npj Imaging

The lifetime of fluorescent molecules provides an orthogonal readout to fluorescence intensity, opening experimental possibilities of measuring changes in local molecular environments, mechanical tension, and metabolism, among other factors. These changes are best studied live and in vivo; however, limitations of slow imaging speeds, high phototoxicity, and increased data size and complexity have significantly impeded progress on this front. Here, we present a complete and transferable pipeline consisting of a light sheet FLIM microscope and an accompanying machine learning model for data processing that renders long-term and/or high-speed volumetric FLIM (vFLIM) tractable in living systems. We benchmark this pipeline across several biological use cases, model systems, lifetime ranges, and spatiotemporal scales, showcasing a suite of possibilities that our workflow enables. This comprehensive pipeline from imaging to analysis is a crucial step forward towards disseminating the power of live vFLIM to the broader bioimaging community.

Virtual staining provides a non-invasive alternative to fluorescence microscopy, yet existing deep learning approaches fundamentally rely on pixel-aligned, multiplexed fluorescence targets for supervision. This dependence on rigidly paired data limits scalability, constrains flexibility in generating diverse subcellular structures, and becomes impractical in data-scarce biological settings. In this work, we introduce a semantic supervision paradigm for virtual staining, demonstrating that domain-knowledge prompts can effectively replace conventional pixel-level supervision. Unlike existing methods constrained by rigidly paired multiplex targets, our framework leverages biological prompts to decouple structural guidance from image translation. This decoupling enables high-fidelity, independent synthesis of multiple subcellular structures using only single-channel data. To ensure high-fidelity generation under weak supervision, we integrate self-supervised representation learning to mitigate data scarcity and incorporate direct preference optimization to suppress structural artifacts. Evaluations on the JUMP benchmark demonstrate that our approach effectively balances flexibility and fidelity, outperforming supervised baselines with a 43.3 % reduction in Average FID and an Average PCC of 0.912, while exhibiting high robustness in channel-deficient scenarios. Furthermore, the model generalizes across four in-house datasets to successfully multiplex six subcellular structures, overcoming the physical constraints of conventional fluorescent staining.

Cell morphology reflects cell health and can distinguish cell-cycle stage, growth arrest, and distinct pathways of cell death. Live, label-free quantitative phase imaging (QPI) captures these features non-invasively and with high temporal resolution, yet many image-based classifiers rely on single frames and cannot separate states whose differences emerge only over time. How much temporal information is needed, and which architecture best exploits it, remain open questions. We assembled 1,874 QPI timelapse sequences spanning six cell states (interphase, mitosis, cell cycle arrest, apoptosis, ferroptosis, and necroptosis) and compared two-dimensional convolutional neural networks (CNNs) with a three-dimensional (3D) spatiotemporal CNN across increasing frame counts. Accuracy improved as frames were added, with the largest gain between one and three frames. The 2D models saturated beyond three frames, whereas the 3D architecture kept improving, reaching 96.5% accuracy and a 3.5% error rate at eleven frames. The temporal information needed tracked the timescale of each process: mitosis was resolved from a single frame, while ferroptosis benefited most from extended sequences. Overall, these results show that dynamic information, rather than static morphology alone, drives accurate cell-state classification, and that 3D architectures are needed to fully exploit it for label-free dynamic phenotyping.

Understanding biological function often requires complementary information on molecular identity, morphology, behavior, and physiology acquired simultaneously from the same specimen. Achieving this capability in miniature microscopes remains challenging because integrating multiple imaging contrasts typically increases optical complexity while compromising field of view (FOV), spatial resolution, or form factor. Here we present dual-mode Bio-CM2, a multimodal computational miniature mesoscope that simultaneously captures fluorescence and label-free reflectance through a shared optical architecture based on distributed computational optics. The system enables frame-interleaved, co-registered dual-mode imaging over a [~]7.5 x 10 mm2 FOV with [~]6 m lateral resolution. We demonstrate the versatility of dual-mode Bio-CM2 across diverse biological systems. In freely moving Caenorhabditis elegans, reflectance captures body posture and locomotor behavior that contextualize fluorescently labeled protein aggregates. In freely swimming larval zebrafish, reflectance captures whole-body morphology and swimming behavior, while fluorescence visualizes cardiac activity. In head-fixed mice, simultaneous fluorescence and reflectance imaging across the bilateral dorsal cortex provides complementary measurements of neuronal calcium activity and intrinsic hemodynamic signals. By integrating molecularly specific fluorescence with complementary reflectance contrast, dual-mode Bio-CM2 extends distributed computational optics into a scalable platform for multimodal miniature imaging.

