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Our current understanding of the molecular basis of embryonic development and the shared machinery underlying this remarkable process has its roots in three papers published 40 years ago, which summarize the results of the Nobel Prize-winning 'Heidelberg screen'. The genesis of these experiments that empowered us and the stories behind the experiments are worth revisiting., Competing Interests: Competing interests The author declares no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)

The discovery of general principles underlying the complexity and diversity of cellular and developmental systems is a central and long-standing aim of biology. While new technologies collect data at an ever-accelerating rate, there is growing concern that conceptual progress is not keeping pace. We contend that this is due to a paucity of conceptual frameworks that support meaningful generalizations. This led us to develop the core and periphery (C&P) hypothesis, which posits that many biological systems can be decomposed into a highly versatile core with a large behavioral repertoire and a specific periphery that configures said core to perform one particular function. Versatile cores tend to be widely reused across biology, which confers generality to theories describing them. Here, we introduce this concept and describe examples at multiple scales, including Turing patterning, actomyosin dynamics, multi-cellular morphogenesis, and vertebrate gastrulation. We also sketch its evolutionary basis and discuss key implications and open questions. We propose that the C&P hypothesis could unlock new avenues of conceptual progress in mesoscale biology., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Gene duplicates, or paralogs, serve as a major source of new genetic material and comprise seeds for evolutionary innovation. While originally thought to be quickly lost or nonfunctionalized following duplication, now a vast number of paralogs are known to be retained in a functional state. Daughter paralogs can provide robustness through redundancy, specialize via sub-functionalization, or neo-functionalize to play new roles. Indeed, the duplication and divergence of developmental genes have played a monumental role in the evolution of animal forms (e.g., Hox genes). Still, despite their prevalence and evolutionary importance, the precise detection of gene duplicates in newly sequenced genomes remains technically challenging and often overlooked. This presents an especially pertinent problem for evolutionary developmental biology, where hypothesis testing requires accurate detection of changes in gene expression and function, often in nontraditional model species. Frequently, these analyses rely on molecular reagents designed within coding sequences that may be highly similar in recently duplicated paralogs, leading to cross-reactivity and spurious results. Thus, care is needed to avoid erroneously assigning diverged functions of paralogs to a single gene, and potentially misinterpreting evolutionary history. This perspective aims to overview the prevalence and importance of paralogs and to shed light on the difficulty of their detection and analysis while offering potential solutions., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.)

Research directly on human embryos has gone through cycles of interest and neglect. The recent revitalization, including the making of 'human developmental biology', depended on fresh supplies of material and demand for medically relevant work. Human studies relied on mice but rejected simple extrapolation from this model mammal. Now, it is time to take stock while scanning the horizon for further change. Will research on human development be facilitated or frustrated? Will comparative approaches bring a greater variety of animal models into the picture? Will human stem-cell-based embryo models secure ever larger roles as exemplars of vertebrate development?, Competing Interests: Competing interests The author declares no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)

Multiplexed spatial profiling of mRNAs has recently gained traction as a tool to explore the cellular diversity and the architecture of tissues. We propose a sensitive, open-source, simple and flexible method for the generation of in situ expression maps of hundreds of genes. We use direct ligation of padlock probes on mRNAs, coupled with rolling circle amplification and hybridization-based in situ combinatorial barcoding, to achieve high detection efficiency, high-throughput and large multiplexing. We validate the method across a number of species and show its use in combination with orthogonal methods such as antibody staining, highlighting its potential value for developmental and tissue biology studies. Finally, we provide an end-to-end computational workflow that covers the steps of probe design, image processing, data extraction, cell segmentation, clustering and annotation of cell types. By enabling easier access to high-throughput spatially resolved transcriptomics, we hope to encourage a diversity of applications and the exploration of a wide range of biological questions., Competing Interests: Competing interests M.N. was scientific advisor for the company 10× Genomics at the time of the initial manuscript submission. H.L., C.M.L., S.M.S. and M.G. are co-founders of spatialist, a data analysis company focused on spatial omics., (© 2024. Published by The Company of Biologists Ltd.)

Delineating developmental events is central to experimental research using early life stages, permitting widespread identification of changes in event timing between species and environments. Yet, identifying developmental events is incredibly challenging, limiting the scale, reproducibility and throughput of using early life stages in experimental biology. We introduce Dev-ResNet, a small and efficient 3D convolutional neural network capable of detecting developmental events characterised by both spatial and temporal features, such as the onset of cardiac function and radula activity. We demonstrate the efficacy of Dev-ResNet using 10 diverse functional events throughout the embryonic development of the great pond snail, Lymnaea stagnalis. Dev-ResNet was highly effective in detecting the onset of all events, including the identification of thermally induced decoupling of event timings. Dev-ResNet has broad applicability given the ubiquity of bioimaging in developmental biology, and the transferability of deep learning, and so we provide comprehensive scripts and documentation for applying Dev-ResNet to different biological systems., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)

