Light: Science & Applications

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 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.

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.

Leveraging its non-invasive and convenient nature, urinary cytology offers a way for the early diagnosis and surveillance of bladder cancer (BCa). However, its sensitivity remains substantially inferior to the histopathological gold standard, rendering it inadequate to support precise clinical diagnosis. Here, we developed Lipid-featured Urine Cytology with Intelligent Detection (LUCID), an automated platform integrating single-cell stimulated Raman scattering (SRS) imaging and explainable AI model for accurate BCa detection. Following a newly developed standardized urine-preprocessing protocol, 19,678 urinary exfoliated cells from 26 BCa patients and 54 non-BCa participants were imaged using two-color SRS microscopy to simultaneously visualize lipid and protein distributions. For cell-level analysis, a customized deep-supervised Cellpose segmentation algorithm and a YOLO-based detection model were developed to enable precise single-cell segmentation and intracellular lipid droplet (LD) identification, respectively. Totally 20 quantitative morphological and compositional features per cell were extracted and 10 related principal components were obtained via principal component analysis. These features were subsequently integrated into a multiple-instance learning (MIL)-XGBoosting model, which achieved robust diagnostic performance, with 92.31% sensitivity, 87.04% specificity, and an AUC of 0.940. We found that morphological and compositional features provided complementary diagnostic information, while LD-associated features provided primary discriminative information, supporting intracellular LD phenotypes as potential urinary cellular markers of BCa. More importantly, LUCID demonstrated exceptional accuracy in detecting early-stage and small tumors, and showed higher concordance than FISH in a preliminary paired subset (5/6 vs. 2/6). Together, LUCID establishes a chemical imaging-AI paradigm that transforms molecular urine cytology.

Functional ultrasound (fUS) imaging provides deep, wide-field access to brain function, but instrument cost and complex acquisition and analysis workflows restrict its wider adoption. Here we introduce @fUS, an agentic transcranial imaging platform with an open architecture that integrates purpose-built hardware with executable experimental skills and persistent structured memory. The system combines 128-channel, 16-bit acquisition at 125 MHz with GPU-based processing and enables functional imaging through the intact scalp and skull of mice, with approximately 100-m spatial resolution and 10-Hz temporal sampling, providing a basis for longitudinal studies without cranial-window surgery. Its agent connects scientific objectives to experimental design, direct instrument control and data analysis, retaining context across interactions and supporting inspectable workflows through text and hands-free voice control. Neuroscience trainees with limited engineering experience independently configured the system within 30 min. Agent-assisted exploration of whisker-stimulation datasets revealed low-frequency vascular changes beyond conventional response mapping, supported by complementary two-photon measurements of single-vessel diameter. By combining transcranial imaging with accessible instrument control and analysis, @fUS provides a foundation for wider adoption and larger, more diverse neuroscience datasets. More broadly, it offers a framework for scientific instruments in which measurement, computation and experimental reasoning are developed as parts of the same system.

Antibiotic resistance is an increasingly serious problem worldwide. Identifying the bacterial species can help guide antibiotic selection and improve patient outcomes. In sepsis, where every minute counts, rapid identification is particularly important, and new approaches are needed. In this study, deep learning models were trained to identify bacterial species from phase-contrast time-lapse images of cells growing in microfluidic traps, without fluorescent labels. We demonstrated this approach in two previous studies using laboratory strains. Here, the previous work is extended to clinical bacterial isolates, evaluating performance on isolates not seen during training. Furthermore, it was tested whether autofluorescence could aid species identification. Two validation sets were acquired in separate experiments from the training data, the first containing clinical-isolate traps with five species (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae, Staphylococcus aureus and Enterobacter cloacae), imaged every two minutes, and the second containing clinical isolates of two species (E. coli and K. pneumoniae) recovered from spiked donor blood, imaged every minute. Training with the best previous CNN method achieved unsatisfactory results. Therefore, vision transformers were explored, and a self-supervised adaptation procedure was developed for pretrained DINOv3 using a frozen teacher, followed by supervised classification using time-lapse montages. Mean macro-F1 was 93.6 {+/-} 1.3% for isolates precultured in Mueller-Hinton medium using 60 frames (118 minutes) and 87.2 {+/-} 2.7% for isolates precultured in blood using 55 frames (54 minutes) (mean {+/-} sample standard deviation across training seeds). Performance was evaluated over time by increasing the number of frames available to the model during testing, and Fourier-domain filtering approximated reduced numerical aperture. The results are promising and suggest that the aperture can be decreased while retaining good results. However, further evaluation on independent isolates, experiments and patient samples is needed to establish generalization. Together with previous findings, these results bring us closer to rapid image-based diagnostics that could transform the initial treatment of acute bacterial infections by enabling better-informed antibiotic selection.

