Nature Photonics

With its unique spatiotemporal resolution at the single-molecule level, Forster resonance energy transfer (FRET) is a powerful tool for investigating biomolecular conformational dynamics and function. However, early photobleaching remains a key limitation restricting the achievable observation time and therefore the information gain and application range of single-molecule FRET. Here, we establish nanophotonic DyeCycling, which overcomes photobleaching by reversible fluorophore binding and efficient background suppression using zero-mode waveguides. Nanoscale conformational changes are sensitively resolved from milliseconds up to the hour range, enabling robust kinetic analyses at the single-molecule level. As a result, outlier molecules reflecting static heterogeneity and time-dependent kinetics within individual molecules reflecting dynamic heterogeneity are reliably resolved. The versatility of DyeCycling is demonstrated using DNA and protein systems. Altogether, nanophotonic DyeCycling extends smFRET observations beyond the conventional photobleaching limit, revealing previously inaccessible kinetic effects and providing deeper insight into biomolecular systems with multiple states and rates.

Small numbers of cells can determine the fate of an entire organism. For example, metastatic cancer cells seed distant organs, therapy-resistant clones repopulate tumors, and T cells infiltrate and expand in tissues with matching antigens. Visualizing the activity of such cells in living organisms requires highly sensitive, specific and repeatable imaging in optically opaque tissues. Here we introduce cell chirp, an ultrasound imaging method for real-time imaging of single cells in living organs inaccessible to light. Cell chirp exploits the harmonic response of genetically encoded gas vesicles to frequency-coded ultrasound to sensitively and specifically visualize single cells in scattering background. We first develop this method in tissue-mimicking phantoms, demonstrating the ability to quantitatively detect single cells as validated by optical cell counts. We then establish the ability of cell chirp to image thousands of genetically labeled cells within a million-cell background tissue, track reporter gene expression over days, and suppress background signals by more than two orders of magnitude. Finally, we use cell chirp to track the fate of antigen-positive or antigen-negative tumor-cell minorities during weeks-long T-cell engager treatments. Cell chirp reveals the dynamics of sparsely distributed, genetically defined cells in opaque tissues.

Capturing fast hemodynamics in deep organs is essential for understanding microvascular regulation of organ responses to physiological demands and pathological stress. However, non-invasive three-dimensional (3D) imaging of these microscale processes remains challenging due to trade-offs between spatial resolution, imaging speed, and penetration depth. Here, we present fluctuation-based high-order super-resolution acoustic microscope (FLAME), a tracking-free 3D ultrasound imaging technology capable of fast microvascular angiography and flow measurement. Using as few as 30 volumes, FLAME improves 3D ultrasound resolution by 8-fold (~50 m) and shortens data acquisition time by over two orders of magnitude, from tens of seconds to tens of milliseconds, compared with conventional tracking-based super-resolution approaches1-6. This high-throughput capability supports high-fidelity imaging of tissue functions under flexible experimental settings. FLAME achieves an unprecedented 3D super-resolution frame rate of ~40 Hz, capturing transient hemodynamic responses to various physiological and pathological stimuli in different mouse organs, from the microvascular level to the whole-body scale. With open-sourced implementation, FLAME provides an accessible platform for real-time, in-depth hemodynamic monitoring across diverse biomedical applications and clinical translation.

Fluorescence microscopy is a powerful tool for dissecting the molecular mechanisms of T-cell antigen recognition in living cells, but its quantitative insight critically depends on non-perturbative, high-quality probes. Here, we repurpose a small (~15 kDa) CD3epsilon-binding DARPin (designed ankyrin repeat proteins) to a fluorescent label for T-cell receptor (TCR)/CD3 complexes on primary human CD8+ T-cells, with the aim of generating a powerful tool for quantitative analysis, single-molecule tracking, and advanced imaging of TCR dynamics. We show that the DARPin binds CD3{varepsilon} with high affinity and selectivity and using single molecule tracking and brightness analysis, we characterize the TCR-CD3 diffusion behavior and show that the DARPin binds to both CD3epsilon; subunits. Importantly, labeling preserves antigen sensitivity: on supported lipid bilayers presenting cognate pMHC, T-cells remain responsive, assemble synapses, form TCR microclusters, and initiate signaling similar to unlabeled controls. We further demonstrate compatibility with lattice light-sheet microscopy for volumetric imaging of T-cell - APC interactions in living cells. Together, these results establish DARPins as versatile, minimally perturbative probes for high resolution, quantitative studies of T cell synapse organization and signaling.

