Dataset Information

Microbial Interaction Network Inference in Microfluidic Droplets.

ABSTRACT: 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.

SUBMITTER: Hsu RH

PROVIDER: S-EPMC6763379 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Microbial Interaction Network Inference in Microfluidic Droplets.

Hsu Ryan H RH Clark Ryan L RL Tan Jin Wen JW Ahn John C JC Gupta Sonali S Romero Philip A PA Venturelli Ophelia S OS

Cell systems 20190904 3

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 ...[more]

PMID: 31494089

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Project description:BACKGROUND:Cell-based experimentation in microfluidic droplets is becoming increasingly popular among biotechnologists and microbiologists, since inherent characteristics of droplets allow high throughput at low cost and space investment. The range of applications for droplet assays is expanding from single cell analysis toward complex cell-cell incubation and interaction studies. As a result of cellular metabolism in these setups, relevant physicochemical alterations frequently occur before functional assays are conducted. However, to use droplets as truly miniaturized bioreactors, parameters like pH and oxygen availability should be controlled similar to large-scale fermentation to ensure reliable research. RESULTS:Here, we introduce a comprehensive strategy to monitor and control pH for large droplet populations during long-term incubation. We show the correlation of fluorescence intensity of 6-carboxyfluorescein and pH in single droplets and entire droplet populations. By taking advantage of inter-droplet transport of pH-mediating molecules, the average pH value of several million droplets is simultaneously adjusted in an a priori defined direction. To demonstrate the need of pH control in practice, we compared the fermentation profiles of two E. coli strains, a K12-strain and a B-strain, in unbuffered medium with 5 g/L glucose for standard 1 L bioreactors and 180 pL droplets. In both fermentation formats, the commonly used B-strain E. coli BL21 is able to consume glucose until depletion and prevent a pH drop, while the growth of the K12-strain E. coli MG1655 is soon inhibited by a low pH caused by its own high acetate production. By regulating the pH during fermentation in droplets with our suggested strategy, we were able to prevent the growth arrest of E. coli MG1655 and obtained an equally high biomass yield as with E. coli BL21. CONCLUSION:We demonstrated a comparable success of pH monitoring and regulation for fermentations in 1 L scale and 180 pL scale for two E. coli strains. This strategy has the potential to improve cell-based experiments for various microbial systems in microfluidic droplets and opens the possibility for new functional assay designs.

Dataset Information

Microbial Interaction Network Inference in Microfluidic Droplets.

Publications

Microbial Interaction Network Inference in Microfluidic Droplets.

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