Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities

Influences of bisphenol A on hydrogen production from food waste by thermophilic dark fermentation

The extensive use of plastic products in food packaging and daily life makes them inevitably enter the treatment process of food waste (FW). Plasticizer as a new pollutant is threatening the dark fermentation of FW. Our study showed that bisphenol A (BPA) at > 250 mg/L had a significant inhibition on hydrogen production from FW by thermophilic dark fermentation. The endogenous ATP content and lactate dehydrogenase (LDH) release showed that high level of BPA not only inhibited the growth of hydrogen-producing consortium, but also led to cell death. In addition, BPA mainly affects the hydrogen-producing consortium by reducing cell membrane fluidity, damaging cell membrane integrity and reducing cell membrane potential, resulting in cell death. This study provides some new insights into the mechanism of the effect of BPA on hydrogen production from FW by thermophilic dark fermentation, and lays the foundation on the utilization of FW.

Design and validation of functionalized redox-responsive hydrogel beads for high-throughput screening of antibody-secreting mammalian cells

Antibody drugs play a vital role in diagnostics and therapy. However, producing antibodies from mammalian cells is challenging owing to cellular heterogeneity, which can be addressed by applying droplet-based microfluidic platforms for high-throughput screening (HTS). Here, we designed an integrated system based on disulfide-bonded redox-responsive hydrogel beads (redox-HBs), which were prepared through enzymatic hydrogelation, to compartmentalize, screen, select, retrieve, and recover selected Chinese hamster ovary (CHO) cells secreting high levels of antibodies. Moreover, redox-HBs were functionalized with protein G as an antibody-binding module to capture antibodies secreted from encapsulated cells. As proof-of-concept, cells co-producing immunoglobulin G (IgG) as the antibody and green fluorescent protein (GFP) as the reporter molecule, denoted as CHO(IgG/GFP), were encapsulated into functionalized redox-HBs. Additionally, antibody-secreting cells were labeled with protein L-conjugated horseradish peroxidase using a tyramide amplification system, enabling fluorescence staining of the antibody captured inside the beads. Redox-HBs were then applied to fluorescence-activated droplet sorting, and selected redox-HBs were degraded by reducing the disulfide bonds to recover the target cells. The results indicated the potential of the developed HTS platform for selecting a single cell viable for biopharmaceutical production.

Raman cell sorting for single-cell research

Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research. Raman cell sorting technology has garnered attention owing to its non-labeling, non-destructive detection features and the capability to analyze samples containing water. In addition, this technology can provide live cells for subsequent genomics analysis and gene sequencing. This paper emphasizes the importance of single-cell research, describes the single-cell research methods that currently exist, including single-cell manipulation and single-cell identification techniques, and highlights the advantages of Raman spectroscopy in the field of single-cell analysis by comparing it with the fluorescence-activated cell sorting (FACS) technique. It describes various existing Raman cell sorting techniques and introduces their respective advantages and disadvantages. The above techniques were compared and analyzed, considering a variety of factors. The current bottlenecks include weak single-cell spontaneous Raman signals and the requirement for a prolonged total cell exposure time, significantly constraining Raman cell sorting technology's detection speed, efficiency, and throughput. This paper provides an overview of current methods for enhancing weak spontaneous Raman signals and their associated advantages and disadvantages. Finally, the paper outlines the detailed information related to the Raman cell sorting technology mentioned in this paper and discusses the development trends and direction of Raman cell sorting.

Flow cytometric assessments of metabolic activity in bacterial assemblages provide insight into ecosystem condition along the Buffalo National River, Arkansas

The Buffalo National River (BNR), on karst terrain in Arkansas, is considered an extraordinary water resource. Water collected in Spring 2017 along BNR was metagenomically analyzed using 16S rDNA, and for 17 months (5/2017-11/2018), bacterial responses were measured in relation to nutrients sampled along a stretch of BNR near a concentrated animal feed operation (CAFO) on Big Creek. Because cell count and esterase activity can increase proportionally with organic enrichment, they were hypothesized to be elevated near the CAFO. Counts (colony forming units; CFUs) were different among sites for 73 % of the months; Big Creek generated highest CFUs 27 % of the time, with the closest downstream site at 13.3 %. Esterase activity was different among sites 94 % of the time, with Big Creek exhibiting lowest activity 71 % of the time. Over the months, activity was similar across sites at ~70 % active, except at Big Creek (56 %). The α-diversity of BNR microbial consortia near a wastewater treatment plant (WWTP) and the CAFO was related to distance from the WWTP and CAFO. The inverse relationship between high CFUs and low esterase activity at Big Creek (r = -0.71) actuated in vitro exposures of bacteria to organic wastewater contaminants (OWC) previously identified in the watershed. Exponential-phase Escherichia coli (stock strain), Streptococcus suis (avirulent, from swine), and S. dysgalactiae (virulent, from silver carp, Hypophthalmichthys molitrix) were incubated with atrazine, pharmaceuticals (17 α-ethynylestradiol and trenbolone), and antimicrobials (tylosin and butylparaben). Bacteria were differentially responsive. Activity varied with exposure time and OWC type, but not concentration; atrazine decreased it most. Taken together - the metagenomic taxonomic similarities along BNR, slightly higher bacterial growth and lower bacterial esterase at the CAFO, and the lab exposures of bacterial strains showing that OWC altered metabolism - the results indicated that bioactive OWC entering the watershed can strongly influence microbial processes in the aquatic ecosystem.

