De novo inference of systems-level mechanistic models of development from live-imaging-based phenotype analysis

Vacuolar H+-ATPase determines daughter cell fates through asymmetric segregation of the nucleosome remodeling and deacetylase complex

Asymmetric cell divisions (ACDs) generate two daughter cells with identical genetic information but distinct cell fates through epigenetic mechanisms. However, the process of partitioning different epigenetic information into daughter cells remains unclear. Here, we demonstrate that the nucleosome remodeling and deacetylase (NuRD) complex is asymmetrically segregated into the surviving daughter cell rather than the apoptotic one during ACDs in Caenorhabditis elegans. The absence of NuRD triggers apoptosis via the EGL-1-CED-9-CED-4-CED-3 pathway, while an ectopic gain of NuRD enables apoptotic daughter cells to survive. We identify the vacuolar H+-adenosine triphosphatase (V-ATPase) complex as a crucial regulator of NuRD's asymmetric segregation. V-ATPase interacts with NuRD and is asymmetrically segregated into the surviving daughter cell. Inhibition of V-ATPase disrupts cytosolic pH asymmetry and NuRD asymmetry. We suggest that asymmetric segregation of V-ATPase may cause distinct acidification levels in the two daughter cells, enabling asymmetric epigenetic inheritance that specifies their respective life-versus-death fates.

A lineage-resolved cartography of microRNA promoter activity in C. elegans empowers multidimensional developmental analysis

Elucidating the expression of microRNAs in developing single cells is critical for functional discovery. Here, we construct scCAMERA (single-cell cartography of microRNA expression based on reporter assay), utilizing promoter-driven fluorescent reporters in conjunction with imaging and lineage tracing. The cartography delineates the transcriptional activity of 54 conserved microRNAs in lineage-resolved single cells throughout C. elegans embryogenesis. The combinatorial expression of microRNAs partitions cells into fine clusters reflecting their function and anatomy. Notably, the expression of individual microRNAs exhibits high cell specificity and divergence among family members. Guided by cellular expression patterns, we identify developmental functions of specific microRNAs, including miR-1 in pharynx development and physiology, miR-232 in excretory canal morphogenesis by repressing NHR-25/NR5A, and a functional synergy between miR-232 and miR-234 in canal development, demonstrating the broad utility of scCAMERA. Furthermore, integrative analysis reveals that tissue-specific fate determinants activate microRNAs to repress protein production from leaky transcripts associated with alternative, especially neuronal, fates, thereby enhancing the fidelity of developmental fate differentiation. Collectively, our study offers rich opportunities for multidimensional expression-informed analysis of microRNA biology in metazoans.

Spatiotemporal analysis of mRNA-protein relationships enhances transcriptome-based developmental inference

Elucidating the complex relationships between mRNA and protein expression at high spatiotemporal resolution is critical for unraveling multilevel gene regulation and enhancing mRNA-based developmental analyses. In this study, we conduct a single-cell analysis of mRNA and protein expression of transcription factors throughout C. elegans embryogenesis. Initially, cellular co-presence of mRNA and protein is low, increasing to a medium-high level (73%) upon factoring in delayed protein synthesis and long-term protein persistence. These factors substantially affect mRNA-protein concordance, leading to potential inaccuracies in mRNA-reliant gene detection and specificity characterization. Building on the learned relationship, we infer protein presence from mRNA expression and demonstrate its utility in identifying tissue-specific genes and elucidating relationships between genes and cells. This approach facilitates identifying the role of sptf-1/SP7 in neuronal lineage development. Collectively, this study provides insights into gene expression dynamics during rapid embryogenesis and approaches for improving the efficacy of transcriptome-based developmental analyses.

Reconstructing cell lineage trees with genomic barcoding: approaches and applications

In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.

Novel metrics reveal new structure and unappreciated heterogeneity in Caenorhabditis elegans development

High throughput experimental approaches are increasingly allowing for the quantitative description of cellular and organismal phenotypes. Distilling these large volumes of complex data into meaningful measures that can drive biological insight remains a central challenge. In the quantitative study of development, for instance, one can resolve phenotypic measures for single cells onto their lineage history, enabling joint consideration of heritable signals and cell fate decisions. Most attempts to analyze this type of data, however, discard much of the information content contained within lineage trees. In this work we introduce a generalized metric, which we term the branch edit distance, that allows us to compare any two embryos based on phenotypic measurements in individual cells. This approach aligns those phenotypic measurements to the underlying lineage tree, providing a flexible and intuitive framework for quantitative comparisons between, for instance, Wild-Type (WT) and mutant developmental programs. We apply this novel metric to data on cell-cycle timing from over 1300 WT and RNAi-treated Caenorhabditis elegans embryos. Our new metric revealed surprising heterogeneity within this data set, including subtle batch effects in WT embryos and dramatic variability in RNAi-induced developmental phenotypes, all of which had been missed in previous analyses. Further investigation of these results suggests a novel, quantitative link between pathways that govern cell fate decisions and pathways that pattern cell cycle timing in the early embryo. Our work demonstrates that the branch edit distance we propose, and similar metrics like it, have the potential to revolutionize our quantitative understanding of organismal phenotype.

