Potential of Flow Cytometric Approaches in Food Industry: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Yvaine Wei and Version 1 by Elena Zand.

Microbial contamination, including the carryover of infectious microbes, is a global public health concern. An alternative technique that serves as a powerful, rapid, and highly sensitive method for the specific and non-specific detection, monitoring, enumeration, and characterization of microorganisms is flow cytometry (FCM).

  • flow cytometry
  • fluorescence in situ hybridization
  • microbial contamination

1. Introduction

Microbial resistance against extrinsic factors is related to their fast adaptability and the formation of microbial biofilms, which can protect spoilage microorganisms and bacterial pathogens from chemical and physical actions [2,3,5,6][1][2][3][4]. Frequently detected pathogens include Salmonella spp., Listeria monocytogenes, Escherichia coli, Shigella spp., Vibrio spp., Campylobacter jejuni, and Yersinia spp., whereas frequent spoilage microorganisms are, for instance, Acinetobacter spp., Pseudomonas spp. or Botrytis spp. [7,8,9,10][5][6][7][8].

Effective qualitative and quantitative monitoring and detection tools are required to minimize the contamination risk. The gold standard among detection tools is still the conventional plating method, with its high sensitivity and selectivity [12,13][9][10]. However, plating is time-consuming, labor-intensive, and detects only viable and cultivable microbes [7,14][5][11]. Complementarily, there are several rapid and culture-independent approaches that overcome these limitations of plating. Among the most widely-known detection methods are molecular methods such as polymerase chain reaction (PCR) or enzyme-linked immunosorbent assay (ELISA) methods [7,13,15,16][5][10][12][13]. Some molecular methods are vulnerable to interference from inhibitory compounds (i.e., the lipid content) or can affect complex matrices such as food. For highly sensitive methods such as PCR, contamination can easily lead to false results. In addition, PCR may be unable to distinguish between viable and non-viable cells [13,17][10][14].

Flow cytometry (FCM) allows a culture-independent quantitative count of microbial cells. In addition, flow cytometry provides information on the physiological and structural characteristics of microbial cells and their viability and can therefore be used as an additional characterization tool. Rapid and reliable detection, quantification and characterization of foodborne pathogens are of great interest to the food industry in order to minimize foodborne diseases [19][15]. The rapid techniques used to detect foodborne pathogens can be categorized into immunological, biosensor, and nucleic acid-based methods [20][16]. Fluorescence in situ hybridization (FISH) is a nucleic acid-based method and is mainly applied in the medical and diagnostic field [19][15].

2. Non-Specific State-of-the-Art Flow Cytometric Applications for Detection and Monitoring

2.1. FCM Principle and Detection Mechanisms

In principle, FCM allows the analysis of the chemical and physical characteristics of any suspended single particle. The optical system of an FCM is illustrated in Figure 1. Usually, it contains the following: a flow chamber, a source of light (i.e., a laser or mercury lamp), dichroic mirrors to bring the light beam into focus, bandpass filters for the detection of different wavelengths, detectors (i.e., photodiodes (PD) and photomultiplier tubes (PMT)) for the detection and amplification of the signals, as well as a data processing unit [36,37,38,39][17][18][19][20]. After transferring the particles into a laminar flow of sheath fluid, scattered light and fluorescence signals are utilized one by one at the interrogation point of the laser beam. To differentiate cells regarding their morphology (i.e., particle size or granularity), forward- (FSC) or side-scattered light (SSC) is detected, respectively. Aside from scattered light, fluorescence appears when fluorochromes or particles labeled with them emit light, which is then excited by a beam of an appropriate wavelength. Some cells can emit fluorescence without fluorochromes, which is called autofluorescence. This phenomenon can be either beneficial for analysis or can impede other fluorescence signals. Most of the time, autofluorescence alone is not sufficient to detect and distinguish between cell populations. Thus, FCM protocols include a staining step with one or more fluorescent dyes before sample analysis [17,40][14][21].
Foods 10 03112 g001 550
Figure 1. Principle of a flow cytometer (FCM). Forward-scattered light (FSC); side-scattered light (SSC); photomultiplier tubes (PMT, fluorescence detectors); detectors at a specific wavelength (FL-1, FL-2, and FL-3); (made in ©BioRender—biorender.com, Toronto, ON, Canada (accessed on 8 June 2021)).

