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Padovani, F.

Publications and source records attributed to Padovani, F..

2 recordsLinked to original sources

Regulation with cell size ensures mitochondrial DNA homeostasis during cell growth

To maintain stable DNA concentrations, proliferating cells need to coordinate DNA replication with cell growth. For nuclear DNA, eukaryotic cells achieve this by coupling DNA replication to cell cycle progression, ensuring that DNA is doubled exactly once per cell cycle. By contrast, mitochondrial DNA replication is typically not strictly coupled to the cell cycle, leaving the open question of how cells maintain the correct amount of mitochondrial DNA during cell growth. Here, we show that in budding yeast, mitochondrial DNA copy number increases with cell volume, both in asynchronously cycling populations and during G1 arrest. Our findings suggest that cell-volume-dependent mitochondrial DNA maintenance is achieved through nuclear encoded limiting factors, including the mitochondrial DNA polymerase Mip1 and the packaging factor Abf2, whose amount increases in proportion to cell volume. By directly linking mitochondrial DNA maintenance to nuclear protein synthesis, and thus cell growth, constant mitochondrial DNA concentrations can be robustly maintained without a need for cell-cycle-dependent regulation.

cell biology↗

Cell-ACDC: a user-friendly toolset embedding state-of-the-art neural networks for segmentation, tracking and cell cycle annotations of live-cell imaging data

Live-cell imaging is a powerful tool to study dynamic cellular processes on the level of single cells with quantitative detail. Microfluidics enables parallel high-throughput imaging, creating a downstream bottleneck at the stage of data analysis. Recent progress on deep learning image analysis dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and broadly used tools spanning the complete range of live-cell imaging analysis, from cell segmentation to pedigree analysis and signal quantification, are still needed. Here, we present Cell-ACDC, a user-friendly graphical user-interface (GUI)-based framework written in Python, for segmentation, tracking and cell cycle annotation. We included two state-of-the-art and high-accuracy deep learning models for single-cell segmentation of yeast and mammalian cells implemented in the most used deep learning frameworks TensorFlow and PyTorch. Additionally, we developed and implemented a cell tracking method and embedded it into an intuitive, semi-automated workflow for label-free cell cycle annotation of single cells. The open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation or downstream image analysis. Source codehttps://github.com/SchmollerLab/Cell_ACDC

cell biology↗