked endosomes in yeast as a dynamic model system, we show that mcPALM recovers a live-cell maturation trajectory linking motion-corrected endosome size and calibrated PI3P content, consistent with fixed-cell benchmarks. Unlike fixed-cell PALM, mcPALM preserves endosome dynamics, revealing stage-dependent directed transport and maturation-associated motility shift. Thus, mcPALM extends PALM from static structural measurements in fixed samples to integrated quantification of nanoscale structure, molecular composition and dynamics in living cells. This framework is broadly applicable to other mobile organelles and biomolecular assemblies, enabling live-cell studies on how molecular organization and dynamics are coupled to biological function.

Direct validation of the nanoscale structural organization of membrane proteins requires localization precision that matches their molecular dimensions. The sodium-potassium pump, or the Na,K-ATPase is an integral membrane protein responsible for maintaining electrochemical gradients and cellular energy homeostasis. Although its crystal structure is characterized, the organization of the Na,K-ATPase within native plasma membranes, particularly whether it forms functional oligomers, remains an open question. Here, we combined 3D MINFLUX nanoscopy with DNA-PAINT with sub-10 nm localization precision to map the clustering topology of the Na,K-ATPase in mammalian cells. By targeting EGFP-tagged Na,K-ATPase 1 and {beta}1 subunits using anti-GFP nanobodies, we obtained high-density 3D localization maps of the protein in the plasma membrane. To evaluate the point patterns, we developed a computational data-driven spatial point assignment approach that segments apical and basal localizations, mitigating clustering artifacts produced by imaging two membranes in close proximity. Furthermore, we used a spatial statistical approach analyzing sequential nearest-neighbour distances to elucidate supramolecular arrangement information. Our data reveal a preferential nearest-neighbour distance of approximately 7 nm, providing direct visual confirmation of Na,K-ATPase dimerization. Additionally, we identified higher-order nanoclusters composed of up to 21 proteins. These findings provide definitive structural evidence of the dimeric configuration of Na,K-ATPase, establishing a foundation for future research on the functional and regulatory implications of Na,K-ATPase clustering.