Biology subjects

Pape, C.

Publications and source records attributed to Pape, C..

At least 19 recordsLinked to original sources

bioRxiv2026

An aorta to insulin producing cell sensor for internal state in Drosophila

The cellular circuits through which the internal state of an organism is sensed and relayed to the brain, and through which they bring about appropriate physiological and behavioral responses, remain largely unelucidated. Using whole-animal electron microscopy reconstruction of a Drosophila larva, we previously identified an Aorta sensory organ whose somata lie on the periphery of the brain, with dendritic arborizations in the wall of the anterior aorta, and whose axons project to the protocerebrum and synapse onto the insulin-producing cells (IPCs). Here we characterize the full anatomy and connectivity of this sensory organ. We show that the aorta sensory neurons make their strongest direct synaptic contacts onto IPCs and the neurosecretory cells producing Dromyosuppressin (DMS), with only weaker direct contacts to diuretic hormone 44 (DH44). They also make direct contacts to the enteric serotonergic neurons modulating swallowing (Se0ens), and to two single mushroom body neurons, DAN-j1 and MBON-d3, of the learning and memory center of Drosophila. Calcium imaging shows that the neurons are activated by fructose, and optogenetic manipulation reveals that their activity influences peptide content in IPCs, DH44 and DMS cells. Polysynaptic paths to the brain run through two morphologically distinct interneuron families. One, formed by the Hugin protocerebral neurons, targets the neuroendocrine system and is associated with bitter taste and pathogen induced feeding suppression. The other, formed by the BAmas projection neurons, targets dopaminergic input neurons and output neurons of the mushroom body. The Aorta sensory pathway therefore couples internal hemolymph state to two parallel central systems -- endocrine state control and mushroom body-associated value updating -- and provides a candidate substrate for a metabolic memory through which post-ingestive state is fed back into future feeding choice.

neuroscience↗

bioRxiv2025

CochleaNet: deep learning-based image analysis for cochlear connectomics and gene therapy

With the emergence of gene and optogenetic therapies targeting deafness, the comprehensive analysis of the molecular anatomy and physiology of the cochlea has become ever more important. Here, we introduce CochleaNet, a deep learning-based framework to analyze volumetric imaging data obtained by light-sheet microscopy of decalcified, cleared and fluorescently labeled cochleae. CochleaNet covers the workflow from reconstruction of the cochlea to segmentation of inner hair cells, spiral ganglion neurons and their afferent synapses, to analyzing the expression of gene therapy products. We validated CochleaNet by comparison to manual image analysis. Trained on high isotropic resolution mouse data, CochleaNet was also applicable to the cochlea of the gerbil, another relevant animal model, and lower-resolution mouse data from a commercially available microscope. We conclude that the combination of light-sheet microscopy and image analysis with CochleaNet paves the way for rapid and reliable quantification of cochlear molecular anatomy and preclinical gene therapy outcomes.

neuroscience↗

bioRxiv2025

The role of aging on endothelial cell-cell junctions and pulmonary microvascular permeability

