Advanced Intelligent Systems

Modern optical microscopes are fully motorised; however, transforming them into truly smart systems requires real-time adjustment of acquisition settings in response to detected objects and dynamic biological events. At the core are classification algorithms that commonly depend on customised softwares and are generally designed for narrowly-defined biological applications. In addition, they often require substantial annotated datasets for effective training. We introduce a semi-supervised generative adversarial network (SGAN) for robust cell-cycle stage classification under low-resource conditions, adaptable to diverse cellular structures. The framework combines unlabelled microscopy images with synthetically generated samples to mitigate limited annotation, while preserving stable performance even when the unlabelled subset is class-imbalanced. Tested on the Mitocheck dataset, which features five mitosis classes, the model achieved 93{+/-}2% accuracy using only 80 labelled per class and 600 unlabelled images. The proposed algorithm is generic and readily adaptable to new labelling schemes, classification targets, cell lines, and microscopy modalities, enabling efficient integration into automated microscopes.

Hematoxylin and eosin (H&E) has been the fundamental method for visualising tissue morphology. Recent advances in tissue clearing and microscopy have enabled the observation of tissue morphology in 3D, but incomplete penetration of nucleic acid dyes has remained the bottleneck. To address this, we develop a new staining chemistry called Cyclodextrin and Organic solvent-assisted deep Labelling of ORgans in 3D (COLOR-3D), which attains the best penetration depth and homogeneity among state-of-the-art methods. We also demonstrate the scalability of COLOR-3D and its compatibility with other staining modalities. To bridge the gap between 3D histology and its wider application in biomedical research and histopathology, we develop a computational pipeline to convert 3D fluorescence images into bright-field H&E images, enabling the creation of a 3D atlas of both normal tissues and pathological specimens. Apart from qualitative observation of tissue morphology, COLOR-3D also enables quantitative analysis for studying biological phenomena. In the mouse liver, we discover rare populations of tetranuclear hepatocytes as well as m16n, t4n and t8n hepatocytes. We also propose the first structural model of the liver lobule based on 3D histology. With a more complete penetration, we reveal the following aging-related changes in tissue microarchitecture, including an increase in extreme nuclear polyploidy, and the disruption of vasculature and portal triad.

Serial-section immunohistochemistry (IHC) is the largest available source of paired hematoxylin and eosin (HE) and IHC whole slide images, yet it remains underexploited for cell-level supervision: adjacent sections sample non-identical cells, and residual registration error prevents direct assignment of IHC labels to individual HE cells. We present CellDF (Cell Displacement Field), which turns registered serial-section data into pairs of HE cells and their IHC labels by solving cell matching at whole-slide scale and assessing its reliability without ground-truth correspondences. CellDF estimates a locally adaptive residual displacement field through iterated kernel regression over each HE cells K nearest IHC candidates; a sparse-kernel variant keeps it tractable at the cell counts of a whole slide, where pairwise matchers are not. The within-tile distribution of the estimated displacements yields two ground-truth-free statistics, the directional scatter{sigma}{theta} and the between-tile angular deviation |{Delta}{theta}|, that localize matching quality more finely than landmark-based target registration error and drive a two-stage outlier filter that withholds labels where matching is unreliable. On 54 same-section HyReCo pairs,{sigma}{theta} correlates only moderately with landmark error and flags localized restaining damage that global error misses; on 30 four-marker Acrobat serial-section cases, the same statistic flags which IHC marker, if any, lies physically close enough to HE to support cell-level transfer. As a proof of concept, IHC labels transferred through CellDF trained a cell classifier on HE embeddings that generalized to held-out cells within the sample (F1 0.85, AUROC 0.88), establishing serial-section IHC as a usable cell-level labeling resource. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/733058v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@a9b3dcorg.highwire.dtl.DTLVardef@15f652corg.highwire.dtl.DTLVardef@1eb3396org.highwire.dtl.DTLVardef@87dda2_HPS_FORMAT_FIGEXP M_FIG C_FIG