Microlasers based on whispering-gallery modes (WGMs) provide bright, spectrally narrow emission, making them ideal optical probes for high-resolution cellular tagging, single-cell tracking, and biosensing. To reproducibly apply WGM microlasers in biomedical or tissue-engineering contexts, control over the number of lasers in a cell population is highly desirable. Here we use a commercial instrument for flow cytometry-based cell sorting to obtain HEK293T cell populations with a controlled number of polystyrene laser particles (LPs). By modifying the particle surface with poly(ethyleneimine) (PEI) or lipofectamine, we enhance LP-association to cells. We demonstrate that our sorting procedure yields viable cells showing unchanged proliferation rates. Manufacturer-dependent differences in particle-cell interactions were observed and investigated by scanning and transmission electron microscopy (SEM/TEM) and flow cytometry. Finally, we show that the same sorting approach enables the generation of multi-cellular spheroids with a defined ratio of LP-containing and LP-free cells. This work lays the foundation for larger tissue engineering studies with defined LP populations and control over LP-placement with respect to the cell.

Expansion microscopy (ExM) physically magnifies preserved biological specimens by using a specimen-permeating hydrogel to pull biomolecules, such as proteins, apart from each other. ExM protocols exhibit a tension between the need to initially fix proteins to each other during sample preservation, to retain proteins and their nanoscale organization, and the need to separate proteins from each other isotropically, to achieve physical magnification. We here propose to address this tension by fixing proteins directly to the hydrogel network itself, which then serves as a common substrate for both initial fixation and final separation. We equip proteins with chemical handles, then form a specimen-permeating hydrogel that binds the handles, in a ~15-minute chemical process conducted entirely at -20oC. This network fixation ExM (nfExM) procedure enables excellent epitope retention and nanoscale organization preservation, in cultured cells. nfExM may help support ultrastructural investigations of protein complexes and interactions.

Cardiac amyloidosis is caused by deposits of misfolded proteins in the heart and can lead to heart failure. Histological confirmation typically involves staining tissue sections with Congo red and examining them under polarized light for apple-green birefringence; however, achieving an interpretable, label-free, and observer-independent assessment remains challenging. Here we show that Pearson kurtosis of channel intensities within the amide I window provides an interpretable, fitting-free contrast for cardiac amyloid in unstained human cardiac tissue sections from ten participants imaged by ultrabroadband multiplex coherent anti-Stokes Raman scattering (CARS). In a discovery participant, high-kurtosis regions corresponded with Congo red-positive tissue in two CARS-histology pairs of adjacent sections (descriptive spatial AUCs, 0.79 and 0.73). Across 15 fields from nine additional participants, field-level kurtosis values did not overlap between severe-deposition and Congo red-negative tissue fields. Among the descriptors examined, kurtosis showed the largest standardized participant-level separation. Exploratory multivariate curve resolution yielded a deposit-associated component with aromatic-residue features shared with reagent transthyretin. In one field from the discovery participant, the second harmonic generation (SHG) signal, acquired simultaneously with CARS, showed a broadly inverse spatial pattern relative to the amide I kurtosis map, revealing distinct tissue-architectural contrast. Together, these findings support a potential label-free approach to detecting cardiac amyloid deposits that combines amide I kurtosis derived from broadband CARS with SHG-derived architectural contrast.

