Cytometric fingerprints: evaluation of new tools for analyzing microbial community dynamics

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.

Single-Cell Techniques in Environmental Microbiology

Environmental microbiology has been an essential part of environmental research because it provides effective solutions to most pollutants. Hence, there is an interest in investigating microorganism behavior, such as observation, identification, isolation of pollutant degraders, and interactions between microbial species. To comprehensively understand cell heterogeneity, diverse approaches at the single-cell level are demanded. Thus far, the traditional bulk biological tools such as petri dishes are technically challenging for single cells, which could mask the heterogeneity. Single-cell technologies can reveal complex and rare cell populations by detecting heterogeneity among individual cells, which offers advantages of higher resolution, higher throughput, more accurate analysis, etc. Here, we overviewed several single-cell techniques on observation, isolation, and identification from aspects of methods and applications. Microscopic observation, sequencing identification, flow cytometric identification and isolation, Raman spectroscopy-based identification and isolation, and their applications are mainly discussed. Further development on multi-technique integrations at the single-cell level may highly advance the research progress of environmental microbiology, thereby giving more indication in the environmental microbial ecology.

Unravelling reasons for variability in the OECD 306 marine biodegradation test

The recommended test for assessing if a chemical can be biodegraded in the marine environment is performed according to the Organisation for Economic Cooperation and Development Marine biodegradation test guideline (OECD 306). However, this test is known to generate highly variable test results when comparing interlaboratory test results for the same compound. One reason can be the relatively low bacterial content compared to the inoculum used for OECD readily biodegradation tests (OECD 301). Some of the variability in data obtained from OECD 306 tests can also be due to the flexibility on how to store the seawater inoculum before starting a test. Another variable in the seawater inoculum is the source of seawater used by different laboratories, i.e., geographical location and anthropogenic activities at the source. In this study, the effect of aging seawater and the source of seawater (sample time and depth) were investigated to determine differences in the biodegradation of the reference compound aniline. Aging the seawater before starting the test is recommended in OECD 306 to reduce the background levels of organic carbon in the water. However, it also functions to acclimatize the bacterial community from the environmental source temperature to the test temperature (normally 20 °C). Herein, the microbial community was monitored using flowcytometer during the aging process. As expected, the microbial community changed over time. In one experiment, aging significantly improved the biodegradation of aniline, while in two experiments, there was no significant difference in biodegradation. Interestingly however, there was significant variability in the biodegradation of aniline between sampling seasons and depths, even when all experiments were performed in the same lab, by the same operator and seawater obtained from the same source. This highlights the need for a more robust and consistent microbial inoculum source to reduce variability in seawater biodegradation tests.

Novel antibody assessment method for microbial compositional alteration in the oral cavity

Recently, it has been demonstrated that dysbiosis, an alteration in commensal microflora composition, is intimately involved in the onset of a variety of diseases. It is becoming increasingly evident that the composition of commensal microflora in the oral cavity is closely connected to oral diseases, such as periodontal disease, and systemic diseases, such as inflammatory bowel disease. Next-generation sequencing techniques are used as a method to examine changes in bacterial flora, but additional analytical methods to assess bacterial flora are needed to understand bacterial activity in more detail. In addition, the oral environment is unique because of the role of secretory antibodies contained in saliva in the formation of bacterial flora. The present study aimed to develop a new method for evaluating the compositional change of microbiota using flow cytometry (FCM) with specific antibodies against the bacterial surface antigen, as well as salivary antibodies. Using specific antibodies against Streptococcus mutans, a causative agent of dental caries, and human IgA, bacterial samples from human saliva were analyzed via FCM. The results showed that different profiles could be obtained depending on the oral hygiene status of the subjects. These results suggest that changes in the amount and type of antibodies that bind to oral bacteria may be an indicator for evaluating abnormalities in the oral flora. Therefore, the protocol established in this report could be applied as an evaluation method for alterations in the oral microbiota.

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We aimed to develop a new method for evaluating dysbiosis using flow cytometry.

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We used bacterial surface antigen-specific antibodies and salivary antibodies.

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Different profiles could be obtained depending on oral hygiene status.

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Changes in antibodies bound to oral bacteria may indicate oral flora abnormalities.

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Our method can be used to evaluate alterations in the oral microbiota.

