characterizing-microbiome-dynamics-flow-cytometry-based-workflows

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical ...

Helmholtz Centre for Environmental Research - UFZ

Analyses of DNA pattern provide an excellent tool to determine activity states of bacteria. Bacterial cell cycle behaviour is generally different from the eukaryotic one and is pre-determined by the bacteria's diversity within the phylogenetic tree, and their metabolic traits. As a result, every species creates its specific proliferation pattern that differs from every other one. Up to now, just few bacterial species have been investigated and little information is available concerning DNA cycling even in already known species. This prevents understanding of the complexity and diversity of ongoing bacterial interactions in many ecosystems or in biotechnology. Flow cytometry is the only possible technique to shed light on the dynamics of bacterial communities and DNA patterns will help to unlock the hidden principles of their life. This review provides basic knowledge about the molecular background of bacterial cell cycling, discusses modes of cell cycle phases and presents techniques to both obtain DNA patterns and to combine the contained information with physiological cell states.

Modes of cytometric bacterial DNA pattern: a tool for pursuing growth.

Cytomics reaches microbiology-population heterogeneity on the protein level caused by chemical stress.

Microbes' heterogeneousness - a focus issue on cytometric technologies in microbial single cell analytics.

Heterogeneity of industrial production cultures is accepted to a certain degree; however, the underlying mechanisms are seldom perceived or included in the development of new bioprocess control strategies. Population heterogeneity and its basics, perceptible in the diverse proficiency of cells, begins with asymmetric birth and is found to recess during the life cycle. Since inefficient subpopulations have significant impact on the productivity of industrial cultures, cellular heterogeneity needs to be detected and quantified by using high speed detection tools like flow cytometry. Possible origins of population heterogeneity, sophisticated fluorescent techniques for detection of individual cell states, and cutting-edge Omics-technologies for extended information beyond the resolution of fluorescent labelling are highlighted.

Origin and analysis of microbial population heterogeneity in bioprocesses.

The still poorly explored world of microbial functioning is about to be uncovered by a combined application of old and new technologies. Bacteria, especially, are still in the dark with respect to their phylogenetic affiliations as well as their metabolic capabilities and functions. However, with the advent of sophisticated flow cytometric and cell sorting technologies in microbiological labs, there is now the possibility to gain this knowledge at the single-cell level without cumbersome cultivation approaches. Cytometry also facilitates the understanding of physiological diversity in seemingly likewise acting populations. Both individuality and diversity lead to the complex and concerted actions of microbial consortia. This review provides an overview of the state of the art in the field. It deals with the handling of microorganisms from the very beginning (i.e. sampling, and detachment and fixation procedures) and goes on to discuss the pitfalls and problems in analysing cells without any further treatment. If information cannot be gained by specific staining procedures, phylogenetic technologies, transcriptomic and proteomic approaches may be options for achieving advanced insights. All in all, flow cytometry will be a mediator technology to gain a deeper insight into the heterogeneity of populations and the functioning of microbial communities.

Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities.

Analyzing natural anaerobic microbial communities is a challenge and interpretation of the respective members' performances arduous. Strict anaerobes are often slow-growing and difficult to cultivate due to their unknown physiological capacities. Additionally, abiotic micro-environmental data are difficult to assess, limiting the information on the eco-chemical background in natural environments. This review describes how qualitative and quantitative data can be obtained on anaerobic microbial communities isolated from anoxic environments and treated under laboratory conditions. It gives information on how community composition ('phylogenetic fingerprint') and community structure ('cytometric fingerprint') can be described by PCR-based and single cell-based techniques, respectively. A cell sorting step combined both approaches and enabled quantitative and more precise community resolution. The community dynamics found were swift and strong, despite low and slow changes in measured abiotic parameters. Therefore, the community structure itself mirrored variation in the constructed long term (6 years) ecosystem in a most sensitive way and can be used as sensor for the ecosystems situation. New statistical tools are presented allowing suddenly changing performances of complex communities to be detected and community (in) stabilities to be monitored and/or predicted.

High resolution single cell analytics to follow microbial community dynamics in anaerobic ecosystems.

Natural microbial systems are highly dynamic due to the short generation times of the comprised organisms and their rapid and distinct reactions to changing environments. Microbial flow cytometry approaches are capable techniques for following such community dynamics in a fast and inexpensive way. Newly developed bioinformatics tools not only enable quantification of single cell dynamics, they also make nearly on-line evaluation of community attributes possible, enable interpretation of community trends, and reveal possible constraints that influence community structure and function. Microbial flow cytometry is poised to make the microbial cytome accessible for ambitious ecosystem studies. Functions of cells within the cytome can be determined either by cell sorting in combination with other omics-approaches of choice or by simple correlation analyses.

Dynamics in the microbial cytome-single cell analytics in natural systems.

Serial repitching of brewing yeast inoculates is an important economic factor in the brewing industry, as their propagation is time and resource intensive. Here, we investigated whether replicative aging and/or the population distribution status changed during serial repitching in three different breweries with the same brewing yeast strain but different abiotic backgrounds and repitching regimes with varying numbers of reuses. Next to bud scar numbers the DNA content of the Saccharomyces pastorianus HEBRU cells was analyzed. Gene expression patterns were investigated using low-density microarrays with genes for aging, stress, storage compound metabolism and cell cycle. Two breweries showed a stable rejuvenation rate during serial repitching. In a third brewery the fraction of virgin cells varied, which could be explained with differing wort aeration rates. Furthermore, the number of bud scars per cell and cell size correlated in all 3 breweries throughout all runs. Transcriptome analyses revealed that from the 6th run on, mainly for the cells positive gene expression could be seen, for example up-regulation of trehalose and glycogen metabolism genes. Additionally, the cells' settling in the cone was dependent on cell size, with the lowest and the uppermost cone layers showing the highest amount of dead cells. In general, cells do not progressively age during extended serial repitching.