Implantation of the human embryo begins a critical developmental stage that comprises profound events including axis formation, gastrulation and the emergence of haematopoietic system 1,2 . Our mechanistic knowledge of this window of human life remains limited due to restricted access to in vivo samples for both technical and ethical reasons 3-5 . Stem cell models of human embryo have emerged to help unlock the mysteries of this stage 6-16 . Here we present a genetically inducible stem cell-derived embryoid model of early post-implantation human embryogenesis that captures the reciprocal codevelopment of embryonic tissue and the extra-embryonic endoderm and mesoderm niche with early haematopoiesis. This model is produced from induced pluripotent stem cells and shows unanticipated self-organizing cellular programmes similar to those that occur in embryogenesis, including the formation of amniotic cavity and bilaminar disc morphologies as well as the generation of an anterior hypoblast pole and posterior domain. The extra-embryonic layer in these embryoids lacks trophoblast and shows advanced multilineage yolk sac tissue-like morphogenesis that harbours a process similar to distinct waves of haematopoiesis, including the emergence of erythroid-, megakaryocyte-, myeloid- and lymphoid-like cells. This model presents an easy-to-use, high-throughput, reproducible and scalable platform to probe multifaceted aspects of human development and blood formation at the early post-implantation stage. It will provide a tractable human-based model for drug testing and disease modelling., (© 2023. The Author(s).)

Understanding the mechanism by which cells coordinate their differentiation and migration is critical to our understanding of many fundamental processes such as wound healing, disease progression, and developmental biology. Mathematical models have been an essential tool for testing and developing our understanding, such as models of cells as soft spherical particles, reaction-diffusion systems that couple cell movement to environmental factors, and multi-scale multi-physics simulations that combine bottom-up rule-based models with continuum laws. However, mathematical models can often be loosely related to data or have so many parameters that model behaviour is weakly constrained. Recent methods in machine learning introduce new means by which models can be derived and deployed. In this review, we discuss examples of mathematical models of aspects of developmental biology, such as cell migration, and how these models can be combined with these recent machine learning methods., Competing Interests: Conflict of interest No conflict of interest., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Technological advances have driven many recent advances in developmental biology. Light sheet imaging can reveal single-cell dynamics in living three-dimensional tissues, whereas single-cell genomic methods open the door to a complete catalogue of cell types and gene expression states. An equally powerful but complementary set of approaches are also becoming available to define development processes from the bottom up. These synthetic approaches aim to reconstruct the minimal developmental patterns, signaling processes, and gene networks that produce the basic set of developmental operations: spatial polarization, morphogen interpretation, tissue movement, and cellular memory. In this review we discuss recent approaches at the intersection of synthetic biology and development, including synthetic circuits to deliver and record signaling stimuli and synthetic reconstitution of pattern formation on multicellular scales., (Copyright © 2022. Published by Elsevier Ltd.)

The Modern Synthesis has received criticism for its purported gene-centrism. That criticism relies on a concept of the gene as a unit of instructional information. In this paper I discuss information concepts and endorse one, developed from Floridi, that sees information as a functional relationship between data and context. I use this concept to inspect developmental criticisms of the Modern Synthesis and argue that the instructional gene arose as an idealization practice when evolutionary biologists made comment on development. However, a closer inspection of key claims shows that at least some associated with the Modern Synthesis were in fact adopting the data led definition I favour and made clear arguments for the role of developmental processes beyond genetic input. There was no instructional gene., (© 2023. The Author(s).)

The application of synthetic biology approaches to study development opens the possibility to build and manipulate developmental processes to understand them better. Researchers have reconstituted fundamental developmental processes, such as cell patterning and sorting, by engineering gene circuits in vitro. Moreover, new tools have been created that allow for the control of developmental processes in more complex organoids and embryos. Synthetic approaches allow testing of which components are sufficient to reproduce a developmental process and under which conditions as well as what effect perturbations have on other processes. We envision that the future of synthetic developmental biology requires an increase in the diversity of available tools and further efforts to combine multiple developmental processes into one system.

Although botanists and horticulturalists often use warm nurseries to increase graft success, little was known about the role of temperature in promoting wound healing and tissue regeneration. Now, a new paper in Development describes the molecular mechanism behind the temperature-dependent enhancement of grafting. We caught up with first authors Phanu Serivichyaswat and Kai Bartusch and corresponding author Charles Melnyk, Assistant Professor at Swedish University of Agricultural Sciences (SLU) in Uppsala, Sweden, to find out more about their research., (© 2022. Published by The Company of Biologists Ltd.)