Light microscopy imaging with histological stains is central to disease diagnosis and research. It is enhanced with immunostaining to reveal cellular composition and complexity linked to clinical utility and biological mechanisms. Emerging multiplex imaging technologies like Phenocycler markedly increase the coverage to capture the cellular diversity but are costly, technically demanding, and inaccessible to most clinical laboratories. We developed DigitAb, a deep learning framework that classifies cell types directly from hematoxylin and eosin (H&E) stained slides, eliminating the need for specialized assays. Using Phenocycler imaging, we generated highlZlresolution ground truths for [~]3.5 million cells from 29 human kidney samples across four multi-institutional datasets to train a semantic segmentation model for 10 cell types, achieving a balanced accuracy of 0.78. By employing an integrated adversarial domain adaptation module, we tested DigitAb on unlabeled and untested biopsy samples from kidney transplant and diabetic samples. We were able to predict several cell types just from histology images, without using any special technology or immunostains, and demonstrate high concordance with clinical gold-standard Banff schema in kidney transplant rejection, and clinical characteristics of diabetic nephropathy. Our cloudlZlbased tool, DigitAb, provides scalable, accessible, labellZlfree cellular segmentation for research and clinical pathology.

The rapid convergence of optical innovation and machine intelligence is reshaping biological imaging by enabling platforms that jointly advance image formation and computational reconstruction for highspeed, high-resolution volumetric microscopy. However, broadly accessible three-dimensional imaging at high spatiotemporal resolution remains limited by the reliance of existing supervised methods on large modality-matched training datasets, the computational burden of conventional iterative reconstruction, and sensitivity to optical mismatch arising from small deviations in the spatial-angular point spread functions. Here, we introduce HYPER-Net, a physics-conditioned self-supervised framework for Fourier light-field microscopy that integrates scan-free volumetric acquisition with fast, robust three-dimensional reconstruction. HYPER-Net incorporates experiment-specific point-spread functions into the learning process in two complementary roles: as the forward operator that enforces measurement consistency and as a conditioning signal that adaptively modulates intermediate feature representations. This design reduces reliance on paired experimental ground-truth volumes, improves robustness to system variation, and enables generalizable reconstruction across diverse biological contexts. Using human colon organoids, embryonic Xenopus laevis hearts, hiPSC-derived cardiac spheroids, and freely moving Caenorhabditis elegans, we demonstrate high-fidelity volumetric imaging of tissue morphology, cardiac function, calcium-contraction coupling, and locomotion-associated neural and muscular dynamics. These results position HYPER-Net as a versatile framework for rapid volumetric imaging and quantitative analysis of dynamic biological systems across basic research and biomedical applications.

Three-dimensional (3D) imaging represents the development of next generation of fluorescence microscopy. However, routine axial down-sampling makes isotropic resolution unrealistic. Here, we propose DeepUI, a physical zero-shot framework designed to achieve isotropic 3D fluorescence images from a low axial sampling rate. DeepUI fully leverages the intrinsic characteristics of 3D images through physics-guided degradation, which incorporates spatial-frequency joint learning to generate a scaled optical transfer function, combined with noise degradation and an up-sampling branch. Typically requiring just 5 minutes for training and 0.5 minutes for high-throughput and fast prediction, we demonstrate the superior performance of DeepUI to get isotropic results, and the exclusivity to axial down-sampling conditions, even in more challenging conditions, including defocused background, noise, and resolution blur.

Optogenetics enables experiments in out-of-equilibrium conditions to clarify biological mechanisms and quantify biophysical parameters. However, modelling and control techniques to study mammalian cell biology under optogenetic perturbation remain underutilised. Here, we benchmark these methods within mammalian cells by steering nucleocytoplasmic transport via the optogenetic LEXY protein in outcome-driven microscopy. First, we employ system identification to obtain models that predict transport dynamics by minimising the prediction error. We quantify this prediction accuracy for one biophysical model and two black-box models. Second, we evaluate closed-loop control efficacy by steering transport along a predefined trajectory using model-free Proportional Integral (PI) control, model-based Linear Quadratic Regulation (LQR) and Model Predictive Control (MPC). Both the predictive models and the applied control techniques demonstrate robust performance against cell-to-cell variation. This biological variation is quantified by the parameter distributions obtained from model identification with single-cell trajectories. While we show that model-free techniques such as PI and gain-scheduled PI achieve steering without explict model knowledge, predictive architectures offer better performance under this cell-to-cell variation and time-varying setpoints. Moreover, black-box predictive accuracy suggests that this model-based control is possible, even when explicit mechanistic understanding is missing. Ultimately, we demonstrate that predictive modelling and optogenetics enable quantitative characterisation and precise manipulation of mammalian cells, while offering practical guidelines for the implementation of these techniques.

