Flow cytometry for fast microbial community fingerprinting

Alternative for HPC22 after repairs in the drinking water distribution system

There is a risk of contamination by (pathogenic) microorganisms from the outside environment into the drinking water during maintenance or pipe breaches in the drinking water distribution system (DWDS) and, consequently, the drinking water distributed to consumers may result in possible detrimental effects on public health. Traditional time-consuming microbiological testing is, therefore, performed to confirm drinking water is not microbially contaminated. This is done by culturing methods of the faecal indicators Escherichia coli, intestinal enterococci and the technical parameters coliform bacteria and heterotrophic plate counts at 22 °C (HPC22). In this study, fast methods (adenosine triphosphate (ATP), flow cytometry, enzyme activity and qPCR) were compared as an alternative for HPC22. Using dilution series and field samples, ATP (ATPtotal-lab and ATPcell-mob) and enzymatic activity (ALP-2) methods proved to be the more reliable and sensitive than flow cytometry and qPCR methods for detecting microbiological contaminations in drinking water. Significant (p < 0.05) and relatively strong correlations (R2 = 0.61-0.76) were obtained between HPC22 and both ATP methods, enzyme activity and qPCR parameters, but relations with flow cytometry were weak (R2 = 0.24 - 0.52). The samples taken after repairs or a calamity from the DWDS showed in general limited variation in the HPC22 count and were in most cases below the guidance level of 1,000 CFU/mL. We recommend that the best performing alternative methods, i.e. ATPtotal-lab and ATPcell-mob and ALP-2, should be included next to HPC22 in additional field studies to further test and compare these methods to be able to decide which fast method can replace HPC22 analysis after maintenance work in the DWDS.

Flow cytometric assessments of metabolic activity in bacterial assemblages provide insight into ecosystem condition along the Buffalo National River, Arkansas

The Buffalo National River (BNR), on karst terrain in Arkansas, is considered an extraordinary water resource. Water collected in Spring 2017 along BNR was metagenomically analyzed using 16S rDNA, and for 17 months (5/2017-11/2018), bacterial responses were measured in relation to nutrients sampled along a stretch of BNR near a concentrated animal feed operation (CAFO) on Big Creek. Because cell count and esterase activity can increase proportionally with organic enrichment, they were hypothesized to be elevated near the CAFO. Counts (colony forming units; CFUs) were different among sites for 73 % of the months; Big Creek generated highest CFUs 27 % of the time, with the closest downstream site at 13.3 %. Esterase activity was different among sites 94 % of the time, with Big Creek exhibiting lowest activity 71 % of the time. Over the months, activity was similar across sites at ~70 % active, except at Big Creek (56 %). The α-diversity of BNR microbial consortia near a wastewater treatment plant (WWTP) and the CAFO was related to distance from the WWTP and CAFO. The inverse relationship between high CFUs and low esterase activity at Big Creek (r = -0.71) actuated in vitro exposures of bacteria to organic wastewater contaminants (OWC) previously identified in the watershed. Exponential-phase Escherichia coli (stock strain), Streptococcus suis (avirulent, from swine), and S. dysgalactiae (virulent, from silver carp, Hypophthalmichthys molitrix) were incubated with atrazine, pharmaceuticals (17 α-ethynylestradiol and trenbolone), and antimicrobials (tylosin and butylparaben). Bacteria were differentially responsive. Activity varied with exposure time and OWC type, but not concentration; atrazine decreased it most. Taken together - the metagenomic taxonomic similarities along BNR, slightly higher bacterial growth and lower bacterial esterase at the CAFO, and the lab exposures of bacterial strains showing that OWC altered metabolism - the results indicated that bioactive OWC entering the watershed can strongly influence microbial processes in the aquatic ecosystem.

Unlocking the mechanism of action: a cost-effective flow cytometry approach for accelerating antimicrobial drug development

Antimicrobial resistance is one of the greatest challenges to global health. While the development of new antimicrobials can combat resistance, low profitability reduces the number of new compounds brought to market. Elucidating the mechanism of action is crucial for developing new antimicrobials. This can become expensive as there are no universally applicable pipelines. Phenotypic heterogeneity of microbial populations resulting from antimicrobial treatment can be captured through flow cytometric fingerprinting. Since antimicrobials are classified into limited groups, the mechanism of action of known compounds can be used for predictive modeling. We demonstrate a cost-effective flow cytometry approach for determining the mechanism of action of new compounds. Cultures of Actinomyces viscosus and Fusobacterium nucleatum were treated with different antimicrobials and measured by flow cytometry. A Gaussian mixture mask was applied over the data to construct phenotypic fingerprints. Fingerprints were used to assess statistical differences between mechanism of action groups and to train random forest classifiers. Classifiers were then used to predict the mechanism of action of cephalothin. Statistical differences were found among the different mechanisms of action groups. Pairwise comparison showed statistical differences for 35 out of 45 pairs for A. viscosus and for 32 out of 45 pairs for F. nucleatum after 3.5 h of treatment. The best-performing random forest classifier yielded a Matthews correlation coefficient of 0.92 and the mechanism of action of cephalothin could be successfully predicted. These findings suggest that flow cytometry can be a cheap and fast alternative for determining the mechanism of action of new antimicrobials.IMPORTANCEIn the context of the emerging threat of antimicrobial resistance, the development of novel antimicrobials is a commonly employed strategy to combat resistance. Elucidating the mechanism of action of novel compounds is crucial in this development but can become expensive, as no universally applicable pipelines currently exist. We present a novel flow cytometry-based approach capable of determining the mechanism of action swiftly and cost-effectively. The workflow aims to accelerate drug discovery and could help facilitate a more targeted approach for antimicrobial treatment of patients.

Domestic hot-water boilers harbour active thermophilic bacterial communities distinctly different from those in the cold-water supply

Running cold and hot water in buildings is a widely established commodity. However, interests regarding hygiene and microbiological aspects had so far been focussed on cold water. Little attention has been given to the microbiology of domestic hot-water installations (DHWIs), except for aspects of pathogenic Legionella. World-wide, regulations consider hot (or warm) water as 'heated drinking water' that must comply (cold) drinking water (DW) standards. However, the few reports that exist indicate presence and growth of microbial flora in DHWIs, even when supplied with water with disinfectant residual. Using flow cytometric (FCM) total cell counting (TCC), FCM-fingerprinting, and 16S rRNA-gene-based metagenomic analysis, the characteristics and composition of bacterial communities in cold drinking water (DW) and hot water from associated boilers (operating at 50 - 60 °C) was studied in 14 selected inhouse DW installations located in Switzerland and Austria. A sampling strategy was applied that ensured access to the bulk water phase of both, supplied cold DW and produced hot boiler water. Generally, 1.3- to 8-fold enhanced TCCs were recorded in hot water compared to those in the supplied cold DW. FCM-fingerprints of cold and corresponding hot water from individual buildings indicated different composition of cold- and hot-water microbial floras. Also, hot waters from each of the boilers sampled had its own individual FCM-fingerprint. 16S rRNA-gene-based metagenomic analysis confirmed the marked differences in composition of microbiomes. E.g., in three neighbouring houses supplied from the same public network pipe each hot-water boiler contained its own thermophilic bacterial flora. Generally, bacterial diversity in cold DW was broad, that in hot water was restricted, with mostly thermophilic strains from the families Hydrogenophilaceae, Nitrosomonadaceae and Thermaceae dominating. Batch growth assays, consisting of cold DW heated up to 50 - 60 °C and inoculated with hot water, resulted in immediate cell growth with doubling times between 5 and 10 h. When cold DW was used as an inoculum no significant growth was observed. Even boilers supplied with UVC-treated cold DW contained an actively growing microbial flora, suggesting such hot-water systems as autonomously operating, thermophilic bioreactors. The generation of assimilable organic carbon from dissolved organic carbon due to heating appears to be the driver for growth of thermophilic microbial communities. Our report suggests that a man-made microbial ecosystem, very close to us all and of potential hygienic importance, may have been overlooked so far. Despite consumers having been exposed to microbial hot-water flora for a long time, with no major pathogens so far been associated specifically with hot-water usage (except for Legionella), the role of harmless thermophiles and their interaction with potential human pathogens able to grow at elevated temperatures in DHWIs remains to be investigated.

