Microbial Interaction Network Inference in Microfluidic Droplets

Hsu, Ryan H.; Clark, Ryan L.; Tan, Jin; Ahn, John C.; Gupta, Sonali; Romero, Philip A.; Venturelli, Ophelia S.

doi:10.1016/j.cels.2019.06.008

Cited by 97 publications

(77 citation statements)

References 61 publications

(69 reference statements)

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“…However, isolation inhibits the growth of organisms that rely on other microbial or host cells, such as Saccharibacteria (formerly TM7) ( He et al, 2015 ), or obligate endosymbionts, such as Wolbachia ( Hosokawa et al, 2010 ). In droplets, co-encapsulation of two cross-feeding auxotrophic strains into a single droplet can induce growth, whereas growth will not occur when only one auxotroph is present within a droplet ( Park et al, 2011 ; Hsu et al, 2019 ). Future studies could stochastically co-encapsulate multiple gut bacteria into droplets (by increasing the loading cell density during droplet generation) and investigate the resulting growth dynamics.…”

Section: Discussionmentioning

confidence: 99%

Droplet-based high-throughput cultivation for accurate screening of antibiotic resistant gut microbes

Watterson

Tanyeri

Watson

et al. 2020

eLife

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Traditional cultivation approaches in microbiology are labor-intensive, low-throughput, and yield biased sampling of environmental microbes due to ecological and evolutionary factors. New strategies are needed for ample representation of rare taxa and slow-growers that are often outcompeted by fast-growers in cultivation experiments. Here we describe a microfluidic platform that anaerobically isolates and cultivates microbial cells in millions of picoliter droplets and automatically sorts them based on colony density to enhance slow-growing organisms. We applied our strategy to a fecal microbiota transplant (FMT) donor stool using multiple growth media, and found significant increase in taxonomic richness and larger representation of rare and clinically relevant taxa among droplet-grown cells compared to conventional plates. Furthermore, screening the FMT donor stool for antibiotic resistance revealed 21 populations that evaded detection in plate-based assessment of antibiotic resistance. Our method improves cultivation-based surveys of diverse microbiomes to gain deeper insights into microbial functioning and lifestyles.

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Section: Discussionmentioning

confidence: 99%

Droplet-based high-throughput cultivation for accurate screening of antibiotic resistant gut microbes

Watterson

Tanyeri

Watson

et al. 2020

eLife

View full text Add to dashboard Cite

show abstract

“…However, isolation inhibits the growth of organisms that rely on other microbial or host cells, such as Saccharibacteria (formerly TM7) [38], or obligate endosymbionts, such as Wolbachia [39]. In droplets, co-encapsulation of two cross-feeding auxotrophic strains into a single droplet can induce growth, whereas growth will not occur when only one auxotroph is present within a droplet [40,41]. Future studies could stochastically co-encapsulate multiple gut bacteria into droplets (by increasing the loading cell density during droplet generation) and investigate the resulting growth dynamics.…”

Section: Discussionmentioning

confidence: 99%

High-throughput isolation and sorting of gut microbes reduce biases of traditional cultivation strategies

Watterson

Tanyeri

Watson³

et al. 2019

Preprint

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Traditional cultivation approaches in microbiology are labor-intensive, low-throughput, and often yield biased sampling of taxa due to ecological and evolutionary factors. New strategies are needed to enable ample representation of rare taxa and slow-growers that are outcompeted by fast-growing organisms. We developed a microfluidic platform that anaerobically isolates and cultivates microbial cells in millions of picoliter droplets and automatically sorts droplets based on colony density. We applied our strategy to mouse and human gut microbiomes and used 16S ribosomal RNA gene amplicons to characterize taxonomic composition of cells grown using different media. We found up to 4-fold increase in richness and larger representation of rare taxa among cells grown in droplets compared to conventional culture plates. Automated sorting of droplets for slow-growing colonies further enhanced the relative abundance of rare populations. Our method improves the cultivation and analysis of diverse microbiomes to gain deeper insights into microbial functioning and lifestyles.

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“…Direct high-throughput measurement of interactions, e.g. through co-culture micro-droplet experiments [15, 16], or spatial visualization of natural communities [17] is possible, but it requires specific technological capabilities, and has yet to be extensively used. In parallel, sequencing data across multiple samples can be used for estimating co-occurrence relationships between taxa.…”

Section: Introductionmentioning

confidence: 99%

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Kishore

Birzu

et al. 2020

Preprint

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Microbes tend to organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, have the potential to reveal which microbes co-occur, providing a glimpse into the network of associations in these communities. The inference of networks from 16S data is prone to statistical artifacts. There are many tools for performing each step of the 16S analysis workflow, but the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and estimate steps that contribute most significantly to the variance. We further determine the tools and parameters that generate the most accurate and robust co-occurrence networks based on comparison with mock and synthetic datasets. Ultimately, we develop a standardized pipeline that follows these default tools and parameters, but that can also help explore the outcome of any other combination of choices. We envisage that this pipeline for processing 16S sequencing data into networks of microbial co-occurrences could be used for integrating multiple data-sets, and for generating comparative analyses and consensus networks that can help understand and control microbial community assembly in different biomes.

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Microbial Interaction Network Inference in Microfluidic Droplets

Cited by 97 publications

References 61 publications

Droplet-based high-throughput cultivation for accurate screening of antibiotic resistant gut microbes

Droplet-based high-throughput cultivation for accurate screening of antibiotic resistant gut microbes

High-throughput isolation and sorting of gut microbes reduce biases of traditional cultivation strategies

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

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