Characteristics, Biodiversity, and Cultivation Strategy of Low Nucleic Acid Content Bacteria

Low nucleic acid content (LNA) bacteria are ubiquitous and estimated to constitute 20%–90% of the total bacterial community in marine and freshwater environment. LNA bacteria with unique physiological characteristics, including small cell size and small genomes, can pass through 0.45-μm filtration. The researchers came up with different terminologies for low nucleic acid content bacteria based on different research backgrounds, such as: filterable bacteria, oligotrophic bacteria, and low-DNA bacteria. LNA bacteria have an extremely high level of genetic diversity and play an important role in material circulation in oligotrophic environment. However, the majority of LNA bacteria in the environment remain uncultivated. Thus, an important challenge now is to isolate more LNA bacteria from oligotrophic environments and gain insights into their unique metabolic mechanisms and ecological functions. Here, we reviewed LNA bacteria in aquatic environments, focusing on their characteristics, community structure and diversity, functions, and cultivation strategies. Exciting future prospects for LNA bacteria are also discussed.

Advances in automated real-time flow cytometry for monitoring of bioreactor processes

Flow cytometry and its technological possibilities have greatly advanced in the past decade as analysis tool for single cell properties and population distributions of different cell types in bioreactors. Along the way, some solutions for automated real-time flow cytometry (ART-FCM) were developed for monitoring of bioreactor processes without operator interference over extended periods with variable sampling frequency. However, there is still great potential for ART-FCM to evolve and possibly become a standard application in bioprocess monitoring and process control. This review first addresses different components of an ART-FCM, including the sampling device, the sample-processing unit, the unit for sample delivery to the flow cytometer and the settings for measurement of pre-processed samples. Also, available algorithms are presented for automated data analysis of multi-parameter fluorescence datasets derived from ART-FCM experiments. Furthermore, challenges are discussed for integration of fluorescence-activated cell sorting into an ART-FCM setup for isolation and separation of interesting subpopulations that can be further characterized by for instance omics-methods. As the application of ART-FCM is especially of interest for bioreactor process monitoring, including investigation of population heterogeneity and automated process control, a summary of already existing setups for these purposes is given. Additionally, the general future potential of ART-FCM is addressed.

When FLOW-FISH met FACS: Combining multiparametric, dynamic approaches for microbial single-cell research in the total environment

In environmental microbiology, the ability to assess, in a high-throughput way, single-cells within microbial communities is key to understand their heterogeneity. Fluorescence in situ hybridization (FISH) uses fluorescently labeled oligonucleotide probes to detect, identify, and quantify single cells of specific taxonomic groups. The combination of Flow Cytometry (FLOW) with FISH (FLOW-FISH) enables high-throughput quantification of complex whole cell populations, which when associated with fluorescence-activated cell sorting (FACS) enables sorting of target microorganisms. These sorted cells may be investigated in many ways, for instance opening new avenues for cytomics at a single-cell scale. In this review, an overview of FISH and FLOW methodologies is provided, addressing conventional methods, signal amplification approaches, common fluorophores for cell physiology parameters evaluation, and model variation techniques as well. The coupling of FLOW-FISH-FACS is explored in the context of different downstream applications of sorted cells. Current and emerging applications in environmental microbiology to outline the interactions and processes of complex microbial communities within soil, water, animal microbiota, polymicrobial biofilms, and food samples, are described.

Potential of Flow Cytometric Approaches for Rapid Microbial Detection and Characterization in the Food Industry-A Review

As microbial contamination is persistent within the food and bioindustries and foodborne infections are still a significant cause of death, the detection, monitoring, and characterization of pathogens and spoilage microorganisms are of great importance. However, the current methods do not meet all relevant criteria. They either show (i) inadequate sensitivity, rapidity, and effectiveness; (ii) a high workload and time requirement; or (iii) difficulties in differentiating between viable and non-viable cells. Flow cytometry (FCM) represents an approach to overcome such limitations. Thus, this comprehensive literature review focuses on the potential of FCM and fluorescence in situ hybridization (FISH) for food and bioindustry applications. First, the principles of FCM and FISH and basic staining methods are discussed, and critical areas for microbial contamination, including abiotic and biotic surfaces, water, and air, are characterized. State-of-the-art non-specific FCM and specific FISH approaches are described, and their limitations are highlighted. One such limitation is the use of toxic and mutagenic fluorochromes and probes. Alternative staining and hybridization approaches are presented, along with other strategies to overcome the current challenges. Further research needs are outlined in order to make FCM and FISH even more suitable monitoring and detection tools for food quality and safety and environmental and clinical approaches.

