WDDD: Worm Developmental Dynamics Database

Effective mechanical potential of cell-cell interaction explains three-dimensional morphologies during early embryogenesis

Mechanical forces are critical for the emergence of diverse three-dimensional morphologies of multicellular systems. However, it remains unclear what kind of mechanical parameters at cellular level substantially contribute to tissue morphologies. This is largely due to technical limitations of live measurements of cellular forces. Here we developed a framework for inferring and modeling mechanical forces of cell-cell interactions. First, by analogy to coarse-grained models in molecular and colloidal sciences, we approximated cells as particles, where mean forces (i.e. effective forces) of pairwise cell-cell interactions are considered. Then, the forces were statistically inferred by fitting the mathematical model to cell tracking data. This method was validated by using synthetic cell tracking data resembling various in vivo situations. Application of our method to the cells in the early embryos of mice and the nematode Caenorhabditis elegans revealed that cell-cell interaction forces can be written as a pairwise potential energy in a manner dependent on cell-cell distances. Importantly, the profiles of the pairwise potentials were quantitatively different among species and embryonic stages, and the quantitative differences correctly described the differences of their morphological features such as spherical vs. distorted cell aggregates, and tightly vs. non-tightly assembled aggregates. We conclude that the effective pairwise potential of cell-cell interactions is a live measurable parameter whose quantitative differences can be a parameter describing three-dimensional tissue morphologies.

Statistical image processing quantifies the changes in cytoplasmic texture associated with aging in Caenorhabditis elegans oocytes

Background

Oocyte quality decreases with aging, thereby increasing errors in fertilization, chromosome segregation, and embryonic cleavage. Oocyte appearance also changes with aging, suggesting a functional relationship between oocyte quality and appearance. However, no methods are available to objectively quantify age-associated changes in oocyte appearance.

Results

We show that statistical image processing of Nomarski differential interference contrast microscopy images can be used to quantify age-associated changes in oocyte appearance in the nematode Caenorhabditis elegans. Max–min value (mean difference between the maximum and minimum intensities within each moving window) quantitatively characterized the difference in oocyte cytoplasmic texture between 1- and 3-day-old adults (Day 1 and Day 3 oocytes, respectively). With an appropriate parameter set, the gray level co-occurrence matrix (GLCM)-based texture feature Correlation (COR) more sensitively characterized this difference than the Max–min Value. Manipulating the smoothness of and/or adding irregular structures to the cytoplasmic texture of Day 1 oocyte images reproduced the difference in Max–min Value but not in COR between Day 1 and Day 3 oocytes. Increasing the size of granules in synthetic images recapitulated the age-associated changes in COR. Manual measurements validated that the cytoplasmic granules in oocytes become larger with aging.

Conclusions

The Max–min value and COR objectively quantify age-related changes in C. elegans oocyte in Nomarski DIC microscopy images. Our methods provide new opportunities for understanding the mechanism underlying oocyte aging.

Using deep reinforcement learning to speed up collective cell migration

BACKGROUND

Collective cell migration is a significant and complex phenomenon that affects many basic biological processes. The coordination between leader cell and follower cell affects the rate of collective cell migration. However, there are still very few papers on the impacts of the stimulus signal released by the leader on the follower. Tracking cell movement using 3D time-lapse microscopy images provides an unprecedented opportunity to systematically study and analyze collective cell migration.

RESULTS

Recently, deep reinforcement learning algorithms have become very popular. In our paper, we also use this method to train the number of cells and control signals. By experimenting with single-follower cell and multi-follower cells, it is concluded that the number of stimulation signals is proportional to the rate of collective movement of the cells. Such research provides a more diverse approach and approach to studying biological problems.

CONCLUSION

Traditional research methods are always based on real-life scenarios, but as the number of cells grows exponentially, the research process is too time consuming. Agent-based modeling is a robust framework that approximates cells to isotropic, elastic, and sticky objects. In this paper, an agent-based modeling framework is used to establish a simulation platform for simulating collective cell migration. The goal of the platform is to build a biomimetic environment to demonstrate the importance of stimuli between the leading and following cells.

Deep reinforcement learning of cell movement in the early stage of C.elegans embryogenesis

Motivation

Cell movement in the early phase of Caenorhabditis elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previous efforts have demonstrated that agent-based, multi-scale modeling systems can integrate physical and biological rules and provide new avenues to study developmental systems. However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulatory networks at the right scales. Recent developments in deep learning and reinforcement learning provide an unprecedented opportunity to explore cell movement using 3D time-lapse microscopy images.

