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bioRxiv2026

Dynamic microtubules drive yolk-cytoplasm segregation in the syncytial Drosophila embryo

Yolk-cytoplasm segregation is among the earliest spatial organization events in the developing embryo of many oviparous animals. The segregation process is intimately linked to early embryonic cleavage and pattern formation, and exhibits a wide range of spatial and temporal diversity. However, the underlying cytoskeletal mechanism remains largely unknown, except for a small number of species. Using quantitative live imaging, we investigated yolk segregation in the Drosophila embryo during the syncytial nuclear cycles 11-14. We find that the yolk vesicles move progressively inward in spatial and temporal coordination with the inward expanding microtubule networks that are nucleated from centrosomes positioned at the cortex, whereas cortical actin meshwork remains spatially restricted. Using the gnu RNAi embryo to decouple nuclear migration and division from cytoskeletal dynamics, we establish causality with targeted pharmacological disruption and find that microtubule dynamics is required for yolk segregation, while depolymerization of actin has no discernible effect. In support of a mechanism of growth-propelled passive displacement, microtubule plus end comets come in apparent contact with yolk vesicles, and injected, inert microbeads are displaced towards the embryo center presumably by the same pushing force. These findings identify microtubule polymerization as a predominant driver of yolk-cytoplasm segregation in Drosophila and suggest that diverse cytoskeletal mechanisms evolved to accomplish this crucial reorganization process

developmental biology↗

bioRxiv2026

Shared neurogenesis onset is sufficient to explain bilateral matching in the vertebrate retina

Bilateral symmetry is a hallmark of many paired organs and often essential for optimal functionality. The vertebrate eyes are a prominent example of this, as the matched development of the two retinas is required for accurate visual processing. While macroscopic aspects of symmetry emergence across systems have been investigated, how bilateral matching is maintained once cells start to differentiate remains less understood. Here we address this question using the zebrafish retina as a model to follow neurogenic programs in vivo at single-cell resolution. We perform quantitative 3D live imaging of both retinas simultaneously and directly compare neurogenesis onset and propagation within and across embryos. We find that neurogenic waves initiate at the retinal poles and progress towards the mid-retina in a conserved spatiotemporal pattern. Within embryos, the two eyes exhibit highly similar neurogenesis dynamics when it comes to timing of neurogenesis onset, cell number increase, and spatial wave progression. Across embryos, however, variability is larger. While these observations hint at active inter-retinal coordination, a stochastic model predicts that a shared onset of neurogenesis can be sufficient to explain bilateral matching. Targeted genetic perturbation experiments support this prediction. We find that altering wave propagation affects patterning but not bilateral similarity. Disrupting neurogenesis onset timing, however, reduces bilateral symmetry between eyes. Thus, the combination of experiment and theory identifies synchronized neurogenesis onset as a key determinant of bilateral symmetry, revealing a minimal principle for how reproducible development of paired organs can emerge from stochastic processes.

developmental biology↗

bioRxiv2026

Clonal memory in human embryonic stem cells biases fate potential during endoderm differentiation

Cell fate decisions during development are shaped not only by extrinsic signals but also by heritable intrinsic states passed on across cell division. The extent to which this phenomenon, termed clonal memory, can explain the persistent heterogeneity observed from directed differentiation of human embryonic stem cells is unclear. Here, we combine lineage tracing with single-cell transcriptomics and chromatin accessibility profiling to track clonal behaviour across human embryonic stem cell differentiation towards definitive endoderm. Using a lentiviral barcoding system coupled with a split-well sampling strategy, we find that clonally related cells exhibit reproducible, probabilistic fate outcomes that cannot be explained by signalling environment alone. Fate-biased clones are transcriptionally indistinguishable at the pluripotent stage yet display distinct chromatin accessibility landscapes at lineage-specific cis-regulatory elements. Pre-existing accessibility at these lineage-specific regulatory regions distinguish clones that undergo successful endoderm differentiation from those that generate off-target mesoderm derivatives. Together, these findings provide an explanation for how off-target populations arise during directed differentiation, identifying heritable chromatin states within pluripotent cultures as a source of variability relevant to stem cell-derived in vitro models and cell therapies.

developmental biology↗

bioRxiv2026

Spatiotemporal expression of the zebrafish pax9 gene that is essential for median fin patterning