Application of flow cytometry for rapid, high-throughput, multiparametric analysis of environmental microbiomes

Quantification of the abundance and understanding of the dynamics of the microbial communities is essential to establish a basis for microbiome characterization. The conventional techniques used for the quantification of microbes are complicated and time-consuming. With scientific advancement, many techniques evolved and came into account. Among them, flow cytometry is a robust, high-throughput technique through which microbial dynamics, morphology, microbial distribution, physiological characteristics, and many more attributes can be studied in a high-throughput manner with comparatively less time and resources. Flow cytometry, when combined with other omics-based methods, offers a rapid and efficient platform to analyze and understand the composition of microbiome at the cellular level. The microbial diversity observed through flow cytometry will not be equivalent to that obtained by sequencing methods, but this integrated approach holds great potential for high throughput characterization of microbiomes. Flow cytometry is regarded as an established characterization tool in haematology, oncology, immunology, and medical microbiology research; however, its application in environmental microbiology is yet to be explored. This comprehensive review aims to delve into the diverse environmental applications of flow cytometry across various domains, including but not limited to bioremediation, landfills, anaerobic digestion, industrial bioprocesses, water quality regulation, and soil quality regulation. By conducting an in-depth analysis, this article seeks to shed light on the potential benefits and challenges associated with the utilization of flow cytometry in addressing environmental concerns.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Multiple fluorescence approaches to identify rapid changes in microbial indicators at karst springs

Karst springs are globally important for drinking water supply but are often also exceptionally vulnerable to contamination. Such springs usually exhibit strong variation in microbial water quality in sharp response to rainfall events, thus, posing a health hazard to consumers of water supplied from these sources. The rapid detection of such changes is extremely important as well as being able to establish a link to the sources of such pollution, so that appropriate measures can be taken both in terms of immediate protection of human health and the management of karst aquifers. In this study, a fluorescence-based multi-parameter approach was trialed in order to evaluate which methods can be used to monitor rainfall-induced rapid changes in microbial water quality at karst springs, as well as determine whether such changes can be linked to sources of human effluent contamination. The results from three monitoring periods at two karst springs revealed marked responses to rainfall events for all of the microbial parameters measured. Total cell count (TCC) measurements using flow cytometry (FCM) showed very strong positive correlations with the more conventionally monitored faecal indicator bacteria (FIB) and total coliforms (TC), indicating that such a fluorescence-based and cultivation-independent technique can be very useful to indicate rapid changes in microbial water quality at karst springs. Furthermore, very strong positive correlations were also found between tryptophan-like fluorescence (TLF) measurements and concentrations of all monitored microbial parameters, again demonstrating that such a fluorescence-based approach can also be useful for detecting rapid changes in concentrations of traditional faecal indicators. Interestingly, it was found that fluorescent whitening compounds (FWCs) signals do not necessarily follow temporal variations of microbial indicators. However, the frequency of detection of positive FWCs signals may still reveal useful information about the overall magnitude of human wastewater effluent impacts on karst aquifer systems.

The r/K selection theory and its application in biological wastewater treatment processes

Understanding the characteristics of functional organisms is the key to managing and updating biological processes for wastewater treatment. This review, for the first time, systematically characterized two typical types of strategists in wastewater treatment ecosystems via the r/K selection theory and provided novel strategies for selectively enriching microbial community. Functional organisms involved in nitrification (e.g., Nitrosomonas and Nitrosococcus), anammox (Candidatus Brocadia), and methanogenesis (Methanosarcinaceae) are identified as r-strategists with fast growth capacities and low substrate affinities. These r-strategists can achieve high pollutant removal loading rates. On the other hand, other organisms such as Nitrosospira spp., Candidatus Kuenenia, and Methanosaetaceae, are characterized as K-strategists with slow growth rates but high substrate affinities, which can decrease the pollutant concentration to low levels. More importantly, K-strategists may play crucial roles in the biodegradation of recalcitrant organic pollutants. The food-to-microorganism ratio, mass transfer, cell size, and biomass morphology are the key factors determining the selection of r-/K-strategists. These factors can be related with operating parameters (e.g., solids and hydraulic retention time), biomass morphology (biofilm or granules), and operating modes (continuous-flow or sequencing batch), etc., to achieve the efficient acclimation of targeted r-/K-strategists. For practical applications, the concept of substrate flux was put forward to further benefit the selective enrichment of r-/K-strategists, fulfilling effective management and improvement of engineered pollution control bioprocesses. Finally, the future perspectives regarding the development of the r/K selection theory in wastewater treatment processes were discussed.

Flow Cytometry and Fecal Indicator Bacteria Analyses for Fingerprinting Microbial Pollution in Karst Aquifer Systems

Microbial pollution of aquifers is a persistent water quality problem globally which poses significant risks to public health. Karst aquifer systems are exceptionally vulnerable to pollution from fecal contamination sources as a result of rapid recharge of water from the surface via discrete pathways linked to highly conductive, solutionally enlarged conduits alongside strong aquifer heterogeneity. Consequently, rapid changes in microbial water quality, which are difficult to monitor with expensive and time-consuming conventional microbiological methods, are a major concern in karst environments. This study examined flow cytometric (FCM) fingerprinting of bacterial cells in groundwater together with fecal indicator bacteria (FIB) at nine separate karst springs of varying catchment size over a 14 month period in order to assess whether such a technique can provide faster and more descriptive information about microbial pollution through such karst aquifer systems. Moreover, the data have also been evaluated with respect to the potential of using turbidity as an easy-to-measure proxy indicator of microbial pollution in a novel way. We argue that FCM provides additional data from which enhanced insights into fecal pollution sources and its fate and transport in such karst catchments can be gained. We also present valuable new information on the potential and limitations of turbidity as an indicator of fecal groundwater contamination in karst. FCM has the potential to become a more widely used tool in the field of contaminant hydrogeology.

Fast quantification of gut bacterial species in cocultures using flow cytometry and supervised classification

A bottleneck for microbial community experiments with many samples and/or replicates is the fast quantification of individual taxon abundances, which is commonly achieved through sequencing marker genes such as the 16S rRNA gene. Here, we propose a new approach for high-throughput and high-quality enumeration of human gut bacteria in a defined community, combining flow cytometry and supervised classification to identify and quantify species mixed in silico and in defined communities in vitro. We identified species in a 5-species in silico community with an F1 score of 71%. In addition, we demonstrate in vitro that our method performs equally well or better than 16S rRNA gene sequencing in two-species cocultures and agrees with 16S rRNA gene sequencing data on the most abundant species in a four-species community. We found that shape and size differences alone are insufficient to distinguish species, and that it is thus necessary to exploit the multivariate nature of flow cytometry data. Finally, we observed that variability of flow cytometry data across replicates differs between gut bacterial species. In conclusion, the performance of supervised classification of gut species in flow cytometry data is species-dependent, but is for some combinations accurate enough to serve as a faster alternative to 16S rRNA gene sequencing.

