Real-time monitoring of population dynamics and physical interactions in a synthetic yeast ecosystem by use of multicolour flow cytometry

Conacher, Cleo G.; Naidoo-Blassoples, R K; Rossouw, Debra; Bauer, Florian F.

doi:10.1007/s00253-020-10607-x

Cited by 22 publications

(22 citation statements)

References 52 publications

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“…Three yeast species representatives of wine-related origin were used to construct a synthetic yeast consortium. The three species were fluorescently labeled, each with a different fluorescent label, namely, S. cerevisiae VIN13 (Anchor Yeast, Cape Town, South Africa) labeled with TagRFP657, L. thermotolerans IWBT Y1240 (CBS: 16374) labeled with mTagBFP2, and T. delbrueckii LO544 (CRBO: LO544) labeled with enhanced green fluorescent protein (eGFP) ( 70 ). All yeast strains were stored as glycerol stocks (25% [wt/vol] glycerol) at −80°C.…”

Section: Methodsmentioning

confidence: 99%

A Transcriptomic Analysis of Higher-Order Ecological Interactions in a Eukaryotic Model Microbial Ecosystem

Conacher

Naidoo-Blassoples

Rossouw

et al. 2022

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Section: Methodsmentioning

confidence: 99%

A Transcriptomic Analysis of Higher-Order Ecological Interactions in a Eukaryotic Model Microbial Ecosystem

Conacher

Naidoo-Blassoples

Rossouw

et al. 2022

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“…Methods to measure the impact of microbial interactions on growth or fitness require either knowledge of the absolute abundance of each member of the community separately, or relative abundance of each member multiplied by the absolute abundance of the community. In the first approach, a quantitatively measurable trait unique to each member is required, to measure each member abundance separately, e.g., colony morphology ( Traxler and Kolter, 2015 ), growth on selective media ( Cheong et al, 2021 ), fluorescent tag ( Conacher et al, 2020 ), specific enzymatic activity ( Rivett et al, 2016 ; Liu et al, 2020a ), specific DNA sequence ( Kumar et al, 2019 ; Blasche et al, 2021 ), or a physical separation allowing for individual quantification ( Liu et al, 2017 ; Moutinho et al, 2017 ; Jo et al, 2021 ). Physical separation is useful to study uncharacterized isolates; however, it precludes the detection of contact-dependent interactions.…”

Section: Experimental Approaches To Measure Ecological Interactionsmentioning

confidence: 99%

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

2021

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Microorganisms are a fundamental part of virtually every ecosystem on earth. Understanding how collectively they interact, assemble, and function as communities has become a prevalent topic both in fundamental and applied research. Owing to multiple advances in technology, answering questions at the microbial system or network level is now within our grasp. To map and characterize microbial interaction networks, numerous computational approaches have been developed; however, experimentally validating microbial interactions is no trivial task. Microbial interactions are context-dependent, and their complex nature can result in an array of outcomes, not only in terms of fitness or growth, but also in other relevant functions and phenotypes. Thus, approaches to experimentally capture microbial interactions involve a combination of culture methods and phenotypic or functional characterization methods. Here, through our perspective of food microbiologists, we highlight the breadth of innovative and promising experimental strategies for their potential to capture the different dimensions of microbial interactions and their high-throughput application to answer the question; are microbial interaction patterns or network architecture similar along different contextual scales? We further discuss the experimental approaches used to build various types of networks and study their architecture in the context of cell biology and how they translate at the level of microbial ecosystem.

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“…Data compensation intends to correct for emission signal spillover from one stain (e.g., Syto59) into the channel designated for another stain (e.g., propidium iodide). Currently, this is only rarely applied due to the limited availability of multicolor FCM protocols to analyze microbial communities, although a few examples can be found in the literature (23,(31)(32)(33)(34). Functions to perform compensation are incorporated into the flowCore package.…”

Section: Preprocessingmentioning

confidence: 99%

Computational Analysis of Microbial Flow Cytometry Data

Rubbens

Props

2021

mSystems

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Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.

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Real-time monitoring of population dynamics and physical interactions in a synthetic yeast ecosystem by use of multicolour flow cytometry

Cited by 22 publications

References 52 publications

A Transcriptomic Analysis of Higher-Order Ecological Interactions in a Eukaryotic Model Microbial Ecosystem

A Transcriptomic Analysis of Higher-Order Ecological Interactions in a Eukaryotic Model Microbial Ecosystem

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

Computational Analysis of Microbial Flow Cytometry Data

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