CellSium: versatile cell simulator for microcolony ground truth generation.

Summary: To train deep learning-based segmentation models, large ground truth datasets 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 are also supported. Availability and implementation: CellSium is free and open source software under the BSD license, implemented in Python, available at github.com/modsim/cellsium (DOI: 10.5281/zenodo.6193033), along with documentation, usage examples and Docker images. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

mycelyso - high-throughput analysis of Streptomyces mycelium live cell imaging data.

BACKGROUND: Streptomycetes are filamentous microorganisms of high biotechnological relevance, especially for the production of antibiotics. In submerged cultures, the productivity of these microorganisms is closely linked to their growth morphology. Microfluidic lab-on-a-chip cultivation systems, coupled with automated time-lapse imaging, generate spatio-temporal insights into the mycelium development of streptomycetes, therewith extending the biotechnological toolset by spatio-temporal screening under well-controlled and reproducible conditions. However, the analysis of the complex mycelial structure formation is limited by the extent of manual interventions required during processing of the acquired high-volume image data. These interventions typically lead to high evaluation times and, therewith, limit the analytic throughput and exploitation of microfluidic-based screenings. RESULTS: We present the tool mycelyso (MYCElium anaLYsis SOftware), an image analysis system tailored to fully automated hyphae-level processing of image stacks generated by time-lapse microscopy. With mycelyso, the developing hyphal streptomycete network is automatically segmented and tracked over the cultivation period. Versatile key growth parameters such as mycelium network structure, its development over time, and tip growth rates are extracted. Results are presented in the web-based exploration tool mycelyso Inspector, allowing for user friendly quality control and downstream evaluation of the extracted information. In addition, 2D and 3D visualizations show temporal tracking for detailed inspection of morphological growth behaviors. For ease of getting started with mycelyso, bundled Windows packages as well as Docker images along with tutorial videos are available. CONCLUSION: mycelyso is a well-documented, platform-independent open source toolkit for the automated end-to-end analysis of Streptomyces image stacks. The batch-analysis mode facilitates the rapid and reproducible processing of large microfluidic screenings, and easy extraction of morphological parameters. The objective evaluation of image stacks is possible by reproducible evaluation workflows, useful to unravel correlations between morphological, molecular and process parameters at the hyphae- and mycelium-levels with statistical power.

Analyzing Microbial Population Heterogeneity-Expanding the Toolbox of Microfluidic Single-Cell Cultivations.

Recent research on population heterogeneity revealed fascinating insights into microbial behavior. In particular emerging single-cell technologies, image-based microfluidics lab-on-chip systems generate insights with spatio-temporal resolution, which are inaccessible with conventional tools. This review reports recent developments and applications of microfluidic single-cell cultivation technology, highlighting fields of broad interest such as growth, gene expression and antibiotic resistance and susceptibility. Combining advanced microfluidic single-cell cultivation technology for environmental control with automated time-lapse imaging as well as smart computational image analysis offers tremendous potential for novel investigation at the single-cell level. We propose on-chip control of parameters like temperature, gas supply, pressure or a change in cultivation mode providing a versatile technology platform to mimic more complex and natural habitats. Digital analysis of the acquired images is a requirement for the extraction of biological knowledge and statistically reliable results demand for robust and automated solutions. Focusing on microbial cultivations, we compare prominent software systems that emerged during the last decade, discussing their applicability, opportunities and limitations. Next-generation microfluidic devices with a high degree of environmental control combined with time-lapse imaging and automated image analysis will be highly inspiring and beneficial for fruitful interdisciplinary cooperation between microbiologists and microfluidic engineers and image analysts in the field of microbial single-cell analysis.

Germination and Growth Analysis of Streptomyces lividans at the Single-Cell Level Under Varying Medium Compositions.

Quantitative single-cell cultivation has provided fundamental contributions to our understanding of heterogeneity among industrially used microorganisms. Filamentous growing Streptomyces species are emerging platform organisms for industrial production processes, but their exploitation is still limited due to often reported high batch-to-batch variations and unexpected growth and production differences. Population heterogeneity is suspected to be one responsible factor, which is so far not systematically investigated at the single-cell level. Novel microfluidic single-cell cultivation devices offer promising solutions to investigate these phenomena. In this study, we investigated the germination and growth behavior of Streptomyces lividans TK24 under varying medium compositions on different complexity levels (i.e., mycelial growth, hyphal growth and tip elongation) on single-cell level. Our analysis reveals a remarkable stability within growth and germination of spores and early mycelium development when exposed to constant and defined environments. We show that spores undergo long metabolic adaptation processes of up to > 30 h to adjust to new medium conditions, rather than using a "persister" strategy as a possibility to cope with rapidly changing environments. Due to this uniform behavior, we conclude that S. lividans can be cultivated quite robustly under constant environmental conditions as provided by microfluidic cultivation approaches. Failure and non-reproducible cultivations are thus most likely to be found in less controllable larger-scale cultivation workflows and as a result of environmental gradients within large-scale cultivations.

Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments.

BACKGROUND: Microfluidic lab-on-chip technology combined with live-cell imaging has enabled the observation of single cells in their spatio-temporal context. The mother machine (MM) cultivation system is particularly attractive for the long-term investigation of rod-shaped bacteria since it facilitates continuous cultivation and observation of individual cells over many generations in a highly parallelized manner. To date, the lack of fully automated image analysis software limits the practical applicability of the MM as a phenotypic screening tool. RESULTS: We present an image analysis pipeline for the automated processing of MM time lapse image stacks. The pipeline supports all analysis steps, i.e., image registration, orientation correction, channel/cell detection, cell tracking, and result visualization. Tailored algorithms account for the specialized MM layout to enable a robust automated analysis. Image data generated in a two-day growth study (≈ 90 GB) is analyzed in ≈ 30 min with negligible differences in growth rate between automated and manual evaluation quality. The proposed methods are implemented in the software molyso (MOther machine AnaLYsis SOftware) that provides a new profiling tool to analyze unbiasedly hitherto inaccessible large-scale MM image stacks. CONCLUSION: Presented is the software molyso, a ready-to-use open source software (BSD-licensed) for the unsupervised analysis of MM time-lapse image stacks. molyso source code and user manual are available at https://github.com/modsim/molyso.

Live cell imaging of SOS and prophage dynamics in isogenic bacterial populations.

Almost all bacterial genomes contain DNA of viral origin, including functional prophages or degenerated phage elements. A frequent but often unnoted phenomenon is the spontaneous induction of prophage elements (SPI) even in the absence of an external stimulus. In this study, we have analyzed SPI of the large, degenerated prophage CGP3 (187 kbp), which is integrated into the genome of the Gram-positive Corynebacterium glutamicum ATCC 13032. Time-lapse fluorescence microscopy of fluorescent reporter strains grown in microfluidic chips revealed the sporadic induction of the SOS response as a prominent trigger of CGP3 SPI but also displayed a considerable fraction (∼30%) of RecA-independent SPI. Whereas approx. 20% of SOS-induced cells recovered from this stress and resumed growth, the spontaneous induction of CGP3 always led to a stop of growth and likely cell death. A carbon source starvation experiment clearly emphasized that SPI only occurs in actively proliferating cells, whereas sporadic SOS induction was still observed in resting cells. These data highlight the impact of sporadic DNA damage on the activity of prophage elements and provide a time-resolved, quantitative description of SPI as general phenomenon of bacterial populations.

Caldariomyces fumago DSM1256 Contains Two Chloroperoxidase Genes, Both Encoding Secreted and Active Enzymes.

Inspection of transcriptome data from the chloroperoxidase (CPO)-producing fungus Caldariomyces fumago DSM1256 led to the discovery of two distinct CPO mRNA sequences. This strain could be shown to contain the newly identified isogene as well as produce and secrete both isoenzymes. The CPO2 enzyme bears high sequence similarity to the well-characterized CPO (87% identity for the mature proteins). It shows two insertions in the signal peptide and in the C-terminal propeptide, and one deletion in the mature polypeptide close to the C-terminus. Furthermore, it lacks one of the serine residues known to be O-glycosylated in the CPO sequence. The demonstration of a CPO isogene which is expressed as a secreted and active CPO clarifies the nature of this isoenzyme already identified in earlier reports. A structure model comparison shows a high conservation of the active site and the substrate channel, suggesting very similar catalytic properties.