Extracting 3D cell parameters from dense tissue environments: application to the development of the mouse heart

3D cell morphology detection by association for embryo heart morphogenesis

Advances in tissue engineering for cardiac regenerative medicine require cellular-level understanding of the mechanism of cardiac muscle growth during embryonic developmental stage. Computational methods to automatize cell segmentation in 3D and deliver accurate, quantitative morphology of cardiomyocytes, are imperative to provide insight into cell behavior underlying cardiac tissue growth. Detecting individual cells from volumetric images of dense tissue, poised with low signal-to-noise ratio and severe intensity in homogeneity, is a challenging task. In this article, we develop a robust segmentation tool capable of extracting cellular morphological parameters from 3D multifluorescence images of murine heart, captured via light-sheet microscopy. The proposed pipeline incorporates a neural network for 2D detection of nuclei and cell membranes. A graph-based global association employs the 2D nuclei detections to reconstruct 3D nuclei. A novel optimization embedding the network flow algorithm in an alternating direction method of multipliers is proposed to solve the global object association problem. The associated 3D nuclei serve as the initialization of an active mesh model to obtain the 3D segmentation of individual myocardial cells. The efficiency of our method over the state-of-the-art methods is observed via various qualitative and quantitative evaluation.

Bioimage Analysis and Cell Motility

Bioimage analysis (BIA) has historically helped study how and why cells move; biological experiments evolved in intimate feedback with the most classical image processing techniques because they contribute objectivity and reproducibility to an eminently qualitative science. Cell segmentation, tracking, and morphology descriptors are all discussed here. Using ameboid motility as a case study, these methods help us illustrate how proper quantification can augment biological data, for example, by choosing mathematical representations that amplify initially subtle differences, by statistically uncovering general laws or by integrating physical insight. More recently, the non-invasive nature of quantitative imaging is fertilizing two blooming fields: mechanobiology, where many biophysical measurements remain inaccessible, and microenvironments, where the quest for physiological relevance has exploded data size. From relief to remedy, this trend indicates that BIA is to become a main vector of biological discovery as human visual analysis struggles against ever more complex data.

Tensor regularized total variation for denoising of third harmonic generation images of brain tumors

Third harmonic generation (THG) microscopy shows great potential for instant pathology of brain tissue during surgery. However, the rich morphologies contained and the noise associated makes image restoration, necessary for quantification of the THG images, challenging. Anisotropic diffusion filtering (ADF) has been recently applied to restore THG images of normal brain, but ADF is hard-to-code, time-consuming and only reconstructs salient edges. This work overcomes these drawbacks by expressing ADF as a tensor regularized total variation model, which uses the Huber penalty and the L1 norm for tensor regularization and fidelity measurement, respectively. The diffusion tensor is constructed from the structure tensor of ADF yet the tensor decomposition is performed only in the non-flat areas. The resulting model is solved by an efficient and easy-to-code primal-dual algorithm. Tests on THG brain tumor images show that the proposed model has comparable denoising performance as ADF while it much better restores weak edges and it is up to 60% more time efficient.

Deciphering tissue morphodynamics using bioimage informatics

In recent years developmental biology has greatly benefited from the latest advances in fluorescence microscopy techniques. Consequently, quantitative and automated analysis of this data is becoming a vital first step in the quest for novel insights into the various aspects of development. Here we present an introductory overview of the various image analysis methods proposed for developmental biology images, with particular attention to openly available software packages. These tools, as well as others to come, are rapidly paving the way towards standardized and reproducible bioimaging studies at the whole-tissue level. Reflecting on these achievements, we discuss the remaining challenges and the future endeavours lying ahead in the post-image analysis era.This article is part of the themed issue 'Systems morphodynamics: understanding the development of tissue hardware'.

Biologically constrained optimization based cell membrane segmentation in C. elegans embryos

BACKGROUND

Recent advances in bioimaging and automated analysis methods have enabled the large-scale systematic analysis of cellular dynamics during the embryonic development of Caenorhabditis elegans. Most of these analyses have focused on cell lineage tracing rather than cell shape dynamics. Cell shape analysis requires cell membrane segmentation, which is challenging because of insufficient resolution and image quality. This problem is currently solved by complicated segmentation methods requiring laborious and time consuming parameter adjustments.

