A workflow to process 3D+time microscopy images of developing organisms and reconstruct their cell lineage

A distinct autofluorescence distribution pattern marks enzymatic deconstruction of plant cell wall

Achieving an economically viable transformation of plant cell walls into bioproducts requires a comprehensive understanding of enzymatic deconstruction. Microscale quantitative analysis offers a relevant approach to enhance our understanding of cell wall hydrolysis, but becomes challenging under high deconstruction conditions. This study comprehensively addresses the challenges of quantifying the impact of extensive enzymatic deconstruction on plant cell wall at microscale. Investigation of highly deconstructed spruce wood provided spatial profiles of cell walls during hydrolysis with remarkable precision. A distinct cell wall autofluorescence distribution pattern marking enzymatic hydrolysis along with an asynchronous impact of hydrolysis on cell wall structure, with cell wall volume reduction preceding cell wall accessible surface area decrease, were revealed. This study provides novel insights into enzymatic deconstruction of cell wall at under-investigated cell scale, and a robust computational pipeline applicable to diverse biomass species and pretreatment types for assessing hydrolysis impact and efficiency.

Unsupervised spatiotemporal classification of deformation patterns of embryonic tissues matches their fate map

During morphogenesis, embryonic tissues display fluid-like behavior with fluctuating strain rates. Digital cell lineages reconstructed from 4D images of developing zebrafish embryos are used to infer representative tissue deformation patterns and their association with developmental events. Finite deformation analysis along cell trajectories and unsupervised machine learning are applied to obtain reduced-order models condensing the collective cell motions, delineating tissue domains with distinct 4D biomechanical behavior. This reduced-order kinematic description is reproducible across specimens and matches fate maps of the zebrafish brain in wild-type and nodal pathway mutants (zoeptz57/tz57 ), shedding light into the morphogenetic defects causing these mutants' cyclopia. Furthermore, the inferred kinematic maps also match expression maps of the gene transcription factor goosecoid (gsc). In summary, this work introduces an objective analytical framework to systematically unravel the complex spatiotemporal patterns of embryonic tissue deformations and couple them with cell fate and gene expression maps.

Coupling of cell shape, matrix and tissue dynamics ensures embryonic patterning robustness

Tissue patterning coordinates morphogenesis, cell dynamics and fate specification. Understanding how precision in patterning is robustly achieved despite inherent developmental variability during mammalian embryogenesis remains a challenge. Here, based on cell dynamics quantification and simulation, we show how salt-and-pepper epiblast and primitive endoderm (PrE) cells pattern the inner cell mass of mouse blastocysts. Coupling cell fate and dynamics, PrE cells form apical polarity-dependent actin protrusions required for RAC1-dependent migration towards the surface of the fluid cavity, where PrE cells are trapped due to decreased tension. Concomitantly, PrE cells deposit an extracellular matrix gradient, presumably breaking the tissue-level symmetry and collectively guiding their own migration. Tissue size perturbations of mouse embryos and their comparison with monkey and human blastocysts further demonstrate that the fixed proportion of PrE/epiblast cells is optimal with respect to embryo size and tissue geometry and, despite variability, ensures patterning robustness during early mammalian development.

Temporal variability and cell mechanics control robustness in mammalian embryogenesis

How living systems achieve precision in form and function despite their intrinsic stochasticity is a fundamental yet ongoing question in biology. We generated morphomaps of preimplantation embryogenesis in mouse, rabbit, and monkey embryos, and these morphomaps revealed that although blastomere divisions desynchronized passively, 8-cell embryos converged toward robust three-dimensional shapes. Using topological analysis and genetic perturbations, we found that embryos progressively changed their cellular connectivity to a preferred topology, which could be predicted by a physical model in which actomyosin contractility and noise facilitate topological transitions, lowering surface energy. This mechanism favored regular embryo packing and promoted a higher number of inner cells in the 16-cell embryo. Synchronized division reduced embryo packing and generated substantially more misallocated cells and fewer inner-cell-mass cells. These findings suggest that stochasticity in division timing contributes to robust patterning.

Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets

Recent advances in computer vision have led to significant progress in the generation of realistic image data, with denoising diffusion probabilistic models proving to be a particularly effective method. In this study, we demonstrate that diffusion models can effectively generate fully-annotated microscopy image data sets through an unsupervised and intuitive approach, using rough sketches of desired structures as the starting point. The proposed pipeline helps to reduce the reliance on manual annotations when training deep learning-based segmentation approaches and enables the segmentation of diverse datasets without the need for human annotations. We demonstrate that segmentation models trained with a small set of synthetic image data reach accuracy levels comparable to those of generalist models trained with a large and diverse collection of manually annotated image data, thereby offering a streamlined and specialized application of segmentation models.

Segmentation-based tracking of macrophages in 2D＋time microscopy movies inside a living animal

The automated segmentation and tracking of macrophages during their migration are challenging tasks due to their dynamically changing shapes and motions. This paper proposes a new algorithm to achieve automatic cell tracking in time-lapse microscopy macrophage data. First, we design a segmentation method employing space-time filtering, local Otsu's thresholding, and the SUBSURF (subjective surface segmentation) method. Next, the partial trajectories for cells overlapping in the temporal direction are extracted in the segmented images. Finally, the extracted trajectories are linked by considering their direction of movement. The segmented images and the obtained trajectories from the proposed method are compared with those of the semi-automatic segmentation and manual tracking. The proposed tracking achieved 97.4% of accuracy for macrophage data under challenging situations, feeble fluorescent intensity, irregular shapes, and motion of macrophages. We expect that the automatically extracted trajectories of macrophages can provide pieces of evidence of how macrophages migrate depending on their polarization modes in the situation, such as during wound healing.

