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.

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.

Refined single-molecule localization microscopy methods demonstrated superior localization precisions on isolated sample but remain limited by labeling density and imaging speed in cells. Here we combine expansion microscopy (ExM) with two-dye-imager (TDI)-DNA-PAINT to resolve fine molecular details of protein assemblies in [~]8-fold expanded cells with nanometer resolution. Using lattice light-sheet (LLS) microscopy, Ex-TDI-DNA-PAINT provides a robust platform for three-dimensional (3D) volumetric nanoscopy of the molecular organization of cells.

We present cleared-tissue direct-view oblique plane microscopy (CtDvOPM), which enables optically sectioned subcellular resolution imaging of centimetre-scale tissues at high-throughput over the full range of clearing media refractive indices (n = 1.33-1.56). CtDvOPM can image conventionally-mounted expanded, aqueous or non-aqueous cleared tissue samples at up to 2 {micro}m lateral by 14 {micro}m axial resolution over a 10 mm x 10 mm x 25 mm sample volume without image tiling, at up to 400 million voxels per second.

1.We report a repetition-controllable gain-managed nonlinear fiber amplifier (GMNA) that delivers near-infrared 50-fs pulses with pulse energies up to 150 nJ and a widely tunable repetition rate from 1-20 MHz, while maintaining stable pulse quality across the full range. Using this source, we demonstrate label-free multiphoton imaging--including metabolic autofluorescence (2PF/3PF), second/third-harmonic generation, and Simultaneous Label-free Autofluorescence Multiharmonic (SLAM) microscopy imaging--across live cells, human lung spheroids, and hard tissues. We further assess the impact of laser repetition rate on photodamage at fixed pulse energy, supported by preliminary measurements indicating lower damage at lower repetition rate. Collectively, the compact architecture and repetition-rate agility of the GMNA enable real-time optimization of imaging speed, depth, and sample safety for advanced biological microscopy.

Simultaneous multiplexed super-resolution imaging remains a central challenge in single-molecule localization microscopy (SMLM), due to the limited number of spectrally distinguishable fluorophores and the trade-off between spatial and spectral precision. Here, we introduce spectroscopic DNA-PAINT (sDNA-PAINT), a framework that integrates DNA-PAINT with spectroscopic SMLM (sSMLM) to enable simultaneous multiplexed imaging with high spatial and spectral fidelity. Using DNA Origami Nanorulers, in vitro and fixed-cell imaging, we show that sDNA-PAINT conditions significantly improve spectral precision and photon budgets compared to conventional glass and antibody-conjugated conditions in sSMLM imaging. Across representative dyes from three fluorophore families (Rhodamine, Cyanine and Oxazine), narrow spectral centroid distributions are observed to enable reliable statistical discrimination at the single-molecule level, even for dyes with heavily overlapping ensemble spectra. In dual-target cellular imaging, sDNA-PAINT achieves accurate spatial reconstruction and high classification accuracy, demonstrating high-fidelity simultaneous multiplexing within a single acquisition. sDNA-PAINT provides a pathway toward high-throughput simultaneous multiplexed super-resolution interaction imaging.

