Biology subjects

Witz, G.

Publications and source records attributed to Witz, G..

5 recordsLinked to original sources

bioRxiv2024

Convpaint - Universal framework for interactive pixel classification using pretrained neural networks

We develop Convpaint, a universal computational framework for interactive pixel classification. Convpaint utilizes pretrained convolutional neural networks (CNNs) or vision transformers (ViTs) for feature extraction and enables easy segmentation across a wide variety of tasks. Available within the Python-based napari software ecosystem, Convpaint integrates seamlessly with other plugins into image processing pipelines, which we demonstrate with three workflows across different data modalities.

bioinformatics↗

bioRxiv2024

CryoVesNet: A Dedicated Framework for Synaptic Vesicle Segmentation in Cryo Electron Tomograms

Cryo-electron Tomography (Cryo-ET) has the potential to reveal cell structure down to atomic resolution. Nevertheless, cellular cryo-ET data is often highly complex, and visualization, as well as quantification, of subcellular structures require image segmentation. Due to a relatively high level of noise and anisotropic resolution in cryo-ET data, automatic segmentation based on classical computer vision approaches usually does not perform satisfactorily. For this reason, cryo-ET researchers have mostly performed manual segmentation. Communication between neurons relies on neurotransmitter-filled synaptic vesicle (SV) exocytosis. Recruitment of SVs to the plasma membrane is an important means of regulating exocytosis and is influenced by interactions between SVs. Cryo-ET study of the spatial organization of SVs and of their interconnections allows a better understanding of the mechanisms of exocytosis regulation. Extremely accurate SV segmentation is a prerequisite to obtaining a faithful representation of SVs state of connectivity. Hundreds to thousands of SVs are present in a typical synapse, and their time-consuming manual segmentation is a bottleneck in this analysis. Several attempts to automate vesicle segmentation by classical computer vision or machine learning algorithms have not yielded robust results. We addressed this problem by designing a workflow consisting of a U-Net convolutional segmentation network followed by post-processing steps. This combination yields highly accurate results. Furthermore, we provide an interactive tool for accurately segmenting spherical vesicles in a fraction of the time required by available manual segmentation methods. This tool can be used to segment vesicles that were missed by the fully automatic procedure or to quickly segment a handful of vesicles while bypassing the fully automatic procedure. Our pipeline can in principle be used to segment any spherical vesicle in any cell type as well as extracellular vesicles.

neuroscience↗

bioRxiv2020

Response to comment on 'Initiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanism'

Last year we published an article (Witz et al., 2019) in which we used time-lapse microscopy in combination with microfluidics to measure growth, division and replication in single E. coli cells on the one hand, and developed a new statistical analysis method to calculate the ability of different cell cycle models to capture the correlation structure observed in the data on the other hand. This led us to propose a new model of cell cycle control in E. coli which we called the double-adder model. Recently Le Treut et al. published a comment (Le Treut et al., 2020) on our article which made a number of highly critical claims, including allegations that our own data support a different model than the one we proposed, and that our model cannot reproduce the adder phenotype observed in the data. We here show that all these allegations are false and based on basic analysis errors. Although our focus is on explaining the errors in the analysis of Le Treut et al, we have attempted to make the presentation of interest to a broader scientific audience by discussing the issues in the context of what our current understanding is of the bacterial cell cycle, and to what extent recent data either support or reject various proposed models.

systems biology↗

bioRxiv2019

Cell-specific targeting by Clostridium perfringens β-toxin unraveled: the role of CD31 as the toxin receptor.

Clostridium perfringens {beta}-toxin (CPB) is a highly active hemolysin {beta}-pore forming toxin and the essential virulence factor for a severe, necro-hemorrhagic enteritis in animals and humans. In vivo and in vitro it exerts a remarkable cell type specificity towards endothelial cells, platelets and some leucocytic cell lines. The target cell specificity of CPB is, however, poorly understood and a receptor explaining this selective toxicity has not been identified. This has hampered further research into the pathogenesis of C. perfringens type C induced enteritis. Here we identify Platelet Endothelial Cell Adhesion Molecule-1 (CD31 or PECAM-1) as the specific membrane receptor for CPB on endothelial cells. CD31 expression is essential for CPB toxicity in endothelial cells and lethality in mice and sufficient to render previously resistant cells highly susceptible to the toxin. We further demonstrate, that the extracellular membrane proximal Ig6 domain of CD31 is required for the interaction with CPB and that expression of CD31 corresponds with the specificity of the toxin towards cultured cell lines. Our results thus provide an explanation for the cell type specificity of CPB and can be linked to the characteristic lesions observed a devastating enteric disease in animals and humans.

molecular biology↗

bioRxiv2019

Initiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanism

Living cells proliferate by completing and coordinating two essential cycles, a division cycle that controls cell size, and a DNA replication cycle that controls the number of chromosomal copies in the cell. Despite lacking dedicated cell cycle control regulators such as cyclins in eukaryotes, bacteria such as E. coli manage to tightly coordinate those two cycles across a wide range of growth conditions, including situations where multiple nested rounds of replication progress simultaneously. Various cell cycle models have been proposed to explain this feat, but it has been impossible to validate them so far due to a lack of experimental tools for systematically testing their different predictions. Recently new insights have been gained on the division cycle through the study of the structure of fluctuations in growth, size, and division in individual cells. In particular, it was found that cell size appears to be controlled by an adder mechanism, i.e. the added volume between divisions is held approximately constant and fluctuates independently of growth rate and cell size at birth. However, how replication initiation is regulated and coupled to cell size control remains unclear, mainly due to scarcity of experimental measurements on replication initiation at the single-cell level. Here, we used time-lapse microscopy in combination with microfluidics to directly measure growth, division and replication in thousands of single E. coli cells growing in both slow and fast growth conditions. In order to compare different phenomenological models of the cell cycle, we introduce a statistical framework which assess their ability to capture the correlation structure observed in the experimental data. Using this in combination with stochastic simulations, our data indicate that, instead of thinking of the cell cycle as running from birth to division, the cell cycle is controlled by two adder mechanisms starting at the initiation of replication: the added volume since the last initiation event controls the timing of both the next division event and the next replication initiation event. Interestingly the double-adder mechanism identified in this study has recently been found to explain the more complex cell cycle of mycobacteria, suggesting shared control strategies across species.

systems biology↗