Biology subjects

Baum, D.

Publications and source records attributed to Baum, D..

17 recordsLinked to original sources

bioRxiv2026

Selection for propagule formation in prebiotic chemical ecosystems

Biological individuals on Earth today are cellular, but the simplest cells are too complex to have arisen spontaneously. We explore the hypothesis that the emergence of biological individuality might have been driven by selection for different molecules to move through space together by forming a propagule. We predicted that propagule formation will be advantageous to an autocatalytic system primarily when 1) sites supporting autocatalysis are distantly spaced and 2) the environment undergoes periodic disturbances. We tested this hypothesis using computational reaction-diffusion models of surface-catalyzed autocatalytic systems in patchy, disturbance-prone environments. We considered two competing autocatalytic cycles, each composed of mutualistic subcycles, one of which diverts flux to the production of propagules. Our analysis shows that propagules tend to be detrimental in well-mixed environments or environments where viable surfaces are close enough together to be reliably seeded through single-species dispersal. However, propagule formation is advantageous when long stretches of empty space must be crossed to reach new resources and/or when environmental disturbances are frequent. These findings suggest spatial structure can selectively favor reliable forms of codispersal. Evolution in response to this mechanism could potentially explain the emergence of vesicles that transported many autocatalytic species and eventually became independent protocells.

evolutionary biology↗

bioRxiv2026

Cartilage canals in sharks and rays show that blood vessels can exist in mature cartilage without triggering endochondral bone formation

Although cartilage in tetrapod skeletons is typically said to lack blood vessels, this is only true for adult cartilage. In young bird and mammal cartilage, a dense network of vasculature-containing tunnels --cartilage canals-- perforate the growing skeleton, helping nourish the cartilage and develop the ossification centers that will later form the skeletons epiphyseal bone. As the canals and their rich vascular network typically recede as animals age, the healthy cartilage of adult animals is typically known to be avascular. Here, however, we use a range of tissue characterization and visualization techniques --including light/electron microscopy and microCT-- to show that the skeletons of rays and sharks (elasmobranch fishes) not only possess cartilage canals, but that these structures persist in the adult skeleton. The morphology and tissue composition of elasmobranch cartilage canals argues homology with mammalian cartilage canals and an ancient invasion of the vascular system into cartilage. However, the anatomical location of canals --extending away from mineralized tissue not toward it-- and the lack of endochondral ossification in ray and shark cartilage suggest that cartilage canals developed early in vertebrates as a transport system for nutrients and mesenchymal cells into the growing skeleton. We describe distinctive features and variation in elasmobranch cartilage canals, discuss their possible roles and their potential for tissue mineralization, and the biomedical implications for their presence in a clade of animals with continuously growing cartilaginous skeletons.

evolutionary biology↗

bioRxiv2026

Bridging disciplines towards wastewater-based surveillance of antimicrobial resistance: frequency, local dynamics, and genomic characteristics of carbapenemase-producing Klebsiella pneumoniae

