Object Segmentation and Ground Truth in 3D Embryonic Imaging

SCALE SPACE DETECTOR FOR ANALYZING SPATIOTEMPORAL VENTRICULAR CONTRACTILITY AND NUCLEAR MORPHOGENESIS IN ZEBRAFISH

In vivo quantitative assessment of structural and functional biomarkers is essential for characterizing the pathophysiology of congenital disorders. In this regard, fixed tissue analysis has offered revolutionary insights into the underlying cellular architecture. However, histological analysis faces major drawbacks with respect to lack of spatiotemporal sampling and tissue artifacts during sample preparation. This study demonstrates the potential of light sheet fluorescence microscopy (LSFM) as a non-invasive, 4D (3days + time) optical sectioning tool for revealing cardiac mechano-transduction in zebrafish. Furthermore, we have described the utility of a scale and size-invariant feature detector, for analyzing individual morphology of fused cardiomyocyte nuclei and characterizing zebrafish ventricular contractility.

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Cardiac defect genes in humans have corresponding zebrafish orthologs

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Light sheet modality is very effective for non-invasive, 4D modeling of zebrafish

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Hessian detector is robust to varying nuclei scales and geometric transformations

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Watershed filter is effective for separating fused cellular volumes

GIANI: open-source software for automated analysis of 3D microscopy images

The study of cellular and developmental processes in physiologically relevant three-dimensional (3D) systems facilitates an understanding of mechanisms underlying cell fate, disease and injury. While cutting-edge microscopy technologies permit the routine acquisition of 3D datasets, there is currently a limited number of open-source software packages to analyse such images. Here, we describe General Image Analysis of Nuclei-based Images (GIANI; https://djpbarry.github.io/Giani), new software for the analysis of 3D images. The design primarily facilitates segmentation of nuclei and cells, followed by quantification of morphology and protein expression. GIANI enables routine and reproducible batch-processing of large numbers of images, and comes with scripting and command line tools. We demonstrate the utility of GIANI by quantifying cell morphology and protein expression in confocal images of mouse early embryos and by segmenting nuclei from light-sheet microscopy images of the flour beetle embryo. We also validate the performance of the software using simulated data. More generally, we anticipate that GIANI will be a useful tool for researchers in a variety of biomedical fields.

Summary: General Image Analysis of Nuclei-based Images (GIANI) is a new plugin for the popular FIJI platform, designed for the automated analysis of 3D microscopy images of a wide range of sample types.

Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification

Identification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio. The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets.

Cell Tracking Profiler - a user-driven analysis framework for evaluating 4D live-cell imaging data

Accurate measurements of cell morphology and behaviour are fundamentally important for understanding how disease, molecules and drugs affect cell function in vivo Here, by using muscle stem cell (muSC) responses to injury in zebrafish as our biological paradigm, we established a 'ground truth' for muSC behaviour. This revealed that segmentation and tracking algorithms from commonly used programs are error-prone, leading us to develop a fast semi-automated image analysis pipeline that allows user-defined parameters for segmentation and correction of cell tracking. Cell Tracking Profiler (CTP) is a package that runs two existing programs, HK Means and Phagosight within the Icy image analysis suite, to enable user-managed cell tracking from 3D time-lapse datasets to provide measures of cell shape and movement. We demonstrate how CTP can be used to reveal changes to cell behaviour of muSCs in response to manipulation of the cell cytoskeleton by small-molecule inhibitors. CTP and the associated tools we have developed for analysis of outputs thus provide a powerful framework for analysing complex cell behaviour in vivo from 4D datasets that are not amenable to straightforward analysis.

3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis

During embryogenesis, cells repeatedly divide and dynamically change their positions in three-dimensional (3D) space. A robust and accurate algorithm to acquire the 3D positions of the cells would help to reveal the mechanisms of embryogenesis. To acquire quantitative criteria of embryogenesis from time-series 3D microscopic images, image processing algorithms such as segmentation have been applied. Because the cells in embryos are considerably crowded, an algorithm to segment individual cells in detail and accurately is needed. To quantify the nuclear region of every cell from a time-series 3D fluorescence microscopic image of living cells, we developed QCANet, a convolutional neural network-based segmentation algorithm for 3D fluorescence bioimages. We demonstrated that QCANet outperformed 3D Mask R-CNN, which is currently considered as the best algorithm of instance segmentation. We showed that QCANet can be applied not only to developing mouse embryos but also to developing embryos of two other model species. Using QCANet, we were able to extract several quantitative criteria of embryogenesis from 11 early mouse embryos. We showed that the extracted criteria could be used to evaluate the differences between individual embryos. This study contributes to the development of fundamental approaches for assessing embryogenesis on the basis of extracted quantitative criteria.

XitoSBML: A Modeling Tool for Creating Spatial Systems Biology Markup Language Models From Microscopic Images

XitoSBML is a software tool designed to create an SBML (Systems Biology Markup Language) Level 3 Version 1 document from microscopic cellular images. It is implemented as an ImageJ plug-in and is designed to create spatial models that reflect the three-dimensional cellular geometry. With XitoSBML, users can perform spatial model simulations based on realistic cellular geometry by using SBML-supported software tools, including simulators such as Virtual Cell and Spatial Simulator. XitoSBML is open-source and is available at https://github.com/spatialsimulator/XitoSBML/. XitoSBML is confirmed to run on most 32/64-bit operating systems: Windows, MacOS, and Linux.

Information flow in the presence of cell mixing and signaling delays during embryonic development

Embryonic morphogenesis is organized by an interplay between intercellular signaling and cell movements. Both intercellular signaling and cell movement involve multiple timescales. A key timescale for signaling is the time delay caused by preparation of signaling molecules and integration of received signals into cells' internal state. Movement of cells relative to their neighbors may introduce exchange of positions between cells during signaling. When cells change their relative positions in a tissue, the impact of signaling delays on intercellular signaling increases because the delayed information that cells receive may significantly differ from the present state of the tissue. The time it takes to perform a neighbor exchange sets a timescale of cell mixing that may be important for the outcome of signaling. Here we review recent theoretical work on the interplay of timescales between cell mixing and signaling delays adopting the zebrafish segmentation clock as a model system. We discuss how this interplay can lead to spatial patterns of gene expression that could disrupt the normal formation of segment boundaries in the embryo. The effect of cell mixing and signaling delays highlights the importance of theoretical and experimental frameworks to understand collective cellular behaviors arising from the interplay of multiple timescales in embryonic developmental processes.

Micro-CT imaging and structural analysis of glomeruli in a model of Adriamycin-induced nephropathy

Glomeruli number and size are important for determining the pathogenesis of glomerular disease, chronic kidney disease, and hypertension. Moreover, renal injury can occur in specific cortical layers and alter glomerular spatial distribution. In this study, we present a comprehensive structural analysis of glomeruli in a model of Adriamycin (doxorubicin) nephropathy. Glomeruli are imaged (micro-CT at 10 × 10 × 10 μm³) in kidney specimens from C57Bl/6 mouse cohorts: control treated with saline ( n = 9) and Adriamycin treated with 20 mg/kg Adriamycin ( n = 7). Several indices were examined, including glomerular number, glomerular volume, glomerular volume heterogeneity, and spatial density at each glomerulus and in each cortical layer (superficial, midcortical, and juxtamedullary). In the Adriamycin-treated animals, glomerular number decreased significantly in the left kidney [control: 8,298 ± 221, Adriamycin: 6,781 ± 630 (mean ± SE)] and right kidney (control: 7,317 ± 367, Adriamycin: 5,522 ± 508), and glomerular volume heterogeneity increased significantly in the left kidney (control: 0.642 ± 0.015, Adriamycin: 0.786 ± 0.018) and right kidney (control: 0.739 ± 0.016, Adriamycin: 0.937 ± 0.023). Glomerular spatial density was not affected. Glomerular volume heterogeneity increased significantly in the superficial and midcortical layers of the Adriamycin cohort. Adriamycin did not affect glomerular volume or density metrics in the juxtamedullary region, suggesting a compensatory mechanism of juxtamedullary glomeruli to injury in the outer cortical layers. Left/right asymmetry was observed in kidney size and various glomeruli metrics. The methods presented here can be used to evaluate renal disease models with subtle changes in glomerular endowment locally or across the entire kidney, and they provide an imaging tool to investigate diverse interventions and therapeutic drugs.

A high-throughput imaging and nuclear segmentation analysis protocol for cleared 3D culture models

Imaging and subsequent segmentation analysis in three-dimensional (3D) culture models are complicated by the light scattering that occurs when collecting fluorescent signal through multiple cell and extracellular matrix layers. For 3D cell culture models to be usable for drug discovery, effective and efficient imaging and analysis protocols need to be developed that enable high-throughput data acquisition and quantitative analysis of fluorescent signal. Here we report the first high-throughput protocol for optical clearing of spheroids, fluorescent high-content confocal imaging, 3D nuclear segmentation, and post-segmentation analysis. We demonstrate nuclear segmentation in multiple cell types, with accurate identification of fluorescently-labeled subpopulations, and develop a metric to assess the ability of clearing to improve nuclear segmentation deep within the tissue. Ultimately this analysis pipeline allows for previously unattainable segmentation throughput of 3D culture models due to increased sample clarity and optimized batch-processing analysis.

A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring

Segmenting touching-pigs in real-time is an important issue for surveillance cameras intended for the 24-h tracking of individual pigs. However, methods to do so have not yet been reported. We particularly focus on the segmentation of touching-pigs in a crowded pig room with low-contrast images obtained using a Kinect depth sensor. We reduce the execution time by combining object detection techniques based on a convolutional neural network (CNN) with image processing techniques instead of applying time-consuming operations, such as optimization-based segmentation. We first apply the fastest CNN-based object detection technique (i.e., You Only Look Once, YOLO) to solve the separation problem for touching-pigs. If the quality of the YOLO output is not satisfied, then we try to find the possible boundary line between the touching-pigs by analyzing the shape. Our experimental results show that this method is effective to separate touching-pigs in terms of both accuracy (i.e., 91.96%) and execution time (i.e., real-time execution), even with low-contrast images obtained using a Kinect depth sensor.

Delta-Notch signalling in segmentation

Modular body organization is found widely across multicellular organisms, and some of them form repetitive modular structures via the process of segmentation. It's vastly interesting to understand how these regularly repeated structures are robustly generated from the underlying noise in biomolecular interactions. Recent studies from arthropods reveal similarities in segmentation mechanisms with vertebrates, and raise the possibility that the three phylogenetic clades, annelids, arthropods and chordates, might share homology in this process from a bilaterian ancestor. Here, we discuss vertebrate segmentation with particular emphasis on the role of the Notch intercellular signalling pathway. We introduce vertebrate segmentation and Notch signalling, pointing out historical milestones, then describe existing models for the Notch pathway in the synchronization of noisy neighbouring oscillators, and a new role in the modulation of gene expression wave patterns. We ask what functions Notch signalling may have in arthropod segmentation and explore the relationship between Notch-mediated lateral inhibition and synchronization. Finally, we propose open questions and technical challenges to guide future investigations into Notch signalling in segmentation.