Novel metrics reveal new structure and unappreciated heterogeneity in C. elegans development

High throughput experimental approaches are increasingly allowing for the quantitative description of cellular and organismal phenotypes. Distilling these large volumes of complex data into meaningful measures that can drive biological insight remains a central challenge. In the quantitative study of development, for instance, one can resolve phenotypic measures for single cells onto their lineage history, enabling joint consideration of heritable signals and cell fate decisions. Most attempts to analyze this type of data, however, discard much of the information content contained within lineage trees. In this work we introduce a generalized metric, which we term the branch distance, that allows us to compare any two embryos based on phenotypic measurements in individual cells. This approach aligns those phenotypic measurements to the underlying lineage tree, providing a flexible and intuitive framework for quantitative comparisons between, for instance, Wild-Type (WT) and mutant developmental programs. We apply this novel metric to data on cell-cycle timing from over 1300 WT and RNAi-treated Caenorhabditis elegans embryos. Our new metric revealed surprising heterogeneity within this data set, including subtle batch effects in WT embryos and dramatic variability in RNAi-induced developmental phenotypes, all of which had been missed in previous analyses. Further investigation of these results suggests a novel, quantitative link between pathways that govern cell fate decisions and pathways that pattern cell cycle timing in the early embryo. Our work demonstrates that the branch distance we propose, and similar metrics like it, have the potential to revolutionize our quantitative understanding of organismal phenotype.

Visualizing Phagocytic Cargo In Vivo from Engulfment to Resolution in Caenorhabditis elegans

The nematode Caenorhabditis elegans offers many experimental advantages to study conserved mechanisms of phagocytosis and phagocytic clearance. These include the stereotyped timing of phagocytic events in vivo for time-lapse imaging, the availability of transgenic reporters labeling molecules involved in different steps of phagocytosis, and the transparency of the animal for fluorescence imaging. Further, the ease of forward and reverse genetics in C. elegans has enabled many of the initial discoveries of proteins involved in phagocytic clearance. In this chapter, we focus on phagocytosis by the large undifferentiated blastomeres of C. elegans embryos, which engulf and eliminate diverse phagocytic cargo from the corpse of the second polar body to cytokinetic midbody remnants. We describe the use of fluorescent time-lapse imaging to observe the distinct steps of phagocytic clearance and methods to normalize this process to distinguish defects in mutant strains. These approaches have enabled us to reveal new insights from the initial signaling to induce phagocytosis up until the final resolution of phagocytic cargo in phagolysosomes.

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.

Defect-buffering cellular plasticity increases robustness of metazoan embryogenesis

Developmental processes are intrinsically robust so as to preserve a normal-like state in response to genetic and environmental fluctuations. However, the robustness and potential phenotypic plasticity of individual developing cells under genetic perturbations remain to be systematically evaluated. Using large-scale gene perturbation, live imaging, lineage tracing, and single-cell phenomics, we quantified the phenotypic landscape of C. elegans embryogenesis in >2,000 embryos following individual knockdown of over 750 conserved genes. We observed that cellular genetic systems are not sufficiently robust to single-gene perturbations across all cells; rather, gene knockdowns frequently induced cellular defects. Dynamic phenotypic analyses revealed many cellular defects to be transient, with cells exhibiting phenotypic plasticity that serves to alleviate, correct, and accommodate the defects. Moreover, potential developmentally related cell modules may buffer the phenotypic effects of individual cell position changes. Our findings reveal non-negligible contributions of cellular plasticity and multicellularity as compensatory strategies to increase developmental robustness.

Deep distributed computing to reconstruct extremely large lineage trees

Phylogeny estimation (the reconstruction of evolutionary trees) has recently been applied to CRISPR-based cell lineage tracing, allowing the developmental history of an individual tissue or organism to be inferred from a large number of mutated sequences in somatic cells. However, current computational methods are not able to construct phylogenetic trees from extremely large numbers of input sequences. Here, we present a deep distributed computing framework to comprehensively trace accurate large lineages (FRACTAL) that substantially enhances the scalability of current lineage estimation software tools. FRACTAL first reconstructs only an upstream lineage of the input sequences and recursively iterates the same produce for its downstream lineages using independent computing nodes. We demonstrate the utility of FRACTAL by reconstructing lineages from >235 million simulated sequences and from >16 million cells from a simulated experiment with a CRISPR system that accumulates mutations during cell proliferation. We also successfully applied FRACTAL to evolutionary tree reconstructions and to an experiment using error-prone PCR (EP-PCR) for large-scale sequence diversification.

A 4D single-cell protein atlas of transcription factors delineates spatiotemporal patterning during embryogenesis

Complex biological processes such as embryogenesis require precise coordination of cell differentiation programs across both space and time. Using protein-fusion fluorescent reporters and four-dimensional live imaging, we present a protein expression atlas of transcription factors (TFs) mapped onto developmental cell lineages during Caenorhabditis elegans embryogenesis, at single-cell resolution. This atlas reveals a spatiotemporal combinatorial code of TF expression, and a cascade of lineage-specific, tissue-specific and time-specific TFs that specify developmental states. The atlas uncovers regulators of embryogenesis, including an unexpected role of a skin specifier in neurogenesis and the critical function of an uncharacterized TF in convergent muscle differentiation. At the systems level, the atlas provides an opportunity to model cell state-fate relationships, revealing a lineage-dependent state diversity within functionally related cells and a winding trajectory of developmental state progression. Collectively, this single-cell protein atlas represents a valuable resource for elucidating metazoan embryogenesis at the molecular and systems levels.

