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Results for “developmental biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 235 records · Page 13Linked to original sources

bioRxiv2025

Long-term ex ovo culture of Caenorhabditis elegans embryos

While the genetic tractability, transparency and invariant development of the Caenorhabditis elegans embryo have led to its broad adoption as a model system for the study of cell and developmental biology, its impermeable eggshell has complicated the use of small-molecule reagents during embryogenesis. Existing genetic approaches for rendering the embryo permeable to acute small molecule treatment have increased the accessibility of early embryogenesis to pharmacological manipulation but compromise long-term viability, preventing their use in studies of later developmental processes or post-exposure physiology. Here, we describe the use of an optimized enzymatic eggshell digestion protocol coupled with a minimal, serum-free culture medium that supports the survival and normal development of ex ovo embryos through larval maturation and adulthood. We show that this approach renders embryos permeable to a wide range of small molecules, enabling precise temporal manipulation of developmental processes previously inaccessible through conventional genetic methods. We demonstrate the utility of this technique through the pharmacological modulation of cytoskeletal components including microtubules and actin, as well as the minus-end-directed microtubule motor protein dynein, highlighting applications for the study of cell division, morphogenesis, and neuronal development, especially at later stages of embryogenesis. As a proof of concept, we use acutely timed dynein inhibition to show that cytoplasmic dynein is required to transport the centriole into the dendrite of embryonically born sensory neurons. This approach expands the experimental toolkit available for labeling and manipulating developmental processes in C. elegans.

cell biology↗

bioRxiv2018

Detecting mosaic patterns in phenotypic disparity

Evolutionary biologists have long sought to understand the full complexity in pattern and process that shapes organismal diversity. Although phylogenetic comparative methods are often used to reconstruct complex evolutionary dynamics, they are typically limited to a single phenotypic trait. Extensions that accommodate multiple traits lack the ability to partition multidimensional datasets into a set of mosaic suites of evolutionarily linked characters. I introduce a comparative framework that identifies heterogeneity in evolutionary patterns across large datasets of continuous traits. Using a model of continuous trait evolution based on the differential accumulation of disparity across lineages in a phylogeny, the approach algorithmically partitions traits into a set of character suites that best explains the data, where each suite displays a distinct pattern in phylogenetic morphological disparity. When applied to empirical data, the approach revealed a mosaic pattern predicted by developmental biology. The evolutionary distinctiveness of individual suites can be investigated in more detail, either by fitting conventional comparative models or by directly studying the phylogenetic patterns in disparity recovered during the analysis. This framework can supplement existing comparative approaches by inferring the complex, integrated patterns that shape evolution across the body plan from disparate developmental, morphometric, and environmental sources of phenotypic data.

evolutionary biology↗

bioRxiv2015

Tissue-specific tagging of endogenous loci in Drosophila melanogaster

Fluorescent protein tags have revolutionized cell and developmental biology, and in combination with binary expression systems they enable diverse tissue-specific studies of protein function. However these binary expression systems often do not recapitulate endogenous protein expression levels, localization, binding partners, and developmental windows of gene expression. To address these limitations, we have developed a method called T-STEP (Tissue-Specific Tagging of Endogenous Proteins) that allows endogenous loci to be tagged in a tissue specific manner. T-STEP uses a combination of efficient gene targeting and tissue-specific recombinase-mediated tag swapping to temporally and spatially label endogenous proteins. We have employed this method to GFP tag OCRL (a phosphoinositide-5-phosphatase in the endocytic pathway) and Vps35 (a Parkinson's disease-implicated component of the endosomal retromer complex) in diverse Drosophila tissues including neurons, glia, muscles, and hemocytes. Selective tagging of endogenous proteins allows for the first time cell type-specific live imaging and proteomics in complex tissues.

