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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. 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.

Bacterial cells can be characterized in terms of their cell properties using flow cytometry. Flow cytometry is able to deliver multiparametric measurements of up to 50,000 cells per second. However, there has not yet been a thorough survey concerning the identification of the population to which bacterial single cells belong based on flow cytometry data. This paper not only aims to assess the quality of flow cytometry data when measuring bacterial populations, but also suggests an alternative approach for analyzing synthetic microbial communities. We created so-called in silico communities, which allow us to explore the possibilities of bacterial flow cytometry data using supervised machine learning techniques. We can identify single cells with an accuracy >90% for more than half of the communities consisting out of two bacterial populations. In order to assess to what extent an in silico community is representative for its synthetic counterpart, we created so-called abundance gradients, a combination of synthetic (i.e., in vitro) communities containing two bacterial populations in varying abundances. By showing that we are able to retrieve an abundance gradient using a combination of in silico communities and supervised machine learning techniques, we argue that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of synthetic communities and bacterial flow cytometry data in general.

Variations in the gut microbiome have been associated with changes in health state such as Crohn’s disease (CD). Most surveys characterize the microbiome through analysis of the 16S rRNA gene. An alternative technology that can be used is flow cytometry. In this report, we reanalyzed a disease cohort that has been characterized by both technologies. Changes in microbial community structure are reflected in both types of data. We demonstrate that cytometric fingerprints can be used as a diagnostic tool in order to classify samples according to CD state. These results highlight the potential of flow cytometry to perform rapid diagnostics of microbiome-associated diseases.

With the increasing demand for global food resources, improving aquaculture production has been the focus for years. Tilapia has become one of the most commonly farmed and economically important fish species globally. Research efforts have recognized the significant roles that microbial communities play in improving the health and aquaculture performance of tilapia. However, interactions between tilapia and its associated microbial communities remain poorly understood. In this review, the current understanding of tilapia microbiomes is summarized. With fish being in intimate relationship with its environment, studies characterizing the microbial communities present in the rearing environment and how they affect tilapia microbiomes and health are also examined. Having an in‐depth understanding of the different microbiomes and their roles and interactions in the tilapia culture system is a crucial step toward managing and modulating these microbial communities to improve tilapia health. This review also sheds light on the different factors that influence tilapia microbiomes such as developmental stages, organ tissues, and types of culture systems. The effects of on‐farm practices such as diet; feeding regimes; use of probiotics, prebiotics, and synbiotics; vaccination; application of antibiotics; disinfection; and pond fertilization on tilapia microbiome are also discussed. Through this review, future research needs are identified that can provide a deeper understanding of the relationships among tilapia microbiomes, health, and productivity. These knowledge in turn can be harnessed into practical applications and potential microbiome‐based management protocols to improve future best management practices for tilapia aquaculture.

Abstract The development of high-throughput sequencing technologies has revolutionised the field of microbial ecology via 16S rRNA gene amplicon sequencing approaches. Clustering those amplicon sequencing reads into operational taxonomic units (OTUs) using a fixed cut-off is a commonly used approach to estimate microbial diversity. A 97% threshold was chosen with the intended purpose that resulting OTUs could be interpreted as a proxy for bacterial species. Our results show that the robustness of such a generalised cut-off is questionable when applied to short amplicons only covering one or two variable regions of the 16S rRNA gene. It will lead to biases in diversity metrics and makes it hard to compare results obtained with amplicons derived with different primer sets. The method introduced within this work takes into account the differential evolutional rates of taxonomic lineages in order to define a dynamic and taxonomic-dependent OTU clustering cut-off score. For a taxonomic family consisting of species showing high evolutionary conservation in the amplified variable regions, the cut-off will be more stringent than 97%. By taking into consideration the amplified variable regions and the taxonomic family when defining this cut-off, such a threshold will lead to more robust results and closer correspondence between OTUs and species. This approach has been implemented in a publicly available software package called DynamiC. More accurate and robust estimates of microbial diversity are generated when performing an OTU clustering step using a dynamic cut-off that integrates bacterial taxonomy and differential evolutionary rates.

