Dev-ResNet: automated developmental event detection using deep learning.

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.

Modelling post-implantation human development to yolk sac blood emergence.

Implantation of the human embryo begins a critical developmental stage that comprises profound events including axis formation, gastrulation and the emergence of haematopoietic system1,2. Our mechanistic knowledge of this window of human life remains limited due to restricted access to in vivo samples for both technical and ethical reasons3-5. Stem cell models of human embryo have emerged to help unlock the mysteries of this stage6-16. 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.

The Role of Information in Evolutionary Biology.

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.

Generative models of morphogenesis in developmental biology.

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.

Synthetic developmental biology: New tools to deconstruct and rebuild developmental systems.

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.

Scaling up complexity in synthetic developmental biology.

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.

Imaging Parhyale hawaiensis embryogenesis with frequency domain photoacoustic microscopy: A novel tool in developmental biology.

We present the application of a low-cost frequency domain photoacoustic (FDPA) microscope for the label-free imaging of live developing embryos of the crustacean model organism Parhyale hawaiensis. By modulating the intensity of a continuous wave laser source at 9.5 MHz, we achieve the excitation of monochromatic PA waves, which are detected to provide amplitude and phase recordings. The data are subsequently processed to generate accurate maximum amplitude projection and surface reconstructions, delineating the morphological features of the embryos with high resolution and contrast. The findings of this study pave the way for the broader adoption of inexpensive PA diagnostic techniques in developmental biology, shedding light on various fundamental processes in established and emerging model organisms.

The people behind the papers - Phanu Serivichyaswat, Kai Bartusch and Charles Melnyk.

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.

Tissue clearing and 3D imaging in developmental biology.

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.

The term "caline" in plant developmental biology.

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.

Deep learning for bioimage analysis in developmental biology.

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.

A roadmap for the Human Developmental Cell Atlas.

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.

The reproductive success of bovine sperm after sex-sorting: a meta-analysis.

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%.

Quantitative developmental biology in vitro using micropatterning.

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.

Size scaling in collective cell growth.

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.

Human neural organoids: Models for developmental neurobiology and disease.

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.

Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification.

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.