Human organoids stand at the forefront of basic and translational research, providing experimentally tractable systems to study human development and disease. These stem cell-derived, in vitro cultures can generate a multitude of tissue and organ types, including distinct brain regions and sensory systems. Neural organoid systems have provided fundamental insights into molecular mechanisms governing cell fate specification and neural circuit assembly and serve as promising tools for drug discovery and understanding disease pathogenesis. In this review, we discuss several human neural organoid systems, how they are generated, advances in 3D imaging and bioengineering, and the impact of organoid studies on our understanding of the human nervous system., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Size is a fundamental feature of living entities and is intimately tied to their function. Scaling laws, which can be traced to D'Arcy Thompson and Julian Huxley, have emerged as a powerful tool for studying regulation of the growth dynamics of organisms and their constituent parts. Yet, throughout the 20th century, as scaling laws were established for single cells, quantitative studies of the coordinated growth of multicellular structures have lagged, largely owing to technical challenges associated with imaging and image processing. Here, we present a supervised learning approach for quantifying the growth dynamics of germline cysts during oogenesis. Our analysis uncovers growth patterns induced by the groupwise developmental dynamics among connected cells, and differential growth rates of their organelles. We also identify inter-organelle volumetric scaling laws, finding that nurse cell growth is linear over several orders of magnitude. Our approach leverages the ever-increasing quantity and quality of imaging data, and is readily amenable for studies of collective cell growth in other developmental contexts, including early mammalian embryogenesis and germline development., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2021. Published by The Company of Biologists Ltd.)

Tissue clearing increases the transparency of late developmental stages and enables deep imaging in fixed organisms. Successful implementation of these methodologies requires a good grasp of sample processing, imaging and the possibilities offered by image analysis. In this Primer, we highlight how tissue clearing can revolutionize the histological analysis of developmental processes and we advise on how to implement effective clearing protocols, imaging strategies and analysis methods for developmental biology., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2021. Published by The Company of Biologists Ltd.)

Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2021. Published by The Company of Biologists Ltd.)

In the 1930s, Frits Warmolt Went conducted a number of seminal studies on pea seedlings that had been germinated in the dark and assessed their growth when either the apical parts, cotyledons, or roots were cut off or grafted, to assess whether coplant growth factors assisted auxin in the development of these organs. Went assigned the term "calines" to all auxin-assisting substances, specifically rhizocaline, caulocaline, and phyllocaline in root, shoot (and axillary buds) and leaf development, respectively. Those experiments were based exclusively on growth assays, and no supplementary biochemical or physiological analyses were ever conducted, and additional proof was only provided by Went using pea or tomato. The lack of independent reproducibility by other groups, combined with the fact that the hormonal control of these developmental events in plants is now fairly well-studied event, even at the molecular level, suggests that these growth factors that Went observed 80 years ago either do not exist or are known by some other term in modern plant development. The terms related to "calines" should thus no longer be used in plant developmental biology., (© 2021. Akadémiai Kiadó Zrt.)

The Human Developmental Cell Atlas (HDCA) initiative, which is part of the Human Cell Atlas, aims to create a comprehensive reference map of cells during development. This will be critical to understanding normal organogenesis, the effect of mutations, environmental factors and infectious agents on human development, congenital and childhood disorders, and the cellular basis of ageing, cancer and regenerative medicine. Here we outline the HDCA initiative and the challenges of mapping and modelling human development using state-of-the-art technologies to create a reference atlas across gestation. Similar to the Human Genome Project, the HDCA will integrate the output from a growing community of scientists who are mapping human development into a unified atlas. We describe the early milestones that have been achieved and the use of human stem-cell-derived cultures, organoids and animal models to inform the HDCA, especially for prenatal tissues that are hard to acquire. Finally, we provide a roadmap towards a complete atlas of human development., (© 2021. Springer Nature Limited.)

In the three decades since its inception, the sex-sorting technology has progressed significantly. However, field studies report conflicting findings regarding reproductive outcomes. Therefore, we conducted this meta-analysis of all trials published between 1999 and 2021. Non-return rates after 24 or 60 d (NRR 24/60), pregnancy, calving, abortion, and stillbirth rates were compared after AI with sex-sorted vs non-sorted sperm. Additionally, the impact of recent developments in the sex-sorting technology was assessed. Of 860 studies found, 45 studies (72 trials) provided extractable data and were included. Overall, the results of this meta-analysis provided evidence that the NRR 24/60 was diminished by 13%, pregnancy rates were reduced by 23% (25% cows, 21% heifers) and calving rates were reduced by 24% when using sex-sorted sperm. Enhancing the dosage to 4 million sex-sorted sperm/straw (including recent improvements, high vs low dose) as well as using fresh sex-sorted sperm (sorted vs non-sorted) increased pregnancy rate ratios by 7 percentage points. The refinement of the sex-sorting technology after 2015 resulted in a lowered reduction of pregnancy and calving rate of 19% and 23%, respectively. Whereas abortion rates were similar, the stillbirth of male calves was increased by 6.3%., (© 2021. The Author(s).)

