Nuclear (Bio)physics in the Embryo.

Deneke et al. (2019) discover that dynamic interactions of cell cycle and actomyosin contractility systems synchronize nuclear cleavages, generating a cytoplasmic flow that results in a spatially uniform distribution of zygotic nuclei in the early Drosophila embryo. This work underscores the importance of self-organizing mechanisms before the onset of zygotic transcription.

Nuclear instance segmentation and tracking for preimplantation mouse embryos.

For investigations into fate specification and morphogenesis in time-lapse images of preimplantation embryos, automated 3D instance segmentation and tracking of nuclei are invaluable. Low signal-to-noise ratio, high voxel anisotropy, high nuclear density, and variable nuclear shapes can limit the performance of segmentation methods, while tracking is complicated by cell divisions, low frame rates, and sample movements. Supervised machine learning approaches can radically improve segmentation accuracy and enable easier tracking, but they often require large amounts of annotated 3D data. Here, we first report a previously unreported mouse line expressing near-infrared nuclear reporter H2B-miRFP720. We then generate a dataset (termed BlastoSPIM) of 3D images of H2B-miRFP720-expressing embryos with ground truth for nuclear instances. Using BlastoSPIM, we benchmark seven convolutional neural networks and identify Stardist-3D as the most accurate instance segmentation method. With our BlastoSPIM-trained Stardist-3D models, we construct a complete pipeline for nuclear instance segmentation and lineage tracking from the eight-cell stage to the end of preimplantation development (>100 nuclei). Finally, we demonstrate the usefulness of BlastoSPIM as pre-train data for related problems, both for a different imaging modality and for different model systems.

Minimal motifs for habituating systems.

Habituation-a phenomenon in which a dynamical system exhibits a diminishing response to repeated stimulations that eventually recovers when the stimulus is withheld-is universally observed in living systems from animals to unicellular organisms. Despite its prevalence, generic mechanisms for this fundamental form of learning remain poorly defined. Drawing inspiration from prior work on systems that respond adaptively to step inputs, we study habituation from a nonlinear dynamics perspective. This approach enables us to formalize classical hallmarks of habituation that have been experimentally identified in diverse organisms and stimulus scenarios. We use this framework to investigate distinct dynamical circuits capable of habituation. In particular, we show that driven linear dynamics of a memory variable with static nonlinearities acting at the input and output can implement numerous hallmarks in a mathematically interpretable manner. This work establishes a foundation for understanding the dynamical substrates of this primitive learning behavior and offers a blueprint for the identification of habituating circuits in biological systems.

Cytoplasmic stirring by active carpets.

Large cells often rely on cytoplasmic flows for intracellular transport, maintaining homeostasis, and positioning cellular components. Understanding the mechanisms of these flows is essential for gaining insights into cell function, developmental processes, and evolutionary adaptability. Here, we focus on a class of self-organized cytoplasmic stirring mechanisms that result from fluid-structure interactions between cytoskeletal elements at the cell cortex. Drawing inspiration from streaming flows in late-stage fruit fly oocytes, we propose an analytically tractable active carpet theory. This model deciphers the origins and three-dimensional spatiotemporal organization of such flows. Through a combination of simulations and weakly nonlinear theory, we establish the pathway of the streaming flow to its global attractor: a cell-spanning vortical twister. Our study reveals the inherent symmetries of this emergent flow, its low-dimensional structure, and illustrates how complex fluid-structure interaction aligns with classical solutions in Stokes flow. This framework can be easily adapted to elucidate a broad spectrum of self-organized, cortex-driven intracellular flows.

The Drosophila tracheal terminal cell as a model for branching morphogenesis.

The terminal cells of the Drosophila larval tracheal system are perhaps the simplest delivery networks, providing an analogue for mammalian vascular growth and function in a system with many fewer components. These cells are a prime example of single-cell morphogenesis, branching significantly over time to adapt to the needs of the growing tissue they supply. While the genetic mechanisms governing local branching decisions have been studied extensively, an understanding of the emergence of a global network architecture is still lacking. Mapping out the full network architecture of populations of terminal cells at different developmental times of Drosophila larvae, we find that cell growth follows scaling laws relating the total edge length, supply area, and branch density. Using time-lapse imaging of individual terminal cells, we identify that the cells grow in three ways: by extending branches, by the side budding of new branches, and by internally growing existing branches. A generative model based on these modes of growth recapitulates statistical properties of the terminal cell network data. These results suggest that the scaling laws arise from the coupled contributions of branching and internal growth. This study establishes the terminal cell as a uniquely tractable model system for further studies of transportation and distribution networks.

