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

Artificial intelligence in bacterial diagnostics and antimicrobial susceptibility testing: Current advances and future prospects

Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and antimicrobial susceptibility testing (AST) by leveraging machine learning models, including Random Forest, Support Vector Machines (SVM), and deep learning architectures such as Convolutional Neural Networks (CNNs) and transformers. The integration of AI into these methods promises to address the current limitations of traditional techniques, offering a path toward more efficient, accessible, and reliable diagnostic solutions. In particular, AI-based approaches have demonstrated significant potential in resource-limited settings by enabling cost-effective and portable diagnostic solutions, reducing dependency on specialized infrastructure, and facilitating remote bacterial detection through smartphone-integrated platforms and telemedicine applications. This review highlights AI's transformative role in automating data analysis, minimizing human error, and delivering real-time diagnostic results, ultimately improving patient outcomes and optimizing healthcare efficiency. In addition, we not only examine the current advances in machine learning and deep learning but also review their applications in plate counting, mass spectrometry, morphology-based and motion-based microscopic detection, holographic microscopy, colorimetric and fluorescence detection, electrochemical sensors, Raman and Surface-Enhanced Raman Spectroscopy (SERS), and Atomic Force Microscopy (AFM) for bacterial diagnostics and AST. Finally, we discuss the future directions and potential advancements in AI-driven bacterial diagnostics.

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.

Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine Learning

Microplastics (MPs) in natural waters are heterogeneously mixed with other natural particles including algal cells and suspended sediments. An easy-to-use and rapid method for directly measuring and distinguishing MPs from other naturally present colloids in the environment would expedite analytical workflows. Here, we established a database of MP scattering and fluorescence properties, either alone or in mixtures with natural particles, by stain-free flow cytometry. The resulting high-dimensional data were analyzed using machine learning approaches, either unsupervised (e.g., viSNE) or supervised (e.g., random forest algorithms). We assessed our approach in identifying and quantifying model MPs of diverse sizes, morphologies, and polymer compositions in various suspensions including phototrophic microorganisms, suspended biofilms, mineral particles, and sediment. We could precisely quantify MPs in microbial phototrophs and natural sediments with high organic carbon by both machine learning models (identification accuracies over 93%), although it was not possible to distinguish between different MP sizes or polymer compositions. By testing the resulting method in environmental samples through spiking MPs into freshwater samples, we further highlight the applicability of the method to be used as a rapid screening tool for MPs. Collectively, this workflow can be easily applied to a diverse set of samples to assess the presence of MPs in a time-efficient manner.

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.

From microbiome composition to functional engineering, one step at a time

SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.

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.

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.

Cybergenetic control of microbial community composition

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

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

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

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

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

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Different profiles could be obtained depending on oral hygiene status.

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

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

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.

Machine Learning Advances in Microbiology: A Review of Methods and Applications

Microorganisms play an important role in natural material and elemental cycles. Many common and general biology research techniques rely on microorganisms. Machine learning has been gradually integrated with multiple fields of study. Machine learning, including deep learning, aims to use mathematical insights to optimize variational functions to aid microbiology using various types of available data to help humans organize and apply collective knowledge of various research objects in a systematic and scaled manner. Classification and prediction have become the main achievements in the development of microbial community research in the direction of computational biology. This review summarizes the application and development of machine learning and deep learning in the field of microbiology and shows and compares the advantages and disadvantages of different algorithm tools in four fields: microbiome and taxonomy, microbial ecology, pathogen and epidemiology, and drug discovery.

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.

Fast custom wavelet analysis technique for single molecule detection and identification

Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. We introduce a powerful, fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT) algorithm. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor. The technique is more accurate than simple peak-finding algorithms and several orders of magnitude faster than existing CWT methods, allowing for real-time data analysis during sensing for the first time. Performance is further increased by applying a custom wavelet to multi-peak signals as demonstrated using amplification-free detection of single bacterial DNAs. A 4x increase in detection rate, a 6x improved error rate, and the ability for extraction of experimental parameters are demonstrated. This cluster-based CWT analysis will enable high-performance, real-time sensing when signal-to-noise is hardware limited, for instance with low-cost sensors in point of care environments.

The authors introduce an accurate, fast and efficient technique to analyze sensory data. They use a continuous wavelet transform concept to look for certain patterns in noisy raw data. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor.

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.

BCNet: A Novel Network for Blood Cell Classification

Aims: Most blood diseases, such as chronic anemia, leukemia (commonly known as blood cancer), and hematopoietic dysfunction, are caused by environmental pollution, substandard decoration materials, radiation exposure, and long-term use certain drugs. Thus, it is imperative to classify the blood cell images. Most cell classification is based on the manual feature, machine learning classifier or the deep convolution network neural model. However, manual feature extraction is a very tedious process, and the results are usually unsatisfactory. On the other hand, the deep convolution neural network is usually composed of massive layers, and each layer has many parameters. Therefore, each deep convolution neural network needs a lot of time to get the results. Another problem is that medical data sets are relatively small, which may lead to overfitting problems. Methods: To address these problems, we propose seven models for the automatic classification of blood cells: BCARENet, BCR5RENet, BCMV2RENet, BCRRNet, BCRENet, BCRSNet, and BCNet. The BCNet model is the best model among the seven proposed models. The backbone model in our method is selected as the ResNet-18, which is pre-trained on the ImageNet set. To improve the performance of the proposed model, we replace the last four layers of the trained transferred ResNet-18 model with the three randomized neural networks (RNNs), which are RVFL, ELM, and SNN. The final outputs of our BCNet are generated by the ensemble of the predictions from the three randomized neural networks by the majority voting. We use four multi-classification indexes for the evaluation of our model. Results: The accuracy, average precision, average F1-score, and average recall are 96.78, 97.07, 96.78, and 96.77%, respectively. Conclusion: We offer the comparison of our model with state-of-the-art methods. The results of the proposed BCNet model are much better than other state-of-the-art methods.

