Deciphering microbial interactions using a label-free microbead sorting approach.

Microorganisms form communities, and their interactions shape the function and stability of these communities. Understanding these interactions can aid in revealing ecosystem dynamics, enhancing community function, and informing the design of synthetic consortia for industrial applications. Deciphering microbial interactions is challenging due to the difficulty of culturing natural microorganisms and the exponential increase in experiments with expanding consortium size. One approach to improving culturing throughput is the use of microcompartments such as agarose microbeads. Microbead-based techniques enable the generation of large numbers of picolitre-sized compartments, facilitating high-throughput, parallel studies of microbial sub-communities. However, the existing microbead-based techniques for deciphering microbial interactions are dependent on single-culture isolates of consortium members and/or labelling of consortium members with fluorescent markers via genetic engineering. We developed a microbead-based, label-free method that eliminates the requirement of single-cell isolates to predict microbial interactions. Our method involves an isolation-independent manner of microbead inoculation with different sub-communities and microbead sorting to separate sub-communities based on growth. Using a probabilistic model, we predict interactions based on cell concentrations and relative abundances in the inoculum and after microbead sorting. We successfully predicted pairwise interactions in two three-member consortia. Additionally, we computationally showcased the validity of our approach for predicting pairwise interactions in larger consortia.