Next Article in Journal / Special Issue
Genome Comparison of ‘Candidatus Phytoplasma rubi’ with Genomes of Other 16SrV Phytoplasmas Highlights Special Group Features
Previous Article in Journal / Special Issue
Salmonella Enteritidis ST11 Prosthetic Valve Endocarditis Complicated by a Paravalvular Abscess: Case Report and Literature Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Microbiology
Volume 3
Issue 3
10.3390/applmicrobiol3030074
applmicrobiol-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

High-Throughput Viability Testing of Microbial Communities in a Probiotic Product Using Flow Cytometry

by
Joanna V. Pereira
1,2,
Hasinika K. A. H. Gamage
1,2,
Amy K. Cain
1,2,3,
Evan Hayes
4,
Ian T. Paulsen
1,2,3 and
Sasha G. Tetu
1,2,3,*
1
School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
2
Australian Research Council Industrial Transformation Training Centre for Facilitated Advancement of Australia’s Bioactives (FAAB), Sydney, NSW 2109, Australia
3
Australian Research Council Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, NSW 2109, Australia
4
Factors Group Australia, Sydney, NSW 2116, Australia
*
Author to whom correspondence should be addressed.
Appl. Microbiol. 2023, 3(3), 1068-1082; https://doi.org/10.3390/applmicrobiol3030074
Submission received: 27 July 2023 / Revised: 8 September 2023 / Accepted: 8 September 2023 / Published: 11 September 2023
(This article belongs to the Special Issue Exclusive Papers Collection of Editorial Board Members and Invited Scholars in Applied Microbiology (2023, 2024))
Browse Figure
Versions Notes

Abstract

:
There is growing scientific and commercial interest in multi-species probiotic products due to their potential benefits in maintaining gut health. Determining the viability of probiotic microorganisms in these products is essential to ensure that they confer maximal health benefits. The gold standard for enumerating probiotic viability is the plate count method. However, this may be inaccurate for enumerating mixed probiotic populations, with recognised limitations including difficulty measuring metabolically active yet unculturable, very slow growing microbes, microencapsulated, enteric coated microbes, or multi-strain formulations that require differing growth media. Here, we developed a flow-cytometry-based approach using SYTOXTM Green dye to assess the viability of probiotic microorganisms in a multi-species, fibre-containing probiotic product and compared this to the traditional plate count method. This method was suitable for enumerating both total bacterial cells and the viable cell fraction in the complete product mixture, and could also be used to assess how stressors, such as gastric digestion and exposure to bile acids, affect bacterial cell viability. Flow cytometry measurements routinely detected higher viable cell counts than plate counting. This work demonstrates that flow cytometry assays can be established as a suitable method for rapid enumeration of viable cells in complex, multi-species probiotics.

