Dataset Information

Dynamic control and quantification of bacterial population dynamics in droplets.

ABSTRACT: Culturing and measuring bacterial population dynamics are critical to develop insights into gene regulation or bacterial physiology. Traditional methods, based on bulk culture to obtain such quantification, have the limitations of higher cost/volume of reagents, non-amendable to small size of population and more laborious manipulation. To this end, droplet-based microfluidics represents a promising alternative that is cost-effective and high-throughput. However, difficulties in manipulating the droplet environment and monitoring encapsulated bacterial population for long-term experiments limit its utilization. To overcome these limitations, we used an electrode-free injection technology to modulate the chemical environment in droplets. This ability is critical for precise control of bacterial dynamics in droplets. Moreover, we developed a trapping device for long-term monitoring of population dynamics in individual droplets for at least 240 h. We demonstrated the utility of this new microfluidic system by quantifying population dynamics of natural and engineered bacteria. Our approach can further improve the analysis for systems and synthetic biology in terms of manipulability and high temporal resolution.

SUBMITTER: Huang S

PROVIDER: S-EPMC4527312 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Publications

Dynamic control and quantification of bacterial population dynamics in droplets.

Huang Shuqiang S Srimani Jaydeep K JK Lee Anna J AJ Zhang Ying Y Lopatkin Allison J AJ Leong Kam W KW You Lingchong L

Biomaterials 20150519

Culturing and measuring bacterial population dynamics are critical to develop insights into gene regulation or bacterial physiology. Traditional methods, based on bulk culture to obtain such quantification, have the limitations of higher cost/volume of reagents, non-amendable to small size of population and more laborious manipulation. To this end, droplet-based microfluidics represents a promising alternative that is cost-effective and high-throughput. However, difficulties in manipulating the ...[more]

PMID: 26005763

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Project description:Progress in molecular methods has enabled the monitoring of bacterial populations in time. Nevertheless, understanding community dynamics and its links with ecosystem functioning remains challenging due to the tremendous diversity of microorganisms. Conceptual frameworks that make sense of time series of taxonomically rich bacterial communities, regarding their potential ecological function, are needed. A key concept for organizing ecological functions is the niche, the set of strategies that enable a population to persist and define its impacts on the surroundings. Here we present a framework based on manifold learning to organize genomic information into potentially occupied bacterial metabolic niches over time. Manifold learning tries to uncover low-dimensional data structures in high-dimensional data sets that can be used to describe the data in reduced dimensions. We apply the method to re-construct the dynamics of putatively occupied metabolic niches using a long-term bacterial time series from the Baltic Sea, the Linnaeus Microbial Observatory (LMO). The results reveal a relatively low-dimensional space of occupied metabolic niches comprising groups of taxa with similar functional capabilities. Time patterns of occupied niches were strongly driven by seasonality. Some metabolic niches were dominated by one bacterial taxon, whereas others were occupied by multiple taxa, depending on the season. These results illustrate the power of manifold learning approaches to advance our understanding of the links between community composition and functioning in microbial systems. IMPORTANCE The increase in data availability of bacterial communities highlights the need for conceptual frameworks to advance our understanding of these complex and diverse communities alongside the production of such data. To understand the dynamics of these tremendously diverse communities, we need tools to identify overarching strategies and describe their role and function in the ecosystem in a comprehensive way. Here, we show that a manifold learning approach can coarse grain bacterial communities in terms of their metabolic strategies and that we can thereby quantitatively organize genomic information in terms of potentially occupied niches over time. This approach, therefore, advances our understanding of how fluctuations in bacterial abundances and species composition can relate to ecosystem functions and it can facilitate the analysis, monitoring, and future predictions of the development of microbial communities.

Dataset Information

Dynamic control and quantification of bacterial population dynamics in droplets.

Publications

Dynamic control and quantification of bacterial population dynamics in droplets.

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