Disabling the Fanconi Anemia Pathway in Stem Cells Leads to Radioresistance and Genomic Instability

Fanconi anemia is an inherited genome instability syndrome characterized by interstrand cross-link hypersensitivity, congenital defects, bone marrow failure, and cancer predisposition. Although DNA repair mediated by Fanconi anemia genes has been extensively studied, how inactivation of these genes leads to specific cellular phenotypic consequences associated with Fanconi anemia is not well understood. Here we report that Fanconi anemia stem cells in the C. elegans germline and in murine embryos display marked nonhomologous end joining (NHEJ)-dependent radiation resistance, leading to survival of progeny cells carrying genetic lesions. In contrast, DNA cross-linking does not induce generational genomic instability in Fanconi anemia stem cells, as widely accepted, but rather drives NHEJ-dependent apoptosis in both species. These findings suggest that Fanconi anemia is a stem cell disease reflecting inappropriate NHEJ, which is mutagenic and carcinogenic as a result of DNA misrepair, while marrow failure represents hematopoietic stem cell apoptosis. SIGNIFICANCE: This study finds that Fanconi anemia stem cells preferentially activate error-prone NHEJ-dependent DNA repair to survive irradiation, thereby conferring generational genomic instability that is instrumental in carcinogenesis.

Single-cell dynamics of chromatin activity during cell lineage differentiation in Caenorhabditis elegans embryos

Elucidating the chromatin dynamics that orchestrate embryogenesis is a fundamental question in developmental biology. Here, we exploit position effects on expression as an indicator of chromatin activity and infer the chromatin activity landscape in every lineaged cell during Caenorhabditis elegans early embryogenesis. Systems-level analyses reveal that chromatin activity distinguishes cellular states and correlates with fate patterning in the early embryos. As cell lineage unfolds, chromatin activity diversifies in a lineage-dependent manner, with switch-like changes accompanying anterior-posterior fate asymmetry and characteristic landscapes being established in different cell lineages. Upon tissue differentiation, cellular chromatin from distinct lineages converges according to tissue types but retains stable memories of lineage history, contributing to intra-tissue cell heterogeneity. However, the chromatin landscapes of cells organized in a left-right symmetric pattern are predetermined to be analogous in early progenitors so as to pre-set equivalent states. Finally, genome-wide analysis identifies many regions exhibiting concordant chromatin activity changes that mediate the co-regulation of functionally related genes during differentiation. Collectively, our study reveals the developmental and genomic dynamics of chromatin activity at the single-cell level.

Alignment of Cell Lineage Trees Elucidates Genetic Programs for the Development and Evolution of Cell Types

A full understanding of the developmental process requires fine-scale characterization of cell divisions and cell types, which are naturally organized as the developmental cell lineage tree (CLT). Technological breakthroughs facilitated determination of more CLTs, but complete comprehension of the data remains difficult without quantitative comparison among CLTs. We hereby quantified phenotypic similarity between CLTs using a novel computational method that exhaustively searches for optimal correspondence between individual cells meanwhile retaining their topological relationships. The revealed CLT similarities allowed us to infer functional similarity at the transcriptome level, identify cell fate transformations, predict functional relationships between mutants, and find evolutionary correspondence between cell types of different species. By allowing quantitative comparison between CLTs, our work is expected to greatly enhance the interpretability of relevant data and help answer the myriad of questions surrounding the developmental process.

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Align cell lineage trees (CLTs) to search/quantify their phenotypic similarities

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Aligning worm CLTs captured known genetic/developmental programs

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Similarities between knockdown CLTs revealed functional relationships between genes

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CLT alignments between species gave insight on the evolution of cell types

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.

Evolution and Developmental System Drift in the Endoderm Gene Regulatory Network of Caenorhabditis and Other Nematodes

Developmental gene regulatory networks (GRNs) underpin metazoan embryogenesis and have undergone substantial modification to generate the tremendous variety of animal forms present on Earth today. The nematode Caenorhabditis elegans has been a central model for advancing many important discoveries in fundamental mechanistic biology and, more recently, has provided a strong base from which to explore the evolutionary diversification of GRN architecture and developmental processes in other species. In this short review, we will focus on evolutionary diversification of the GRN for the most ancient of the embryonic germ layers, the endoderm. Early embryogenesis diverges considerably across the phylum Nematoda. Notably, while some species deploy regulative development, more derived species, such as C. elegans, exhibit largely mosaic modes of embryogenesis. Despite the relatively similar morphology of the nematode gut across species, widespread variation has been observed in the signaling inputs that initiate the endoderm GRN, an exemplar of developmental system drift (DSD). We will explore how genetic variation in the endoderm GRN helps to drive DSD at both inter- and intraspecies levels, thereby resulting in a robust developmental system. Comparative studies using divergent nematodes promise to unveil the genetic mechanisms controlling developmental plasticity and provide a paradigm for the principles governing evolutionary modification of an embryonic GRN.

Systems Properties and Spatiotemporal Regulation of Cell Position Variability during Embryogenesis

An intriguing question in developmental biology is how do developmental processes achieve high reproducibility among individuals? An in-depth analysis of information contained in phenotypic variability provides an important perspective to address this question. In this work, we present a quantitative and functional analysis of cell position variability during Caenorhabditis elegans embryogenesis. We find that cell position variability is highly deterministic and regulated by intrinsic and extrinsic mechanisms. Positional variability is determined by cell lineage identity and is coupled to diverse developmental properties of cells, including embryonic localization, cell contact, and left-right symmetry. Temporal dynamics of cell position variability are highly concordant, and fate specification contributes to a systems-wide reduction of variability that could provide a buffering strategy. Positional variability is stringently regulated throughout embryogenesis and cell-cell junctions function to restrict variability. Our results provide insight into systems properties and spatiotemporal control of cellular variability during development.

Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification

Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or a new analytical approach is needed to deal with these phenomics data. We proposed a statistical framework for the analysis of phenomics data by integrating DM and ML methods. The most popular supervised ML methods; Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine with linear (SVM-l) and radial basis (SVM-r) kernel are used for classification/prediction plant status (stress/non-stress) to validate our proposed approach. Several simulated and real plant phenotype datasets were analyzed. The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy.