1. Introduction

The gut microbiome plays a crucial role in human metabolism and in maintaining the integrity of the gut mucosal layer and immune homeostasis [1]. Specific alterations in the composition and functions of the gut microbiome have been associated with various diseases [1]. Therapeutic modulation of the gut microbiome offers promising strategies to improve and maintain host health through prevention and treatment of diseases. Probiotics are studied and marketed for their ability to alter the gut microbiome and potential health-promoting benefits [2]. Consequently, the global market for probiotics has grown rapidly in the past few years [3,4]. Probiotics are defined as “live microorganisms, which, when administered in adequate amounts, confer a health benefit to the host” [5]. These probiotic microorganisms are generally consumed either in the form of capsules, powder, or tablets [6], or are contained within whole foods such as yogurt and milk [7,8], with the latter accounting for the largest share of the market [9].
Currently available probiotic products are diverse in their formulations and in the microbial strains they contain. Some products contain microorganisms belonging to a single genus, such as yogurt containing only Lactobacillus species [10], while other products contain mixed microbial populations including representatives of more than one genus, e.g., Lactobacillus, Bifidobacterium, and Streptococcus [2]. Lactic acid bacteria belonging to Lactobacillus and Bifidobacterium genera represent groups commonly included in probiotics [11]. In addition to bacterial strains, some probiotic products also contain yeasts such as Saccharomyces cerevisiae and Saccharomyces cerevisiae var. boulardii which have been shown to improve human gastrointestinal health [12,13]. Probiotic products can additionally contain certain additives that are included to support viability and help stimulate the growth of probiotic bacteria in the gut. Among the most common additives are prebiotics, which are non-digestible food ingredients that are utilised as substrates by selective intestinal microflora, including externally applied probiotics [2,14]. Examples of prebiotic additives include oligosaccharides, such as lactulose, oligofructose, galacto-oligosaccharides, and polysaccharides, such as inulin [2,15].
For probiotic microbes to confer their beneficial effects, they need to be viable and have the capability to survive and maintain their properties during manufacturing processes and storage conditions [15,16]. Ideally, once ingested, these viable cells will reach the colon, adhere to the intestinal mucosa, colonise the gut [17,18], and become a crucial effector of host–microbial interactions [19]. There are also site-specific interactions where probiotics can confer a benefit at a certain point of the gastrointestinal (GI) tract, and this benefit can differ if the probiotic is viable, dead, or degraded entirely [20]. Therefore, the viability of microorganisms is an important property of probiotic products and accurate enumeration is paramount to assessing and predicting the efficacy of products [21]. The current industry standard for enumerating probiotic viability is the traditional culture-based plate count method, where the product is spread onto solid microbiological media in petri dishes, then incubated at growth-permissive temperatures for some period of time, following which visible colony forming units are counted. However, this method is limited in its abilities to accurately enumerate viable cells, particularly from products that contain multiple species. Firstly, plate counting is reliant on the ability of bacterial cells to form colonies on the specific microbiological media under the set incubation conditions [22] and is recognised to underestimate viable microbes by excluding viable but non-culturable cells (VBNCs) [23]. Bacteria in the VBNC state exhibit metabolic activity and are considered to be live but are unable to grow on culture medium [24]. They enter this state to resist stress and it has been suggested that stress associated with probiotic product processing and formulation may result in some proportion of cells entering the VBNC state [25]. Another limitation of the plate count method is the assumption that one microbial cell will give rise to a single colony [25]; however, there is a risk of clumping in probiotic dry powders where multiple cells may give rise to a single colony, resulting in an underestimation of viable cells in products. Multi-species probiotic products containing bacterial and yeast strains require selective media [25] and additional additives (cysteine, antibiotics, etc.) to enumerate different sets of microorganisms within the mixture [19,25]. Furthermore, competition among mixed microbial populations for nutrients can favour the growth of certain microorganisms over others, with those that are slow growing most likely to be under-considered by culture-based enumeration techniques [25].
Non-culture-based techniques such as PCR-based methods (qPCR and digital PCR) and flow cytometry have been used previously to assess viable microbial cells from culture isolates and simple microbial mixtures [26,27]. Application of qPCR-based methods for mixed-species products have some drawbacks as routine enumeration techniques requiring validated primer sets for different members of the mixture and are limited in their capacity to determine absolute rather than relative abundances without the use of spiked-in synthetic standards [28]. For studies involving cross-domain mixtures, such approaches also must consider two different taxonomic marker genes, running separate reactions to amplify both the 16S rRNA gene in prokaryotes and the ITS regions in eukaryotes [28]. Furthermore, there are chances of PCR-related biases being introduced, for example, primer biases, and the complications of data interpretation due to varying 16S rRNA copy numbers in different bacteria. Flow-cytometric-based techniques have been shown to overcome many of the issues encountered with plate counting and those associated with PCR-based enumeration and have been successfully used to assess probiotic viability in products containing relatively simple mixes of bacteria [23,29,30].
Flow cytometry is a multi-parametric single cell analysis technique that identifies and enumerates cells from both homogeneous and heterogenous populations [22] based on the shape and size of cells [19]. Flow cytometry can be used to enumerate viable microbial cells by applying this technique in tandem with cell staining using established viability dyes such as SYTOXTM Green, a nucleic acid stain that indicates bacterial cell membrane integrity status, to help discriminate live cells from dead cells [23,29,30,31]. Other nucleic acid stains, such as propidium iodide (PI), SYTO, and combinations of nucleic acid dyes including SYTO 9/PI [4,23], and dyes that can discriminate based on metabolic activity such as 5-cyano-2,3-ditolyl tetrazolium chloride (CTC), are also used to assess the viability of bacterial cells using flow cytometry [32]. In some instances, flow cytometry is already used as an alternative for probiotic viability assessment [33]. The International Organization for Standardization and the International Dairy Federation have suggested the use of flow cytometry as the standard method for the enumeration of active lactic acid bacteria and other probiotic microorganisms from dairy products [30,33]. However, uptake of flow cytometry for product assessment is still limited within the broader probiotic industry. Furthermore, studies involving single probiotics species comparing flow cytometry and traditional plate counting have reported a strong agreement between the numbers of dead/live cells enumerated using both methods [34,35]. In contrast, examinations of probiotic bacterial blends containing both Lactobacillus spp. and Bifidobacterium spp. have reported a higher number of viable cells when assessed using flow cytometry compared to plate counting [23,29], demonstrating a discrepancy in viable cell counts observed between the two methods when more than one bacterial species or genera are enumerated. Given the increasing numbers of probiotic products with multiple species of bacteria, yeast, and other active ingredients such as prebiotic fibres, there is a clear and timely need to develop protocols to better utilise high-throughput techniques, such as flow cytometry, to enumerate the viable cell counts of probiotic products.
Here, we developed a flow cytometric approach using a single viability dye to accurately and rapidly analyse and quantify viable microbial cells from heterogenous bacterial and yeast populations from a complex commercial probiotic product (Factors Group Australia). This product contains multiple bacterial strains including representatives of Lactobacillus, Bifidobacterium, and Streptococcus genera, as well as strains of the yeast Saccharomyces cerevisiae, prebiotics, and other plant-based ingredients. As a proof-of-principle, we then applied this protocol to enumerate viable cells following simulated GI tract digestion of the probiotic product, comparing the results obtained from flow cytometry with those derived from standard plate count methods.