Deep Learning (DL) holds exciting potential in automating the prediction of organoid differentiation results. Nevertheless, current models lack adaptability, openness, and robustness in performance. Additionally, broad employments of predictive models in wet-lab settings necessitate machine learning expertise, often not readily available in biologically oriented laboratories. To offer an intuitive solution, we present ColabViTAMIn-O, a code-free platform together with ViTAMIn-O. ViTAMIn-O is a fully open organoid-specific DL model trained and tested on a total of 34 organoid categories, incorporating annotated images across transmitted light microscopy (TLM) modalities at single-organoid resolution. It is adaptable to downstream prediction tasks of varying dataset sizes and outperforms established models even with linear-probing. It performs reliably within a few-shot framework and is even extensible to human embryo TLM imaging data at single specimen level. By releasing our platform, centralized model hub, and datasets, we hope to encourage broader deployments of specialized DL models in stem-cell laboratories.

Multimodal 3D imaging has emerged as a powerful approach for investigating complex tissue architecture in pathological specimens. Techniques such as propagation-based phase-contrast computed tomography (PCT), light-sheet microscopy (LSM), and three-photon microscopy (3PM) provide complementary information on unlabeled tissue morphology based on distinct intrinsic contrast mechanisms. However, integrating these heterogeneous datasets into a unified spatial framework remains challenging due to differences in imaging geometry, spatial resolution, and modality-specific distortions. In this study, we present a registration pipeline for spatially aligning volumetric datasets acquired with PCT, LSM, and 3PM from formalin-fixed paraffin-embedded (FFPE) human colon cancer specimens. Biopsies from theses specimens were optically cleared and imaged sequentially using the three high-resolution modalities. To compensate for large positional differences between acquisitions, a three-stage cascade registration strategy was developed, consisting of coarse global alignment on down-sampled data, followed by rigid refinement at intermediate resolution. Mutual information was used as the similarity metric to ensure robust multimodal registration. The resulting framework enables the generation of spatially aligned multi-channel 3D datasets that combine structural information from X-ray phase-contrast imaging with complementary optical contrast signals. Beyond registration, we demonstrate that the fused six-dimensional feature space can be further exploited for unsupervised tissue characterization using a Gaussian Mixture Model (GMM), enabling data-driven identification of spatially coherent tissue regions without manual annotation. Qualitative evaluation confirms consistent alignment of major anatomical structures across modalities, while the unsupervised clustering reveals biologically meaningful patterns despite modality-specific noise and resolution differences. While further optimization and validation across larger datasets will enhance its computational efficiency and breadth of application, the approach already demonstrates strong potential for comprehensive tissue analysis and enables scalable, label-free 3D characterization of colon cancer tissue architecture.

Advances in multi-dimensional imaging method and probe developments have brought super-resolution fluorescence microscopy into a functional era. They capture additional single-molecule fluorescence information concurrently with spatial localization, enabling simultaneous identification of molecular species and interrogation of nanoscale environments with rich, high-dimensional imaging information. However, the adoption of multi-dimensional imaging has been hindered by fragmented analysis workflows, complex parameter tuning, and limited integration of advanced computational methods. Here, we introduce an agentic single-molecule multi-dimensional bioimaging AI, referred to as SIMBA, an AI-driven platform that unifies single-molecule localization, spectral processing and deep learning-based denoising within a single agentic and interactive framework. SIMBA incorporates large language model-based agents capable of interpreting user intent, orchestrating analysis pipelines, and dynamically selecting computational tools for automated data processing. We demonstrate that SIMBA enables supports standard single-molecule localization workflow, functional mapping of nanoscale environmental heterogeneity through single-molecule spectral analysis and denoising using developed supervised learning methods. By integrating extensible tool architectures with human language-guided workflows, SIMBA establishes a new paradigm for intelligent microscopy analysis, lowering barriers to multi-dimensional imaging adoption while enabling scalable, reproducible, and adaptive analysis of complex imaging datasets.