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.

Planar Asymmetries in the C. elegans Embryo Emerge by Differential Retention of aPARs at Cell-Cell Contacts

Formation of the anteroposterior and dorsoventral body axis in Caenorhabditis elegans depends on cortical flows and advection of polarity determinants. The role of this patterning mechanism in tissue polarization after formation of cell-cell contacts is not fully understood. Here, we demonstrate that planar asymmetries are established during left-right symmetry breaking: Centripetal cortical flows asymmetrically and differentially advect anterior polarity determinants (aPARs) from contacts to the medial cortex, resulting in their unmixing from apical myosin. Contact localization and advection of PAR-6 requires balanced CDC-42 activation, while asymmetric retention and advection of PAR-3 can occur independently of PAR-6. Concurrent asymmetric retention of PAR-3, E-cadherin/HMR-1 and opposing retention of antagonistic CDC-42 and Wnt pathway components leads to planar asymmetries. The most obvious mark of planar asymmetry, retention of PAR-3 at a single cell-cell contact, is required for proper cytokinetic cell intercalation. Hence, our data uncover how planar polarity is established in a system without the canonical planar cell polarity pathway through planar asymmetric retention of aPARs.

Digging deeper: methodologies for high-content phenotyping in Caenorhabditis elegans

Deep phenotyping is an emerging conceptual paradigm and experimental approach aimed at measuring and linking many aspects of a phenotype to understand its underlying biology. To date, deep phenotyping has been applied mostly in cultured cells and used less in multicellular organisms. However, in the past decade, it has increasingly been recognized that deep phenotyping could lead to a better understanding of how genetics, environment and stochasticity affect the development, physiology and behavior of an organism. The nematode Caenorhabditis elegans is an invaluable model system for studying how genes affect a phenotypic trait, and new technologies have taken advantage of the worm's physical attributes to increase the throughput and informational content of experiments. Coupling of these technical advancements with computational and analytical tools has enabled a boom in deep-phenotyping studies of C. elegans. In this Review, we highlight how these new technologies and tools are digging into the biological origins of complex, multidimensional phenotypes.

Lineage context switches the function of a C. elegans Pax6 homolog in determining a neuronal fate

The sensory nervous system of C. elegans comprises cells with varied molecular and functional characteristics, and is, therefore, a powerful model for understanding mechanisms that generate neuronal diversity. We report here that VAB-3, a C. elegans homolog of the homeodomain-containing protein Pax6, has opposing functions in regulating expression of a specific chemosensory fate. A homeodomain-only short isoform of VAB-3 is expressed in BAG chemosensory neurons, where it promotes gene expression and cell function. In other cells, a long isoform of VAB-3, comprising a Paired homology domain and a homeodomain, represses expression of ETS-5, a transcription factor required for expression of BAG fate. Repression of ets-5 requires the Eyes Absent homolog EYA-1 and the Six-class homeodomain protein CEH-32. We determined sequences that mediate high-affinity binding of ETS-5, VAB-3 and CEH-32. The ets-5 locus is enriched for ETS-5-binding sites but lacks sequences that bind VAB-3 and CEH-32, suggesting that these factors do not directly repress ets-5 expression. We propose that a promoter-selection system together with lineage-specific expression of accessory factors allows VAB-3/Pax6 to either promote or repress expression of specific cell fates in a context-dependent manner. This article has an associated 'The people behind the papers' interview.

A high-content imaging approach to profile C. elegans embryonic development

The Caenorhabditis elegans embryo is an important model for analyzing mechanisms of cell fate specification and tissue morphogenesis. Sophisticated lineage-tracing approaches for analyzing embryogenesis have been developed but are labor intensive and do not naturally integrate morphogenetic readouts. To enable the rapid classification of developmental phenotypes, we developed a high-content method that employs two custom strains: a Germ Layer strain that expresses nuclear markers in the ectoderm, mesoderm and endoderm/pharynx; and a Morphogenesis strain that expresses markers labeling epidermal cell junctions and the neuronal cell surface. We describe a procedure that allows simultaneous live imaging of development in 80-100 embryos and provide a custom program that generates cropped, oriented image stacks of individual embryos to facilitate analysis. We demonstrate the utility of our method by perturbing 40 previously characterized developmental genes in variants of the two strains containing RNAi-sensitizing mutations. The resulting datasets yielded distinct, reproducible signature phenotypes for a broad spectrum of genes that are involved in cell fate specification and morphogenesis. In addition, our analysis provides new in vivo evidence for MBK-2 function in mesoderm fate specification and LET-381 function in elongation.

'Building a perfect body': control of vertebrate organogenesis by PBX-dependent regulatory networks

Pbx genes encode transcription factors that belong to the TALE (three-amino-acid loop extension) superclass of homeodomain proteins. We have witnessed a surge in information about the roles of this gene family as leading actors in the transcriptional control of development. PBX proteins represent a clear example of how transcription factors can regulate developmental processes by combinatorial properties, acting within multimeric complexes to implement activation or repression of transcription depending on their interaction partners. Here, we revisit long-emphasized functions of PBX transcription factors as cofactors for HOX proteins, major architects of the body plan. We further discuss new knowledge on roles of PBX proteins in different developmental contexts as upstream regulators of Hox genes-as factors that interact with non-HOX proteins and can work independently of HOX-as well as potential pioneer factors. Committed to building a perfect body, PBX proteins govern regulatory networks that direct essential morphogenetic processes and organogenesis in vertebrate development. Perturbations of PBX-dependent networks can cause human congenital disease and cancer.