The World Health Organization (WHO) has designated carbapenemase-producing Klebsiella pneumoniae (CP-KP) as a critical priority pathogen due to its increasing importance for human health. As wastewater-based surveillance (WBS) is discussed as a complementary tool for classical systems regarding hazard forecasting and early-warning, we designed a "wet-lab to genomics" workflow to target CP-KP in raw influent wastewater samples to support method development processes across different scientific disciplines. The CP-KP screening workflow was set up based on membrane filtration, selective chromogenic media for selective cultivation and the modified carbapenem inactivation method (mCIM) for confirming carbapenemase-production using 33 samples from four different wastewater treatment plants in North-Eastern Germany. All samples tested positive for CP-KP, with concentrations ranging between 102 and 104 colony-forming units (cfu) per 100 ml across the sample set. As a result, 320 isolates belonged to the Klebsiella, Enterobacter, Citrobacter (KEC)- group, with the majority being identified as KP (n= 297; 93%), including n= 253 (79%) verified CP-KP. Genotypic characterization of CP-KP by PCR revealed the predominance of blaOXA-48-related genes (n= 83) among isolates from all WWTPs. As quality parameters, colony counts for viable Escherichia coli (EC) were employed as a proxy for valid wastewater samples and extended-spectrum beta-lactamase-producing E. coli (ESBL-EC) as indicator for AMR, with cfu/100 ml ranges from 10 to 10 and 102 to 10, respectively. To verify the screening outcome, a subset of 58 CP-KP from two WWTPs were subjected to whole genome sequencing (WGS). As a result, eight different sequence types (STs), i.e., ST147 and ST273 (both: clonal group 147), ST258, ST35, ST15, ST37, ST307, and ST485 were identified. These include clinically relevant STs clustering closest with fecal isolates from Germany when compared with Pathogenwatch-database entries. Moreover, WGS data enabled the identification of antibiotic resistance genes (ARGs), and the detection of closely related isolates within the WWTP dataset.

microbiology↗

bioRxiv2026

TomoSegNet: Augmented membrane segmentation for cryo-electron tomography by simulating the cellular context

Membrane segmentation is an essential task in the workflow for processing cryo-electron tomography data. Recently, machine learning algorithms have successfully been adopted to perform membrane segmentation. However, the performance of these approaches is limited by the training dataset, as models are trained from manual annotations, thereby hindering the models generalization and preventing the recovery of membranes that have vanished due to distortions. Here, we address these limitations by generating training data with a simulator. To provide a representative and realistic dataset, we have extended the current state-of-the-art in simulators for cryo-electron tomography by incorporating a biophysical model for membranes. We demonstrate that our machine learning model, trained solely from synthetic data, and thanks to the physical knowledge learned from the simulator, outperforms the current state-of-the-art for membrane segmentation in a diverse set of experimental data. This performance is particularly noteworthy in terms of recovering membranes lost due to imaging distortions.

bioinformatics↗

bioRxiv2026

Simple case of prebiotic evolution: vesicle populations can respond to selection for greater turbidity via emergent cooperative dynamics

Adaptive evolution has long been hypothesized to be possible in the absence of genetic molecules, but experimental evidence remains lacking. Fatty acid vesicles represent an intriguing model to study the emergence of prebiotic evolution, since they can spontaneously grow and divide and have been hypothesized to be capable of non-genetic inheritance. In this study we conducted multiple experiments to test whether vesicle populations can respond to artificial selection for greater turbidity and, if so, whether that response can be tied to an inheritance-like mechanism. We prepared 96 independent vesicle populations, incubated them for 24 hours and then selected half the populations to propagate into the next generation. The populations to propagate were picked either randomly, representing drift controls, or were the populations with the greatest turbidity, representing selection. Population propagation involved resuspension, transfer into fresh buffer, feeding with an amphiphile stock, and then incubating for the next 24 hours. In three replicate experiments run for at least 10 generations, we observed consistently greater turbidity in selection lineages compared to drift, as well as a reduction in the heritability (i.e., the correlation between parent and offspring turbidities). We conducted additional experiments to evaluate whether this response to selection is caused by a simple carryover effect or reflects cooperative dynamics, where vesicles from the parental generation affect newly formed vesicles in the offspring generation. The response to selection is much lower if we omitted the resuspension step and/or if we did not feed transfers with amphiphiles but instead mixed them we pre-formed vesicles. Combined with imaging and other analyses of the resuspension and feeding process, these results suggest that cooperative vesicle dynamics occur, where a small number of intact vesicles from a parental generation alters the dynamics of new vesicle formation following food addition. Overall, this study represents the first experimental finding of a response to artificial selection in prebiotic chemistry.

evolutionary biology↗

bioRxiv2025

Cell size reduction scales spindle elongation but not chromosome segregation in C. elegans