Linking observation to intervention at cellular resolution makes it possible to move from measuring network activity to testing the contribution of defined neurons or ensembles within the same preparation. All-optical probing provides this capability by combining fluorescence-based readout with targeted optogenetic manipulation. Yet the platforms that deliver this capability remain complex, expensive, and difficult to maintain, requiring specialized expertise that has confined them to a small number of laboratories. They also typically provide fields of view too limited for studies of large, distributed neuronal populations. We address these constraints with the Spatiotemporal Illumination Microscope (STIMscope), a one-photon benchtop platform that integrates large-aperture tandem optics with a small-pixel back-illuminated CMOS sensor, a digital micromirror device for patterned illumination, and a GPU-based processing unit coordinated by a microcontroller for hardware-level synchronization. Ray-tracing simulations and point-spread-function measurements confirm cellular-scale resolution, with imaging lateral FWHM of 5.6 {micro}m at the field center and 5.8 {micro}m at the edge, and excitation lateral FWHM of 5.8 {micro}m at the center and 6.2 {micro}m at the edge, supporting fields of view as large as 14 mm x 11 mm in the demagnified configuration. The accompanying Closed-loop ready Real-time Imaging and Stimulation Pipeline (CRISPI) provides GPU-accelerated calibrated mask projection (26.3 ms latency), online ROI trace extraction, and modular ZeroMQ-based control, with a measured imaging-to-stimulation loop benchmark of 91.6 ms. We validate STIMscope in fixed mouse brain tissue, live iPSC-derived human neuronal cultures, and ex vivo organotypic slices of mouse auditory cortex. In organotypic slices, we show that both static and spatiotemporal stimulus identity can be decoded from population activity, revealing reservoir-like population dynamics, and that this decodability remains stable in the same neuronal population over hours. We further show that post-stimulus activity retains information about recent stimuli for several seconds, consistent with short-term memory dynamics. With a bill of materials under $5,000 USD and all mechanical designs, firmware, and software released open-source, STIMscope makes all-optical neuroscience experiments a routine capability accessible to laboratories without specialized optical engineering expertise.

For decades, the photon counting histogram (PCH) was used as the sole method to quantify fluorophore numbers in a diffraction-limited focal volume. This technique combines spatial excitation profiles, and the distribution of photon counts to register the photon emission statistics of individual fluorophores. However, this approach has not yet been transferred to widefield fluorescent imaging due to the lack of fast and single photon sensitive camera sensors which can capture the photon emission statistics of a single fluorophore. Here, we explore avenues towards quantitative analysis of the active fluorophore number by leveraging recent advancements in single photon avalanche diode (SPAD) array technology. Binary exposures of a SPAD array can be synchronized with picosecond laser pulses to measure the PCH in a widefield setting. Then, by modeling the statistical relationship between the active fluorophore number and the PCH in a region of interest following a laser pulse, we can perform Bayesian inference of this number. The model is demonstrated experimentally by counting quantum dots and various numbers of fluorescent dye molecules bound to DNA origamis. We find that this method has several important applications in widefield microscopy, including enhanced localization microscopy and constrained fitting of multiple unresolvable fluorescent emitters.

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.

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.

Background constrains analytical sensitivity: surveying larger sensor areas samples more analyte molecules but also accumulates false positives, limiting gains in detection performance. Here we introduce FINDER--Fluorogenic INstantaneous Digital Enumeration and Recognition--a single-molecule platform that combines kinetic fingerprinting with fluorogenic transient probes for rapid molecular classification under near-zero-background conditions. By suppressing both solution and surface-associated background at micromolar probe concentrations, FINDER classifies individual molecules within seconds-scale observation windows per field of view. This regime allows sensitivity to scale with surveyed sensor area, enabling amplification-free quantification of the miRNA cancer biomarker hsa-miR-16 with an 11 aM detection limit. FINDER further generalizes to HPV16 DNA biomarker detection, two-color RNA/DNA co-profiling, and rapid discrimination of clinically relevant EGFR single-nucleotide variants using multidimensional kinetic filtering. Rapid per-field classification permits tens of fields to be surveyed within minutes. By converting kinetic specificity into area-scalable sensitivity, FINDER enables semi-automated attomolar biomarker counting without amplification in practical workflows.