Maps of variability in cell lineage trees

New approaches to lineage tracking have allowed the study of differentiation in multicellular organisms over many generations of cells. Understanding the phenotypic variability observed in these lineage trees requires new statistical methods. Whereas an invariant cell lineage, such as that for the nematode Caenorhabditis elegans, can be described by a lineage map, defined as the pattern of phenotypes overlaid onto the binary tree, a traditional lineage map is static and does not describe the variability inherent in the cell lineages of higher organisms. Here, we introduce lineage variability maps which describe the pattern of second-order variation in lineage trees. These maps can be undirected graphs of the partial correlations between every lineal position, or directed graphs showing the dynamics of bifurcated patterns in each subtree. We show how to infer these graphical models for lineages of any depth from sample sizes of only a few pedigrees. This required developing the generalized spectral analysis for a binary tree, the natural framework for describing tree-structured variation. When tested on pedigrees from C. elegans expressing a marker for pharyngeal differentiation potential, the variability maps recover essential features of the known lineage map. When applied to highly-variable pedigrees monitoring cell size in T lymphocytes, the maps show that most of the phenotype is set by the founder naive T cell. Lineage variability maps thus elevate the concept of the lineage map to the population level, addressing questions about the potency and dynamics of cell lineages and providing a way to quantify the progressive restriction of cell fate with increasing depth in the tree.

Illumination of neural development by in vivo clonal analysis

Single embryonic and adult neural stem cells (NSCs) are characterized by their self-renewal and differentiation potential. Lineage tracing via clonal analysis allows for specific labeling of a single NSC and tracking of its progeny throughout development. Over the past five decades, a plethora of clonal analysis methods have been developed in tandem with integration of chemical, genetic, imaging and sequencing techniques. Applications of these approaches have gained diverse insights into the heterogeneous behavior of NSCs, lineage relationships between cells, molecular regulation of fate specification and ontogeny of complex neural tissues. In this review, we summarize the history and methods of clonal analysis as well as highlight key findings revealed by single-cell lineage tracking of stem cells in developing and adult brains across different animal models.

Selenocysteine mimics the effect of dietary restriction on lifespan via SKN‑1 and retards age‑associated pathophysiological changes in Caenorhabditis elegans

Selenocysteine, a sulfur-containing amino acid, can modulate cellular oxidative stress defense systems by incorporating into anti-oxidant enzymes such as glutathione peroxidase and thioredoxin reductase. Selenocysteine can also prevent cancer, neurodegenerative diseases and cardiovascular diseases. A recent study revealed that dietary supplementation with selenocysteine can increase the resistance of Caenorhabditis elegans to environmental stressors and its lifespan. The objective of the present study was to identify the underlying mechanism involved in the lifespan-extending effect of selenocysteine and the effect of selenocysteine on age-associated pathophysiological changes. Lifespan assays with known long-lived mutants of age-1 (the ortholog of the phosphoinositide 3-kinase), clk-1 (the ortholog of demethoxyubiquinone hydroxylase) and eat-2 (a ligand-gated ion channel subunit) revealed that the effect of selenocysteine on lifespan specifically overlapped with that of the eat-2 mutation, a genetic model of dietary restriction (DR). Selenocysteine mimicked the effect of DR on the bacterial dilution method. It required SKN-1 (the ortholog of mammalian nuclear factor-erythroid-related factor) for lifespan extension. In addition, selenocysteine significantly delayed the paralysis induced by human amyloid-β gene, positively correlated with the incidence of Alzheimer's disease. The effect of selenocysteine on amyloid-β-induced toxicity was dependent on the nuclear localization of DAF-16. Reduced survival caused by high-glucose-diet was recovered by selenocysteine. Selenocysteine also reduced the cellular level of reactive oxygen species known to be increased by high-glucose-diet. The results of the present study suggested that selenocysteine can mimic the effect of DR on lifespan and age-associated pathophysiological alterations, providing scientific evidence for the development of DR mimetics using selenocysteine.

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.

Establishment of Signaling Interactions with Cellular Resolution for Every Cell Cycle of Embryogenesis

Intercellular signaling interactions play a key role in breaking fate symmetry during animal development. Identification of signaling interactions at cellular resolution is technically challenging, especially in a developing embryo. Here, we develop a platform that allows automated inference and validation of signaling interactions for every cell cycle of Caenorhabditis elegans embryogenesis. This is achieved by the generation of a systems-level cell contact map, which consists of 1114 highly confident intercellular contacts, by modeling analysis and is validated through cell membrane labeling coupled with cell lineage analysis. We apply the map to identify cell pairs between which a Notch signaling interaction takes place. By generating expression patterns for two ligands and two receptors of the Notch signaling pathway with cellular resolution using the automated expression profiling technique, we are able to refine existing and identify novel Notch interactions during C. elegans embryogenesis. Targeted cell ablation followed by cell lineage analysis demonstrates the roles of signaling interactions during cell division in breaking fate symmetry. Finally, we describe the development of a website that allows online access to the cell-cell contact map for mapping of other signaling interactions by the community. The platform can be adapted to establish cellular interactions from any other signaling pathway.

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.