Delineating the mechanisms and design principles of Caenorhabditis elegans embryogenesis using in toto high-resolution imaging data and computational modeling

The nematode (roundworm) Caenorhabditis elegans is one of the most popular animal models for the study of developmental biology, as its invariant development and transparent body enable in toto cellular-resolution fluorescence microscopy imaging of developmental processes at 1-min intervals. This has led to the development of various computational tools for the systematic and automated analysis of imaging data to delineate the molecular and cellular processes throughout the embryogenesis of C. elegans, such as those associated with cell lineage, cell migration, cell morphology, and gene activity. In this review, we first introduce C. elegans embryogenesis and the development of techniques for tracking cell lineage and reconstructing cell morphology during this process. We then contrast the developmental modes of C. elegans and the customized technologies used for studying them with the ones of other animal models, highlighting its advantage for studying embryogenesis with exceptional spatial and temporal resolution. This is followed by an examination of the physical models that have been devised-based on accurate determinations of developmental processes afforded by analyses of imaging data-to interpret the early embryonic development of C. elegans from subcellular to intercellular levels of multiple cells, which focus on two key processes: cell polarization and morphogenesis. We subsequently discuss how quantitative data-based theoretical modeling has improved our understanding of the mechanisms of C. elegans embryogenesis. We conclude by summarizing the challenges associated with the acquisition of C. elegans embryogenesis data, the construction of algorithms to analyze them, and the theoretical interpretation.

nAdder: A scale-space approach for the 3D analysis of neuronal traces

Tridimensional microscopy and algorithms for automated segmentation and tracing are revolutionizing neuroscience through the generation of growing libraries of neuron reconstructions. Innovative computational methods are needed to analyze these neuronal traces. In particular, means to characterize the geometric properties of traced neurites along their trajectory have been lacking. Here, we propose a local tridimensional (3D) scale metric derived from differential geometry, measuring for each point of a curve the characteristic length where it is fully 3D as opposed to being embedded in a 2D plane or 1D line. The larger this metric is and the more complex the local 3D loops and turns of the curve are. Available through the GeNePy3D open-source Python quantitative geometry library (https://genepy3d.gitlab.io), this approach termed nAdder offers new means of describing and comparing axonal and dendritic arbors. We validate this metric on simulated and real traces. By reanalysing a published zebrafish larva whole brain dataset, we show its ability to characterize different population of commissural axons, distinguish afferent connections to a target region and differentiate portions of axons and dendrites according to their behavior, shedding new light on the stereotypical nature of neurites' local geometry.

From Cell States to Cell Fates: How Cell Proliferation and Neuronal Differentiation Are Coordinated During Embryonic Development

The central nervous system (CNS) exhibits an extraordinary diversity of neurons, with the right cell types and proportions at the appropriate sites. Thus, to produce brains with specific size and cell composition, the rates of proliferation and differentiation must be tightly coordinated and balanced during development. Early on, proliferation dominates; later on, the growth rate almost ceases as more cells differentiate and exit the cell cycle. Generation of cell diversity and morphogenesis takes place concomitantly. In the vertebrate brain, this results in dramatic changes in the position of progenitor cells and their neuronal derivatives, whereas in the spinal cord morphogenetic changes are not so important because the structure mainly grows by increasing its volume. Morphogenesis is under control of specific genetic programs that coordinately unfold over time; however, little is known about how they operate and impact in the pools of progenitor cells in the CNS. Thus, the spatiotemporal coordination of these processes is fundamental for generating functional neuronal networks. Some key aims in developmental neurobiology are to determine how cell diversity arises from pluripotent progenitor cells, and how the progenitor potential changes upon time. In this review, we will share our view on how the advance of new technologies provides novel data that challenge some of the current hypothesis. We will cover some of the latest studies on cell lineage tracing and clonal analyses addressing the role of distinct progenitor cell division modes in balancing the rate of proliferation and differentiation during brain morphogenesis. We will discuss different hypothesis proposed to explain how progenitor cell diversity is generated and how they challenged prevailing concepts and raised new questions.

3D fluorescence microscopy data synthesis for segmentation and benchmarking

Automated image processing approaches are indispensable for many biomedical experiments and help to cope with the increasing amount of microscopy image data in a fast and reproducible way. Especially state-of-the-art deep learning-based approaches most often require large amounts of annotated training data to produce accurate and generalist outputs, but they are often compromised by the general lack of those annotated data sets. In this work, we propose how conditional generative adversarial networks can be utilized to generate realistic image data for 3D fluorescence microscopy from annotation masks of 3D cellular structures. In combination with mask simulation approaches, we demonstrate the generation of fully-annotated 3D microscopy data sets that we make publicly available for training or benchmarking. An additional positional conditioning of the cellular structures enables the reconstruction of position-dependent intensity characteristics and allows to generate image data of different quality levels. A patch-wise working principle and a subsequent full-size reassemble strategy is used to generate image data of arbitrary size and different organisms. We present this as a proof-of-concept for the automated generation of fully-annotated training data sets requiring only a minimum of manual interaction to alleviate the need of manual annotations.

Automated Deep Lineage Tree Analysis Using a Bayesian Single Cell Tracking Approach

Single-cell methods are beginning to reveal the intrinsic heterogeneity in cell populations, arising from the interplay of deterministic and stochastic processes. However, it remains challenging to quantify single-cell behaviour from time-lapse microscopy data, owing to the difficulty of extracting reliable cell trajectories and lineage information over long time-scales and across several generations. Therefore, we developed a hybrid deep learning and Bayesian cell tracking approach to reconstruct lineage trees from live-cell microscopy data. We implemented a residual U-Net model coupled with a classification CNN to allow accurate instance segmentation of the cell nuclei. To track the cells over time and through cell divisions, we developed a Bayesian cell tracking methodology that uses input features from the images to enable the retrieval of multi-generational lineage information from a corpus of thousands of hours of live-cell imaging data. Using our approach, we extracted 20,000 + fully annotated single-cell trajectories from over 3,500 h of video footage, organised into multi-generational lineage trees spanning up to eight generations and fourth cousin distances. Benchmarking tests, including lineage tree reconstruction assessments, demonstrate that our approach yields high-fidelity results with our data, with minimal requirement for manual curation. To demonstrate the robustness of our minimally supervised cell tracking methodology, we retrieve cell cycle durations and their extended inter- and intra-generational family relationships in 5,000 + fully annotated cell lineages. We observe vanishing cycle duration correlations across ancestral relatives, yet reveal correlated cyclings between cells sharing the same generation in extended lineages. These findings expand the depth and breadth of investigated cell lineage relationships in approximately two orders of magnitude more data than in previous studies of cell cycle heritability, which were reliant on semi-manual lineage data analysis.