3.2. Food-Related FCM Applications

2.2. Food-Related FCM Applications

For food-related research, FCM is mainly used for the performance testing of food preservation or disinfection approaches, i.e., sodium hypochlorite or peracetic acid disinfection, ultraviolet light (UV-C), supercritical CO2 pasteurization, ohmic heating applications, non-thermal inactivation technologies, including pulsed electric fields and cold atmospheric pressure plasma treatment, as well as natural preservatives such as essential oils [47,54,55,56,57,58,59,60,61,62][22][23][24][25][26][27][28][29][30][31]. The most commonly investigated microorganism was E. coli [3,55,56,57,59,60,61,63,64,65,66][2][24][25][26][28][29][30][32][33][34][35].
A study by Coronel-Leon et al. [67][36] tested the antimicrobial effect of the surfactant Nα-lauroyl L arginine ethylester monohydrochloride as a food additive and used FCM to understand the inactivation mechanisms better and to indicate the presence of sublethally injured cells. The most popular cell target for viability staining is membrane integrity, in which DNA-intercalating dyes are applied. Moreover, esterase activity is a suitable detection target, as potential sublethal injured cells after inefficient inactivation procedures are observed. For this purpose, FDA or cFDA are combined with PI [44,59][37][28]. Tamburini et al. [60][29] concluded that FCM was the most suitable viability assessment method compared to PCR, plate counts, and fluorescence microscopy.
FCM is not only a suitable tool for detecting sublethally injured cells but also for cells in the viable but non-culturable (VBNC) state. This is important as environmental stresses present during food processes, such as temperature change, pH, or the absence of nutrients, can introduce cells into the VBNC state. With culture-based techniques, only viable and culturable cells are detected. VBNC cells, however, are able to resuscitate and become culturable again [68][38]. Thus, FCM viability staining, in combination with plate count, can be conducted to detect the VBNC state of cells. 

43. Specific State-of-the-Art Flow-FISH Methods and Applications for Monitoring and Detection

4.1. Principle of FISH

3.1. Principle of FISH

DeLong, Wickham, and Pace [96][39] were the first to describe FISH for microorganisms. The method based on the use of fluorescently-labeled oligonucleotide probes that target a specific region of rRNA (16S/23S in Bacteria/Archaea or 18S/28S in Eukarya) enables the specific identification of microorganisms from the domain to the subspecies level [96,97,98,99][39][40][41][42]. It is now a well-established technique [100][43]. In addition to oligonucleotide probes, fluorescently labeled antibodies can also be used for the identification of microbial cells, but the low cost of oligonucleotide probes and the availability of a large number of rRNA sequences, as well as the associated possibility of the in silico design of oligonucleotide probes, have led to the preferred use of oligonucleotide probes [101][44].
In contrast to culture-dependent methods, microorganisms that are difficult to cultivate can be identified. FISH visualizes whole cells, and since abundant structures in living cells are targeted, it is possible to distinguish between viable and dead cells, which is the main advantage over other molecular techniques such as PCR-based methods [1,98][45][41]. Additionally, the direct observation of cells within their native environment is possible [102,103][46][47]. Flow-FISH, a combination of FISH and flow cytometry, was described in the early 1990s by R.I. Amann et al. [104][48]. The advantage of Flow-FISH is that the method enables the rapid analysis of larger sample volumes, while being more convenient since manual counting is omitted [105][49].
In general, FISH consists of four preparation steps: (i) fixation and permeabilization, (ii) probe hybridization with the target sequence, (iii) washing of excess and unbound probes, and (iv) observation of cells with epifluorescence microscopic techniques, scanning microscopy, or flow cytometry (Flow-FISH) [98,100,106][41][43][50]. Sampling, pre-preparation, and hybridization steps, compared to a typical FCM protocol for quantitative analysis, are illustrated in Figure 2. 
Foods 10 03112 g002 550
Figure 2. Standard sample preparation protocols and detection mechanisms of specific flow fluorescence in situ hybridization (FISH) and non-specific flow cytometry (FCM). LNA, locked nucleic acids; PNA, peptide nucleic acids.

4.2. Flow-FISH in Food Microbiology

3.2. Flow-FISH in Food Microbiology

Flow-FISH in food microbiology is used to detect microorganisms in food products or for biofilm studies on abiotic surfaces, e.g., food contact surfaces. For food products, the range of food products examined is wide, from vegetables, meat, fish products, and dairy products to vinegar, wine, beer, and water.
FISH DNA or PNA probes are used for biofilm characterization, and the analyzed biofilms are either natural biofilms or those formed under laboratory conditions. FISH was used by Almeida, Azevedo, Santos, Keevil, and Vieira [158][51] to characterize and quantify the biofilm formation of S. enterica, L. monocytogenes, and E. coli on different surfaces (e.g., glass, stainless steel) using PNA probes. Stainless steel was also used as a surface to capture biofilm formation of various Arcobacter species using FISH in a study by Šilhová, Moťková, Šilha, and Vytřasová [161][52]. Bragança, Azevedo, Simões, Keevil, and Vieira [159][53] screened natural biofilms in a drinking water distribution system for the occurrence of H. pylori using PNA-FISH and evaluated the composition of natural biofilms from conveyors in breweries.