Unlocking the mechanism of action: a cost-effective flow cytometry approach for accelerating antimicrobial drug development

Antimicrobial resistance is one of the greatest challenges to global health. While the development of new antimicrobials can combat resistance, low profitability reduces the number of new compounds brought to market. Elucidating the mechanism of action is crucial for developing new antimicrobials. This can become expensive as there are no universally applicable pipelines. Phenotypic heterogeneity of microbial populations resulting from antimicrobial treatment can be captured through flow cytometric fingerprinting. Since antimicrobials are classified into limited groups, the mechanism of action of known compounds can be used for predictive modeling. We demonstrate a cost-effective flow cytometry approach for determining the mechanism of action of new compounds. Cultures of Actinomyces viscosus and Fusobacterium nucleatum were treated with different antimicrobials and measured by flow cytometry. A Gaussian mixture mask was applied over the data to construct phenotypic fingerprints. Fingerprints were used to assess statistical differences between mechanism of action groups and to train random forest classifiers. Classifiers were then used to predict the mechanism of action of cephalothin. Statistical differences were found among the different mechanisms of action groups. Pairwise comparison showed statistical differences for 35 out of 45 pairs for A. viscosus and for 32 out of 45 pairs for F. nucleatum after 3.5 h of treatment. The best-performing random forest classifier yielded a Matthews correlation coefficient of 0.92 and the mechanism of action of cephalothin could be successfully predicted. These findings suggest that flow cytometry can be a cheap and fast alternative for determining the mechanism of action of new antimicrobials.IMPORTANCEIn the context of the emerging threat of antimicrobial resistance, the development of novel antimicrobials is a commonly employed strategy to combat resistance. Elucidating the mechanism of action of novel compounds is crucial in this development but can become expensive, as no universally applicable pipelines currently exist. We present a novel flow cytometry-based approach capable of determining the mechanism of action swiftly and cost-effectively. The workflow aims to accelerate drug discovery and could help facilitate a more targeted approach for antimicrobial treatment of patients.

Spectral analysis and sorting of microbial organisms using a spectral sorter

This chapter discusses the problems related to the application of conventional flow cytometers to microbiology. To address some of those limitations, the concept of spectral flow cytometry is introduced and the advantages over conventional flow cytometry for bacterial sorting are presented. We demonstrate by using ThermoFisher's Bigfoot spectral sorter where the spectral signatures of different stains for staining bacteria are demonstrated with an example of performing unmixing on spectral datasets. In addition to the Bigfoot's spectral analysis, the special biosafety features of this instrument are discussed. Utilizing these biosafety features, the sorting and patterning at the single cell level is optimized using non-pathogenic bacteria. Finally, the chapter is concluded by presenting a novel, label free, non-destructive, and rapid phenotypic method called Elastic Light Scattering (ELS) technology for identification of the patterned bacterial cells based on their unique colony scatter patterns.

Rapid detection of viable microbes with 5-cyano-2,3-di-(p-tolyl)tetrazolium chloride and 5(6)-carboxyfluorescein diacetate using a fibre fluorescence spectroscopy system

AIMS

To assess the efficacy of two commercially available viability dyes, 5-cyano-2,3-di-(p-tolyl)tetrazolium chloride (CTC) and 5(6)-carboxyfluorescein diacetate (CFDA), in reporting on viable cell concentration and species using an all-fibre fluorometer.

METHODS AND RESULTS

Four bacterial species (two Gram-positive and two Gram-negative) commonly associated with food poisoning or food spoilage (Escherichia coli, Salmonella enterica, Staphylococcus aureus, and Bacillus cereus) were stained with CTC or CFDA and the fibre fluorometer was used to collect full fluorescence emission spectra. A good correlation between concentration and fluorescence intensity was found for Gram-negative bacteria between 107 and 108 colony-forming units (CFU) ml-1. There was no correlation with concentration for Gram-positive bacteria; however, the information in the CTC and CFDA spectra shows the potential to distinguish Gram-negative cells from Gram-positive cells, although it may simply reflect the overall bacterial metabolic activity under staining conditions from this study.

CONCLUSIONS

The limit of detection (LoD) is too high in the dip-probe approach for analysis; however, the development of an approach measuring the fluorescence of single cells may improve this limitation. The development of new bacteria-specific fluorogenic dyes may also address this limitation. The ability to differentiate bacteria using these dyes may add value to measurements made to enumerate bacteria using CTC and CFDA.

Sorting and simultaneous quantitation of intact mixed-cell samples via capillary isotachophoresis

A great need currently exists for rapid, inexpensive, and accurate methods for microbial analysis in the medical, food, industrial, and water quality fields. Here, a novel capillary isotachophoresis (CITP) method is presented for the focusing, sorting, and quantitation of intact cells in mixed samples based on their electrophoretic mobility ranges. Using a series of ion spacers dissolved in the sample, this technique results in several efficient cell peaks in the electropherogram corresponding to specific cell electrophoretic mobility ranges. The concentrations of different species in mixed-cell samples are determined from the cell peak areas and the known peak response factors for the cell species using a series of linear equations. Method design and optimization are discussed, including the choice of running buffer, pH, and ion spacers. Mixed-cell samples of up to four different species were focused and quantified as a proof-of-principle of the method. When sample cell concentrations were toward the middle of the linear response range, accuracies between 1% and 11% and relative standard deviations of 1%-14% were obtained, depending on the number of cell species in the mixture. This work provides a useful basis for future studies of cell quantitation using CITP, which could be potentially applied to a variety of fields including cell growth studies, microbial contamination testing, and sterility testing.