Quantification of cell behaviors and computational modeling show that cell directional behaviors drive zebrafish pectoral fin morphogenesis

MOTIVATION

Understanding the mechanisms by which the zebrafish pectoral fin develops is expected to produce insights on how vertebrate limbs grow from a 2D cell layer to a 3D structure. Two mechanisms have been proposed to drive limb morphogenesis in tetrapods: a growth-based morphogenesis with a higher proliferation rate at the distal tip of the limb bud than at the proximal side, and directed cell behaviors that include elongation, division and migration in a non-random manner. Based on quantitative experimental biological data at the level of individual cells in the whole developing organ, we test the conditions for the dynamics of pectoral fin early morphogenesis.

RESULTS

We found that during the development of the zebrafish pectoral fin, cells have a preferential elongation axis that gradually aligns along the proximodistal (PD) axis of the organ. Based on these quantitative observations, we build a center-based cell model enhanced with a polarity term and cell proliferation to simulate fin growth. Our simulations resulted in 3D fins similar in shape to the observed ones, suggesting that the existence of a preferential axis of cell polarization is essential to drive fin morphogenesis in zebrafish, as observed in the development of limbs in the mouse, but distal tip-based expansion is not.

AVAILABILITYAND IMPLEMENTATION

Upon publication, biological data will be available at http://bioemergences.eu/modelingFin, and source code at https://github.com/guijoe/MaSoFin.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Current approaches to fate mapping and lineage tracing using image data

Visualizing, tracking and reconstructing cell lineages in developing embryos has been an ongoing effort for well over a century. Recent advances in light microscopy, labelling strategies and computational methods to analyse complex image datasets have enabled detailed investigations into the fates of cells. Combined with powerful new advances in genomics and single-cell transcriptomics, the field of developmental biology is able to describe the formation of the embryo like never before. In this Review, we discuss some of the different strategies and applications to lineage tracing in live-imaging data and outline software methodologies that can be applied to various cell-tracking challenges.

Synthetic living machines: A new window on life

Increased control of biological growth and form is an essential gateway to transformative medical advances. Repairing of birth defects, restoring lost or damaged organs, normalizing tumors, all depend on understanding how cells cooperate to make specific, functional large-scale structures. Despite advances in molecular genetics, significant gaps remain in our understanding of the meso-scale rules of morphogenesis. An engineering approach to this problem is the creation of novel synthetic living forms, greatly extending available model systems beyond evolved plant and animal lineages. Here, we review recent advances in the emerging field of synthetic morphogenesis, the bioengineering of novel multicellular living bodies. Emphasizing emergent self-organization, tissue-level guided self-assembly, and active functionality, this work is the essential next generation of synthetic biology. Aside from useful living machines for specific functions, the rational design and analysis of new, coherent anatomies will greatly increase our understanding of foundational questions in evolutionary developmental and cell biology.

Contact area-dependent cell communication and the morphological invariance of ascidian embryogenesis

Marine invertebrate ascidians display embryonic reproducibility: Their early embryonic cell lineages are considered invariant and are conserved between distantly related species, despite rapid genomic divergence. Here, we address the drivers of this reproducibility. We used light-sheet imaging and automated cell segmentation and tracking procedures to systematically quantify the behavior of individual cells every 2 minutes during Phallusia mammillata embryogenesis. Interindividual reproducibility was observed down to the area of individual cell contacts. We found tight links between the reproducibility of embryonic geometries and asymmetric cell divisions, controlled by differential sister cell inductions. We combined modeling and experimental manipulations to show that the area of contact between signaling and responding cells is a key determinant of cell communication. Our work establishes the geometric control of embryonic inductions as an alternative to classical morphogen gradients and suggests that the range of cell signaling sets the scale at which embryonic reproducibility is observed.

Tools and Concepts for Interrogating and Defining Cellular Identity

Defining the mechanisms that generate specialized cell types and coordinate their functions is critical for understanding organ development and renewal. New tools and discoveries are challenging and refining our definitions of a cell type. A rapidly growing toolkit for single-cell analyses has expanded the number of markers that can be assigned to a cell simultaneously, revealing heterogeneity within cell types that were previously regarded as homogeneous populations. Additionally, cell types defined by specific molecular markers can exhibit distinct, context-dependent functions; for example, between tissues in homeostasis and those responding to damage. Here we review the current technologies used to identify and characterize cells, and we discuss how experimental and pathological perturbations are adding increasing complexity to our definitions of cell identity.

A review on multiplatform evaluations of semi-automatic open-source based image segmentation for cranio-maxillofacial surgery

BACKGROUND AND OBJECTIVES

Computer-assisted technologies, such as image-based segmentation, play an important role in the diagnosis and treatment support in cranio-maxillofacial surgery. However, although many segmentation software packages exist, their clinical in-house use is often challenging due to constrained technical, human or financial resources. Especially technological solutions or systematic evaluations of open-source based segmentation approaches are lacking. The aim of this contribution is to assess and review the segmentation quality and the potential clinical use of multiple commonly available and license-free segmentation methods on different medical platforms.

