Cytometric fingerprinting for analyzing microbial intracommunity structure variation and identifying subcommunity function

Single-cell multi-parametric characterization of microbiota by flow cytometry

It has become evident, that the microbes colonizing the human body have a great impact on health and disease. Investigations of microbiota currently primarily rely on culturomics, high-throughput sequencing and metaproteomics which have considerably advanced our knowledge regarding the role of the microbiota in our environment and for our health. While single-cell phenotyping of immune cells and other somatic cells by flow cytometry has become widely used, the detailed analysis of bacterial cells such as the human microbiota on the single-cell level, is lagging behind. Here, we outline a protocol for the single-cell characterization of bacterial cells from complex microbiota samples, such as stool, by multi-parametric flow cytometry. Our protocol describes the isotype-specific detection of host-antibody coating of intestinal bacteria ex vivo, which together with quantitative DNA staining and light scatter detection comprise an individual's microbiota fingerprint. Cryoconservation and appropriate staining controls ensure reliable, reproducible data generation and analysis. We have automated the analysis of the multi-dimensional data using a segmentation approach by self-organizing map (SOM) algorithm for downstream comparative analyses. Our protocol can be adapted to integrate further phenotypic markers and uses the power of analytical cytometry for the characterization of bacteria on the single-cell level.

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.

Morphine-induced intestinal microbial dysbiosis drives TLR-dependent IgA targeting of gram-positive bacteria and upregulation of CD11b and TLR2 on a sub-population of IgA+ B cells

IgA binding dictates the composition of the intestinal microbiome and reflects dysbiotic states during chronic disease. Both pathogenic and commensal bacteria differentially bind to IgA with varying outcomes. Little is known regarding IgA dynamics immediately following microbial dysbiosis. Recent work shows that morphine treatment rapidly induces microbial dysbiosis within hours of administration. This microbial shift is characterized by the expansion of pathogenic bacteria with a concurrent decrease in commensal bacteria. Because of this rapid microbial shift, a murine model of chronic morphine treatment was used to gain insight on the host IgA response during early microbial disruption. Within 24 h, morphine treatment induces microbial dysbiosis which disrupts IgA-bacterial homeostasis, resulting in an increased concentration of unbound IgA with a corresponding decrease in the frequency of IgA-bound bacteria. Additionally, the increased concentration of unbound IgA is dependent on the microbiome, as microbial depletion abolishes the increase. At 48 h of morphine treatment, the frequency of IgA-bound bacteria increases and IgA-seq reveals increased IgA targeting of gram-positive bacteria. Both a whole-body TLR2 KO and treatment with the TLR inhibitor OxPAPC resulted in abrogation of IgA binding to bacteria, implicating modulation of IgA binding through TLR signaling. Finally, we identify that a sub-population of IgA+ B cells in the intestinal lamina propria has increased CD11b and TLR2 expression at 24 h of morphine treatment which could be a potential source of the observed IgA that targets gram-positive bacteria. Together, we demonstrate for the first time the role of TLR2 in IgA targeting of intestinal bacteria, and this study sheds light on the IgA dynamics during the initial hours of microbial dysbiosis.

Single-cell phenotyping of bacteria combined with deep sequencing for improved contextualization of microbiome analyses

Great effort was made to characterize the bacterial communities inhabiting the human body as a factor in disease, resulting in the realization that a wide spectrum of diseases is associated with an altered composition of the microbiome. However, the identification of disease-relevant bacteria has been hindered by the high cross-sectional diversity of individual microbiomes, and in most cases, it remains unclear whether the observed alterations are cause or consequence of disease. Hence, innovative analysis approaches are required that enable inquiries of the microbiome beyond mere taxonomic cataloging. This review highlights the utility of microbiota flow cytometry, a single-cell analysis platform to directly interrogate cellular interactions, cell conditions, and crosstalk with the host's immune system within the microbiome to take into consideration the role of microbes as critical interaction partners of the host and the spectrum of microbiome alterations, beyond compositional changes. In conjunction with advanced sequencing approaches it could reveal the genetic potential of target bacteria and advance our understanding of taxonomic diversity and gene usage in the context of the microenvironment. Single-cell bacterial phenotyping has the potential to change our perspective on the human microbiome and empower microbiome research for the development of microbiome-based therapy approaches and personalized medicine.

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.

Full-electric microfluidic platform to capture, analyze and selectively release single cells

Current single-cell technologies require large and expensive equipment, limiting their use to specialized labs. In this paper, we present for the first time a microfluidic device which demonstrates a combined method for full-electric cell capturing, analyzing, and selectively releasing with single-cell resolution. All functionalities are experimentally demonstrated on Saccharomyces cerevisiae. Our microfluidic platform consists of traps centered around a pair of individually accessible coplanar electrodes, positioned under a microfluidic channel. Using this device, we validate our novel Two-Voltage method for trapping single cells by positive dielectrophoresis (pDEP). Cells are attracted to the trap when a high voltage (VH) is applied. A low voltage (VL) holds the already trapped cell in place without attracting additional cells, allowing full control over the number of trapped cells. After trapping, the cells are analyzed by broadband electrochemical impedance spectroscopy. These measurements allow the detection of single cells and the extraction of cell parameters. Additionally, these measurements show a strong correlation between average phase change and cell size, enabling the use of our system for size measurements in biological applications. Finally, our device allows selectively releasing trapped cells by turning off the pDEP signal in their trap. The experimental results show the techniques potential as a full-electric single-cell analysis tool with potential for miniaturization and automation which opens new avenues towards small-scale, high throughput single-cell analysis and sorting lab-on-CMOS devices.