Fluorescent proteins (FPs) are the workhorse of modern-day cellular imaging and biosensing. However, rationally predicting and engineering the photophysical properties of FPs remain challenging, as these are governed by complex three-dimensional microenvironments and post-translational modifications. Here, we present a novel surface-mutation-guided engineering strategy combined with iterative rounds of rational mutagenesis to improve key photophysical properties of red fluorescent proteins (RFPs). This approach generated two bright, monomeric RFP variants from the mFruits lineage, mCranberry and mCranberry2, with quantum yields of 85% and 80%, respectively. Strategic incorporation of charged amino acid residues on the protein surface enhanced fluorescence quantum yield without compromising photostability. Comprehensive photophysical characterization and live-cell microscopy demonstrate enhanced performance of these variants and establish their utility as robust probes for fluorescence imaging. However, the exact mechanism of such improvements is subject to further investigation. Collectively, this work introduces a strategy for optimizing fluorescence properties and provides a framework that may be broadly applicable to other pigment-protein systems such as genetically encoded metal-ion indicators and photosynthetic light-harvesting complexes.

Whole-cell biosensors can detect many types of chemicals in complex environments, but existing designs force a tradeoff in multiplexed sensing: separating sensor strains into individual reservoirs limits how many analytes can be monitored at once, while pooling strains in open co-culture lets faster-growing strains take over within days, corrupting the signal. The microfluidic "mother machine" has been used for over a decade, mostly to study individual bacterial strains with single-cell resolution, but never to house multiple engineered strains simultaneously for sensing applications. We repurpose this device to hold a library of genetically distinct biosensor strains, physically isolating each strain in its own growth channel while continuous flow keeps every strain fed. Each strain reports on a target chemical through a unique combination of three fluorescent proteins, so seven distinguishable signals emerge from only three optical channels. We characterized turn-on and turn-off kinetics of several biosensors in the mother machine. Over a multi-day experiment with repeated chemical exposures, biosensors housed this way matched the expected on/off state and held stable population ratios, while biosensors in an open batch co-culture drifted and lost reliable signal. This establishes mother-machine-based multiplexing as a scalable route to long-term chemical monitoring with living sensors.

Fluorescence microscopy is a central tool in biomedical image analysis, but its practical use is often constrained by incomplete and heterogeneous data. This is especially true in rare disease research, where patient cohorts are small, biological material is limited, and complex study designs frequently yield images with missing, degraded, or low-quality channels. We introduce Reliability-Mask-Conditioned DDPM (RM-DDPM), a conditional diffusion model that formulates multichannel image generation, missing-channel imputation, and degraded-channel restoration as a single reliability-guided inference problem. The model is trained on a curated subset of complete, high-quality multichannel images using a self-supervised masking objective to learn rich cross-channel dependencies from the available channels. At inference time, reliable observations condition the reverse diffusion process, allowing the same trained model to solve multiple reconstruction tasks without task-specific retraining. We evaluate RM-DDPM against task-specific baselines on one public and two in-house fluorescence microscopy datasets, comprising a rare-disease preclinical model study and a clinical patient cohort. Across these settings, RM-DDPM matches the specialized baselines in reconstruction quality, hallucinates fewer false structures, and remains more robust as degradation severity increases, while repeated sampling provides per-pixel uncertainty maps that flag unreliable reconstructions.

Forster resonance energy transfer (FRET) is commonly used to monitor protein-protein interactions in situ. The high spatiotemporal resolution and facile implementation inside complex molecular environments have spearheaded FRET's widespread adoption in biosensing. Despite these advantages, current FRET biosensors are largely restricted to the detection of the presence/absence of individual inputs and are thus unable to sense several multiplexable inputs simultaneously within complex milieu of biological environments. In this work, we introduce a generalizable strategy to construct genetically encoded protein-based FRET biosensors capable of recognizing multiple inputs following Boolean logic-type (YES/OR/AND) operations. These topologically specified FRET sensors powerfully expand the input capacity in sensing protein-protein interactions while providing a user-programmable platform for monitoring heterogeneous biological activities both in vitro and in living cells.