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.

Machine learning classification of cellular states based on the impedance features derived from microfluidic single-cell impedance flow cytometry

Mitosis is a crucial biological process where a parental cell undergoes precisely controlled functional phases and divides into two daughter cells. Some drugs can inhibit cell mitosis, for instance, the anti-cancer drugs interacting with the tumor cell proliferation and leading to mitosis arrest at a specific phase or cell death eventually. Combining machine learning with microfluidic impedance flow cytometry (IFC) offers a concise way for label-free and high-throughput classification of drug-treated cells at single-cell level. IFC-based single-cell analysis generates a large amount of data related to the cell electrophysiology parameters, and machine learning helps establish correlations between these data and specific cell states. This work demonstrates the application of machine learning for cell state classification, including the binary differentiations between the G1/S and apoptosis states and between the G2/M and apoptosis states, as well as the classification of three subpopulations comprising a subgroup insensitive to the drug beyond the two drug-induced states of G2/M arrest and apoptosis. The impedance amplitudes and phases used as input features for the model training were extracted from the IFC-measured datasets for the drug-treated tumor cells. The deep neural network (DNN) model was exploited here with the structure (e.g., hidden layer number and neuron number in each layer) optimized for each given cell type and drug. For the H1650 cells, we obtained an accuracy of 78.51% for classification between the G1/S and apoptosis states and 82.55% for the G2/M and apoptosis states. For HeLa cells, we achieved a high accuracy of 96.94% for classification between the G2/M and apoptosis states, both of which were induced by taxol treatment. Even higher accuracy approaching 100% was achieved for the vinblastine-treated HeLa cells for the differentiation between the viable and non-viable states, and between the G2/M and apoptosis states. We also demonstrate the capability of the DNN model for high-accuracy classification of the three subpopulations in a complete cell sample treated by taxol or vinblastine.

New insights into the roles of fungi and bacteria in the development of medicinal plant

BACKGROUND

The interaction between microorganisms and medicinal plants is a popular topic. Previous studies consistently reported that microorganisms were mainly considered pathogens or contaminants. However, with the development of microbial detection technology, it has been demonstrated that fungi and bacteria affect beneficially the medicinal plant production chain.

AIM OF REVIEW

Microorganisms greatly affect medicinal plants, with microbial biosynthesis a high regarded topic in medicinal plant-microbial interactions. However, it lacks a systematic review discussing this relationship. Current microbial detection technologies also have certain advantages and disadvantages, it is essential to compare the characteristics of various technologies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review first illustrates the role of fungi and bacteria in various medicinal plant production procedures, discusses the development of microbial detection and identification technologies in recent years, and concludes with microbial biosynthesis of natural products. The relationship between fungi, bacteria, and medicinal plants is discussed comprehensively. We also propose a future research model and direction for further studies.

Application of flow cytometry for rapid, high-throughput, multiparametric analysis of environmental microbiomes

Quantification of the abundance and understanding of the dynamics of the microbial communities is essential to establish a basis for microbiome characterization. The conventional techniques used for the quantification of microbes are complicated and time-consuming. With scientific advancement, many techniques evolved and came into account. Among them, flow cytometry is a robust, high-throughput technique through which microbial dynamics, morphology, microbial distribution, physiological characteristics, and many more attributes can be studied in a high-throughput manner with comparatively less time and resources. Flow cytometry, when combined with other omics-based methods, offers a rapid and efficient platform to analyze and understand the composition of microbiome at the cellular level. The microbial diversity observed through flow cytometry will not be equivalent to that obtained by sequencing methods, but this integrated approach holds great potential for high throughput characterization of microbiomes. Flow cytometry is regarded as an established characterization tool in haematology, oncology, immunology, and medical microbiology research; however, its application in environmental microbiology is yet to be explored. This comprehensive review aims to delve into the diverse environmental applications of flow cytometry across various domains, including but not limited to bioremediation, landfills, anaerobic digestion, industrial bioprocesses, water quality regulation, and soil quality regulation. By conducting an in-depth analysis, this article seeks to shed light on the potential benefits and challenges associated with the utilization of flow cytometry in addressing environmental concerns.

Methylomics and cancer: the current state of methylation profiling and marker development for clinical care

Epigenetic modifications have long been recognized as an essential level in transcriptional regulation linking behavior and environmental conditions or stimuli with biological processes and disease development. Among them, methylation is the most abundant of these reversible epigenetic marks, predominantly occurring on DNA, RNA, and histones. Methylation modification is intimately involved in regulating gene transcription and cell differentiation, while aberrant methylation status has been linked with cancer development in several malignancies. Early detection and precise restoration of dysregulated methylation form the basis for several epigenetics-based therapeutic strategies. In this review, we summarize the current basic understanding of the regulation and mechanisms responsible for methylation modification and cover several cutting-edge research techniques for detecting methylation across the genome and transcriptome. We then explore recent advances in clinical diagnostic applications of methylation markers of various cancers and address the current state and future prospects of methylation modifications in therapies for different diseases, especially comparing pharmacological methylase/demethylase inhibitors with the CRISPRoff/on methylation editing systems. This review thus provides a resource for understanding the emerging role of epigenetic methylation in cancer, the use of methylation-based biomarkers in cancer detection, and novel methylation-targeted drugs.