Automated flow cytometry as a flexible tool for comparing disinfection characteristics of indigenous bacterial communities and pure cultures

Bacterial inactivation efficiency of chlorine varies with organisms and environmental conditions. The comparability of different samples/studies, especially comparing indigenous bacterial communities with pure cultures, is impeded by inconsistent experimental conditions and analytical methods used in various studies. We tested a novel 96-well plate FCM experimental and automated analytical approach, where bacterial communities and pure cultures were suspended in the same natural water matrix prior to chlorination directly in the plate. We demonstrated the ability to rapidly monitor the efficiency of 32 different combinations of chlorine concentration and time (i.e. chlorine exposure) on bacterial pure cultures and indigenous aquatic communities, which enabled correct comparison of the data from different samples under the exact same experimental conditions. In this study, the 96-well plate automated FCM approach enabled large sets (896) of independent chlorination experiments to be carried out in a short time period. To our knowledge, this is the largest dataset of chlorination experiments which consumed least time (within 18 h after sampling) until now. Staining with SYBR Green I (SG) and SG combined with propidium iodide (SGPI) was used to assess cellular damage during chlorination. The results showed that with the same chlorine exposure, a higher chlorine concentration with a shorter contact time is favorable for inactivation of bacteria. Our research provides a promising framework to compare disinfection characteristics of various microorganism and can be further developed to diagnose effect of antimicrobial products.

The dynamics of picocyanobacteria from a hypereutrophic shallow lake is affected by light-climate and small-bodied zooplankton: a 10-year cytometric time-series analysis

In aquatic systems, an interplay between bottom-up and top-down processes determines the dynamic of picocyanobacteria (Pcy) abundance and community structure. Here, we analyzed a 10-year time series (sampled fortnightly) from a hypereutrophic turbid shallow lake located within the Pampa Region of South America, generating the first long-term record of freshwater Pcy from the Southern Hemisphere. We used a cytometric approach to study Pcy community, and focused on its relations with nutrient and light conditions (bottom-up) and potential grazers (top-down). A novel Pcy abundance seasonality with winter maximums was observed for years with relatively stable hydrological levels, related with decreased abundance of seasonal rotifers during colder seasons. Pcy showed lower abundance and higher cytometric alpha diversity during summer, probably due to a strong predation exerted by rotifers. In turn, a direct effect of the non-seasonal small cladocerans Bosmina spp. decreased Pcy abundance and induced a shift from single-cell Pcy into aggregated forms. This structuring effect of Bosmina spp. was further confirmed by Pcy cytometric (dis)similarity analyses from the time series and in situ experimental data. Remarkably, Pcy showed acclimatization to underwater light variations, resembling the relevance of light in this turbid system.

Microbial fuel cells: a comprehensive review for beginners

Microbial fuel cells (MFCs) have shown immense potential as a one-stop solution for three major sustainability issues confronting the world today-energy security, global warming and wastewater management. MFCs represent a cross-disciplinary platform for research at the confluence of the natural and engineering sciences. The diversity of variables influencing performance of MFCs has garnered research interest across varied scientific disciplines since the beginning of this century. The increasing number of research publications has made it necessary to keep track of work being carried out by research groups across the globe and consolidate significant findings on a regular basis. Review articles are often the nodal points for beginners who are required to undertake an exploratory survey of literature to identify a suitable research problem. This 'review of reviews' is a ready-reckoner that directs readers to relevant reviews and research articles reporting significant developments in MFC research in the last two decades. The article also highlights the areas needing research attention which when addressed could take this technology a few more steps closer to practical implementation.

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.

