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.

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.

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

The quantitative and systematic analysis of embryonic cell dynamics from in vivo 3D+time image data sets is a major challenge at the forefront of developmental biology. Despite recent breakthroughs in the microscopy imaging of living systems, producing an accurate cell lineage tree for any developing organism remains a difficult task. We present here the BioEmergences workflow integrating all reconstruction steps from image acquisition and processing to the interactive visualization of reconstructed data. Original mathematical methods and algorithms underlie image filtering, nucleus centre detection, nucleus and membrane segmentation, and cell tracking. They are demonstrated on zebrafish, ascidian and sea urchin embryos with stained nuclei and membranes. Subsequent validation and annotations are carried out using Mov-IT, a custom-made graphical interface. Compared with eight other software tools, our workflow achieved the best lineage score. Delivered in standalone or web service mode, BioEmergences and Mov-IT offer a unique set of tools for in silico experimental embryology.

Engineered decellularized matrices to instruct bone regeneration processes

Despite the significant progress in the field of bone tissue engineering, cell-based products have not yet reached the stage of clinical adoption. This is due to the uncertain advantages from the standard-of-care, combined with challenging cost-and regulatory-related issues. Novel therapeutic approaches could be based on exploitation of the intrinsic regenerative capacity of bone tissue, provided the development of a deeper understanding of its healing mechanisms. While it is well-established that endogenous progenitors can be activated toward bone formation by overdoses of single morphogens, the challenge to stimulate the healing processes by coordinated and controlled stimulation of specific cell populations remains open. Here, we review the recent approaches to generate osteoinductive materials based on the use of decellularized extracellular matrices (ECM) as reservoirs of multiple factors presented at physiological doses and through the appropriate ligands. We then propose the generation of customized engineered and decellularized ECM (i) as a tool to better understand the processes of bone regeneration and (ii) as safe and effective "off-the-shelf" bone grafts for clinical use. This article is part of a Special Issue entitled Stem Cells and Bone.

A digital framework to build, visualize and analyze a gene expression atlas with cellular resolution in zebrafish early embryogenesis

A gene expression atlas is an essential resource to quantify and understand the multiscale processes of embryogenesis in time and space. The automated reconstruction of a prototypic 4D atlas for vertebrate early embryos, using multicolor fluorescence in situ hybridization with nuclear counterstain, requires dedicated computational strategies. To this goal, we designed an original methodological framework implemented in a software tool called Match-IT. With only minimal human supervision, our system is able to gather gene expression patterns observed in different analyzed embryos with phenotypic variability and map them onto a series of common 3D templates over time, creating a 4D atlas. This framework was used to construct an atlas composed of 6 gene expression templates from a cohort of zebrafish early embryos spanning 6 developmental stages from 4 to 6.3 hpf (hours post fertilization). They included 53 specimens, 181,415 detected cell nuclei and the segmentation of 98 gene expression patterns observed in 3D for 9 different genes. In addition, an interactive visualization software, Atlas-IT, was developed to inspect, supervise and analyze the atlas. Match-IT and Atlas-IT, including user manuals, representative datasets and video tutorials, are publicly and freely available online. We also propose computational methods and tools for the quantitative assessment of the gene expression templates at the cellular scale, with the identification, visualization and analysis of coexpression patterns, synexpression groups and their dynamics through developmental stages.

We propose a workflow to map the expression domains of multiple genes onto a series of 3D templates, or “atlas”, during early embryogenesis. It was applied to the zebrafish at different stages between 4 and 6.3 hpf, generating 6 templates. Our system overcomes the lack of significant morphological landmarks in early development by relying on the expression of a reference gene (goosecoid, gsc) and nuclear staining to guide the registration of the analyzed genes. The proposed method also successfully maps gene domains from partially imaged embryos, thus allowing greater microscope magnification and cellular resolution. By using the workflow to construct a spatiotemporal database of zebrafish, we opened the way to a systematic analysis of vertebrate embryogenesis. The atlas database, together with the mapping software (Match-IT), a custom-made visualization platform (Atlas-IT), and step-by-step user guides are available from the Supplementary Material. We expect that this will encourage other laboratories to generate, map, visualize and analyze new gene expression datasets.

3D+t morphological processing: applications to embryogenesis image analysis

We propose to directly process 3D + t image sequences with mathematical morphology operators using a new classification of the 3D+t structuring elements. Several methods (filtering, tracking, segmentation) dedicated to the analysis of 3D + t datasets of zebrafish embryogenesis are introduced and validated through a synthetic dataset. Then, we illustrate the application of these methods to the analysis of datasets of zebrafish early development acquired with various microscopy techniques. This processing paradigm produces spatio-temporal coherent results as it benefits from the intrinsic redundancy of the temporal dimension and minimizes the needs for human intervention in semi-automatic algorithms.