How embryos adapt their internal cellular machinery to reductions in cell size during development remains a fundamental question in cell biology1-11. Here, we use high-resolution lattice light-sheet fluorescence microscopy and automated image analysis to quantify lineage-resolved mitotic spindle and chromosome segregation dynamics from the 2-to 64-cell stages in Caenorhabditis elegans embryos. While spindle length scales with cell size across both wild-type and size-perturbed embryos, chromosome segregation dynamics remain largely invariant, suggesting that distinct mechanisms govern these mitotic processes. Combining femtosecond laser ablation12,13 with large-scale electron tomography14, we find that central spindle microtubules mediate chromosome segregation dynamics and remain uncoupled from cell size across all stages of early development. In contrast, spindle elongation is driven by cortically anchored motor proteins and astral microtubules, rendering it sensitive to cell size12,13,15-17. Incorporating these experimental results into an extended stoichiometric model for both the spindle and chromosomes, we find that allowing only cell size and microtubule catastrophe rates to vary reproduces elongation dynamics across development. The same model also accounts for centrosome separation and pronuclear positioning in the one-cell C. elegans embryo18, spindle-length scaling across nematode species spanning ~100 million years of divergence17, and spindle rotation in human cells19. Thus, a unified stoichiometric framework provides a predictive, mechanistic account of spindle and nuclear dynamics across scales and species.

cell biology↗

bioRxiv2025

Fishexplorer: A multimodal cellular atlas platform for neuronal circuit dissection in larval zebrafish

Understanding how neural circuits give rise to behavior requires comprehensive knowledge of neuronal morphology, connectivity, and function. Atlas platforms play a critical role in enabling the visualization, exploration, and dissemination of such information. Here, we present FishExplorer, an interactive and expandable community platform designed to integrate and analyze multimodal brain data from larval zebrafish. FishExplorer supports datasets acquired through light microscopy (LM), electron microscopy (EM), and X-ray imaging, all co-registered within a unified spatial coordinate system which enables seamless comparison of neuronal morphologies and synaptic connections. To further assist circuit analysis, FishExplorer includes a suite of tools for querying and visualizing connectivity at the whole-brain scale. By integrating data from recent large-scale EM reconstructions (presented in companion studies), FishExplorer enables researchers to validate circuit models, explore wiring principles, and generate new hypotheses. As a continuously evolving resource, FishExplorer is designed to facilitate collaborative discovery and serve the growing needs of the teleost neuroscience community.

neuroscience↗

bioRxiv2025

Semi-automatic Geometrical Reconstruction and Analysis of Filopodia Dynamics in 4D Two-Photon Microscopy Images

BackgroundFilopodia are thin and dynamic membrane protrusions that play a crucial role in cell migration, axon guidance, and other processes where cells explore and interact with their surroundings. Historically, filopodial dynamics have been studied in great detail in 2D in cultured cells, and more recently in 3D culture as well as living brains. However, there is a lack of efficient tools to trace and track filopodia in 4D images of complex brain cells. ResultsTo address this issue, we have developed a semi-automatic workflow for tracing filopodia in 3D images and tracking the traced filopodia over time. The workflow was developed based on high-resolution data of photoreceptor axon terminals in the in vivo context of normal Drosophila brain development, but devised to be applicable to filopodia in any system, including at different temporal and spatial scales. In contrast to the pre-existing methods, our workflow relies solely on the original intensity images without the requirement for segmentation or complex preprocessing. The workflow was realized in C++ within the Amira software system and consists of two main parts, dataset pre-processing, and geometrical filopodia reconstruction, where each of the two parts comprises multiple steps. In this paper, we provide an extensive workflow description and demonstrate its versatility for two different axo-dendritic morphologies, R7 and Dm8 cells. Finally, we provide an analysis of the time requirements for user input and data processing. ConclusionTo facilitate simple application within Amira or other frameworks, we share the source code, which is available at https://github.com/zibamira/filopodia-tool.