Monitoring the Early Stages of Formation of Oil-Water Emulsions Using Flow Cytometry

Characterization of complex oil emulsions is critical yet challenging both in science and in many industrial applications. Here we demonstrate for the first time the use of flow cytometry as a fast method for characterizing complex, polydisperse oil-water emulsions. Owing to our interest in understanding how the presence of specific ions might affect the properties of oil-water emulsions including size, polydispersity, and complexity, we present a systematic study of oil emulsions in deionized water and various brines of different ionic strength. Forward scatter (FSC) and side scatter (SSC) intensities associated with detailed statistics were judiciously combined to provide a better understanding of these complex systems. We find that the type and concentration profiles of ions around the oil droplets affect significantly the properties of the emulsion. Weakly hydrated cations NH₄⁺ and Ca²⁺ appear to be more effective in screening the charge of oil droplets compared to the monovalent Na⁺ and divalent Mg²⁺ ions, respectively. As a result, coalescence and formation of larger droplets are seen in the case of NH₄Cl and CaCl₂ compared to NaCl and MgCl₂, respectively. In addition, weakly hydrated anions such as Cl^- can come closer to the oil surface and, thus, decrease the effective screening that the Na⁺ ions provide as compared to SO₄^2- ions, which leads to more stable emulsions in NaCl compared to Na₂SO₄. In addition to these specific findings, the work demonstrates the utility of the technique as a new tool for characterizing oil emulsions in a wide spectrum of fields ranging from food to oil and gas applications.

Drinking Water Source and Intake Are Associated with Distinct Gut Microbiota Signatures in US and UK Populations

BACKGROUND

The microbiome of the digestive tract exerts fundamental roles in host physiology. Extrinsic factors including lifestyle and diet are widely recognized as key drivers of gut and oral microbiome compositions. Although drinking water is among the food items consumed in the largest amount, little is known about its potential impact on the microbiome.

OBJECTIVES

We explored the associations of plain drinking water source and intake with gut and oral microbiota compositions in a population-based cohort.

METHODS

Microbiota, health, lifestyle, and food intake data were extracted from the American Gut Project public database. Associations of drinking water source (bottled, tap, filtered, or well water) and intake with global microbiota composition were evaluated using linear and logistic models adjusted for anthropometric, diet, and lifestyle factors in 3413 and 3794 individuals, respectively (fecal samples; 56% female, median [IQR] age: 48 [36-59] y; median [IQR] BMI: 23.3 [20.9-26.3] kg/m2), and in 283 and 309 individuals, respectively (oral samples).

RESULTS

Drinking water source ranked among the key contributing factors explaining the gut microbiota variation, accounting for 13% [Faith's phylogenetic diversity (Faith's PD)] and 47% (Bray-Curtis dissimilarity) of the age effect size. Drinking water source was associated with differences in gut microbiota signatures, as revealed by β diversity analyses (P < 0.05; Bray-Curtis dissimilarity, weighted UniFrac distance). Subjects drinking mostly well water had higher fecal α diversity (P < 0.05; Faith's PD, observed amplicon sequence variants), higher Dorea, and lower Bacteroides, Odoribacter, and Streptococcus than the other groups. Low water drinkers also exhibited gut microbiota differences compared with high water drinkers (P < 0.05; Bray-Curtis dissimilarity, unweighted UniFrac distance) and a higher abundance of Campylobacter. No associations were found between oral microbiota composition and drinking water consumption.

CONCLUSIONS

Our results indicate that drinking water may be an important factor in shaping the human gut microbiome and that integrating drinking water source and intake as covariates in future microbiome analyses is warranted.

Triangulation of microbial fingerprinting in anaerobic digestion reveals consistent fingerprinting profiles

The anaerobic digestion microbiome has been puzzling us since the dawn of molecular methods for mixed microbial community analysis. Monitoring of the anaerobic digestion microbiome can either take place via a non-targeted holistic evaluation of the microbial community through fingerprinting or by targeted monitoring of selected taxa. Here, we compared four different microbial community fingerprinting methods, i.e., amplicon sequencing, metaproteomics, metabolomics and cytomics, in their ability to characterise the full-scale anaerobic digestion microbiome. Cytometric fingerprinting through cytomics reflects a, for anaerobic digestion, novel, single cell-based approach of direct microbial community fingerprinting by flow cytometry. Three different digester types, i.e., sludge digesters, digesters treating agro-industrial waste and dry anaerobic digesters, each reflected different operational parameters. The α-diversity analysis yielded inconsistent results, especially for richness, across the different methods. In contrast, β-diversity analysis resulted in comparable profiles, even when translated into phyla or functions, with clear separation of the three digester types. In-depth analysis of each method's features i.e., operational taxonomic units, metaproteins, metabolites, and cytometric traits, yielded certain similar features, yet, also some clear differences between the different methods, which was related to the complexity of the anaerobic digestion process. In conclusion, cytometric fingerprinting through flow cytometry is a reliable, fast method for holistic monitoring of the anaerobic digestion microbiome, and the complementary identification of key features through other methods could give rise to a direct interpretation of anaerobic digestion process performance.

PhenoGMM: Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure

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.

Computational Analysis of Microbial Flow Cytometry Data

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.

Exopolymeric Substances Control Microbial Community Structure and Function by Contributing to both C and Fe Nutrition in Fe-Limited Southern Ocean Provinces

Organic ligands such as exopolymeric substances (EPS) are known to form complexes with iron (Fe) and modulate phytoplankton growth. However, the effect of organic ligands on bacterial and viral communities remains largely unknown. Here, we assessed how Fe associated with organic ligands influences phytoplankton, microbial, and viral abundances and their diversity in the Southern Ocean. While the particulate organic carbon (POC) was modulated by Fe chemistry and bioavailability in the Drake Passage, the abundance and diversity of microbes and viruses were not governed by Fe bioavailability. Only following amendments with bacterial EPS did bacterial abundances increase, while phenotypic alpha diversity of bacterial and viral communities decreased. The latter was accompanied by significantly enhanced POC, pointing toward the relief of C limitation or other drivers of the microbial loop. Based on the literature and our findings, we propose a conceptual framework by which EPS may affect phytoplankton, bacteria, and viruses. Given the importance of the Southern Ocean for Earth's climate as well as the prevalence of viruses and their increasingly recognized impact on marine biogeochemistry and C cycling; the role of microbe-virus interactions on primary productivity in the Southern Ocean needs urgent attention.

Spatial variation in bacterial biomass, community composition and driving factors across a eutrophic river

Eutrophication is a global problem, and bacterial diversity and community composition are usually affected by eutrophication. However, limited information on the ecological significance of bacterial community during algae blooms of rivers has been given, more studies should be focused on the bacterial diversity and distribution characteristics in eutrophic rivers. In this study, we explored the spatial variations of bacterial biomass, community structure, and their relationship with environmental factors in the eutrophic Xiangxi River. The content of Chlorophyll (Chl) was about 16 mg/L in the midstream (S2, S3), which was in the range of light eutrophication. Significant spatial variation of bacterial community structure was found at different sites and depths (p < 0.05), and the driving environmental factor was found to be nitrogen, mainly detected as total nitrogen (TN), Kjeldahl nitrogen (KN), and ammonia nitrogen (NH₄⁺) (p < 0.05). The midstream sites had some significantly different bacteria, including algicidal bacteria and dominant lineages during algal blooms. This result was consistent with the functional prediction, where significant higher abundance of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was associated with algicidal substances in the midstream. At different water depths, some populations adapted to the surface layer, such as the class Flavobacteriia, and others preferred to inhabit in the bottom layer, such as Betaproteobacteria and Acidobacteria. The bacterial biomass was higher in the bottom layer than that in the surface and middle layer, and temperature and pH were found to be the major driving factors. The bacterial diversity increased with the increasing of depths in most sampling sites according to operational taxonomic units (OTUs), Chao1 and ACE indexes, and PO₄^3- was demonstrated to be the most significant factor. In summary, this study offered the evidence for microbial distribution characteristics across different sites and depths in summer, and its relationship with environmental variables in a eutrophic river.