RESULTS

Our new framework BCOMS (Biologically Constrained Optimization based cell Membrane Segmentation) automates the extraction of the cell shape of C. elegans embryos. Both the segmentation and evaluation processes are automated. To automate the evaluation, we solve an optimization problem under biological constraints. The performance of BCOMS was validated against a manually created ground truth of the 24-cell stage embryo. The average deviation of 25 cell shape features was 5.6%. The deviation was mainly caused by membranes parallel to the focal planes, which either contact the surfaces of adjacent cells or make no contact with other cells. Because segmentation of these membranes was difficult even by manual inspection, the automated segmentation was sufficiently accurate for cell shape analysis. As the number of manually created ground truths is necessarily limited, we compared the segmentation results between two adjacent time points. Across all cells and all cell cycles, the average deviation of the 25 cell shape features was 4.3%, smaller than that between the automated segmentation result and ground truth.

CONCLUSIONS

BCOMS automated the accurate extraction of cell shapes in developing C. elegans embryos. By replacing image processing parameters with easily adjustable biological constraints, BCOMS provides a user-friendly framework. The framework is also applicable to other model organisms. Creating the biological constraints is a critical step requiring collaboration between an experimentalist and a software developer.

Amotl1 mediates sequestration of the Hippo effector Yap1 downstream of Fat4 to restrict heart growth

Although in flies the atypical cadherin Fat is an upstream regulator of Hippo signalling, the closest mammalian homologue, Fat4, has been shown to regulate tissue polarity rather than growth. Here we show in the mouse heart that Fat4 modulates Hippo signalling to restrict growth. Fat4 mutant myocardium is thicker, with increased cardiomyocyte size and proliferation, and this is mediated by an upregulation of the transcriptional activity of Yap1, an effector of the Hippo pathway. Fat4 is not required for the canonical activation of Hippo kinases but it sequesters a partner of Yap1, Amotl1, out of the nucleus. The nuclear translocation of Amotl1 is accompanied by Yap1 to promote cardiomyocyte proliferation. We, therefore, identify Amotl1, which is not present in flies, as a mammalian intermediate for non-canonical Hippo signalling, downstream of Fat4. This work uncovers a mechanism for the restriction of heart growth at birth, a process which impedes the regenerative potential of the mammalian heart.

Structure Tensor Based Analysis of Cells and Nuclei Organization in Tissues

Extracting geometrical information from large 2D or 3D biomedical images is important to better understand fundamental phenomena such as morphogenesis. We address the problem of automatically analyzing spatial organization of cells or nuclei in 2D or 3D images of tissues. This problem is challenging due to the usually low quality of microscopy images as well as their typically large sizes. The structure tensor is a simple and robust descriptor that was developed to analyze textures orientation. Contrarily to segmentation methods which rely on an object based modeling of images, the structure tensor considers the sample at a macroscopic scale, like a continuous medium. We show that this tool allows quantifying two important features of nuclei in tissues: their privileged orientation as well as the ratio between the length of their main axes. A quantitative evaluation of the method is provided for synthetic and real 2D and 3D images. As an application, we analyze the nuclei orientation and anisotropy on multicellular tumor spheroids cryosections. This analysis reveals that cells are elongated in a privileged direction that is parallel to the spheroid boundary. A MATLAB toolbox and an Icy plugin are available to use the proposed method.

Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images

BACKGROUND

Analysis of single cells in their native environment is a powerful method to address key questions in developmental systems biology. Confocal microscopy imaging of intact tissues, followed by automatic image segmentation, provides a means to conduct cytometric studies while at the same time preserving crucial information about the spatial organization of the tissue and morphological features of the cells. This technique is rapidly evolving but is still not in widespread use among research groups that do not specialize in technique development, perhaps in part for lack of tools that automate repetitive tasks while allowing experts to make the best use of their time in injecting their domain-specific knowledge.

RESULTS

Here we focus on a well-established stem cell model system, the C. elegans gonad, as well as on two other model systems widely used to study cell fate specification and morphogenesis: the pre-implantation mouse embryo and the developing mouse olfactory epithelium. We report a pipeline that integrates machine-learning-based cell detection, fast human-in-the-loop curation of these detections, and running of active contours seeded from detections to segment cells. The procedure can be bootstrapped by a small number of manual detections, and outperforms alternative pieces of software we benchmarked on C. elegans gonad datasets. Using cell segmentations to quantify fluorescence contents, we report previously-uncharacterized cell behaviors in the model systems we used. We further show how cell morphological features can be used to identify cell cycle phase; this provides a basis for future tools that will streamline cell cycle experiments by minimizing the need for exogenous cell cycle phase labels.

CONCLUSIONS

High-throughput 3D segmentation makes it possible to extract rich information from images that are routinely acquired by biologists, and provides insights - in particular with respect to the cell cycle - that would be difficult to derive otherwise.

