Biology subjects

Haering, M.

Publications and source records attributed to Haering, M..

4 recordsLinked to original sources

bioRxiv2024

mitoXplorer 3.0, a web tool for exploring mitochondrial dynamics in single-cell RNA-seq data.

Mitochondria are important eukaryotic organelles, best known for their function in ATP production and in cellular metabolism and signalling. It is widely accepted that their structure, composition and function differ across cell types. However, little is known about mitochondrial variability within the same cell type. To truly understand mitochondrial function and dynamics, we need to study individual cell types, as well as mitochondrial variability on a single-cell level. Based on our mitoXplorer 2.0 web tool, we introduce mitoXplorer 3.0 with new features adapted for analysing single-cell sequencing data, focusing only on mitochondria. We provide a formatting script, scXplorer to generate mitoXplorer 3.0 compatible files for upload. This script creates pseudo-bulk transcriptomes of cell types from scRNA-seq data for differential expression analysis and subsequent mitochondria-centric analysis with mitoXplorer classical interfaces. It also creates a single-cell expression matrix only containing mitochondria-associated genes (mito-genes), which can be analysed for cell-to-cell variability with novel, interactive interfaces created for mitoXplorer 3.0: these new interfaces help to identify sub-clusters of cell types based only on mito-genes and offer in-depth mitochondria-centric analysis of subpopulations. We demonstrate the usability and predictive power of mitoXplorer 3.0 using single-cell transcriptome data from a single-cell study of Spinocerebellar Ataxia Type 1. We identified several mito-processes and mito-genes that are majorly affected in SCA1 Purkinje cells and which might contribute to our understanding of mitochondrial decline and subsequent Purkinje cell loss in this disease. MitoXplorer 3.0 is freely available at https://mitoxplorer3.ibdm.univ-amu.fr.

bioinformatics↗

bioRxiv2024

Developmental control of E-cadherin junctions by mechanical contractility in Drosophila embryos

Adherens junctions are very plastic structures that change their composition and structure depending on the state of the epithelial tissue, involving mechanical stress and tissue dynamics. In Drosophila embryos, adherens junctions mature only during gastrulation following their formation a few minutes before during cellularization. Although the developmental maturation is obvious during gastrulation, the factors and conditions controlling it have remained unclear. Here, we assay the levels, distribution pattern, and mobility of E-cadherin during Drosophila gastrulation. Indicating a maturation, we find an increase in total levels at junctions and a drop in the mobile fraction of E-cadherin molecules. Both developmental changes depend on Myo-II contractility. Consistent with stereotypic Myo-II regulation, we find that interference with Rho signaling leads to corresponding changes in E-cadherin levels and mobility, at least in part. Besides Rho signaling, Src42A contributes to junction maturation, as tested by both loss-function and gain-of-function situations. Together, we demonstrate developmental maturation of adherens junctions and identify Rho signaling and Src42A as upstream regulatory pathways.

developmental biology↗

bioRxiv2022

The mitoXplorer 2.0 update: integrating and interpreting mitochondrial expression dynamics within a cellular context

Mitochondria are subcellular organelles present in almost all eukaryotic cells, which play a central role in cellular metabolism. Different tissues, health and age conditions are characterised by a difference in mitochondrial structure and composition. The visual data mining platform mitoXplorer 1.0 was developed to explore the expression dynamics of genes associated with mitochondrial functions that could help explain these differences. It however lacked functions aimed at integrating mitochondria in the cellular context and thus, identifying regulators that help mitochondria adapt to cellular needs. To fill this gap, we upgraded the mitoXplorer platform to version 2.0 (mitoXplorer 2.0). In this upgrade we implemented two novel integrative functions, Network Analysis and the transcription factor- (TF-) Enrichment, to specifically help identify signalling or transcriptional regulators of mitochondrial processes. In addition, we implemented several other novel functions to allow the platform to go beyond simple data visualisation, such as an enrichment function for mitochondrial processes, a function to explore time-series data, the possibility to compare datasets across species as well as an IDconverter to help facilitate data upload. We demonstrate the usefulness of these functions in 3 specific use cases. mitoXplorer 2.0 is freely available without login at http://mitoxplorer2.ibdm.univ-mrs.fr.

bioinformatics↗

bioRxiv2018

Automated Segmentation of Epithelial Tissue Using Cycle-Consistent Generative Adversarial Networks

A central problem in biomedical imaging is the automated segmentation of images for further quantitative analysis. Recently, fully convolutional neural networks, such as the U-Net, were applied successfully in a variety of segmentation tasks. A downside of this approach is the requirement for a large amount of well-prepared training samples, consisting of image - ground truth mask pairs. Since training data must be created by hand for each experiment, this task can be very costly and time-consuming. Here, we present a segmentation method based on cycle consistent generative adversarial networks, which can be trained even in absence of prepared image - mask pairs. We show that it successfully performs image segmentation tasks on samples with substantial defects and even generalizes well to different tissue types.

bioinformatics↗