Oral Microbiota Display Profound Differential Metabolic Kinetics and Community Shifts upon Incubation with Sucrose, Trehalose, Kojibiose, and Xylitol

This study compares the metabolic properties of kojibiose, trehalose, sucrose, and xylitol upon incubation with representative oral bacteria as monocultures or synthetic communities or with human salivary bacteria in a defined medium. Compared to sucrose and trehalose, kojibiose resisted metabolism during a 48-h incubation with monocultures, except for Actinomyces viscosus Incubations with Lactobacillus-based communities, as well as salivary bacteria, displayed kojibiose metabolism, yet to a lesser extent than sucrose and trehalose. Concurring with our in vitro findings, screening for carbohydrate-active enzymes revealed that only Lactobacillus spp. and A. viscosus possess enzymes from glycohydrolase (GH) families GH65 and GH15, respectively, which are associated with kojibiose metabolism. Donor-dependent differences in salivary microbiome composition were noted, and differences in pH drop during incubation indicated different rates of sugar metabolism. However, functional analysis indicated that lactate, acetate, and formate evenly dominated the metabolic profile for all sugars except for xylitol. 16S rRNA gene sequencing analysis and α-diversity markers revealed that a significant shift of the microbiome community by sugars was more pronounced in sucrose and trehalose than in kojibiose and xylitol. In Streptococcus spp., a taxon linked to cariogenesis dominated in sucrose (mean ± standard deviation, 91.8 ± 6.4%) and trehalose (55.9 ± 38.6%), representing a high diversity loss. In contrast, Streptococcus (5.1 ± 3.7%) was less abundant in kojibiose, which instead was dominated by Veillonella (26.8 ± 19.6%), while for xylitol, Neisseria (29.4 ± 19.1%) was most abundant. Overall, kojibiose and xylitol incubations stimulated cariogenic species less yet closely maintained an abundance of key phyla and genera of the salivary microbiome, suggesting that kojibiose has low cariogenic properties.IMPORTANCE This study provides a detailed scientific insight on the metabolism of a rare disaccharide, kojibiose, whose mass production has recently been made possible. While the resistance of kojibiose was established with monocultures, delayed utilization of kojibiose was observed with communities containing lactobacilli and A. viscosus as well as with complex communities of bacteria from human saliva. Kojibiose is, therefore, less metabolizable than sucrose and trehalose. Moreover, although conventional sugars cause distinct shifts in salivary microbial communities, our study has revealed that kojibiose is able to closely maintain the salivary microbiome composition, suggesting its low cariogenic properties. This study furthermore underscores the importance and relevance of microbial culture and ex vivo mixed cultures to study cariogenicity and substrate utilization; this is in sharp contrast with tests that solely rely on monocultures such as Streptococcus mutans, which clearly fail to capture complex interactions between oral microbiota.

Stochasticity in microbiology: managing unpredictability to reach the Sustainable Development Goals

Pure (single) cultures of microorganisms and mixed microbial communities (microbiomes) have been important for centuries in providing renewable energy, clean water and food products to human society and will continue to play a crucial role to pursue the Sustainable Development Goals. To use microorganisms effectively, microbial engineered processes require adequate control. Microbial communities are shaped by manageable deterministic processes, but also by stochastic processes, which can promote unforeseeable variations and adaptations. Here, we highlight the impact of stochasticity in single culture and microbiome engineering. First, we discuss the concepts and mechanisms of stochasticity in relation to microbial ecology of single cultures and microbiomes. Second, we discuss the consequences of stochasticity in relation to process performance and human health, which are reflected in key disadvantages and important opportunities. Third, we propose a suitable decision tool to deal with stochasticity in which monitoring of stochasticity and setting the boundaries of stochasticity by regulators are central aspects. Stochasticity may give rise to some risks, such as the presence of pathogens in microbiomes. We argue here that by taking the necessary precautions and through clever monitoring and interpretation, these risks can be mitigated.

Development of fungal spore staining methods for flow cytometric quantification and their application in chlorine-based disinfection

Fungal contamination in drinking water has been becoming a hot topic. The routine enumeration method of fungal spores is heterotrophic plate counts (HPC). However, this method is time-consuming and labor-intensive and there is also the difficulty of enumerating viable but non-culturable cells. In this study, a rapid, simple and accurate method for quantifying fungal spores and discriminating their viability in water was established using flow cytometry (FCM) combined with fluorescence dyes. The optimal staining conditions are as follows: spores suspensions are sonicated at 495 W for 5 min as pretreatment, and then 10 μL of SYBR Green I (100×) and 30 mM Ethylene diamine tetraacetic acid are added to a 500 μL water sample, which incubate at 35 °C for 20 min in dark. The concentration of fungal spores measured by FCM was highly correlated with HPC results and microscope observations, with correlation coefficient of 0.996 and 0.988, respectively. This staining method can be widely applied to the enumeration and viability evaluation of fungal spores. In addition, chlorine-based inactivation of three genera of fungal spores was assessed by plating and FCM. The result showed that all three genera of fungal spores lost culturability firstly and then membrane integrity decreased, preliminarily revealing the inactivation mechanism. The inactivation rate constants of membrane damage varied in the following order: chlorine dioxide > chlorine > chloramine. This study concluded that FCM is an appropriate and alternative tool to detect fungal spores' viability and can be used for evaluating the fungal inactivation by disinfectants.

Gut microbiota generation of protein-bound uremic toxins and related metabolites is not altered at different stages of chronic kidney disease

Chronic kidney disease (CKD) is characterized by accumulation of protein-bound uremic toxins such as p-cresyl sulfate, p-cresyl glucuronide, indoxyl sulfate and indole-3-acetic acid, which originate in the gut. Intestinal bacteria metabolize aromatic amino acids into p-cresol and indole, (further conjugated in the colon mucosa and liver) and indole-3-acetic acid. Here we measured fecal, plasma and urine metabolite concentrations; the contribution of gut bacterial generation to plasma protein-bound uremic toxins accumulation; and influx into the gut of circulating protein-bound uremic toxins at different stages of CKD. Feces, blood and urine were collected from 14 control individuals and 141 patients with CKD. Solutes were quantified by ultra-high performance liquid chromatography. To assess the rate of bacterial generation of p-cresol, indole and indole-3-acetic acid, fecal samples were cultured ex vivo. With CKD progression, an increase in protein-bound uremic toxins levels was observed in plasma, whereas the levels of these toxins and their precursors remained the same in feces and urine. Anaerobic culture of fecal samples showed no difference in ex vivo p-cresol, indole and indole-3-acetic acid generation. Therefore, differences in plasma protein-bound uremic toxins levels between different CKD stages cannot be explained by differences in bacterial generation rates in the gut, suggesting retention due to impaired kidney function as the main contributor to their increased plasma levels. Thus, as fractional clearance decreased with the progression of CKD, tubular clearance appeared to be more affected than the glomerular filtration rate, and there was no net increase in protein-bound uremic toxins influx into the gut lumen with increased plasma levels.