PAX9 is an evolutionarily conserved paired-box transcription factor that is critical for embryonic development and human diseases. The mouse model has been predominantly used to investigate Pax9 functions. Zebrafish has emerged as a complementary vertebrate model for various human diseases, including cancers. Until recently, the functions of the zebrafish pax9 gene in jaw and hematopoiesis have started to be uncovered. However, detailed pax9 spatiotemporal expression, molecular mechanisms, and potential functions in other zebrafish organs remain largely unknown. With the technical advances in CRISPR-Cas9, non-homologous end joining (NHEJ) has made knockin and knockout a convenient way to examine endogenous gene expression in vivo and to generate a loss-of-function allele simultaneously. Here, we first generated pax9 knockin fish lines by inserting fluorescent proteins at the start of the endogenous pax9 coding region. Then, we examined pax9 expression in real time from early embryonic stages through adulthood. Except for previously reported expression domains, we were able to identify pax9 expression in high resolution in the paired and median fins, where pax9 marks anterior fin rays. Moreover, our knockin and knockout mutants showed increased fin ray number in median fins, but no evident effect on paired fins in pax9 null mutants. Thus, PAX9 is critical for median fin patterning in zebrafish.

developmental biology↗

bioRxiv2018

Morphogenetic processes as data: Quantitative structure in the Drosophila eye imaginal disc

We can improve our understanding of biological processes through the use of computational and mathematical modeling. One such morphogenetic process (ommatidia formation in the Drosophila eye imaginal disc) provides us with an opportunity to demonstrate the power of this approach. We use a high-resolution image that catches the spatially- and temporally-dependent process of ommatidia formation in the act. This image is converted to quantitative measures and models that provide us with new information about the dynamics and geometry of this process. We approach this by addressing three computational hypotheses, and provide a publicly-available repository containing data and images for further analysis. Potential spatial patterns in the morphogenetic furrow and ommatidia are summarized, while the ommatidia cells are projected to a spherical map in order to identify higher-level spatiotemporal features. In the conclusion, we discuss the implications of our approach and findings for developmental complexity and biological theory.

developmental biology↗

bioRxiv2017

Synthesizing developmental trajectories

Dynamical processes in biology are studied using an ever-increasing number of techniques, each of which brings out unique features of the system. One of the current challenges is to develop systematic approaches for fusing heterogeneous datasets into an integrated view of multivariable dynamics. We demonstrate that heterogeneous data fusion can be successfully implemented within a semi-supervised learning framework that exploits the intrinsic geometry of high-dimensional datasets. We illustrate our approach using a dataset from studies of pattern formation in Drosophila. The result is a continuous trajectory that reveals the joint dynamics of gene expression, subcellular protein localization, protein phosphorylation, and tissue morphogenesis. Our approach can be readily adapted to other imaging modalities and forms a starting point for further steps of data analytics and modeling of biological dynamics.

developmental biology↗

bioRxiv2017

Developmental transcriptomics of the brittle star Amphiura filiformis reveals gene regulatory network rewiring in echinoderm larval skeleton evolution

Amongst the echinoderms the class Ophiuroidea is of particular interest for its phylogenetic position, ecological importance, developmental and regenerative biology. However, compared to other echinoderms, notably echinoids (sea urchins), relatively little is known about developmental changes in gene expression in ophiuroids. To address this issue we have generated and assembled a large RNAseq data set of four key stages of development in the brittle star Amphiura filiformis and a de novo reference transcriptome of comparable quality to that of a model echinoderm - the sea urchin Strongyloncentrotus purpuratus. Furthermore, we provide access to the new data via a web interface: http://www.echinonet.eu/shiny/Amphiura_filiformis/. With a focus on skeleton development, we have identified highly conserved genes associated with the development of a biomineralized skeleton. We also identify important class-specific characters, including the independent duplication of the msp130 class of genes in different echinoderm classes and the unique occurrence of spicule matrix (sm) genes in echinoids. Using a new quantification pipeline for our de novo transcriptome, validated with other methodologies, we find major differences between brittle stars and sea urchins in the temporal expression of many transcription factor genes. This divergence in developmental regulatory states is more evident in early stages of development when cell specification begins, than when cells initiate differentiation. Our findings indicate that there has been a high degree of gene regulatory network rewiring in the evolution of echinoderm larval development.\n\nData DepositionsAll sequence reads are available at Genbank SRR4436669 - SRR4436674. Any sequence alignments used are available by the corresponding author upon request.