Tumor necrosis factor-alpha (TNF-) represents a major pro-inflammatory cytokine associated with chronic inflammation. Enhanced understanding of intracellular trafficking of TNF- enables the identification of drug targets that modulate TNF- secretion at the cellular source. However, conventional cytokine assays provide only bulk measurements of secreted TNF- or static measurements in fixed cells and therefore cannot provide mechanistic information. Here, we report a fluorescence imaging platform to visualize and quantify intracellular TNF- trafficking in human T cells. Using a Jurkat T cell line stably expressing TNF- fused to mStayGold as a model system, we revealed through single-particle tracking (SPT) that T cell activation increased the motility of TNF- vesicles and that microtubule perturbation disrupted this increased motility. 3D structured illumination microscopy uncovered spatial associations between TNF- vesicles and lysosomes, and SPT further demonstrated their coordinated transport. Using total internal reflection fluorescence microscopy, we captured the sub-second splitting of single TNF- vesicles into multiple puncta that subsequently separated, suggesting delivery of TNF- from the vesicle to the plasma membrane. Confocal-based super-resolution microscopy further resolved TNF- vesicles fusing with the plasma membrane and transmembrane TNF-. Together, these imaging capabilities for tracking and super-resolving the intracellular transport and membrane delivery of TNF- in live T cells enable mechanistic studies of TNF- trafficking and release.

Unlike nucleic acids, proteins cannot be directly amplified. Ultrasensitive detection must therefore couple efficient analyte-to-signal conversion with strong suppression of target-independent background. Consistently detecting ~100 molecules (below which Poisson sampling noise alone causes CV to exceed 10%) across protein targets in a format as scalable and accessible as PCR remains unsolved. Separation-based ultrasensitive immunoassays require target binding stable enough to survive stringent washing and are therefore constrained by wash-induced signal loss and, more fundamentally, the relative scarcity of exceptionally tight binders. Performance consequently varies across targets; detection limits below 1,000 molecules remain rare. Conventional separation-free proximity assays avoid washing but incur background from random probe collisions, with reported detection limits frequently exceeding 100,000 molecules. Here we introduce Successive Proximity Extension Amplification Reaction (SPEAR), a separation-free architecture in which autonomous engineered DNA nanodevices emulate biological kinetic proofreading. Two successive irreversible proximity-extension steps compound discrimination between analyte-dependent and background reactions, suppressing spurious signals while preserving efficient analyte-to-signal conversion. This design reduces reliance on high-affinity binders and enables sub-fM detection from 1 uL samples through simple 3-step mixing followed by standard qPCR readout. Across 11 biomarkers tested, SPEAR consistently achieved sub-100-molecule detection limits. Where compared, it outperformed ultrasensitive separation-based platforms, analytically and clinically, including for low-abundance Alzheimer's disease biomarkers. SPEAR also precisely quantified SARS-CoV-2 neutralizing antibody titers from finger-prick dried blood spots, concordant with the cell-based reference assay on venous blood. It detected a distinct mixed phosphorylation state of Tau, and targeting this state resolved previously observed capillary-venous discordance and improved amyloid-status discrimination. These results establish engineered autonomous successive molecular discrimination as a practical strategy for near-single-molecule protein detection, extending the sensitivity, scalability and accessibility of PCR-based testing to non-amplifiable analytes.