Systems biology of embryonic development: Prospects for a complete understanding of the Caenorhabditis elegans embryo

The convergence of developmental biology and modern genomics tools brings the potential for a comprehensive understanding of developmental systems. This is especially true for the Caenorhabditis elegans embryo because its small size, invariant developmental lineage, and powerful genetic and genomic tools provide the prospect of a cellular resolution understanding of messenger RNA (mRNA) expression and regulation across the organism. We describe here how a systems biology framework might allow large-scale determination of the embryonic regulatory relationships encoded in the C. elegans genome. This framework consists of two broad steps: (a) defining the "parts list"-all genes expressed in all cells at each time during development and (b) iterative steps of computational modeling and refinement of these models by experimental perturbation. Substantial progress has been made towards defining the parts list through imaging methods such as large-scale green fluorescent protein (GFP) reporter analysis. Imaging results are now being augmented by high-resolution transcriptome methods such as single-cell RNA sequencing, and it is likely the complete expression patterns of all genes across the embryo will be known within the next few years. In contrast, the modeling and perturbation experiments performed so far have focused largely on individual cell types or genes, and improved methods will be needed to expand them to the full genome and organism. This emerging comprehensive map of embryonic expression and regulatory function will provide a powerful resource for developmental biologists, and would also allow scientists to ask questions not accessible without a comprehensive picture. This article is categorized under: Invertebrate Organogenesis > Worms Technologies > Analysis of the Transcriptome Gene Expression and Transcriptional Hierarchies > Gene Networks and Genomics.

An Observation Data Driven Simulation and Analysis Framework for Early Stage <i>C. elegans</i> Embryogenesis

Recent developments in cutting-edge live microscopy and image analysis provide a unique opportunity to systematically investigate individual cell’s dynamics as well as simulation-based hypothesis testing. After a summary of data generation and analysis in the observation and modeling efforts related to C. elegans embryogenesis, we develop a systematic approach to model the basic behaviors of individual cells. Next, we present our ideas to model cell fate, division, and movement using 3D time-lapse images within an agent-based modeling framework. Then, we summarize preliminary result and discuss efforts in cell fate, division, and movement modeling. Finally, we discuss the ongoing efforts and future directions for C. elegans embryo modeling, including an inferred developmental landscape for cell fate, a quasi-equilibrium model for cell division, and multi-agent, deep reinforcement learning for cell movement.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

PCP and SAX-3/Robo Pathways Cooperate to Regulate Convergent Extension-Based Nerve Cord Assembly in C. elegans

Formation and resolution of multicellular rosettes can drive convergent extension (CE) type cell rearrangements during tissue morphogenesis. Rosette dynamics are regulated by both planar cell polarity (PCP)-dependent and -independent pathways. Here we show that CE is involved in ventral nerve cord (VNC) assembly in Caenorhabditis elegans. We show that a VANG-1/Van Gogh and PRKL-1/Prickle containing PCP pathway and a Slit-independent SAX-3/Robo pathway cooperate to regulate, via rosette intermediaries, the intercalation of post-mitotic neuronal cell bodies during VNC formation. We show that VANG-1 and SAX-3 are localized to contracting edges and rosette foci and act to specify edge contraction during rosette formation and to mediate timely rosette resolution. Simultaneous loss of both pathways severely curtails CE resulting in a shortened, anteriorly displaced distribution of VNC neurons at hatching. Our results establish rosette-based CE as an evolutionarily conserved mechanism of nerve cord morphogenesis and reveal a role for SAX-3/Robo in this process.

Imaging the pharmacology of nanomaterials by intravital microscopy: Toward understanding their biological behavior

Therapeutic nanoparticles (NPs) can deliver cytotoxic chemotherapeutics and other drugs more safely and efficiently to patients; furthermore, selective delivery to target tissues can theoretically be accomplished actively through coating NPs with molecular ligands, and passively through exploiting physiological "enhanced permeability and retention" features. However, clinical trial results have been mixed in showing improved efficacy with drug nanoencapsulation, largely due to heterogeneous NP accumulation at target sites across patients. Thus, a clear need exists to better understand why many NP strategies fail in vivo and not result in significantly improved tumor uptake or therapeutic response. Multicolor in vivo confocal fluorescence imaging (intravital microscopy; IVM) enables integrated pharmacokinetic and pharmacodynamic (PK/PD) measurement at the single-cell level, and has helped answer key questions regarding the biological mechanisms of in vivo NP behavior. This review summarizes progress to date and also describes useful technical strategies for successful IVM experimentation.

ImaEdge: a platform for the quantitative analysis of cortical proteins spatiotemporal dynamics during cell polarization

Cell polarity involves the compartmentalization of the cell cortex. The establishment of cortical compartments arises from the spatial bias in the activity and concentration of cortical proteins. The mechanistic dissection of cell polarity requires the accurate detection of dynamic changes in cortical proteins, but the fluctuations of cell shape and the inhomogeneous distributions of cortical proteins greatly complicate the quantitative extraction of their global and local changes during cell polarization. To address these problems, we introduce an open-source software package, ImaEdge, which automates the segmentation of the cortex from time-lapse movies, and enables quantitative extraction of cortical protein intensities. We demonstrate that ImaEdge enables efficient and rigorous analysis of the dynamic evolution of cortical PAR proteins during C. elegans embryogenesis. It is also capable of accurate tracking of varying levels of transgene expression and discontinuous signals of the actomyosin cytoskeleton during multiple rounds of cell division. ImaEdge provides a unique resource for the quantitative studies of cortical polarization, with the potential for application to many types of polarized cells.

Long-Term High-Resolution Imaging of Developing C. elegans Larvae with Microfluidics

Long-term studies of Caenorhabditis elegans larval development traditionally require tedious manual observations because larvae must move to develop, and existing immobilization techniques either perturb development or are unsuited for young larvae. Here, we present a simple microfluidic device to simultaneously follow development of ten C. elegans larvae at high spatiotemporal resolution from hatching to adulthood (∼3 days). Animals grown in microchambers are periodically immobilized by compression to allow high-quality imaging of even weak fluorescence signals. Using the device, we obtain cell-cycle statistics for C. elegans vulval development, a paradigm for organogenesis. We combine Nomarski and multichannel fluorescence microscopy to study processes such as cell-fate specification, cell death, and transdifferentiation throughout post-embryonic development. Finally, we generate time-lapse movies of complex neural arborization through automated image registration. Our technique opens the door to quantitative analysis of time-dependent phenomena governing cellular behavior during C. elegans larval development.