2. Materials and Methods

2.1. Factors Group Australia Multi-Species Probiotic Product

The multi-species probiotic product was provided by the manufacturer Factors Group Australia. Each capsule (transparent hard shell) contained a freeze-dried mixture of the live bacterial strains Bifidobacterium longum subsp. infantis, Lactobacillus acidophilus, Lacticaseibacillus casei (formerly Lactobacillus casei), Lactobacillus gasseri, Lacticaseibacillus paracasei subsp. paracasei (formerly Lactobacillus paracasei), Lactiplantibacillus plantarum (formerly Lactobacillus plantarium), Lactobacillus reuteri, Lacticaseibacillus rhamnosus (formerly Lactobacillus rhamnosus), Ligilactobacillus salivarius subsp. salivarius (formerly Lactobacillus salivarius), and Streptococcus thermophilus. Additionally, the capsules contained yeast strains including Saccharomyces cerevisiae and Saccharomyces cerevisiae (boulardii), as well as plant and fungal extracts. Probiotic capsules containing only the lyophilised bacterial mixture without addition of yeast or other active ingredients were also provided.

2.2. Sample Processing

Each capsule was processed by dissolving capsule contents in 50 mL of 0.20 µm filtered phosphate buffered saline (PBS, Oxoid, Sydney, Australia), applying vortexing to ensure a homogenous mixture was generated. For flow cytometry assays where prebiotic fibre removal was necessary, the above-described protocol was carried out and the resulting solution was then passed through a 100 µm cell strainer (FalconA®, In Vitro Technologies, Sydney, Australia) to filter out larger particles such as plant material. The samples were then 1:10 serially diluted in PBS and the technique appropriate dilutions were used for enumeration via plate count and flow cytometry.

2.3. Enumeration and Sequencing of Viable Cells by the Plate Count Method

Bacterial cells within the Factors Group Australia probiotic product were enumerated by spread plating serially diluted capsule contents onto de Man, Rogosa and Sharpe (MRS, Merck, Sydney, Australia), MRS L-cysteine agar and Streptococcus thermophilus agar solid culture media. For each assay, three separate capsules (as biological replicates) were independently prepared as detailed in Section 2.2 and appropriate dilutions were spread plated onto agar plates in triplicate. The MRS and Streptococcus thermophilus agar plates were incubated aerobically at 37 °C for 72 h, while MRS L-cysteine agar plates were incubated anaerobically at 37 °C for 72 h. The plate count results were expressed as colony forming units/capsule (CFU/capsule). Visibly identifiable colonies were enumerated only from plates containing 30–300 colonies per plate. The 16S rRNA gene (using 27F 5′-AGAGTTTGATCMTGGCTCAG-3′ and 1492R 5′-TACGGYTACCTTGTTACGACTT-3′ primers) of individual colony forming units presented on MRS L-cysteine agar and Streptococcus thermophilus agar plates were sequenced at Macrogen, Korea. The resulting nucleotide sequences were analysed using the basic local alignment search tool nucleotide (blastn) against the National Library of Medicine (NLM) NCBI database, considering the percent identify score when determining the taxonomy assignment.

2.4. Generation of Dead Cell Control Populations for Flow Cytometric Analysis

To locate the position of dead cells in the product on flow cytometric plots, a membrane compromised (dead) cell population was generated by exposing an aliquot of the diluted samples to heat at 70 °C for 30 min (water bath incubator, Thermo Fisher Scientific, Sydney, Australia). The resulting cell suspension served as the cell membrane compromised (dead) control population in subsequent viability assessment assays run with the SYTOXTM Green stain [36,37].

2.5. Cell Viability Staining for Flow Cytometric Analysis

Bacterial cell viability assays were carried out using SYTOXTM Green nucleic acid dye (Thermo Fisher Scientific, Sydney, Australia) which stains only membrane compromised cells [32], thus distinguishing membrane compromised (dead) from membrane intact cells (viable cells). Briefly, the diluted probiotic suspension samples along with the dead cell control population and filtered PBS negative controls were stained with SYTOXTM Green at a final concentration of 1 µM. These were incubated in the dark for 15 min at room temperature. The filtered PBS control was used to detect and measure any background non-specific fluorescence. All flow cytometry analyses were performed on three separate capsules with and without SYTOX TM Green stain (stained cells and unstained cells), with each biological replicate analysed using three technical replicates.

2.6. Flow Cytometric Data Acquisition and Analysis

The samples were analysed on a CytoFLEX S (Beckman Coulter, Brea, CA, USA) flow cytometer using the CytExpert software (Beckman Coulter, Brea, CA, USA) for data analysis. The following settings were used: gain for forward scatter (FSC)—100, side scatter (SSC)—100, and FITC (green fluorescence detector)—40, while the threshold for FSC was set to 1500 and that for SSC to 500 (the flow rate of the cells was set at the slow setting). The total cell counts were determined by applying gates based on the dot-plot SSC versus FSC (SSC-A vs. FSC-A). The positioning of the dead cell control population was used to set gates to separate the membrane compromised cells from the membrane intact cells. These gates were applied on the dot-plot SSC vs. FITC (SSC-A vs. FITC-A); both plots were set on log10-scale. The SYTOX green fluorescence intensity peak (indicative of membrane compromised cells) was also observed via a histogram plot of FITC vs. count, with FITC-A set on log scale and count set on linear scale. To identify and gate the bacterial population separately from the yeast population present in the multi-species probiotic product, probiotic capsules containing only the lyophilised lactic acid bacterial component of the product mixture were analysed. To account for the possible presence of instrument background noise, filtered PBS-only (blank) controls, which were prepared the same way as for the samples, were analysed on the flow cytometer and events were recorded at the beginning of each flow cytometry assay. To determine the total cell counts, any events detected in PBS controls were subtracted from the total events detected as the proportion of events attributable to background noise. The number of viable cells of the product was expressed as cells/capsule.

2.7. Tolerance to Conditions Simulating Gastrointestinal (GI) Tract Passage

The multi-species probiotic capsules were exposed to gastric digestion as per the protocol described by Shehata et al., with slight modifications [38]. Briefly, simulated gastric juice was prepared by suspending 0.3% (w/v) pepsin (Merck, Sydney, Australia) in PBS at pH 2.0. The simulated gastric juice and 0.5% NaCl were added to nine independent 15 mL sterile tubes, each containing a single probiotic capsule. The tubes were incubated at 37 °C with agitation at 100 rpm. At 0, 1.5, and 3 h of incubation, three tubes were removed for processing. Tube contents were vortexed and centrifuged at 7800 rpm at 4 °C for 10 min, followed by PBS washes of the cells until the pH values of the samples were 7.0. The resulting cell suspensions were then processed as described in Section 2.2. Tolerance to gastric digestion was assessed by determining viable counts at each time point using both plate counts and flow cytometric analyses in parallel by splitting the contents of each tube into two for use in each technique. The percent survivability was calculated by comparing the treatment samples (1.5 and 3 h) to the samples collected at 0 h.
For assessing resistance to bile salts, capsule contents were dispensed into tubes containing 0.3% (w/v) of bile salts (Merck, Sydney, Australia) suspended in PBS (pH 7.0). The samples were incubated at 37 °C with agitation at 100 rpm for 3 h. Samples were collected at intervals of 0, 1.5, and 3 h and processed as described above. Three technical replicates of each of the nine samples were diluted and assessed for the viable cell counts and % survivability by plate count method and flow cytometry as described above.

2.8. Statistical Analysis

Data were analysed using GraphPad Prism (version 9.4.1, GraphPad Software Inc., San Diego, CA, USA). Data is displayed as mean ± standard deviation (SD). The CFU/capsule and cells/capsule counts were transformed to Log10 values and for all experiments normality tests were performed using the D’Agostino and Pearson omnibus test to assess the distribution of data. Statistical analysis was performed using Student t-tests to compare the mean differences between two groups, while one-way ANOVA tests were used to compare the mean differences of more than two groups, with p ≤ 0.05 used as the significance level cut-off for both.

3. Results

3.1. Determining Viable Cell Number Variability for Factors Group Australia Probiotic Capsules

The standard plate count technique using MRS culture media was used to count colony forming units and assess the cell count variability between three capsules of the multi-species probiotic product. Viable cell numbers between capsules were determined to be consistent (Table 1), with no significant differences recorded (one way ANOVA, p = 0.59).
Serial dilutions of the complete product with mixed bacteria were plated on MRS L-cysteine agar and Streptococcus thermophilus agar plates. While colony forming units were seen on both media, sequencing of these colonies revealed taxonomic assignments to Lactobacillus spp. that are present in the product. Therefore, colony forming units obtained from MRS L-cysteine agar and Streptococcus thermophilus agar plates were not included in any of the plate counting results presented in this study.

3.2. Impact of Filtering out Prebiotic Fibres on the Viable Cell Counts of the Multi-Species Probiotic Product

While flow cytometry is a relatively versatile technique, to be suitable for analysis using this technique, any large particles or clumps must first be removed to ensure flow conditions are maintained and prevent clogs in the fluidic path. For this reason, prior to processing probiotic capsule contents, it was necessary to perform a pre-filtering step to remove particles over 100 µm in size. We performed a set of initial experiments to examine how such filtering impacts the flow-cytometry-derived viable cell counts in the multi-species probiotic product. The plate counting method was used to determine viable cell numbers for both the filtrate and the residue retained on the filter to determine the proportion of the viable cell population that was removed by the filtration process. There was a 4.3% reduction in the viable cell numbers in the filtrate compared to the viable cell numbers in capsules not subjected to filtration (Table 2), suggesting that filtration has a minimal impact on the viable cell counts of the product. As the difference in viable cell counts for filtered and unfiltered capsule suspensions was found not to be significant (Student t-test, p = 0.39), and removing the larger particles was necessary to enable reliable flow cytometry analysis of multi-component fibre-containing products, this filtering step was applied in all subsequent work.