Monitoring stratification of anode biofilms in bioelectrochemical laminar flow reactors using flow cytometry

A laminar flow bioelectrochemical systems (BES) was designed and benchmarked using microbial anodes dominated with Geobacter spp. The reactor architecture was based on modeled flow fields, the resulting structure was 3D printed and used for BES manufacturing. Stratification of the substrate availability within the reactor channels led to heterogeneous biomass distribution, with the maximum biomass found mainly in the initial/middle channels. The anode performance was assessed for different hydraulic retention times while coulombic efficiencies of up to 100% (including also hydrogen recycling from the cathode) and current densities of up to 75 μA cm^-2 at an anode surface to volume ratio of 1770 cm² L^-1 after 35 days were achieved. This low current density can be clearly attributed to the heterogeneous distributions of biomass and the stratification of the microbial community structure. Further, it was shown that time and space resolved analysis of the reactor microbiomes per channel is feasible using flow cytometry.

Water and microbial monitoring technologies towards the near future space exploration

Space exploration is demanding longer lasting human missions and water resupply from Earth will become increasingly unrealistic. In a near future, the spacecraft water monitoring systems will require technological advances to promptly identify and counteract contingent events of waterborne microbial contamination, posing health risks to astronauts with lowered immune responsiveness. The search for bio-analytical approaches, alternative to those applied on Earth by cultivation-dependent methods, is pushed by the compelling need to limit waste disposal and avoid microbial regrowth from analytical carryovers. Prospective technologies will be selected only if first validated in a flight-like environment, by following basic principles, advantages, and limitations beyond their current applications on Earth. Starting from the water monitoring activities applied on the International Space Station, we provide a critical overview of the nucleic acid amplification-based approaches (i.e., loop-mediated isothermal amplification, quantitative PCR, and high-throughput sequencing) and early-warning methods for total microbial load assessments (i.e., ATP-metry, flow cytometry), already used at a high readiness level aboard crewed space vehicles. Our findings suggest that the forthcoming space applications of mature technologies will be necessarily bounded by a compromise between analytical performances (e.g., speed to results, identification depth, reproducibility, multiparametricity) and detrimental technical requirements (e.g., reagent usage, waste production, operator skills, crew time). As space exploration progresses toward extended missions to Moon and Mars, miniaturized systems that also minimize crew involvement in their end-to-end operation are likely applicable on the long-term and suitable for the in-flight water and microbiological research.

Development and characterization of Escherichia coli triple reporter strains for investigation of population heterogeneity in bioprocesses

Background

Today there is an increasing demand for high yielding robust and cost efficient biotechnological production processes. Although cells in these processes originate from isogenic cultures, heterogeneity induced by intrinsic and extrinsic influences is omnipresent. To increase understanding of this mechanistically poorly understood phenomenon, advanced tools that provide insights into single cell physiology are needed.

Results

Two Escherichia coli triple reporter strains have been designed based on the industrially relevant production host E. coli BL21(DE3) and a modified version thereof, E. coli T7E2. The strains carry three different fluorescence proteins chromosomally integrated. Single cell growth is followed with EmeraldGFP (EmGFP)-expression together with the ribosomal promoter rrnB. General stress response of single cells is monitored by expression of sigma factor rpoS with mStrawberry, whereas expression of the nar-operon together with TagRFP657 gives information about oxygen limitation of single cells. First, the strains were characterized in batch operated stirred-tank bioreactors in comparison to wildtype E. coli BL21(DE3). Afterwards, applicability of the triple reporter strains for investigation of population heterogeneity in bioprocesses was demonstrated in continuous processes in stirred-tank bioreactors at different growth rates and in response to glucose and oxygen perturbation simulating gradients on industrial scale. Population and single cell level physiology was monitored evaluating general physiology and flow cytometry analysis of fluorescence distributions of the triple reporter strains. Although both triple reporter strains reflected physiological changes that were expected based on the expression characteristics of the marker proteins, the triple reporter strain based on E. coli T7E2 showed higher sensitivity in response to environmental changes. For both strains, noise in gene expression was observed during transition from phases of non-growth to growth. Apparently, under some process conditions, e.g. the stationary phase in batch cultures, the fluorescence response of EmGFP and mStrawberry is preserved, whereas TagRFP657 showed a distinct response.

Conclusions

Single cell growth, general stress response and oxygen limitation of single cells could be followed using the two triple reporter strains developed in this study. They represent valuable tools to study population heterogeneity in bioprocesses significantly increasing the level of information compared to the use of single reporter strains.

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.