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

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

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Flow Cytometry: The Next Revolution

Unmasking the subtleties of the immune system requires both a comprehensive knowledge base and the ability to interrogate that system with intimate sensitivity. That task, to a considerable extent, has been handled by an iterative expansion in flow cytometry methods, both in technological capability and also in accompanying advances in informatics. As the field of fluorescence-based cytomics matured, it reached a technological barrier at around 30 parameter analyses, which stalled the field until spectral flow cytometry created a fundamental transformation that will likely lead to the potential of 100 simultaneous parameter analyses within a few years. The simultaneous advance in informatics has now become a watershed moment for the field as it competes with mature systematic approaches such as genomics and proteomics, allowing cytomics to take a seat at the multi-omics table. In addition, recent technological advances try to combine the speed of flow systems with other detection methods, in addition to fluorescence alone, which will make flow-based instruments even more indispensable in any biological laboratory. This paper outlines current approaches in cell analysis and detection methods, discusses traditional and microfluidic sorting approaches as well as next-generation instruments, and provides an early look at future opportunities that are likely to arise.

Single-Cell Techniques in Environmental Microbiology

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

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

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

Detection of airborne pathogens with single photon counting and a real-time spectrometer on microfluidics

The common practice for monitoring pathogenic bioaerosols is to collect bioaerosols from air and then detect them, which lacks timeliness and accuracy. In order to improve the detection speed, here we demonstrate an innovative airflow-based optical detection method for directly identifying aerosol pathogens, and built a microfluidic-based counter composite spectrometer detection platform, which simplifies sample preparation and collection detection from two steps to one step. The method is based on principal component analysis and partial least squares discriminant analysis for particle species identification and dynamic transmission spectroscopy analysis, and single-photon measurement is used for particle counting. Compared with traditional microscopic counting and identification methods, the particle counting accuracy is high, the standard deviation is small, and the counting accuracy exceeds 92.2%. The setup of dynamic transmission spectroscopy analysis provides high-precision real-time particle identification with an accuracy rate of 93.75%. As the system is further refined, we also foresee potential applications of this method in agricultural disease control, environmental control, and infectious disease control in aerosol pathogen detection.

Phenotypic convergence of bacterial adaption to sub-lethal antibiotic treatment

Microorganisms can adapt quickly to changes in their environment, leading to various phenotypes. The dynamic for phenotypic plasticity caused by environmental variations has not yet been fully investigated. In this study, we analyzed the time-series of phenotypic changes in Staphylococcus cells during adaptive process to antibiotics stresses using flow cytometry and Raman spectroscopy. The nine antibiotics with four different mode of actions were treated in bacterial cells at a sub-lethal concentration to give adaptable stress. Although the growth rate initially varied depending on the type of antibiotic, most samples reached the maximum growth comparable to the control through the short-term adaptation after 24 h. The phenotypic diversity, which showed remarkable changes depending on antibiotic treatment, converged identical to the control over time. In addition, the phenotype with cellular biomolecules converted into a bacterial cell that enhance tolerance to antibiotic stress with increases in cytochrome and lipid. Our findings demonstrated that the convergence into the phenotypes that enhance antibiotic tolerance in a short period when treated with sub-lethal concentrations, and highlight the feasibility of phenotypic approaches in the advanced antibiotic treatment.

The identification of Pseudomonas aeruginosa persisters using flow cytometry

Pseudomonas aeruginosa persisters are a rare and poorly characterized subpopulation of cells that are responsible for many recurrent infections. The lack of knowledge on the mechanisms that lead to persister cell development is mainly a result of the difficulty in isolating and characterizing this rare population. Flow cytometry is an ideal method for identifying such subpopulations because it allows for high-content single-cell analysis. However, there are fewer established protocols for bacterial flow cytometry compared to mammalian cell work. Herein, we describe and propose a flow cytometry protocol to identify and isolate P. aeruginosa persister cells. Additionally, we show that the percentage of potential persister cells increases with increasing antibiotic concentrations above the MIC.

Physiological changes and growth behavior of Corynebacterium glutamicum cells in biofilm

Biofilm cells are well-known for their increased survival and metabolic capabilities and have been increasingly implemented in industrial and biotechnological processes. Corynebacterium glutamicum is one of the most widely used microorganisms in the fermentation industry. However, C. glutamicum biofilm has been rarely reported and little is known about its cellular basis. Here, the physiological changes and characteristics of C. glutamicum biofilm cells during long-term fermentation were studied for the first time. Results showed that the biofilm cells maintained stable metabolic activity and cell size was enlarged after repeated-batch of fermentation. Cell division was slowed, and chromosome content and cell proliferation efficiency were reduced during long-term fermentation. Compared to free cells, more biofilm cells were stained by the apoptosis indicator dyes Annexin V-FITC and propidium iodide (PI). Overall, these results suggested slow-growing, long-lived cells of C. glutamicum biofilm during fermentation, which could have important industrial implications. This study presents first insights into the physiological changes and growth behavior of C. glutamicum biofilm cell population, which would be valuable for understanding and developing biofilm-based processes.