References

  1. Havelaar, A.H.; Brul, S.; De Jong, A.; De Jonge, R.; Zwietering, M.H.; Ter Kuile, B.H. Future challenges to microbial food safety. Int. J. Food Microbiol. 2010, 139, S79–S94.
  2. Juzwa, W.; Duber, A.; Myszka, K.; Bialas, W.; Czaczyk, K. Identification of microbes from the surfaces of food-processing lines based on the flow cytometric evaluation of cellular metabolic activity combined with cell sorting. Biofouling 2016, 32, 841–851.
  3. Wang, R. Biofilms and meat safety: A mini-review. J. Food Prot. 2019, 82, 120–127.
  4. Weber, M.; Liedtke, J.; Plattes, S.; Lipski, A. Bacterial community composition of biofilms in milking machines of two dairy farms assessed by a combination of culture-dependent and –independent methods. PLoS ONE 2019, 14, e0222238.
  5. Hameed, S.; Xie, L.; Ying, Y. Conventional and emerging detection techniques for pathogenic bacteria in food science: A review. Trends Food Sci. Technol. 2018, 81, 61–73.
  6. Marušić, A. Food safety and security: What were favourite topics for research in the last decade? J. Glob. Health 2011, 1, 72–78.
  7. Pepe, T.; De Dominicis, R.; Esposito, G.; Ventrone, I.; Fratamico, P.M.; Cortesi, M.L. Detection of Campylobacter from poultry carcass skin samples at slaughter in Southern Italy. J. Food Prot. 2009, 72, 1718–1721.
  8. Rohde, A.; Hammerl, J.A.; Al Dahouk, S. Detection of foodborne bacterial zoonoses by fluorescence in situ hybridization. Food Control 2016, 69, 297–305.
  9. Ge, B.; Meng, J. Advanced technologies for pathogen and toxin detection in foods: Current applications and future directions. JALA J. Assoc. Lab. Autom. 2009, 14, 235–241.
  10. Rajapaksha, P.; Elbourne, A.; Gangadoo, S.; Brown, R.; Cozzolino, D.; Chapman, J. A review of methods for the detection of pathogenic microorganisms. Analyst 2019, 144, 396–411.
  11. Velusamy, V.; Arshak, K.; Korostynska, O.; Oliwa, K.; Adley, C. An overview of foodborne pathogen detection: In the perspective of biosensors. Biotechnol. Adv. 2010, 28, 232–254.
  12. Ricke, S.C.; Feye, K.M.; Chaney, W.E.; Shi, Z.; Pavlidis, H.; Yang, Y. Developments in rapid detection methods for the detection of foodborne campylobacterin the United States. Front. Microbiol. 2019, 10, 3280.
  13. Wei, Q.Y.; Wang, X.M.; Sun, D.W.; Pu, H.B. Rapid detection and control of psychrotrophic microorganisms in cold storage foods: A review. Trends Food Sci. Technol. 2019, 86, 453–464.
  14. Safford, H.R.; Bischel, H.N. Flow cytometry applications in water treatment, distribution, and reuse: A review. Water Res. 2019, 151, 110–133.
  15. Dias, G.; Rathnayaka, U. Fluorescence in situ hybridization (FISH) in food pathogen detection. Int. J. Mol. Biol. 2018, 3, 143–149.
  16. Law, J.W.-F.; Ab Mutalib, N.-S.; Chan, K.-G.; Lee, L.-H. Rapid methods for the detection of foodborne bacterial pathogens: Principles, applications, advantages and limitations. Front. Microbiol. 2015, 5, 770.
  17. Comas-Riu, J.; Rius, N. Flow cytometry applications in the food industry. J. Ind. Microbiol. Biotechnol. 2009, 36, 999–1011.
  18. Paparella, A.; Serio, A.; Chaves, C. Flow cytometry applications in food safety studies. Flow Cytom.-Recent Perspect. 2012, 69–102.
  19. Veal, D.A.; Deere, D.; Ferrari, B.; Piper, J.; Attfield, P.V. Fluorescence staining and flow cytometry for monitoring microbial cells. J. Immunol. Methods 2000, 243, 191–210.
  20. Wu, L.N.; Wang, S.; Song, Y.Y.; Wang, X.; Yan, X.M. Applications and challenges for single-bacteria analysis by flow cytometry. Sci. China-Chem. 2016, 59, 30–39.