Quantitative imaging of extrachromosomal DNA (ecDNA) is increasingly important for studying cancer heterogeneity and adaptation, yet automated analysis has been limited by the absence of accessible imaging data, gold standard annotations and adaptable computational tools. Here we establish an open resource for computational ecDNA imaging, integrating 2,986 native-resolution metaphase FISH image sets with manual annotations, standardized benchmarks and open-source quantification frameworks. We use this resource to systematically compare existing and newly developed approaches spanning rule-based computer vision, deep-learning-based segmentation and probabilistic localization. This comparison reveals count-dependent underestimation that distorts ecDNA copy-number distributions and motivates ecCount, a probabilistic localization method developed here to preserve individual ecDNA signals and quantitative burden. ecCount achieves an object-level F1 score of 0.939 on held-out images with minimal count bias. Together, the images, annotations, retrainable models, evaluation tools and guided workflows provide community infrastructure for applying, adapting and improving automated ecDNA quantification across experimental systems.

High-speed three-dimensional imaging in scattering tissues remains challenging because volumetric microscopy generally requires scanning, whereas snapshot light-field approaches divide limited detector pixels among multiple views. This constraint is particularly severe in the second near-infrared window (NIR-II), where commonly used InGaAs cameras typically have relatively small sensor formats and high detector noise. Here we introduce NIR-II squeezed light-field microscopy (NIR-II SLIM), which optically rotates and compresses multiple perspective views before detection, allowing efficient use of camera pixels while retaining complementary spatial information for three-dimensional reconstruction. NIR-II SLIM acquires volumes at up to 600 volumes s-1 with a reconstructed lateral sampling grid of 512 x 512 pixels. We use the method for label-free four-dimensional imaging of cardiac dynamics in pigmented late-larval zebrafish, resolving chamber deformation and millisecond-scale atrioventricular-valve motion, and for NIR-II fluorescence imaging of vascular and lymphatic transport in mice. NIR-II SLIM provides a detector-efficient approach for high-speed volumetric imaging of rapid biological dynamics in scattering tissues.

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.

While light-sheet microscopy is emerging as a robust method for volumetric imaging with improved axial resolution, its capability regarding two-dimensional, surface-level mapping is often hindered by limitations in data redundancy and reconstruction efficiency stemming from volumetric registration methods. We demonstrate that a multiview imaging approach in an axially-swept, dithered light-sheet microscope paired with computational image reconstruction of view projections is able to address these trade-offs to enable large-scale mapping of surface structural features, leveraging the advantages of multiview light-sheet in scalable field of view, working distance, and near isotropic resolution across the entire imaging depth. To aid in the acquisition and analysis of two-dimensional surface structures, we present a tailored surface mapping workflow and a Fiji plugin for computational reconstruction, promoting robust and comprehensive visualization of surface features of uncleared volumetric samples. Our strategy, termed projection reconstruction for imaging surface morphology (PRISM), integrates axially swept dithered light-sheet microscopy and post-processing software for multiview imaging. The imaging hardware enables near-isotropic resolution across its entire field of view, while the software implementation leverages rigid and affine transformations to align two-dimensional projections of multiview samples. It is designed to work with the BigStitcher pipeline, leveraging its robust algorithm to provide support for two-dimensional image alignment and stitching. We demonstrate the capability of PRISM in studies of lymphatic network mapping in the epicardial layer of intact mouse hearts, as well as surface profiles of FaDu spheroids labeled with antibody-nanodiamond conjugates. This method allows us to quantify cardiac lymphatic branch numbers, diameters, and lengths of a Prox1-tdTomato mouse cardiac model, as well as cluster number and diameters of epidermal growth factor receptor within a FaDu spheroid labeled with a nanodiamond-antibody conjugate, with a significant reduction of post-processing data size. PRISM leverages multiview image projections to promote studies of cardiac lymphatics in mouse models and surface receptor distributions within spheroid models, enabling efficient surface mapping of large, intact, and uncleared biological samples across a variety of scales.

