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

Abstract

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. In addition, the rapid adoption and widespread popularity of deep-learning methods by the scientific community raises an important question: to what extent can users trust the results generated by such “black box” methods? We explicitly demonstrate “What You Put Is What You Get” (WYPIWYG); i.e., the image analysis results can reflect the user bias encoded in the training dataset. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over a decade in our lab, we also provide useful information for those who want to implement mother-machine-based high-throughput imaging and image analysis methods in their research. This includes our guiding principles and best practices to ensure transparency and reproducible results.

Scaling of stochastic growth and division dynamics: A comparative study of individual rod-shaped cells in the Mother Machine and SChemostat platforms

“…Phase contrast microscopy provides a suitable alternative (40); although used less frequently due to challenges in detecting cells within the close walls of the Mother Machine, a number of tools have been developed for automated analysis of such data (41). Nonetheless, an extensive comparison of these tools noted systematic discrepancies in values obtained by different methods, necessitating care in interpretation of absolute quantities (42). In addition, unintended gradients in nutrient concentrations may well be unavoidable across each channel, and mechanical confinement in narrow growth channels may disrupt growth and morphology (43).…”