Deep phenotyping unveils hidden traits and genetic relations in subtle mutants

Discovering mechanistic insights from phenotypic information is critical for the understanding of biological processes. For model organisms, unlike in cell culture, this is currently bottlenecked by the non-quantitative nature and perceptive biases of human observations, and the limited number of reporters that can be simultaneously incorporated in live animals. An additional challenge is that isogenic populations exhibit significant phenotypic heterogeneity. These difficulties limit genetic approaches to many biological questions. To overcome these bottlenecks, we developed tools to extract complex phenotypic traits from images of fluorescently labelled subcellular landmarks, using C. elegans synapses as a test case. By population-wide comparisons, we identified subtle but relevant differences inaccessible to subjective conceptualization. Furthermore, the models generated testable hypotheses of how individual alleles relate to known mechanisms or belong to new pathways. We show that our model not only recapitulates current knowledge in synaptic patterning but also identifies novel alleles overlooked by traditional methods.

Experimenter scoring of cellular imaging data can be biased. This study describes an automated and unbiased multidimensional phenotyping method that relies on machine learning and complex feature computation of imaging data, and identifies weak alleles affecting synapse morphology in live C. elegans.

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.

The Skp1 Homologs SKR-1/2 Are Required for the Caenorhabditis elegans SKN-1 Antioxidant/Detoxification Response Independently of p38 MAPK

SKN-1/Nrf are the primary antioxidant/detoxification response transcription factors in animals and they promote health and longevity in many contexts. SKN-1/Nrf are activated by a remarkably broad-range of natural and synthetic compounds and physiological conditions. Defining the signaling mechanisms that regulate SKN-1/Nrf activation provides insights into how cells coordinate responses to stress. Nrf2 in mammals is regulated in part by the redox sensor repressor protein named Keap1. In C. elegans, the p38 MAPK cascade in the intestine activates SKN-1 during oxidative stress by promoting its nuclear accumulation. Interestingly, we find variation in the kinetics of p38 MAPK activation and tissues with SKN-1 nuclear accumulation among different pro-oxidants that all trigger strong induction of SKN-1 target genes. Using genome-wide RNAi screening, we identify new genes that are required for activation of the core SKN-1 target gene gst-4 during exposure to the natural pro-oxidant juglone. Among 10 putative activators identified in this screen was skr-1/2, highly conserved homologs of yeast and mammalian Skp1, which function to assemble protein complexes. Silencing of skr-1/2 inhibits induction of SKN-1 dependent detoxification genes and reduces resistance to pro-oxidants without decreasing p38 MAPK activation. Global transcriptomics revealed strong correlation between genes that are regulated by SKR-1/2 and SKN-1 indicating a high degree of specificity. We also show that SKR-1/2 functions upstream of the WD40 repeat protein WDR-23, which binds to and inhibits SKN-1. Together, these results identify a novel p38 MAPK independent signaling mechanism that activates SKN-1 via SKR-1/2 and involves WDR-23.

Identifying Multi-Dimensional Co-Clusters in Tensors Based on Hyperplane Detection in Singular Vector Spaces

Co-clustering, often called biclustering for two-dimensional data, has found many applications, such as gene expression data analysis and text mining. Nowadays, a variety of multi-dimensional arrays (tensors) frequently occur in data analysis tasks, and co-clustering techniques play a key role in dealing with such datasets. Co-clusters represent coherent patterns and exhibit important properties along all the modes. Development of robust co-clustering techniques is important for the detection and analysis of these patterns. In this paper, a co-clustering method based on hyperplane detection in singular vector spaces (HDSVS) is proposed. Specifically in this method, higher-order singular value decomposition (HOSVD) transforms a tensor into a core part and a singular vector matrix along each mode, whose row vectors can be clustered by a linear grouping algorithm (LGA). Meanwhile, hyperplanar patterns are extracted and successfully supported the identification of multi-dimensional co-clusters. To validate HDSVS, a number of synthetic and biological tensors were adopted. The synthetic tensors attested a favorable performance of this algorithm on noisy or overlapped data. Experiments with gene expression data and lineage data of embryonic cells further verified the reliability of HDSVS to practical problems. Moreover, the detected co-clusters are well consistent with important genetic pathways and gene ontology annotations. Finally, a series of comparisons between HDSVS and state-of-the-art methods on synthetic tensors and a yeast gene expression tensor were implemented, verifying the robust and stable performance of our method.

Emerging Imaging and Genomic Tools for Developmental Systems Biology

Animal development is a complex and dynamic process orchestrated by exquisitely timed cell lineage commitment, divisions, migration, and morphological changes at the single-cell level. In the past decade, extensive genetic, stem cell, and genomic studies provided crucial insights into molecular underpinnings and the functional importance of genetic pathways governing various cellular differentiation processes. However, it is still largely unknown how the precise coordination of these pathways is achieved at the whole-organism level and how the highly regulated spatiotemporal choreography of development is established in turn. Here, we discuss the latest technological advances in imaging and single-cell genomics that hold great promise for advancing our understanding of this intricate process. We propose an integrated approach that combines such methods to quantitatively decipher in vivo cellular dynamic behaviors and their underlying molecular mechanisms at the systems level with single-cell, single-molecule resolution.