Due to its ability to establish a pool of undetectable and latently infected cells that can initiate viral production through random reactivation, a cure to human immunodeficiency virus (HIV) infections has remained elusive. Many approaches have been proposed, including the "shock and kill" method where latency reversing agents (LRAs) are administered to reactivate latently infected cells out of latency and remove them through immune targeting and clearance, and the "block and lock" method where latency promoting agents (LPAs) are administered to inhibit reactivation and potentially induce a "deep latency" state where infected cells can no longer reactivate. Previous large scale drug screen studies have demonstrated a correlation between a compound's capability to modulate the fluctuations (or "noise") in HIV gene expression and its potential to modulate HIV latency. However, measurements of gene expression noise are labor- and cost-intensive. To circumvent these drawbacks, we trained a variational autoencoder (VAE) on a previously published large scale time-lapse fluorescence microscopy dataset, and performed an in silico screening of ~175,000 compounds for HIV latency modulators. Out of the top 113 predicted modulators that were experimentally tested, 16 latency reversing agent (LRA) synergizers and 2 latency promoting agents (LPAs) were confirmed, yielding an overall experimental hit rate of 15.9%. Our work demonstrates that in silico drug screening modalities, guided by existing large-scale experimental datasets, can yield high experimental hit rates, reducing costs incurred from labor-intensive wet lab-focused methodologies.

1Three-dimensional organoid cultures have emerged as powerful models for studying human tissue biology, disease mechanisms, and drug responses. Fluorescence confocal microscopy of organoids generates complex volumetric image data that is increasingly analyzed using deep learning pipelines for cell segmentation, morphometry, and phenotyping. However, training and benchmarking such pipelines requires large annotated datasets, the manual curation of which is prohibitively expensive and time-consuming. Here we present a parametric, physics-based computational framework for generating synthetic 3D fluorescence organoid images with exact ground-truth cell body and nucleus label masks. The framework models cell placement using force-directed sphere packing with optional hollow lumen exclusion for cyst-forming organoids, cell morphology using power-diagram (Laguerre) tessellation with apical-basal elongation and surface flattening for polarized epithelial cells, membrane curvature using low-frequency coordinate displacement, nuclear shape using irregular ellipsoid deformation with smooth radial eccentricity direction blending, and optical effects using depth-dependent point-spread function broadening, a physically motivated staining diffusion gradient with residual interior plateau, z-attenuation, haze, shot noise, and channel crosstalk. The necrotic core model uses a three-phenotype nuclear population, pyknotic, ghost, and karyorrhectic, reflecting the histological diversity of real necrotic zones. Five condition-specific presets are provided, calibrated to published biological measurements and covering PDAC osmotic stress, HMECyst normal and cyst-forming organoids, and a large primary PDAC organoid with a necrotic core. Unlike generative adversarial network approaches, our method requires no training data, produces exact ground truth by construction, and allows systematic and interpretable control over every morphological and optical parameter. The framework is released as open-source Python software with a PyQt5 graphical interface and produces OME-TIFF output compatible with arivis Pro, FIJI, and napari, as well as most other microscopy image analysis software.

Light is a modifiable determinant of health, yet real-world exposure assessment is often reduced to illuminance alone, lacks environmental context, or relies on privacy-sensitive sensing. We present SpectraVita, a low-cost, compact multispectral wearable that continuously samples 11 ultraviolet-to-near-infrared bands and, through a privacy-preserving pipeline without cameras or location tracking, produces interpretable digital phenotypes of lighting environment (natural vs. artificial and source type) and vegetation context alongside standard visual and non-visual light metrics. In extensive in-the-wild recordings spanning diverse scenes, times of day, weather conditions, and light sources, we observe distinctive spectral signatures that enable supervised models to achieve a macro-averaged F1 score of 0.988{+/-}0.004 for light-source classification and green-space detection in boundary-free environments. A sensor-derived normalized difference vegetation index (NDVI) emerges as an explainable, physically grounded marker linking natural light exposure and greenness. Robustness is supported by scenario-shift testing, image-segmentation validation, and mixed-environment experiments that demonstrate sensitivity to partial and transient exposures, as well as by longitudinal stationary monitoring and deployment in a cohort of thousands of participants capturing seasonal and behavioral variability. SpectraVita enables individualized, privacy-preserving, longitudinal monitoring of light and greenness exposure at scale, addressing a key measurement gap for precision and population health studies of daily photic environments.

Pathologist-guided distinctions within histology and spatial omic images provide insights into health and disease, with digital pathology leveraging artificial intelligence to automate such assessments. To train computational models, current digital pathology methods rely on upfront manual annotations, which are time-consuming to generate. Pre-annotation is poorly suited to investigating novel spatial behaviors--a major need driven by advances in spatial profiling--for which annotation criteria and data needs will be uncertain. To address these challenges, we present DIANNE, a digital pathology approach for rapid training and inference of spatial differential attributes based on train-time Positive Class Mixup Augmentation. DIANNE can compute foundation model-derived segmentation-free localization of differential classifiers across whole slide H&E images within seconds on a workstation, enabling interactive investigation of spatial niches. Predictive models can be re-trained in real-time in response to patch or regional annotation changes, clarifying determinative biological attributes across slides from only a few dozen annotated patches. We demonstrate the effectiveness of DIANNE for tumor detection, artifact identification, and exploration of pancreatic, fetal membranes and kidney tissue structures. DIANNE also provides analogous capabilities for IHC, multiplex immunofluorescence, and registered spatial transcriptomic+H&E images. DIANNE is implemented in a Jupyter toolkit, enabling rapid development of high-resolution classifiers from weakly-supervised training. DIANNE provides a practical system to quantitatively understand known and novel spatial phenotypes.

While Single Molecule Localization Microscopy (SMLM) aims to generate precise coordinates of molecular targets in cells, the resulting point clouds are inherently blurred by additive noise sources across the experimental, imaging, and processing workflow. This blurring often limits SMLMs ability to accurately quantify complex assembled structures required to address biological issues, despite reported localization precision down to a couple of nanometers. Here, we present SMLMFlow, a machine learning framework for improving structural resolution in SMLM datasets that combines a graph neural network and a hierarchical transformer with flow matching. We show that SMLMFlow improves structural resolution and downstream quantification across different structures, including filaments and protein nano-clusters, and generalizes to new unseen photophysics models.

Optical fluorescence microscopy enables visualization of biological structures and dynamics. However, the intrinsic diffraction limit, especially axially, and depth-related scattering noise compromise the image resolution and fidelity. Computational 3D deconvolution is a promising approach for mitigating these issues, yet its execution is hindered by inaccurate and cumbersome theoretical modeling or experimental measurement of 3D point spread function (PSF), as well as ineffective 3D noise regularization. Furthermore, in the 3D super-resolution regime, there remains a lack of standardized tools for evaluating 3D super-resolution fidelity. Here, we present the 3D adaptive deconvolution and evaluation (3D-ADE) toolkit, which comprises 3D-Ada deconvolution with physics-oriented automatic 3D-PSF calibration, and 3D-SQUIRREL for 3D super-resolution quality assessment. It effectively resolves noise instability, eliminates the need for 3D-PSF calibration, and reliably assesses the fidelity of 3D resolution extension via deconvolution, physical, and deep-learning-based methods. Accessible via multiple software platforms, 3D-ADE enhances the versatility of 3D deconvolution and fills the gap in 3D super-resolution evaluation tools, and thereby advances volumetric fluorescence imaging applications.

Digital Pathology (DP) is a fast-emerging branch of pathology focused on digitizing pathology data. A key challenge of DP usage for pathology laboratories, especially mid- to small-sized clinical labs, are the upfront costs associated with instrumentation and the logistical challenges of implementation. In the current project, we built an end-to-end DP solution using low-cost, open-source components that is user-friendly at a small scale. We repurposed readily available microscopy components in a pathology lab to assemble a fully functional DP pipeline for translational research applications. We tested multiple low-cost complementary metal-oxide semiconductor (CMOS) cameras in this project and chose a user-friendly Canon camera for image acquisition. An open-source DP server solution, OMERO v.5.6.4, was used as the image management system (IMS) to host and serve the WSIs on an Ubuntu 22.04 operating system. The server-hosted WSI images were evaluated remotely and asynchronously by multiple pathologists physically situated in Albuquerque, NM; Salt Lake City, UT; and Palo Alto, CA. Each pathologist assessed the quality of the WSI pipeline, image quality, and WSI interaction experience using a 23-question survey. Overall, the custom, low-cost WSI pipeline was noted to be a robust and user-friendly experience by the pathologists. The current DP setup is unlikely to be useful as a commercial, scalable DP pipeline for large-scale clinical applications. However, it demonstrates the feasibility of creating customized, small-scale DP solutions (at a low price point) for asynchronous translational pathology research applications. Additionally, building customized DP pipelines provides excellent educational opportunities for pathology residents to gain in-depth knowledge of the various technical elements of a DP workflow. In summary, we have established a low-cost, end-to-end WSI DP pipeline useful for spatiotemporally asynchronous translational pathology research, in an academic setting.

Caenorhabditis elegans is a premier model organism for aging and neurobiology research, valued for its short lifespan, optical transparency, genetic tractability, and well-mapped nervous system. Non-invasive automated recording of biomarkers is a fundamental goal in modern biology because it preserves natural physiology and eliminates confounds from anesthesia, restraint, or repeated handling in C. elegans. Yet high-magnification imaging of freely moving worms remains a persistent challenge: as magnification increases, the narrowing field of view compounds target loss, motion blur, and focal drift, pushing researchers toward immobilization strategies that compromise physiology, suppress natural behavior, and preclude the continuous longitudinal observation essential for aging and neurobiological studies. Here, we present a real-time tracking workflow for imaging individual worms in a microfluidic platform under controlled culture conditions. The system integrates deep learning head detection, image-based autofocus, and rapid motorized-stage feedback to support stable imaging across multiple magnifications, including neuronal-scale imaging. Hundreds of individually housed worms in separate incubation chambers enable repeated daily imaging of the same animals throughout their lifespan. Built entirely on a commercially available inverted microscope without additional custom hardware, the platform features a modular, user-configurable interface adaptable to diverse microscope setups, specimens, and experimental goals. Fluorescence images from freely moving worms were visually comparable to those from immobilized animals, supporting longitudinal phenotyping in aging and neurobiology studies.

Generative Lung Architecture Modeling (GLAM) is an integrated bioengineering framework that couples high-resolution three-dimensional tissue imaging with generative artificial intelligence to de novo design and 3D-bioprint anatomically detailed lung microtissue models. Native extracellular 3D matrix architectures of pulmonary parenchyma were extracted from healthy, fibrotic, and emphysematous in vivo mouse disease models and processed through a computational pipeline containing pre-trained image segmentation and 3D mesh generation. The resulting datasets were used to train a U-Net generative diffusion model with attention layers capable of synthesizing healthy and diseased lung tissue architectures. Microtissue cubes of about 200 - 300 {micro}m edge length of native and synthetic datasets were fabricated through high-resolution two-photon stereolithography with gelatin-methacryloyl biomaterial ink and successfully seeded with cells, demonstrating biological compatibility. In closing the loop between biological imaging, generative modeling, and high-resolution biofabrication, this integrated framework establishes generative AI as a functional design layer for tissue engineering. The resulting lung microtissues retained architectural features of the native and original tissues, making them an application-ready platform for customizable and scalable fabrication of biological tissue surrogates for preclinical modeling, drug testing, and precision regenerative bioengineering.

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.