Archived formalin-fixed, paraffin-embedded (FFPE) tissue represents a vast clinical resource, yet three-dimensional imaging and molecular profiling remain largely incompatible because current antigen retrieval and clearing can degrade nucleic acids. We developed REZCUE, a depth-invariant antigen retrieval chemistry based on in situ protein refolding that enables 3D multiplexed immunofluorescence imaging of intact FFPE tissue while preserving DNA, RNA, and protein for subsequent assays. REZCUE restores antigenicity in tissue archived for up to 30 years, supports 160 commercial antibodies across tissue types, and enables imaging to 4 mm depth. Imaging before destructive molecular profiling establishes a 3D spatial reference for registering molecular assays to intact tissue architecture. In a 25-year-archived breast cancer specimen, REZCUE integrated 3D image-guided clonal segmentation with spatial transcriptomics, mass spectrometry, miRNA sequencing, and whole-genome sequencing, identifying a luminalB/HER2-positive clone missed by conventional section-based spatial-omics. REZCUE transforms archived FFPE specimens into spatially registered substrates for sequential 3D multi-omic interrogation.

Nanopore sensors have transformed single-molecule analysis, enabling real-time detection of biomolecules with unprecedented resolution. Understanding and modelling noise in nanopore sensing is essential to unlocking their full analytical potential. Although mechanistic models of nanopore noise exist, a rigorous statistical framework that characterises noise structure across diverse experimental conditions is still lacking. Here, we present a comprehensive empirical analysis of nanopore noise using twelve diverse experimental conditions acquired with different nanopores, analytes, and electronic recording systems. By employing multiple, mutually reinforcing statistical methods, we establish that nanopore noise is well described by a composite fractional model comprising multiple fractional Gaussian noise and fractional Brownian motion components. This finding is supported by confirmation of Gaussianity, rigorous interpretation of second-order exponents, multifractal analysis, and evidence that transient deviations are attributable to deterministic or experimental artefacts rather than alternative stochastic mechanisms. We further resolve quasi-deterministic structures, such as baseline trends and structural breaks, and demonstrate their separability from the underlying stochastic profile. Collectively, these results provide the first formal statistical validation of a composite fractional model for nanopore noise and establish a principled basis for noise reconstruction, realistic simulation, and statistically grounded algorithm design for nanopore sensing applications. This study provides the foundation for the creation of high-fidelity performance evaluation datasets for signal processing, and the controlled generation and augmentation of data for machine learning while maintaining statistical fidelity to experimental conditions.

Biomolecular condensates formed via phase separation play critical roles in regulating cellular processes, and their dysregulation is increasingly associated with neurodegeneration. A key challenge in understanding condensate pathology lies in distinguishing dynamic condensates from neurotoxic solid aggregates, the formation of which can, in some systems, occur within existing condensates. Yet current detection techniques often lack the sensitivity and resolution to directly monitor these assemblies and their transitions in dynamic and complex environments. Here, we combined fluorescent probes, based on molecular rotors, with fluorescence lifetime imaging microscopy to quantify changes in protein dynamics during phase separation-associated aggregate formation. Molecular rotors respond to local viscosity changes, which in our study allowed discrimination between fluid condensates and rigid amyloids based on rotors' fluorescence lifetime. Using -synuclein, a hallmark protein in Parkinson's disease, as a model system, we demonstrate real-time visualization of phase separation and condensate maturation within a unified experimental framework. Our method preserves protein integrity and provides quantitative, spatially resolved insights into structural transitions. This technology bridges a critical gap in the study of pathological protein aggregation and offers a powerful platform for mechanistic studies, biomarker discovery, and drug discovery in neurodegenerative disease research.

Image scanning microscopy (ISM) is emerging as the natural evolution of confocal laser-scanning microscopy, combining optical sectioning and a two-fold improvement in lateral resolution with high signal-to-noise ratio. Single-photon avalanche diode (SPAD) array detectors enable an effective implementation of ISM by recording a micro-image at each scan position with single-photon sensitivity, negligible readout noise, and without the fixed frame-readout constraints of conventional cameras. Nevertheless, SPAD array-based ISM remains largely confined to laboratories with expertise in microscope development. Here, we present BrightEyes-Scope, a reference implementation of a SPAD array-based ISM system. We provide step-by-step guidelines for optical design, system construction, alignment, calibration, data acquisition, and image reconstruction. Presented as a complete ISM microscope, BrightEyes-Scope also serves as a reference for upgrading existing laser-scanning microscopes. We anticipate that it will accelerate the adoption of ISM for quantitative super-resolution imaging.