Araújo DS, Quiroga MV, Amado AM, Neto ADD, Sarmento H, Metz SD, Unrein F. flowDiv: a new pipeline for analyzing flow cytometric diversity

BACKGROUND

Flow cytometry (FCM) is one of the most commonly used technologies for analysis of numerous biological systems at the cellular level, from cancer cells to microbial communities. Its high potential and wide applicability led to the development of various analytical protocols, which are often not interchangeable between fields of expertise. Environmental science in particular faces difficulty in adapting to non-specific protocols, mainly because of the highly heterogeneous nature of environmental samples. This variety, although it is intrinsic to environmental studies, makes it difficult to adjust analytical protocols to maintain both mathematical formalism and comprehensible biological interpretations, principally for questions that rely on the evaluation of differences between cytograms, an approach also termed cytometric diversity. Despite the availability of promising bioinformatic tools conceived for or adapted to cytometric diversity, most of them still cannot deal with common technical issues such as the integration of differently acquired datasets, the optimal number of bins, and the effective correlation of bins to previously known cytometric populations.

RESULTS

To address these and other questions, we have developed flowDiv, an R language pipeline for analysis of environmental flow cytometry data. Here, we present the rationale for flowDiv and apply the method to a real dataset from 31 freshwater lakes in Patagonia, Argentina, to reveal significant aspects of their cytometric diversities.

CONCLUSIONS

flowDiv provides a rather intuitive way of proceeding with FCM analysis, as it combines formal mathematical solutions and biological rationales in an intuitive framework specifically designed to explore cytometric diversity.

Long-Term Behavior of Defined Mixed Cultures of Geobacter sulfurreducens and Shewanella oneidensis in Bioelectrochemical Systems

This work aims to investigate the long-term behavior of interactions of electrochemically active bacteria in bioelectrochemical systems. The electrochemical performance and biofilm characteristics of pure cultures of Geobacter sulfurreducens and Shewanella oneidensis are being compared to a defined mixed culture of both organisms. While S. oneidensis pure cultures did not form cohesive and stable biofilms on graphite anodes and only yielded 0.034 ± 0.011 mA/cm² as maximum current density by feeding of each 5 mM lactate and acetate, G. sulfurreducens pure cultures formed 69 μm thick, area-wide biofilms with 10 mM acetate as initial substrate concentration and yielded a current of 0.39 ± 0.09 mA/cm². Compared to the latter, a defined mixed culture of both species was able to yield 38% higher maximum current densities of 0.54 ± 0.07 mA/cm² with each 5 mM lactate and acetate. This increase in current density was associated with a likewise increased thickness of the anodic biofilm to approximately 93 μm. It was further investigated whether a sessile incorporation of S. oneidensis into the mixed culture biofilm, which has been reported previously for short-term experiments, is long-term stable. The results demonstrate that S. oneidensis was not stably incorporated into the biofilm; rather, the planktonic presence of S. oneidensis has a positive effect on the biofilm growth of G. sulfurreducens and thus on current production.

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.

Monitoring microbial communities' dynamics during the start-up of microbial fuel cells by high-throughput screening techniques

Microbial Electrochemical Technologies are based on the use of electrochemically active microorganisms that can carry out extracellular electron transfer to an electrode while they are oxidizing the organic compounds. The dynamics and changes of the bacterial community in the anode biofilm and planktonic broth of an acetate fed batch single chamber air cathode MFC have been studied by combing flow-cytometry and Illumina sequencing techniques. At the beginning of the test, from 0 h to 70 h, microbial planktonic communities changed from four groups to two groups, as revealed by DNA content, and from three groups to one group based on the cell membrane polarization revealed by a DiOC₆(3) probe. Between 4^th day and 13^th day, microbial communities changed from one group to a maximum of three groups, monitoring DNA content, and from one group to two based on the cell membrane polarization. The 16S rDNA gene profiling confirmed the shift in microbial communities, with Acinetobacter (39.34%), Azospirillum (27.66%), Arcobacter (4.17%) and Comamonas (2.62%) being the most abundant genera at the beginning of MFC activation. After 70 h the main genera detected were Azospirillum (46.42%), Acinetobacter (34.66%), Enterococcus (2.32%), Dysgonomonas (2.14%). Data obtained have shown that flow cytometry and illumina sequencing are useful tools to monitor "online" the changes in microbial communities during the MFCs start-up and the increase of Azospirillum and Acinetobacter genera is in good agreement with the MFC voltage generation. Moreover, monitoring planktonic populations, instead of the less accessible anode biofilm, was in good agreement with the evolution of MFC voltage.

AgNPs Change Microbial Community Structures of Wastewater

Due to their strong antimicrobial activity, silver nanoparticles (AgNPs) are massively produced, applied, consumed and, as a negative consequence, released into wastewater treatment plants. Most AgNPs are assumed to be bound by sludge, and thus bear potential risk for microbial performance and stability. In this lab-scale study, flow cytometry as a high-throughput method and 16S rRNA gene amplicon Illumina MiSeq sequencing were used to track microbial community structure changes when being exposed to AgNPs. Both methods allowed deeper investigation of the toxic impact of chemicals on microbial communities than classical EC₅₀ determination. In addition, ecological metrics were used to quantify microbial community variations depending on AgNP types (10 and 30 nm) and concentrations. Only low changes in α- and intra-community β-diversity values were found both in successive negative and positive control batches and batches that were run with AgNPs below the EC₅₀ value. Instead, AgNPs at EC₅₀ concentrations caused upcoming of certain and disappearance of formerly dominant subcommunities. Flavobacteriia were among those that almost disappeared, while phylotypes affiliated with Gammaproteobacteria (3.6-fold) and Bacilli (8.4-fold) increased in cell abundance in comparison to the negative control. Thus, silver amounts at the EC₅₀ value affected community structure suggesting a potential negative impact on functions in wastewater treatment systems.

Neutral mechanisms and niche differentiation in steady-state insular microbial communities revealed by single cell analysis

In completely insular microbial communities, evolution of community structure cannot be shaped by the immigration of new members. In addition, when those communities are run in steady state, the influence of environmental factors on their assembly is reduced. Therefore, one would expect similar community structures under steady‐state conditions. Yet, in parallel setups, variability does occur. To reveal ecological mechanisms behind this phenomenon, five parallel reactors were studied at the single‐cell level for about 100 generations and community structure variations were quantified by ecological measures. Whether community variability can be controlled was tested by implementing soft temperature stressors as potential synchronizers. The low slope of the lognormal rank‐order abundance curves indicated a predominance of neutral mechanisms, i.e., where species identity plays no role. Variations in abundance ranks of subcommunities and increase in inter‐community pairwise β‐diversity over time support this. Niche differentiation was also observed, as indicated by steeper geometric‐like rank‐order abundance curves and increased numbers of correlations between abiotic and biotic parameters during initial adaptation and after disturbances. Still, neutral forces dominated community assembly. Our findings suggest that complex microbial communities in insular steady‐state environments can be difficult to synchronize and maintained in their original or desired structure, as they are non‐equilibrium systems.

Long-Term Biogas Production from Glycolate by Diverse and Highly Dynamic Communities

Generating chemical energy carriers and bulk chemicals from solar energy by microbial metabolic capacities is a promising technology. In this long-term study of over 500 days, methane was produced by a microbial community that was fed by the mono-substrate glycolate, which was derived from engineered algae. The microbial community structure was measured on the single cell level using flow cytometry. Abiotic and operational reactor parameters were analyzed in parallel. The R-based tool flowCyBar facilitated visualization of community dynamics and indicated sub-communities involved in glycolate fermentation and methanogenesis. Cell sorting and amplicon sequencing of 16S rRNA and mcrA genes were used to identify the key organisms involved in the anaerobic conversion process. The microbial community allowed a constant fermentation, although it was sensitive to high glycolate concentrations in the feed. A linear correlation between glycolate loading rate and biogas amount was observed (R² = 0.99) for glycolate loading rates up to 1.81 g L⁻¹ day⁻¹ with a maximum in biogas amount of 3635 mL day⁻¹ encompassing 45% methane. The cytometric diversity remained high during the whole cultivation period. The dominating bacterial genera were Syntrophobotulus, Clostridia genus B55_F, Aminobacterium, and Petrimonas. Methanogenesis was almost exclusively performed by the hydrogenotrophic genus Methanobacterium.

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.

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.