Results

We present a deep reinforcement learning approach within an agent-based modeling system to characterize cell movement in the embryonic development of C.elegans. Our modeling system captures the complexity of cell movement patterns in the embryo and overcomes the local optimization problem encountered by traditional rule-based, agent-based modeling that uses greedy algorithms. We tested our model with two real developmental processes: the anterior movement of the Cpaaa cell via intercalation and the rearrangement of the superficial left-right asymmetry. In the first case, the model results suggested that Cpaaa's intercalation is an active directional cell movement caused by the continuous effects from a longer distance (farther than the length of two adjacent cells), as opposed to a passive movement caused by neighbor cell movements. In the second case, a leader-follower mechanism well explained the collective cell movement pattern in the asymmetry rearrangement. These results showed that our approach to introduce deep reinforcement learning into agent-based modeling can test regulatory mechanisms by exploring cell migration paths in a reverse engineering perspective. This model opens new doors to explore the large datasets generated by live imaging.

Availability and implementation

Source code is available at https://github.com/zwang84/drl4cellmovement.

Supplementary information

Supplementary data are available at Bioinformatics online.

Three-Dimensional Embryonic Image Segmentation and Registration Based on Shape Index and Ellipsoid-Fitting Method

Quantitative analysis based on three-dimensional differential interference contrast (DIC) images is currently the mainstream in analyzing gene functions involved in early cell fate specifications. Segmentation and registration are the two most important steps in analysis. Many image segmentation methods have poor performance on embryonic DIC images because of the interference of egg shells, blurs, and nonuniform intensity background. A novel segmentation method is presented based on the shape index (SI) of local intensity variation in DIC images. To compute the SI, the intensity values of a local neighborhood are regarded as z coordinates over x-y planes. After calculating the SI for each pixel by evaluating the shape of intensity surface over the corresponding local neighborhood, SI thresholding is used to detect cytoplasm granules within embryonic boundaries. As a scalar and rotation invariant, the SI is both insensitive to directional changes and different ranges of intensity variations. Embryonic registration methods are usually based on the orientation of vertebrate anteroposterior (AP) axes computed from segmented embryonic boundaries. Due to the blur of marginal slices in DIC images, usually the segmented boundaries are incomplete, which may make the computed AP axes shift away from the correct orientation when using the principal component analysis method. A method calculating AP axes based on ellipsoid-fitting is proposed, which can achieve high accuracy when handling incomplete segmented boundaries. Using Worm Developmental Dynamics Database, we evaluated the performance of the proposed segmentation method and the computation of AP axes. Experimental results show that the two methods outperform existing methods.

Phenotype Analysis Method for Identification of Gene Functions Involved in Asymmetric Division of Caenorhabditis elegans

In gene function analysis, it is arduous to identify gene function individually, and the way to screen out all involved genes according to a particular phenotype or disease usually shows us little information for a specific problem. We present a data-driven analysis system based on wild type (WT) embryos to study the concrete function of each gene associated with certain category of abnormal phenotypes. It can be applied to genes with very few RNAi embryos. Instead of presupposing the particular function of a gene, its function is confirmed by the statistical testing of built models. The scheme includes the following five: first, verify the to be detected genes and determine related recognized features according to the given category; second, compute the value of each feature based on WT embryos and merge them by principal component analysis (PCA); third, for each of the selected components of PCA, build a normal distribution and verify its normality; fourth, project the RNAi embryos to each component and probe them; and finally, analyze the more detailed functions of each gene based on the physical or biological meaning of each component. Choosing the first-round asymmetric division process of Caenorhabditis elegans as the phenotype, experimental results show that on the different aspects of the asymmetric division process, par-2, par-3, and let-754 are related to scalar differences; dcn-1 and mcm-5 are associated with the divergences of scalar variation, which may reflect the disaccord in development; and dcn-1, par-2, and par-3 are involved with morphological discrepancies.

An Observation-Driven Agent-Based Modeling and Analysis Framework for C. elegans Embryogenesis

With cutting-edge live microscopy and image analysis, biologists can now systematically track individual cells in complex tissues and quantify cellular behavior over extended time windows. Computational approaches that utilize the systematic and quantitative data are needed to understand how cells interact in vivo to give rise to the different cell types and 3D morphology of tissues. An agent-based, minimum descriptive modeling and analysis framework is presented in this paper to study C. elegans embryogenesis. The framework is designed to incorporate the large amounts of experimental observations on cellular behavior and reserve data structures/interfaces that allow regulatory mechanisms to be added as more insights are gained. Observed cellular behaviors are organized into lineage identity, timing and direction of cell division, and path of cell movement. The framework also includes global parameters such as the eggshell and a clock. Division and movement behaviors are driven by statistical models of the observations. Data structures/interfaces are reserved for gene list, cell-cell interaction, cell fate and landscape, and other global parameters until the descriptive model is replaced by a regulatory mechanism. This approach provides a framework to handle the ongoing experiments of single-cell analysis of complex tissues where mechanistic insights lag data collection and need to be validated on complex observations.