  21. Wilkinson, M.G. Flow cytometry as a potential method of measuring bacterial viability in probiotic products: A review. Trends Food Sci. Technol. 2018, 78, 1–10.
  22. Tracy, B.P.; Gaida, S.M.; Papoutsakis, E.T. Flow cytometry for bacteria: Enabling metabolic engineering, synthetic biology and the elucidation of complex phenotypes. Curr. Opin. Biotechnol. 2010, 21, 85–99.
  23. Barros, C.P.; Pires, R.P.S.; Guimarães, J.T.; Abud, Y.K.D.; Almada, C.N.; Pimentel, T.C.; Sant’Anna, C.; De-Melo, L.D.B.; Duarte, M.C.K.H.; Silva, M.C.; et al. Ohmic heating as a method of obtaining paraprobiotics: Impacts on cell structure and viability by flow cytometry. Food Res. Int. 2021, 140, 110061.
  24. Braschi, G.; Patrignani, F.; Siroli, L.; Lanciotti, R.; Schlueter, O.; Froehling, A. Flow Cytometric Assessment of the Morphological and Physiological Changes of Listeria monocytogenes and Escherichia coli in Response to Natural Antimicrobial Exposure. Front. Microbiol. 2018, 9, 2783.
  25. Fröhling, A.; Baier, M.; Ehlbeck, J.; Knorr, D.; Schlüter, O. Atmospheric pressure plasma treatment of Listeria innocua and Escherichia coli at polysaccharide surfaces: Inactivation kinetics and flow cytometric characterization. Innov. Food Sci. Emerg. Technol. 2012, 13, 142–150.
  26. Fröhling, A.; Wienke, M.; Rose-Meierhofer, S.; Schlüter, O. Improved method for mastitis detection and evaluation of disinfectant efficiency during milking process. Food Bioprocess Technol. 2010, 3, 892–900.
  27. Jaeger, H.; Schulz, A.; Karapetkov, N.; Knorr, D. Protective effect of milk constituents and sublethal injuries limiting process effectiveness during PEF inactivation of Lb. rhamnosus. Int. J. Food Microbiol. 2009, 134, 154–161.
  28. Schenk, M.; Raffellini, S.; Guerrero, S.; Blanco, G.A.; Alzamora, S.M. Inactivation of Escherichia coli, Listeria innocua and Saccharomyces cerevisiae by UV-C light: Study of cell injury by flow cytometry. LWT-Food Sci. Technol. 2011, 44, 191–198.
  29. Tamburini, S.; Ballarini, A.; Ferrentino, G.; Moro, A.; Foladori, P.; Spilimbergo, S.; Jousson, O. Comparison of quantitative PCR and flow cytometry as cellular viability methods to study bacterial membrane permeabilization following supercritical CO2 treatment. Microbiology 2013, 159, 1056–1066.
  30. Teixeira, P.; Fernandes, B.; Silva, A.M.; Dias, N.; Azeredo, J. Evaluation by flow cytometry of Escherichia coli viability in lettuce after disinfection. Antibiotics 2020, 9, 14.
  31. Zand, E.; Schottroff, F.; Steinacker, E.; Mae-Gano, J.; Schoenher, C.; Wimberger, T.; Wassermann, K.J.; Jaeger, H. Advantages and limitations of various treatment chamber designs for reversible and irreversible electroporation in life sciences. Bioelectrochemistry 2021, 141, 107841.
  32. Fröhling, A.; Schlüter, O. Flow cytometric evaluation of physico-chemical impact on Gram-positive and Gram-negative bacteria. Front. Microbiol. 2015, 6, 939.
  33. Khan, M.M.T.; Pyle, B.H.; Camper, A.K. Specific and Rapid Enumeration of Viable but Nonculturable and Viable-Culturable Gram-Negative Bacteria by Using Flow Cytometry. Appl. Environ. Microbiol. 2010, 76, 5088–5096.
  34. Mao, C.; Xue, C.; Wang, X.; He, S.; Wu, L.; Yan, X. Rapid quantification of pathogenic Salmonella Typhimurium and total bacteria in eggs by nano-flow cytometry. Talanta 2020, 217, 121020.
  35. Yu, M.X.; Wu, L.N.; Huang, T.X.; Wang, S.; Yan, X.M. Rapid detection and enumeration of total bacteria in drinking water and tea beverages using a laboratory-built high-sensitivity flow cytometer. Anal. Methods 2015, 7, 3072–3079.