Scientific imaging is undergoing a fundamental transformation. Advances in multiplex immunofluorescence, computational pathology, and electron microscopy now enable observation of biological and physical systems at unprecedented spatial resolution. Rather than simply classifying known objects, these data offer the opportunity to discover previously unknown organizational principles. However, scientific image datasets are typically highly curated, information-rich, and limited in size, making reliable representation learning particularly challenging. We show that standard continuous self-supervised adaptation can become unstable in this regime, allowing teacher representations to drift and degrading downstream transfer. We introduce Kodiak, a discrete representation learning architecture that discovers latent scientific concepts through Sinkhorn-balanced codebook assignments. Instead of learning unconstrained continuous embeddings, Kodiak constructs a reusable vocabulary of discrete concepts that provides stable supervision for both masked-patch prediction and cross-view representation learning within a single-stage framework. Across five scientific imaging domains, including multiplex immunofluorescence for pancreatic cancer pathology and scanning electron microscopy, Kodiak consistently improves representation quality under both continued foundation-model adaptation and training from scratch. Beyond improved classification and transfer,the learned concepts are interpretable, reusable, and produce spatially coherent concept maps that support applications such as cell phenotyping, segmentation-free patch phenotyping, spatial niche discovery, tissue organization, and materials characterization. We view Kodiak as the representation-learning engine of our foundation model for spatial biology. More broadly, our results suggest that discrete representation learning provides a principled foundation for discovering reusable latent scientific concepts that can accelerate scientific interpretation across imaging domains.

In materials science, the integrity of scanning electron microscopy (SEM) images is paramount for quality control and validation of research outcomes. However, the introduction of sophisticated generative artificial intelligence, particularly Generative Adversarial Networks (GANs), has introduced a novel vulnerability: the potential for highly realistic, artificially synthesized SEM images to be used fraudulently in scientific literature. To address this challenge, we present a deep learning-based framework capable of distinguishing between authentic SEM images and those synthesized by Generative Adversarial Networks (GANs). Using FastGAN and StyleGAN2-ADA, two state-of-the-art GAN models, we generated synthetic SEM datasets to complement real imaging data. We fine-tuned a pre-trained Contrastive Language-Image Pre-training (CLIP) Vision Transformer (ViT-L-14) for binary classification. By unfreezing the final transformer blocks and appending a custom classification head, the model effectively captures the subtle, high-level artifacts inherent in GAN-generated upsampling. This work highlights the potential of deep learning to safeguard scientific imaging workflows and provides an important step toward detecting and mitigating image forgeries in materials science publications.

Understanding distributed biological systems, particularly neural circuits, requires simultaneous cellular-resolution imaging across millimeter-scale fields of view (FOV). Existing miniature microscopes remain fundamentally constrained by trade-offs among FOV, spatial resolution, and optical complexity, limiting their ability to bridge cellular microscopy with cortex-scale imaging. Here we introduce distributed computational optics, a framework that distributes image formation across coordinated optical modules and computationally integrates their measurements into a unified image. We realize this framework in Bio-CM2, a computational miniature mesoscope that partitions the imaging field across four optical modules while converging their measurements onto a common image sensor. This architecture overcomes the aberration-scaling limitations of conventional miniature optics while avoiding the hardware complexity of multi-camera systems and the contrast degradation associated with optical multiplexing. Bio-CM2 achieves a 7.5 x 10 mm2 FOV while enabling cellular-resolution in vivo imaging at video rates. We demonstrate its utility through two complementary imaging modalities in head-fixed mice: cortex-wide functional vascular imaging, enabling simultaneous quantification of pial arteriole vasomotion and mesoscale hemodynamic functional connectivity, and cellular-resolution calcium imaging, resolving the activity of over 3,000 neurons together with mesoscale neuronal functional connectivity. We further demonstrate the versatility of the platform through cellular-resolution imaging of entire coronal mouse brain sections, population-scale imaging of freely behaving Caenorhabditis elegans, and odor-evoked calcium imaging of the main olfactory bulb in head-fixed mice, highlighting its broad applicability across diverse biological systems and imaging modalities. By overcoming the conventional trade-off between FOV and spatial resolution in a compact miniature platform, Bio-CM2 establishes distributed computational optics as a scalable framework for multiscale biological imaging.

Photoacoustic (PA) and ultrasound (US) imaging provide complementary molecular, functional, and anatomical contrasts. Here, we present a panoramic PA-US imaging platform that integrates multispectral PA computed tomography (PACT) along with reflection-mode and transmission-mode US imaging through a single shared full-ring ultrasound array. We employ an ultrafast planewave transmission scheme in reflection-mode US for power Doppler (PWD) imaging and ultrasound localization microscopy (ULM). Additionally, we use the transmission-mode US to reconstruct a spatially resolved speed of sound (SoS) map that corrects both PA and US reconstruction. Such correction sharpens the resolution of PACT, suppresses the artifacts of PWD, and improves microbubble localization of ULM. Elevational scanning further enables whole-body volumetric imaging with co-registered PA and US contrasts. The integrated system maps photoswitchable DrBphP1-expressing tumors alongside their blood perfusion and oxygenation environment. Applying the platform to monitor unilateral renal ischemia-reperfusion injury, we report that microvascular perfusion and renal oxygenation recover at different rates. Collectively, we demonstrate that the integrated PA-US imaging platform provides a unified framework for multiparametric study of anatomy, perfusion, microvascular flow, oxygenation, and molecular activities.

