Flow Cytometric Single-Cell Identification of Populations in Synthetic Bacterial Communities

Enhancing Bacterial Phenotype Classification Through the Integration of Autogating and Automated Machine Learning in Flow Cytometric Analysis

Although flow cytometry produces reliable results, the data processing from gating to fingerprinting is prone to subjective bias. Here, we integrated autogating with Automated Machine Learning in flow cytometry to enhance the classification of bacterial phenotypes. We analyzed six bacterial strains prevalent in the soil and groundwater- Bacillus subtilis , Burkholderia thailandensis , Corynebacterium glutamicum , Escherichia coli , Pseudomonas putida , and Pseudomonas stutzeri . Using the H2O-AutoML framework, we applied gradient-boosting machine (GBM) models to classify bacteria across different metabolic phases. Our results demonstrated an overall classification accuracy of 82.34% for GBM. Notably, accuracy varied across metabolic phases, with the highest observed during the late log (88.06%), lag (88.43%), and early log phases (89.37%), whereas the stationary phase showed a slightly lower accuracy of 80.73%. P. stutzeri exhibited consistently high sensitivity and specificity across all the phases, which indicated that it was the most distinctly identifiable strain. In contrast, E. coli showed low sensitivity, particularly in the stationary phase, which indicated challenges in its classification. Overall, this study with incorporating autogating and the AutoML framework, substantially reduces subjective biases and enhances the reproducibility and accuracy of microbial classification. Our methodology offers a robust framework for microbial classification in flow cytometric analysis, paving the way for more precise and comprehensive analyses of microbial ecology.

Quantifying synthetic bacterial community composition with flow cytometry: efficacy in mock communities and challenges in co-cultures

Determination of bacterial community composition in synthetic communities is critical for understanding microbial systems. The community composition is typically determined through bacterial plating or through PCR-based methods, which can be labor-intensive, expensive, or prone to bias. Simultaneously, flow cytometry has been suggested as a cheap and fast alternative. However, since the technique captures the phenotypic state of bacterial cells, accurate determination of community composition could be affected when bacteria are co-cultured. We investigated the performance of flow cytometry for quantifying oral synthetic communities and compared it to the performance of strain specific qPCR and 16S rRNA gene amplicon sequencing. Therefore, axenic cultures, mock communities and co-cultures of oral bacteria were prepared. Random forest classifiers trained on flow cytometry data of axenic cultures were used to determine the composition of the synthetic communities, as well as strain specific qPCR and 16S rRNA gene amplicon sequencing. Flow cytometry was shown to have a lower average root mean squared error and outperformed the PCR-based methods in even mock communities (flow cytometry: 0.11 ± 0.04; qPCR: 0.26 ± 0.09; amplicon sequencing: 0.15 ± 0.01). When bacteria were co-cultured, neither flow cytometry, strain-specific qPCR, nor 16S rRNA gene amplicon sequencing resulted in similar community composition. Performance of flow cytometry was decreased compared with mock communities due to changing phenotypes. Finally, discrepancies between flow cytometry and strain-specific qPCR were found. These findings highlight the challenges ahead for quantifying community composition in co-cultures by flow cytometry.IMPORTANCEQuantification of bacterial composition in synthetic communities is crucial for understanding and steering microbial interactions. Traditional approaches like plating, strain-specific qPCR, and amplicon sequencing are often labor-intensive and expensive and limit high-throughput experiments. Recently, flow cytometry has been suggested as a swift and cheap alternative for quantifying communities and has been successfully demonstrated on simple bacterial mock communities. However, since flow cytometry measures the phenotypic state of cells, measurements can be affected by differing phenotypes. Especially, changing phenotypes resulting from co-culturing bacteria can have a profound effect on the applicability of the technique in this context. This research illustrates the feasibility and challenges of flow cytometry for the determination of community structure in synthetic mock communities and co-cultures.

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.