Supercharged MPNs? Automated Determination of High-Throughput Most Probable Number (htMPN) Using Chip-Based 3D Digital PCR

Surface plating on agar and most probable number (MPN) are the standard methods for determining bacterial viability but both have limitations. Here we present a novel cell count method, high-throughput MPN (htMPN), that uses a chip-based digital PCR instrument to accelerate and to improve the quantification of viable or sublethally injured cells. This method tracks growth of up to 20,000 individual bacterial cells on a single chip. Single cells were grown in the individual wells of the chip at their optimal temperature until the cell density was high enough to detect the fluorescent signal with cell-permeant or cell-impermeant DNA-intercalating fluorescent dyes. This method based on microfluidic devices implemented in digital PCR equipment was equivalent to surface plating in determining cell counts of Escherichia coli, Salmonella enterica serovar Typhimurium, Fructilactobacillus sanfranciscensis, Pseudomonas putida, and vegetative cells but not spores of Bacillus subtilis. Viable E. coli could be enumerated within 7 h. Culture of strict aerobes was restricted to strains that are capable of nitrate respiration; organisms requiring complex media that also contain double-stranded DNA were detected after treatment of growth media with DNase before inoculation. Our approach not only monitors the frequency distribution of bacterial growth and determines cell counts with high reliability but also detected heat-injured cells of S. Typhimurium that escaped detection by the surface plating. Overall, the method accelerates detection of viable bacterial cells, facilitates automation, and offers new possibilities for the analysis of individual bacterial cells. IMPORTANCE htMPN uses chip-based fluorescence acquisition and is a simple and compact tool for automatic viable cell enumeration with applications in microbiological research. This method applies to a wide range of anaerobic or facultative anaerobic species and improves accuracy by reducing the number of pipetting steps. In addition, the method offers an additional tool for single-cell microbiology. The single cell time-to-detection times have been used as an important criterion for the physiological state of bacterial cells after sublethal stress, and htMPNs support the acquisition of such data with an unprecedented number of cells. In particular, htMPN provides an anaerobic environment and enables a long incubation time to increase the recovery rate of sublethally injured cells. Given its reproducibility and reliability, our approach can potentially be applied to quantify viable cells in samples from environmental, clinical, or food samples to reduce the risk of underestimation of the number of viable bacterial cells.

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.

Synergistic Action of Mild Heat and Essential Oil Treatments on Culturability and Viability of Escherichia coli ATCC 25922 Tested In Vitro and in Fruit Juice

The strengthening effect of a mild temperature treatment on the antimicrobial efficacy of essential oils has been widely reported, often leading to an underestimation or a misinterpretation of the product’s microbial status. In the present study, both a traditional culture-based method and Flow Cytometry (FCM) were applied to monitor the individual or combined effect of Origanum vulgare essential oil (OEO) and mild heat treatment on the culturability and viability of Escherichia coli in a conventional culture medium and in a fruit juice challenge test. The results obtained in the culture medium showed bacterial inactivation with an increasing treatment temperature (55 °C, 60 °C, 65 °C), highlighting an overestimation of the dead population using the culture-based method; in fact, when the FCM method was applied, the prevalence of injured bacterial cells in a viable but non-culturable (VBNC) state was observed. When commercial fruit juice with a pH of 3.8 and buffered at pH 7.0 was inoculated with E. coli ATCC 25922, a bactericidal action of OEO and a higher efficiency of the mild heat at 65 °C for 5′ combined with OEO were found. Overall, the combination of mild heat and OEO treatment represents a promising antimicrobial alternative to improve the safety of fruit juice.

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.

Xylanase production by Thermobacillus xylanilyticus is impaired by population diversification but can be mitigated based on the management of cheating behavior

Background

The microbial production of hemicellulasic cocktails is still a challenge for the biorefineries sector and agro-waste valorization. In this work, the production of hemicellulolytic enzymes by Thermobacillus xylanilyticus has been considered. This microorganism is of interest since it is able to produce an original set of thermostable hemicellulolytic enzymes, notably a xylanase GH11, Tx-xyn11. However, cell-to-cell heterogeneity impairs the production capability of the whole microbial population.

Results

Sequential cultivations of the strain on xylan as a carbon source has been considered in order to highlight and better understand this cell-to-cell heterogeneity. Successive cultivations pointed out a fast decrease of xylanase activity (loss of ~ 75%) and Tx-xyn11 gene expression after 23.5 generations. During serial cultivations on xylan, flow cytometry analyses pointed out that two subpopulations, differing at their light-scattering properties, were present. An increase of the recurrence of the subpopulation exhibiting low forward scatter (FSC) signal was correlated with a progressive loss of xylanase activity over several generations. Cell sorting and direct observation of the sorted subpopulations revealed that the low-FSC subpopulation was not sporulating, whereas the high-FSC subpopulation contained cells at the onset of the sporulation stage. The subpopulation differences (growth and xylanase activity) were assessed during independent growth. The low-FSC subpopulation exhibited a lag phase of 10 h of cultivation (and xylanase activities from 0.15 ± 0.21 to 3.89 ± 0.14 IU/mL along the cultivation) and the high-FSC subpopulation exhibited a lag phase of 5 h (and xylanase activities from 0.52 ± 0.00 to 4.43 ± 0.61 over subcultivations). Serial cultivations on glucose, followed by a switch to xylan led to a ~ 1.5-fold to ~ 15-fold improvement of xylanase activity, suggesting that alternating cultivation conditions could lead to an efficient population management strategy for the production of xylanase.