Micropatterning encompasses a set of methods aimed at precisely controlling the spatial distribution of molecules onto the surface of materials. Biologists have borrowed the idea and adapted these methods, originally developed for electronics, to impose physical constraints on biological systems with the aim of addressing fundamental questions across biological scales from molecules to multicellular systems. Here, I approach this topic from a developmental biologist's perspective focusing specifically on how and why micropatterning has gained in popularity within the developmental biology community in recent years. Overall, this Primer provides a concise overview of how micropatterns are used to study developmental processes and emphasises how micropatterns are a useful addition to the developmental biologist's toolbox., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2021. Published by The Company of Biologists Ltd.)

Identification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio. The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets.

Morphological scaling relationships, or allometries, describe how traits grow coordinately and covary among individuals in a population. The developmental regulation of scaling is essential to generate correctly proportioned adults across a range of body sizes, while the mis-regulation of scaling may result in congenital birth defects. Research over several decades has identified the developmental mechanisms that regulate the size of individual traits. Nevertheless, we still have poor understanding of how these mechanisms work together to generate correlated size variation among traits in response to environmental and genetic variation. Conceptually, morphological scaling can be generated by size-regulatory factors that act directly on multiple growing traits (trait-autonomous scaling), or indirectly via hormones produced by central endocrine organs (systemically regulated scaling), and there are a number of well-established examples of such mechanisms. There is much less evidence, however, that genetic and environmental variation actually acts on these mechanisms to generate morphological scaling in natural populations. More recent studies indicate that growing organs can themselves regulate the growth of other organs in the body. This suggests that covariation in trait size can be generated by network-regulated scaling mechanisms that respond to changes in the growth of individual traits. Testing this hypothesis, and one of the main challenges of understanding morphological scaling, requires connecting mechanisms elucidated in the laboratory with patterns of scaling observed in the natural world. This article is categorized under: Establishment of Spatial and Temporal Patterns > Regulation of Size, Proportion, and Timing Comparative Development and Evolution > Organ System Comparisons Between Species., (© 2020 Wiley Periodicals LLC.)

How does morphological complexity evolve? This study suggests that the likelihood of mutations increasing phenotypic complexity becomes smaller when the phenotype itself is complex. In addition, the complexity of the genotype-phenotype map (GPM) also increases with the phenotypic complexity. We show that complex GPMs and the above mutational asymmetry are inevitable consequences of how genes need to be wired in order to build complex and robust phenotypes during development. We randomly wired genes and cell behaviors into networks in EmbryoMaker. EmbryoMaker is a mathematical model of development that can simulate any gene network, all animal cell behaviors (division, adhesion, apoptosis, etc.), cell signaling, cell and tissues biophysics, and the regulation of those behaviors by gene products. Through EmbryoMaker we simulated how each random network regulates development and the resulting morphology (i.e. a specific distribution of cells and gene expression in 3D). This way we obtained a zoo of possible 3D morphologies. Real gene networks are not random, but a random search allows a relatively unbiased exploration of what is needed to develop complex robust morphologies. Compared to the networks leading to simple morphologies, the networks leading to complex morphologies have the following in common: 1) They are rarer; 2) They need to be finely tuned; 3) Mutations in them tend to decrease morphological complexity; 4) They are less robust to noise; and 5) They have more complex GPMs. These results imply that, when complexity evolves, it does so at a progressively decreasing rate over generations. This is because as morphological complexity increases, the likelihood of mutations increasing complexity decreases, morphologies become less robust to noise, and the GPM becomes more complex. We find some properties in common, but also some important differences, with non-developmental GPM models (e.g. RNA, protein and gene networks in single cells)., Competing Interests: The authors have declared that no competing interests exist.

Biomineralization is the process by which organisms use minerals to harden their tissues and provide them with physical support. Biomineralizing cells concentrate the mineral in vesicles that they secret into a dedicated compartment where crystallization occurs. The dynamics of vesicle motion and the molecular mechanisms that control it, are not well understood. Sea urchin larval skeletogenesis provides an excellent platform for investigating the kinetics of mineral-bearing vesicles. Here we used lattice light-sheet microscopy to study the three-dimensional (3D) dynamics of calcium-bearing vesicles in the cells of normal sea urchin embryos and of embryos where skeletogenesis is blocked through the inhibition of Vascular Endothelial Growth Factor Receptor (VEGFR). We developed computational tools for displaying 3D-volumetric movies and for automatically quantifying vesicle dynamics. Our findings imply that calcium vesicles perform an active diffusion motion in both, calcifying (skeletogenic) and non-calcifying (ectodermal) cells of the embryo. The diffusion coefficient and vesicle speed are larger in the mesenchymal skeletogenic cells compared to the epithelial ectodermal cells. These differences are possibly due to the distinct mechanical properties of the two tissues, demonstrated by the enhanced f-actin accumulation and myosinII activity in the ectodermal cells compared to the skeletogenic cells. Vesicle motion is not directed toward the biomineralization compartment, but the vesicles slow down when they approach it, and probably bind for mineral deposition. VEGFR inhibition leads to an increase of vesicle volume but hardly changes vesicle kinetics and doesn't affect f-actin accumulation and myosinII activity. Thus, calcium vesicles perform an active diffusion motion in the cells of the sea urchin embryo, with diffusion length and speed that inversely correlate with the strength of the actomyosin network. Overall, our studies provide an unprecedented view of calcium vesicle 3D-dynamics and point toward cytoskeleton remodeling as an important effector of the motion of mineral-bearing vesicles., Competing Interests: The authors have declared that no competing interests exist.