METHODS

In this contribution, the quality and accuracy of open-source segmentation methods was assessed on different platforms using patient-specific clinical CT-data and reviewed with the literature. The image-based segmentation algorithms GrowCut, Robust Statistics Segmenter, Region Growing 3D, Otsu & Picking, Canny Segmentation and Geodesic Segmenter were investigated in the mandible on the platforms 3D Slicer, MITK and MeVisLab. Comparisons were made between the segmentation algorithms and the ground truth segmentations of the same anatomy performed by two clinical experts (n = 20). Assessment parameters were the Dice Score Coefficient (DSC), the Hausdorff Distance (HD), and Pearsons correlation coefficient (r).

RESULTS

The segmentation accuracy was highest with the GrowCut (DSC 85.6%, HD 33.5 voxel) and the Canny (DSC 82.1%, HD 8.5 voxel) algorithm. Statistical differences between the assessment parameters were not significant (p < 0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the segmentation methods and the ground truth schemes. Functionally stable and time-saving segmentations were observed.

CONCLUSION

High quality image-based semi-automatic segmentation was provided by the GrowCut and the Canny segmentation method. In the cranio-maxillofacial complex, these segmentation methods provide algorithmic alternatives for image-based segmentation in the clinical practice for e.g. surgical planning or visualization of treatment results and offer advantages through their open-source availability. This is the first systematic multi-platform comparison that evaluates multiple license-free, open-source segmentation methods based on clinical data for the improvement of algorithms and a potential clinical use in patient-individualized medicine. The results presented are reproducible by others and can be used for clinical and research purposes.

Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures

Methods for measuring the properties of individual cells within their native 3D environment will enable a deeper understanding of embryonic development, tissue regeneration, and tumorigenesis. However, current methods for segmenting nuclei in 3D tissues are not designed for situations in which nuclei are densely packed, nonspherical, or heterogeneous in shape, size, or texture, all of which are true of many embryonic and adult tissue types as well as in many cases for cells differentiating in culture. Here, we overcome this bottleneck by devising a novel method based on labelling the nuclear envelope (NE) and automatically distinguishing individual nuclei using a tree-structured ridge-tracing method followed by shape ranking according to a trained classifier. The method is fast and makes it possible to process images that are larger than the computer's memory. We consistently obtain accurate segmentation rates of >90%, even for challenging images such as mid-gestation embryos or 3D cultures. We provide a 3D editor and inspector for the manual curation of the segmentation results as well as a program to assess the accuracy of the segmentation. We have also generated a live reporter of the NE that can be used to track live cells in 3 dimensions over time. We use this to monitor the history of cell interactions and occurrences of neighbour exchange within cultures of pluripotent cells during differentiation. We provide these tools in an open-access user-friendly format.

Embryo development stage prediction algorithm for automated time lapse incubators

BACKGROUND AND OBJECTIVE

Time-lapse microscopy has become an important tool for studying the embryo development process. Embryologists can monitor the entire embryo growth process and thus select the best embryos for fertilization. This time and the resource consuming process are among the key factors for success of pregnancies. Tools for automated evaluation of the embryo quality and development stage prediction are developed for improving embryo selection.

METHODS

We present two-classifier vote-based method for embryo image classification. Our classification algorithms have been trained with features extracted using a Convolutional Neural Network (CNN). Prediction of embryo development stage is then completed by comparing confidence of two classifiers. Images are labeled depending on which one receives a larger confidence rating.

RESULTS

The evaluation has been done with imagery of real embryos, taken in the ESCO Time Lapse incubator from four different developing embryos. The results illustrate the most effective combination of two classifiers leading to an increase of prediction accuracy and achievement of overall 97.62% accuracy for a test set classification.

CONCLUSIONS

We have presented an approach for automated prediction of the embryo development stage for microscopy time-lapse incubator image. Our algorithm has extracted high-complexity image feature using CNN. Classification is done by comparing prediction of two classifiers and selecting the label of that classifier, which has a higher confidence value. This combination of two classifiers has allowed us to increase the overall accuracy of CNN from 96.58% by 1.04% up to 97.62%. The best results are achieved when combining the CNN and Discriminant classifiers. Practical implications include improvement of embryo selection process for in vitro fertilization.

Fast determination of coarse-grained cell anisotropy and size in epithelial tissue images using Fourier transform

Mechanical strain and stress play a major role in biological processes such as wound healing or morphogenesis. To assess this role quantitatively, fixed or live images of tissues are acquired at a cellular precision in large fields of views. To exploit these data, large numbers of cells have to be analyzed to extract cell shape anisotropy and cell size. Most frequently, this is performed through detailed individual cell contour determination, using so-called segmentation computer programs, complemented if necessary by manual detection and error corrections. However, a coarse-grained and faster technique can be recommended in at least three situations: first, when detailed information on individual cell contours is not required; for instance, in studies which require only coarse-grained average information on cell anisotropy. Second, as an exploratory step to determine whether full segmentation can be potentially useful. Third, when segmentation is too difficult, for instance due to poor image quality or too large a cell number. We developed a user-friendly, Fourier-transform-based image analysis pipeline. It is fast (typically 10^{4} cells per minute with a current laptop computer) and suitable for time, space, or ensemble averages. We validate it on one set of artificial images and on two sets of fully segmented images, one from a Drosophila pupa and the other from a chicken embryo; the pipeline results are robust. Perspectives include in vitro tissues, nonbiological cellular patterns such as foams and xyz stacks.

3D+time imaging of normal and twin sea urchin embryos for the reconstruction of their cell lineage

The Mediterranean sea urchin, Paracentrotus lividus, has been a powerful model to study embryonic development since the late 1800s. As a model, it has the advantage of having external fertilization, it can easily be manipulated experimentally, and it has semi-transparent embryonic stages, which makes it ideal for live imaging. Embryogenesis is a highly dynamic process with intrinsic variability. The reconstruction of cell dynamics and an assessment of such variability from in vivo observations has proven to be a challenge. Here, we provide an innovative methodology for manipulation and immobilization of embryos and their long-term 3D+time imaging. We then describe the twinning procedure that allows us to assess the variability and robustness of developmental processes. We demonstrate the reconstruction of cell lineages based on automated image processing and cell tracking using the BioEmergences workflow as well as the use of interactive visualization tools (Mov-IT software) for lineage validation, correction and analysis.