Convergence of flow cytometry and bacteriology. Current and future applications: a focus on food and clinical microbiology

Since its development in the 1960s, flow cytometry (FCM) was quickly revealed a powerful tool to analyse cell populations in medical studies, yet, for many years, was almost exclusively used to analyse eukaryotic cells. Instrument and methodological limitations to distinguish genuine bacterial signals from the background, among other limitations, have hampered FCM applications in bacteriology. In recent years, thanks to the continuous development of FCM instruments and methods with a higher discriminatory capacity to detect low-size particles, FCM has emerged as an appealing technique to advance the study of microbes, with important applications in research, clinical and industrial settings. The capacity to rapidly enumerate and classify individual bacterial cells based on viability facilitates the monitoring of bacterial presence in foodstuffs or clinical samples, reducing the time needed to detect contamination or infectious processes. Besides, FCM has stood out as a valuable tool to advance the study of complex microbial communities, or microbiomes, that are very relevant in the context of human health, as well as to understand the interaction of bacterial and host cells. This review highlights current developments in, and future applications of, FCM in bacteriology, with a focus on those related to food and clinical microbiology.

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.

Development of an Automated Online Flow Cytometry Method to Quantify Cell Density and Fingerprint Bacterial Communities

Cell density is an important factor in all microbiome research, where interactions are of interest. It is also the most important parameter for the operation and control of most biotechnological processes. In the past, cell density determination was often performed offline and manually, resulting in a delay between sampling and immediate data processing, preventing quick action. While there are now some online methods for rapid and automated cell density determination, they are unable to distinguish between the different cell types in bacterial communities. To address this gap, an online automated flow cytometry procedure is proposed for real-time high-resolution analysis of bacterial communities. On the one hand, it allows for the online automated calculation of cell concentrations and, on the other, for the differentiation between different cell subsets of a bacterial community. To achieve this, the OC-300 automation device (onCyt Microbiology, Zürich, Switzerland) was coupled with the flow cytometer CytoFLEX (Beckman Coulter, Brea, USA). The OC-300 performs the automatic sampling, dilution, fixation and 4',6-diamidino-2-phenylindole (DAPI) staining of a bacterial sample before sending it to the CytoFLEX for measurement. It is demonstrated that this method can reproducibly measure both cell density and fingerprint-like patterns of bacterial communities, generating suitable data for powerful automated data analysis and interpretation pipelines. In particular, the automated, high-resolution partitioning of clustered data into cell subsets opens up the possibility of correlation analysis to identify the operational or abiotic/biotic causes of community disturbances or state changes, which can influence the interaction potential of organisms in microbiomes or even affect the performance of individual organisms.

Exploration of microbiome diversity of stacked fermented grains by flow cytometry and cell sorting

Sauce-flavor baijiu is one of the twelve flavor types of Chinese distilled fermented product. Microbial composition plays a key role in the stacked fermentation of Baijiu, which uses grains as raw materials and produces flavor compounds, however, the active microbial community and its relationship remain unclear. Here, we investigated the total and active microbial communities of stacked fermented grains of sauce-flavored Baijiu using flow cytometry and high-throughput sequencing technology, respectively. By using traditional high-throughput sequencing technology, a total of 24 bacterial and 14 fungal genera were identified as the core microbiota, the core bacteria were Lactobacillus (0.08-39.05%), Acetobacter (0.25-81.92%), Weissella (0.03-29.61%), etc. The core fungi were Issatchenkia (23.11-98.21%), Monascus (0.02-26.36%), Pichia (0.33-37.56%), etc. In contrast, using flow cytometry combined with high-throughput sequencing, the active dominant bacterial genera after cell sorting were found to be Herbaspirillum, Chitinophaga, Ralstonia, Phenylobacterium, Mucilaginibacter, and Bradyrhizobium, etc., whereas the active dominant fungal genera detected were Aspergillus, Pichia, Exophiala, Candelabrochaete, Italiomyces, and Papiliotrema, etc. These results indicate that although the abundance of Acetobacter, Monascus, and Issatchenkia was high after stacked fermentation, they may have little biological activity. Flow cytometry and cell sorting techniques have been used in the study of beer and wine, but exploring the microbiome in such a complex environment as Chinese baijiu has not been reported. The results also reveal that flow cytometry and cell sorting are convenient methods for rapidly monitoring complex microbial flora and can assist in exploring complex environmental samples.

Ecological forces dictate microbial community assembly processes in bioreactor systems

Microbial communities are indispensable for future biotechnology to produce valuable platform chemicals and reduce the exploitation of fossil resources. Yet, the stability of microbial communities in classical continuous reactor setups is best brief or non-existent. This is due to ecological forces such as stochastic and deterministic properties of communities that contribute to rapid changes in structure and function to varying degrees. The review highlights the differences between these two properties, provides tools for their estimation, and gives an outlook on overcoming instabilities of microbial communities in biotechnological reactor systems.