Methodology for reconstructing early zebrafish development from in vivo multiphoton microscopy

Investigating cell dynamics during early zebrafish embryogenesis requires specific image acquisition and analysis strategies. Multiharmonic microscopy, i.e., second- and third-harmonic generations, allows imaging cell divisions and cell membranes in unstained zebrafish embryos from 1- to 1000-cell stage. This paper presents the design and implementation of a dedicated image processing pipeline (tracking and segmentation) for the reconstruction of cell dynamics during these developmental stages. This methodology allows the reconstruction of the cell lineage tree including division timings, spatial coordinates, and cell shape until the 1000-cell stage with minute temporal accuracy and micrometer spatial resolution. Data analysis of the digital embryos provides an extensive quantitative description of early zebrafish embryogenesis.

Image processing challenges in the creation of spatiotemporal gene expression atlases of developing embryos

To properly understand and model animal embryogenesis it is crucial to obtain detailed measurements, both in time and space, about their gene expression domains and cell dynamics. Such challenge has been confronted in recent years by a surge of atlases which integrate a statistically relevant number of different individuals to get robust, complete information about their spatiotemporal locations of gene patterns. This paper will discuss the fundamental image analysis strategies required to build such models and the most common problems found along the way. We also discuss the main challenges and future goals in the field.

Cell lineage reconstruction of early zebrafish embryos using label-free nonlinear microscopy

Quantifying cell behaviors in animal early embryogenesis remains a challenging issue requiring in toto imaging and automated image analysis. We designed a framework for imaging and reconstructing unstained whole zebrafish embryos for their first 10 cell division cycles and report measurements along the cell lineage with micrometer spatial resolution and minute temporal accuracy. Point-scanning multiphoton excitation optimized to preferentially probe the innermost regions of the embryo provided intrinsic signals highlighting all mitotic spindles and cell boundaries. Automated image analysis revealed the phenomenology of cell proliferation. Blastomeres continuously drift out of synchrony. After the 32-cell stage, the cell cycle lengthens according to cell radial position, leading to apparent division waves. Progressive amplification of this process is the rule, contrasting with classical descriptions of abrupt changes in the system dynamics.

An automatic quantification and registration strategy to create a gene expression atlas of zebrafish embryogenesis

In order to properly understand and model the gene regulatory networks in animals development, it is crucial to obtain detailed measurements, both in time and space, about their gene expression domains. In this paper, we propose a complete computational framework to fulfill this task and create a 3D Atlas of the early zebrafish embryogenesis annotated with both the cellular localizations and the level of expression of different genes at different developmental stages. The strategy to construct such an Atlas is described here with the expression pattern of 5 different genes at 6 hours of development post fertilization.

Cells segmentation from 3-D confocal images of early zebrafish embryogenesis

We designed a strategy for extracting the shapes of cell membranes and nuclei from time lapse confocal images taken throughout early zebrafish embryogenesis using a partial-differential-equation-based segmentation. This segmentation step is a prerequisite for an accurate quantitative analysis of cell morphodynamics during embryogenesis and it is the basis for an integrated understanding of biological processes. The segmentation of embryonic cells requires live zebrafish embryos fluorescently labeled to highlight sub-cellular structures and designing specific algorithms by adapting classical methods to image features. Our strategy includes the following steps: the signal-to-noise ratio is first improved by an edge-preserving filtering, then the cell shape is reconstructed applying a fully automated algorithm based on a generalized version of the Subjective Surfaces technique. Finally we present a procedure for the algorithm validation either from the accuracy and the robustness perspective.

Combining sea urchin embryo cell lineages by error-tolerant graph matching

Obtaining the complete cell lineage tree of an embryo's development is a very appealing and ambitious goal, but fortunately recent developments both in optical imaging and digital image processing are bringing it closer. However, when imaging the embryos (sea urchin embryos for this work) with high enough spatial resolution and short enough time-step to make cell segmentation and tracking possible, it is currently not possible to image the specimen throughout its all embryogenesis. For this reason it is interesting to explore how cell lineage trees extracted from two different embryos of the same species and imaged for overlapping periods of time can be concatenated, resulting in a single lineage tree covering both embryos' development time frames. To achieve this we used an error-tolerant graph matching strategy by selecting a time point at which both lineage trees overlap, and representing the information about each embryo at that time point as a graph in which nodes stand for cells and edges for neighborhood relationships among cells. The expected output of the graph matching algorithm is the minimal-cost correspondence between cells of both specimens, allowing us to perform the lineage combination.

Cell tracking in fluorescence images of embryogenesis processes with morphological reconstruction by 4D-tubular structuring elements

We present a simple and parameter-free nuclei tracking method for reconstructing cell dynamics in fluorescence 3D+t images of embryogenesis. The strategy is based on the use of the mathematical morphology operators directly in the 4D image. The morphological reconstruction of a marker -manually or automatically selected- in an initial spatio-temporal position generates a connected path over the time representing the cell migration. Thus, the processing provides a coherent spatiotemporal estimation of cell movement. The algorithm has been validated on in vivo images of early zebrafish and sea urchin embryogenesis acquired with two-photon laser scanning microscopy providing mean tracking rates above 98% per time step.

Cells tracking in a live zebrafish embryo

We designed a set of procedures for achieving the tracking of cell nuclei and the identification of cell divisions in live zebrafish embryos using 3D+time images acquired by confocal laser scanning microscopy (CLSM). Our strategy includes image signal enhancement with feature preserving denoising algorithm, automated identification of the nuclei position, extraction of the optical flow from 3D images sequences and tracking of nuclei.

3-D zebrafish embryo image filtering by nonlinear partial differential equations

We discuss application of nonlinear PDE based methods to filtering of 3-D confocal images of embryogenesis. We focus on the mean curvature driven and the regularized Perona-Malik equations, where standard as well as newly suggested edge detectors are used. After presenting the related mathematical models, the practical results are given and discussed by visual inspection and quantitatively using the mean Hausdorff distance.

Segmentation of cells from 3-D confocal images of live zebrafish embryo

In this paper, we use partial-differential-equation-based segmentation to accurately extract the shapes of membranes and nuclei from time lapse confocal microscopy images, taken throughout early Zebrafish embryogenesis. This strategy is a prerequisite for an accurate quantitative analysis of cell shape and morphodynamics during organogenesis and is the basis for an integrated understanding of biological processes. This data will also serve for the measurement of the variability between individuals in a population. The segmentation of cellular structures is achieved by first using an edge-preserving image filtering method for noise reduction and then applying an algorithm for cell shape reconstruction based on the Subjective Surfaces technique.

Autopoiesis and cognition

This article revisits the concept of autopoiesis and examines its relation to cognition and life. We present a mathematical model of a 3D tesselation automaton, considered as a minimal example of autopoiesis. This leads us to a thesis T1: "An autopoietic system can be described as a random dynamical system, which is defined only within its organized autopoietic domain." We propose a modified definition of autopoiesis: "An autopoietic system is a network of processes that produces the components that reproduce the network, and that also regulates the boundary conditions necessary for its ongoing existence as a network." We also propose a definition of cognition: "A system is cognitive if and only if sensory inputs serve to trigger actions in a specific way, so as to satisfy a viability constraint." It follows from these definitions that the concepts of autopoiesis and cognition, although deeply related in their connection with the regulation of the boundary conditions of the system, are not immediately identical: a system can be autopoietic without being cognitive, and cognitive without being autopoietic. Finally, we propose a thesis T2: "A system that is both autopoietic and cognitive is a living system."

Topological and causal structure of the yeast transcriptional regulatory network

Interpretation of high-throughput biological data requires a knowledge of the design principles underlying the networks that sustain cellular functions. Of particular importance is the genetic network, a set of genes that interact through directed transcriptional regulation. Genes that exert a regulatory role encode dedicated transcription factors (hereafter referred to as regulating proteins) that can bind to specific DNA control regions of regulated genes to activate or inhibit their transcription. Regulated genes may themselves act in a regulatory manner, in which case they participate in a causal pathway. Looping pathways form feedback circuits. Because a gene can have several connections, circuits and pathways may crosslink and thus represent connected components. We have created a graph of 909 genetically or biochemically established interactions among 491 yeast genes. The number of regulating proteins per regulated gene has a narrow distribution with an exponential decay. The number of regulated genes per regulating protein has a broader distribution with a decay resembling a power law. Assuming in computer-generated graphs that gene connections fulfill these distributions but are otherwise random, the local clustering of connections and the number of short feedback circuits are largely underestimated. This deviation from randomness probably reflects functional constraints that include biosynthetic cost, response delay and differentiative and homeostatic regulation.