Cdh2 coordinates Myosin-II dependent internalisation of the zebrafish neural plate

Tissue internalisation is a key morphogenetic mechanism by which embryonic tissues generate complex internal organs and a number of studies of epithelia have outlined a general view of tissue internalisation. Here we have used quantitative live imaging and mutant analysis to determine whether similar mechanisms are responsible for internalisation in a tissue that apparently does not have a typical epithelial organisation - the zebrafish neural plate. We found that although zebrafish embryos begin neurulation without a conventional epithelium, medially located neural plate cells adopt strategies typical of epithelia in order to constrict their dorsal surface membrane during cell internalisation. Furthermore, we show that Myosin-II activity is a significant driver of this transient cell remodeling which also depends on Cdh2 (N-cadherin). Abrogation of Cdh2 results in defective Myosin-II distribution, mislocalised internalisation events and defective neural plate morphogenesis. Our work suggests Cdh2 coordinates Myosin-II dependent internalisation of the zebrafish neural plate.

Brownian-like deviation of neighboring cells in the early embryogenesis of the zebrafish

We investigate cell trajectories during zebrafish early embryogenesis based on 3D+time photonic microscopy imaging data. To remove the collective flow motion and focus on fluctuations, we analyze the deviations of pairs of neighboring cells. These deviations resemble Brownian motion and reveal different behaviors between pairs containing daughter cells generated by cell division and other pairs of neighboring cells. This observation justifies a common practice of using white noise fluctuations in modeling cell movement.

Three-dimensional microscopy and image analysis methodology for mapping and quantification of nuclear positions in tissues with approximate cylindrical geometry

Organogenesis involves extensive and dynamic changes of tissue shape during development. It is associated with complex morphogenetic events that require enormous tissue plasticity and generate a large variety of transient three-dimensional geometries that are achieved by global tissue responses. Nevertheless, such global responses are driven by tight spatio-temporal regulation of the behaviours of individual cells composing these tissues. Therefore, the development of image analysis tools that allow for extraction of quantitative data concerning individual cell behaviours is central to study tissue morphogenesis. There are many image analysis tools available that permit extraction of cell parameters. Unfortunately, the majority are developed for tissues with relatively simple geometries such as flat epithelia. Problems arise when the tissue of interest assumes a more complex three-dimensional geometry. Here, we use the endothelium of the developing zebrafish dorsal aorta as an example of a tissue with cylindrical geometry and describe the image analysis routines developed to extract quantitative data on individual cells in such tissues, as well as the image acquisition and sample preparation methodology.This article is part of the Theo Murphy meeting issue 'Mechanics of development'.

Quantitative analysis of cell shape and the cytoskeleton in developmental biology

Computational approaches that enable quantification of microscopy data have revolutionized the field of developmental biology. Due to its inherent complexity, elucidating mechanisms of development requires sophisticated analysis of the structure, shape, and kinetics of cellular processes. This need has prompted the creation of numerous techniques to visualize, quantify, and merge microscopy data. These approaches have defined the order and structure of developmental events, thus, providing insight into the mechanisms that drive them. This review describes current computational approaches that are being used to answer developmental questions related to morphogenesis and describe how these approaches have impacted the field. Our intent is not to comprehensively review techniques, but to highlight examples of how different approaches have impacted our understanding of development. Specifically, we focus on methods to quantify cell shape and cytoskeleton structure and dynamics in developing tissues. Finally, we speculate on where the future of computational analysis in developmental biology might be headed. This article is categorized under: Technologies > Analysis of Cell, Tissue, and Animal Phenotypes Early Embryonic Development > Gastrulation and Neurulation Early Embryonic Development > Development to the Basic Body Plan.

The devil is in the mesoscale: Mechanical and behavioural heterogeneity in collective cell movement

Heterogeneity within cell populations can be an important aspect affecting their collective movement and tissue-mechanical properties, determining for example their effective viscoelasticity. Differences in cell-level properties and behaviour within a group of moving cells can give rise to unexpected and non-intuitive behaviours at the tissue level. Such emergent phenomena often manifest themselves through spatiotemporal patterns at an intermediate 'mesoscale' between cell and tissue scales, typically involving tens of cells. Focussing on the development of embryonic animal tissues, we review recent evidence for the importance of heterogeneity at the mesoscale for collective cell migration and convergence and extension movements. We further discuss approaches to incorporate heterogeneity into computational models to complement experimental investigations.

Technical Review: Microscopy and Image Processing Tools to Analyze Plant Chromatin: Practical Considerations

In situ nucleus and chromatin analyses rely on microscopy imaging that benefits from versatile, efficient fluorescent probes and proteins for static or live imaging. Yet the broad choice in imaging instruments offered to the user poses orientation problems. Which imaging instrument should be used for which purpose? What are the main caveats and what are the considerations to best exploit each instrument's ability to obtain informative and high-quality images? How to infer quantitative information on chromatin or nuclear organization from microscopy images? In this review, we present an overview of common, fluorescence-based microscopy systems and discuss recently developed super-resolution microscopy systems, which are able to bridge the resolution gap between common fluorescence microscopy and electron microscopy. We briefly present their basic principles and discuss their possible applications in the field, while providing experience-based recommendations to guide the user toward best-possible imaging. In addition to raw data acquisition methods, we discuss commercial and noncommercial processing tools required for optimal image presentation and signal evaluation in two and three dimensions.

