Rapid microbial interaction network inference in microfluidic droplets

Microbial interactions are major drivers of microbial community dynamics and functions. However, microbial interactions are challenging to decipher due to limitations in parallel culturing of sub-communities across many environments and accurate absolute abundance quantification of constituent members of the consortium. To this end, we developed M icrobial Interaction N etwork Inference in microdroplets (MINI-Drop), a high-throughput method to rapidly infer microbial interactions in microbial consortia in microfluidic droplets. Fluorescence microscopy coupled to automated computational droplet and cell detection was used to rapidly determine the absolute abundance of each strain in hundreds to thousands of droplets per experiment. We show that MINI-Drop can accurately infer pairwise as well as higher-order interactions using a microbial interaction toolbox of defined microbial interactions mediated by distinct molecular mechanisms. MINI-Drop was used to investigate how the molecular composition of the environment alters the interaction network of a three-member consortium. To provide insight into the variation in community states across droplets, we developed a probabilistic model of cell growth modified by microbial interactions. In sum, we demonstrate a robust and generalizable method to probe cellular interaction networks by random encapsulation of sub-communities into microfluidic droplets.