Combinatorial decoding of the invariant C. elegans embryonic lineage in space and time

Understanding how a single cell, the zygote, can divide and differentiate to produce the diverse animal cell types is a central goal of developmental biology research. The model organism Caenorhabditis elegans provides a system that enables a truly comprehensive understanding of this process across all cells. Its invariant cell lineage makes it possible to identify all of the cells in each individual and compare them across organisms. Recently developed methods automate the process of cell identification, allowing high-throughput gene expression characterization and phenotyping at single cell resolution. In this Review, we summarize the sequences of events that pattern the lineage including establishment of founder cell identity, the signaling pathways that diversify embryonic fate, and the regulators involved in patterning within these founder lineages before cells adopt their terminal fates. We focus on insights that have emerged from automated approaches to lineage tracking, including insights into mechanisms of robustness, context-specific regulation of gene expression, and temporal coordination of differentiation. We suggest a model by which lineage history produces a combinatorial code of transcription factors that act, often redundantly, to ensure terminal fate.

Long-term time-lapse live imaging reveals extensive cell migration during annelid regeneration

BACKGROUND

Time-lapse imaging has proven highly valuable for studying development, yielding data of much finer resolution than traditional "still-shot" studies and allowing direct examination of tissue and cell dynamics. A major challenge for time-lapse imaging of animals is keeping specimens immobile yet healthy for extended periods of time. Although this is often feasible for embryos, the difficulty of immobilizing typically motile juvenile and adult stages remains a persistent obstacle to time-lapse imaging of post-embryonic development.

RESULTS

Here we describe a new method for long-duration time-lapse imaging of adults of the small freshwater annelid Pristina leidyi and use this method to investigate its regenerative processes. Specimens are immobilized with tetrodotoxin, resulting in irreversible paralysis yet apparently normal regeneration, and mounted in agarose surrounded by culture water or halocarbon oil, to prevent dehydration but allowing gas exchange. Using this method, worms can be imaged continuously and at high spatial-temporal resolution for up to 5 days, spanning the entire regeneration process. We performed a fine-scale analysis of regeneration growth rate and characterized cell migration dynamics during early regeneration. Our studies reveal the migration of several putative cell types, including one strongly resembling published descriptions of annelid neoblasts, a cell type suggested to be migratory based on "still-shot" studies and long hypothesized to be linked to regenerative success in annelids.

CONCLUSIONS

Combining neurotoxin-based paralysis, live mounting techniques and a starvation-tolerant study system has allowed us to obtain the most extensive high-resolution longitudinal recordings of full anterior and posterior regeneration in an invertebrate, and to detect and characterize several cell types undergoing extensive migration during this process. We expect the tetrodotoxin paralysis and time-lapse imaging methods presented here to be broadly useful in studying other animals and of particular value for studying post-embryonic development.

Tunicates: exploring the sea shores and roaming the open ocean. A tribute to Thomas Huxley

This review is a tribute to the remarkable contributions of Thomas Huxley to the biology of tunicates, the likely sister group of vertebrates. In 1851, the great biologist and philosopher published two landmark papers on pelagic tunicates in the Philosophical Transactions of the Royal Society. They were dedicated to the description of the adult anatomy and life cycle of thaliaceans and appendicularians, the pelagic relatives of ascidians. In the first part of this review, we discuss the novel anatomical observations and evolutionary hypotheses made by Huxley, which would have a lasting influence on tunicate biology. We also briefly comment on the more philosophical reflections of Huxley on individuality. In the second part, we stress the originality and relevance of past and future studies of tunicates in the resolution of major biological issues. In particular, we focus on the complex relationship between genotype and phenotype and the phenomenon of developmental system drift. We propose that more than 150 years after Huxley's papers, tunicate embryos are still worth studying in their own right, independently of their evolutionary proximity to vertebrates, as they provide original and crucial insights into the process of animal evolution. Tunicates are still at the forefront of biological research.

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.

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Quantitatively capturing developmental processes is crucial to derive mechanistic models and key to identify and describe mutant phenotypes. Here protocols are presented for preparing embryos and adult C. elegans animals for short- and long-term time-lapse microscopy and methods for tracking and quantification of developmental processes. The methods presented are all based on C. elegans strains available from the Caenorhabditis Genetics Center and on open-source software that can be easily implemented in any laboratory independently of the microscopy system used. A reconstruction of a 3D cell-shape model using the modelling software IMOD, manual tracking of fluorescently-labeled subcellular structures using the multi-purpose image analysis program Endrov, and an analysis of cortical contractile flow using PIVlab (Time-Resolved Digital Particle Image Velocimetry Tool for MATLAB) are shown. It is discussed how these methods can also be deployed to quantitatively capture other developmental processes in different models, e.g., cell tracking and lineage tracing, tracking of vesicle flow.

Efficient processing and analysis of large-scale light-sheet microscopy data

Light-sheet microscopy is a powerful method for imaging the development and function of complex biological systems at high spatiotemporal resolution and over long time scales. Such experiments typically generate terabytes of multidimensional image data, and thus they demand efficient computational solutions for data management, processing and analysis. We present protocols and software to tackle these steps, focusing on the imaging-based study of animal development. Our protocols facilitate (i) high-speed lossless data compression and content-based multiview image fusion optimized for multicore CPU architectures, reducing image data size 30-500-fold; (ii) automated large-scale cell tracking and segmentation; and (iii) visualization, editing and annotation of multiterabyte image data and cell-lineage reconstructions with tens of millions of data points. These software modules are open source. They provide high data throughput using a single computer workstation and are readily applicable to a wide spectrum of biological model systems.