Biology subjects

Chessel, A.

Publications and source records attributed to Chessel, A..

7 recordsLinked to original sources

bioRxiv2025

Cranial suture integrity is maintained by Fgfr3 in zebrafish

Cranial suture formation is a dynamic process requiring precise cellular and molecular coordination to regulate bone growth and suture homeostasis. Despite the recognized role of Fibroblast Growth Factor Receptor 3 (FGFR3) in this process and its association with Muenke syndrome, one of the most common syndromic craniosynostoses, its precise function remains undefined. Through analyses of cranial suture formation and maintenance in a fgfr3 LoF zebrafish model displaying abnormal suture morphology, we demonstrate for the first time that Fgfr3 plays a pleiotropic role during these events. Transmission electron microscopy and second harmonic generation imaging revealed that Fgfr3 is involved in the proper organization of the collagen network within the suture. By employing specific transgenic reporter lines and studying gene expression of suture mesenchymal stem cell markers via RNAscope in situ hybridization, we show that Fgfr3 is crucial for regulating osteogenesis within the suture. Specifically, Fgfr3 limits the number of osteoprogenitors at the osteogenic front and promotes osteoblast maturation at the suture edge. Finally, our results reveal that Fgfr3-mediated regulation of osteogenesis involves cross-talk between FGF, canonical Wnt, and potentially BMP signaling pathways. In conclusion, these data position Fgfr3 as a central regulator of cranial suture formation and open new perspectives for understanding suture homeostasis and FGFR3-related craniosynostoses.

developmental biology↗

bioRxiv2024

BiaPy: A unified framework for versatile bioimage analysis with deep learning

BiaPy is an open-source library and application that streamlines the use of common deep learning approaches for bioimage analysis. Designed to simplify technical complexities, it offers an intuitive interface, zero-code notebooks, and Docker integration, catering to both users and developers. While focused on deep learning workflows for 2D and 3D image data, it enhances performance with multi-GPU capabilities, memory optimization, and scalability for large datasets. Although BiaPy does not encompass all aspects of bioimage analysis, such as visualization and manual annotation tools, it empowers researchers by providing a ready-to-use environment with customizable templates that facilitate sophisticated bioimage analysis workflows.

bioinformatics↗

bioRxiv2020

A scale-space approach for 3D neuronal traces analysis

AO_SCPLOWBSTRACTC_SCPLOWTridimensional microscopy and algorithms for automated segmentation and tracing are revolutionizing neuroscience through the generation of growing libraries of neuron reconstructions. Innovative computational methods are needed to analyze these neural traces. In particular, means to analyse the geometric properties of traced neurites along their trajectory have been lacking. Here, we propose a local tridimensional (3D) scale metric derived from differential geometry, which is the distance in micrometers along which a curve is fully 3D as opposed to being embedded in a 2D plane or 1D line. We apply this metric to various neuronal traces ranging from single neurons to whole brain data. By providing a local readout of the geometric complexity, it offers a new mean of describing and comparing axonal and dendritic arbors from individual neurons or the behavior of axonal projections in different brain regions. This broadly applicable approach termed nAdder is available through the GeNePy3D open-source Python quantitative geometry library.

neuroscience↗

bioRxiv2019

BIAFLOWS: A collaborative framework to benchmark bioimage analysis workflows

Automated image analysis has become key to extract quantitative information from scientific microscopy bioimages, but the methods involved are now often so refined that they can no longer be unambiguously described using written protocols. We introduce BIAFLOWS, a software tool with web services and a user interface specifically designed to document, interface, reproducibly deploy, and benchmark image analysis workflows. BIAFLOWS allows image analysis workflows to be compared fairly and shared in a reproducible manner, safeguarding research results and promoting the highest quality standards in bioimage analysis. A curated instance of BIAFLOWS is available online; it is currently populated with 34 workflows that can be triggered to process image datasets illustrating 15 common bioimage analysis problems organized in 9 major classes. As a complete case study, the open benchmarking of 7 nuclei segmentation workflows, including classical and deep learning techniques, was performed on this online instance. All the results presented can be reproduced online.

bioinformatics↗

bioRxiv2018

Activation of polarized cell growth by inhibition of cell polarity

A key feature of cells is the capacity to activate new functional polarized domains contemporaneously to pre-existing ones. How cells accomplish this is not clear. Here, we show that in fission yeast inhibition of cell polarity at pre-existing domains of polarized cell growth is required to activate new growth. This inhibition is mediated by the ERM-related polarity factor Tea3, which antagonizes the activation of the Rho-GTPase Cdc42 by its co-factor Scd2. We demonstrate that Tea3 acts in a phosphorylation-dependent manner controlled by the PAK kinase Shk1 and that, like Scd2, Tea3 is direct substrate of Shk1. Importantly, we show that Tea3 and Scd2 compete for their binding to Shk1, indicating that their biochemical competition for Shk1 underpins their antagonistic roles in controlling polarity. Thus, by preventing pre-existing growth domains from becoming overpowering, Tea3 allows cells to redistribute their polarity-activating machinery to prospective sites and control their timing of activation.

cell biology↗

bioRxiv2017

Reconstructing regulatory pathways by systematically mapping protein localization interdependency networks

A key goal of functional genomics is to elucidate how genes and proteins act together in space and time, wired as pathways, to control specific aspects of cell biological function. Here, we develop a method to quantitatively determine proteins localization interdependencies at high throughput. We show that this method can be used to systematically obtain weighted, signed and directional pathway relationships, and hence to reconstruct a detailed pathway wiring. As proof-of-principle, we focus on 42 factors that control cell polarity in fission yeast (Schizosaccharomyces pombe) and use high-throughput confocal microscopy and quantitative image analysis to reconstruct their Localization Interdependency Network (LIN). Through this approach we identify 554 pairwise interactions across the factors, including 98% putative new directed links. Validation of an unexpected interaction between two polarity factor subgroups - the polarity landmark proteins and the cell integrity pathway components - by orthogonal phenotyping demonstrates the power of the LIN approach in detecting subtle, systems-level causal connections.

cell biology↗

bioRxiv2016

The Image Data Resource: A Scalable Platform for Biological Image Data Access, Integration, and Dissemination

Access to primary research data is vital for the advancement of science. To extend the data types supported by community repositories, we built a prototype Image Data Resource (IDR) that collects and integrates imaging data acquired across many different imaging modalities. IDR links high-content screening, super-resolution microscopy, time-lapse and digital pathology imaging experiments to public genetic or chemical databases, and to cell and tissue phenotypes expressed using controlled ontologies. Using this integration, IDR facilitates the analysis of gene networks and reveals functional interactions that are inaccessible to individual studies. To enable re-analysis, we also established a computational resource based on IPython notebooks that allows remote access to the entire IDR. IDR is also an open source platform that others can use to publish their own image data. Thus IDR provides both a novel on-line resource and a software infrastructure that promotes and extends publication and re-analysis of scientific image data.

bioinformatics↗