A model of replicating coupled oscillators generates naturally occurring cell networks.

When a founder cell and its progeny divide with incomplete cytokinesis, a network forms in which each intercellular bridge corresponds to a past mitotic event. Such networks are required for gamete production in many animals, and different species have evolved diverse final network topologies. Although mechanisms regulating network assembly have been identified in particular organisms, we lack a quantitative framework to understand network assembly and inter-species variability. Motivated by cell networks responsible for oocyte production in invertebrates, where the final topology is typically invariant within each species, we devised a mathematical model for generating cell networks, in which each node is an oscillator and, after a full cycle, the node produces a daughter to which it remains connected. These cell cycle oscillations are transient and coupled via diffusion over the edges of the network. By variation of three biologically motivated parameters, our model generates nearly all such networks currently reported across invertebrates. Furthermore, small parameter variations can rationalize cases of intra-species variation. Because cell networks outside of the ovary often form less deterministically, we propose model generalizations to account for sources of stochasticity.

Dynamics of an incoherent feedforward loop drive ERK-dependent pattern formation in the early Drosophila embryo.

Positional information in development often manifests as stripes of gene expression, but how stripes form remains incompletely understood. Here, we use optogenetics and live-cell biosensors to investigate the posterior brachyenteron (byn) stripe in early Drosophila embryos. This stripe depends on interpretation of an upstream ERK activity gradient and the expression of two target genes, tailless (tll) and huckebein (hkb), that exert antagonistic control over byn. We find that high or low doses of ERK signaling produce transient or sustained byn expression, respectively. Although tll transcription is always rapidly induced, hkb converts graded ERK inputs into a variable time delay. Nuclei thus interpret ERK amplitude through the relative timing of tll and hkb transcription. Antagonistic regulatory paths acting on different timescales are hallmarks of an incoherent feedforward loop, which is sufficient to explain byn dynamics and adds temporal complexity to the steady-state model of byn stripe formation. We further show that 'blurring' of an all-or-none stimulus through intracellular diffusion non-locally produces a byn stripe. Overall, we provide a blueprint for using optogenetics to dissect developmental signal interpretation in space and time.

Publisher Correction: Mechanics of a multilayer epithelium instruct tumour architecture and function.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mechanics of a multilayer epithelium instruct tumour architecture and function.

Loss of normal tissue architecture is a hallmark of oncogenic transformation1. In developing organisms, tissues architectures are sculpted by mechanical forces during morphogenesis2. However, the origins and consequences of tissue architecture during tumorigenesis remain elusive. In skin, premalignant basal cell carcinomas form 'buds', while invasive squamous cell carcinomas initiate as 'folds'. Here, using computational modelling, genetic manipulations and biophysical measurements, we identify the biophysical underpinnings and biological consequences of these tumour architectures. Cell proliferation and actomyosin contractility dominate tissue architectures in monolayer, but not multilayer, epithelia. In stratified epidermis, meanwhile, softening and enhanced remodelling of the basement membrane promote tumour budding, while stiffening of the basement membrane promotes folding. Additional key forces stem from the stratification and differentiation of progenitor cells. Tumour-specific suprabasal stiffness gradients are generated as oncogenic lesions progress towards malignancy, which we computationally predict will alter extensile tensions on the tumour basement membrane. The pathophysiologic ramifications of this prediction are profound. Genetically decreasing the stiffness of basement membranes increases membrane tensions in silico and potentiates the progression of invasive squamous cell carcinomas in vivo. Our findings suggest that mechanical forces-exerted from above and below progenitors of multilayered epithelia-function to shape premalignant tumour architectures and influence tumour progression.

Evolutionary history of MEK1 illuminates the nature of deleterious mutations.

Mutations in signal transduction pathways lead to various diseases including cancers. MEK1 kinase, encoded by the human MAP2K1 gene, is one of the central components of the MAPK pathway and more than a hundred somatic mutations in the MAP2K1 gene were identified in various tumors. Germline mutations deregulating MEK1 also lead to congenital abnormalities, such as the cardiofaciocutaneous syndrome and arteriovenous malformation. Evaluating variants associated with a disease is a challenge, and computational genomic approaches aid in this process. Establishing evolutionary history of a gene improves computational prediction of disease-causing mutations; however, the evolutionary history of MEK1 is not well understood. Here, by revealing a precise evolutionary history of MEK1, we construct a well-defined dataset of MEK1 metazoan orthologs, which provides sufficient depth to distinguish between conserved and variable amino acid positions. We matched known and predicted disease-causing and benign mutations to evolutionary changes observed in corresponding amino acid positions and found that all known and many suspected disease-causing mutations are evolutionarily intolerable. We selected several variants that cannot be unambiguously assessed by automated prediction tools but that are confidently identified as "damaging" by our approach, for experimental validation in Drosophila. In all cases, evolutionary intolerant variants caused increased mortality and severe defects in fruit fly embryos confirming their damaging nature. We anticipate that our analysis will serve as a blueprint to help evaluate known and novel missense variants in MEK1 and that our approach will contribute to improving automated tools for disease-associated variant interpretation.