EmbryoMiner: A new framework for interactive knowledge discovery in large-scale cell tracking data of developing embryos

State-of-the-art light-sheet and confocal microscopes allow recording of entire embryos in 3D and over time (3D+t) for many hours. Fluorescently labeled structures can be segmented and tracked automatically in these terabyte-scale 3D+t images, resulting in thousands of cell migration trajectories that provide detailed insights to large-scale tissue reorganization at the cellular level. Here we present EmbryoMiner, a new interactive open-source framework suitable for in-depth analyses and comparisons of entire embryos, including an extensive set of trajectory features. Starting at the whole-embryo level, the framework can be used to iteratively focus on a region of interest within the embryo, to investigate and test specific trajectory-based hypotheses and to extract quantitative features from the isolated trajectories. Thus, the new framework provides a valuable new way to quantitatively compare corresponding anatomical regions in different embryos that were manually selected based on biological prior knowledge. As a proof of concept, we analyzed 3D+t light-sheet microscopy images of zebrafish embryos, showcasing potential user applications that can be performed using the new framework.

Control of the proportion of inner cells by asymmetric divisions and the ensuing resilience of cloned rabbit embryos

Mammalian embryo cloning by nuclear transfer has a low success rate. This is hypothesized to correlate with a high variability of early developmental steps that segregate outer cells, which are fated to extra-embryonic tissues, from inner cells, which give rise to the embryo proper. Exploring the cell lineage of wild-type embryos and clones, imaged in toto until hatching, highlights the respective contributions of cell proliferation, death and asymmetric divisions to phenotypic variability. Preferential cell death of inner cells in clones, probably pertaining to the epigenetic plasticity of the transferred nucleus, is identified as a major difference with effects on the proportion of inner cell. In wild type and clones, similar patterns of outer cell asymmetric divisions are shown to be essential to the robust proportion of inner cells observed in wild type. Asymmetric inner cell division, which is not described in mice, is identified as a regulator of the proportion of inner cells and likely gives rise to resilient clones.

Summary: A unique quantitative approach based on complete reconstruction of the cell lineage that unveils an unknown mechanism of size control in cell populations of rabbit blastocysts, wild types or clones.

Multi-view light-sheet imaging and tracking with the MaMuT software reveals the cell lineage of a direct developing arthropod limb

During development, coordinated cell behaviors orchestrate tissue and organ morphogenesis. Detailed descriptions of cell lineages and behaviors provide a powerful framework to elucidate the mechanisms of morphogenesis. To study the cellular basis of limb development, we imaged transgenic fluorescently-labeled embryos from the crustacean Parhyale hawaiensis with multi-view light-sheet microscopy at high spatiotemporal resolution over several days of embryogenesis. The cell lineage of outgrowing thoracic limbs was reconstructed at single-cell resolution with new software called Massive Multi-view Tracker (MaMuT). In silico clonal analyses suggested that the early limb primordium becomes subdivided into anterior-posterior and dorsal-ventral compartments whose boundaries intersect at the distal tip of the growing limb. Limb-bud formation is associated with spatial modulation of cell proliferation, while limb elongation is also driven by preferential orientation of cell divisions along the proximal-distal growth axis. Cellular reconstructions were predictive of the expression patterns of limb development genes including the BMP morphogen Decapentaplegic.

During early life, animals develop from a single fertilized egg cell to hundreds, millions or even trillions of cells. These cells specialize to do different tasks; forming different tissues and organs like muscle, skin, lungs and liver. For more than a century, scientists have strived to understand the details of how animal cells become different and specialize, and have created many new techniques and technologies to help them achieve this goal.

Limbs – such as arms, legs and wings – form from small lumps of cells called limb buds. Scientists use the shrimp-like crustacean, Parhyale hawaiensis, to study development, including limb growth. This species is useful because it is easy to grow, manipulate and observe its developing young in the laboratory. Understanding how its limbs develop offers important new insights into how limbs develop in other animals too. Wolff, Tinevez, Pietzsch et al. have now combined advanced microscopy with custom computer software, called Massive Multi-view Tracker (MaMuT) to investigate this.

As limbs develop in Parhyale, the MaMuT software tracks how cells behave, and how they are organized. This analysis revealed that for cells to produce a limb bud, they need to split at an early stage into separate groups. These groups are organized along two body axes, one that goes from head to tail, and one that runs from back to belly. The limb grows perpendicular to these main body axes, along a new ‘proximal-distal’ axis that goes from nearest to furthest from the body. Wolff et al. found that the cells that contribute to the extremities of the limb divide faster than the ones that stay closer to the body. Finally, the results show that when cells in a limb divide, they mostly divide along the proximal-distal axis, producing one cell that is further from the body than the other. These cell activities may help limbs to get longer as they grow.

Notably, the groups of cells seen by Wolff et al. were expressing genes that had previously been identified in developing limbs. This helps to validate the new results and to identify which active genes control the behaviors of the analyzed cells.

These findings reveal new ways to study animal development. This approach could have many research uses and may help to link the mechanisms of cell biology to their effects. It could also contribute to new understanding of developmental and genetic conditions that affect human health.

Image analysis of neural stem cell division patterns in the zebrafish brain

Proliferating stem cells in the adult body are the source of constant regeneration. In the brain, neural stem cells (NSCs) divide to maintain the stem cell population and generate neural progenitor cells that eventually replenish mature neurons and glial cells. How much spatial coordination of NSC division and differentiation is present in a functional brain is an open question. To quantify the patterns of stem cell divisions, one has to (i) identify the pool of NSCs that have the ability to divide, (ii) determine NSCs that divide within a given time window, and (iii) analyze the degree of spatial coordination. Here, we present a bioimage informatics pipeline that automatically identifies GFP expressing NSCs in three-dimensional image stacks of zebrafish brain from whole-mount preparations. We exploit the fact that NSCs in the zebrafish hemispheres are located on a two-dimensional surface and identify between 1,500 and 2,500 NSCs in six brain hemispheres. We then determine the position of dividing NSCs in the hemisphere by EdU incorporation into cells undergoing S-phase and calculate all pairwise NSC distances with three alternative metrics. Finally, we fit a probabilistic model to the observed spatial patterns that accounts for the non-homogeneous distribution of NSCs. We find a weak positive coordination between dividing NSCs irrespective of the metric and conclude that neither strong inhibitory nor strong attractive signals drive NSC divisions in the adult zebrafish brain. © 2017 International Society for Advancement of Cytometry.

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'.

Fluorescent Polymer Nanoparticles for Cell Barcoding In Vitro and In Vivo

Fluorescent polymer nanoparticles for long-term labeling and tracking of living cells with any desired color code are developed. They are built from biodegradable poly(lactic-co-glycolic acid) polymer loaded with cyanine dyes (DiO, DiI, and DiD) with the help of bulky fluorinated counterions, which minimize aggregation-caused quenching. At the single particle level, these particles are ≈20-fold brighter than quantum dots of similar color. Due to their identical 40 nm size and surface properties, these nanoparticles are endocytosed equally well by living cells. Mixing nanoparticles of three colors in different proportions generates a homogeneous RGB (red, green, and blue) barcode in cells, which is transmitted through many cell generations. Cell barcoding is validated on 7 cell lines (HeLa, KB, embryonic kidney (293T), Chinese hamster ovary, rat basophilic leucemia, U97, and D2A1), 13 color codes, and it enables simultaneous tracking of co-cultured barcoded cell populations for >2 weeks. It is also applied to studying competition among drug-treated cell populations. This technology enabled six-color imaging in vivo for (1) tracking xenografted cancer cells and (2) monitoring morphogenesis after microinjection in zebrafish embryos. In addition to a robust method of multicolor cell labeling and tracking, this work suggests that multiple functions can be co-localized inside cells by combining structurally close nanoparticles carrying different functions.

Determining the impact of cell mixing on signaling during development

Cell movement and intercellular signaling occur simultaneously to organize morphogenesis during embryonic development. Cell movement can cause relative positional changes between neighboring cells. When intercellular signals are local such cell mixing may affect signaling, changing the flow of information in developing tissues. Little is known about the effect of cell mixing on intercellular signaling in collective cellular behaviors and methods to quantify its impact are lacking. Here we discuss how to determine the impact of cell mixing on cell signaling drawing an example from vertebrate embryogenesis: the segmentation clock, a collective rhythm of interacting genetic oscillators. We argue that comparing cell mixing and signaling timescales is key to determining the influence of mixing. A signaling timescale can be estimated by combining theoretical models with cell signaling perturbation experiments. A mixing timescale can be obtained by analysis of cell trajectories from live imaging. After comparing cell movement analyses in different experimental settings, we highlight challenges in quantifying cell mixing from embryonic timelapse experiments, especially a reference frame problem due to embryonic motions and shape changes. We propose statistical observables characterizing cell mixing that do not depend on the choice of reference frames. Finally, we consider situations in which both cell mixing and signaling involve multiple timescales, precluding a direct comparison between single characteristic timescales. In such situations, physical models based on observables of cell mixing and signaling can simulate the flow of information in tissues and reveal the impact of observed cell mixing on signaling.

Assembly of embryonic and extraembryonic stem cells to mimic embryogenesis in vitro

Mammalian embryogenesis requires intricate interactions between embryonic and extraembryonic tissues to orchestrate and coordinate morphogenesis with changes in developmental potential. Here, we combined mouse embryonic stem cells (ESCs) and extraembryonic trophoblast stem cells (TSCs) in a three-dimensional scaffold to generate structures whose morphogenesis is markedly similar to that of natural embryos. By using genetically modified stem cells and specific inhibitors, we show that embryogenesis of ESC- and TSC-derived embryos-ETS-embryos-depends on cross-talk involving Nodal signaling. When ETS-embryos develop, they spontaneously initiate expression of mesoderm and primordial germ cell markers asymmetrically on the embryonic and extraembryonic border, in response to Wnt and BMP signaling. Our study demonstrates the ability of distinct stem cell types to self-assemble in vitro to generate embryos whose morphogenesis, architecture, and constituent cell types resemble those of natural embryos.

All-in-one 3D printed microscopy chamber for multidimensional imaging, the UniverSlide

While live 3D high resolution microscopy techniques are developing rapidly, their use for biological applications is partially hampered by practical difficulties such as the lack of a versatile sample chamber. Here, we propose the design of a multi-usage observation chamber adapted for live 3D bio-imaging. We show the usefulness and practicality of this chamber, which we named the UniverSlide, for live imaging of two case examples, namely multicellular systems encapsulated in sub-millimeter hydrogel shells and zebrafish larvae. We also demonstrate its versatility and compatibility with all microscopy devices by using upright or inverted microscope configurations after loading the UniverSlide with fixed or living samples. Further, the device is applicable for medium/high throughput screening and automatized multi-position image acquisition, providing a constraint-free but stable and parallelized immobilization of the samples. The frame of the UniverSlide is fabricated using a stereolithography 3D printer, has the size of a microscopy slide, is autoclavable and sealed with a removable lid, which makes it suitable for use in a controlled culture environment. We describe in details how to build this chamber and we provide all the files necessary to print the different pieces in the lab.

A cell-based computational model of early embryogenesis coupling mechanical behaviour and gene regulation

The study of multicellular development is grounded in two complementary domains: cell biomechanics, which examines how physical forces shape the embryo, and genetic regulation and molecular signalling, which concern how cells determine their states and behaviours. Integrating both sides into a unified framework is crucial to fully understand the self-organized dynamics of morphogenesis. Here we introduce MecaGen, an integrative modelling platform enabling the hypothesis-driven simulation of these dual processes via the coupling between mechanical and chemical variables. Our approach relies upon a minimal 'cell behaviour ontology' comprising mesenchymal and epithelial cells and their associated behaviours. MecaGen enables the specification and control of complex collective movements in 3D space through a biologically relevant gene regulatory network and parameter space exploration. Three case studies investigating pattern formation, epithelial differentiation and tissue tectonics in zebrafish early embryogenesis, the latter with quantitative comparison to live imaging data, demonstrate the validity and usefulness of our framework.

Distribution of neurosensory progenitor pools during inner ear morphogenesis unveiled by cell lineage reconstruction

Reconstructing the lineage of cells is central to understanding how the wide diversity of cell types develops. Here, we provide the neurosensory lineage reconstruction of a complex sensory organ, the inner ear, by imaging zebrafish embryos in vivo over an extended timespan, combining cell tracing and cell fate marker expression over time. We deliver the first dynamic map of early neuronal and sensory progenitor pools in the whole otic vesicle. It highlights the remodeling of the neuronal progenitor domain upon neuroblast delamination, and reveals that the order and place of neuroblasts' delamination from the otic epithelium prefigure their position within the SAG. Sensory and non-sensory domains harbor different proliferative activity contributing distinctly to the overall growth of the structure. Therefore, the otic vesicle case exemplifies a generic morphogenetic process where spatial and temporal cues regulate cell fate and functional organization of the rudiment of the definitive organ.

Segmentation of 3D images of plant tissues at multiple scales using the level set method

BACKGROUND

Developmental biology has made great strides in recent years towards the quantification of cellular properties during development. This requires tissues to be imaged and segmented to generate computerised versions that can be easily analysed. In this context, one of the principal technical challenges remains the faithful detection of cellular contours, principally due to variations in image intensity throughout the tissue. Watershed segmentation methods are especially vulnerable to these variations, generating multiple errors due notably to the incorrect detection of the outer surface of the tissue.

RESULTS

We use the level set method (LSM) to improve the accuracy of the watershed segmentation in different ways. First, we detect the outer surface of the tissue, reducing the impact of low and variable contrast at the surface during imaging. Second, we demonstrate a new edge function for a level set, based on second order derivatives of the image, to segment individual cells. Finally, we also show that the LSM can be used to segment nuclei within the tissue.

CONCLUSION

The watershed segmentation of the outer cell layer is demonstrably improved when coupled with the LSM-based surface detection step. The tool can also be used to improve watershed segmentation at cell-scale, as well as to segment nuclei within a tissue. The improved segmentation increases the quality of analysis, and the surface detected by our algorithm may be used to calculate local curvature or adapted for other uses, such as mathematical simulations.

An integrated modelling framework from cells to organism based on a cohort of digital embryos

We conducted a quantitative comparison of developing sea urchin embryos based on the analysis of five digital specimens obtained by automatic processing of in toto 3D+ time image data. These measurements served the reconstruction of a prototypical cell lineage tree able to predict the spatiotemporal cellular organisation of a normal sea urchin blastula. The reconstruction was achieved by designing and tuning a multi-level probabilistic model that reproduced embryo-level dynamics from a small number of statistical parameters characterising cell proliferation, cell surface area and cell volume evolution along the cell lineage. Our resulting artificial prototype was embedded in 3D space by biomechanical agent-based modelling and simulation, which allowed a systematic exploration and optimisation of free parameters to fit the experimental data and test biological hypotheses. The spherical monolayered blastula and the spatial arrangement of its different cell types appeared tightly constrained by cell stiffness, cell-adhesion parameters and blastocoel turgor pressure.

Joint level-set and spatio-temporal motion detection for cell segmentation

BACKGROUND

Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images.

METHODS

In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result.

RESULTS

We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan-Vese techniques, and 4 % compared to the nonlinear spatio-temporal diffusion method.

CONCLUSIONS

Despite the wide variation in cell shape, density, mitotic events, and image quality among the datasets, our proposed method produced promising segmentation results. These results indicate the efficiency and robustness of this method especially for mitotic events and low SNR imaging, enabling the application of subsequent quantification tasks.

Self-organization of the human embryo in the absence of maternal tissues

Remodelling of the human embryo at implantation is indispensable for successful pregnancy. Yet it has remained mysterious because of the experimental hurdles that beset the study of this developmental phase. Here, we establish an in vitro system to culture human embryos through implantation stages in the absence of maternal tissues and reveal the key events of early human morphogenesis. These include segregation of the pluripotent embryonic and extra-embryonic lineages, and morphogenetic rearrangements leading to generation of a bilaminar disc, formation of a pro-amniotic cavity within the embryonic lineage, appearance of the prospective yolk sac, and trophoblast differentiation. Using human embryos and human pluripotent stem cells, we show that the reorganization of the embryonic lineage is mediated by cellular polarization leading to cavity formation. Together, our results indicate that the critical remodelling events at this stage of human development are embryo-autonomous, highlighting the remarkable and unanticipated self-organizing properties of human embryos.

Repulsive cues combined with physical barriers and cell-cell adhesion determine progenitor cell positioning during organogenesis

The precise positioning of organ progenitor cells constitutes an essential, yet poorly understood step during organogenesis. Using primordial germ cells that participate in gonad formation, we present the developmental mechanisms maintaining a motile progenitor cell population at the site where the organ develops. Employing high-resolution live-cell microscopy, we find that repulsive cues coupled with physical barriers confine the cells to the correct bilateral positions. This analysis revealed that cell polarity changes on interaction with the physical barrier and that the establishment of compact clusters involves increased cell–cell interaction time. Using particle-based simulations, we demonstrate the role of reflecting barriers, from which cells turn away on contact, and the importance of proper cell–cell adhesion level for maintaining the tight cell clusters and their correct positioning at the target region. The combination of these developmental and cellular mechanisms prevents organ fusion, controls organ positioning and is thus critical for its proper function.

The precise positioning of organ progenitor cells is essential for organ development and function. Here the authors use live imaging and mathematical modelling to show that the confinement of a motile progenitor cell population results from coupled physical barriers and cell-cell interactions.