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Automated microscopy techniques combined with high-throughput microfluidic cultivation systems provide unique insights into living microorganisms, enabling hundreds of experiments to be performed in parallel. Such setups generate large data sets with rich cellular features that need to be extracted to arrive at quantitative insights. The sheer amount of recorded time-lapse images requires reliable automated processing. While recent advances in deep learning methods have enabled automated processing, these methods rely on large-scale and precisely annotated data sets, often not available for new organisms or custom imaging modalities.To overcome the annotated data bottleneck, particularly in the microbial domain, we present the open-source ObiWan-Microbi platform providing a fast workflow for large-scale annotation of up to hundred thousands of segmentation and tracking annotations in time-lapse imaging data. ObiWan-Microbi focuses on easy-to-use semi-automated annotation in the browser eliminating the need for local installation or accelerator hardware, encourages FAIR data management using OMERO, and provides convenient collaborative cloud deployment to simplify the creation and long-term development of large-scale annotated data sets. The public availability of such benchmark data sets has the potential to improve data-driven methods, increase comparability among them, and is an essential step towards reliable automated image processing in microbial live-cell microscopy.

Microfluidic cultivation devices that facilitate O2 control enable unique studies of the complex interplay between environmental O2 availability and microbial physiology at the single-cell level. Therefore, microbial single-cell analysis based on time-lapse microscopy is typically used to resolve microbial behavior at the single-cell level with spatiotemporal resolution. Time-lapse imaging then provides large image-data stacks that can be efficiently analyzed by deep learning analysis techniques, providing new insights into microbiology. This knowledge gain justifies the additional and often laborious microfluidic experiments. Obviously, the integration of on-chip O2 measurement and control during the already complex microfluidic cultivation, and the development of image analysis tools, can be a challenging endeavor. A comprehensive experimental approach to allow spatiotemporal single-cell analysis of living microorganisms under controlled O2 availability is presented here. To this end, a gas-permeable polydimethylsiloxane microfluidic cultivation chip and a low-cost 3D-printed mini-incubator were successfully used to control O2 availability inside microfluidic growth chambers during time-lapse microscopy. Dissolved O2 was monitored by imaging the fluorescence lifetime of the O2-sensitive dye RTDP using FLIM microscopy. The acquired image-data stacks from biological experiments containing phase contrast and fluorescence intensity data were analyzed using in-house developed and open-source image-analysis tools. The resulting oxygen concentration could be dynamically controlled between 0% and 100%. The system was experimentally tested by culturing and analyzing an E. coli strain expressing green fluorescent protein as an indirect intracellular oxygen indicator. The presented system allows for innovative microbiological research on microorganisms and microbial ecology with single-cell resolution.

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.