Christian Carsten Sachs

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.

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.