Multicellular entities are characterized by intricate spatial patterns, intimately related to the functions they perform. These patterns are often created from isotropic embryonic structures, without external information cues guiding the symmetry breaking process. Mature biological structures also display characteristic scales with repeating distributions of signals or chemical species across space. Many candidate patterning modules have been used to explain processes during development and typically include a set of interacting and diffusing chemicals or agents known as morphogens . Great effort has been put forward to better understand the conditions in which pattern-forming processes can occur in the biological domain. However, evidence and practical knowledge allowing us to engineer symmetry-breaking is still lacking. Here we follow a different approach by designing a synthetic gene circuit in E. coli that implements a local activation long-range inhibition mechanism. The synthetic gene network implements an artificial differentiation process that changes the physicochemical properties of the agents. Using both experimental results and modeling, we show that the proposed system is capable of symmetry-breaking leading to regular spatial patterns during colony growth. Studying how these patterns emerge is fundamental to further our understanding of the evolution of biocomplexity and the role played by self-organization. The artificial system studied here and the engineering perspective on embryogenic processes can help validate developmental theories and identify universal properties underpinning biological pattern formation, with special interest for the area of synthetic developmental biology.

The failure of adult cardiomyocytes to reproduce themselves to repair an injury results in the development of severe cardiac disability leading to death in many cases. The quest for an understanding of the inability of cardiac myocytes to repair an injury has been ongoing for decades with the identification of various factors which have a temporary effect on cell-cycle activity. Fetal cardiac myocytes are continuously replicating until the time that the developing fetus reaches a stage of maturity sufficient for postnatal life around the time of birth. Recent reports of the ability for early neonatal mice and pigs to completely repair after the severe injury has stimulated further study of the regulators of the cardiomyocyte cell cycle to promote replication for the remuscularization of injured heart. In all mammals just before or after birth, single-nucleated hyperplastically growing cardiomyocytes, 1X2N, undergo ≥1 additional DNA replications not followed by cytokinesis, resulting in cells with ≥2 nuclei or as in primates, multiple DNA replications (polyploidy) of 1 nucleus, 2X2(+)N or 1X4(+)N. All further growth of the heart is attributable to hypertrophy of cardiomyocytes. Animal studies ranging from zebrafish with 100% 1X2N cells in the adult to some strains of mice with up to 98% 2X2N cells in the adult and other species with variable ratios of 1X2N and 2X2N cells are reviewed relative to the time of conversion. Various structural, physiologic, metabolic, genetic, hormonal, oxygenation, and other factors that play a key role in the inability of post-neonatal and adult myocytes to undergo additional cytokinesis are also reviewed.

In developmental biology as well as in other biological systems, emerging structure and organization can be captured using time-series data of protein locations. In analyzing this time-dependent data, it is a common challenge not only to determine whether topological features emerge, but also to identify the timing of their formation. For instance, in most cells, actin filaments interact with myosin motor proteins and organize into polymer networks and higher-order structures. Ring channels are examples of such structures that maintain constant diameters over time and play key roles in processes such as cell division, development, and wound healing. Given the limitations in studying interactions of actin with myosin in vivo, we generate time-series data of protein polymer interactions in cells using complex agent-based models. Since the data has a filamentous structure, we propose sampling along the actin filaments and analyzing the topological structure of the resulting point cloud at each time. Building on existing tools from persistent homology, we develop a topological data analysis (TDA) method that assesses effective ring generation in this dynamic data. This method connects topological features through time in a path that corresponds to emergence of organization in the data. In this work, we also propose methods for assessing whether the topological features of interest are significant and thus whether they contribute to the formation of an emerging hole (ring channel) in the simulated protein interactions. In particular, we use the MEDYAN simulation platform to show that this technique can distinguish between the actin cytoskeleton organization resulting from distinct motor protein binding parameters.