Combining Flow Cytometry and Metagenomics Improves Recovery of Metagenome-Assembled Genomes in a Cell Culture from Activated Sludge

The recovery of metagenome-assembled genomes is biased towards the most abundant species in a given community. To improve the identification of species, even if only dominant species are recovered, we investigated the integration of flow cytometry cell sorting with bioinformatics tools to recover metagenome-assembled genomes. We used a cell culture of a wastewater microbial community as our model system. Cells were separated based on fluorescence signals via flow cytometry cell sorting into sub-communities: dominant gates, low abundant gates, and outer gates into subsets of the original community. Metagenome sequencing was performed for all groups. The unsorted community was used as control. We recovered a total of 24 metagenome-assembled genomes (MAGs) representing 11 species-level genome operational taxonomic units (gOTUs). In addition, 57 ribosomal operational taxonomic units (rOTUs) affiliated with 29 taxa at species level were reconstructed from metagenomic libraries. Our approach suggests a two-fold increase in the resolution when comparing sorted and unsorted communities. Our results also indicate that species abundance is one determinant of genome recovery from metagenomes as we can recover taxa in the sorted libraries that are not present in the unsorted community. In conclusion, a combination of cell sorting and metagenomics allows the recovery of MAGs undetected without cell sorting.

Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity

Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.

Cybergenetic control of microbial community composition

The use of bacterial communities in bioproduction instead of monocultures has potential advantages including increased productivity through division of labour, ability to utilise cheaper substrates, and robustness against perturbations. A key challenge in the application of engineered bacterial communities is the ability to reliably control the composition of the community in terms of its constituent species. This is crucial to prevent faster growing species from outcompeting others with a lower relative fitness, and to ensure that all species are present at an optimal ratio during different steps in a biotechnological process. In contrast to purely biological approaches such as synthetic quorum sensing circuits or paired auxotrophies, cybergenetic control techniques - those in which computers interface with living cells-are emerging as an alternative approach with many advantages. The community composition is measured through methods such as fluorescence intensity or flow cytometry, with measured data fed real-time into a computer. A control action is computed using a variety of possible control algorithms and then applied to the system, with actuation taking the form of chemical (e.g., inducers, nutrients) or physical (e.g., optogenetic, mechanical) inputs. Subsequent changes in composition are then measured and the cycle repeated, maintaining or driving the system to a desired state. This review discusses recent and future developments in methods for implementing cybergenetic control systems, contrasts their capabilities with those of traditional biological methods of population control, and discusses future directions and outstanding challenges for the field.

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.

Stabilizing microbial communities by looped mass transfer

Building and changing a microbiome at will and maintaining it over hundreds of generations has so far proven challenging. Despite best efforts, complex microbiomes appear to be susceptible to large stochastic fluctuations. Current capabilities to assemble and control stable complex microbiomes are limited. Here, we propose a looped mass transfer design that stabilizes microbiomes over long periods of time. Five local microbiomes were continuously grown in parallel for over 114 generations and connected by a loop to a regional pool. Mass transfer rates were altered and microbiome dynamics were monitored using quantitative high-throughput flow cytometry and taxonomic sequencing of whole communities and sorted subcommunities. Increased mass transfer rates reduced local and temporal variation in microbiome assembly, did not affect functions, and overcame stochasticity, with all microbiomes exhibiting high constancy and increasing resistance. Mass transfer synchronized the structures of the five local microbiomes and nestedness of certain cell types was eminent. Mass transfer increased cell number and thus decreased net growth rates μ′. Subsets of cells that did not show net growth μ′SCx were rescued by the regional pool R and thus remained part of the microbiome. The loop in mass transfer ensured the survival of cells that would otherwise go extinct, even if they did not grow in all local microbiomes or grew more slowly than the actual dilution rate D would allow. The rescue effect, known from metacommunity theory, was the main stabilizing mechanism leading to synchrony and survival of subcommunities, despite differences in cell physiological properties, including growth rates.

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.

Validating Flow Cytometry as a Method for Quantifying Bdellovibrio Predatory Bacteria and Its Prey for Microbial Ecology

Bdellovibrio bacteriovorus is a predatory, Gram-negative bacteria that feeds on many pathogenic bacteria and has been investigated as a possible solution for mitigating biofilms in different fields. The application depends on more fundamental ecological studies into the dynamics between Bdellovibrio and their prey. To do so requires an accurate, reliable, and, preferably rapid, way of enumerating the cells. Flow cytometry (FCM) is potentially a rapid, accurate, and inexpensive tool for this, but it has yet to be validated in the enumeration of Bdellovibrio. In this study, we developed a protocol to measure the number of Bdellovibrio in samples of various densities using FCM and compared the results with those of other methods: optical density (OD), PFU assay (PFU), and quantitative PCR (qPCR). We observed a strong correlation between values obtained using FCM and PFU (ρ = 0.923) and FCM and qPCR (ρ = 0.987). Compared to optical density there was a much weaker correlation (ρ = 0.784), which was to be expected given the well-documented uncertainty in converting optical density (OD) to cell numbers. The FCM protocol was further validated by demonstrating its ability to distinguish and count mixed populations of Bdellovibrio and the prey Pseudomonas. Thus, the accuracy of FCM as well as its speed and reproducibility make it a suitable alternative for measuring Bdellovibrio cell numbers, especially where many samples are required to capture the dynamics of predator-prey interactions. IMPORTANCE The rise of antibiotic resistance and the unwanted growth of bacteria is a universally growing problem. Predatory bacteria can be used as a biological alternative to antibiotics because they grow by feeding on other bacteria. To apply this effectively requires further study and a deeper understanding of the forces that drive a prey population to elimination. Initially, such studies require more reliable methods to count these cells. Flow cytometry (FCM) is potentially a rapid, accurate, and inexpensive tool for this, but it has yet to be validated for predatory bacteria. This study develops a protocol to count the predatory bacteria Bdellovibrio bacteriovorus and its Pseudomonas prey using FCM and compare the results with those of other methods, demonstrating its ability for studies into B. bacteriovorus predation dynamics. This could lead to the use of B. bacteriovorus for killing bacterial biofilms in fields, such as drinking water and agriculture.

Recent advances in microbial community analysis from machine learning of multiparametric flow cytometry data

Dynamic analysis of microbial composition is crucial for understanding community functioning and detecting dysbiosis. Compositional information is mostly obtained through sequencing of taxonomic markers or whole meta-genomes, which may be productively complemented by real-time quantitative community multiparametric flow cytometry data (FCM). Patterns and clusters in FCM community data can be distinguished and compared by unsupervised machine learning. Alternatively, FCM data from preselected individual strain phenotypes can be used for supervised machine-training in order to differentiate similar cell types within communities. Both types of machine learning can quantitatively deconvolute community FCM data sets and rapidly analyse global changes in response to treatment. Procedures may further be optimized for recurrent microbiome samples to simultaneously quantify physiological and compositional states.

Microbial disruption in the gut promotes cerebral endothelial dysfunction

Cerebrovascular disease is a group of conditions characterized by disorders of the cerebral vessels. Endothelial dysfunction renders the vasculature at risk of impaired blood flow and increases the potential of developing cerebrovascular disease. The gut microbiota has been recently identified as a possible risk factor of cerebrovascular disease. However, a direct link between gut microbiota and cerebral vascular function has not been established. Therefore, the aim of this study was to determine the effect of gut bacterial disruption on cerebral endothelial function. Male inbred Sprague-Dawley rats were randomly assigned to receive either drinking water with (n = 4) or without (n = 4) a cocktail of nonabsorbable broad-spectrum antibiotics (streptomycin, neomycin, bacitracin, and polymyxin B). Three weeks of antibiotic treatment resulted in a significant reduction in bacterial load and shifts within the bacterial sub-populations as assessed using flow cytometry. Using pressure myography, we found that spontaneous tone significantly increased and L-NAME-induced vasoconstriction was significantly blunted in middle cerebral arteries (MCAs) harvested from antibiotic-treated rats. ATP-mediated dilations were significantly blunted in MCAs from antibiotic-treated rats compared to their control counterparts. Immunoblotting revealed that the eNOS-P/total eNOS ratio was significantly reduced in cerebral artery lysates from antibiotic-treated rats compared to controls. Our findings suggest that disruption of the gut microbiota leads to cerebral endothelial dysfunction through reduction of eNOS activity. This study highlights the potential of the microbiota as a target to reverse endothelial dysfunction and a preventative approach to reducing risk of stroke and aneurysms.

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.

Effects of oxygen exposure on relative nucleic acid content and membrane integrity in the human gut microbiota

While the diversity of the human gut microbiota is becoming increasingly well characterized, bacterial physiology is still a critical missing link in understanding how the gut microbiota may be implicated in disease. The current best practice for studying bacterial physiology involves the immediate storage of fecal samples in an anaerobic chamber. This reliance on immediate access to anaerobic chambers greatly limits the scope of sample populations that can be studied. Here, we assess the effects of short-term oxygen exposure on gut bacterial physiology and diversity. We use relative nucleic acid content and membrane integrity as markers of bacterial physiology, and 16S rRNA gene amplicon sequencing to measure bacterial diversity. Samples were stored for up to 6 h in either ambient conditions or in anoxic environments created with gas packs or in an anaerobic chamber. Our data indicate that AnaeroGen sachets preserve bacterial membrane integrity and nucleic acid content over the course of 6 h similar to storage in an anaerobic chamber. Short-term oxygen exposure increases bacterial membrane permeability, without exceeding inter-individual differences. As oxygen exposure remains an important experimental consideration for bacterial metabolism, our data suggest that AnaeroGen sachets are a valid alternative limiting loss of membrane integrity for short-term storage of samples from harder-to-access populations.

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.

Bacterial mock communities as standards for reproducible cytometric microbiome analysis

Flow cytometry has recently established itself as a tool to track short-term dynamics in microbial community assembly and link those dynamics with ecological parameters. However, instrumental configurations of commercial cytometers and variability introduced through differential handling of the cells and instruments frequently cause data set variability at the single-cell level. This is especially pronounced with microorganisms, which are in the lower range of optical resolution. Although alignment beads are valuable to generally minimize instrumental noise and align overall machine settings, an artificial microbial cytometric mock community (mCMC) is mandatory for validating lab workflows and enabling comparison of data between experiments, thus representing a necessary reference standard for the reproducible cytometric characterization of microbial communities, especially in long-term studies. In this study, the mock community consisted of two Gram-positive and two Gram-negative bacterial strains, which can be assembled with respective subsets of cells, including spores, in any selected ratio or concentration. The preparation of the four strains takes a maximum of 5 d, and the stains are storable with either PFA/ethanol fixation at -20 °C or drying at 4 °C for at least 6 months. Starting from this stock, an mCMC can be assembled within 1 h. Fluorescence staining methods are presented and representatively applied with two high-resolution cell sorters and three benchtop flow cytometers. Benchmarked data sets allow the use of bioinformatic evaluation procedures to decode community behavior or convey qualified cell sorting decisions for subsequent high-resolution sequencing or proteomic routines.

