Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.
Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
Cell Syst. 2019 Sep 25;9(3):229-242.e4. doi: 10.1016/j.cels.2019.06.008. Epub 2019 Sep 4.
Microbial interactions are major drivers of microbial community dynamics and functions but remain challenging to identify because of limitations in parallel culturing and absolute abundance quantification of community members across environments and replicates. To this end, we developed Microbial Interaction Network Inference in microdroplets (MINI-Drop). Fluorescence microscopy coupled to computer vision techniques were used to rapidly determine the absolute abundance of each strain in hundreds to thousands of droplets per condition. We showed that MINI-Drop could accurately infer pairwise and higher-order interactions in synthetic consortia. We developed a stochastic model of community assembly to provide insight into the heterogeneity in community states across droplets. Finally, we elucidated the complex web of interactions linking antibiotics and different species in a synthetic consortium. In sum, we demonstrated a robust and generalizable method to infer microbial interaction networks by random encapsulation of sub-communities into microfluidic droplets.
微生物相互作用是微生物群落动态和功能的主要驱动因素，但由于在环境和重复实验中对群落成员进行平行培养和绝对丰度定量的限制，微生物相互作用的识别仍然具有挑战性。为此，我们开发了微滴中的微生物相互作用网络推断（MINI-Drop）。荧光显微镜结合计算机视觉技术，可快速确定每个菌株在数百到数千个微滴中的绝对丰度。我们表明，MINI-Drop 可以准确推断合成群落中的成对和更高阶相互作用。我们开发了一种群落组装的随机模型，以深入了解不同液滴之间群落状态的异质性。最后，我们阐明了抗生素和合成群落中不同物种之间相互联系的复杂网络。总之，我们通过将亚群落随机封装到微流控液滴中，展示了一种强大且可推广的推断微生物相互作用网络的方法。