Selection for desirable traits and breed-specific phenotypes has left distinctive footprints in the genome of pigs. As representative of a breed with strong selective traces aiming for robustness, health and performance, the Mangalitza pig, a native curly-haired pig breed from Hungary, was investigated in this study. Whole genome sequencing and SNP chip genotyping was performed to detect runs of homozygosity (ROH) in Mangalitza and Mangalitza-crossbreeds. We identified breed specific ROH regions harboring genes associated with the development of the curly hair type and further characteristics of this breed. Further analysis of two matings of Mangalitza with straight-coated pig breeds confirmed an autosomal dominant inheritance of curly hair. Subsequent scanning of the genome for variant effects on this trait revealed two variants potentially affecting hair follicle development and differentiation. Validation in a large sample set as well as in imputed SNP data confirmed these variants to be Mangalitza-specific. Herein, we demonstrated how strong artificial selection has shaped the genome in Mangalitza pigs and left traces in the form of selection signatures. This knowledge on genomic variation promoting unique phenotypes like curly hair provides an important resource for futures studies unraveling genetic effects for special characteristics in livestock.

The celebrated 1917 work "On Growth and Form" of D'Arcy W. Thompson has established a landmark for mathematical biology, introducing new perspectives of study and research in biology, providing mathematical methods to morphology of biological systems. In this brief historical essay, we recall the novelties and relevance of the work from a retrospective stance, above all pointing out the crucial role played by it in the dawning of epigenetic standpoint. The role of underlying epigenetic processes in generation of biological forms via similarity transformations is analyzed within the framework of D'Arcy Thompson. The significance of D'Arcy Thompson as a predecessor of the relational biology and of the epigenetic concepts of evolution is discussed., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Living systems are subject to the arrow of time; from birth, they undergo complex transformations (self-organization) in a constant battle for survival, but inevitably ageing and disease trap them to death. Can ageing be understood and eventually reversed? What tools can be employed to further our understanding of ageing? The present article is an invitation for biologists and clinicians to consider key conceptual ideas and computational tools (known to mathematicians and physicists), which potentially may help dissect some of the underlying processes of ageing and disease. Specifically, we first discuss how to classify and analyse complex systems, as well as highlight critical theoretical difficulties that make complex systems hard to study. Subsequently, we introduce Topological Data Analysis - a novel Big Data tool - which may help in the study of complex systems since it extracts knowledge from data in a holistic approach via topological considerations. These conceptual ideas and tools are discussed in a relatively informal way to pave future discussions and collaborations between mathematicians and biologists studying ageing., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The zebrafish Danio rerio is a valuable and common model for scientists in the fields of genetics and developmental biology. Since zebrafish are also amenable to genetic manipulation, modelling of human diseases or behavioral experiments have moved into the focus of zebrafish research. Consequently, gene expression data beyond embryonic and larval stages become more important, yet there is a dramatic knowledge gap of gene expression beyond day four of development. Like in other model organisms, the visualization of spatial and temporal gene expression by whole mount in situ hybridization (ISH) becomes increasingly difficult when zebrafish embryos develop further and hence the growing tissues become dense and less permeable. Here we introduce a modified method for whole mount ISH, which overcomes these penetration and detection problem. The method is an all in one solution that enables the detection and visualization of gene expression patterns up to the late larval stage in a 3D manner without the need for tissue sectioning and offers a valuable extension for whole mount ISH by immunohistochemistry in the zebrafish field., Competing Interests: The authors have declared that no competing interests exist.

Although apoptosis has been widely observed during the regenerative process, the mechanisms by which it is regulated and its roles in regeneration remained unclear. In this study, we introduced Aeolosoma viride, a fresh water annelid with an extraordinary regenerative ability as our model organism to study the functions and regulations of apoptotic caspases. Here we showed that major events of apoptosis were detected near the wounded area and showed spatial correlation with the expression patterns of caspase gene namely Avi-caspase X and two apoptosis regulators namely Avi-Bax and Avi-Bcl-xL. Next, we investigated how Avi-caspase X gene expression and apoptosis influence regeneration following head amputation. RNA interference of Avi-caspase X reduced the amounts of apoptotic cells, as well as the percentage of successful regeneration, suggesting a critical role for apoptosis in anterior regeneration of A. viride. In addition, we also discovered that the expression of apoptotic caspases was regulated by the canonical Wnt signaling pathway. Together, our study showed that caspase dependent apoptosis was critical to the anterior regeneration of A. viride, and could be regulated by the canonical Wnt signaling pathway.