Measuring the precise dynamics of specific neuromodulators in neurons is key to understanding how information is transmitted and processed in the brain. Despite the growing use of intensity-based neural activity sensors such as dLight in recent years, it remains challenging to monitor the dynamics of multiple neurochemicals simultaneously in live neurons. Unlike intensiometric sensors that are susceptible to artifacts caused by fluctuations in excitation power and rapid photobleaching, fluorescence lifetime-based sensors are less affected by these factors and can provide more stable measurements. Here we introduce dHaloLife635, a novel genetically encoded fluorescence-lifetime-based dopamine (DA) sensor that exhibits a fluorescence lifetime change up to -0.20 ns upon dopamine binding. dHaloLife635 consists of a G protein-coupled dopamine receptor (DRD1), a circularly permuted HaloTag (cpHaloTag), and a cell-permeable dye JF635 functionalized with a HaloTag ligand, a reactive chloroalkane linker (JF635-HTL). When dopamine binds to the dHaloLife635 sensor, it induces a conformational change in the cpHaloTag that alters the local environment of the conjugated dye, thereby changing its fluorescence lifetime. In live HEK293T cells and primary neurons, dHaloLife635 responds selectively to dopamine but not to serotonin, L-DOPA, or GABA, demonstrating excellent specificity. This dHaloLife635 sensor represents an initial step toward the establishment of a broader family of chemigenetic lifetime sensors that exhibit distinct fluorescence lifetime signatures upon binding their respective targets, thereby enabling highly specific and sensitive, real-time monitoring of multiple neurochemical dynamics in vivo.

The visualisation of large biological samples in cryo-electron tomography requires both few-nanometre resolution and a wide, ideally >10 micrometrre, field of view. For a single image, increasing magnification trades increased resolution for a smaller field of view. Montaging, where high-magnification images are stitched together to increase field of view, addresses the trade-off but is dose-inefficient with round beams that are standard in transmission electron microscopy (TEM), since this illumination shape does not tessellate. Our workflow greatly simplifies montaging schemes for dose-sensitive specimens, allowing large montages of any shape - from individual cells or other features of interest up to whole lamellae - to be collected at high resolution without compromising electron dose efficiency. We demonstrate the workflow by acquiring montage tomograms of whole yeast (Saccharomyces cerevisiae) cells in cryo-FIB lamellae and of human neurons.

Hormone dynamics are central to a multitude of conditions in women's health; however, limitations in the cost, convenience, and multiplexing capacity of current hormone assays preclude routine longitudinal hormone testing. This limitation severely restricts our understanding in this historically neglected field, presenting a need for improved bioanalytical tools to support hormone-informed care. Silicon photonic resonator sensors have excellent multiplexing capacity and scalable fabrication, and offer strong potential to meet this important need. Here we demonstrate, for the first time, multiplexed silicon photonic detection of two urinary hormone biomarkers, follicle-stimulating hormone (FSH) and pregnanediol-3-glucuronide (PdG), using sub-wavelength grating ring resonators and a polydopamine-based, regenerable surface chemistry. We combine a sandwich immunoassay for FSH and an antigen-down competitive immunoassay for PdG on the same chip, both amplified using a commercially available quantum-dot-streptavidin conjugate and with on-chip regeneration over multiple measurement cycles. In artificial urine, this proof-of-principle multiplexed assay achieves limits of detection of 0.594 ng/mL for FSH and 19.1 ng/mL for PdG. This architecture is inherently scalable to tens of hormone targets on a millimetre-scale chip (a >10x multiplexing improvement over typical lateral-flow immunoassays) enabling future low-cost, quantitative at-home hormone panels that could transform research, diagnostics, and treatment in women's health.