Tools and methods for high-throughput single-cell imaging with the mother machine.

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

Bacillus subtilis produces (p)ppGpp in response to the bacteriostatic antibiotic chloramphenicol to prevent its potential bactericidal effect.

Antibiotics combat bacteria through their bacteriostatic (by growth inhibition) or bactericidal (by killing bacteria) action. Mechanistically, it has been proposed that bactericidal antibiotics trigger cellular damage, while bacteriostatic antibiotics suppress cellular metabolism. Here, we demonstrate how the difference between bacteriostatic and bactericidal activities of the antibiotic chloramphenicol can be attributed to an antibiotic-induced bacterial protective response: the stringent response. Chloramphenicol targets the ribosome to inhibit the growth of the Gram-positive bacterium Bacillus subtilis. Intriguingly, we found that chloramphenicol becomes bactericidal in B. subtilis mutants unable to produce (p)ppGpp. We observed a similar (p)ppGpp-dependent bactericidal effect of chloramphenicol in the Gram-positive pathogen Enterococcus faecalis. In B. subtilis, chloramphenicol treatment induces (p)ppGpp accumulation through the action of the (p)ppGpp synthetase RelA. (p)ppGpp subsequently depletes the intracellular concentration of GTP and antagonizes GTP action. This GTP regulation is critical for preventing chloramphenicol from killing B. subtilis, as bypassing (p)ppGpp-dependent GTP regulation potentiates chloramphenicol killing, while reducing GTP synthesis increases survival. Finally, chloramphenicol treatment protects cells from the classical bactericidal antibiotic vancomycin, reminiscent of the clinical phenomenon of antibiotic antagonism. Taken together, our findings suggest a role of (p)ppGpp in the control of the bacteriostatic and bactericidal activity of antibiotics in Gram-positive bacteria, which can be exploited to potentiate the efficacy of existing antibiotics.