  36. Coronel-Leon, J.; Lopez, A.; Espuny, M.J.; Beltran, M.T.; Molinos-Gomez, A.; Rocabayera, X.; Manresa, A. Assessment of antimicrobial activity of N-alpha-lauroyl arginate ethylester (LAE (R)) against Yersinia enterocolitica and Lactobacillus plantarum by flow cytometry and transmission electron microscopy. Food Control 2016, 63, 1–10.
  37. Carrillo, M.G.; Ferrario, M.; Guerrero, S. Effectiveness of UV-C light assisted by mild heat on Saccharomyces cerevisiae KE 162 inactivation in carrot-orange juice blend studied by flow cytometry and transmission electron microscopy. Food Microbiol. 2018, 73, 1–10.
  38. Barer, M.R.; Gribbon, L.T.; Harwood, C.R.; Nwoguh, C.E. The viable but non-culturable hypothesis and medical bacteriology. Rev. Med Microbiol. 1993, 4, 183–191.
  39. DeLong, E.F.; Wickham, G.S.; Pace, N.R. Phylogenetic stains: Ribosomal RNA-based probes for the identification of single cells. Science 1989, 243, 1360–1363.
  40. Amann, R.I.; Binder, B.J.; Olson, R.J.; Chisholm, S.W.; Devereux, R.; Stahl, D.A. Combination of 16S ribosomal-RNA-targeted oligonucleotide probes with flow-cytometry for analyzing mixed microbial-populations. Appl. Environ. Microbiol. 1990, 56, 1919–1925.
  41. Oliveira, R.; Almeida, C.; Azevedo, N.F. Detection of microorganisms by fluorescence in situ hybridization using peptide nucleic acid. In Peptide Nucleic Acids: Methods and Protocols; Nielsen, P.E., Ed.; Springer: New York, NY, USA, 2020; pp. 217–230.
  42. Wagner, M.; Haider, S. New trends in fluorescence in situ hybridization for identification and functional analyses of microbes. Curr. Opin. Biotechnol. 2012, 23, 96–102.
  43. Amann, R.; Fuchs, B.M.; Behrens, S. The identification of microorganisms by fluorescence in situ hybridisation. Curr. Opin. Biotechnol. 2001, 12, 231–236.
  44. Amann, R.; Fuchs, B.M. Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat. Rev. Microbiol. 2008, 6, 339–348.
  45. Rohde, A.; Hammerl, J.A.; Appel, B.; Dieckmann, R.; Al Dahouk, S. FISHing for bacteria in food—A promising tool for the reliable detection of pathogenic bacteria? Food Microbiol. 2015, 46, 395–407.
  46. Amann, R.I.; Ludwig, W.; Schleifer, K.H. Phylogenetic identification and in-situ detection of individual microbial-cells without cultivation. Microbiol. Rev. 1995, 59, 143–169.
  47. Bokulich, N.A.; Mills, D.A. Next-generation approaches to the microbial ecology of food fermentations. BMB Rep. 2012, 45, 377–389.
  48. Amann, R.I.; Krumholz, L.; Stahl, D.A. Fluorescent-oligonucleotide probing of whole cells for determinative, phylogenetic, and environmental studies in microbiology. J. Bacteriol. 1990, 172, 762–770.
  49. Gunasekera, T.S.; Veal, D.A.; Attfield, P.V. Potential for broad applications of flow cytometry and fluorescence techniques in microbiological and somatic cell analyses of milk. Int. J. Food Microbiol. 2003, 85, 269–279.
  50. Zwirglmaier, K. Fluorescence in situ hybridisation (FISH)—The next generation. FEMS Microbiol. Lett. 2005, 246, 151–158.
  51. Almeida, C.; Azevedo, N.F.; Santos, S.; Keevil, C.W.; Vieira, M.J. Correction: Discriminating multi-species populations in biofilms with peptide nucleic acid fluorescence in situ hybridization (PNA FISH). PLoS ONE 2013, 8, e14786.
  52. Šilhová, L.; Moťková, P.; Šilha, D.; Vytřasová, J. FISH detection of Campylobacter and Arcobacter adhered to stainless steel coupons. J. Microbiol. Biotechnol. Food Sci. 2015, 4, 347–351.
  53. Bragança, S.M.; Azevedo, N.F.; Simões, L.C.; Keevil, C.W.; Vieira, M.J. Use of fluorescent in situ hybridisation for the visualisation of Helicobacter pylori in real drinking water biofilms. Water Sci. Technol. 2007, 55, 387–393.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback