High-resolution microbiota flow cytometry reveals dynamic colitis-associated changes in fecal bacterial composition

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.

A flow cytometry method for safe detection of bacterial viability

Flow cytometry is a relevant tool to meet the requirements of academic and industrial research projects aimed at estimating the features of a bacterial population (e.g., quantity, viability, activity). One of the remaining challenges is now the safe assessment of bacterial viability while minimizing the risks inherent to existing protocols. In our core facility at the Paris-Saclay University, we have addressed this issue with two objectives: measuring bacterial viability in biological samples and preventing bacterial contamination and chemical exposure of the staff and cytometers used on the platform. Here, we report the development of a protocol achieving these two objectives, including a viability labeling step before bacteria fixation, which removes the risk of biological exposure, and the decrease of the use of reagents such as propidium iodide (PI), which are dangerous for health (CMR: carcinogenic, mutagenic, and reprotoxic). For this purpose, we looked for a non-CMR viability dye that can irreversibly label dead bacteria before fixation procedures and maintain intense fluorescence after further staining. We decided to test on the bacteria, eFluor Fixable Viability dyes, which are usually used on eukaryotic cells. Since the bacteria had size and granularity characteristics very similar to those associated with flow cytometry background signals, a step of bacterial DNA labeling with SYTO or DRAQ5 was necessarily added to differentiate them from the background. Three marker combinations (viability-DNA) were tested on LSR Fortessa and validated on pure bacterial populations (Gram+ , Gram- ) and polybacterial cultures. Any of the three methods can be used and adapted to the needs of each project and allow users to adapt the combination according to the configuration of their cytometer. Having been tested on six bacterial populations, validated on two cytometers, and repeated at least two times in each evaluated condition, we consider this method reliable in the context of these conditions. The reliability of the results obtained in flow cytometry was successfully validated by applying this protocol to confocal microscopy, permeabilization, and also to follow cultures over time. This flow cytometry protocol for measuring bacterial viability under safer conditions also opens the prospect of its use for further bacterial characterization.

Pathobionts in Inflammatory Bowel Disease: Origins, Underlying Mechanisms, and Implications for Clinical Care

The gut microbiota plays a significant role in the pathogenesis of both forms of inflammatory bowel disease (IBD), namely, Crohn's disease (CD) and ulcerative colitis (UC). Although evidence suggests dysbiosis and loss of beneficial microbial species can exacerbate IBD, many new studies have identified microbes with pathogenic qualities, termed "pathobionts," within the intestines of patients with IBD. The concept of pathobionts initiating or driving the chronicity of IBD has largely focused on the putative aggravating role that adherent invasive Escherichia coli may play in CD. However, recent studies have identified additional bacterial and fungal pathobionts in patients with CD and UC. This review will highlight the characteristics of these pathobionts and their implications for IBD treatment. Beyond exploring the origins of pathobionts, we discuss those associated with specific clinical features and the potential mechanisms involved, such as creeping fat (Clostridium innocuum) and impaired wound healing (Debaryomyces hansenii) in patients with CD as well as the increased fecal proteolytic activity (Bacteroides vulgatus) seen as a biomarker for UC severity. Finally, we examine the potential impact of pathobionts on current IBD therapies, and several new approaches to target pathobionts currently in the early stages of development. Despite recognizing that pathobionts likely contribute to the pathogenesis of IBD, more work is needed to define their modes of action. Determining whether causal relationships exist between pathobionts and specific disease characteristics could pave the way for improved care for patients, particularly for those not responding to current IBD therapies.

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.

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.

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.

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.

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.

•

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

•

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

•

Different profiles could be obtained depending on oral hygiene status.