Shape is data and data is shape. Biologists are accustomed to thinking about how the shape of biomolecules, cells, tissues, and organisms arise from the effects of genetics, development, and the environment. Less often do we consider that data itself has shape and structure, or that it is possible to measure the shape of data and analyze it. Here, we review applications of topological data analysis (TDA) to biology in a way accessible to biologists and applied mathematicians alike. TDA uses principles from algebraic topology to comprehensively measure shape in data sets. Using a function that relates the similarity of data points to each other, we can monitor the evolution of topological features-connected components, loops, and voids. This evolution, a topological signature, concisely summarizes large, complex data sets. We first provide a TDA primer for biologists before exploring the use of TDA across biological sub-disciplines, spanning structural biology, molecular biology, evolution, and development. We end by comparing and contrasting different TDA approaches and the potential for their use in biology. The vision of TDA, that data are shape and shape is data, will be relevant as biology transitions into a data-driven era where the meaningful interpretation of large data sets is a limiting factor., (© 2020 The Authors. Developmental Dynamics published by Wiley Periodicals, Inc. on behalf of American Association of Anatomists.)

Size is a fundamental feature of biology that affects physiology at all levels, from the organism to organs and tissues to cells and subcellular structures. How size is determined at these different levels, and how biological structures scale to fit together and function properly are important open questions. Historically, amphibian systems have been extremely valuable to describe scaling phenomena, as they occupy some of the extremes in biological size and are amenable to manipulations that alter genome and cell size. More recently, the application of biochemical, biophysical, and embryological techniques to amphibians has provided insight into the molecular mechanisms underlying scaling of subcellular structures to cell size, as well as how perturbation of normal size scaling impacts other aspects of cell and organism physiology., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Implantation represents a critical step for embryonic development and pregnancy. Its success depends on the complex interplay between a receptive endometrium and a competent embryo. Implantation-related events remain hardly accessible, making implantation a true "black box" in developmental biology. Improved in vitro models are becoming useful experimental tools, as they are considerably more accessible than in vivo models, easier to manipulate, and permit the use of human cells or tissues, thus increasing the translational value of the studies. In this Review, we briefly summarize the relevant cell types and structures involved into the process of implantation, in order to outline which compartments are indispensable for creating the perfect in vitro model. We also critically address advantages and limitations of available models and assess their application potential. Moreover, we examine the chances and challenges brought by the latest approaches to recapitulate the endometrial compartment, as well as by peri-implantational embryoids., Competing Interests: Declaration of competing interest None., (Copyright © 2020 Elsevier Inc. All rights reserved.)

In this article I critically evaluate the thesis that DNA is an ontologically distinctive developmental cause. I shall critically analyse different versions of the latter thesis by taking into consideration concrete developmental cases. I shall argue that DNA is neither a developmental determinant nor an ontologically distinctive developmental cause. Instead, I shall argue that mechanistic analysis shows that DNA's causal role in development depends on the higher robustness of the developmental processes in which it exerts its causal capacities. The focus on process and developmental system implies a metaphysical shift: rather than attributing to DNA molecules biochemically unique properties, I suggest that it might be better to think about DNA's causal role in development in terms of the causal capacities that DNA molecules manifest in a rich developmental milieu. I shall also suggest that my position is distinct both from the view advocating the instrumental primacy of DNA-centric biology and developmental constructionism. It is different from the former because it provides a substantial answer to the question of what makes DNA causally central in developmental processes. Finally, I argue that evolutionary considerations pose an important challenge to developmental constructionism., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Nonhuman primate (NHP) studies are crucial to biomedical research. NHPs are the species most similar to humans in lifespan, body size, and hormonal profiles. Planning research requires statistical power evaluation, which is difficult to perform when lacking directly relevant preliminary data. This is especially true for NHP developmental programming studies, which are scarce. We review the sample sizes reported, challenges, areas needing further work, and goals of NHP maternal nutritional programming studies. The literature search included 27 keywords, for example, maternal obesity, intrauterine growth restriction, maternal high-fat diet, and maternal nutrient reduction. Only fetal and postnatal offspring studies involving tissue collection or imaging were included. Twenty-eight studies investigated maternal over-nutrition and 33 under-nutrition; 23 involved macaques and 38 baboons. Analysis by sex was performed in 19; minimum group size ranged from 1 to 8 (mean 4.7 ± 0.52, median 4, mode 3) and maximum group size from 3 to 16 (8.3 ± 0.93, 8, 8). Sexes were pooled in 42 studies; minimum group size ranged from 2 to 16 (mean 5.3 ± 0.35, median 6, mode 6) and maximum group size from 4 to 26 (10.2 ± 0.92, 8, 8). A typical study with sex-based analyses had group size minimum 4 and maximum 8 per sex. Among studies with sexes pooled, minimum group size averaged 6 and maximum 8. All studies reported some significant differences between groups. Therefore, studies with group sizes 3-8 can detect significance between groups. To address deficiencies in the literature, goals include increasing age range, more frequently considering sex as a biological variable, expanding topics, replicating studies, exploring intergenerational effects, and examining interventions.