Flow cytometry for rapid characterisation of microbial community dynamics in waste stabilisation ponds

Algal and bacterial communities play a major role in the treatment performance and efficiency of waste stabilisation ponds (WSPs); however, the study of these WSP microbial communities has been challenging. Flow cytometry (FCM) has been used widely as a rapid, culture-independent method of characterising algae and/or bacteria in a range of freshwater and marine environments, and in conventional wastewater treatment processes, but its application to WSP wastewater has been underexplored. In this study, a method for the characterisation of both algal and bacterial microbial populations in WSP wastewater is presented and standardised, using cultures and field samples. We show that SYTO 16 dye is more effective than SYBR Green I for the concurrent detection of both algae and bacteria in samples. Through gating and phenotypic diversity analysis, the FCM results show both spatial and temporal shifts in pond microbial communities. The ability to rapidly determine the spatiotemporal shifts in pond populations is not only important for the improvement of pond operation and monitoring strategies, but also for the planning and management. Flow cytometry has the potential to become a diagnostic tool for ponds to assess treatment performance and determine the most optimal operating conditions.

Discriminating Bacterial Phenotypes at the Population and Single-Cell Level: A Comparison of Flow Cytometry and Raman Spectroscopy Fingerprinting

Investigating phenotypic heterogeneity can help to better understand and manage microbial communities. However, characterizing phenotypic heterogeneity remains a challenge, as there is no standardized analysis framework. Several optical tools are available, such as flow cytometry and Raman spectroscopy, which describe optical properties of the individual cell. In this work, we compare Raman spectroscopy and flow cytometry to study phenotypic heterogeneity in bacterial populations. The growth stages of three replicate Escherichia coli populations were characterized using both technologies. Our findings show that flow cytometry detects and quantifies shifts in phenotypic heterogeneity at the population level due to its high-throughput nature. Raman spectroscopy, on the other hand, offers a much higher resolution at the single-cell level (i.e., more biochemical information is recorded). Therefore, it can identify distinct phenotypic populations when coupled with analyses tailored toward single-cell data. In addition, it provides information about biomolecules that are present, which can be linked to cell functionality. We propose a computational workflow to distinguish between bacterial phenotypic populations using Raman spectroscopy and validated this approach with an external data set. We recommend using flow cytometry to quantify phenotypic heterogeneity at the population level, and Raman spectroscopy to perform a more in-depth analysis of heterogeneity at the single-cell level. © 2019 International Society for Advancement of Cytometry.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

The Dynamic of a River Model Bacterial Community in Two Different Media Reveals a Divergent Succession and an Enhanced Growth of Most Strains Compared to Monocultures

The dynamic of a community of 20 bacterial strains isolated from river water was followed in R2 broth and in autoclaved river water medium for 27 days in batch experiments. At an early stage of incubation, a fast-growing specialist strain, Acinetobater sp., dominated the community in both media. Later on, the community composition in both media diverged but was highly reproducible across replicates. In R2, several strains previously reported to degrade multiple simple carbon sources prevailed. In autoclaved river water, the community was more even and became dominated by several strains growing faster or exclusively in that medium. Those strains have been reported in the literature to degrade complex compounds. Their growth rate in the community was 1.5- to 7-fold greater than that observed in monoculture. Furthermore, those strains developed simultaneously in the community. Together, our results suggest the existence of cooperative interactions within the community incubated in autoclaved river water.

Population Structure and Morphotype Analysis of "Candidatus Accumulibacter" Using Fluorescence In Situ Hybridization-Staining-Flow Cytometry

"Candidatus Accumulibacter" is the dominant polyphosphate-accumulating organism (PAO) in denitrifying phosphorus removal (DPR) systems. In order to investigate the community structure and clade morphotypes of "Candidatus Accumulibacter" in DPR systems through flow cytometry (FCM), denitrifying phosphorus removal of almost 100% using nitrite and nitrate as the electron acceptor was achieved in sequencing batch reactors (SBRs). An optimal method of flow cytometry combined with fluorescence in situ hybridization and SYBR green I staining (FISH-staining-flow cytometry) was developed to quantify PAOs in DPR systems. By setting the width value of FCM, bacterial cells in a sludge sample were divided into three groups in different morphotypes, namely, coccus, coccobacillus, and bacillus. Average percentages that the three different PAO populations accounted for among total bacteria from SBR1 (SBR2) were 42% (45%), 14% (13%), and 4% (2%). FCM showed that the ratios of PAOs to total bacteria in the two reactors were 61% and 59%, and the quantitative PCR (qPCR) results indicated that IIC was the dominant "Candidatus Accumulibacter" clade in both denitrifying phosphorus removal systems, reaching 50% of the total "Candidatus Accumulibacter" bacteria. The subdominant clade in the reactor with nitrite as the electron acceptor was IID, accounting for 31% of the total "Candidatus Accumulibacter" bacteria. The FCM and qPCR results suggested that clades IIC and IID were both coccus, clade IIF was coccobacillus, and clade IA was bacillus. FISH analysis also indicated that PAOs were major cocci in the systems. An equivalence test of FCM-based quantification confirmed the accuracy of FISH-staining-flow cytometry, which can meet the quantitative requirements for PAOs in complex activated sludge samples.IMPORTANCE As one group of the most important functional phosphorus removal organisms, "Candidatus Accumulibacter," affiliated with the Rhodocyclus group of the Betaproteobacteria, is a widely recognized and studied PAO in the field of biological wastewater treatment. The morphotypes and population structure of clade-level "Candidatus Accumulibacter" were studied through novel FISH-staining-flow cytometry, which involved denitrifying phosphorus removal (DPR) achieving carbon and energy savings and simultaneous removal of N and P, thus inferring the different denitrifying phosphorus removal abilities of these clades. Additionally, based on this method, in situ quantification for specific polyphosphate-accumulating organisms (PAOs) enables a more efficient process and more accurate result. The establishment of FISH-staining-flow cytometry makes cell sorting of clade-level noncultivated organisms available.

Flow cytometry applications in water treatment, distribution, and reuse: A review

Ensuring safe and effective water treatment, distribution, and reuse requires robust methods for characterizing and monitoring waterborne microbes. Methods widely used today can be limited by low sensitivity, high labor and time requirements, susceptibility to interference from inhibitory compounds, and difficulties in distinguishing between viable and non-viable cells. Flow cytometry (FCM) has recently gained attention as an alternative approach that can overcome many of these challenges. This article critically and systematically reviews for the first time recent literature on applications of FCM in water treatment, distribution, and reuse. In the review, we identify and examine nearly 300 studies published from 2000 to 2018 that illustrate the benefits and challenges of using FCM for assessing source-water quality and impacts of treatment-plant discharge on receiving waters, wastewater treatment, drinking water treatment, and drinking water distribution. We then discuss options for combining FCM with other indicators of water quality and address several topics that cut across nearly all applications reviewed. Finally, we identify priority areas in which more work is needed to realize the full potential of this approach. These include optimizing protocols for FCM-based analysis of waterborne viruses, optimizing protocols for specifically detecting target pathogens, automating sample handling and preparation to enable real-time FCM, developing computational tools to assist data analysis, and improving standards for instrumentation, methods, and reporting requirements. We conclude that while more work is needed to realize the full potential of FCM in water treatment, distribution, and reuse, substantial progress has been made over the past two decades. There is now a sufficiently large body of research documenting successful applications of FCM that the approach could reasonably and realistically see widespread adoption as a routine method for water quality assessment.

