Sachs, C. C. | Search bioRxiv

bioRxiv2022

CellSium - Versatile Cell Simulator for Microcolony Ground Truth Generation

SummaryTo train deep learning based segmentation models, large ground truth data sets are needed. To address this need in microfluidic live-cell imaging, we present CellSium, a flexibly configurable cell simulator built to synthesize realistic image sequences of bacterial microcolonies growing in monolayers. We illustrate that the simulated images are suitable for training neural networks. Synthetic time-lapse videos with and without fluorescence, using programmable cell growth models, and simulation-ready 3D colony geometries for computational fluid dynamics (CFD) are also supported. Availability and ImplementationCellSium is free and open source software under the BSD license, implemented in Python, available at https://github.com/modsim/cellsium (DOI: 10.5281/zenodo.6193033), along with documentation, usage examples and Docker images. Contactk.noeh@fz-juelich.de Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2021

Growth-rate dependency of ribosome abundance and translation elongation rate in Corynebacterium glutamicum differs from Escherichia coli

The growth rate {micro} of bacteria depends on the protein synthesis capacity of the cell and thus on the number of active ribosomes and their translation elongation rate. The relationship between these fundamental growth parameters have only been described for a few bacterial species, in particular Escherichia coli, but are missing for most bacterial phyla. In this study, we systematically analysed the growth-rate dependency of ribosome abundance and translation elongation rate for Corynebacterium glutamicum, a gram-positive model species differing from E. coli by a lower growth temperature optimum and a lower {micro}max. Ribosomes were quantified via single-molecule localization microscopy (SMLM) using fluorescently tagged ribosomal proteins and via RNA/protein ratio. Both methods revealed a non-linear relationship with little change in ribosome abundance below {micro} = 0.4 h-1 and a steep increase at higher {micro}. Unlike E. coli, C. glutamicum keeps a large pool of active ribosomes at low {micro}, but the translation elongation rate declines from [~]9 amino acids s-1 at {micro}max to <2 aa s-1 at {micro} < 0.1 h-1. A model-based approach shows that depletion of translation precursors at low growth rates can explain the observed decrease in translation elongation rate. Nutrient up-shift experiments support the hypothesis that maintenance of excess ribosomes during poor nutrient conditions enables C. glutamicum to quickly restart growth when conditions improve.

microbiology↗