Fluorescence-based tools for single-cell approaches in food microbiology

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.

Recombinant Protein Production with Escherichia coli in Glucose and Glycerol Limited Chemostats

Recently, there has been a resurgence of interest in continuous bioprocessing as a cost-optimised production strategy, driven by a rising global requirement for recombinant proteins used as biological drugs. This strategy could provide several benefits over traditional batch processing, including smaller bioreactors, smaller facilities, and overall reduced plant footprints and investment costs. Continuous processes may also offer improved product quality and minimise heterogeneity, both in the culture and in the product. In this paper, a model protein, green fluorescent protein (GFP) mut3*, was used to test the recombinant protein expression in an Escherichia coli strain with industrial relevance grown in chemostat. An important factor in enabling stable productivity in continuous cultures is the carbon source. We have studied the viability and heterogeneity of the chemostat cultures using a chemically defined medium based on glucose or glycerol as the single carbon source. As a by-product of biodiesel production, glycerol is expected to become a sustainable alternative substrate to glucose. We have found that although glycerol gives a higher cell density, it also generates higher heterogeneity in the culture and a less stable recombinant protein production. We suggest that manipulating the balance between different subpopulations to increase the proportion of productive cells may be a possible solution for making glycerol a successful alternative to glucose.

A cytofluorimetric analysis of a Saccharomyces cerevisiae population cultured in a fed-batch bioreactor

The yeast Saccharomyces cerevisiae is a reference model system and one of the widely used microorganisms in many biotechnological processes. In industrial yeast applications, combined strategies aim to maximize biomass/product yield, with the fed-batch culture being one of the most frequently used. Flow cytometry (FCM) is widely applied in biotechnological processes and represents a key methodology to monitor cell population dynamics. We propose here an application of FCM in the analysis of yeast cell cycle along the time course of a typical S. cerevisiae fed-batch culture. We used two different dyes, SYTOX Green and SYBR Green, with the aim to better define each stage of cell cycle during S. cerevisiae fed-batch culture. The results provide novel insights in the use of FCM cell cycle analysis for the real-time monitoring of S. cerevisiae bioprocesses.

Rapid Detection of Escherichia coli Antibiotic Susceptibility Using Live/Dead Spectrometry for Lytic Agents

Antibiotic resistance is a serious threat to public health. The empiric use of the wrong antibiotic occurs due to urgency in treatment combined with slow, culture-based diagnostic techniques. Inappropriate antibiotic choice can promote the development of antibiotic resistance. We investigated live/dead spectrometry using a fluorimeter (Optrode) as a rapid alternative to culture-based techniques through application of the LIVE/DEAD® BacLightTM Bacterial Viability Kit. Killing was detected by the Optrode in near real-time when Escherichia coli was treated with lytic antibiotics-ampicillin and polymyxin B-and stained with SYTO 9 and/or propidium iodide. Antibiotic concentration, bacterial growth phase, and treatment time used affected the efficacy of this detection method. Quantification methods of the lethal action and inhibitory action of the non-lytic antibiotics, ciprofloxacin and chloramphenicol, respectively, remain to be elucidated.

Shaving and breaking bacterial chains with a viscous flow

Some food and ferment manufacturing steps such as spray-drying result in the application of viscous stresses to bacteria. This study explores how a viscous flow impacts both bacterial adhesion functionality and bacterial cell organization using a combined experimental and modeling approach. As a model organism we study Lactobacillus rhamnosus GG (LGG) "wild type" (WT), known to feature strong adhesive affinities towards beta-lactoglobulin thanks to pili produced by the bacteria on cell surfaces, along with three cell-surface mutant strains. Applying repeated flows with high shear-rates reduces bacterial adhesive abilities up to 20% for LGG WT. Bacterial chains are also broken by this process, into 2-cell chains at low industrial shear rates, and into single cells at very high shear rates. To rationalize the experimental observations we study numerically and analytically the Stokes equations describing viscous fluid flow around a chain of elastically connected spheroidal cell bodies. In this model setting we examine qualitatively the relationship between surface traction (force per unit area), a proxy for pili removal rate, and bacterial chain length (number of cells). Longer chains result in higher maximal surface tractions, particularly at the chain extremities, while inner cells enjoy a small protection from surface tractions due to hydrodynamic interactions with their neighbors. Chain rupture therefore may act as a mechanism to preserve surface adhesive functionality in bacteria.

Recent advances in single-cell analysis by mass spectrometry

Cells are the most basic structural units that play vital roles in the functioning of living organisms. Analysis of the chemical composition and content of a single cell plays a vital role in ensuring precise investigations of cellular metabolism, and is a crucial aspect of lipidomic and proteomic studies. In addition, structural knowledge provides a better understanding of cell behavior as well as the cellular and subcellular mechanisms. However, single-cell analysis can be very challenging due to the very small size of each cell as well as the large variety and extremely low concentrations of substances found in individual cells. On account of its high sensitivity and selectivity, mass spectrometry holds great promise as an effective technique for single-cell analysis. Numerous mass spectrometric techniques have been developed to elucidate the molecular profiles at the cellular level, including electrospray ionization mass spectrometry (ESI-MS), secondary ion mass spectrometry (SIMS), laser-based mass spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). In this review, the recent advances in single-cell analysis by mass spectrometry are summarized. The strategies of different ionization modes to achieve single-cell analysis are classified and discussed in detail.

Flow Cytometry Analysis of Antibacterial Effects of Universal Dentin Bonding Agents on Streptococcus mutans

There is no consensus on the antibacterial activity of dentin bonding systems (DBS). Many study models have been used to evaluate the antimicrobial activity of dental materials. In this study, a novel detection method, flow cytometry, was introduced. It allows for evaluation of the antibacterial activity of DBS, based on assessment of the disruption of the bacterial physical membrane induced by DBS. The aim of the study was to evaluate the antibacterial properties of selected dentin bonding systems against Streptococcus mutans. The highest antibacterial activity against S. mutans was observed for Adhese Universal (99.68% dead cells) and was comparable to that of Prime&Bond Universal, OptiBond Universal, or Clearfil Universal Bond Quick (p > 0.05). The lowest activity of all tested systems was displayed by the multi-mode adhesive, Universal Bond (12.68% dead bacteria cells), followed by the self-etch adhesive, OptiBond FL (15.58% dead bacteria cells). The present study showed that in the case of two-component DBS, the primer exhibited higher antimicrobial activity than the adhesive (or bond) itself.

True one cell chemical analysis: a review

The constantly growing field of True One Cell (TOC) analysis has provided important information on the direct chemical composition of various cells and cellular components.

Use of flow cytometry and total viable count to determine the effects of orange juice composition on the physiology of Escherichia coli

Orange juice (OJ) contains numerous compounds some of which are known to play key roles in growth and survival of bacteria. This study aimed to investigate the effects of natural or processing-induced variations in OJ composition on the physiology of Escherichia coli. OJ and model OJ (MOJ) samples containing various sugars, organic acids, amino acids, or ascorbic acid were inoculated with E. coli K-12 MG1655 in different growth phases. The culturability, viability, and physiology of the cells were investigated during storage using plate counting and flow cytometry. Generally, stationary-phase cells displayed the greatest survival in both MOJ and OJ. Increase in incubation temperature from 4 to 22.5ºC caused a significant decrease in both healthy and culturable cell populations. Supplementation of MOJ with ascorbic acid and amino acids increased both the viability and culturability of the cells. Similar trends were observed in amino acid-supplemented OJ, albeit at a slower rate. In contrast, variations in sugar or organic acid composition had negligible effects on the physiological status of the cells. In summary, natural variation in ascorbic acid or amino acid concentrations could potentially have an adverse effect on the microbiological safety of orange juice.

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

BACKGROUND

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

RESULTS

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

CONCLUSION

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

Antibacterial Activity of Commercial Dentine Bonding Systems against E. faecalis-Flow Cytometry Study

Literature presents inconsistent results on the antibacterial activity of dentine bonding systems (DBS). Antibacterial activity of adhesive systems depends on several factors, including composition and acidity. Flow cytometry is a novel detection method to measure multiple characteristics of a single cell: total cell number, structural (size, shape), and functional parameters (viability, cell cycle). The LIVE/DEAD^® BacLight™ bacterial viability assay was used to evaluate an antibacterial activity of DBS by assessing physical membrane disruption of bacteria mediated by DBS. Ten commercial DBSs: four total-etching (TE), four self-etching (SE) and two selective enamel etching (SEE) were tested. Both total-etching DBS ExciTE F and OptiBond Solo Plus showed comparatively low antibacterial activity against E. faecalis. The lowest activity of all tested TE systems showed Te-Econom Bond. Among SE DBS, G-ænial Bond (92.24% dead cells) followed by Clearfil S3 Bond Plus (88.02%) and Panavia F 2.0 ED Primer II (86.67%) showed the highest antibacterial activity against E. faecalis, which was comparable to isopropranol (positive control). In the present study, self-etching DBS exhibited higher antimicrobial activity than tested total-etching adhesives against E. faecalis.

Current Perspectives on Viable but Non-culturable State in Foodborne Pathogens

The viable but non-culturable (VBNC) state, a unique state in which a number of bacteria respond to adverse circumstances, was first discovered in 1982. Unfortunately, it has been reported that many foodborne pathogens can be induced to enter the VBNC state by the limiting environmental conditions during food processing and preservation, such as extreme temperatures, drying, irradiation, pulsed electric field, and high pressure stress, as well as the addition of preservatives and disinfectants. After entering the VBNC state, foodborne pathogens will introduce a serious crisis to food safety and public health because they cannot be detected using conventional plate counting techniques. This review provides an overview of the various features of the VBNC state, including the biological characteristics, induction and resuscitation factors, formation and resuscitation mechanisms, detection methods, and relationship to food safety.