Growth-dependent recombinant product formation kinetics can be reproduced through engineering of glucose transport and is prone to phenotypic heterogeneity

Background

Escherichia coli W3110 and a group of six isogenic derivatives, each displaying distinct specific rates of glucose consumption were characterized to determine levels of GFP production and population heterogeneity. These strains have single or combinatory deletions in genes encoding phosphoenolpyruvate:sugar phosphotransferase system (PTS) permeases as PtsG and ManX, as well as common components EI, Hpr protein and EIIA, also the non-PTS Mgl galactose/glucose ABC transporter. They have been transformed for expressing GFP based on a lac-based expression vector, which is subject to bistability.

Results

These strains displayed specific glucose consumption and growth rates ranging from 1.75 to 0.45 g/g h and 0.54 to 0.16 h⁻¹, respectively. The rate of acetate production was strongly reduced in all mutant strains when compared with W3110/pV21. In bioreactor cultures, wild type W3110/pV21 produced 50.51 mg/L GFP, whereas strains WG/pV21 with inactive PTS IICBGlc and WGM/pV21 with the additional inactivation of PTS IIABMan showed the highest titers of GFP, corresponding to 342 and 438 mg/L, respectively. Moreover, we showed experimentally that bistable expression systems, as lac-based ones, induce strong phenotypic segregation among microbial populations.

Conclusions

We have demonstrated that reduction on glucose consumption rate in E. coli leads to an improvement of GFP production. Furthermore, from the perspective of phenotypic heterogeneity, we observed in this case that heterogeneous systems are also the ones leading to the highest performance. This observation suggests reconsidering the generally accepted proposition stating that phenotypic heterogeneity is generally unwanted in bioprocess applications.

Electronic supplementary material

The online version of this article (10.1186/s12934-019-1073-5) contains supplementary material, which is available to authorized users.

Reduced TCA cycle rates at high hydrostatic pressure hinder hydrocarbon degradation and obligate oil degraders in natural, deep-sea microbial communities

Petroleum hydrocarbons reach the deep-sea following natural and anthropogenic factors. The process by which they enter deep-sea microbial food webs and impact the biogeochemical cycling of carbon and other elements is unclear. Hydrostatic pressure (HP) is a distinctive parameter of the deep sea, although rarely investigated. Whether HP alone affects the assembly and activity of oil-degrading communities remains to be resolved. Here we have demonstrated that hydrocarbon degradation in deep-sea microbial communities is lower at native HP (10 MPa, about 1000 m below sea surface level) than at ambient pressure. In long-term enrichments, increased HP selectively inhibited obligate hydrocarbon-degraders and downregulated the expression of beta-oxidation-related proteins (i.e., the main hydrocarbon-degradation pathway) resulting in low cell growth and CO₂ production. Short-term experiments with HP-adapted synthetic communities confirmed this data, revealing a HP-dependent accumulation of citrate and dihydroxyacetone. Citrate accumulation suggests rates of aerobic oxidation of fatty acids in the TCA cycle were reduced. Dihydroxyacetone is connected to citrate through glycerol metabolism and glycolysis, both upregulated with increased HP. High degradation rates by obligate hydrocarbon-degraders may thus be unfavourable at increased HP, explaining their selective suppression. Through lab-scale cultivation, the present study is the first to highlight a link between impaired cell metabolism and microbial community assembly in hydrocarbon degradation at high HP. Overall, this data indicate that hydrocarbons fate differs substantially in surface waters as compared to deep-sea environments, with in situ low temperature and limited nutrients availability expected to further prolong hydrocarbons persistence at deep sea.

Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data

Detecting disturbances in microbial communities is an important aspect of managing natural and engineered microbial communities. Here, we implemented a custom-built continuous staining device in combination with real-time flow cytometry (RT-FCM) data acquisition, which, combined with advanced FCM fingerprinting methods, presents a powerful new approach to track and quantify disturbances in aquatic microbial communities. Through this new approach we were able to resolve various natural community and single-species microbial contaminations in a flow-through drinking water reactor. Next to conventional FCM metrics, we applied metrics from a recently developed fingerprinting technique in order to gain additional insight into the microbial dynamics during these contamination events. Importantly, we found that multiple community FCM metrics based on different statistical approaches were required to fully characterize all contaminations. Furthermore we found that for accurate cell concentration measurements and accurate inference from the FCM metrics (coefficient of variation ≤ 5%), at least 1000 cells should be measured, which makes the achievable temporal resolution a function of the prevalent bacterial concentration in the system-of-interest. The integrated RT-FCM acquisition and analysis approach presented herein provides a considerable improvement in the temporal resolution by which microbial disturbances can be observed and simultaneously provides a multi-faceted toolset to characterize such disturbances.

Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes

BACKGROUND

Flow cytometry, with its high throughput nature, combined with the ability to measure an increasing number of cell parameters at once can surpass the throughput of prevalent genomic and metagenomic approaches in the study of microbiomes. Novel computational approaches to analyze flow cytometry data will result in greater insights and actionability as compared to traditional tools used in the analysis of microbiomes. This paper is a demonstration of the fruitfulness of machine learning in analyzing microbial flow cytometry data generated in anaerobic microbiome perturbation experiments.

RESULTS

Autoencoders were found to be powerful in detecting anomalies in flow cytometry data from nanoparticles and carbon sources perturbed anaerobic microbiomes but was marginal in predicting perturbations due to antibiotics. A comparison between different algorithms based on predictive capabilities suggested that gradient boosting (GB) and deep learning, i.e. feed forward artificial neural network with three hidden layers (DL) were marginally better under tested conditions at predicting overall community structure while distributed random forests (DRF) worked better for predicting the most important putative microbial group(s) in the anaerobic digesters viz. methanogens, and it can be optimized with better parameter tuning. Predictive classification patterns with DL (feed forward artificial neural network with three hidden layers) were found to be comparable to previously demonstrated multivariate analysis. The potential applications of this approach have been demonstrated for monitoring the syntrophic resilience of the anaerobic microbiomes perturbed by synthetic nanoparticles as well as antibiotics.

CONCLUSION

Machine learning can benefit the microbial flow cytometry research community by providing rapid screening and characterization tools to discover patterns in the dynamic response of microbiomes to several stimuli.

Characterizing Microbiome Dynamics - 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 applications driven by microorganisms. Next generation sequencing methods are widely utilized to resolve microbiomes, but they are generally resource and time intensive and deliver mostly qualitative information. Flow cytometric microbiome analysis does not suffer from those disadvantages and can provide relative subcommunity abundances and absolute cell numbers at-line. Although it does not deliver direct phylogenetic information, it can enhance the analysis depth and resolution of sequencing approaches. In sharp contrast to medical applications in both research and routine settings, flow cytometry is still not widely used for microbiome analysis. Missing information on sample preparation and data analysis pipelines may create an entry barrier for the researchers facing microbiome analysis challenges that would often be textbook flow cytometry applications. Here, we present three comprehensive workflows for pure cultures, complex communities in clear medium and complex communities in challenging matrices, respectively. We describe individual sampling and fixation procedures and optimized staining protocols for the respective sample sets. We elaborate the cytometric analysis with a complex research centered and an application focused bench top device, describe the cell sorting procedure and suggest data analysis packages. We furthermore propose important experimental controls and apply the presented workflows to the respective sample sets.