In February 2020, the European Molecular Biology Laboratory (EMBL) and the Institute for Bioengineering of Catalonia (IBEC) joined forces to unite researchers from all over the globe to discuss emerging topics in 'Engineering Multicellular Systems'. As we review here, key themes that arose throughout the meeting included the ethics of organoids in developmental biology, bottom-up versus top-down models, tissue organizing principles, and the future of improving these systems to better mimic the natural world., Competing Interests: Competing interestsDr Freedman is an inventor on patent applications related to kidney organoids and is an advisor for Chinook Therapeutics. The authors declare no other competing or financial interests., (© 2020. Published by The Company of Biologists Ltd.)

Cell differentiation in multicellular organisms requires cells to respond to complex combinations of extracellular cues, such as morphogen concentrations. Some models of phenotypic plasticity conceptualise the response as a relatively simple function of a single environmental cues (e.g. a linear function of one cue), which facilitates rigorous analysis. Conversely, more mechanistic models such those implementing GRNs allows for a more general class of response functions but makes analysis more difficult. Therefore, a general theory describing how cells integrate multi-dimensional signals is lacking. In this work, we propose a theoretical framework for understanding the relationships between environmental cues (inputs) and phenotypic responses (outputs) underlying cell plasticity. We describe the relationship between environment and cell phenotype using logical functions, making the evolution of cell plasticity equivalent to a simple categorisation learning task. This abstraction allows us to apply principles derived from learning theory to understand the evolution of multi-dimensional plasticity. Our results show that natural selection is capable of discovering adaptive forms of cell plasticity associated with complex logical functions. However, developmental dynamics cause simpler functions to evolve more readily than complex ones. By using conceptual tools derived from learning theory we show that this developmental bias can be interpreted as a learning bias in the acquisition of plasticity functions. Because of that bias, the evolution of plasticity enables cells, under some circumstances, to display appropriate plastic responses to environmental conditions that they have not experienced in their evolutionary past. This is possible when the selective environment mirrors the bias of the developmental dynamics favouring the acquisition of simple plasticity functions-an example of the necessary conditions for generalisation in learning systems. These results illustrate the functional parallelisms between learning in neural networks and the action of natural selection on environmentally sensitive gene regulatory networks. This offers a theoretical framework for the evolution of plastic responses that integrate information from multiple cues, a phenomenon that underpins the evolution of multicellularity and developmental robustness., Competing Interests: The authors have declared that no competing interests exist.

Fifty years ago, Lewis Wolpert defined an important question in developmental biology: how are cell fates determined by the positions of cells within a system? He proposed that cells retain positional values as if they lie within a coordinate system and that the interpretation of these values produces patterns in development. He referred to this concept as positional information. Though initially controversial, this concept of positional information has proven to be profoundly influential in developmental biology. One area in which the influence of Wolpert's theoretical work can be clearly demonstrated is the study of limb regeneration in salamanders. Here, we review the work in limb regeneration leading up to Wolpert defining the concept of positional information and how his theory has guided regeneration research over the subsequent 50 years., (© 2020 Wiley Periodicals, Inc.)

Embryonic development is a largely self-organizing process, in which the adult body plan arises from a ball of cells with initially nearly equal potency. The reaction-diffusion theory first proposed by Alan Turing states that the initial symmetry in embryos can be broken by the interplay between two diffusible molecules, whose interactions lead to the formation of patterns. The reaction-diffusion theory provides a valuable framework for self-organized pattern formation, but it has been difficult to relate simple two-component models to real biological systems with multiple interacting molecular species. Recent studies have addressed this shortcoming and extended the reaction-diffusion theory to realistic multi-component networks. These efforts have challenged the generality of previous central tenets derived from the analysis of simplified systems and guide the way to a new understanding of self-organizing processes. Here, we discuss the challenges in modeling multi-component reaction-diffusion systems and how these have recently been addressed. We present a synthesis of new pattern formation mechanisms derived from these analyses, and we highlight the significance of reaction-diffusion principles for developmental and synthetic pattern formation., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Mosaic analysis provides a means to probe developmental processes in situ by generating loss-of-function mutants within otherwise wildtype tissues. Combining these techniques with quantitative microscopy enables researchers to rigorously compare RNA or protein expression across the resultant clones. However, visual inspection of mosaic tissues remains common in the literature because quantification demands considerable labor and computational expertise. Practitioners must segment cell membranes or cell nuclei from a tissue and annotate the clones before their data are suitable for analysis. Here, we introduce Fly-QMA, a computational framework that automates each of these tasks for confocal microscopy images of Drosophila imaginal discs. The framework includes an unsupervised annotation algorithm that incorporates spatial context to inform the genetic identity of each cell. We use a combination of real and synthetic validation data to survey the performance of the annotation algorithm across a broad range of conditions. By contributing our framework to the open-source software ecosystem, we aim to contribute to the current move toward automated quantitative analysis among developmental biologists., Competing Interests: The authors have declared that no competing interests exist.