Neuroscience↗

bioRxiv2021

Axial-circular magnetic levitation assisted biofabrication and manipulation of cellular structures

Diamagnetic levitation is an emerging technology for remote manipulation of cells in cell and tissue level applications. Low-cost magnetic levitation configurations using permanent magnets are commonly composed of a culture chamber physically sandwiched between two block magnets that limit working volume and applicability. This work describes a single ring magnet-based magnetic levitation system to eliminate physical limitations for biofabrication. Developed configuration utilizes sample culture volume for construct size manipulation and long-term maintenance. Furthermore, our configuration enables convenient transfer of liquid or solid phases during the levitation. Prior to biofabrication, we first calibrated the platform for levitation with polymeric beads, considering the single cell density range of viable cells. By taking advantage of magnetic focusing and cellular self-assembly, millimeter-sized 3D structures were formed and maintained in the system allowing easy and on-site intervention in cell culture with an open operational space. We demonstrated that the levitation protocol could be adapted for levitation of various cell types (i.e., stem cell, adipocyte and cancer cell) representing cells of different densities by modifying the paramagnetic ion concentration that could be also reduced by manipulating the density of the medium. This technique allowed the manipulation and merging of separately formed 3D biological units, as well as the hybrid biofabrication with biopolymers. In conclusion, we believe that this platform will serve as an important tool in broad fields such as bottom-up tissue engineering, drug discovery and developmental biology.

bioengineering↗

bioRxiv2020

Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis

Lineage tracing is a powerful tool traditionally used in developmental biology to interrogate the evolutionary time course of tissue formation, but the dense, three-dimensional nature of tissue limits the ability to assemble individual traces into complete reconstructions of development. Human induced pluripotent stem cells (hiPSCs) enable recapitulation of various aspects of developmental processes, thereby providing an in vitro platform to assess the dynamic collective behaviors directing tissue morphogenesis. Here, we trained an ensemble of independent convolutional neural networks to identify individual hiPSCs imaged via time lapse microscopy in order to generate longitudinal measures of individual cell and dense cellular neighborhood properties simultaneously on timescales ranging from minutes to days. Our analysis reveals that while individual cell parameters are not strongly affected by extracellular microenvironmental conditions such as pluripotency maintenance regime or soluble morphogenic cues, regionally specific cell behaviors change in a manner predictive of organization dynamics. By generating complete multicellular reconstructions of hiPSC behavior, our cell tracking pipeline enables fine-grained understanding of developmental organization by elucidating the role of regional behavior stratification in early tissue formation.

bioengineering↗

bioRxiv2026

Transient cell states encode positional information to direct asymmetric growth

How tissues generate asymmetric growth from spatially and temporally restricted signals remains a fundamental challenge in developmental biology. We show that transient cell states encode positional information that instructs asymmetric growth in plants. Using spatial single-cell transcriptomics and multi-omics, we uncover a surprisingly complex landscape of transient cell states in the apical hook, including a previously unrecognized population in the apical hypocotyl region that integrates developmental and hormonal cues and bifurcates into opposing growth trajectories. Gene regulatory network analysis and functional perturbation establish GATA TRANSCRIPTION FACTOR 2 (GATA2) as a central regulator that promotes cell elongation through gibberellin signaling. These results reveal that diverse, spatially localized transient cell states function as regulatory hubs that convert positional information into divergent growth programs, providing a unifying framework for how dynamic cellular states shape organ morphology.

plant biology↗

bioRxiv2025

Levy Flight Patterns in the Cortical Architecture of Macaca mulatta

Levy flights (LF), a concept originating in statistical physics, describe random walks in which the step lengths follow a heavy-tailed probability distribution, often a power law. Unlike Brownian motion, where step lengths are constrained within a narrow range, LF are characterized by the coexistence of many short steps interspersed with occasional long jumps. Applying advanced computational techniques, we looked for LF-like patterns in high-resolution histological images of Macaca mulatta (Rhesus macaque) cortical area 4 from BrainMaps.org. Step-length distributions, derived from pairwise distances between neuronal somata, exhibited heavy-tailed behavior consistent with power-law models across all samples. Maximum likelihood estimation of power-law exponents ( values: 0.87-1.08) strongly supported the heavy-tailed nature of these patterns, showing a better fit with power-law models compared to exponential or normal distributions. Connectivity analyses revealed a dual organizational structure within cortical layers: densely interconnected local clusters coexisting with sparse long-range connections. k-Nearest neighbors graphs demonstrated small-world network properties, with average clustering coefficients ranging from 0.622 to 0.630 across samples. This consistent structural organization aligns with LF principles, wherein local processing is optimized alongside global integration for efficiency and functionality. The implications extend to developmental biology, as the emergence of LF-like patterns likely reflects intrinsic self-organizing processes during embryonic and fetal development. This LF-like organization provides a natural framework for designing artificial networks that optimize performance in tasks requiring both localized specialization and global integration. Moreover, understanding the developmental origins of these patterns could guide strategies for neural repair and regeneration in stroke or neurodegenerative diseases.