Analysis of aging in lager brewing yeast during serial repitching.

Clonal populations employ many strategies of diversification to deal with constraints. All these strategies result in the generation of different phenotypes with diverse functions. Events like cell division are major sources of phenotypic variability due to the unequal partitioning of cellular components. In this review we concentrate on passive and active mechanisms cells employ to distribute macromolecules between their offspring. Different types of segregation are described, addressing both metabolically pertinent molecules such as PHA/PHB or polyphosphates, and components that adversely affect cells by promoting aging, such as damaged protein complexes or extrachromosomal rDNA circles. We also refer to mechanisms generating plasmid copy number (PCN) variation between cells in a population, and how elaborate partitioning systems counteract partitioning errors and ensure equal distribution. Finally, we demonstrate how simple differences in chromosomal copy number determine the fate of a cell, in this case the effect of gene dosage on the onset of sporulation in Bacillus subtilis or on a functional trait in Sinorhizobium meliloti. 

Non-random distribution of macromolecules as driving forces for phenotypic variation.

Editorial overview: Environmental microbiology: Revisiting the physiology of microorganisms on the single cell scale.

Plasmids are widely used for molecular cloning or production of proteins in laboratory and industrial settings. Constant modification has brought forth countless plasmid vectors whose characteristics in terms of average plasmid copy number (PCN) and stability are rarely known. The crucial factor determining the PCN is the replication system; most replication systems in use today belong to a small number of different classes and are available through repositories like the Standard European Vector Architecture (SEVA).

Copy number variability of expression plasmids determined by cell sorting and Droplet Digital PCR.

Microbial flow cytometry is an established fast and economic technique for complex ecosystem studies and enables visualization of rapidly changing community structures by measuring characteristics of single microbial cells. Cytometric evaluation routines are available such as flowCyBar which are useful for automatic data processing. Here, a cytometric workflow was established which allows to routinely analyze salivary microbiomes on the example of ten oral healthy subjects. First, saliva was collected within a 3-month period, cytometrically analyzed and the evolution of the microbiomes followed as well as the calculation of their intra- and inter-subject similarity. Second, the respective microbiomes were stressed by exposition to high sugar or acid concentrations and immediate changes were recorded. Third, bactericide solutions were tested on their impact on the microbiomes. In all three set ups huge intra-individual variations in cytometric community structures were found to be largely absent, even under stress, while inter-individual diversity was obvious. The bacterial cell counts of saliva samples were found to vary between 3.0×10 and 6.2×10 cells per sample and subject in undisturbed environments. The application of the two bactericides did not cause noteworthy diversity changes but the loss in cell numbers by about 50% was high after treatment. Illumina® sequencing of whole microbiomes or sorted sub-microbiomes revealed typical phyla such as Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes and Fusobacteria. This approach is useful for fast monitoring of individual salivary microbiomes and automatic calculation of intra- and inter-individual dynamic changes and variability and opens insight into ecological principles leading to their sustainment in their individual environment.

A cytometric approach to follow variation and dynamics of the salivary microbiota.

The widely established production of CH from renewable biomass in industrial scale anaerobic reactors may play a major role in the future energy supply. It relies on methanogenic archaea as key organisms which represent the bottleneck in the process. The quantitative analysis of these organisms can help to maximize process performance, uncover disturbances before failure, and may ultimately lead to community-based process control schemes. Existing qPCR and fluorescence microscopy-based methods are very attractive but can be cost-intensive and laborious.

Article	Year
Modes of cytometric bacterial DNA pattern: a tool for pursuing growth. Cell proliferation\| PubMed ID: 17877606	2007
Cytomics reaches microbiology-population heterogeneity on the protein level caused by chemical stress. Cytometry. Part A : the journal of the International Society for Analytical Cytology\| PubMed ID: 18061956	2008
Microbes' heterogeneousness - a focus issue on cytometric technologies in microbial single cell analytics. Biotechnology journal\| PubMed ID: 19452474	2009
Origin and analysis of microbial population heterogeneity in bioprocesses. Current opinion in biotechnology\| PubMed ID: 20138500	2010
Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities. FEMS microbiology reviews\| PubMed ID: 20337722	2010
High resolution single cell analytics to follow microbial community dynamics in anaerobic ecosystems. Methods (San Diego, Calif.)\| PubMed ID: 22504724	2012
Dynamics in the microbial cytome-single cell analytics in natural systems. Current opinion in biotechnology\| PubMed ID: 24525083	2014
Analysis of aging in lager brewing yeast during serial repitching. Journal of biotechnology\| PubMed ID: 25026460	2014
Non-random distribution of macromolecules as driving forces for phenotypic variation. Current opinion in microbiology\| PubMed ID: 25974411	2015
Editorial overview: Environmental microbiology: Revisiting the physiology of microorganisms on the single cell scale. Current opinion in microbiology\| PubMed ID: 26117799	2015
Copy number variability of expression plasmids determined by cell sorting and Droplet Digital PCR. Microbial cell factories\| PubMed ID: 27993152	2016
A cytometric approach to follow variation and dynamics of the salivary microbiota. Methods (San Diego, Calif.)\| PubMed ID: 28842259	2018
Flow cytometric quantification, sorting and sequencing of methanogenic archaea based on F autofluorescence. Microbial cell factories\| PubMed ID: 29084543	2017

Susann Mueller

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