Syracuse

Automated high-content image-based characterization of microorganism behavioral diversity and distribution

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Lupatelli, Carlotta Aurora | Attard, Agnès | Kuhn, Marie-Line | Cohen, Celine | Thomen, Philippe | Noblin, Xavier | Galiana, Eric

Edité par CCSD ; springer -

International audience. Abstract Background Microorganisms evolved complex systems to respond to environmental signals. Gradients of particular molecules alter microbe behavior and distribution within their environment. Microdevice tools coupled to automated image-based methods are now employed to analyze instantaneous distribution and motion behaviors of microbial species in controlled environments at small temporal scales, mimicking to some extent macro conditions. Such technologies have already been adopted for investigations mainly on individual species. Similar versatile approaches must now be developed for the characterization of multiple and complex interactions between the microbial community and environment. Results Here, we provide a comprehensive step-by-step method for the characterization of species-specific behavior of a synthetic mixed microbial suspension in response to an environmental driver. By coupling accessible microfluidic devices with automated image analysis approaches, we evaluated the behavior response of three morphologically different telluric species (Phytophthora parasitica, Vorticella microstoma, Enterobacter aerogenes) to a potassium gradient driver. Using the TrackMate plug-in algorithms, we performed morphometric and then motion analysis to characterize each microbial species response to the driver. Such approach enabled us to confirm the different shape features of the three species, and to simultaneously characterize their particular motion adaptation to the driver, as well as their co-interaction dynamics. Conclusions The results obtained demonstrated the feasibility of the method to screen mixed-species suspension dynamics at high spatial and temporal scale. By increasing the complexity of suspensions, this approach could be integrated to support conventional omics methods, contributing to characterizing how the main drivers operate at the microbiota-host-environment interfaces. In its current advances, the method can integrate screening strategy, for example, for biocontrol agent evaluation, enlightening possible beneficial-pathogenic interactions based on co-colonization of micro habitats.

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