A dynamical model of growth and maturation in Drosophila.

The decision to stop growing and mature into an adult is a critical point in development that determines adult body size, impacting multiple aspects of an adult's biology. In many animals, growth cessation is a consequence of hormone release that appears to be tied to the attainment of a particular body size or condition. Nevertheless, the size-sensing mechanism animals use to initiate hormone synthesis is poorly understood. Here, we develop a simple mathematical model of growth cessation in Drosophila melanogaster, which is ostensibly triggered by the attainment of a critical weight (CW) early in the last instar. Attainment of CW is correlated with the synthesis of the steroid hormone ecdysone, which causes a larva to stop growing, pupate, and metamorphose into the adult form. Our model suggests that, contrary to expectation, the size-sensing mechanism that initiates metamorphosis occurs before the larva reaches CW; that is, the critical-weight phenomenon is a downstream consequence of an earlier size-dependent developmental decision, not a decision point itself. Further, this size-sensing mechanism does not require a direct assessment of body size but emerges from the interactions between body size, ecdysone, and nutritional signaling. Because many aspects of our model are evolutionarily conserved among all animals, the model may provide a general framework for understanding how animals commit to maturing from their juvenile to adult form.

ERK signaling dissolves ERF repression condensates in living embryos.

Phase separation underlies the organization of the nucleus, including the biogenesis of nucleoli and the packaging of heterochromatin. Here we explore the regulation of transcription factor condensates involved in gene repression by ERK signaling in gastrulating embryos of a simple proto-vertebrate (Ciona). ERK signaling induces nuclear export of the transcriptional repressor Ets-2 repressive factor (ERF), which has been linked to various human developmental disorders. Using high-resolution imaging, we show that ERF is localized within discrete nuclear condensates that dissolve upon ERK activation. Interestingly, we observe dynamic pulses of assembly and dissociation during interphase, providing visualization of a nuclear phase separation process regulated by cell signaling. We discuss the implications of these observations for producing sharp on/off switches in gene activity and suppressing noise in cell-cell signaling events.

Dynamics of Drosophila endoderm specification.

During early Drosophila embryogenesis, a network of gene regulatory interactions orchestrates terminal patterning, playing a critical role in the subsequent formation of the gut. We utilized CRISPR gene editing at endogenous loci to create live reporters of transcription and light-sheet microscopy to monitor the individual components of the posterior gut patterning network across 90 min prior to gastrulation. We developed a computational approach for fusing imaging datasets of the individual components into a common multivariable trajectory. Data fusion revealed low intrinsic dimensionality of posterior patterning and cell fate specification in wild-type embryos. The simple structure that we uncovered allowed us to construct a model of interactions within the posterior patterning regulatory network and make testable predictions about its dynamics at the protein level. The presented data fusion strategy is a step toward establishing a unified framework that would explore how stochastic spatiotemporal signals give rise to highly reproducible morphogenetic outcomes.

Uncoupling neurogenic gene networks in the Drosophila embryo.

The EGF signaling pathway specifies neuronal identities in the Drosophila embryo by regulating developmental patterning genes such as intermediate neuroblasts defective (ind). EGFR is activated in the ventral midline and neurogenic ectoderm by the Spitz ligand, which is processed by the Rhomboid protease. CRISPR/Cas9 was used to delete defined rhomboid enhancers mediating expression at each site of Spitz processing. Surprisingly, the neurogenic ectoderm, not the ventral midline, was found to be the dominant source of EGF patterning activity. We suggest that Drosophila is undergoing an evolutionary transition in central nervous system (CNS)-organizing activity from the ventral midline to the neurogenic ectoderm.

Computing whole embryo strain maps during gastrulation.