Ecological Stability Properties of Microbial Communities Assessed by Flow Cytometry

Natural microbial communities affect human life in countless ways, ranging from global biogeochemical cycles to the treatment of wastewater and health via the human microbiome. In order to probe, monitor, and eventually control these communities, fast detection and evaluation methods are required. In order to facilitate rapid community analysis and monitor a community's dynamic behavior with high resolution, we here apply community flow cytometry, which provides single-cell-based high-dimensional data characterizing communities with high acuity over time. To interpret time series data, we draw inspiration from macroecology, in which a rich set of concepts has been developed for describing population dynamics. We focus on the stability paradigm as a promising candidate to interpret such data in an intuitive and actionable way and present a rapid workflow to monitor stability properties of complex microbial ecosystems. Based on single-cell data, we compute the stability properties resistance, resilience, displacement speed, and elasticity. For resilience, we also introduce a method which can be implemented for continuous online community monitoring. The proposed workflow was tested in a long-term continuous reactor experiment employing both an artificial and a complex microbial community, which were exposed to identical short-term disturbances. The computed stability properties uncovered the superior stability of the complex community and demonstrated the global applicability of the protocol to any microbiome. The workflow is able to support high temporal sample densities below bacterial generation times. This may provide new opportunities to unravel unknown ecological paradigms of natural microbial communities, with applications to environmental, biotechnological, and health-related microbiomes. IMPORTANCE Microbial communities drive many processes which affect human well-being directly, as in the human microbiome, or indirectly, as in natural environments or in biotechnological applications. Due to their complexity, their dynamics over time is difficult to monitor, and current sequence-based approaches are limited with respect to the temporal resolution. However, in order to eventually control microbial community dynamics, monitoring schemes of high temporal resolution are required. Flow cytometry provides single-cell-based data in the required temporal resolution, and we here use such data to compute stability properties as easy to interpret univariate indicators of microbial community dynamics. Such monitoring tools will allow for a fast, continuous, and cost-effective screening of stability states of microbiomes. Applicable to various environments, including bioreactors, surface water, and the human body, it will contribute to the development of control schemes to manipulate microbial community structures and performances.

Initial evenness determines diversity and cell density dynamics in synthetic microbial ecosystems

The effect of initial evenness on the temporal trajectory of synthetic communities in comprehensive, low-volume microcosm studies remains unknown. We used flow cytometric fingerprinting and 16S rRNA gene amplicon sequencing to assess the impact of time on community structure in one hundred synthetic ecosystems of fixed richness but varying initial evenness. Both methodologies uncovered a similar reduction in diversity within synthetic communities of medium and high initial evenness classes. However, the results of amplicon sequencing showed that there were no significant differences between and within the communities in all evenness groups at the end of the experiment. Nevertheless, initial evenness significantly impacted the cell density of the community after five medium transfers. Highly even communities retained the highest cell densities at the end of the experiment. The relative abundances of individual species could be associated to particular evenness groups, suggesting that their presence was dependent on the initial evenness of the synthetic community. Our results reveal that using synthetic communities for testing ecological hypotheses requires prior assessment of initial evenness, as it impacts temporal dynamics.

Deconvolution model to resolve cytometric microbial community patterns in flowing waters

Flow cytometry is suitable to discriminate and quantify aquatic microbial cells within a spectrum of fluorescence and light scatter signals. Using fixed gating and operational settings, we developed a finite distribution mixture model, followed by the Voronoi tessellation, to resolve bivariate cytometric profiles into cohesive subgroups of events. This procedure was applied to outline recurrent patterns and quantitative changes of the aquatic microbial community along a river hydrologic continuum. We found five major subgroups within each of the commonly retrieved populations of cells with Low and High content of Nucleic Acids (namely, LNA, and HNA cells). Moreover, the advanced analysis allowed assessing changes of community patterns perturbed by a wastewater feed. Our approach for cytometric data deconvolution confirmed that flow cytometry could represent a prime candidate technology for assessing microbial community patterns in flowing waters. © 2017 International Society for Advancement of Cytometry.

Cupriavidus pinatubonensis AEO106 deals with copper-induced oxidative stress before engaging in biodegradation of the herbicide 4-chloro-2-methylphenoxyacetic acid

BACKGROUND

Microbial degradation of phenoxy acid (PA) herbicides in agricultural soils is important to minimize herbicide leaching to groundwater reservoirs. Degradation may, however, be hampered by exposure of the degrader bacteria to toxic metals as copper (Cu) in the soil environment. Exposure to Cu leads to accumulation of intracellular reactive oxygen species (ROS) in some bacteria, but it is not known how Cu-derived ROS and an ensuing oxidative stress affect the degradation of PA herbicides. Based on the previously proposed paradigm that bacteria deal with environmental stress before they engage in biodegradation, we studied how the degradation of the PA herbicide 2-methyl-4-chlorophenoxyacetic acid (MCPA) by the model PA degrader Cupriavidus pinatubonensis AEO106 was affected by Cu exposure.

RESULTS

Exposure of C. pinatubonensis in batch culture to sublethal concentrations of Cu increased accumulation of ROS measured by the oxidant sensing probe 2,7-dichlorodihydrofluorescein diacetate and flow cytometry, and resulted in upregulation of a gene encoding a protein belong to the Ohr/OsmC protein family. The ohr/osmC gene was also highly induced by H₂O₂ exposure suggesting that it is involved in the oxidative stress response in C. pinatubonensis. The increased ROS accumulation and increased expression of the oxidative stress defense coincided with a delay in the catabolic performance, since both expression of the catabolic tfdA gene and MCPA mineralization were delayed compared to unexposed control cells.

CONCLUSIONS

The current study suggests that Cu-induced ROS accumulation in C. pinatubonensis activates a stress response involving the product of the ohr/osmC gene. Further, the stress response is launched before induction of the catabolic tfdA gene and mineralization occurs.

Flow cytometric characterization of bacterial abundance and physiological status in a nitrifying-denitrifying activated sludge system treating landfill leachate

Flow cytometry has recently been presented as a research tool in the assessment of the viability/activity of activated sludge from municipal wastewater treatment plants, but it has not put in practice for industrial biotreatments yet. In this study, for the first time ever, the reliability and significance of the multiparameter flow cytometry applied to the biological nitrification-denitrification treatment of leachate have been evaluated. Using a double staining procedure (cFDA/PI), the viable, damaged, and dead subpopulations were determined, and the results were compared to those obtained with conventional methods, such as nitrogen and oxygen uptake rates or plate counting. Flow cytometry showed that viable cells represented approximately 47% of the total population, whereas active cells accounted for 90%. For both sludge from nitrification and denitrification processes, with less than 1% of them being also culturable in plate. Either flow cytometry or uptake rates revealed that health status of sludge remained constant throughout the biotreatment, which is consistent with the high recirculation rates. Under anaerobic starvation conditions, physiological status of sludge remained constant as well as specific oxygen and denitrification rates. Nevertheless, both the culturability in plate and the nitrification rate significantly decreased. These findings proved that multiparameter flow cytometry is a useful tool for the assessment of the viability and activity of sludge from a nitrification-denitrification biotreatment process. These results gathered all the bacterial communities in the sludge, so the decay in minority populations, such as nitrifying bacteria, requires the use of a complementary technique to evaluate specific activities.