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.

The Transport-of-Intensity Equation (TIE) and polarization differential phase contrast (pDPC) exhibit transfer characteristics that limit their use as standalone quantitative phase imaging modalities. TIE provides stable recovery of low spatial frequencies but attenuates fine structure, while pDPC enhances phase gradients but is intrinsically insensitive near zero frequency. We present a hybrid reconstruction framework that combines a single-shot pDPC measurement with an in-focus-defocused TIE image pair and fuses the resulting phase estimates in the Fourier domain using frequency-selective weighting. The illumination geometry is co-designed to enforce angular separation between modalities, enabling high-frequency phase-gradient acquisition in a single exposure while preserving low-frequency sensitivity through low-numerical-aperture symmetric illumination. The proposed method requires three intensity measurements without requiring interferometric stability or sequential asymmetric illumination. Experimental results demonstrate improved phase fidelity across spatial frequencies, reduced halo artifacts, and enhanced recovery of both smooth and fine structural features. This approach provides a compact and robust method that moves towards extended spatial-frequency phase imaging for dynamic and resource-constrained applications.

Experimental surgery is one of the most fundamental fields of biomedical research, recreating clinical conditions on living organisms similar to clinical patients, to facilitate the design of novel therapies. For modeling neurological and psychiatric disorders, the majority of operative techniques mandatorily include craniotomy for consecutive interactions with the open brain. The most wide-spread methodology involves stereotactic surgery in rodents, implementing a specific head holder, attached to the coordinate table, providing the precise animal head positioning according to the brain structures topology. Despite the high efficiency, such systems require complex instrumental skills and long procedure duration per animal even for simple implantation protocols. Hence, novel alternative model species could be a valuable supplementary tool for such manipulations. Therefore, the zebrafish (Danio rerio) fits perfectly, due to its low cost, easy husbandry, rapid development, optic transparency, and high genetic and physiological similarity to rodents and humans. Furthermore, the everted zebrafish telencephalon provides excellent accessibility to critical mammalian-like structures involved in emotion processing and cognition. However, conventional avian- or rodent-inspired craniotomies in adult zebrafish often trigger inadvertent disruption of the brain vessels network, resulting in high operative mortality. A novel, geometry-driven, non-lethal cranial window protocol preserving neurovascular integrity and ensuring 90-100% post-operative survival is presented to overcome above mentioned limitations. This rapid, simple, and high reproducible technique opens new avenues for precise region-specific in vivo neurosurgery, high-throughput psychopharmacological screening, and neurostimulation in a powerful non-mammalian model species

Extracellular signal-regulated kinase (ERK) governs many cellular processes, and its spatiotemporal activity dynamics are important for downstream signaling specificity. Existing fluorescent ERK activity biosensors, however, fall short in enabling spectrally multiplexed, spatially resolved, or deep tissue measurements. Here, we introduce a chemigenetic, single-fluorophore biosensor, WHaloEKAR, that reports ERK activities through changes in fluorescence lifetime. WHaloEKAR combines large lifetime changes (-0.81 ns) with the spectral versatility of the self-labeling protein HaloTag giving access to orange, red, and far-red ERK biosensors. The latter enabled the investigation of heterogeneous ERK activity at distinct subcellular locations in cultured cell lines and primary cortical rat neurons via fluorescence lifetime imaging microscopy (FLIM). Moreover, multiplexed FLIM measurements allowed to study ERK activity in parallel to the second messengers calcium and cyclic adenosine monophosphate. Finally, WHaloEKAR enabled the investigation of ERK activity in 3D samples as well as in live animals.

