(188l) Extractable Microwell Arrays for Screening Microbial Interaction Networks

Microbes often exist and function in dynamic and diverse communities that are shaped by host-microbe and microbe-microbe interactions, species abundance, spatial organization, and environmental cues. Due to these complexities, a fundamental understanding of the critical microbe-microbe interactions that impact the function of specific organisms is lacking in many communities. To address this knowledge gap, we have developed a new tool designed to rapidly uncover critical pair-wise or microbial interaction networks that effect the function of a focal species. The platform consists of a microwell array that randomly combines the fluorescently labeled focal species with a controlled number of microbiome isolates. When using a diverse microbiome, each well becomes compositionally unique in terms of the combination of species present. This allows the user to screen thousands of unique interaction networks in parallel using a fluorescence microscope in order to identify those that influence focal species function. Recently, we have developed a crosslinked, photo-degradable polyethylene glycol hydrogel membrane for use with this platform. The membrane is designed to trap motile bacteria cells in wells during co-culture and screening and can then be removed over wells of interest using a patterned light source. This allows for the unique capability of on-demand extraction of live cells from wells of interest with high spatial precision, enabling 16S rRNA sequencing and other molecular characterizations to identify and characterize interacting bacteria. With this new capability, we are screening plant root microbiome members against Agrobacterium tumefaciens, the cause of Crown Gall Disease, in search for microbes that inhibit its growth or quorum activation. These findings will inform more robust and reliable soil inoculants for biocontrol applications. In a broader sense, the tool will assist in mapping higher-order interaction networks that can be combined with metagenomic data to provide an improved, systems-level understanding of microbial community structure and function in a variety of different ecosystems.