Technical versus biological variability in a synthetic human gut community

Synthetic communities grown in well-controlled conditions are an important tool to decipher the mechanisms driving community dynamics. However, replicate time series of synthetic human gut communities in chemostats are rare, and it is thus still an open question to what extent stochasticity impacts gut community dynamics. Here, we address this question with a synthetic human gut bacterial community using an automated fermentation system that allows for a larger number of biological replicates. We collected six biological replicates for a community initially consisting of five common gut bacterial species that fill different metabolic niches. After an initial 12 hours in batch mode, we switched to chemostat mode and observed the community to stabilize after 2-3 days. Community profiling with 16S rRNA resulted in high variability across replicate vessels and high technical variability, while the variability across replicates was significantly lower for flow cytometric data. Both techniques agree on the decrease in the abundance of Bacteroides thetaiotaomicron, accompanied by an initial increase in Blautia hydrogenotrophica. These changes occurred together with reproducible metabolic shifts, namely a fast depletion of glucose and trehalose concentration in batch followed by a decrease in formic acid and pyruvic acid concentrations within the first 12 hours after the switch to chemostat mode. In conclusion, the observed variability in the synthetic bacterial human gut community, as assessed with 16S rRNA gene sequencing, is largely due to technical variability. The low variability seen in HPLC and flow cytometry data suggests a highly deterministic system.

Investigation of the effect of sustainable magnetic treatment on the microbiological communities in drinking water

The drinking water scarcity is posing a threat to mankind, hence better water quality management methods are required. Magnetic water treatment, which has been reported to improve aesthetic water quality and reduce scaling problems, can be an important addition to the traditional disinfectant dependent treatment. Despite the extensive market application opportunities, the effect of magnetic fields on (microbial) drinking water communities and subsequently the biostability is still largely unexplored, although the first patent was registered already 1945. Here flow cytometry was applied to assess the effect of weak magnetic fields (≤10 G) with strong gradients (≈800 G/m) on drinking water microbial communities. Drinking water was collected from the tap and placed inside the magnetic field (treated) and 5 m away from the magnet to avoid any background interferences (control) using both a static set-up and a shaking set-up. Samples were collected during a seven-day period for flow cytometry examination. Additionally, the effects of magnetic fields on the growth of Pseudomonas aeruginosa in autoclaved tap water were examined. Based on the fluorescent intensity of the stained nucleic acid content, the microbial cells were grouped into low nucleic acid content (LNA) and high nucleic acid content (HNA). Our results show that the LNA was dominant under nutrient limited condition while the HNA dominates when nutrient is more available. Such behavior of LNA and HNA matches well with the long discussed r/K selection model where r-strategists adapted to eutrophic conditions and K-strategists adapted to oligotrophic conditions. The applied magnetic fields selectively promote the growth of LNA under nutrient rich environment, which indicates a beneficial effect on biostability enhancement. Inhibition on an HNA representative Pseudomonas aeruginosa has also been observed. Based on our laboratory observations, we conclude that magnetic field treatment can be a sustainable method for microbial community management with great potential.

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

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

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.

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.

Electrochemical and spectroelectrochemical characterization of bacteria and bacterial systems

Microbes, such as bacteria, can be described, at one level, as small, self-sustaining chemical factories. Based on the species, strain, and even the environment, bacteria can be useful, neutral or pathogenic to human life, so it is increasingly important that we be able to characterize them at the molecular level with chemical specificity and spatial and temporal resolution in order to understand their behavior. Bacterial metabolism involves a large number of internal and external electron transfer processes, so it is logical that electrochemical techniques have been employed to investigate these bacterial metabolites. In this mini-review, we focus on electrochemical and spectroelectrochemical methods that have been developed and used specifically to chemically characterize bacteria and their behavior. First, we discuss the latest mechanistic insights and current understanding of microbial electron transfer, including both direct and mediated electron transfer. Second, we summarize progress on approaches to spatiotemporal characterization of secreted factors, including both metabolites and signaling molecules, which can be used to discern how natural or external factors can alter metabolic states of bacterial cells and change either their individual or collective behavior. Finally, we address in situ methods of single-cell characterization, which can uncover how heterogeneity in cell behavior is reflected in the behavior and properties of collections of bacteria, e.g. bacterial communities. Recent advances in (spectro)electrochemical characterization of bacteria have yielded important new insights both at the ensemble and the single-entity levels, which are furthering our understanding of bacterial behavior. These insights, in turn, promise to benefit applications ranging from biosensors to the use of bacteria in bacteria-based bioenergy generation and storage.