•

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

•

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

Flow cytometry can reliably capture gut microbial composition in healthy adults as well as dysbiosis dynamics in patients with aggressive B-cell non-Hodgkin lymphoma

Modulation of commensal gut microbiota is increasingly recognized as a promising strategy to reduce mortality in patients with malignant diseases, but monitoring for dysbiosis is generally not routine clinical practice due to equipment, expertise and funding required for sequencing analysis. A low-threshold alternative is microbial diversity profiling by single-cell flow cytometry (FCM), which we compared to 16S rRNA sequencing in human fecal samples and employed to characterize longitudinal changes in the microbiome composition of patients with aggressive B-cell non-Hodgkin lymphoma undergoing chemoimmunotherapy. Diversity measures obtained from both methods were correlated and captured identical trends in microbial community structures, finding no difference in patients' pretreatment alpha or beta diversity compared to healthy controls and a significant and progressive loss of alpha diversity during chemoimmunotherapy. Our results highlight the potential of FCM-based microbiome profiling as a reliable and accessible diagnostic tool that can provide novel insights into cancer therapy-associated dysbiosis dynamics.

Species-targeted sorting and cultivation of commensal bacteria from the gut microbiome using flow cytometry under anaerobic conditions

BACKGROUND

There is a growing interest in using gut commensal bacteria as "next generation" probiotics. However, this approach is still hampered by the fact that there are few or no strains available for specific species that are difficult to cultivate. Our objective was to adapt flow cytometry and cell sorting to be able to detect, separate, isolate, and cultivate new strains of commensal species from fecal material. We focused on the extremely oxygen sensitive (EOS) species Faecalibacterium prausnitzii and the under-represented, health-associated keystone species Christensenella minuta as proof-of-concept.

RESULTS

A BD Influx® cell sorter was equipped with a glovebox that covered the sorting area. This box was flushed with nitrogen to deplete oxygen in the enclosure. Anaerobic conditions were maintained during the whole process, resulting in only minor viability loss during sorting and culture of unstained F. prausnitzii strains ATCC 27766, ATCC 27768, and DSM 17677. We then generated polyclonal antibodies against target species by immunizing rabbits with heat-inactivated bacteria. Two polyclonal antibodies were directed against F. prausnitzii type strains that belong to different phylogroups, whereas one was directed against C. minuta strain DSM 22607. The specificity of the antibodies was demonstrated by sorting and sequencing the stained bacterial fractions from fecal material. In addition, staining solutions including LIVE/DEAD™ BacLight™ Bacterial Viability staining and polyclonal antibodies did not severely impact bacterial viability while allowing discrimination between groups of strains. Finally, we combined these staining strategies as well as additional criteria based on bacterial shape for C. minuta and were able to detect, isolate, and cultivate new F. prausnitzii and C. minuta strains from healthy volunteer's fecal samples.

CONCLUSIONS

Targeted cell-sorting under anaerobic conditions is a promising tool for the study of fecal microbiota. It gives the opportunity to quickly analyze microbial populations, and can be used to sort EOS and/or under-represented strains of interest using specific antibodies, thus opening new avenues for culture experiments. Video abstract.

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.

Intestinal Microbiome in Hematopoietic Stem Cell Transplantation For Autoimmune Diseases: Considerations and Perspectives on Behalf of Autoimmune Diseases Working Party (ADWP) of the EBMT

Over the past decades, hematopoietic stem cell transplantation (HSCT) has been evolving as specific treatment for patients with severe and refractory autoimmune diseases (ADs), where mechanistic studies have provided evidence for a profound immune renewal facilitating the observed beneficial responses. The intestinal microbiome plays an important role in host physiology including shaping the immune repertoire. The relationships between intestinal microbiota composition and outcomes after HSCT for hematologic diseases have been identified, particularly for predicting the mortality from infectious and non-infectious causes. Furthermore, therapeutic manipulations of the gut microbiota, such as fecal microbiota transplant (FMT), have emerged as promising therapeutic approaches for restoring the functional and anatomical integrity of the intestinal microbiota post-transplantation. Although changes in the intestinal microbiome have been linked to various ADs, studies investigating the effect of intestinal dysbiosis on HSCT outcomes for ADs are scarce and require further attention. Herein, we describe some of the landmark microbiome studies in HSCT recipients and patients with chronic ADs, and discuss the challenges and opportunities of microbiome research for diagnostic and therapeutic purposes in the context of HSCT for ADs.

Predicting the Presence and Abundance of Bacterial Taxa in Environmental Communities through Flow Cytometric Fingerprinting

Microbiome management research and applications rely on temporally resolved measurements of community composition. Current technologies to assess community composition make use of either cultivation or sequencing of genomic material, which can become time-consuming and/or laborious in case high-throughput measurements are required. Here, using data from a shrimp hatchery as an economically relevant case study, we combined 16S rRNA gene amplicon sequencing and flow cytometry data to develop a computational workflow that allows the prediction of taxon abundances based on flow cytometry measurements. The first stage of our pipeline consists of a classifier to predict the presence or absence of the taxon of interest, with yielded an average accuracy of 88.13% ± 4.78% across the top 50 operational taxonomic units (OTUs) of our data set. In the second stage, this classifier was combined with a regression model to predict the relative abundances of the taxon of interest, which yielded an average R² of 0.35 ± 0.24 across the top 50 OTUs of our data set. Application of the models to flow cytometry time series data showed that the generated models can predict the temporal dynamics of a large fraction of the investigated taxa. Using cell sorting, we validated that the model correctly associates taxa to regions in the cytometric fingerprint, where they are detected using 16S rRNA gene amplicon sequencing. Finally, we applied the approach of our pipeline to two other data sets of microbial ecosystems. This pipeline represents an addition to the expanding toolbox for flow cytometry-based monitoring of bacterial communities and complements the current plating- and marker gene-based methods.

IMPORTANCE Monitoring of microbial community composition is crucial for both microbiome management research and applications. Existing technologies, such as plating and amplicon sequencing, can become laborious and expensive when high-throughput measurements are required. In recent years, flow cytometry-based measurements of community diversity have been shown to correlate well with those derived from 16S rRNA gene amplicon sequencing in several aquatic ecosystems, suggesting that there is a link between the taxonomic community composition and phenotypic properties as derived through flow cytometry. Here, we further integrated 16S rRNA gene amplicon sequencing and flow cytometry survey data in order to construct models that enable the prediction of both the presence and the abundances of individual bacterial taxa in mixed communities using flow cytometric fingerprinting. The developed pipeline holds great potential to be integrated into routine monitoring schemes and early warning systems for biotechnological applications.

Metabolomics activity screening of T cell-induced colitis reveals anti-inflammatory metabolites

[Figure: see text].

Gut Microbiota-Modulated Metabolomic Profiling Shapes the Etiology and Pathogenesis of Autoimmune Diseases

Autoimmunity is a complex and multifaceted process that contributes to widespread functional decline that affects multiple organs and tissues. The pandemic of autoimmune diseases, which are a global health concern, augments in both the prevalence and incidence of autoimmune diseases, including type 1 diabetes, multiple sclerosis, and rheumatoid arthritis. The development of autoimmune diseases is phenotypically associated with gut microbiota-modulated features at the molecular and cellular levels. The etiology and pathogenesis of autoimmune diseases comprise the alterations of immune systems with the innate and adaptive immune cell infiltration into specific organs and the augmented production of proinflammatory cytokines stimulated by commensal microbiota. However, the relative importance and mechanistic interrelationships between the gut microbial community and the immune system during progression of autoimmune diseases are still not well understood. In this review, we describe studies on the profiling of gut microbial signatures for the modulation of immunological homeostasis in multiple inflammatory diseases, elucidate their critical roles in the etiology and pathogenesis of autoimmune diseases, and discuss the implications of these findings for these disorders. Targeting intestinal microbiome and its metabolomic associations with the phenotype of autoimmunity will enable the progress of developing new therapeutic strategies to counteract microorganism-related immune dysfunction in these autoimmune diseases.

Culturing Human Gut Microbiomes in the Laboratory

The human gut microbiota is a complex community of prokaryotic and eukaryotic microbes and viral particles that is increasingly associated with many aspects of host physiology and health. However, the classical microbiology approach of axenic culture cannot provide a complete picture of the complex interactions between microbes and their hosts in vivo. As such, recently there has been much interest in the culture of gut microbial ecosystems in the laboratory as a strategy to better understand their compositions and functions. In this review, we discuss the model platforms and methods available in the contemporary microbiology laboratory to study human gut microbiomes, as well as current knowledge surrounding the isolation of human gut microbes for the potential construction of defined communities for use in model systems. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Long-term evolution and short-term adaptation of microbiota strains and sub-strains in mice