Laboratory-Scale Simulation and Real-Time Tracking of a Microbial Contamination Event and Subsequent Shock-Chlorination in Drinking Water

Rapid contamination of drinking water in distribution and storage systems can occur due to pressure drop, backflow, cross-connections, accidents, and bio-terrorism. Small volumes of a concentrated contaminant (e.g., wastewater) can contaminate large volumes of water in a very short time with potentially severe negative health impacts. The technical limitations of conventional, cultivation-based microbial detection methods neither allow for timely detection of such contaminations, nor for the real-time monitoring of subsequent emergency remediation measures (e.g., shock-chlorination). Here we applied a newly developed continuous, ultra high-frequency flow cytometry approach to track a rapid pollution event and subsequent disinfection of drinking water in an 80-min laboratory scale simulation. We quantified total (TCC) and intact (ICC) cell concentrations as well as flow cytometric fingerprints in parallel in real-time with two different staining methods. The ingress of wastewater was detectable almost immediately (i.e., after 0.6% volume change), significantly changing TCC, ICC, and the flow cytometric fingerprint. Shock chlorination was rapid and detected in real time, causing membrane damage in the vast majority of bacteria (i.e., drop of ICC from more than 380 cells μl^-1 to less than 30 cells μl^-1 within 4 min). Both of these effects as well as the final wash-in of fresh tap water followed calculated predictions well. Detailed and highly quantitative tracking of microbial dynamics at very short time scales and for different characteristics (e.g., concentration, membrane integrity) is feasible. This opens up multiple possibilities for targeted investigation of a myriad of bacterial short-term dynamics (e.g., disinfection, growth, detachment, operational changes) both in laboratory-scale research and full-scale system investigations in practice.

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

Microbial flow cytometry is an established fast and economic technique for complex ecosystem studies and enables visualization of rapidly changing community structures by measuring characteristics of single microbial cells. Cytometric evaluation routines are available such as flowCyBar which are useful for automatic data processing. Here, a cytometric workflow was established which allows to routinely analyze salivary microbiomes on the example of ten oral healthy subjects. First, saliva was collected within a 3-month period, cytometrically analyzed and the evolution of the microbiomes followed as well as the calculation of their intra- and inter-subject similarity. Second, the respective microbiomes were stressed by exposition to high sugar or acid concentrations and immediate changes were recorded. Third, bactericide solutions were tested on their impact on the microbiomes. In all three set ups huge intra-individual variations in cytometric community structures were found to be largely absent, even under stress, while inter-individual diversity was obvious. The bacterial cell counts of saliva samples were found to vary between 3.0×10⁷ and 6.2×10⁸ cells per sample and subject in undisturbed environments. The application of the two bactericides did not cause noteworthy diversity changes but the loss in cell numbers by about 50% was high after treatment. Illumina® sequencing of whole microbiomes or sorted sub-microbiomes revealed typical phyla such as Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes and Fusobacteria. This approach is useful for fast monitoring of individual salivary microbiomes and automatic calculation of intra- and inter-individual dynamic changes and variability and opens insight into ecological principles leading to their sustainment in their individual environment.

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.

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.

Flow cytometry community fingerprinting and amplicon sequencing for the assessment of landfill leachate cellulolytic bioaugmentation

Flow cytometry (FCM) is a high throughput single cell technology that is actually becoming widely used for studying phenotypic and genotypic diversity among microbial communities. This technology is considered in this work for the assessment of a bioaugmentation treatment in order to enhance cellulolytic potential of landfill leachate. The experimental results reveal the relevant increase of leachate cellulolytic potential due to bioaugmentation. Cytometric monitoring of microbial dynamics along these assays is then realized. The flow FP package is used to establish microbial samples fingerprint from initial 2D cytometry histograms. This procedure allows highlighting microbial communities' variation along the assays. Cytometric and 16S rRNA gene sequencing fingerprinting methods are then compared. The two approaches give same evidence about microbial dynamics throughout digestion assay. There are however a lack of significant correlation between cytometric and amplicon sequencing fingerprint at genus or species level. Same phenotypical profiles of microbiota during assays matched to several 16S rRNA gene sequencing ones. Flow cytometry fingerprinting can thus be considered as a promising routine on-site method suitable for the detection of stability/variation/disturbance of complex microbial communities involved in bioprocesses.