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.

Fluorescence microscopy applications often require specialized instruments that are optimized for different experimental goals. Here, we present a reconfigurable microscopy module that integrates highly inclined swept tile (HIST) illumination for high-sensitivity single-molecule imaging and line-scanning confocal microscopy for rapid and optically sectioned volumetric acquisition. The system shares major hardware components, including lasers, scanning optics, and detection hardware, while employing unique beam shaping pathways to enable rapid switching between modalities without realignment. We characterize the module performance by measuring the excitation beam profiles, the point spread functions (PSF), and the optical transfer functions (OTF) across 40x, 60x, and 100x magnifications and demonstrate imaging applications including diffraction-limited fixed and live-cell volumetric imaging, fluorescence recovery after photobleaching, and super-resolution DNA-PAINT and single particle tracking (SPT). We also demonstrate the capability to execute multimodal imaging workflows by performing confocal imaging for chromatin density classification correlated with SPT data of nuclear proteins with diverse functions. Together, these results demonstrate a versatile imaging platform capable of supporting complementary fluorescence imaging modalities within a single instrument.

Fluorescence lifetime imaging microscopy provides quantitative, concentration-independent contrast for probing molecular interactions, biochemical environments and cellular physiology. However, the requirement to acquire sufficient time-resolved photon statistics makes FLIM inherently slow, limiting its application to dynamic biological processes. In live-cell applications, including calcium signalling and vesicular trafficking, acquisition times can exceed the timescale of the underlying biology, causing temporal averaging, motion artefacts and loss of transient information. Methods that increase FLIM acquisition speed while preserving quantitative lifetime accuracy are therefore required. We report on a high-speed, compressive, multiphoton fluorescence lifetime imaging technique (TomoFLIM). Two-photon fluorescence is excited using a line focus projected tomographically across the sample, while time-tagged fluorescence is acquired using time-correlated single-photon counting. Time-resolved fluorescence data are reconstructed using Lucy-Richardson deconvolution followed by a computationally efficient centre-of-mass method lifetime estimator. In addition, TomoFLIM Net, a physics-informed neural-network, directly reconstructs fluorescence intensity and lifetime from compressed time-resolved tomographic data. TomoFLIM was benchmarked against raster-scanned fluorescence lifetime measurements using calibrated fluorescence lifetime beads and biological specimens. We demonstrate imaging at compression ratios exceeding 90%, with Pearson correlation coefficients above 80% relative to reference images. A raster-scanned FLIM dataset acquired in 60 s was reproduced using TomoFLIM in 3.75 s, representing a 16-fold increase in frame rate and equivalent reduction in accumulated dark counts. TomoFLIM Net recovered distinct experimental bead lifetime populations, demonstrating a direct route from compressed measurements to quantitative lifetime maps. TomoFLIM therefore offers significant potential for rapid live-cell imaging of dynamic biological processes, including deep within turbid biological specimens.

Coarse to fine segmentation is a natural strategy for three dimensional lesion delineation becausefull resolution processing of an entire scan is expensive while only a small fraction of the volume contains pathology. Its main failure mode, however, is structural: a lesion missed by the coarse stage is usually absent from the region of interest and therefore cannot be recovered by the fine stage. This problem is most severe for small, low contrast, and multifocal lesions. We introduce RECALLREFINE, a coverage constrained coarse to fine framework that treats candidate-region selection as a recall critical learning problem rather than a deterministic crop around the coarse mask.A full-volume network first predicts coarse lesion probabilities and multi-scale features. A candidate score network then combines coarse probability, predictive uncertainty, feature novelty, and spatial context to score overlapping 3D blocks. During training, a differentiable selector is optimized with a component-wise coverage objective that requires every annotated lesion instance to remain covered by at least one selected block under a fixed refinement budget. Selected blocks are processed at native resolution by a local refiner with a context-ring branch, and overlapping predictions are merged back into the full volume. Across four public 3D lesion benchmarks, RECALLREFINE improves mean tumor Dice from 76.5% for the strongest matched coarse-to-fine baseline to 78.4%, while increasing sensitivity for lesions below 10 mm from 60.9% to 70.8%.At a 20% candidate budget, the selector covers 94.4% of lesion instances, substantially reducing irreversible coarse-stage misses. The results suggest that the central problem in cascaded lesion segmentation is not only how to refine a detected lesion, but how to guarantee that difficult lesions are still represented in the refinement set.