Lung injury leads to pulmonary microvascular endothelial cell (PMVEC) damage, disruption of cell-cell junctions, and increased permeability. Previously, we demonstrated in a mechanical ventilation-induced model of lung injury that aging exacerbated pulmonary microvascular permeability. Based on this, we hypothesized that aging was associated with increased PMVEC barrier dysfunction due to impaired cell-cell junction integrity. PMVEC were isolated from young and aged mice and cultured to confluence in vitro. Barrier function and cell-cell junction integrity were assessed through electric cell-substrate impedance sensing, XPerT permeability assay, immunofluorescence, and western blot analysis. Further studies were conducted to examine alterations in the proteome, markers of inflammation, and actin cytoskeleton organization. To model injurious conditions, PMVEC were stimulated with inflammatory cytokines; permeability and actin cytoskeletal alterations were subsequently assessed. We observed increased basal permeability in PMVEC from aged mice, which was associated with disrupted cell-surface localization of the adherens junction protein, vascular endothelial (VE)-cadherin. Protein abundance of VE-cadherin was significantly increased with age; however, levels of the adapter protein,{gamma} -catenin, and the tight junction protein, claudin-5, were decreased. Measures of inflammation, including cytokine expression and cell surface abundance of adhesion molecules, did not differ with age. Alterations in actin cytoskeleton organization, characterized by augmented presence of actin stress fibers, were observed in aged PMVEC. Under inflammatory conditions, permeability and actin stress fiber formation were exacerbated with age. It is concluded that aging predisposes PMVEC to elevated injury, due to inherent deficiencies in cell-cell junctions and barrier function, potentially mediated through altered actin cytoskeleton organization. New and NoteworthyCompared with pulmonary microvascular endothelial cells (PMVEC) from young mice, PMVEC isolated from aged mice had higher permeability, which was directly associated with impairments in cell-cell junctions. The higher permeability in aged PMVEC was not associated with augmented inflammatory signaling but was associated with actin cytoskeletal alterations. Following an inflammatory insult, PMVEC from aged mice had further exacerbated permeability. These findings may begin to highlight why older patients exhibit higher mortality during lung injury.

cell biology↗

bioRxiv2025

Structure and function of otoferlin, a synaptic protein of sensory hair cells essential for hearing

Our sense of hearing relies upon speedy synaptic transmission of sound information from cochlear inner hair cells (IHCs) to spiral ganglion neurons (SGNs). To accomplish this, IHCs employ a sophisticated presynaptic machinery including the multi-C2-domain protein otoferlin which is affected by human deafness mutations. Otoferlin is essential for IHC-exocytosis but how it binds Ca2+ and the target membrane to serve synaptic vesicle (SV) tethering, docking and fusion remained unclear. Here, we obtained cryo-electron-microscopy structures of Ca2+-bound otoferlin and employed molecular dynamics simulations of membrane binding. We show that membrane binding involves C2B-C2G-domains and repositions C2F- and C2G-domains. Progressive disruption of Ca2+-binding by the C2D-domain in mice increasingly altered synaptic sound encoding and eliminated the Ca2+-cooperativity of SV-exocytosis, indicating that this Ca2+-cooperativity reflects binding of several Ca2+-ions to otoferlin. Together, our findings elucidate molecular mechanisms underlying otoferlin-mediated SV-docking and support a role of otoferlin as Ca2+-sensor of SV-fusion in IHCs.

neuroscience↗

bioRxiv2025

A correlative workflow streamlines synaptic imaging by cryo-electron tomography

Despite decades of intense research, the molecular organization of the synapse is not well understood. To address this issue, we sought to develop a method for systematic imaging of synapses by cryo-electron tomography (cryo-ET), a technology capable of mapping cellular architecture at molecular resolution. Thinning of cellular samples by cryo-focused ion beam milling is a prerequisite for high-quality cryo-ET imaging, but this process needs to be guided to the structures of interest. To allow robust synaptic targeting, we established a correlative cryo-light/electron microscopy approach by which synapses are fluorescently labeled in a minimally invasive manner, using a synthetic binder of the postsynaptic scaffold PSD-95 and antibodies against the presynaptic protein Synaptotagmin-1. Cryo-ET imaging at sites of colocalization consistently revealed excitatory synapses. Our method allows structural studies of synaptic protein complexes in situ, facilitating investigations of the molecular architecture of synapses.

neuroscience↗

bioRxiv2024

A Spatiotemporal Atlas of Mouse Gastrulation and Early Organogenesis to Explore Axial Patterning and Project In Vitro Models onto In Vivo Space