Automated Identification and Localization of Hematopoietic Stem Cells in 3D Intravital Microscopy Data

Measuring three-dimensional (3D) localization of hematopoietic stem cells (HSCs) within the bone marrow microenvironment using intravital microscopy is a rapidly expanding research theme. This approach holds the key to understanding the detail of HSC-niche interactions, which are critical for appropriate stem cell function. Due to the complex tissue architecture of the bone marrow and to the progressive introduction of scattering and signal loss at increasing imaging depths, there is no ready-made software to handle efficient segmentation and unbiased analysis of the data. To address this, we developed an automated image analysis tool that simplifies and standardizes the biological interpretation of 3D HSC microenvironment images. The algorithm identifies HSCs and measures their localization relative to surrounding osteoblast cells and bone collagen. We demonstrate here the effectiveness, consistency, and accuracy of the proposed approach compared to current manual analysis and its wider applicability to analyze other 3D bone marrow components.

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A new tool allows automated 3D image analysis of HSCs and their niche

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It performs automated segmentation of heterogeneous HSCs and bone marrow components

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This tool identifies real HSCs and eliminates false-positive signals

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3D distance measurements of HSC to the nearest osteoblast/bone are demonstrated

Imaging and analyzing primary cilia in cardiac cells

The primary cilium is a small sensory organelle that is required for different aspects of embryonic development, including the formation of the heart. The structure and composition of cilia have been extensively studied, so that several markers of primary cilia have now been identified. However, the role of cilia in specific cell types remains poorly understood. We describe here a series of approaches to image primary cilia in the rodent heart or in primary cultures of cells dissociated from the heart. As the cilium is a marker of cell polarity, we also provide, for quantitative image analysis of cilium orientation, tools which are generally applicable to other types of tissues.

Light sheet-based imaging and analysis of early embryogenesis in the fruit fly

The fruit fly is an excellent model system for investigating the sequence of epithelial tissue invaginations constituting the process of gastrulation. By combining recent advancements in light sheet fluorescence microscopy (LSFM) and image processing, the three-dimensional fly embryo morphology and relevant gene expression patterns can be accurately recorded throughout the entire process of embryogenesis. LSFM provides exceptionally high imaging speed, high signal-to-noise ratio, low level of photoinduced damage, and good optical penetration depth. This powerful combination of capabilities makes LSFM particularly suitable for live imaging of the fly embryo.The resulting high-information-content image data are subsequently processed to obtain the outlines of cells and cell nuclei, as well as the geometry of the whole embryo tissue by image segmentation. Furthermore, morphodynamics information is extracted by computationally tracking objects in the image. Towards that goal we describe the successful implementation of a fast fitting strategy of Gaussian mixture models.The data obtained by image processing is well-suited for hypothesis testing of the detailed biomechanics of the gastrulating embryo. Typically this involves constructing computational mechanics models that consist of an objective function providing an estimate of strain energy for a given morphological configuration of the tissue, and a numerical minimization mechanism of this energy, achieved by varying morphological parameters.In this chapter, we provide an overview of in vivo imaging of fruit fly embryos using LSFM, computational tools suitable for processing the resulting images, and examples of computational biomechanical simulations of fly embryo gastrulation.

Whole population cell analysis of a landmark-rich mammalian epithelium reveals multiple elongation mechanisms

Tissue elongation is a fundamental component of developing and regenerating systems. Although localised proliferation is an important mechanism for tissue elongation, potentially important contributions of other elongation mechanisms, specifically cell shape change, orientated cell division and cell rearrangement, are rarely considered or quantified, particularly in mammalian systems. Their quantification, together with proliferation, provides a rigorous framework for the analysis of elongation. The mammalian palatal epithelium is a landmark-rich tissue, marked by regularly spaced ridges (rugae), making it an excellent model in which to analyse the contributions of cellular processes to directional tissue growth. We captured confocal stacks of entire fixed mouse palate epithelia throughout the mid-gestation growth period, labelled with membrane, nuclear and cell proliferation markers and segmented all cells (up to ∼20,000 per palate), allowing the quantification of cell shape and proliferation. Using the rugae as landmarks, these measures revealed that the so-called growth zone is a region of proliferation that is intermittently elevated at ruga initiation. The distribution of oriented cell division suggests that it is not a driver of tissue elongation, whereas cell shape analysis revealed that both elongation of cells leaving the growth zone and apico-basal cell rearrangements do contribute significantly to directional growth. Quantitative comparison of elongation processes indicated that proliferation contributes most to elongation at the growth zone, but cell shape change and rearrangement contribute as much as 40% of total elongation. We have demonstrated the utility of an approach to analysing the cellular mechanisms underlying tissue elongation in mammalian tissues. It should be broadly applied to higher-resolution analysis of links between genotypes and malformation phenotypes.