Gastrulation is a critical process during embryonic development that transforms a single-layered blastula into a multilayered embryo with distinct germ layers, which eventually give rise to all the tissues and organs of the organism. Studies across species have uncovered the mechanisms underlying the building blocks of gastrulation movements, such as localized in-plane and out-of-plane epithelial deformations. The next challenge is to understand dynamics on the scale of the embryo: this requires quantifying strain tensors, which rigorously describe the differences between the deformed configurations taken on by local clusters of cells at time instants of observation and their reference configuration at an initial time. We present a systematic strategy for computing such tensors from the local dynamics of cell clusters, which are chosen across the embryo from several regions whose morphogenetic fate is central to viable gastrulation. As an application of our approach, we demonstrate a strategy of identifying distinct Drosophila morphological domains using strain tensors.

Phase plane dynamics of ERK phosphorylation.

The extracellular signal-regulated kinase (ERK) controls multiple critical processes in the cell and is deregulated in human cancers, congenital abnormalities, immune diseases, and neurodevelopmental syndromes. Catalytic activity of ERK requires dual phosphorylation by an upstream kinase, in a mechanism that can be described by two sequential Michaelis-Menten steps. The estimation of individual reaction rate constants from kinetic data in the full mechanism has proved challenging. Here, we present an analytically tractable approach to parameter estimation that is based on the phase plane representation of ERK activation and yields two combinations of six reaction rate constants in the detailed mechanism. These combinations correspond to the ratio of the specificities of two consecutive phosphorylations and the probability that monophosphorylated substrate does not dissociate from the enzyme before the second phosphorylation. The presented approach offers a language for comparing the effects of mutations that disrupt ERK activation and function in vivo. As an illustration, we use phase plane representation to analyze dual phosphorylation under heterozygous conditions, when two enzyme variants compete for the same substrate.

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.

Fusome topology and inheritance during insect gametogenesis.

From insects to mammals, oocytes and sperm develop within germline cysts comprising cells connected by intercellular bridges (ICBs). In numerous insects, formation of the cyst is accompanied by growth of the fusome-a membranous organelle that permeates the cyst. Fusome composition and function are best understood in Drosophila melanogaster: during oogenesis, the fusome dictates cyst topology and size and facilitates oocyte selection, while during spermatogenesis, the fusome synchronizes the cyst's response to DNA damage. Despite its distinct and sex-specific roles during insect gametogenesis, elucidating fusome growth and inheritance in females and its structure and connectivity in males has remained challenging. Here, we take advantage of advances in three-dimensional (3D) confocal microscopy and computational image processing tools to reconstruct the topology, growth, and distribution of the fusome in both sexes. In females, our experimental findings inform a theoretical model for fusome assembly and inheritance and suggest that oocyte selection proceeds through an 'equivalency with a bias' mechanism. In males, we find that cell divisions can deviate from the maximally branched pattern observed in females, leading to greater topological variability. Our work consolidates existing disjointed experimental observations and contributes a readily generalizable computational approach for quantitative studies of gametogenesis within and across species.

Temporal integration of inductive cues on the way to gastrulation.

Markers for the endoderm and mesoderm germ layers are commonly expressed together in the early embryo, potentially reflecting cells' ability to explore potential fates before fully committing. It remains unclear when commitment to a single-germ layer is reached and how it is impacted by external signals. Here, we address this important question in Drosophila, a convenient model system in which mesodermal and endodermal fates are associated with distinct cellular movements during gastrulation. Systematically applying endoderm-inducing extracellular signal-regulated kinase (ERK) signals to the ventral medial embryo-which normally only receives a mesoderm-inducing cue-reveals a critical time window during which mesodermal cell movements and gene expression are suppressed by proendoderm signaling. We identify the ERK target gene huckebein (hkb) as the main cause of the ventral furrow suppression and use computational modeling to show that Hkb repression of the mesoderm-associated gene snail is sufficient to account for a broad range of transcriptional and morphogenetic effects. Our approach, pairing precise signaling perturbations with observation of transcriptional dynamics and cell movements, provides a general framework for dissecting the complexities of combinatorial tissue patterning.

Chemical Embryology Redux: Metabolic Control of Development.

New studies of metabolic reactions and networks in embryos are making important additions to regulatory models of development, so far dominated by genes and signals. Metabolic control of development is not a new idea and can be traced back to Joseph Needham's 'Chemical Embryology', published in the 1930s. Even though Needham's ideas fell by the wayside with the advent of genetic studies of embryogenesis, they demonstrated that embryos provide convenient models for addressing fundamental questions in biochemistry and are now experiencing a comeback, enabled by the powerful merger of detailed mechanistic studies and systems-level techniques. Here we review recent results from studies that quantified the energy budget of embryogenesis in Drosophila and started to untangle the intricate connections between core anabolic processes and developmental transitions. Dynamic coordination of metabolic, genetic, and signaling networks appears to be essential for seamless progression of development.