At the onset of murine gastrulation, pluripotent epiblast cells migrate through the primitive streak, generating mesodermal and endodermal precursors, while the ectoderm arises from the remaining epiblast. Together, these germ layers establish the body plan, defining major body axes and initiating organogenesis. Although comprehensive single cell transcriptional atlases of dissociated mouse embryos across embryonic stages have provided valuable insights during gastrulation, the spatial context for cell differentiation and tissue patterning remain underexplored. In this study, we employed spatial transcriptomics to measure gene expression in mouse embryos at E6.5 and E7.5 and integrated these datasets with previously published E8.5 spatial transcriptomics1 and a scRNA-seq2 atlas spanning E6.5 to E9.5. This approach resulted in a comprehensive spatiotemporal atlas, comprising over 150,000 cells with 88 refined cell type annotations as well as genome-wide transcriptional imputation during mouse gastrulation and early organogenesis. The atlas facilitates exploration of gene expression dynamics along anterior-posterior and dorsal-ventral axes at cell type, tissue, and organismal scales, revealing insights into mesodermal fate decisions within the primitive streak. Moreover, we developed a bioinformatics pipeline to project additional scRNA-seq datasets into a spatiotemporal framework and demonstrate its utility by analysing cardiovascular models of gastrulation3. To maximise impact, the atlas is publicly accessible via a user-friendly web portal empowering the wider developmental and stem cell biology communities to explore mechanisms of early mouse development in a spatiotemporal context.

developmental biology↗

bioRxiv2024

SynapseNet: Deep Learning for Automatic Synapse Reconstruction

Electron microscopy is an important technique for the study of synaptic morphology and its relation to synaptic function. The data analysis for this task requires the segmentation of the relevant synaptic structures, such as synaptic vesicles, active zones, mitochondria, presynaptic densities, synaptic ribbons, and synaptic compartments. Previous studies were predominantly based on manual segmentation, which is very time-consuming and prevented the systematic analysis of large datasets. Here, we introduce SynapseNet, a tool for the automatic segmentation and analysis of synapses in electron micrographs. It can reliably segment synaptic vesicles and other synaptic structures in a wide range of electron microscopy approaches, thanks to a large annotated dataset, which we assembled, and domain adaptation functionality we developed. We demonstrated its capability for (semi-)automatic biological analysis in two applications and made it available as an easy-to-use tool to enable novel data-driven insights into synapse organization and function.

bioinformatics↗

bioRxiv2024

Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis

Vesicle exocytosis is a fundamental component of intercellular communication, in all organisms. It has been studied for decades, using various imaging tools. Nevertheless, exocytosis research is still limited by the lack of reliable automated analysis procedures. To address this, we developed the Intelligent Vesicle Exocytosis Analysis Platform (IVEA), a nearly universal solution for analyzing exocytosis acquired with live cell imaging. IVEA is applicable to a wide variety of experimental model systems, microscopes and reporter fluorophores. IVEA combines state-of-the-art deep-learning and computer vision regimes to enable fully automated analysis of large data. IVEA runs as a FIJI plugin and does not require prior training or human intervention. IVEA is 60 times faster than manual analysis and is able to detect rare events often missed by the human eye. Overall, IVEA represents a breakthrough in the analysis of cellular secretory mechanisms and has a transformative potential for the exocytosis imaging field.

bioengineering↗

bioRxiv2023

Segment Anything for Microscopy

We present Segment Anything for Microscopy, a tool for interactive and automatic segmentation and tracking of objects in multi-dimensional microscopy data. Our method is based on Segment Anything, a vision foundation model for image segmentation. We extend it by training specialized models for microscopy data that significantly improve segmentation quality for a wide range of imaging conditions. We also implement annotation tools for interactive (volumetric) segmentation and tracking, that speed up data annotation significantly compared to established tools. Our work constitutes the first application of vision foundation models to microscopy, laying the groundwork for solving image analysis problems in these domains with a small set of powerful deep learning architectures.

bioinformatics↗

bioRxiv2023

CebraEM: A practical workflow to segmentcellular organelles in volume SEM datasetsusing a transferable CNN-based membraneprediction