Conclusions

Taken altogether, the data from this study point out that a cheating behavior is responsible for the progressive reduction in xylanase activity during serial cultivations of T. xylanilyticus. Alternating cultivation conditions between glucose and xylan could be used as an efficient strategy for promoting population stability and higher enzymatic productivity from this bacterium.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01762-z.

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.

Application of an Escherichia coli triple reporter strain for at-line monitoring of single-cell physiology during L-phenylalanine production

Biotechnological production processes are sustainable approaches for the production of biobased components such as amino acids for food and feed industry. Scale-up from ideal lab-scale bioreactors to large-scale processes is often accompanied by loss in productivity. This may be related to population heterogeneities of cells originating from isogenic cultures that arise due to dynamic non-ideal conditions in the bioreactor. To better understand this phenomenon, deeper insights into single-cell physiologies in bioprocesses are mandatory before scale-up. Here, a triple reporter strain (3RP) was developed by chromosomally integrating the fluorescent proteins mEmerald, CyOFP1, and mTagBFP2 into the L-phenylalanine producing Escherichia coli strain FUS4 (pF81_kan) to allow monitoring of growth, oxygen availability, and general stress response of the single cells. Functionality of the 3RP was confirmed in well-mixed lab-scale fed-batch processes with glycerol as carbon source in comparison to the strain without fluorescent proteins, leading to no difference in process performance. Fluorescence levels could successfully reflect the course of related process state variables, revealed population heterogeneities during the transition between different process phases and potentially subpopulations that exhibit superior process performance. Furthermore, indications were found for noise in gene expression as regulation strategy against environmental perturbation.

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

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

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.

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

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

Microfluidic Compartmentalization Platforms for Single Cell Analysis

Many cellular analytical technologies measure only the average response from a cell population with an assumption that a clonal population is homogenous. The ensemble measurement often masks the difference among individual cells that can lead to misinterpretation. The advent of microfluidic technology has revolutionized single-cell analysis through precise manipulation of liquid and compartmentalizing single cells in small volumes (pico- to nano-liter). Due to its advantages from miniaturization, microfluidic systems offer an array of capabilities to study genomics, transcriptomics, and proteomics of a large number of individual cells. In this regard, microfluidic systems have emerged as a powerful technology to uncover cellular heterogeneity and expand the depth and breadth of single-cell analysis. This review will focus on recent developments of three microfluidic compartmentalization platforms (microvalve, microwell, and microdroplets) that target single-cell analysis spanning from proteomics to genomics. We also compare and contrast these three microfluidic platforms and discuss their respective advantages and disadvantages in single-cell analysis.

Single-Walled Carbon Nanotube Probes for the Characterization of Biofilm-Degrading Enzymes Demonstrated against Pseudomonas aeruginosa Extracellular Matrices

Hydrolase co-therapies that degrade biofilm extracellular polymeric substances (EPS) allow for a better diffusion of antibiotics and more effective treatment; current methods for quantitatively measuring the enzymatic degradation of EPS are not amendable to high-throughput screening. Herein, we present biofilm EPS-functionalized single-walled carbon nanotube (SWCNT) probes for rapid screening of hydrolytic enzyme selectivity and activity on EPS. The extent of biofilm EPS degradation is quantified by monitoring the quenching of the SWCNT fluorescence. We used this platform to screen 16 hydrolases with varying bond breaking selectivity against a panel of wild-type Pseudomonas aeruginosa and mutants deficient or altered in one or more EPS. Next, we performed concentration-dependent studies of six enzymes on two common strains found in cystic fibrosis (CF) environments and, for each enzyme, extracted three first-order rate constants and their relative contributions by fitting a parallel, multi-site degradation model, with a good model fit (R² from 0.65 to 0.97). Reaction rates (turnover rates) are dependent on the enzyme concentration and range from 6.67 × 10^-11 to 2.80 × 10^-3 *s^-1 per mg/mL of enzymes. Lastly, we confirmed findings from this new assay using an established crystal-violet staining assay for a subset of hydrolase panels. In summary, our work shows that this modular sensor is amendable to the high-throughput screening of EPS degradation, thereby improving the rate of discovery and development of novel hydrolases.

Single-cell genomics for resolution of conserved bacterial genes and mobile genetic elements of the human intestinal microbiota using flow cytometry

As our understanding of the importance of the human microbiota in health and disease grows, so does our need to carefully resolve and delineate its genomic content. 16S rRNA gene-based analyses yield important insights into taxonomic composition, and metagenomics-based approaches reveal the functional potential of microbial communities. However, these methods generally fail to directly link genetic features, including bacterial genes and mobile genetic elements, to each other and to their source bacterial genomes. Further, they are inadequate to capture the microdiversity present within a genus, species, or strain of bacteria within these complex communities. Here, we present a method utilizing fluorescence-activated cell sorting for isolation of single bacterial cells, amplifying their genomes, screening them by 16S rRNA gene analysis, and selecting cells for genomic sequencing. We apply this method to both a cultured laboratory strain of Escherichia coli and human stool samples. Our analyses reveal the capacity of this method to provide nearly complete coverage of bacterial genomes when applied to isolates and partial genomes of bacterial species recovered from complex communities. Additionally, this method permits exploration and comparison of conserved and variable genomic features between individual cells. We generate assemblies of novel genomes within the Ruminococcaceae family and the Holdemanella genus by combining several 16S rRNA gene-matched single cells, and report novel prophages and conjugative transposons for both Bifidobacterium and Ruminococcaceae. Thus, we demonstrate an approach for flow cytometric separation and sequencing of single bacterial cells from the human microbiota, which yields a variety of critical insights into both the functional potential of individual microbes and the variation among those microbes. This method definitively links a variety of conserved and mobile genomic features, and can be extended to further resolve diverse elements present in the human microbiota.