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

Cells maintain homeostasis by dynamically reorganizing their organelles to tune metabolism in response to stress. Fluorescence microscopy maps organelle locations with subcellular resolution but provides limited information on their chemical composition. Infrared (IR) imaging offers a label-free alternative for probing intrinsic molecular vibrations that report on lipids, carbohydrates, and nucleic acids. However, its broader application to subcellular biology has been limited by spatial resolution and hyperspectral data complexity. Here, we combine submicron optical photothermal IR imaging with machine learning to classify subcellular structures in fixed U-2 OS cells. Using fluorescent-labeled organelles as ground truth, we trained and evaluated random forest (RF) classifiers and U-Net convolutional neural networks to identify organelles from IR spectra. The RF model converged rapidly, requiring fewer than 75 spectra per class per cell and fewer than 25 cells, indicating that models trained on small cellular regions can be extended to classify whole-cell images. The resulting classifiers accurately identified multiple organelles, including the endoplasmic reticulum, Golgi apparatus, mitochondria, nucleus, nucleolus, and stress granules. In contrast, classification was unsuccessful for nuclear speckles, actin, and microtubules, suggesting that some structures lack sufficiently distinct IR signatures under these conditions. Classifiers trained in U-2 OS cells generalized to HEK 293 cells, consistent with conserved organelle biochemical composition across cell types. However, the classifiers failed under cellular stress, indicating sensitivity to stress-induced changes in organelle state. Together, these results establish a scalable, label-free strategy for high-resolution mapping of organelle biochemical composition and provide a foundation for subcellular biomarker discovery and disease-state diagnostics.

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.

Modulating neuronal activity with light is a powerful tool for neuroscience research. However, currently available technologies often require invasive fibers for delivery of visible light, causing tissue damage and limiting behavioral studies. Although near-infrared (NIR) neuronal modulation improves tissue penetration depth, sustained NIR illumination during neuromodulation raises significant concerns about photothermal effects. Here, we introduce a dual-wavelength near-infrared switch (Dual-NIR Switch) that uses two transcranial NIR inputs to initiate and terminate neuronal activation, enabling tether-free neuromodulation in freely moving mice. Dual-NIR Switch employs orthogonal dichromatic upconversion nanoparticles that emit blue and green light under 980 nm and 808 nm excitation, respectively, to activate and inactivate the step-function opsin SOUL. Therefore, transient 980 nm NIR illumination initiates neuronal excitation, which will remain excited without further stimulation but will be rapidly terminated on demand by a subsequent 808 nm illumination. Upon transcranial 980 nm and 808 nm NIR illuminations in freely moving mice, we achieve on-demand control of behavioral paradigms across tunable timescales, ranging from seconds to minutes and even extending to sub-hour durations. By eliminating the need for sustained NIR irradiation, Dual-NIR Switch offers an on-demand, duration-tunable neuromodulation tool for both basic neuroscience and potential therapeutic applications in treating brain diseases.

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.

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.

Human retina can achieve single-photon sensitivity through specialised photoreceptors that convert light into electrical signals via phototransduction. Among microbial light-sensitive proteins, proteorhodopsins stand out for their intrinsic light-driven ion transport and spectral tunability, making them promising candidates for bio-inspired photonic devices. A central challenge for acellular integration, however, is the fragility of most bacterial rhodopsins under extreme conditions. Here, we exploit the exceptional robustness of TARA76, a microbial rhodopsin that retains structural integrity even upon complete dehydration, to demonstrate its functional reconstitution in an artificial black lipid membrane within a biocompatible microfluidic platform. By recording light-induced ionic currents with picoampere sensitivity across a broad range of pH, illumination power, electrolyte composition, and applied voltages, we establish TARA76 as a high-performance photoelectric transducer in a fully acellular environment. Strikingly, we uncover a strong and previously unreported dependence of the photocurrent on Na+ ions, which appears to play a key structural and functional role in stabilising the proteins active conformation. Furthermore, we demonstrate that the orientation of TARA76 within the artificial membrane can be externally controlled by applying a defined electric field during bilayer formation, enabling deterministic tuning of photocurrent directionality. Together, these results establish a robust and miniaturisable bio-photonic platform with direct implications for quantum light sensing, neuromorphic bioelectronics, and next-generation artificial retinal interfaces.

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.