Recent Advances on Multi-Parameter Flow Cytometry to Characterize Antimicrobial Treatments

The investigation on antimicrobial mechanisms is a challenging and crucial issue in the fields of food or clinical microbiology, as it constitutes a prerequisite to the development of new antimicrobial processes or compounds, as well as to anticipate phenomenon of microbial resistance. Nowadays it is accepted that a cells population exposed to a stress can cause the appearance of different cell populations and in particular sub-lethally compromised cells which could be defined as viable but non-culturable (VBNC). Recent advances on flow cytometry (FCM) and especially on multi-parameter flow cytometry (MP-FCM) provide the opportunity to obtain high-speed information at real time on damage at single-cell level. This review gathers MP-FCM methodologies based on individual and simultaneous staining of microbial cells employed to investigate their physiological state following different physical and chemical antimicrobial treatments. Special attention will be paid to recent studies exploiting the possibility to corroborate MP-FCM results with additional techniques (plate counting, microscopy, spectroscopy, molecular biology techniques, membrane modeling) in order to elucidate the antimicrobial mechanism of action of a given antimicrobial treatment or compound. The combination of MP-FCM methodologies with these additional methods is namely a promising and increasingly used approach to give further insight in differences in microbial sub-population evolutions in response to antimicrobial treatments.

Individual cell heterogeneity in Predictive Food Microbiology: Challenges in predicting a "noisy" world

Gene expression is a fundamentally noisy process giving rise to a significant cell to cell variability at the phenotype level. The phenotypic noise is manifested in a wide range of microbial traits. Heterogeneous behavior of individual cells is observed at the growth, survival and inactivation responses and should be taken into account in the context of Predictive Food Microbiology (PMF). Recent methodological advances can be employed for the study and modeling of single cell dynamics leading to a new generation of mechanistic models which can provide insight into the link between phenotype, gene-expression, protein and metabolic functional units at the single cell level. Such models however, need to deal with an enormous amount of interactions and processes that influence each other, forming an extremely complex system. In this review paper, we discuss the importance of noise and present the future challenges in predicting the "noisy" microbial responses in foods.

Biochemical Engineering Approaches for Increasing Viability and Functionality of Probiotic Bacteria

The literature presents a growing body of evidence demonstrating the positive effect of probiotics on health. Probiotic consumption levels are rising quickly in the world despite the fluctuation of their viability and functionality. Technological methods aiming at improving probiotic characteristics are thus highly wanted. However, microbial metabolic engineering toolbox is not available for this kind of application. On the other hand, basic microbiology teaches us that bacteria are able to exhibit adaptation to external stresses. It is known that adequately applied sub-lethal stress, i.e., controlled in amplitude and frequency at a given stage of the culture, is able to enhance microbial robustness. This property could be potentially used to improve the viability of probiotic bacteria, but some technical challenges still need to be overcome before any industrial implementation. This review paper investigates the different technical tools that can be used in order to define the proper condition for improving viability of probiotic bacteria and their implementation at the industrial scale. Based on the example of Bifidobacterium bifidum, potentialities for simultaneously improving viability, but also functionality of probiotics will be described.

Colonial vs. planktonic type of growth: mathematical modeling of microbial dynamics on surfaces and in liquid, semi-liquid and solid foods

Predictive models are mathematical expressions that describe the growth, survival, inactivation, or biochemical processes of foodborne bacteria. During processing of contaminated raw materials and food preparation, bacteria are entrapped into the food residues, potentially transferred to the equipment surfaces (abiotic or inert surfaces) or cross-contaminate other foods (biotic surfaces). Growth of bacterial cells can either occur planktonically in liquid or immobilized as colonies. Colonies are on the surface or confined in the interior (submerged colonies) of structured foods. For low initial levels of bacterial population leading to large colonies, the immobilized growth differs from planktonic growth due to physical constrains and to diffusion limitations within the structured foods. Indeed, cells in colonies experience substrate starvation and/or stresses from the accumulation of toxic metabolites such as lactic acid. Furthermore, the micro-architecture of foods also influences the rate and extent of growth. The micro-architecture is determined by (i) the non-aqueous phase with the distribution and size of oil particles and the pore size of the network when proteins or gelling agent are solidified, and by (ii) the available aqueous phase within which bacteria may swarm or swim. As a consequence, the micro-environment of bacterial cells when they grow in colonies might greatly differs from that when they grow planktonically. The broth-based data used for modeling (lag time and generation time, the growth rate, and population level) are poorly transferable to solid foods. It may lead to an over-estimation or under-estimation of the predicted population compared to the observed population in food. If the growth prediction concerns pathogen bacteria, it is a major importance for the safety of foods to improve the knowledge on immobilized growth. In this review, the different types of models are presented taking into account the stochastic behavior of single cells in the growth of a bacterial population. Finally, the recent advances in the rules controlling different modes of growth, as well as the methodological approaches for monitoring and modeling such growth are detailed.