In vitro Increased Respiratory Activity of Selected Oral Bacteria May Explain Competitive and Collaborative Interactions in the Oral Microbiome

Understanding the driving forces behind the shifts in the ecological balance of the oral microbiota will become essential for the future management and treatment of periodontitis. As the use of competitive approaches for modulating bacterial outgrowth is unexplored in the oral ecosystem, our study aimed to investigate both the associations among groups of functional compounds and the impact of individual substrates on selected members of the oral microbiome. We employed the Phenotype Microarray high-throughput technology to analyse the microbial cellular phenotypes of 15 oral bacteria. Multivariate statistical analysis was used to detect respiratory activity triggers and to assess similar metabolic activities. Carbon and nitrogen were relevant for the respiration of health-associated bacteria, explaining competitive interactions when grown in biofilms. Carbon, nitrogen, and peptides tended to decrease the respiratory activity of all pathobionts, but not significantly. None of the evaluated compounds significantly increased activity of pathobionts at both 24 and 48 h. Additionally, metabolite requirements of pathobionts were dissimilar, suggesting that collective modulation of their respiratory activity may be challenging. Flow cytometry indicated that the metabolic activity detected in the Biolog plates may not be a direct result of the number of bacterial cells. In addition, damage to the cell membrane may not influence overall respiratory activity. Our methodology confirmed previously reported competitive and collaborative interactions among bacterial groups, which could be used either as marker of health status or as targets for modulation of the oral environment.

Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses

Phenotypic plasticity of microbial cells has attracted much attention and several research efforts have been dedicated to the description of methods aiming at characterizing phenotypic heterogeneity and its impact on microbial populations. However, different approaches have also been suggested in order to take benefit from noise in a bioprocess perspective, e.g. by increasing the robustness or productivity of a microbial population. This review is dedicated to outline these controlling methods. A common issue, that has still to be addressed, is the experimental identification and the mathematical expression of noise. Indeed, the effective interfacing of microbial physiology with external parameters that can be used for controlling physiology depends on the acquisition of reliable signals. Latest technologies, like single cell microfluidics and advanced flow cytometric approaches, enable linking physiology, noise, heterogeneity in productive microbes with environmental cues and hence allow correctly mapping and predicting biological behavior via mathematical representations. However, like in the field of electronics, signals are perpetually subjected to noise. If appropriately interpreted, this noise can give an additional insight into the behavior of the individual cells within a microbial population of interest. This review focuses on recent progress made at describing, treating and exploiting biological noise in the context of microbial populations used in various bioprocess applications.

Characterization of suspended bacteria from processing units in an advanced drinking water treatment plant of China

For the drinking water treatment plant (DWTP), the organic pollutant removal was the primary focus, while the suspended bacterial was always neglected. In this study, the suspended bacteria from each processing unit in a DWTP employing an ozone-biological activated carbon process was mainly characterized by using heterotrophic plate counts (HPCs), a flow cytometer, and 454-pyrosequencing methods. The results showed that an adverse changing tendency of HPC and total cell counts was observed in the sand filtration tank (SFT), where the cultivability of suspended bacteria increased to 34%. However, the cultivability level of other units stayed below 3% except for ozone contact tank (OCT, 13.5%) and activated carbon filtration tank (ACFT, 34.39%). It meant that filtration processes promoted the increase in cultivability of suspended bacteria remarkably, which indicated biodegrading capability. In the unit of OCT, microbial diversity indexes declined drastically, and the dominant bacteria were affiliated to Proteobacteria phylum (99.9%) and Betaproteobacteria class (86.3%), which were also the dominant bacteria in the effluent of other units. Besides, the primary genus was Limnohabitans in the effluents of SFT (17.4%) as well as ACFT (25.6%), which was inferred to be the crucial contributors for the biodegradable function in the filtration units. Overall, this paper provided an overview of community composition of each processing units in a DWTP as well as reference for better developing microbial function for drinking water treatment in the future.

Flow cytometric bacterial cell counts challenge conventional heterotrophic plate counts for routine microbiological drinking water monitoring

Drinking water utilities and researchers continue to rely on the century-old heterotrophic plate counts (HPC) method for routine assessment of general microbiological water quality. Bacterial cell counting with flow cytometry (FCM) is one of a number of alternative methods that challenge this status quo and provide an opportunity for improved water quality monitoring. After more than a decade of application in drinking water research, FCM methodology is optimised and established for routine application, supported by a considerable amount of data from multiple full-scale studies. Bacterial cell concentrations obtained by FCM enable quantification of the entire bacterial community instead of the minute fraction of cultivable bacteria detected with HPC (typically < 1% of all bacteria). FCM measurements are reproducible with relative standard deviations below 3% and can be available within 15 min of samples arriving in the laboratory. High throughput sample processing and complete automation are feasible and FCM analysis is arguably less expensive than HPC when measuring more than 15 water samples per day, depending on the laboratory and selected staining procedure(s). Moreover, many studies have shown FCM total (TCC) and intact (ICC) cell concentrations to be reliable and robust process variables, responsive to changes in the bacterial abundance and relevant for characterising and monitoring drinking water treatment and distribution systems. The purpose of this critical review is to initiate a constructive discussion on whether FCM could replace HPC in routine water quality monitoring. We argue that FCM provides a faster, more descriptive and more representative quantification of bacterial abundance in drinking water.

Flow cytometry for immediate follow-up of drinking water networks after maintenance

Drinking water networks need maintenance every once in a while, either planned interventions or emergency repairs. When this involves opening of the water pipes, precautionary measures need to be taken to avoid contamination of the drinking water at all time. Drinking water suppliers routinely apply plating for faecal indicator organisms as quality control in such a situation. However, this takes at least 21 h of waiting time, which can be crucial when dealing with major supply pipes. A combination of flow cytometric (FCM) bacterial cell counts with FCM fingerprinting techniques is proposed in this study as a fast and sensitive additional technique. In three full scale situations, major supply pipes with 400-1050 mm diameter were emptied for maintenance, shock-chlorinated and flushed with large amounts of clean drinking water before taking back in operation. FCM measurements of the discharged flushing water revealed fast lowering and stabilizing bacterial concentrations once flushing is initiated. Immediate comparison with clean reference drinking water used for flushing was done, and the moment when both waters had similar bacterial concentrations was considered as the endpoint of the necessary flushing works. This was usually after 2-4 h of flushing. FCM fingerprinting, based on both bacteria and FCM background, was used as additional method to verify how similar flushing and reference samples were and yielded similar results. The FCM approved samples were several hours later approved as well by the drinking water supplier after plating and incubation for total Coliforms and Enterococci. These were used as decisive control to set the pipes back in operation. FCM proved to be a more conservative test than plating, yet it yielded immediate results. Application of these FCM methods can therefore avoid long unnecessary waiting times and large drinking water losses.

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic processing of biological samples typically involves differential manipulations of suspended particles under various force fields in order to spatially fractionate the sample based on a biological property of interest. For the resultant spatial distribution to be used as the assay readout, microfluidic devices are often subjected to microscopic analysis requiring complex instrumentation with higher cost and reduced portability. To address this limitation, we have developed an integrated electronic sensing technology for multiplexed detection of particles at different locations on a microfluidic chip. Our technology, called Microfluidic CODES, combines Resistive Pulse Sensing with Code Division Multiple Access to compress 2D spatial information into a 1D electrical signal. In this paper, we present a practical demonstration of the Microfluidic CODES technology to detect and size cultured cancer cells distributed over multiple microfluidic channels. As validated by the high-speed microscopy, our technology can accurately analyze dense cell populations all electronically without the need for an external instrument. As such, the Microfluidic CODES can potentially enable low-cost integrated lab-on-a-chip devices that are well suited for the point-of-care testing of biological samples.