3.3. Enumeration of Viable Cells in the Multi-Species Probiotic Product

Following preliminary experiments to establish that individual capsules contained relatively consistent numbers of viable cells and that separation of viable cells from fibre was possible, we worked to develop a routine protocol for flow-cytometry-based viable cell enumeration. In addition to multiple strains of bacteria, the tested product also contained yeast. Therefore, to enable the positioning of separate gates for bacterial and yeast populations on the flow cytometric plots, we analysed capsules containing only the lyophilised bacterial mixture alongside standard capsules. The gating for yeast was consistent with the expected trend for larger and more internally complex cells (brown-coloured gate, Figure 1). To discriminate membrane compromised cells from membrane intact viable cells, flow cytometry assays were performed on cells stained with SYTOXTM Green dye. To manually gate the membrane compromised bacterial and yeast cell populations in the product using flow cytometry, the cytograph positioning of heat-killed control populations (green- and brown-coloured gates, respectively, Figure 1A–C) was used. Analyses of the complete product with flow cytometry was then performed to allow both the viable and non-viable (membrane compromised) bacterial populations to be enumerated (representative results shown in Figure 1D–F). Due to the relatively small size of the bacterial cells in the product, it was not possible to set a flow cytometry threshold capable of completely removing all background noise, so PBS-only (blank) control mean cell counts were subtracted from the total bacterial cell counts to more accurately determine viable bacterial cell counts.
In order to compare the flow cytometry-derived viable counts for this mixed-species probiotic with that obtained by traditional plate count methods, the standard capsule analyses were performed in parallel. Comparing the culture-based CFU per capsule count to the viable cell counts obtained via flow cytometry, we found the latter showed significantly higher (Student t-test, p < 0.001) counts (Table 3).

3.4. Testing the Reproducibility of the Flow Cytometry Protocol

To examine whether results obtained by flow cytometry and plate count methods are reproducible, testing was performed with six additional capsules using both techniques. Similar to our previous observations, higher viable cell counts were obtained using flow cytometry compared with the plate count method (Table 4). Notably, flow cytometry detected consistent numbers of viable cells across the six biological replicates, while for plate count assays there was greater variability, with fold change differences in the CFU/capsule count determined for capsules 1–3 compared to capsules 4–6 (Table 4). The results suggested a higher reproducibility of flow-cytometry-based enumeration of viable cells compared to traditional plate counting using MRS media.

3.5. Comparison of Flow Cytometry and Plate Counting Methods for Assessing Viability following Simulated GI Tract Digestion

To ascertain how each enumeration approach compared when used to determine cell survivability following a challenge, we performed assays examining the impact of exposure to gastric juice (0.3% pepsin at pH 2.0) on bacterial viability for the complete probiotic product. Both techniques showed that sizable populations of viable bacterial cells remained after each of the tested exposure periods (Table 5). However, flow cytometry assay-based enumeration indicated considerably higher proportions of bacteria remained viable at both 1.5 and 3 h than what was indicated by plate counting at each time point (Table 5).
Following the stomach digestion mimicking assays, we performed standard in vitro bile salt exposure assays, again examining viable cell survivability following different exposure times. Similar trends were observed in this second assay, with flow cytometry indicating substantially higher viable counts after both 1.5 and 3 h bile salt exposure than plate count assays (Table 6).