SSBD: a database of quantitative data of spatiotemporal dynamics of biological phenomena

Motivation: Rapid advances in live-cell imaging analysis and mathematical modeling have produced a large amount of quantitative data on spatiotemporal dynamics of biological objects ranging from molecules to organisms. There is now a crucial need to bring these large amounts of quantitative biological dynamics data together centrally in a coherent and systematic manner. This will facilitate the reuse of this data for further analysis.

Results: We have developed the Systems Science of Biological Dynamics database (SSBD) to store and share quantitative biological dynamics data. SSBD currently provides 311 sets of quantitative data for single molecules, nuclei and whole organisms in a wide variety of model organisms from Escherichia coli to Mus musculus. The data are provided in Biological Dynamics Markup Language format and also through a REST API. In addition, SSBD provides 188 sets of time-lapse microscopy images from which the quantitative data were obtained and software tools for data visualization and analysis.

Availability and Implementation: SSBD is accessible at http://ssbd.qbic.riken.jp.

Contact:sonami@riken.jp

Minimizing the Number of Edges via Edge Concentration in Dense Layered Graphs

Edge concentration in dense bipartite graphs is a technique for reducing the numbers of edges and edge crossings in graph drawings. The conventional method proposed by Newbery is designed to reduce the number of edge crossings; however, it does not always reduce the number of edges. Reducing the number of edges is also an important factor for improving the readability of graphs. However, no edge concentration method with the explicit purpose of minimizing the number of edges has previously been studied. In this study, we propose a novel, efficient heuristic method for minimizing the number of edges during edge concentration. We demonstrate the efficiency of the proposed method via a comparison using randomly generated graphs. We find that Newbery's method fails to reduce the number of edges when the number of vertices is large. By contrast, the proposed method achieves an average compression ratio of 47 to 82 percent for all generated graph groups. We also present a real-world application of the proposed method using a causality network of biological data.

Digital development: a database of cell lineage differentiation in C. elegans with lineage phenotypes, cell-specific gene functions and a multiscale model

Developmental systems biology is poised to exploit large-scale data from two approaches: genomics and live imaging. The combination of the two offers the opportunity to map gene functions and gene networks in vivo at single-cell resolution using cell tracking and quantification of cellular phenotypes. Here we present Digital Development (http://www.digital-development.org), a database of cell lineage differentiation with curated phenotypes, cell-specific gene functions and a multiscale model. The database stores data from recent systematic studies of cell lineage differentiation in the C. elegans embryo containing ∼ 200 conserved genes, 1400 perturbed cell lineages and 600,000 digitized single cells. Users can conveniently browse, search and download four categories of phenotypic and functional information from an intuitive web interface. This information includes lineage differentiation phenotypes, cell-specific gene functions, differentiation landscapes and fate choices, and a multiscale model of lineage differentiation. Digital Development provides a comprehensive, curated, multidimensional database for developmental biology. The scale, resolution and richness of biological information presented here facilitate exploration of gene-specific and systems-level mechanisms of lineage differentiation in Metazoans.

Biological Dynamics Markup Language (BDML): an open format for representing quantitative biological dynamics data

Motivation: Recent progress in live-cell imaging and modeling techniques has resulted in generation of a large amount of quantitative data (from experimental measurements and computer simulations) on spatiotemporal dynamics of biological objects such as molecules, cells and organisms. Although many research groups have independently dedicated their efforts to developing software tools for visualizing and analyzing these data, these tools are often not compatible with each other because of different data formats.

Results: We developed an open unified format, Biological Dynamics Markup Language (BDML; current version: 0.2), which provides a basic framework for representing quantitative biological dynamics data for objects ranging from molecules to cells to organisms. BDML is based on Extensible Markup Language (XML). Its advantages are machine and human readability and extensibility. BDML will improve the efficiency of development and evaluation of software tools for data visualization and analysis.

Availability and implementation: A specification and a schema file for BDML are freely available online at http://ssbd.qbic.riken.jp/bdml/.

Contact:sonami@riken.jp

Supplementary Information:Supplementary data are available at Bioinformatics online.