Isobiotic mice, with an identical stable microbiota composition, potentially allow models of host-microbial mutualism to be studied over time and between different laboratories. To understand microbiota evolution in these models, we carried out a 6-year experiment in mice colonized with 12 representative taxa. Increased non-synonymous to synonymous mutation rates indicate positive selection in multiple taxa, particularly for genes annotated for nutrient acquisition or replication. Microbial sub-strains that evolved within a single taxon can stably coexist, consistent with niche partitioning of ecotypes in the complex intestinal environment. Dietary shifts trigger rapid transcriptional adaptation to macronutrient and micronutrient changes in individual taxa and alterations in taxa biomass. The proportions of different sub-strains are also rapidly altered after dietary shift. This indicates that microbial taxa within a mouse colony adapt to changes in the intestinal environment by long-term genomic positive selection and short-term effects of transcriptional reprogramming and adjustments in sub-strain proportions.

Assessing Biodegradability of Chemical Compounds from Microbial Community Growth Using Flow Cytometry

Compound biodegradability tests with natural microbial communities form an important keystone in the ecological assessment of chemicals. However, biodegradability tests are frequently limited by a singular focus either on the chemical and potential transformation products or on the individual microbial species degrading the compound. Here, we investigated a methodology to simultaneously analyze community compositional changes and biomass growth on dosed test compound from flow cytometry (FCM) data coupled to machine-learned cell type recognition. We quantified the growth of freshwater microbial communities on a range of carbon dosages of three readily biodegradable reference compounds, phenol, 1-octanol, and benzoate, in comparison to three fragrances, methyl jasmonate, myrcene, and musk xylene (as a nonbiodegradable control). Compound mass balances with between 0.1 to 10 mg C · liter-1 phenol or 1-octanol, inferred from cell numbers, parent compound analysis, and CO2 evolution, as well as use of 14C-labeled compounds, showed between 6 and 25% mg C · mg C-1 substrate incorporation into biomass within 2 to 4 days and 25 to 45% released as CO2 In contrast, similar dosage of methyl jasmonate and myrcene supported slower (4 to 10 days) and less (2.6 to 6.6% mg C · mg C-1 with 4.9 to 22% CO2) community growth. Community compositions inferred from machine-learned cell type recognition and 16S rRNA amplicon sequencing showed substrate- and concentration-dependent changes, with visible enrichment of microbial subgroups already at 0.1 mg C · liter-1 phenol and 1-octanol. In general, community compositions were similar at the start and after the stationary phase of the microbial growth, except at the highest used substrate concentrations of 100 to 1,000 mg C · liter-1 Flow cytometry cell counting coupled to deconvolution of communities into subgroups is thus suitable to infer biodegradability of organic chemicals, permitting biomass balances and near-real-time assessment of relevant subgroup changes.IMPORTANCE The manifold effects of potentially toxic compounds on microbial communities are often difficult to discern. Some compounds may be transformed or completely degraded by few or multiple strains in the community, whereas others may present inhibitory effects. In this study, we benchmark a new method based on machine-learned microbial cell recognition to rapidly follow dynamic changes in aquatic communities. We further determine productive biodegradation upon dosing of a number of well-known readily biodegradable tester compounds at a variety of concentrations. Microbial community growth was quantified using flow cytometry, and the multiple cell parameters measured were used in parallel to deconvolute the community on the basis of similarity to previously standardized cell types. Biodegradation was further confirmed by chemical analysis, showing how distinct changes in specific populations correlate to degradation. The method holds great promise for near-real-time community composition changes and deduction of compound biodegradation in natural microbial communities.

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.

Accurate identification and quantification of commensal microbiota bound by host immunoglobulins

BACKGROUND

Identifying which taxa are targeted by immunoglobulins can uncover important host-microbe interactions. Immunoglobulin binding of commensal taxa can be assayed by sorting bound bacteria from samples and using amplicon sequencing to determine their taxonomy, a technique most widely applied to study Immunoglobulin A (IgA-Seq). Previous experiments have scored taxon binding in IgA-Seq datasets by comparing abundances in the IgA bound and unbound sorted fractions. However, as these are relative abundances, such scores are influenced by the levels of the other taxa present and represent an abstract combination of these effects. Diversity in the practical approaches of prior studies also warrants benchmarking of the individual stages involved. Here, we provide a detailed description of the design strategy for an optimised IgA-Seq protocol. Combined with a novel scoring method for IgA-Seq datasets that accounts for the aforementioned effects, this platform enables accurate identification and quantification of commensal gut microbiota targeted by host immunoglobulins.