Targeted radiopharmaceutical development for ovarian cancer (OC) has been limited by the lack of molecular targets that combine broad tumor expression with minimal normal-tissue distribution. TRA-1-60 (TRA) is a cancer-associated glycoepitope carried by podocalyxin. Here, we evaluated TRA as a target for OC and developed a TRA-directed immunoPET imaging platform. Immunohistochemical analysis demonstrated significantly higher TRA expression in ovarian tumors than in normal adjacent ovarian tissue, with expression maintained across epithelial OC histotypes and disease stages. An engineered anti-TRA single-chain variable fragment-Fc (scFv-Fc) demonstrated robust penetration of three-dimensional tumor spheroids and selective accumulation in intraperitoneal tumors in an immunocompetent syngeneic OC model. Radiolabeling with zirconium-89 generated [Zr]Zr-DFO-anti-TRA scFv-Fc with >98% radiochemical yield. Serial PET/CT imaging demonstrated progressive and sustained radiotracer accumulation at tumor sites through 96 hours, accompanied by declining liver-associated activity and low uptake in most normal tissues. Together, these findings identify TRA as a broadly expressed and accessible tumor-associated glycoepitope and establish TRA-targeted immunoPET as a promising strategy for noninvasive detection of OC. The selective and sustained tumor localization of this platform further provides a foundation for development of TRA-directed radiopharmaceutical therapy, supporting a potential theranostic approach for OC.

Point-spread-function engineering by depth-encoding phase masks enables volumetric super-resolution imaging by 3D single-molecule localization microscopy (SMLM) but usually requires cumbersome relay optics. We demonstrate simple 3D SMLM implementation by phase mask insertion directly into the infinity space of a commercial microscope, along with appropriate computational compensation for field-dependence. The entire inserted component, including the phase mask, is 3D printing-based.

Drosophila melanogaster is a powerful model for studying systemic metabolism. Although metabolism is compartmentalized across tissues, prior work in adult Drosophila has mostly measured it in dissected tissues rather than in situ, limiting the spatial information that can be gained. Here, we combine whole-fly mass-spectrometry imaging with stable-isotope tracing and flux modeling to quantify metabolic activities across the adult fruit fly in a spatially resolved manner. Across 68 tracer-fed Oregon-R female flies and 187,709 pixels, we map 81 metabolites onto eight annotated regions, including muscle, fat body, gut, and nervous system. Glucose labeling and fitted relative fluxes indicate higher glycolytic and tricarboxylic acid cycle turnover in thoracic muscle than in abdominal fat body; proline labels the nervous system and eye; glutamine the abdominal reproductive system and gut. Alanine carries glucose-derived carbon through the hemolymph and transfers nitrogen into glutamate. In flies bearing yorkie-driven gut tumors, alanine contribution to malate rises in gut and abdomen, and reducing gut alanine uptake lowers tumor burden. Together, this work establishes a framework for quantitative spatial metabolic analysis, showing compartmentalized nutrient use across the adult fruit fly.

Formalin-fixed prostate tissue samples were imaged by propagation-based synchrotron phase contrast micro computed tomography ({micro}CT) with a 3D spatial resolution of ca. 3 {micro}m. Post-{micro}CT, samples were prepared for histology with sections close to coplanar with the transverse {micro}CT image planes. Haematoxylin and eosin stained sections were examined by an expert prostate histopathologist and compared qualitatively with corresponding {micro}CT-visible microstructure features. There is potential for {micro}CT to provide complimentary information to conventional histology and light microscopy without the need for preparation of stained thin sections. For the imaging conditions and spatial resolution of our study, {micro}CT may provide tissue architectural features similar to those used in Gleason grading, albeit without clear subcellular microstructure detail. At the spatial resolution of our study {micro}CT may provide novel 3D microstructure information for validation of diffusion weighted magnetic resonance imaging (MRI) methods. As an example, we demonstrate a qualitative correlation between {micro}CT-derived stromal fibre orientation and preferential water diffusion direction measured by diffusion tensor MRI microscopy of the same sample.