evolutionary biology↗

bioRxiv2018

A homing CRISPR mouse resource for barcoding and lineage tracing

Cellular barcoding using nuclease-induced genetic mutations is an effective approach that is emerging for recording biological information, including developmental lineages. We have previously introduced the homing CRISPR system as a promising methodology for generating such barcodes with scalable diversity and without crosstalk. Here, we present a mouse line (MARC1) with multiple genomically-integrated and heritable homing guide RNAs (hgRNAs). We determine the genomic locations of these hgRNAs, their activity profiles during gestation, and the diversity of their mutants. We apply the line for unique barcoding of mouse embryos and differential barcoding of embryonic tissues. We conclude that this mouse line can address the unique challenges associated with in vivo barcoding in mammalian model organisms and is thus an enabling platform for recording and lineage tracing applications in a mammalian model system.

synthetic biology↗

bioRxiv2018

Toward deciphering developmental patterning with deep neural network

Complex biological functions are carried out by the interaction of genes and proteins. Uncovering the gene regulation network behind a function is one of the central themes in biology. Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN), a form of machine learning or artificial intelligence. Specifically, the DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation - the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN "black box". Our interpretable DNN approach should have broad applications in genotype-phenotype mapping. SignificanceComplex biological functions are carried out by gene regulation networks. The mapping between gene network and function is a central theme in biology. The task usually involves extensive experiments with perturbations to the system (e.g. gene deletion). Here, we demonstrate that machine learning, or deep neural network (DNN), can help reveal the underlying gene regulation for a given function or phenotype with minimal perturbation data. Specifically, after training with wild-type gene expression dynamics data and a few mutant snapshots, the DNN learns to behave like an accurate simulator for the genetic system, which can be used to predict other mutants behaviors. Furthermore, our DNN approach is biochemically interpretable, which helps uncover possible gene regulatory mechanisms underlying the observed phenotypic behaviors.

developmental biology↗

bioRxiv2014

Conservation and structural analysis of the Xenopus laevis phospho-proteome

The African clawed frog Xenopus laevis is an important model organism for studies in developmental and cell biology, including cell-signaling. However, our knowledge of X. laevis protein post-translational modifications remains scarce. Here, we used a mass spectrometry-based approach to survey the phosphoproteome of this species, compiling a list of 3225 phosphosites. We used this resource to study the conservation between the phosphoproteomes of X. laevis and 13 other species. We found that the degree of conservation of phosphorylation across species is predictive of sites with known molecular function, kinase interactions and functionally relevant phospho-regulatory interactions. In addition, using comparative protein structure models, we find that phosphosites within structured domains tend to be located at positions with high conformational flexibility. A fraction of sites appear to occur in inaccessible positions and have the potential to regulate protein conformation.

Evolutionary Biology↗

bioRxiv2017

An efficient FLP-based toolkit for spatiotemporal control of gene expression in Caenorhabditis elegans

Site-specific recombinases are potent tools to regulate gene expression. In particular, the Cre and FLP enzymes are widely used to either activate or inactivate genes in a precise spatiotemporal manner. Both recombinases work efficiently in the popular model organism Caenorhabditis elegans but their use in this nematode is still only sporadic. To increase the utility of the FLP system in C. elegans we have generated a series of single-copy transgenic strains that stably express an optimized version of FLP in specific tissues or by heat induction. We show that recombination efficiencies reach 100 percent in several cell types, such as muscles, intestine and serotonin producing neurons. Moreover, we demonstrate that most promoters drive recombination exclusively in the expected tissues. As examples of the potentials of the FLP lines we describe novel tools for induced cell ablation by expression of the PEEL-1 toxin and a versatile FLP-out cassette for generation of GFP-tagged conditional knockout alleles. Together with other recombinase-based reagents created by the C. elegans community this toolkit increases the possibilities for detailed analyses of specific biological processes at developmental stages inside intact animals.

genetics↗

bioRxiv2017

Ecological plant epigenetics: Evidence from model and non-model species, and the way forward

Growing evidence makes a strong case that epigenetic mechanisms contribute to complex traits, with implications across many fields of biology from dissecting developmental processes to understanding aspects of human health and disease. In ecology, recent studies have merged ecological experimental design with epigenetic analyses to elucidate the contribution of epigenetics to plant phenotypes, stress response, adaptation to habitat, or species range distributions. While there has been some progress in revealing the role of epigenetics in ecological processes, many studies with non-model species have so far been limited to describing broad patterns based on anonymous markers of DNA methylation. In contrast, studies with model species have benefited from powerful genomic resources, which allow for a more mechanistic understanding but have limited ecological realism. To understand the true significance of epigenetics for plant ecology and evolution, we must combine both approaches transferring knowledge and methods from model-species research to genomes of evolutionarily divergent species, and examining responses to complex natural environments at a more mechanistic level. This requires transforming genomics tools specifically for studying non-model species, which is challenging given the large and often polyploid genomes of plants. Collaboration between molecular epigeneticists, ecologists and bioinformaticians promises to enhance our understanding of the mutual links between genome function and ecological processes.

ecology↗

bioRxiv2018

Integration and segregation in Autism Spectrum Disorders modulated by age, disease, and interaction: A graph-theoretic study of intrinsic functional connectivity

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting 1 in 50 children between the ages of 6 and 17 years. Brain connectivity and graph theoretic methods have been particularly very useful in shedding light on the differences between high functioning autistic children compared to typically developing (TD) ones. However, very recent developments in network measures raise a cautionary note by highlighting gross under- and over-connectivity in ASD may be an oversimplified hypothesis. Thus the primary aim of our study is to investigate these notions in functional connectomics of ASD versus TD by subjecting the data to reproducibility experiments using two independent datasets.\n\nFurther, we tested the hypothesis of alteration in network segregation and integration in the ASD subjects. We have analyzed the resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) data from the University of California Los Angeles (UCLA) multimodal connectivity database (n=42 ASD, n=37 TD) and rs-fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) (n=187 ASD, n=176 TD) dataset. We assessed the differences in connection strength between TD and ASD subjects. We also performed graph theoretical analysis to analyze the effect of disease on various network measures. Further, using the larger ABIDE dataset, we performed two-factor ANOVA test, to study the effect of age, disease and their interaction by classifying the TD and ASD participants into two cohorts: children (9-12 years, n=73 TD and n=87 ASD) and adolescents (13-16 years, n=103 TD and n=100 ASD). In ASD, we show the existence of atypical connectivity within and between functional networks as compared to TD. We also found in ASD both hypo-and hyper-connectivity within functional networks such as the default mode network (DMN). Further, graph theoretic analysis showed that there is significant effect of age and disease on modularity, clustering coefficient, and local efficiency. We also identified specific areas within the DMN, sensorimotor, visual and attention networks that are affected by age, disease and their interaction. Overall, our findings suggest that maturation, disease and their interaction are critical for unraveling the biological basis and developmental trajectory in ASD and other neuropsychiatric disorders.

neuroscience↗

bioRxiv2018

R2C2: Improving nanopore read accuracy enables the sequencing of highly-multiplexed full-length single-cell cDNA

High-throughput short-read sequencing has revolutionized how transcriptomes are quantified and annotated. However, while Illumina short-read sequencers can be used to analyze entire transcriptomes down to the level of individual splicing events with great accuracy, they fall short of analyzing how these individual events are combined into complete RNA transcript isoforms. Because of this shortfall, long-read sequencing is required to complement short-read sequencing to analyze transcriptomes on the level of full-length RNA transcript isoforms. However, there are issues with both Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) long-read sequencing technologies that prevent their widespread adoption. Briefly, PacBio sequencers produce low numbers of reads with high accuracy, while ONT sequencers produce higher numbers of reads with lower accuracy. Here we introduce and validate a new long-read ONT based sequencing method. At the same cost, our Rolling Circle Amplification to Concatemeric Consensus (R2C2) method generates more accurate reads of full-length RNA transcript isoforms than any other available long-read sequencing method. These reads can then be used to generate isoform-level transcriptomes for both genome annotation and differential expression analysis in bulk or single cell samples.\n\nSignificance StatementSubtle changes in RNA transcript isoform expression can have dramatic effects on cellular behaviors in both health and disease. As such, comprehensive and quantitative analysis of isoform-level transcriptomes would open an entirely new window into cellular diversity in fields ranging from developmental to cancer biology. The R2C2 method we are presenting here is the first method with sufficient throughput and accuracy to make the comprehensive and quantitative analysis of RNA transcript isoforms in bulk and single cell samples economically feasible.

genomics↗

bioRxiv2017

Identification And Evolutionary Analysis Of Eight Non-Coding Genomic Elements Regulating Neurodevelopmental Genes

Many non-coding regulatory elements conserved in vertebrates regulate the expression of genes involved in development and play an important role in the evolution of morphology through the rewiring of developmental gene networks. Available biological datasets allow the identification of non-coding regulatory elements with high confidence; furthermore, chromatin conformation data can be used to confirm enhancer-promoter interactions in specific tissue types and developmental stages. We have devised an analysis pipeline that integrates datasets about gene expression, enhancer activity, chromatin accessibility, epigenetic marks, and Hi-C contact frequencies in various brain tissues and developmental stages, leading to the identification of eight non-coding elements that might regulate the expression of three genes with important roles in brain development in vertebrates. We have then performed comparative sequence and microsynteny analyses in order to reconstruct the evolutionary history of the regulatory landscape around these genes; we observe a general pattern of ancient regulatory elements conserved across most vertebrate lineages, together with younger elements that appear to be mammal and primate innovations. This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100035)

evolutionary biology↗

bioRxiv2018

RT States: systematic annotation of the human genome using cell type-specific replication timing programs

The replication timing (RT) program has been linked to many key biological processes including cell fate commitment, 3D chromatin organization and transcription regulation. Significant technology progress now allows to characterize the RT program in the entire human genome in a high-throughput and high-resolution fashion. These experiments suggest that RT changes dynamically during development in coordination with gene activity. Since RT is such a fundamental biological process, we believe that an effective quantitative profile of the local RT program from a diverse set of cell types in various developmental stages and lineages can provide crucial biological insights for a genomic locus. In the present study, we explored recurrent and spatially coherent combinatorial profiles from 42 RT programs collected from multiple lineages at diverse differentiation states. We found that a Hidden Markov Model with 15 hidden states provide a good model to describe these genome-wide RT profiling data. Each of the hidden state represents a unique combination of RT profiles across different cell types which we refer to as \"RT states\". To understand the biological properties of these RT states, we inspected their relationship with chromatin states, gene expression, functional annotation and 3D chromosomal organization. We found that the newly defined RT states possess interesting genome-wide functional properties that add complementary information to the existing annotation of the human genome.\n\nAUTHOR SUMMARYThe replication timing (RT) program is an important cellular mechanism and has been linked to many key biological processes including cell fate commitment, 3D chromatin organization and transcription regulation. Significant technology progress now allows us to characterize the RT program in the entire human genome. Results from these experiments suggest that RT changes dynamically across different developmental stages. Since RT is such a fundamental biological process, we believe that the local RT program from a diverse set of cell types in various developmental stages can provide crucial biological insights for a genomic locus. In the present study, we explored combinatorial profiles from 42 RT programs collected from multiple lineages at diverse differentiation states. We developed a statistical model consist of 15 \"RT states\" to describe these genome-wide RT profiling data. To understand the biological properties of these RT states, we inspected the relationship between RT states and other types of functional annotations of the genome. We found that the newly defined RT states possess interesting genome-wide functional properties that add complementary information to the existing annotation of the human genome.

bioinformatics↗

bioRxiv2017

Live Tracking Of Moving Samples In Confocal Microscopy For Vertically Grown Plant Roots

Roots navigate through soil integrating environmental signals to orient their growth. The Arabidopsis root is a widely used model for developmental, physiological and cell biological studies. Live imaging greatly aids these efforts, but the horizontal sample position and continuous root tip displacement present significant difficulties. Here, we develop a confocal microscope setup for vertical sample mounting and integrated directional illumination. We present TipTracker - a custom software for automatic tracking of diverse moving objects usable on various microscope setups. Combined, this enables observation of root tips growing along the natural gravity vector over prolonged periods of time, as well as the ability to induce rapid gravity or light stimulation. We also track migrating cells in the developing zebrafish embryo, demonstrating the utility of this system in the acquisition of high resolution data sets of dynamic samples. We provide detailed descriptions of the tools enabling the easy implementation on other microscopes.

plant biology↗

bioRxiv2018

Binary transcriptional control of pattern formation in development

Predicting developmental outcomes from regulatory DNA sequence and transcription factor patterns remains an open challenge in physical biology. Using stripe 2 of the even-skipped gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key step along the developmental decision-making cascade: the generation of cytoplasmic mRNA patterns via the control of transcription in individual cells. Using live imaging and computational approaches, we found that the transcriptional burst frequency is modulated across the stripe to control the mRNA production rate. However, we discovered that bursting alone cannot quantitatively recapitulate the formation of the stripe, and that control of the window of time over which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical modeling revealed that these regulatory strategies--bursting and the time window--obey different kinds of regulatory logic, suggesting that the stripe is shaped by the interplay of two distinct underlying molecular processes.

developmental biology↗