RESULTS

Using germ-free and Rag1^-/- mice as negative controls, and a strain-specific IgA antibody as a positive control, we determine optimal reagents and fluorescence-activated cell sorting (FACS) parameters for IgA-Seq. Using simulated IgA-Seq data, we show that existing IgA-Seq scoring methods are influenced by pre-sort relative abundances. This has consequences for the interpretation of case-control studies where there are inherent differences in microbiota composition between groups. We show that these effects can be addressed using a novel scoring approach based on posterior probabilities. Finally, we demonstrate the utility of both the IgA-Seq protocol and probability-based scores by examining both novel and published data from in vivo disease models.

CONCLUSIONS

We provide a detailed IgA-Seq protocol to accurately isolate IgA-bound taxa from intestinal samples. Using simulated and experimental data, we demonstrate novel probability-based scores that adjust for the compositional nature of relative abundance data to accurately quantify taxon-level IgA binding. All scoring approaches are made available in the IgAScores R package. These methods should improve the generation and interpretation of IgA-Seq datasets and could be applied to study other immunoglobulins and sample types. Video abstract.

Cytometric fingerprints of gut microbiota predict Crohn's disease state

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

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.

Trash Your Agar Plates! Blood Stream Bacteria Are Now Quantified by in vivo Flow Cytometry

© 2020 International Society for Advancement of Cytometry.

Specific microbiota enhances intestinal IgA levels by inducing TGF-β in T follicular helper cells of Peyer's patches in mice

In humans and mice, mucosal immune responses are dominated by IgA antibodies and the cytokine TGF-β, suppressing unwanted immune reactions but also targeting Ig class switching to IgA. It had been suggested that eosinophils promote the generation and maintenance of mucosal IgA-expressing plasma cells. Here, we demonstrate that not eosinophils, but specific bacteria determine mucosal IgA production. Co-housing of eosinophil-deficient mice with mice having high intestinal IgA levels, as well as the intentional microbiota transfer induces TGF-β expression in intestinal T follicular helper cells, thereby promoting IgA class switching in Peyer's patches, enhancing IgA⁺ plasma cell numbers in the small intestinal lamina propria and levels of mucosal IgA. We show that bacteria highly enriched for the genus Anaeroplasma are sufficient to induce these changes and enhance IgA levels when adoptively transferred. Thus, specific members of the intestinal microbiota and not the microbiota as such regulate gut homeostasis, by promoting the expression of immune-regulatory TGF-β and of mucosal IgA.

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.

Raman-deuterium isotope probing to study metabolic activities of single bacterial cells in human intestinal microbiota

Human intestinal microbiota is important to host health and is associated with various diseases. It is a challenge to identify the functions and metabolic activity of microorganisms at the single-cell level in gut microbial community. In this study, we applied Raman microspectroscopy and deuterium isotope probing (Raman-DIP) to quantitatively measure the metabolic activities of intestinal bacteria from two individuals and analysed lipids and phenylalanine metabolic pathways of functional microorganisms in situ. After anaerobically incubating the human faeces with heavy water (D₂ O), D₂ O with specific substrates (glucose, tyrosine, tryptophan and oleic acid) and deuterated glucose, the C-D band in single-cell Raman spectra appeared in some bacteria in faeces, due to the Raman shift from the C-H band. Such Raman shift was used to indicate the general metabolic activity and the activities in response to the specific substrates. In the two individuals' intestinal microbiota, the structures of the microbial communities were different and the general metabolic activities were 76 ± 1.0% and 30 ± 2.0%. We found that glucose, but not tyrosine, tryptophan and oleic acid, significantly stimulated metabolic activity of the intestinal bacteria. We also demonstrated that the bacteria within microbiota preferably used glucose to synthesize fatty acids in faeces environment, whilst they used glucose to synthesize phenylalanine in laboratory growth environment (e.g. LB medium). Our work provides a useful approach for investigating the metabolic activity in situ and revealing different pathways of human intestinal microbiota at the single-cell level.