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.

From Biogas and Hydrogen to Microbial Protein Through Co-Cultivation of Methane and Hydrogen Oxidizing Bacteria

Increasing efforts are directed towards the development of sustainable alternative protein sources among which microbial protein (MP) is one of the most promising. Especially when waste streams are used as substrates, the case for MP could become environmentally favorable. The risks of using organic waste streams for MP production-the presence of pathogens or toxicants-can be mitigated by their anaerobic digestion and subsequent aerobic assimilation of the (filter-sterilized) biogas. Even though methane and hydrogen oxidizing bacteria (MOB and HOB) have been intensively studied for MP production, the potential benefits of their co-cultivation remain elusive. Here, we isolated a diverse group of novel HOB (that were capable of autotrophic metabolism), and co-cultured them with a defined set of MOB, which could be grown on a mixture of biogas and H2/O2. The combination of MOB and HOB, apart from the CH4 and CO2 contained in biogas, can also enable the valorization of the CO2 that results from the oxidation of methane by the MOB. Different MOB and HOB combinations were grown in serum vials to identify the best-performing ones. We observed synergistic effects on growth for several combinations, and in all combinations a co-culture consisting out of both HOB and MOB could be maintained during five days of cultivation. Relative to the axenic growth, five out of the ten co-cultures exhibited 1.1-3.8 times higher protein concentration and two combinations presented 2.4-6.1 times higher essential amino acid content. The MP produced in this study generally contained lower amounts of the essential amino acids histidine, lysine and threonine, compared to tofu and fishmeal. The most promising combination in terms of protein concentration and essential amino acid profile was Methyloparacoccus murrelli LMG 27482 with Cupriavidus necator LMG 1201. Microbial protein from M. murrelli and C. necator requires 27-67% less quantity than chicken, whole egg and tofu, while it only requires 15% more quantity than the amino acid-dense soybean to cover the needs of an average adult. In conclusion, while limitations still exist, the co-cultivation of MOB and HOB creates an alternative route for MP production leveraging safe and sustainably-produced gaseous substrates.

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.

Measurement Techniques to Resolve and Control Population Dynamics of Mixed-Culture Processes

Microbial mixed cultures are gaining increasing attention as biotechnological production systems, since they offer a large but untapped potential for future bioprocesses. Effects of secondary metabolite induction and advantages of labor division for the degradation of complex substrates offer new possibilities for process intensification. However, mixed cultures are highly complex, and, consequently, many biotic and abiotic parameters are required to be identified, characterized, and ideally controlled to establish a stable bioprocess. In this review, we discuss the advantages and disadvantages of existing measurement techniques for identifying, characterizing, monitoring, and controlling mixed cultures and highlight promising examples. Moreover, existing challenges and emerging technologies are discussed, which lay the foundation for novel analytical workflows to monitor mixed-culture bioprocesses.

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

Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA.

Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics.

IMPORTANCE Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technology to characterize microbial community diversity and dynamics. The technology enables a fast measurement of optical properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian mixture models. We evaluated our workflow on data sets from both synthetic and natural ecosystems, illustrating its general applicability for the analysis of microbial flow cytometry data. PhenoGMM supports a rapid and quantitative analysis of microbial community structure using flow cytometry.

Computational Analysis of Microbial Flow Cytometry Data

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.