Imaging Flow Cytometry to Study Biofilm-Associated Microbial Aggregates

The aim of the research was to design an advanced analytical tool for the precise characterization of microbial aggregates from biofilms formed on food-processing surfaces. The approach combined imaging flow cytometry with a machine learning-based interpretation protocol. Biofilm samples were collected from three diagnostic points of the food-processing lines at two independent time points. The samples were investigated for the complexity of microbial aggregates and cellular metabolic activity. Thus, aggregates and singlets of biofilm-associated microbes were simultaneously examined for the percentages of active, mid-active, and nonactive (dead) cells to evaluate the physiology of the microbial cells forming the biofilm structures. The tested diagnostic points demonstrated significant differences in the complexity of microbial aggregates. The significant percentages of the bacterial aggregates were associated with the dominance of active microbial cells, e.g., 75.3% revealed for a mushroom crate. This confirmed the protective role of cellular aggregates for the survival of active microbial cells. Moreover, the approach enabled discriminating small and large aggregates of microbial cells. The developed tool provided more detailed characteristics of bacterial aggregates within a biofilm structure combined with high-throughput screening potential. The designed methodology showed the prospect of facilitating the detection of invasive biofilm forms in the food industry environment.

Flow cytometry method for absolute counting and single-cell phenotyping of mycobacteria

Detection and accurate quantitation of viable Mycobacterium tuberculosis is fundamental to understanding mycobacterial pathogenicity, tuberculosis (TB) disease progression and outcomes; TB transmission; drug action, efficacy and drug resistance. Despite this importance, methods for determining numbers of viable bacilli are limited in accuracy and precision owing to inherent characteristics of mycobacterial cell biology—including the tendency to clump, and “differential” culturability—and technical challenges consequent on handling an infectious pathogen under biosafe conditions. We developed an absolute counting method for mycobacteria in liquid cultures using a bench-top flow cytometer, and the low-cost fluorescent dyes Calcein-AM (CA) and SYBR-gold (SG). During exponential growth CA + cell counts are highly correlated with CFU counts and can be used as a real-time alternative to simplify the accurate standardisation of inocula for experiments. In contrast to CFU counting, this method can detect and enumerate cell aggregates in samples, which we show are a potential source of variance and bias when using established methods. We show that CFUs comprise a sub-population of intact, metabolically active mycobacterial cells in liquid cultures, with CFU-proportion varying by growth conditions. A pharmacodynamic application of the flow cytometry method, exploring kinetics of fluorescent probe defined subpopulations compared to CFU is demonstrated. Flow cytometry derived Mycobacterium bovis bacillus Calmette-Guérin (BCG) time-kill curves differ for rifampicin and kanamycin versus isoniazid and ethambutol, as do the relative dynamics of discrete morphologically-distinct subpopulations of bacilli revealed by this high-throughput single-cell technique.

Beating the Bio-Terror Threat with Rapid Antimicrobial Susceptibility Testing

A bioterror event using an infectious bacterium may lead to catastrophic outcomes involving morbidity and mortality as well as social and psychological stress. Moreover, a bioterror event using an antibiotic resistance engineered bacterial agent may raise additional concerns. Thus, preparedness is essential to preclude and control the dissemination of the bacterial agent as well as to appropriately and promptly treat potentially exposed individuals or patients. Rates of morbidity, death, and social anxiety can be drastically reduced if the rapid delivery of antimicrobial agents for post-exposure prophylaxis and treatment is initiated as soon as possible. Availability of rapid antibiotic susceptibility tests that may provide key recommendations to targeted antibiotic treatment is mandatory, yet, such tests are only at the development stage. In this review, we describe the recently published rapid antibiotic susceptibility tests implemented on bioterror bacterial agents and discuss their assimilation in clinical and environmental samples.

Investigation of the pathway dependent endocytosis of gold nanoparticles by surface-enhanced Raman scattering

Endocytosis is a critical mechanism providing not only internalization of biomacromolecular structures but also communication with the environment where cells reside. Due to being the first step at the interaction interface, the route of cellular uptake has a major role governing the intracellular destinations and behaviors of molecular and non-molecular species including nanoparticles. To this end, various methods employing variety of techniques are investigated. In this study, surface-enhanced Raman spectroscopy (SERS) based approach for the investigation of endocytosis of gold nanoparticles (AuNPs) is reported. Internalization pathways of AuNPs were examined by flow cytometry via specific inhibitors for each endocytosis pathway type using three model cell lines Beas-2b, A549 and PNT1A. Macropinocytosis was blocked by cytochalasin D (CytoD), clathrin mediated endocytosis (CME) by sucrose (Scr), and caveolae mediated endocytosis (CE) by filipin (Fil). The results showed that cell type dependent AuNPs internalization affects not only the response of the cells to the inhibitors but also the obtained SERS spectra. SERS spectra of PNT1A cells treated with inhibitors was influenced most. The inhibition of each endocytosis pathway significantly affected the SERS spectral pattern and the spectral changes in different endocytosis pathways were clearly discriminated from each other. This means that SERS can significantly contribute to the investigation of different endosomal pathways from single living cells without any disruption of the cells or labeling.