neuroscience↗

bioRxiv2026

High-Fidelity Long-term Whole-embryo Lineage and Fate Reconstruction by Iterative Tracking with Error Correction

Reconstructing the complete cell lineages and fate maps of a living embryo has remained a central challenge in developmental biology. Here we introduce ITEC (Iterative Tracking with Error Correction), a fully unsupervised method that automatically reconstructs the lineage of every cell in the embryo with high fidelity. ITEC was validated with manually annotated lineages on four cross-species datasets including zebrafish, mouse, and Drosophila. We reconstructed the developmental lineages of a zebrafish embryo from terabyte-scale data of total 18.5 million cells with an estimated accuracy of over 99.7%. With ITEC, we revealed spatiotemporal dynamics of key morphogenetic processes such as somite boundary formation, demonstrated the various patterns of spatial sorting among adjacent organs or anatomical regions, and identified associations between cellular movement and spatial transcriptomics. ITEC provides a powerful platform for retrospective fate mapping and systematic exploration of developmental dynamics at the cellular scale and single-embryo level.

bioinformatics↗

bioRxiv2023

Analysis of histone H3K4me3 modifications in bovine placenta derived from different calf-production methods

In ruminants, overgrowth of offspring produced by in vitro fertilization (IVF) is a common problem. Abnormal epigenetic modifications caused by environmental factors during the early embryonic period are suspected as an etiology of overgrowth. In this study, we investigated the genome-wide histone H3K4me3 profiles of bovine placentae that play a pivotal role in fetal development and compared their characteristics between artificial insemination (AI)- and IVF-derived samples. Cotyledons were harvested from the placentae obtained at parturition of 5 AI- and 13 IVF-derived calves, and chromatin immunoprecipitation sequencing was performed for H3K4me3. We confirmed no significant maternal tissue contamination in the samples we used. The revealed H3K4me3 profiles reflected the general characteristics of the H3K4me3 modification, which is abundantly distributed in the promoter region of active genes. By extracting common modifications from multiple samples, the genes involved in placenta-specific biological processes could be enriched. Comparison with the H3K4me3 modifications of blastocyst samples was also effective for enriching the placenta-specific features. Principal component analysis suggested the presence of differential H3K4me3 modifications in AI- and IVF-derived samples. The genes contributing to the difference were related to the developmental biological processes. Imprinted genes such as BEGAIN, ZNF215, and DLX5 were among the extracted genes. Principal component and discriminant analyses using only male samples categorized the samples into three groups based on fetal weight and calf-production methods. To our knowledge, this is the first study to profile the genome-wide histone modifications of bovine fetal placentae and reveal their differential characteristics between different calf-production methods.

genomics↗

bioRxiv2024

Small molecule and cell contact-inducible systems for controlling expression and differentiation in stem cells

Synthetic developmental biology uses engineering approaches to understand multicellularity with goals ranging from recapitulating development to building synthetic organisms. Current approaches include engineering multicellular patterning, controlling differentiation, and implementing cooperative cellular behaviors in model systems. Synthetic biology tools enable these pursuits with genetic circuits that drive customized responses to arbitrary stimuli, synthetic receptors that enable orthogonal signaling channels, and light- or drug-inducible systems that enable precise spatial and temporal control of cell function. Mouse embryonic stem cells (mESCs) offer a well-studied and genetically tractable pluripotent chassis for pursuing synthetic development questions however, there is minimal characterization of existing synthetic biology tools in mESCs and we lack genetic toolkits for rapid iterative engineering of synthetic development workflows. Here, we began to address this challenge by characterizing small molecule and cell contact-inducible systems for gene expression in and differentiation of mESCs. We show that small molecule and cell-contact inducible systems work reliably and efficiently for controlling expression of arbitrary genetic payloads. Furthermore, we show that these systems can drive direct differentiation of mESCs into neurons. Each of these systems can readily be used on their own or in combination, opening many possibilities for studying developmental principles with high precision.

synthetic biology↗

bioRxiv2024

OneSC: A computational platform for recapitulating cell state transitions

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high- resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico synthetic cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.

systems biology↗

bioRxiv2026

Development positions malignant cellular states but does notexplain their diversification

Epithelial cancers are often described as aberrant reactivations of embryonic or tissue-forming programs, but whether malignant cellular-state diversification is actually constrained by developmental trajectories remains unclear. Here, we present a quantitative framework to test this idea in pancreatic ductal adenocarcinoma (PDAC). Using representation learning on large-scale single-cell data, we build a reference space that captures the main axes of normal foregut and pancreatic epithelial variation. Malignant cells can be mapped into this space, showing that developmental biology helps interpret their identity. However, these coordinates account for only part of the variation. A substantial portion lay outside the reference space, and the residual component was structured rather than diffuse. Furthermore, displacements between cancer states showed weak alignment with canonical axes and did not correspond to simple progression along them, even when they retained measurable components within the broader subspace. Thus, developmental programs offer a useful coordinate system for describing where PDAC states sit relative to normal epithelia, but they do not explain the directions along which malignant states differ. Instead, malignant diversification follows additional structured, cancer-adaptive axes outside the dominant geometry of normal development. More generally, these results demonstrate how disease-state variation can be decomposed into shared coordinates of normal identity and distinct directions of pathological reprogramming. Significance StatementTumors are often said to reuse genetic programs from early tissue formation, but similarity to those programs does not mean cancer cells follow the same trajectories. We developed a quantitative framework to separate these ideas in pancreatic ductal adenocarcinoma. Variation tied to normal epithelial differentiation provided a useful coordinate system for locating malignant cell states, yet differences between those states showed weak alignment with canonical differentiation directions and included structured, cancer-adaptive programs. Thus, pancreatic cancer retains aspects of normal cell identity while diversifying along additional malignant axes. This distinction clarifies how insights from normal tissue programs should be applied to cancer plasticity and provides a general method for comparing disease states with normal cellular programs.

systems biology↗

bioRxiv2025

A chromosome-level assembly and functional genomic resources for the model annelid Capitella teleta

BackgroundThe polychaete Capitella teleta is a commonly used annelid for studies in evolutionary developmental biology, comparative genomics, conservation, and ecotoxicology. Over a decade ago, it was the first polychaete to have its genome sequenced and assembled, contributing to pioneering studies that transformed our understanding of animal genomes and their evolution. However, this early resource is now outdated compared to current genome sequencing standards, limiting the use of modern functional genomic tools that could further our understanding of numerous biological processes. ResultsWe combine long-read and short-read sequencing with Hi-C chromatin conformation capture data to assemble the chromosome-level nuclear and mitochondrial genomes of the laboratory strain of C. teleta. This reference assembly more accurately reflects the expected genome size for this polychaete ([~]243.6 Mb) and contains a highly complete, evolutionarily conserved gene repertoire. Notably, the nuclear and mitochondrial genomes are heavily rearranged, indicating a decoupling between gene family repertoire and chromosomal evolution. The analyses of multi-omic datasets available for C. teleta, including developmental time courses of bulk and single-cell RNA-seq, ATAC-seq, and EM-seq, using the new reference assembly, resulted in a significant quality improvement, allowing us to identify new cell-type-specific gene markers and gain additional insights of biological relevance. Finally, we generated a publicly available genome browser that ensures all these resources are easily findable, accessible, interoperable, and reusable. ConclusionsOur study provides state-of-the-art genomic resources for the polychaete model C. teleta, addressing a pressing community need that will open new research opportunities in animal and genome evolution.

genomics↗

bioRxiv2017

Massively parallel single cell lineage tracing using CRISPR/Cas9 induced genetic scars

A key goal of developmental biology is to understand how a single cell transforms into a full-grown organism consisting of many different cell types. Single-cell RNA-sequencing (scRNA-seq) has become a widely-used method due to its ability to identify all cell types in a tissue or organ in a systematic manner 1-3. However, a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here, we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes, we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses, LINNAEUS (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences) can be used as a systematic approach for identifying the lineage origin of novel cell types, or of known cell types under different conditions.