Individual cell heterogeneity in Predictive Food Microbiology: Challenges in predicting a "noisy" world

Gene expression is a fundamentally noisy process giving rise to a significant cell to cell variability at the phenotype level. The phenotypic noise is manifested in a wide range of microbial traits. Heterogeneous behavior of individual cells is observed at the growth, survival and inactivation responses and should be taken into account in the context of Predictive Food Microbiology (PMF). Recent methodological advances can be employed for the study and modeling of single cell dynamics leading to a new generation of mechanistic models which can provide insight into the link between phenotype, gene-expression, protein and metabolic functional units at the single cell level. Such models however, need to deal with an enormous amount of interactions and processes that influence each other, forming an extremely complex system. In this review paper, we discuss the importance of noise and present the future challenges in predicting the "noisy" microbial responses in foods.

Changes in Microbial Plankton Assemblages Induced by Mesoscale Oceanographic Features in the Northern Gulf of Mexico

Mesoscale circulation generated by the Loop Current in the Northern Gulf of Mexico (NGOM) delivers growth-limiting nutrients to the microbial plankton of the euphotic zone. Consequences of physicochemically driven community shifts on higher order consumers and subsequent impacts on the biological carbon pump remain poorly understood. This study evaluates microbial plankton <10 μm abundance and community structure across both cyclonic and anti-cyclonic circulation features in the NGOM using flow cytometry (SYBR Green I and autofluorescence parameters). Non-parametric multivariate hierarchical cluster analyses indicated that significant spatial variability in community structure exists such that stations that clustered together were defined as having a specific ‘microbial signature’ (i.e. statistically homogeneous community structure profiles based on relative abundance of microbial groups). Salinity and a combination of sea surface height anomaly and sea surface temperature were determined by distance based linear modeling to be abiotic predictor variables significantly correlated to changes in microbial signatures. Correlations between increased microbial abundance and availability of nitrogen suggest nitrogen-limitation of microbial plankton in this open ocean area. Regions of combined coastal water entrainment and mesoscale convergence corresponded to increased heterotrophic prokaryote abundance relative to autotrophic plankton. The results provide an initial assessment of how mesoscale circulation potentially influences microbial plankton abundance and community structure in the NGOM.

Fluorescence-based tools for single-cell approaches in food microbiology

The better understanding of the functioning of microbial communities is a challenging and crucial issue in the field of food microbiology, as it constitutes a prerequisite to the optimization of positive and technological microbial population functioning, as well as for the better control of pathogen contamination of food. Heterogeneity appears now as an intrinsic and multi-origin feature of microbial populations and is a major determinant of their beneficial or detrimental functional properties. The understanding of the molecular and cellular mechanisms behind the behavior of bacteria in microbial communities requires therefore observations at the single-cell level in order to overcome "averaging" effects inherent to traditional global approaches. Recent advances in the development of fluorescence-based approaches dedicated to single-cell analysis provide the opportunity to study microbial communities with an unprecedented level of resolution and to obtain detailed insights on the cell structure, metabolism activity, multicellular behavior and bacterial interactions in complex communities. These methods are now increasingly applied in the field of food microbiology in different areas ranging from research laboratories to industry. In this perspective, we reviewed the main fluorescence-based tools used for single-cell approaches and their concrete applications with specific focus on food microbiology.

Microbiology and Molecular Biology Tools for Biogas Process Analysis, Diagnosis and Control

Many biotechnological processes such as biogas production or defined biotransformations are carried out by microorganisms or tightly cooperating microbial communities. Process breakdown is the maximum credible accident for the operator. Any time savings that can be provided by suitable early-warning systems and allow for specific countermeasures are of great value. Process disturbance, frequently due to nutritional shortcomings, malfunction or operational deficits, is evidenced conventionally by process chemistry parameters. However, knowledge on systems microbiology and its function has essentially increased in the last two decades, and molecular biology tools, most of which are directed against nucleic acids, have been developed to analyze and diagnose the process. Some of these systems have been shown to indicate changes of the process status considerably earlier than the conventionally applied process chemistry parameters. This is reasonable because the triggering catalyst is determined, activity changes of the microbes that perform the reaction. These molecular biology tools have thus the potential to add to and improve the established process diagnosis system. This chapter is dealing with the actual state of the art of biogas process analysis in practice, and introduces molecular biology tools that have been shown to be of particular value in complementing the current systems of process monitoring and diagnosis, with emphasis on nucleic acid targeted molecular biology systems.