Evaluation of the feed composition for an effective medium chain carboxylic acid production in an open culture fermentation

The objective of this study was to investigate the effect of substrate composition on chain elongation pathways and on shaping reactor microbiome during open culture fermentation (OCF). The process was performed in a continuous mode in an upflow anaerobic sludge blanket (UASB) reactor fed with either fresh acid whey (AW) or AW at controlled stage of prefermentation (with controlled content of electron donors). Dosing AW with an increasing ethanol loading rate led to ethanol oxidation and short chain carboxylic acids (SCCAs) generation. Change of the feedstock composition (higher lactate and lactose content and ethanol cut off) shifted the process outcome towards medium chain carboxylic acids (MCCAs) production, with caproate as the main product. The MCCAs production rate has grown from 0.7 ± 0 to 4.12 ± 1 g/L/day (38.3 ± 5 to 212.6 ± 60 mmol C/L/day) and reached specificity of 48 ± 18% mol C. The differentiation between microbiome samples confirmed the reactor microbiome shaped according to the feed composition. The only known caproic acid producers were represented by Caproiciproducens ssp., that reached a relative OTU abundance between 3 and 7%. The developed method enables to substitute the use of fossil resources with products from the OCF of waste and wastewater. Thus, it contributes to reduce the carbon footprint and enhance the sustainability of the chemical industry.

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data

The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flow cytometry data using a supervised machine learning algorithm of standard cell type recognition (CellCognize). As a proof-of-concept, we trained neural networks with 32 microbial cell and bead standards. The resulting classifiers were extensively validated in silico on known microbiota, showing on average 80% prediction accuracy. Furthermore, the classifiers could detect shifts in microbial communities of unknown composition upon chemical amendment, comparable to results from 16S-rRNA-amplicon analysis. CellCognize was also able to quantify population growth and estimate total community biomass productivity, providing estimates similar to those from ¹⁴C-substrate incorporation. CellCognize complements current sequencing-based methods by enabling rapid routine cell diversity analysis. The pipeline is suitable to optimize cell recognition for recurring microbiota types, such as in human health or engineered systems.

Duygan et al. develop a supervised machine learning algorithm, CellCognize, to quantify cell type diversity from multidimensional flow cytometry data. Their model achieves 80% prediction accuracy, detects shifts in microbial communities of unknown composition and quantifies population growth and biomass productivity. Their work will be useful to study microbiota in human health or engineered systems.

Following the community development of SIHUMIx - a new intestinal in vitro model for bioreactor use

Diverse intestinal microbiota is frequently used in in vitro bioreactor models to study the effects of diet, chemical contaminations, or medication. However, the reproducible cultivation of fecal microbiota is challenging and the resultant communities behave highly dynamic. To approach the issue of reproducibility in in vitro models, we established an intestinal microbiota model community of reduced complexity, SIHUMIx, as a valuable model for in vitro use. The development of the SIHUMIx community was monitored over time with methods covering the cellular and the molecular level. We used microbial flow cytometry, intact protein profiling and terminal restriction fragment length polymorphism analysis to assess community structure. In parallel, we analyzed the functional level by targeted analysis of short-chain fatty acids and untargeted metabolomics. The stability properties constancy, resistance, and resilience were approached both on the structural and functional level of the community. We show that the SIHUMIx community is highly reproducible and constant since day 5 of cultivation. Furthermore, SIHUMIx has the ability to resist and recover from a pulsed perturbation, with changes in community structure recovered earlier than functional changes. Since community structure and function changed divergently, both levels need to be monitored at the same time to gain a full overview of the community development. All five methods are highly suitable to follow the community dynamics of SIHUMIx and indicated stability on day five. This makes SIHUMIx a suitable in vitro model to investigate the effects of e.g. medical, chemical, or dietary interventions.

Assessment of Gram- and Viability-Staining Methods for Quantifying Bacterial Community Dynamics Using Flow Cytometry

Over the past years, gut microbiota became a major field of interest with increasing reports suggesting its association with a large number of human diseases. In this context, there is a major interest to develop analysis tools allowing simple and cost-effective population pattern analysis of these complex ecosystems to follow changes over time. Whereas sequence-based metagenomics profiling is widely used for microbial ecosystems characterization, it still requires time and specific expertise for analysis. Flow cytometry overcomes these disadvantages, providing key information on communities within hours. In addition, it can potentially be used to select, isolate and cultivate specific bacteria of interest. In this study, we evaluated the culturability of strictly anaerobic bacteria that were stained with a classical Live/Dead staining, and then sorted using flow cytometry under anaerobic conditions. This sorting of “viable” fraction demonstrated that 10–80% of identified “viable” cells of pure cultures of strictly anaerobic bacteria were culturable. In addition, we tested the use of a combination of labeled vancomycin and Wheat Germ Agglutinin (WGA) lectin to discriminate Gram-positive from Gram-negative bacteria in complex ecosystems. After validation on both aerobic/anaerobic facultative and strictly anaerobic bacteria, the staining methods were applied on complex ecosystems, revealing differences between culture conditions and demonstrating that minor pH variations have strong impacts on microbial community structure, which was confirmed by 16S rRNA gene sequencing. This combination of staining methods makes it possible to follow-up evolutions of complex microbial communities, supporting its future use as a rapid analysis tool in various applications. The flow cytometry staining method that was developed has the potential to facilitate the analysis of complex ecosystems by highlighting changes in bacterial communities’ dynamics. It is assumed to be applicable as an efficient and fast approach to improve the control of processes linked to a wide range of ecosystems or known communities of bacterial species in both research and industrial contexts.