systems biology↗

bioRxiv2026

A single-cell transcriptome atlas of the barley root apical meristem uncovers conserved and divergent roles of HvWOX5

Single-cell approaches have transformed plant developmental biology; however, cell-type-resolved resources for cereals remain limited. Here, we present a single-cell transcriptome atlas of the barley root apical meristem (RAM), which resolves 24 transcriptionally distinct cell populations. We assigned major root cell identities by integrating marker gene validation using Hybridization Chain Reaction (HCR) RNA fluorescence in situ hybridization and spatial transcriptomics with cross-species comparisons of published root atlases. Pseudotime analysis reconstructed developmental trajectories from the quiescent center to differentiating tissues, supporting the spatial and developmental organization of the atlas. We further demonstrated the utility of this resource by identifying HvWOX5 expression in the quiescent center and metaxylem and showing that HvWOX5 loss-of-function mutants displayed reduced root and meristem lengths, altered stem cell niche homeostasis, and disrupted metaxylem organization. Taken together, this atlas provides a framework for dissecting barley root development and identifies HvWOX5 as a key regulator of RAM organization and metaxylem patterning.

Plant Biology↗

bioRxiv2026

Near-equiprobable binary branching decisions underlie filament patterning in the moss Physcomitrium patens

Branching forms are ubiquitous in nature and have evolved repeatedly across scales and species. An important goal of developmental biology remains to identify similarities and differences in the regulatory mechanisms underlying branching. Here, we investigate the branching filaments that form upon spore germination in mosses, using Physcomitrium patens as a model species. To identify the macroscopic rules governing filament patterning, we developed a pipeline to acquire high-resolution 3D images of whole sporelings, reconstruct filament architecture at single-cell resolution, and formalize cell organization using mathematical tree representations. Our quantitative analysis reveals that branch patterning in moss filaments can be captured by a simple probabilistic model in which subapical cells have a near-equal probability of producing - or not producing - a side-branch between successive apical cell divisions. This framework provides a quantitative basis for comparing the developmental rules driving branching morphogenesis within and beyond the plant kingdom.

plant biology↗

bioRxiv2014

What to compare and how: comparative transcriptomics for Evo-Devo

Evolutionary developmental biology has grown historically from the capacity to relate patterns of evolution in anatomy to patterns of evolution of expression of specific genes, whether between very distantly related species, or very closely related species or populations. Scaling up such studies by taking advantage of modern transcriptomics brings promising improvements, allowing us to estimate the overall impact and molecular mechanisms of convergence, constraint or innovation in anatomy and development. But it also presents major challenges, including the computational definitions of anatomical homology and of organ function, the criteria for the comparison of developmental stages, the annotation of transcriptomics data to proper anatomical and developmental terms, and the statistical methods to compare transcriptomic data between species to highlight significant conservation or changes. In this article, we review these challenges, and the ongoing efforts to address them, which are emerging from bioinformatics work on ontologies, evolutionary statistics, and data curation, with a focus on their implementation in the context of the development of our database Bgee (http://bgee.org).

Evolutionary Biology↗

bioRxiv2023

Approximate Bayesian Computation for Inferring Waddington Landscapes from Single Cell Data

Single cell technologies allow us to gain insights into cellular processes at unprecedented resolution. In stem cell and developmental biology snapshot data allows us to characterise how the transcriptional state of cells changes between successive cell types. Here we show how approximate Bayesian computation (ABC) can be employed to calibrate mathematical models against single cell data. In our simulation study we demonstrate the pivotal role of the adequate choice of distance measures appropriate for single cell data. We show that for good distance measures, notably optimal transport distances, we can infer parameters for mathematical models from simulated single cell data. We show that the ABC posteriors can be used to characterise parameter sensitivity and identify dependencies between different parameters, and to infer representations of the Waddington or epigenetic landscape, which forms a popular and interpretable representation of the developmental dynamics. In summary, these results pave the way for fitting mechanistic models of stem cell differentiation to single cell data.

systems biology↗