Innovations to culturing the uncultured microbial majority

Despite the surge of microbial genome data, experimental testing is important to confirm inferences about the cell biology, ecological roles and evolution of microorganisms. As the majority of archaeal and bacterial diversity remains uncultured and poorly characterized, culturing is a priority. The growing interest in and need for efficient cultivation strategies has led to many rapid methodological and technological advances. In this Review, we discuss common barriers that can hamper the isolation and culturing of novel microorganisms and review emerging, innovative methods for targeted or high-throughput cultivation. We also highlight recent examples of successful cultivation of novel archaea and bacteria, and suggest key microorganisms for future cultivation attempts.

Flow-FISH Using Nucleic Acid Mimic Probes for the Detection of Bacteria

Flow-Fluorescence in situ hybridization (Flow-FISH) enables multiparametric high-throughput detection of target nucleic acid sequences at the single cell-level, allowing an accurate quantification of different cell populations by using a combination of flow cytometry and fluorescent in situ hybridization (FISH). In this chapter, a flow-FISH protocol is described with labeled nucleic acid mimics (NAMs) (e.g. LNA/2'OMe and PNA) acting as the reporter molecules. This protocol allows for the specific detection of bacterial cells. Hence, this protocol can be carried out with minor adjustments, in order to simultaneously detect different species of bacteria in different types of clinical, food, or environmental samples.

High-throughput functional profiling of single adherent cells via hydrogel drop-screen

Single-adherent-cell phenotyping on an extracellular matrix (ECM) is essential to determine cellular biological functions, such as morphological adaptations and biomolecule secretions, correlated to medical treatments and metastasis, yet there is no available platform for such high-throughput screening. Here, a novel hydrogel drop-screen device was developed to rapidly measure large-scale single-cell morphologies and multiple secretions on substrates for phenotype profiling. Single cells were first anchored to microfluidically fabricated gelatin particles providing mechanical stimulations similar to those from ECM in vivo. The cellular morphologies were then examined by quantifying the amount of cytoskeleton expressed on the particles. With droplet encapsulation, adherent single-cell multiplexed secretion analysis of a disintegrin and metalloproteinases (ADAMs) and matrix metalloproteinases (MMPs) was conducted at a throughput of ∼102 cells per second, revealing distinct functional heterogeneities associated with extracellular mechanical stimulations. The level of cell heterogeneity increased with increasing substrate stuffiness. Moreover, because of the promising screening capability, a database related to both nontumorigenic and tumorigenic breast cells (MCF10A, MCF-7, and MDA-MB-231) was constructed. The respective cell distributions and heterogeneities based on the morphologies and secreted bioindicators, such as MMP-2, MMP-3, MMP-9, and ADAM-8, were measured and found to correspond to the progress of tumor metastasis.

Pathogens electrogenicity as a tool for in-situ metabolic activity monitoring and drug assessment in biofilms

Concerns regarding increased antibiotic resistance arising from the emergent properties of biofilms have spurred interest in the discovery of novel antibiotic agents and techniques to directly estimate metabolic activity in biofilms. Although a number of methods have been developed to quantify biofilm formation, real-time quantitative assessment of metabolic activity in label-free biofilms remains a challenge. Production of electrical current via extracellular electron transport (EET) has recently been found in pathogens and appears to correlate with their metabolic activity. Accordingly, monitoring the production of electrical currents as an indicator of cellular metabolic activity in biofilms represents a new direction for research aiming to assess and screen the effects of antimicrobials on biofilm activity. In this article, we reviewed EET-capable pathogens and the methods to monitor biofilm activity to discuss advantages of using the capability of pathogens to produce electrical currents and effective combination of these methods. Moreover, we discussed EET mechanisms by pathogenic and environmental bacteria and open questions for the physiological roles of EET in pathogen's biofilm. The present limitations and possible future directions of in situ biofilm metabolic activity assessment for large-scale screening of antimicrobials are also discussed.

Bioprospecting of Novel Extremozymes From Prokaryotes-The Advent of Culture-Independent Methods

Extremophiles are remarkable organisms that thrive in the harshest environments on Earth, such as hydrothermal vents, hypersaline lakes and pools, alkaline soda lakes, deserts, cold oceans, and volcanic areas. These organisms have developed several strategies to overcome environmental stress and nutrient limitations. Thus, they are among the best model organisms to study adaptive mechanisms that lead to stress tolerance. Genetic and structural information derived from extremophiles and extremozymes can be used for bioengineering other nontolerant enzymes. Furthermore, extremophiles can be a valuable resource for novel biotechnological and biomedical products due to their biosynthetic properties. However, understanding life under extreme conditions is challenging due to the difficulties of in vitro cultivation and observation since > 99% of organisms cannot be cultivated. Consequently, only a minor percentage of the potential extremophiles on Earth have been discovered and characterized. Herein, we present a review of culture-independent methods, sequence-based metagenomics (SBM), and single amplified genomes (SAGs) for studying enzymes from extremophiles, with a focus on prokaryotic (archaea and bacteria) microorganisms. Additionally, we provide a comprehensive list of extremozymes discovered via metagenomics and SAGs.

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.