Segmentation of large-volume datasets obtained by volume SEM techniques is a challenging task that generally requires a considerable amount of human effort. Despite recent advances in deep learning leading to the successful segmentation of cellular organelles in a variety of datasets, it is still challenging and time-consuming to produce the necessary data for training a convolutional neural network as well as to set up targeted post-processing pipelines to obtain a good quality full-volume semantic instance segmentation. We present CebraEM, a software package that uses a novel workflow for the segmentation of organelles in volume EM datasets, which helps to minimize the annotation time for the generation of training data. It relies on a generic CNN-based membrane prediction, followed by a well-established machine-learning pipeline that includes over-segmentation before random forest classification and graph multi-cut grouping. The workflow was tested for the segmentation of organelles on different datasets originating from various sample preparations and imaging modalities in volume SEM, in each case resulting in state-of-the-art semantic instance segmentations without additional post-processing. Importantly, by considerably simplifying the segmentation problem, CebraEM empowers single users with the ability to efficiently segment hundreds of gigabytes of data.

cell biology↗

bioRxiv2023

OME-Zarr: a cloud-optimized bioimaging file format with international community support

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself - OME-Zarr - along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

bioinformatics↗

bioRxiv2022

BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis

Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based workflows for imaging scientists remain far from sufficient. Several tools have recently risen to the challenge of democratizing deep learning by providing user-friendly interfaces to analyze new data with pre-trained or fine-tuned models. Still, few of the existing pre-trained models are interoperable between these tools, critically restricting a models overall utility and the possibility of validating and reproducing scientific analyses. Here, we present the BioImage Model Zoo (https://bioimage.io): a community-driven, fully open resource where standardized pre-trained models can be shared, explored, tested, and downloaded for further adaptation or direct deployment in multiple end user-facing tools (e.g., ilastik, deepImageJ, QuPath, StarDist, ImJoy, ZeroCostDL4Mic, CSBDeep). To enable everyone to contribute and consume the Zoo resources, we provide a model standard to enable cross-compatibility, a rich list of example models and practical use-cases, developer tools, documentation, and the accompanying infrastructure for model upload, download and testing. Our contribution aims to lay the groundwork to make deep learning methods for microscopy imaging findable, accessible, interoperable, and reusable (FAIR) across software tools and platforms.

bioinformatics↗

bioRxiv2022

MoBIE: A Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data

Facing the challenge of exploring and sharing multi-terabyte, multi-modal and multi-scale image data of heterogeneous dimensionality, we developed MoBIE, a Fiji plugin that provides rich visualization features to enable browsing data from numerous biomedical applications on a standard laptop computer. MoBIE also supports segmentations, associated measurements and annotations. Users can configure complex views of datasets, share them with collaborators, and use them for interactive figure panels. The MoBIE plugin also offers a convenient interface for converting data into compatible data formats; an additional Python library facilitates managing diverse MoBIE projects.

bioinformatics↗

bioRxiv2022

Convolutional networks for supervised mining of molecular patterns within cellular context

Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (Deep Picker in Context), an open-source deep-learning framework for supervised structure segmentation and macromolecular complex localization in cellular cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles and cytosol. By comparing our method to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally-distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, by applying pre-trained networks to a HeLa cell dataset, we demonstrate that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate exploitation by the community.

bioinformatics↗

bioRxiv2021

From shallow to deep: exploiting feature-based classifiers for domain adaptation in semantic segmentation

The remarkable performance of Convolutional Neural Networks on image segmentation tasks comes at the cost of a large amount of pixelwise annotated images that have to be segmented for training. In contrast, feature-based learning methods, such as the Random Forest, require little training data, but never reach the segmentation accuracy of CNNs. This work bridges the two approaches in a transfer learning setting. We show that a CNN can be trained to correct the errors of the Random Forest in the source domain and then be applied to correct such errors in the target domain without retraining, as the domain shift between the Random Forest predictions is much smaller than between the raw data. By leveraging a few brushstrokes as annotations in the target domain, the method can deliver segmentations that are sufficiently accurate to act as pseudo-labels for target-domain CNN training. We demonstrate the performance of the method on several datasets with the challenging tasks of mitochondria, membrane and nuclear segmentation. It yields excellent performance compared to microscopy domain adaptation baselines, especially when a significant domain shift is involved.

bioinformatics↗

bioRxiv2021

Microglia complement signaling promotes neuronal elimination and normal brain functional connectivity

Complement signaling is thought to serve as an opsonization signal to promote the phagocytosis of synapses by microglia. However, while its role in synaptic remodeling has been demonstrated in the retino-thalamic system, it remains unclear whether complement signaling mediates synaptic pruning in the brain more generally. Here we show that mice lacking the complement 3 receptor (C3r), the major microglia complement receptor, fail to show a deficit in either synaptic pruning or axon elimination in the developing mouse cortex. Instead, mice lacking C3r show a deficit in the perinatal elimination of neurons, both in the retina as well as in the cortex, a deficit that is associated with increased cortical thickness and enhanced functional connectivity in these regions in adulthood. These data demonstrate a preferential role for complement in promoting neuronal elimination in the developing brain and argue for a reconsideration of the role of complement in synaptic pruning.

neuroscience↗

bioRxiv2021

Unsupervised temporal consistency improvement for microscopy video segmentation with Siamese networks

We introduce a simple mechanism by which a CNN trained to perform semantic segmentation of individual images can be re-trained - with no additional annotations - to improve its performance for segmentation of videos. We put the segmentation CNN in a Siamese setup with shared weights and train both for segmentation accuracy on annotated images and for segmentation similarity on unlabelled consecutive video frames. Our main application is live microscopy imaging of membrane-less organelles where the fluorescent groundtruth for virtual staining can only be acquired for individual frames. The method is directly applicable to other microscopy modalities, as we demonstrate by experiments on the Cell Segmentation Benchmark. Our code is available at https://github.com/kreshuklab/learning-temporal-consistency.

bioinformatics↗

bioRxiv2020

A versatile reporter system to monitor virus infected cells and its application to dengue virus and SARS-CoV-2

Positive-strand RNA viruses have been the etiological agents in several major disease outbreaks over the last few decades. Examples of that are flaviviruses, such as dengue virus and Zika virus that cause millions of yearly infections and spread around the globe, and coronaviruses, such as SARS-CoV-2, which is the cause of the current pandemic. The severity of outbreaks caused by these viruses stresses the importance of virology research in determining mechanisms to limit virus spread and to curb disease severity. Such studies require molecular tools to decipher virus-host interactions and to develop effective interventions. Here, we describe the generation and characterization of a reporter system to visualize dengue virus and SARS-CoV-2 replication in live cells. The system is based on viral protease activity causing cleavage and nuclear translocation of an engineered fluorescent protein that is expressed in the infected cells. We show the suitability of the system for live cell imaging and visualization of single infected cells as well as for screening and testing of antiviral compounds. Given the modular building blocks, the system is easy to manipulate and can be adapted to any virus encoding a protease, thus offering a high degree of flexibility. IMPORTANCEReporter systems are useful tools for fast and quantitative visualization of viral replication and spread within a host cell population. Here we describe a reporter system that takes advantage of virus-encoded proteases that are expressed in infected cells to cleave an ER-anchored fluorescent protein fused to a nuclear localization sequence. Upon cleavage, the fluorescent protein translocates to the nucleus, allowing for rapid detection of the infected cells. Using this system, we demonstrate reliable reporting activity for two major human pathogens from the Flaviviridae and the Coronaviridae families: dengue virus and SARS-CoV-2. We apply this reporter system to live cell imaging and use it for proof-of-concept to validate antiviral activity of a nucleoside analogue. This reporter system is not only an invaluable tool for the characterization of viral replication, but also for the discovery and development of antivirals that are urgently needed to halt the spread of these viruses.

microbiology↗