Mitochondrial genomes (mtDNA) encode essential subunits of the mitochondrial respiratory chain. Mutations in mtDNA can cause a shortage in cellular energy supply, which can lead to numerous mitochondrial diseases. How cells secure mtDNA integrity over generations has remained unanswered. Here, we show that the single-celled yeast Saccharomyces cerevisiae can intracellularly distinguish between functional and defective mtDNA and promote generation of daughter cells with increasingly healthy mtDNA content. Purifying selection for functional mtDNA occurs in a continuous mitochondrial network and does not require mitochondrial fission but necessitates stable mitochondrial subdomains that depend on intact cristae morphology. Our findings support a model in which cristae-dependent proximity between mtDNA and the proteins it encodes creates a spatial "sphere of influence," which links a lack of functional fitness to clearance of defective mtDNA.

Summary Here, we introduce YeastMate, a user-friendly deep learning-based application for automated detection and segmentation of Saccharomyces cerevisiae cells and their mating and budding events in microscopy images. We build upon Mask R-CNN with a custom segmentation head for the subclassification of mother and daughter cells during lifecycle transitions. YeastMate can be used directly as a Python library or through a stand-alone GUI application and a Fiji plugin as easy to use frontends. Availability and Implementation The source code for YeastMate is freely available at https://github.com/hoerlteam/YeastMate under the MIT license. We offer installers for our software stack for Windows, macOS and Linux. A detailed user guide is available at https://yeastmate.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.

Background Invasiveness is a major factor contributing to metastasis of tumour cells. Given the broad variety and plasticity of invasion mechanisms, assessing potential metastasis-promoting effects of irradiation for specific mechanisms is important for further understanding of potential adverse effects of radiotherapy. In fibroblast-led invasion mechanisms, fibroblasts produce tracks in the extracellular matrix in which cancer cells with epithelial traits can follow. So far, the influence of irradiation on this type of invasion mechanisms has not been assessed. Methods By matrix-embedding coculture spheroids consisting of breast cancer cells (MCF-7, BT474) and normal fibroblasts, we established a model for fibroblast-led invasion. To demonstrate applicability of this model, spheroid growth and invasion behaviour after irradiation with 5 Gy were investigated by microscopy and image analysis. Results When not embedded, irradiation caused a significant growth delay in the spheroids. When irradiating the spheroids with 5 Gy before embedding, we find comparable maximum migration distance in fibroblast monoculture and in coculture samples as seen in unirradiated samples. Depending on the fibroblast strain, the number of invading cells remained constant or was reduced. Conclusion In this spheroid model and with the cell lines and fibroblast strains used, irradiation does not have a major invasion-promoting effect. 3D analysis of invasiveness allows to uncouple effects on invading cell number and maximum invasion distance when assessing radiation effects.

Here, we introduce YeastMate, a user-friendly deep learning- based application for automated detection and segmentation of Saccharomyces cerevisiae cells and their mating and budding events in microscopy images. We build upon Mask R-CNN with a custom segmentation head for the subclassification of mother and daughter cells during lifecycle transitions. YeastMate can be used directly as a Python library or through a stand-alone GUI application and a Fiji plugin as easy to use frontends. The source code for YeastMate is freely available at https://github.com/hoerlteam/YeastMate under the MIT license. We offer packaged installers for our whole software stack for Windows, macOS and Linux. A detailed user guide is available at https://yeastmate.readthedocs.io.