Biotransformation of Chemicals at the Water–Sediment Interface─Toward a Robust Simulation Study Setup

Studying aquatic biotransformation of chemicals in laboratory experiments, i.e., OECD 308 and OECD 309 studies, is required by international regulatory frameworks to prevent the release of persistent chemicals into natural water bodies. Here, we aimed to address several previously described shortcomings of OECD 308/309 studies regarding their variable outcomes and questionable environmental relevance by broadly testing and characterizing a modified biotransformation test system in which an aerated water column covers a thin sediment layer. Compared to standard OECD 308/309 studies, the modified system showed little inter-replicate variability, improved observability of biotransformation, and consistency with first-order biotransformation kinetics for the majority of 43 test compounds, including pharmaceuticals, pesticides, and artificial sweeteners. To elucidate the factors underlying the decreased inter-replicate variability compared to OECD 309 outcomes, we used multidimensional flow cytometry data and a machine learning-based cell type assignment pipeline to study cell densities and cell type diversities in the sediment and water compartments. Our here presented data on cell type composition in both water and sediment allows, for the first time, to study the behavior of microbial test communities throughout different biotransformation simulation studies. We found that sediment-associated microbial communities were generally more stable throughout the experiments and exhibited higher cell type diversity than the water column-associated communities. Consistently, our data indicate that aquatic biotransformation of chemicals can be most robustly studied in test systems providing a sufficient amount of sediment-borne biomass. While these findings favor OECD 308-type systems over OECD 309-type systems to study biotransformation at the water-sediment interface, our results suggest that the former should be modified toward lower sediment-water ratios to improve observability and interpretability of biotransformation.

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.

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.

Early detection of drug-resistant Streptococcus pneumoniae and Haemophilus influenzae by quantitative flow cytometry

Early detection of drug resistance contributes to combating drug-resistant bacteria and improving patient outcomes. Microbial testing in the laboratory is essential for treating infectious diseases because it can provide critical information related to identifying pathogenic bacteria and their resistance profiles. Despite these clinical requirements, conventional phenotypic testing is time-consuming. Additionally, recent rapid drug resistance tests are not compatible with fastidious bacteria such as Streptococcus and Haemophilus species. In this study, we validated the feasibility of direct bacteria counting using highly sensitive quantitative flow cytometry. Furthermore, by combining flow cytometry and a nucleic acid intercalator, we constructed a highly sensitive method for counting viable fastidious bacteria. These are inherently difficult to measure due to interfering substances from nutrients contained in the medium. Based on the conventional broth microdilution method, our method acquired a few microliter samples in a time series from the same microplate well to exclude the growth curve inconsistency between the samples. Fluorescent staining and flow cytometry measurements were completed within 10 min. Therefore, this approach enabled us to determine antimicrobial resistance for these bacteria within a few hours. Highly sensitive quantitative flow cytometry presents a novel avenue for conducting rapid antimicrobial susceptibility tests.

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.

Shifts in methanogenic archaea communities and methane dynamics along a subtropical estuarine land use gradient

In coastal aquatic ecosystems, prokaryotic communities play an important role in regulating the cycling of nutrients and greenhouse gases. In the coastal zone, estuaries are complex and delicately balanced systems containing a multitude of specific ecological niches for resident microbes. Anthropogenic influences (i.e. urban, industrial and agricultural land uses) along the estuarine continuum can invoke physical and biochemical changes that impact these niches. In this study, we investigate the relative abundance of methanogenic archaea and other prokaryotic communities, distributed along a land use gradient in the subtropical Burnett River Estuary, situated within the Great Barrier Reef catchment, Australia. Microbiological assemblages were compared to physicochemical, nutrient and greenhouse gas distributions in both pore and surface water. Pore water samples from within the most urbanised site showed a high relative abundance of methanogenic Euryarchaeota (7.8% of all detected prokaryotes), which coincided with elevated methane concentrations in the water column, ranging from 0.51 to 0.68 μM at the urban and sewage treatment plant (STP) sites, respectively. These sites also featured elevated dissolved organic carbon (DOC) concentrations (0.66 to 1.16 mM), potentially fuelling methanogenesis. At the upstream freshwater site, both methane and DOC concentrations were considerably higher (2.68 μM and 1.8 mM respectively) than at the estuarine sites (0.02 to 0.66 μM and 0.39 to 1.16 mM respectively) and corresponded to the highest relative abundance of methanotrophic bacteria. The proportion of sulfate reducing bacteria in the prokaryotic community was elevated within the urban and STP sites (relative abundances of 8.0%– 10.5%), consistent with electron acceptors with higher redox potentials (e.g. O₂, NO₃^-) being scarce. Overall, this study showed that ecological niches in anthropogenically altered environments appear to give an advantage to specialized prokaryotes invoking a potential change in the thermodynamic landscape of the ecosystem and in turn facilitating the generation of methane–a potent greenhouse gas.