Fluorescence microscopy is routinely used in the quantitative analysis of Giant Unilamellar Vesicles (GUVs) because it makes GUVs visible and easy to detect. Nonetheless, it requires fluorescence-equipped microscopes and, especially, the addition of tagged molecules that may introduce undesired and unaccounted variations compared to the pristine formulation. Alternatively, phase-contrast microscopy can be used to observe GUVs without labelling, yet the automatic detection of GUVs in phase-contrast images by standard image processing techniques is difficult. To overcome this limitation, we developed a framework for the automated detection and size characterization of label-free GUVs based on YOLOv11 model, a state-of-art lightweight Convolutional Neural Network (CNN) for object detection. Trained on 164 phase-contrast microscope images containing 4666 manually annotated vesicles, the model enables robust detection under low contrast-to-noise ratio and heterogeneous illumination. The trained model is implemented in a stand-alone desktop application supporting multiple analysis in a single run, pixel-to-micrometer calibration, and automated size distribution analysis. This approach achieves high detection accuracy and competitive cross-microscope generalization using YOLOv11 nano on greyscale image. Retraining scripts compatible with standard hardware are also provided to enhance adaptability. This tool enables fast and reliable analysis of GUVs without altering their composition, supporting experimental reproducibility.

Nuclear segmentation in 3D microscopy remains challenging, particularly for low signal-to-noise ratio (SNR) data from timelapse imaging, in vivo experiments, or dense tissue samples. Yet accurate 3D nuclear segmentation is a prerequisite for quantitative single-cell analysis, making this a key bottleneck for high-throughput study of challenging datasets. We present NucLogic, a fast and robust 3D nuclear segmentation algorithm that combines the maturity of 2D segmentation with geometric stitching rules based on nuclear shape, orientation, and intensity, to reconstruct accurate 3D volumes. NucLogic reliably segments challenging datasets including long-term timelapse imaging of organoids, and large dense structures such as the zebrafish brain. Benchmarked against other state-of-the-art segmentation tools, NucLogic matches F1 scores at high SNR, with increasing outperformance as SNR decreases. Importantly, segmentation completes at 20-50% of the computation time, with greater speed gains on larger images. Beyond segmentation, NucLogic includes an intuitive graphical user interface allowing non-experts to segment, quantify, and visualize 3D and 4D data. This enables easy quantification of cell numbers and integration of a variety of both cell-intrinsic properties, such as phenotype, morphology, and local environment metrics, including density and spatial interactions. Together, NucLogic makes quantitative 3D nuclear analysis of previously unmanageable datasets accessible to a broad research community.

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.

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

Interpretable computational pathology is constrained by a trade-off between scalable learned representations and biologically explicit phenotypes. Here we present NucXplore, a nuclear phenomics framework that converts each hematoxylin-and-eosin (H&E)-stained nucleus into 129 explicitly defined features spanning morphology, chromatin, intensity, texture, color, and spatial context. A Rust-based implementation accelerates extraction 17.6-fold over the Python reference implementation. Across three histopathology cohorts, NucXplore outperformed classical descriptors and larger deep-learning embeddings while retaining feature attribution. Applied to human skin, hierarchical multiple-instance models resolved eight cellular compartments and decoded age and sun-exposure states, revealing a largely shared aging program with age and cell-specific exposure-dependent recalibration. Conditional flow matching and neural ODE integration transformed cross-sectional phenotypes into model-implied trajectories, uncovering a midlife velocity minimum, late-life re-acceleration, and cell-specific changes in trajectory magnitude and direction. Source-free domain adaptation enabled preliminary transfer of age-associated predictions to an independent hospital cohort. NucXplore thus unifies interpretable representation, high-performance computing, and dynamical modeling for cellular phenotyping from routine histology.

Defining tumour regions on haematoxylin and eosin (H&E) sections is a routine first step in spatial-omics studies, yet it is usually done by hand and is difficult to reproduce. We present OmiCoreTumorDetector, an openly licensed model that maps tumour-enriched regions in colorectal cancer (CRC) H&E sections and exports them as QuPath-compatible annotations. The released model (omicore-tumordetector-crc-he-v0.1) is an ensemble of three convolutional classifiers trained on 100,000 public tissue tiles, combined with Macenko stain normalisation at inference. During development we found that the main obstacle to reuse was calibration under stain-domain shift rather than discrimination: a single model kept an area under the ROC curve (AUROC) of 0.955 on unseen slides while its sensitivity at the conventional 0.5 threshold fell to 0.48. Training on non-normalised tiles raised tumour AUROC on an independently collected tile set from 0.836 to 0.992, and normalising at inference reduced false-positive tumour area in normal-adjacent tissue by 16- to 26-fold. On five 10x Visium HD CRC sections that share no material with the training data, the released model called 27.7-48.5% of tissue as tumour in three carcinoma sections and 0.08% and 1.82% in two normal-adjacent sections, exporting no tumour region from either normal section. On the carcinoma section with matched single-cell-resolution transcriptomics, agreement with transcriptome-derived tumour-cell identities reached an AUROC of 0.985 (95% spatial-block bootstrap CI 0.975-0.993). The image model never observes gene expression, so this is orthogonal evidence. The model localises tumour-enriched regions at 112 um resolution; it does not identify individual malignant cells and has not yet been validated across scanners, institutions or histological variants. Code, weights and evaluation are released under Apache-2.0 and installable with pip install omicoretumordetector.

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.

Text-driven medical image segmentation demands unified models based on multi-modal learning, capable of processing diverse imaging modalities while providing spatially reliable prediction estimates for clinical practice. Nevertheless, existing vision-language model solutions suffer from poor domain generalization and exhibit limited capability for uncertainty estimation to model the cumulative unreliability of cross-modal alignment across network layers. In this paper, we propose UniMedSeg, a unified vision-language framework for multi-modal medical image segmentation built upon frozen pre-trained vision-language backbones. Our framework incorporates modality-aware adaptation, cross-layer uncertainty-guided encoding, and a frequency-spatial dual-prompt decoder, wherein the progressive uncertainty propagation mechanism of the encoder suppresses misleading cross-modal features, whereas the decoder disentangles anatomical structure and boundary details in the frequency domain under dual textual prompts. Auxiliary losses further sharpen boundary representations and calibrate pixel-wise uncertainty maps. Extensive experiments demonstrate that UniMedSeg preserves competitive in-domain segmentation performance, improves domain generalization, and yields clinically useful spatial risk estimation.

Super-resolution methods, such as single molecule localisation microscopy (SMLM), can determine the distribution of specific proteins at the nanometre scale. However, three-dimensional (3D) SMLM is typically limited to thin sections and requires prohibitively long acquisition times to image complete cells. Here we introduce a 3D SMLM imaging modality, Intrinsic3D, that combines the speed and optical sectioning of oblique plane microscopy (OPM) with the nanometre resolution of SMLM to determine the position of single molecule emitters throughout complete volumes more than an order-of-magnitude faster over regular DNA-PAINT. By scanning the oblique light sheet across the volume and combining localization information from two or more adjacent planes, an emitter's x, y and z position is determined, eliminating the need for post-processing steps to stitch multiple image stacks together. We demonstrate resolution and speed by imaging nuclear pore complexes in U2OS cells and Drosophila egg chambers. As a proof-of-concept, we map the 3D distribution of the cell-adhesion protein DE-cadherin in the Drosophila follicular epithelium and show that spot adherens junctions are enriched near tricellular junctions and contain more E-cadherin than the apical adherens junctions, suggesting that spot junctions play a wider role in cell-cell adhesion than previously appreciated. Finally, we examine the organization of Gliotactin, a tricellular septate junction protein relative to E-cadherin using exchange-PAINT. These investigations demonstrate that Intrinsic3D delivers rapid 3D protein mapping in whole cells. Combining Intrinsic3D with existing multiplex and resolution enhancing methods (e.g. exchange-PAINT and RESI) will enable multicolour, 3D in situ spatial proteomics in tissue samples and bridge the gap between SMLM and systems-level cell biology.

Current state of the art computational pathology foundation models attain satisfying accuracy most general pathology tasks including detection of pancreatic ductal adenocarcinoma (PDAC) but fail to provide sufficient explainability, operating as "black boxes". Some interpretability effort has been made, with models providing attention maps that project back on the slide the models high attended areas that pushed for the given prediction result, without further naming or reasoning explanation which can limit pathologists trust toward the prediction. We developed C3PRO, a cell-centric framework attempting to provide deeper understanding and explainability, by using embeddings extracted using a pathology foundation model, from cell centered tiles containing both the cell and its close neighborhood, which were then clustered into recurrent morphological phenotypes. A spatial graph was additionally constructed on top of the back-projected vocabulary to account for recurrent motifs. The evaluation of the model performance involved a cohort of 809 patients (825 diagnostic biopsies) using nested cross validation. C3PRO was benchmarked against four different multiple instance learning frameworks and achieved on-par performance for diagnostic (0.958 AUC), survival (0.617) and resectability (0.696) prediction, while providing additional insights compared to the strongest baseline models. Each clustered phenotype was probed and extracted as a representative tile mosaic and annotated by two expert pathologists, constituting a phenotype vocabulary available for statistical analysis. These phenotypes were scored by pathologists with physical brightfield microscopy and replicated the independent statistical significance impact on overall survival prediction from diagnostic biopsies.