Direct quantification of ecological drift at the population level in synthetic bacterial communities

In community ecology, drift refers to random births and deaths in a population. In microbial ecology, drift is estimated indirectly via community snapshots but in this way, it is almost impossible to distinguish the effect of drift from the effect of other ecological processes. Controlled experiments where drift is quantified in isolation from other processes are still missing. Here we isolate and quantify drift in a series of controlled experiments on simplified and tractable bacterial communities. We detect drift arising randomly in the populations within the communities and resulting in a 1.4–2% increase in their growth rate variability on average. We further use our experimental findings to simulate complex microbial communities under various conditions of selection and dispersal. We find that the importance of drift increases under high selection and low dispersal, where it can lead to ~5% of species loss and to ~15% increase in β-diversity. The species extinct by drift are mainly rare, but they become increasingly less rare when selection increases, and dispersal decreases. Our results provide quantitative insights regarding the properties of drift in bacterial communities and suggest that it accounts for a consistent fraction of the observed stochasticity in natural surveys.

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.

Fast Pathogen Identification Using Single-Cell Matrix-Assisted Laser Desorption/Ionization-Aerosol Time-of-Flight Mass Spectrometry Data and Deep Learning Methods

In diagnostics of infectious diseases, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) can be applied for the identification of pathogenic microorganisms. However, to achieve a trustworthy identification from MALDI-TOF MS data, a significant amount of biomass should be considered. The bacterial load that potentially occurs in a sample is therefore routinely amplified by culturing, which is a time-consuming procedure. In this paper, we show that culturing can be avoided by conducting MALDI-TOF MS on individual bacterial cells. This results in a more rapid identification of species with an acceptable accuracy. We propose a deep learning architecture to analyze the data and compare its performance with traditional supervised machine learning algorithms. We illustrate our workflow on a large data set that contains bacterial species related to urinary tract infections. Overall we obtain accuracies up to 85% in discriminating five different species.

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.

Learning Single-Cell Distances from Cytometry Data

Recent years have seen an increased interest in employing data analysis techniques for the automated identification of cell populations in the field of cytometry. These techniques highly depend on the use of a distance metric, a function that quantifies the distances between single-cell measurements. In most cases, researchers simply use the Euclidean distance metric. In this article, we exploit the availability of single-cell labels to find an optimal Mahalanobis distance metric derived from the data. We show that such a Mahalanobis distance metric results in an improved identification of cell populations compared with the Euclidean distance metric. Once determined, it can be used for the analysis of multiple samples that were measured under the same experimental setup. We illustrate this approach for cytometry data from two different origins, that is, flow cytometry applied to microbial cells and mass cytometry for the analysis of human blood cells. We also illustrate that such a distance metric results in an improved identification of cell populations when clustering methods are employed. Generally, these results imply that the performance of data analysis techniques can be improved by using a more advanced distance metric. © 2019 International Society for Advancement of Cytometry.

Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities

Isogenic bacterial populations are known to exhibit phenotypic heterogeneity at the single-cell level. Because of difficulties in assessing the phenotypic heterogeneity of a single taxon in a mixed community, the importance of this deeper level of organization remains relatively unknown for natural communities. In this study, we have used membrane-based microcosms that allow the probing of the phenotypic heterogeneity of a single taxon while interacting with a synthetic or natural community. Individual taxa were studied under axenic conditions, as members of a coculture with physical separation, and as a mixed culture. Phenotypic heterogeneity was assessed through both flow cytometry and Raman spectroscopy. Using this setup, we investigated the effect of microbial interactions on the individual phenotypic heterogeneities of two interacting drinking water isolates. Through flow cytometry we have demonstrated that interactions between these bacteria lead to a reduction of their individual phenotypic diversities and that this adjustment is conditional on the bacterial taxon. Single-cell Raman spectroscopy confirmed a taxon-dependent phenotypic shift due to the interaction. In conclusion, our data suggest that bacterial interactions may be a general driver of phenotypic heterogeneity in mixed microbial populations.IMPORTANCE Laboratory studies have shown the impact of phenotypic heterogeneity on the survival and functionality of isogenic populations. Because phenotypic heterogeneity plays an important role in pathogenicity and virulence, antibiotic resistance, biotechnological applications, and ecosystem properties, it is crucial to understand its influencing factors. An unanswered question is whether bacteria in mixed communities influence the phenotypic heterogeneity of their community partners. We found that coculturing bacteria leads to a reduction in their individual phenotypic heterogeneities, which led us to the hypothesis that the individual phenotypic diversity of a taxon is dependent on the community composition.

Dispersal homogenizes communities via immigration even at low rates in a simplified synthetic bacterial metacommunity

Selection and dispersal are ecological processes that have contrasting roles in the assembly of communities. Variable selection diversifies and strong dispersal homogenizes them. However, we do not know whether dispersal homogenizes communities directly via immigration or indirectly via weakening selection across habitats due to physical transfer of material, e.g., water mixing in aquatic ecosystems. Here we examine how dispersal homogenizes a simplified synthetic bacterial metacommunity, using a sequencing-independent approach based on flow cytometry and mathematical modeling. We show that dispersal homogenizes the metacommunity via immigration, not via weakening selection, and even when immigration is four times slower than growth. This finding challenges the current view that dispersal homogenizes communities only at high rates and explains why communities are homogeneous at small spatial scales. It also offers a benchmark for sequence-based studies in natural microbial communities where immigration rates can be inferred solely by using neutral models.

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

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

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

BACKGROUND

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

RESULTS

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

CONCLUSION

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

A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data

Imaging flow cytometry is a powerful experimental technique combining the strength of microscopy and flow cytometry to enable high-throughput characterization of cell populations on a detailed microscopic scale. This approach has an increasing importance for distinguishing between different cellular phenotypes such as proliferation, cell division and cell death. In the course of undergoing these different pathways, each cell is characterized by a high amount of properties. This makes it hard to filter the most relevant information for cell state discrimination. The traditional methods for cell state discrimination rely on dye based two-dimensional gating strategies ignoring information that is hidden in the high-dimensional property space. In order to make use of the information ignored by the traditional methods, we present a simple and efficient approach to distinguish biological states within a cell population based on machine learning techniques. We demonstrate the advantages and drawbacks of filter techniques combined with different classification schemes. These techniques are illustrated with two case studies of apoptosis detection in HeLa cells. Thereby we highlight (i) the aptitude of imaging flow cytometry regarding automated, label-free cell state discrimination and (ii) pitfalls that are frequently encountered. Additionally a MATLAB script is provided, which gives further insight regarding the computational work presented in this study.

Population heterogeneity in microbial bioprocesses: origin, analysis, mechanisms, and future perspectives

Population heterogeneity is omnipresent in all bioprocesses even in homogenous environments. Its origin, however, is only so well understood that potential strategies like bet-hedging, noise in gene expression and division of labour that lead to population heterogeneity can be derived from experimental studies simulating the dynamics in industrial scale bioprocesses. This review aims at summarizing the current state of the different parts of single cell studies in bioprocesses. This includes setups to visualize different phenotypes of single cells, computational approaches connecting single cell physiology with environmental influence and special cultivation setups like scale-down reactors that have been proven to be useful to simulate large-scale conditions. A step in between investigation of populations and single cells is studying subpopulations with distinct properties that differ from the rest of the population with sub-omics methods which are also presented here. Moreover, the current knowledge about population heterogeneity in bioprocesses is summarized for relevant industrial production hosts and mixed cultures, as they provide the unique opportunity to distribute metabolic burden and optimize production processes in a way that is impossible in traditional monocultures. In the end, approaches to explain the underlying mechanism of population heterogeneity and the evidences found to support each hypothesis are presented. For instance, population heterogeneity serving as a bet-hedging strategy that is used as coordinated action against bioprocess-related stresses while at the same time spreading the risk between individual cells as it ensures the survival of least a part of the population in any environment the cells encounter.

Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling

Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale.