Light-Sheet Microscopy and Its Potential for Understanding Developmental Processes.

The ability to visualize and quantitatively measure dynamic biological processes in vivo and at high spatiotemporal resolution is of fundamental importance to experimental investigations in developmental biology. Light-sheet microscopy is particularly well suited to providing such data, since it offers exceptionally high imaging speed and good spatial resolution while minimizing light-induced damage to the specimen. We review core principles and recent advances in light-sheet microscopy, with a focus on concepts and implementations relevant for applications in developmental biology. We discuss how light-sheet microcopy has helped advance our understanding of developmental processes from single-molecule to whole-organism studies, assess the potential for synergies with other state-of-the-art technologies, and introduce methods for computational image and data analysis. Finally, we explore the future trajectory of light-sheet microscopy, discuss key efforts to disseminate new light-sheet technology, and identify exciting opportunities for further advances.

Expansion Microscopy: Scalable and Convenient Super-Resolution Microscopy.

Expansion microscopy (ExM) is a physical form of magnification that increases the effective resolving power of any microscope. Here, we describe the fundamental principles of ExM, as well as how recently developed ExM variants build upon and apply those principles. We examine applications of ExM in cell and developmental biology for the study of nanoscale structures as well as ExM's potential for scalable mapping of nanoscale structures across large sample volumes. Finally, we explore how the unique anchoring and hydrogel embedding properties enable postexpansion molecular interrogation in a purified chemical environment. ExM promises to play an important role complementary to emerging live-cell imaging techniques, because of its relative ease of adoption and modification and its compatibility with tissue specimens up to at least 200 µm thick.

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.

Perspective.

I am a developmental biologist, but I started off as a civil engineer. I did some research on soil mechanics but decided to change to biology. A friend changed my life when he told me about the mechanics of cell division, on which I did my PhD at Kings College. I then worked on the morphogenesis of the sea urchin embryo and became interested in how embryos are patterned, and I proposed positional information as a basic mechanism. I was a professor at the Middlesex Hospital Medical School, where we concentrated on how the chick limb developed.

Toward a synthesis of developmental biology with evolutionary theory and ecology.

The evolutionary conservation of developmental mechanisms is a truism in biology, but few attempts have been made to integrate development with evolutionary theory and ecology. To work toward such a synthesis, we summarize studies in the nematode model Pristionchus pacificus, focusing on the development of the dauer, a stress-resistant, alternative larval stage. Integrative approaches combining molecular and genetic principles of development with natural variation and ecological studies in wild populations have identified a key role for a developmental switch mechanism in dauer development and evolution, one that involves the nuclear hormone receptor DAF-12. DAF-12 is a crucial regulator and convergence point for different signaling inputs, and its function is conserved among free-living and parasitic nematodes. Furthermore, DAF-12 is the target of regulatory loops that rely on novel or fast-evolving components to control the intraspecific competition of dauer larvae. We propose developmental switches as paradigms for understanding the integration of development, evolution, and ecology at the molecular level.

Open-source, high-throughput targeted in situ transcriptomics for developmental and tissue biology.

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.

Past and future of human developmental 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?

40†years since the Heidelberg genetic screen that revolutionized developmental and cell biology.

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.

Lineage tracing.

Lineage tracing is the identification of all progeny of a single cell. Although its origins date back to developmental biology of invertebrates in the 19(th) century, lineage tracing is now an essential tool for studying stem cell properties in adult mammalian tissues. Lineage tracing provides a powerful means of understanding tissue development, homeostasis, and disease, especially when it is combined with experimental manipulation of signals regulating cell-fate decisions. Recently, the combination of inducible recombinases, multicolor reporter constructs, and live-cell imaging has provided unprecedented insights into stem cell biology. Here we discuss the different experimental strategies currently available for lineage tracing, their associated caveats, and new opportunities to integrate lineage tracing with the monitoring of intracellular signaling pathways.

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.

Pattern, growth, and control.

Systems biology seeks not only to discover the machinery of life but to understand how such machinery is used for control, i.e., for regulation that achieves or maintains a desired, useful end. This sort of goal-directed, engineering-centered approach also has deep historical roots in developmental biology. Not surprisingly, developmental biology is currently enjoying an influx of ideas and methods from systems biology. This Review highlights current efforts to elucidate design principles underlying the engineering objectives of robustness, precision, and scaling as they relate to the developmental control of growth and pattern formation. Examples from vertebrate and invertebrate development are used to illustrate general lessons, including the value of integral feedback in achieving set-point control; the usefulness of self-organizing behavior; the importance of recognizing and appropriately handling noise; and the absence of "free lunch." By illuminating such principles, systems biology is helping to create a functional framework within which to make sense of the mechanistic complexity of organismal development.

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.

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.

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.

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.

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.

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.

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.

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.