Whole genome shotgun sequencing of POPs degrading bacterial community dwelling tannery effluents and petrol contaminated soil

The present study involved identification of genes which are present in the genome of native bacteria to make them effective tools for bioremediation of persistent organic pollutants (POPs). During this study, forty-one POPs (naphthalene, toluene and petrol) metabolizing bacteria were isolated from tannery effluents and petrol contaminated soil samples by successive enrichment culturing. The taxonomic diversity and gene repertoire conferring POPs degradation ability to the isolated bacterial community were studied through whole genome shotgun sequencing of DNA consortium. The DNA consortium contained equimolar concentration of DNA extracted from each bacterial isolate using organic method. To add a double layer of confirmation the established DNA consortium was subjected to 16S rRNA metagenome sequencing and whole genome shotgun sequencing analysis. Biodiversity analysis revealed that the consortium was composed of phyla Firmicutes (80 %), Proteobacteria (12 %) and Actinobacteria (5%). Genera found included Bacillus (45 %), Burkholderia (25 %), Brevibacillus (9%) and Geobacillus (4%). Functional profiling of consortium helped us to identify genes associated with degradation pathways of a variety of organic compounds including toluene, naphthalene, caprolactam, benzoate, aminobenzoate, xylene, 4-hydroxyphenyl acetic acid, biphenyl, anthracene, aminobenzoate, chlorocyclohexane, chlorobenzene, n-phenylalkanoic acid, phenylpropanoid, salicylate, gentisate, central meta cleavage of aromatic compounds, cinnamic acid, catechol and procatechuate branch of β-ketoadipate pathway, phenyl-acetyl CoA and homogentisate catabolic pathway. The information thus generated has ensured not only biodegradation potential but also revealed many possible future applications of the isolated bacteria.

The Activation of Mucosal-Associated Invariant T (MAIT) Cells Is Affected by Microbial Diversity and Riboflavin Utilization in vitro

Recent research has demonstrated that MAIT cells are activated by individual bacterial or yeasts species that possess the riboflavin biosynthesis pathway. However, little is known about the MAIT cell activating potential of microbial communities and the contribution of individual community members. Here, we analyze the MAIT cell activating potential of a human intestinal model community (SIHUMIx) as well as intestinal microbiota after bioreactor cultivation. We determined the contribution of individual SIHUMIx community members to the MAIT cell activating potential and investigated whether microbial stress can influence their MAIT cell activating potential. The MAIT cell activating potential of SIHUMIx was directly related to the relative species abundances in the community. We therefore suggest an additive relationship between the species abundances and their MAIT cell activating potential. In diverse microbial communities, we found that a low MAIT cell activating potential was associated with high microbial diversity and a high level of riboflavin demand and vice versa. We suggest that microbial diversity might affect MAIT cell activation via riboflavin utilization within the community. Microbial acid stress significantly reduced the MAIT cell activating potential of SIHUMIx by impairing riboflavin availability through increasing the riboflavin demand. We show that MAIT cells can perceive microbial stress due to changes in riboflavin utilization and that riboflavin availability might also play a central role for the MAIT cell activating potential of diverse microbiota.

Next-generation physiology approaches to study microbiome function at single cell level

The function of cells in their native habitat often cannot be reliably predicted from genomic data or from physiology studies of isolates. Traditional experimental approaches to study the function of taxonomically and metabolically diverse microbiomes are limited by their destructive nature, low spatial resolution or low throughput. Recently developed technologies can offer new insights into cellular function in natural and human-made systems and how microorganisms interact with and shape the environments that they inhabit. In this Review, we provide an overview of these next-generation physiology approaches and discuss how the non-destructive analysis of cellular phenotypes, in combination with the separation of the target cells for downstream analyses, provide powerful new, complementary ways to study microbiome function. We anticipate that the widespread application of next-generation physiology approaches will transform the field of microbial ecology and dramatically improve our understanding of how microorganisms function in their native environment.

Bacterial Community Diversity Dynamics Highlight Degrees of Nestedness and Turnover Patterns

Bacterial communities change their structure rapidly due to short generation times of their members. How bacteria assemble to certain structures provides insight into ecological mechanisms that shape a bacterial community. Microbial community flow cytometry was used to create community fingerprints based on subcommunity distributions and to visualize the dynamic variations of 10 independently grown communities under equal conditions. Inventory diversity values were recorded by α- and γ-diversity whereas the degree of subsistence of subcommunities (nestedness) and the degree of gain or loss of subcommunities (turnover) was calculated as multi-sites ß-diversity terms ß_NES and ß_SIM . Numbers of unique subcommunities of pairwise samples were determined by intra- and inter-community ß-diversity values. Although all communities were exposed to niche-differentiating conditions they assembled to disparate structures. In our study, the turnover coefficients were high (> 0.6), while the nestedness coefficients were complementary low in the separate 10 bioreactors. Intra- and inter-community ß-diversity values indicated fast community shifts. Microbial community flow cytometry straightforwardly identifies the dominance and subsistence of subsets of cells in a community or the degree of their replacement. The calculation of either turnover or nestedness patterns might have implications in medical, biotechnological, or environmental research. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations

Phenotypic heterogeneity within microbial populations arises even when the cells are exposed to putatively constant and homogeneous conditions. The outcome of this phenomenon can affect the whole function of the population, resulting in, for example, new "adapted" metabolic strategies and impacting its fitness at given environmental conditions. Accounting for phenotypic heterogeneity becomes thus necessary, due to its relevance in medical and applied microbiology as well as in environmental processes. Still, a comprehensive evaluation of this phenomenon requires a common and unique method of quantitation, which allows for the comparison between different studies carried out with different approaches. Consequently, in this study, two widely applicable indices for quantitation of heterogeneity were developed. The heterogeneity coefficient (HC) is valid when the population follows unimodal activity, while the differentiation tendency index (DTI) accounts for heterogeneity implying outbreak of subpopulations and multimodal activity. We demonstrated the applicability of HC and DTI for heterogeneity quantitation on stable isotope probing with nanoscale secondary ion mass spectrometry (SIP-nanoSIMS), flow cytometry, and optical microscopy datasets. The HC was found to provide a more accurate and precise measure of heterogeneity, being at the same time consistent with the coefficient of variation (CV) applied so far. The DTI is able to describe the differentiation in single-cell activity within monoclonal populations resolving subpopulations with low cell abundance, individual cells with similar phenotypic features (e.g., isotopic content close to natural abundance, as detected with nanoSIMS). The developed quantitation approach allows for a better understanding on the impact and the implications of phenotypic heterogeneity in environmental, medical and applied microbiology, microbial ecology, cell biology, and biotechnology.

flowEMMi: an automated model-based clustering tool for microbial cytometric data

Background

Flow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM.

Results

We bridge this gap with flowEMMi, a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. flowEMMi outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further.

Conclusions

flowEMMi is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.

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.

How the bacterial community of a tannery effluent responds to bioaugmentation with the consortium SFC 500-1. Impact of environmental variables

Bioaugmentation with the consortium SFC 500-1 is a promising alternative to remediate wastewaters, such as tannery effluents. With the aim of assessing the changes produced in response to bioaugmentation, bacterial 16S rDNA genes were sequenced with Illumina MiSeq Platform. Additionally, bacterial and fungal groups were analyzed through standard culture dependent methods. The impact of diverse physico-chemical and microbiological parameters on the prokaryotic diversity was also evaluated throughout. Bacteroidetes, Firmicutes and Proteobacteria, represented together up to 91% of the total number of sequences obtained from the tannery effluent. Diversity decreased immediately after inoculation, due to an increase in the representation of the taxa to which the added consortium belongs. However, bioaugmentation produced no greater variations since only a 10% of unique operational taxonomic units were found in the inoculated treatment. An increase in the abundance of Myroides and a reduction in the representation of Proteiniclasticum and Halomonas were major observed variations. On the other hand, pH and dissolved oxygen constituted main environmental factors affecting the structure of the prokaryotic communities. In all treatments yeasts increased over time, to the detriment of filamentous fungi. Together, data from this report may contribute to the development of improved bioremediation strategies of industrial wastewaters.

Key sub-community dynamics of medium-chain carboxylate production

BACKGROUND

The carboxylate platform is a promising technology for substituting petrochemicals in the provision of specific platform chemicals and liquid fuels. It includes the chain elongation process that exploits reverse β-oxidation to elongate short-chain fatty acids and forms the more valuable medium-chain variants. The pH value influences this process through multiple mechanisms and is central to effective product formation. Its influence on the microbiome dynamics was investigated during anaerobic fermentation of maize silage by combining flow cytometric short interval monitoring, cell sorting and 16S rRNA gene amplicon sequencing.

RESULTS

Caproate and caprylate titres of up to 6.12 g L-1 and 1.83 g L-1, respectively, were achieved in a continuous stirred-tank reactor operated for 241 days. Caproate production was optimal at pH 5.5 and connected to lactate-based chain elongation, while caprylate production was optimal at pH 6.25 and linked to ethanol utilisation. Flow cytometry recorded 31 sub-communities with cell abundances varying over 89 time points. It revealed a highly dynamic community, whereas the sequencing analysis displayed a mostly unchanged core community. Eight key sub-communities were linked to caproate or caprylate production (rS > | ± 0.7|). Amongst other insights, sorting and subsequently sequencing these sub-communities revealed the central role of Bifidobacterium and Olsenella, two genera of lactic acid bacteria that drove chain elongation by providing additional lactate, serving as electron donor.

CONCLUSIONS

High-titre medium-chain fatty acid production in a well-established reactor design is possible using complex substrate without the addition of external electron donors. This will greatly ease scaling and profitable implementation of the process. The pH value influenced the substrate utilisation and product spectrum by shaping the microbial community. Flow cytometric single cell analysis enabled fast, short interval analysis of this community and was coupled with 16S rRNA gene amplicon sequencing to reveal the major role of lactate-producing bacteria.

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.

Impact of planktonic low nucleic acid-content bacteria to bacterial community structure and associated ecological functions in a shallow lake

In this study, 0.45 μm filtration, flow cytometric fingerprint, 16S rRNA amplicon sequencing, and bioinformation tools were adopted to analyze the structural diversity and potential functions of planktonic low nucleic acid (LNA)- content bacteria in a shallow lake. Three bacterial groups, namely, "LNA," "high nucleic acid (HNA)-Small," and "HNA-Large," were classified through flow cytometric fingerprint, among which the "HNA-Small" group was possibly in the proliferation stage of the "LNA" group. Total nitrogen and phosphate were the key factors that influence the growth of LNA bacteria. Results of 16S rRNA amplicon sequencing showed that LNA bacteria were phylogenetically less diverse than HNA bacteria, and Actinobacteria and Proteobacteria (especially Gamma-Proteobacteria) were the dominant phyla in LNA bacterial operational taxonomic units (OTUs). Accordingly, hgcI_clade and Pseudomonas were the most abundant bacterial genera in LNA bacterial OTUs. The fraction of low-abundance LNA bacteria was sensitive to several environmental factors, indicating that environmental factors only determined the fraction distribution of low-abundance bacteria. The prediction of metabolic and ecological functions showed that LNA and HNA bacteria had distinct metabolic and ecological functions, which were mainly attributed to the dominant and exclusive bacterial groups.

Co-digestion of organic and mineral wastes for enhanced biogas production: Reactor performance and evolution of microbial community and function

Mineral wastes (MWs) from municipal solid waste incineration plants and construction demolition sites are rich in minerals, heavy metals and have acid neutralising capacity. This renders such MWs a promising source of bulk and trace elements to enhance and stabilize biogas production in anaerobic processes. However, finding a MW with typical heavy metal concentrations, which promotes anaerobic digestion (AD) without adverse effects on the microbial community of the reactor is of major importance. To investigate the impact of several MW additives (1. incineration bottom ash; 2. fly ash; 3. boiler ash; 4. cement-based waste) as AD co-substrates, six 5 L single stage mesophilic, continuously stirred tank reactors (CSTR) were setup. Two different feeding regimes were employed including: (a) a liquid-recycled feeding method (LRFM); (b) a draw-and-fill feeding method (DFFM). Under the LRFM regime, one gram MW per gram organic waste enhanced process stability (pH), increased methane production (25-45% increase), and yielded (450-520 mL CH₄/g VS); DFFM enhanced digestibility to a lesser degree. Illumina HiSeq 16S rRNA community sequencing of reactors showed that the microbial community compositions were unaffected by the presence of MW additives in comparison to unamended controls, but MW amendment accelerated bacterial growth (determined by qPCR). In contrast, different feeding regimes altered the microbial communities; Methanoculleus (hydrogenotrophic) and Methanosaeta (acetoclastic) were the most abundant methanogenic genera in the LRFM reactors, and the more metabolically versatile Methanosarcina genus dominated under DFFM.

Effect of large magnetotactic bacteria with polyphosphate inclusions on the phosphate profile of the suboxic zone in the Black Sea

The Black Sea is the world's largest anoxic basin and a model system for studying processes across redox gradients. In between the oxic surface and the deeper sulfidic waters there is an unusually broad layer of 10-40 m, where neither oxygen nor sulfide are detectable. In this suboxic zone, dissolved phosphate profiles display a pronounced minimum at the upper and a maximum at the lower boundary, with a peak of particulate phosphorus in between, which was suggested to be caused by the sorption of phosphate on sinking particles of metal oxides. Here we show that bacterial polyphosphate inclusions within large magnetotactic bacteria related to the genus Magnetococcus contribute substantially to the observed phosphorus peak, as they contain 26-34% phosphorus compared to only 1-5% in metal-rich particles. Furthermore, we found increased gene expression for polyphosphate kinases by several groups of bacteria including Magnetococcaceae at the phosphate maximum, indicating active bacterial polyphosphate degradation. We propose that large magnetotactic bacteria shuttle up and down within the suboxic zone, scavenging phosphate at the upper and releasing it at the lower boundary. In contrast to a passive transport via metal oxides, this bacterial transport can quantitatively explain the observed phosphate profiles.

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.

Composition of heterotrophic specialized sub-guilds defined by a positive RNA and polyhydroxyalkanoate correlation in activated sludge

Microbial heterotrophic guilds in activated sludge wastewater treatment systems have complex population structures and functions. A previously proposed heterotrophic-specialist model states that heterotrophs consist of sub-guilds specialized in consuming specific classes of compounds, either readily degradable substrate (RDS) or slowly degradable substrate (SDS) according to current mathematical modeling practices for wastewater treatment processes. It follows from metabolic considerations that the levels of RNA and polyhydroxyalkanoate (PHA) are correlated for strains of the same species growing in different environments; a conjecture previously tested. The proposed classification of heterotrophs into RDS or SDS consumers predicts that the same correlation would also be found across heterotrophic species in conventional activated sludge systems; this prediction was tested in the current study. The positive correlation between the RNA and PHA levels was observed in 9 conventional activated sludge plants in two independent sampling times and it was also found stable over a 6-month regular sampling period at one of these plants. Together, these results imply that the levels of RNA and PHA can be used to define heterotrophic-specialist sub-guilds. In order to gain insight in the species composition of the defined sub-guilds, flow cytometry cell sorting was used to further analyze one of the activated sludge samples. Four sorted sub-samples were obtained (high-RNA/high-PHA, low-RNA/high-PHA, high-RNA/low-PHA, and low-RNA/low-PHA), and the phylogenetic composition of each was determined using 16S rRNA gene amplicon pyrosequencing. Heterotrophic genera were identified across 12 phyla, and their representation in each sorted sub-sample showed that the high-RNA/high-PHA and low-RNA/low-PHA groups were most dissimilar. The enriched genera in these sorted sub-samples are suggested to represent the composition of heterotrophic-specialized sub-guilds defined by the kinetics of substrate consumption.

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.

Investigation of the population dynamics within a Pseudomonas aeruginosa biofilm using a flow based biofilm model system and flow cytometric evaluation of cellular physiology

In this study a flow based biofilm model system was used to simulate the formation of Pseudomonas aeruginosa biofilms on a stainless steel surface. To investigate the complexity of biofilm-associated P. aeruginosa populations a combination of microscopic observations and flow cytometric analysis (FCM) was adopted. Biofilm-associated P. aeruginosa cells were evaluated (1) under optimal vs reduced nutrient-availability at the initial adhesion stage, and (2) irrespective of nutrient-availability within a mature biofilm. Microscopic estimation of the extent of attachment revealed more effective colonization upon optimal vs starvation conditions. FCM allowed an in situ evaluation of P. aeruginosa vitality, using cellular redox potential measurements to discriminate active, mid-active and non-active sub-populations. Samples from recently attached cells and mature biofilms showed significant differences in the percentages of bacterial cells from the defined sub-populations. The approach demonstrated that distribution of individual P. aeruginosa sub-populations was influenced by the stage of the biofilm life-cycle and nutrient availability.

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.