Physicochemical water disinfection methods result in the reduction of bacterial concentrations by orders of magnitude, but not in the total elimination of the bacterial community. As such, the dead bacterial biomass may act as a carbon and nutrient source for the survivor populations. The ability of bacterial strains to grow on dead bacterial cells has been described before as necrotrophy. We investigated the impact of killed bacterial biomass of two different bacterial strains on the growth potential of natural drinking water microbial communities. Many indigenous bacterial taxa could grow on dead biomass, with the total bacterial concentration increasing from 10 4 to 10 8 cells/ml. Necrotrophic growth was specific (43 enriched taxa) and selective (i.e. enriched taxa were dependent on the type of dead biomass). The potential of natural water communities to grow necrotrophically has remained underexplored. Nevertheless the phenomenon can have a big impact in water quality and deserves more attention.

As evidence is emerging of the key roles that host-associated microbiomes often play in regulating the physiology, fitness, and ecology of their eukaryotic hosts, human activities are causing declines in biological diversity, including within the microbial world. Here, we use a multifactorial manipulative experiment to test the effects of declining diversity within host microbiomes both alone and in tandem with the effects of emerging global changes, including climate warming and shifts in nutrient bioavailability, which are inflicting increasing abiotic stress on host organisms. Using single-celled eukaryotic phytoplankton that harbor an external microbiome as a model system, we demonstrate that diversity within host-associated microbiomes impacts multiple tiers of biological organization, including host physiology, the host population and community ecology, and ecosystem nutrient cycling. Notably, these microbiome diversity-driven effects became magnified in abiotically stressful environments, suggesting that the importance of microbiome diversity may have increased over time during the Anthropocene.

By combining a genome-centric metagenomic approach with a culture-based approach, we investigated the genomic adaptations of prevalent populations in an engineered oligotrophic freshwater system. We found evidence for widespread positive selection on genes involved in phosphorus and carbon scavenging pathways and for gene expansions in motility and environmental sensing to be important genomic adaptations of the abundant taxon in this system. In addition, microscopic and flow cytometric analysis of the first freshwater representative of this population ( Ramlibacter aquaticus LMG 30558 T ) demonstrated phenotypic plasticity, possibly due to the metabolic versatility granted by its larger genome, to be a strategy to cope with nutrient limitation. Our study clearly demonstrates the need for the use of a broad set of genomic tools combined with culture-based physiological characterization assays to investigate and validate genomic adaptations. We examined the genomic adaptations of prevalent bacterial taxa in a highly nutrient- and ion-depleted freshwater environment located in the secondary cooling water system of a nuclear research reactor. Using genome-centric metagenomics, we found that none of the prevalent bacterial taxa were related to typical freshwater bacterial lineages. We also did not identify strong signatures of genome streamlining, which has been shown to be one of the ecoevolutionary forces shaping the genome characteristics of bacterial taxa in nutrient-depleted environments. Instead, focusing on the dominant taxon, a novel Ramlibacter sp. which we propose to name Ramlibacter aquaticus , we detected extensive positive selection on genes involved in phosphorus and carbon scavenging pathways. These genes were involved in the high-affinity phosphate uptake and storage into polyphosphate granules, metabolism of nitrogen-rich organic matter, and carbon/energy storage into polyhydroxyalkanoate. In parallel, comparative genomics revealed a high number of paralogs and an accessory genome significantly enriched in environmental sensing pathways (i.e., chemotaxis and motility), suggesting extensive gene expansions in R. aquaticus . The type strain of R. aquaticus (LMG 30558 T ) displayed optimal growth kinetics and productivity at low nutrient concentrations, as well as substantial cell size plasticity. Our findings with R. aquaticus LMG 30558 T demonstrate that positive selection and gene expansions may represent successful adaptive strategies to oligotrophic environments that preserve high growth rates and cellular productivity. IMPORTANCE By combining a genome-centric metagenomic approach with a culture-based approach, we investigated the genomic adaptations of prevalent populations in an engineered oligotrophic freshwater system. We found evidence for widespread positive selection on genes involved in phosphorus and carbon scavenging pathways and for gene expansions in motility and environmental sensing to be important genomic adaptations of the abundant taxon in this system. In addition, microscopic and flow cytometric analysis of the first freshwater representative of this population ( Ramlibacter aquaticus LMG 30558 T ) demonstrated phenotypic plasticity, possibly due to the metabolic versatility granted by its larger genome, to be a strategy to cope with nutrient limitation. Our study clearly demonstrates the need for the use of a broad set of genomic tools combined with culture-based physiological characterization assays to investigate and validate genomic adaptations.

Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics. IMPORTANCE Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technology to characterize microbial community diversity and dynamics. The technology enables a fast measurement of optical properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian mixture models. We evaluated our workflow on data sets from both synthetic and natural ecosystems, illustrating its general applicability for the analysis of microbial flow cytometry data. PhenoGMM supports a rapid and quantitative analysis of microbial community structure using flow cytometry.

Microbial cells that live in the same community can exist in different physiological and morphological states that change as a function of spatiotemporal variations in environmental conditions. This phenomenon is commonly known as phenotypic heterogeneity and/or diversity. Measuring this plethora of cellular expressions is needed to better understand and manage microbial processes. However, most tools to study phenotypic diversity only average the behavior of the sampled community. In this work, we present a way to quantify the phenotypic diversity of microbial samples by inferring the (bio)molecular profile of its constituent cells using Raman spectroscopy. We demonstrate how this tool can be used to quantify the phenotypic diversity that arises after the exposure of microbes to stress. Raman spectroscopy holds potential for the detection of stressed cells in bioproduction. Microbial cells experience physiological changes due to environmental change, such as pH and temperature, the release of bactericidal agents, or nutrient limitation. This has been shown to affect community assembly and physiological processes (e.g., stress tolerance, virulence, or cellular metabolic activity). Metabolic stress is typically quantified by measuring community phenotypic properties such as biomass growth, reactive oxygen species, or cell permeability. However, bulk community measurements do not take into account single-cell phenotypic diversity, which is important for a better understanding and the subsequent management of microbial populations. Raman spectroscopy is a nondestructive alternative that provides detailed information on the biochemical makeup of each individual cell. Here, we introduce a method for describing single-cell phenotypic diversity using the Hill diversity framework of Raman spectra. Using the biomolecular profile of individual cells, we obtained a metric to compare cellular states and used it to study stress-induced changes. First, in two Escherichia coli populations either treated with ethanol or nontreated and then in two Saccharomyces cerevisiae subpopulations with either high or low expression of a stress reporter. In both cases, we were able to quantify single-cell phenotypic diversity and to discriminate metabolically stressed cells using a clustering algorithm. We also described how the lipid, protein, and nucleic acid compositions changed after the exposure to the stressor using information from the Raman spectra. Our results show that Raman spectroscopy delivers the necessary resolution to quantify phenotypic diversity within individual cells and that this information can be used to study stress-driven metabolic diversity in microbial populations. IMPORTANCE Microbial cells that live in the same community can exist in different physiological and morphological states that change as a function of spatiotemporal variations in environmental conditions. This phenomenon is commonly known as phenotypic heterogeneity and/or diversity. Measuring this plethora of cellular expressions is needed to better understand and manage microbial processes. However, most tools to study phenotypic diversity only average the behavior of the sampled community. In this work, we present a way to quantify the phenotypic diversity of microbial samples by inferring the (bio)molecular profile of its constituent cells using Raman spectroscopy. We demonstrate how this tool can be used to quantify the phenotypic diversity that arises after the exposure of microbes to stress. Raman spectroscopy holds potential for the detection of stressed cells in bioproduction.