neuroscience↗

bioRxiv2025

Correlative light and electron microscopy reveals the fine circuit structure underlying evidence accumulation in larval zebrafish

AbstractEvidence accumulation is a fundamental neural computation essential for adaptive behavior, yet its synaptic implementation remains unclear. Addressing this challenge critically depends on linking neural dynamics to circuit structure within the same brain. Here, we combine functional calcium imaging with large-scale ultrastructural electron microscopy (EM) to uncover the wiring logic of visual evidence accumulation in larval zebrafish. In a functionally imaged EM dataset of the anterior hindbrain, we identify conserved morphological cell types whose activity patterns define distinct computational roles. Bilateral inhibition, disinhibition, and recurrent connectivity emerge as key circuit motifs shaping these dynamics. To generalize our findings across animals, we develop a photoconversion-based pipeline to label and reconstruct functionally characterized neurons, enabling us to train a classifier that predicts functional identity from morphology alone. Applying this classifier to a second, whole-brain EM dataset lacking functional data reveals matching connectivity patterns, significantly augmenting its applicability for detailed circuit dissections. Based on these results, we develop and constrain a biophysically realistic neural network model that captures observed dynamics and yields predictions we tested and confirmed experimentally. Our work illustrates how hypothesis-driven connectomics can uncover the synaptic basis of sensory-motor computations and establishes a novel framework for cross-animal circuit dissection in the vertebrate brain.

neuroscience↗

bioRxiv2024

Dissecting origins of wiring specificity in dense cortical connectomes

What are the origins of the highly specific wiring patterns that are formed by the neurons in the brain? To address this question, we introduce a method to predict the empirically observed wiring diagram - the connectome - at synaptic resolution based on dense electron-microscopic reconstructions of neural tissue. Our method generates the connectome based on the morphological properties of the neurons in combination with synaptic specificity models, whose parameters capture how neurons wire conditional on their subcellular, cellular and cell type properties. We employ simulation-based Bayesian inference to identify all values for these parameters that can generate the observed connectome. Finally, for each synapse in a dense reconstruction, our method provides quantitative measures to reveal which synaptic specificity models are necessary, sufficient and best-suited to generate it. The output of our method are experimentally testable predictions of wiring preferences from subcellular to cell type levels that could account for each synapse in dense reconstructions. We demonstrate our method on dense datasets from mouse primary visual and human temporal cortex. Strikingly, this demonstration shows that just three assumptions, with nearly the same synaptic specificity values, predict the connectivity in both datasets. Our method is openly accessible as a computational framework that includes a comprehensive workflow for the analysis of wiring specificity, and which provides users with full flexibility to define and test their hypotheses. Our method sets the stage to uncover the principles by which neural networks are organized, and to compare these principles across brain areas, species and time points.

neuroscience↗

bioRxiv2024

DeepOrientation: Deep Orientation Estimation of Macromolecules in Cryo-electron tomography

Orientation estimation of macromolecules in cryo-electron tomography (cryo-ET) images is one of the fundamental steps in applying subtomogram averaging. The standard method in particle picking and orientation estimation is template matching (TM), which is computationally very expensive, with its performance depending linearly on the number of template orientations. In addition to conventional image processing methods like TM, the investigation of crowded cell environments using cryo-ET has also been attempted with deep learning (DL) methods. These attempts were restricted to macromolecule localization and identification while orientation estimation was not addressed due to a lack of a large enough dataset of ground truth annotations suitable for DL. To this end, we first generate a large-scale synthetic dataset of 450 tomograms containing almost 200K samples of two macromolecular structures using the PolNet simulator. Utilizing this synthetic dataset, we address the problem of particle orientation estimation as a regression problem by proposing a DL-based model based on multi-layer perceptron networks and a six-degree-of-freedom orientation representation. The iso-surface visualizations of the averaged subtomograms show that the predicted results by the network are overly similar to that of ground truth. Our work shows that orientation estimation of particles using DL methods is in principle possible provided that ground truth data is available. What remains to be solved is the gap between synthetic and experimental data. The source code is available at https://github.com/noushinha/DeepOrientation.

molecular biology↗

bioRxiv2024

Multi-species community platform for comparative neuroscience in teleost fish

Studying neural mechanisms in complementary model organisms from different ecological niches in the same animal class can leverage the comparative brain analysis at the cellular level. To advance such a direction, we developed a unified brain atlas platform and specialized tools that allowed us to quantitatively compare neural structures in two teleost larvae, medaka (Oryzias latipes) and zebrafish (Danio rerio). Leveraging this quantitative approach we found that most brain regions are similar but some subpopulations are unique in each species. Specifically, we confirmed the existence of a clear dorsal pallial region in the telencephalon in medaka lacking in zebrafish. Further, our approach allows for extraction of differentially expressed genes in both species, and for quantitative comparison of neural activity at cellular resolution. The web-based and interactive nature of this atlas platform will facilitate the teleost communitys research and its easy extensibility will encourage contributions to its continuous expansion.

neuroscience↗

bioRxiv2023

Simulation-based inference for efficient identification of generative models in connectomics

Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the posterior distribution over parameters conditioned on the data) that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data. Author summaryThe brain is composed of an intricately connected network of cells--what are the principles that contribute to constructing these patterns of connectivity, and how? To answer these questions, amassing connectivity data alone is not enough. We must also be able to efficiently develop and test our ideas about the underlying connectivity principles. For example, we could simulate a hypothetical wiring rule like "neurons near each other are more likely to form connections" in a computational model and generate corresponding synthetic data. If the synthetic, simulated data resembles the real, measured data, then we have some confidence that our hypotheses might be correct. However, the proposed wiring rules usually have unknown parameters that we need to "tune" such that simulated data matches the measurements. The central challenge thus lies in finding all the potential parametrizations of a wiring rule that can reproduce the measured data, as this process is often idiosyncratic and labor-intensive. To tackle this challenge, we introduce an approach combining computational modeling in connectomics, deep learning, and Bayesian statistical inference to automatically infer a probability distribution over the model parameters likely to explain the data. We demonstrate our approach by inferring the parameters of a wiring rule in a detailed model of the rat barrel cortex and find that the inferred distribution identifies multiple data-compatible model parameters, reveals biologically plausible parameter interactions, and allows us to make experimentally testable predictions.

neuroscience↗

bioRxiv2022

MCRS1 modulates the heterogeneity of microtubule minus-end morphologies in mitotic spindles

Faithful chromosome segregation requires the assembly of a bipolar spindle, consisting of two antiparallel microtubule (MT) arrays having most of their minus ends focused at the spindle poles and their plus ends overlapping in the spindle midzone. Spindle assembly, chromosome alignment and segregation require highly dynamic MTs. The plus ends of MTs have been extensively investigated; instead, their minus end structure remains poorly characterized. Here, we used large-scale electron tomography to study the morphology of the MT minus ends in 3D-reconstructed metaphase spindles in HeLa cells. In contrast to the homogeneous open morphology of the MT plus ends at the kinetochores, we found that MT minus ends are heterogeneous showing either open or closed morphologies. Silencing the minus-end specific stabilizer, MCRS1 increased the proportion of open MT minus ends. Altogether, these data suggest a correlation between the morphology and the dynamic state of the MT ends. Taking this heterogeneity of the MT minus end morphologies into account, our work indicates an unsynchronized behavior of MTs at the spindle poles, thus laying the ground for further studies on the complexity of MT dynamics regulation.

cell biology↗

bioRxiv2021

Automated segmentation of insect anatomy from micro-CT images using deep learning

Three-dimensional (3D) imaging, such as micro-computed tomography (micro-CT), is increasingly being used by organismal biologists for precise and comprehensive anatomical characterization. However, the segmentation of anatomical structures remains a bottleneck in research, often requiring tedious manual work. Here, we propose a pipeline for the fully-automated segmentation of anatomical structures in micro-CT images utilizing state-of-the-art deep learning methods, selecting the ant brain as a test case. We implemented the U-Net architecture for 2D image segmentation for our convolutional neural network (CNN), combined with pixel-island detection. For training and validation of the network, we assembled a dataset of semi-manually segmented brain images of 94 ant species. The trained network predicted the brain area in ant images fast and accurately; its performance tested on validation sets showed good agreement between the prediction and the target, scoring 80% Intersection over Union (IoU) and 90% Dice Coefficient (F1) accuracy. While manual segmentation usually takes many hours for each brain, the trained network takes only a few minutes. Furthermore, our network is generalizable for segmenting the whole neural system in full-body scans, and works in tests on distantly related and morphologically divergent insects (e.g., fruit flies). The latter suggest that methods like the one presented here generally apply across diverse taxa. Our method makes the construction of segmented maps and the morphological quantification of different species more efficient and scalable to large datasets, a step toward a big data approach to organismal anatomy.

evolutionary biology↗

bioRxiv2020

Semi-automatic stitching of filamentous structures in image stacks from serial-section electron tomography

We present a software-assisted workflow for the alignment and matching of filamentous structures across a 3D stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After an initial alignment, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labor. The software tool was developed to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite the problems related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time consuming. Furthermore, an interactive visualization of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialized, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. The key to our solution is a careful design of the visualization and interaction tools for each processing step to guarantee real-time response, and an optimized workflow that efficiently guides the user through datasets. Author summaryElectron tomography of biological samples is used for a 3D reconstruction of filamentous structures, such as microtubules (MTs) in mitotic and meiotic spindles. Large-scale electron tomography can be applied to increase the reconstructed volume for the visualization of full spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. Previously, we presented fully automatic approaches for this 3D reconstruction pipeline. However, large volumes often suffer from imperfections (i.e. physical distortions) caused during sectioning and imaging, making it difficult to apply fully automatic approaches for matching and stitching of numerous tomograms. Therefore, we developed an interactive, semi-automatic solution that considers all requirements for large-scale stitching of microtubules in serial-section image stacks. We achieved this by combining automatic methods, visual validation and interactive error correction, thus allowing the user to continuously improve the result by interactively correcting landmarks or matches of filaments. We present large-scale reconstructions of spindles in which the automatic workflow failed and where different steps of manual corrections were needed. Our approach is also applicable to other biological samples showing 3D distributions of MTs in a number of different cellular contexts.

cell biology↗

bioRxiv2020

Establishing cell-intrinsic limitations in cell cycle progression controls graft growth and promotes differentiation of pancreatic endocrine cells

Limitations in cell proliferation are a key barrier to reprogramming differentiated cells to pluripotent stem cells, and conversely, acquiring these limitations may be important to establish the differentiated state. The pancreas, and beta cells in particular have a low proliferative potential, which limits regeneration, but how these limitations are established is largely unknown. Understanding proliferation potential is important for the safty of cell replacement therapy with cell products made from pluripotent stem cell which have unlimited proliferative potential. Here we test a novel hypothesis, that these limitations are established through limitations in S-phase progression. We used a stem cell-based system to expose differentiating stem cells to small molecules that interfere with cell cycle progression either by inducing G1 arrest, impairing S-phase entry, or S-phase completion. Upon release from these molecules, we determined growth potential, differentiation and function of insulin-producing endocrine cells both in vitro and after grafting in vivo. We found that the combination of G1 arrest with a compromised ability to complete DNA replication promoted the differentiation of pancreatic progenitor cells towards insulin-producing cells, improved the stability of the differentiated state, and protected mice from diabetes without the formation of cystic growths. Therefore, a compromised ability to enter S-phase and replicate the genome is a functionally important property of pancreatic endocrine differentiation, and can be exploited to generate insulin-producing organoids with predictable growth potential after transplantation.

cell biology↗