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.

Microbiota-gut brain axis involvement in neuropsychiatric disorders

Introduction: The microbiota-gut brain (MGB) axis is the bidirectional communication between the intestinal microbiota and the brain. An increasing body of preclinical and clinical evidence has revealed that the gut microbial ecosystem can affect neuropsychiatric health. However, there is still a need of further studies to elucidate the complex gene-environment interactions and the role of the MGB axis in neuropsychiatric diseases, with the aim of identifying biomarkers and new therapeutic targets, to allow early diagnosis and improving treatments. Areas covered: To review the role of MGB axis in neuropsychiatric disorders, prediction and prevention of disease through exploitation, integration, and combination of data from existing gut microbiome/microbiota projects and appropriate other International '-Omics' studies. The authors also evaluated the new technological advances to investigate and modulate, through nutritional and other interventions, the gut microbiota. Expert opinion: The clinical studies have documented an association between alterations in gut microbiota composition and/or function, whereas the preclinical studies support a role for the gut microbiota in impacting behaviors which are of relevance to psychiatry and other central nervous system (CNS) disorders. Targeting MGB axis could be an additional approach for treating CNS disorders and all conditions in which alterations of the gut microbiota are involved.

c-Maf-dependent Treg cell control of intestinal TH17 cells and IgA establishes host-microbiota homeostasis

Foxp3+ regulatory T cells (Treg cells) are crucial for the maintenance of immune homeostasis both in lymphoid tissues and in non-lymphoid tissues. Here we demonstrate that the ability of intestinal Treg cells to constrain microbiota-dependent interleukin (IL)-17-producing helper T cell (TH17 cell) and immunoglobulin A responses critically required expression of the transcription factor c-Maf. The terminal differentiation and function of several intestinal Treg cell populations, including RORγt+ Treg cells and follicular regulatory T cells, were c-Maf dependent. c-Maf controlled Treg cell-derived IL-10 production and prevented excessive signaling via the kinases PI(3)K (phosphatidylinositol-3-OH kinase) and Akt and the metabolic checkpoint kinase complex mTORC1 (mammalian target of rapamycin) and expression of inflammatory cytokines in intestinal Treg cells. c-Maf deficiency in Treg cells led to profound dysbiosis of the intestinal microbiota, which when transferred to germ-free mice was sufficient to induce exacerbated intestinal TH17 responses, even in a c-Maf-competent environment. Thus, c-Maf acts to preserve the identity and function of intestinal Treg cells, which is essential for the establishment of host-microbe symbiosis.

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.

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.

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.

The intestinal microbiota determines the colitis-inducing potential of T-bet-deficient Th cells in mice

Conflicting evidence has been provided as to whether induction of intestinal inflammation by adoptive transfer of naïve T cells into Rag^-/- mice requires expression of the transcription factor T-bet by the T cells. Here, we formally show that the intestinal microbiota composition of the Rag^-/- recipient determines whether or not T-bet-deficient Th cells can induce colitis and we have resolved the differences of the two microbiomes, permissive or non-permissive to T-bet-independent colitis. Our data highlight the dominance of the microbiota over particular T cell differentiation programs in the pathogenesis of chronic intestinal inflammation.

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.

The microbiota revolution: Excitement and caution

Scientific progress is characterized by important technological advances. Next-generation DNA sequencing has, in the past few years, led to a major scientific revolution: the microbiome revolution. It has become possible to generate a fingerprint of the whole microbiota of any given environment. As it becomes clear that the microbiota affects several aspects of our lives, each new scientific finding should ideally be analyzed in light of these communities. For instance, animal experimentation should consider animal sources and husbandry; human experimentation should include analysis of microenvironmental cues that might affect the microbiota, including diet, antibiotic, and drug use, genetics. When analyzing the activity of a drug, we should remember that, according to the microbiota of the host, different drug activities might be observed, either due to modification or degradation by the microbiota, or because the microbiota changes the immune system of the host in a way that makes that drug more or less effective. This minireview will not be a comprehensive review on the interaction between the host and microbiota, but it will aim at creating awareness on why we should not forget the contribution of the microbiota in any single aspect of biology.