4. Discussion

Here, we developed a flow-cytometry-based technique using a commercial viability dye to enumerate viable bacterial cells in a multi-species probiotic product containing bacteria, yeast, and other components. In this work we observed that viable cell counts based on flow enumeration were higher than those derived from plate counting method. This finding is consistent with what has been reported in a number of previous studies on probiotic blends containing Lactobacillus and Bifidobacterium spp. [23,29,39] and on multi-species probiotic products Lacidofil®, Protecflor®, and ProbioKid® (Mirabel, QC, Canada) [40]. It should be noted that the flow cytometry and plate count methods presented in our study are not measuring precisely the same thing, with flow cytometry providing estimates of membrane intact cells per capsule and plate count determining the numbers of colony forming units per capsule under the specific culture conditions applied (MRS medium, incubated aerobically at 37 °C for 72 h). These culture media and conditions used for plate counting typically favour growth of Lactobacillus spp., especially if they are present in a multi-species population. The product tested in this work, whilst comprised predominantly of strains of Lactobacillus spp., did also contain Bifidobacterium longum subsp. infantis and Streptococcus thermophilus. MRS L-cysteine agar and Streptococcus thermophilus agar, which are known to favour the growth of Bifidobacterium and Streptococcus thermophilus, respectively, were used to account for the different genera, but only Lactobacillus spp. were recovered on them. Similarly, work by Sohrabvandi et al., isolating lactic acid bacteria from yogurt, showed Lactobacillus spp. consistently outgrew Bifidobacterium spp., despite providing anaerobic conditions which are considered to be tolerated by both groups [41]. It is possible that competition amongst these multiple species and genera, when co-plated as present in the product, contributed to the lack of viable Bifidobacterium and Streptococcus spp. on the MRS L-cysteine and Streptococcus thermophilus agar plates.
We consistently observed a lower count of colony forming units and higher variation in results when plate counting was used compared to the number of viable cells detected through flow cytometry. This could also be due to the competition between bacterial species and genera induced due to co-plating the mixed population of bacteria present in the tested product. Furthermore, the ability of flow cytometry to detect injured yet membrane intact cells [25], including cells in the VBNC state, may also have contributed to the higher number of cells detected by flow cytometry. The ability of flow cytometry to detect VBNCs and dormant cells, which are generally not detected by plate counting, may have been the reason that the stress tolerance assays resulted in particularly large discrepancies in viable cell counts between flow cytometry and plate counting, as these assays may have resulted in cells within this product entering into a VBNC or dormant state [25], in addition to the competition between the multiple species of bacteria for growth conditions as described above. Furthermore, these limitations associated with plate counting may have exacerbated any minor variation in the proportion of different bacteria present in the product, leading to a greater variation in colony forming units detected. It is also worth noting the technical limitations in enumerating multiple species and genera of bacteria present in products using plate counting due to the different growth conditions required to culture them. This is a significant limitation in assessing the viability of probiotics in complete products containing mixed populations of bacteria, which can be minimised using flow cytometry as we demonstrate here.
The ability of probiotic microorganisms to tolerate GI transit is considered to be an important feature of an effective probiotic product [42] and is therefore assessed at the initial stages of identifying microorganisms with probiotic characteristics. Probiotic survival through the gastrointestinal tract is very much dependent on the bacterial strain and the delivery mechanism of the bacteria (e.g., capsule or powder) [19]. Characteristically, when assessed in a simulated GI tract, probiotic cultures show a decrease in concentration in the stomach followed by an increase in concentration in the intestine, with factors such as the time in the stomach and the composition of foods in the stomach at the time influencing probiotic concentrations throughout the process [43]. Here, we applied flow cytometric enumeration of viable cells to assess the response of a mixed bacterial population in a probiotic product to conditions mimicking independent sections of GI transit. While such testing is routine, it is generally performed on pure cultures during single strain selection [16], instead of on the final probiotic product, and there are limited evaluations of products after freeze drying and formulation [44]. The stage at which such testing is conducted is important, as the survival after exposure to gastric acid and bile can differ significantly, sometimes by several log units depending on formulation, freeze-drying, and storage conditions [45]. One reason for this is that during production, dried lyophilised cells generally undergo rehydration which is challenging to the cell as it moves from a gel-like state to a crystalline liquid state [44]. In this work, lower survivability observed in plate growth assays may have been due to cells entering a transition phase where they could maintain their metabolic activity but were unable to form colonies on the culture medium. Studies that have compared the GI tolerance of the unencapsulated probiotics with the encapsulated probiotics (different encapsulation materials) have shown higher tolerance rates when probiotics are encapsulated [46,47,48]; however, this literature involved capsules that dissolved in bile salt assays, i.e., simulated intestinal conditions rather than in the stomach-mimicking conditions [46,47,48]. In our case, the capsule dissolved in the gastric juice with gentle agitation, suggesting that further work comparing effects of different capsule formulations on viable cell concentrations at different stages of continuous GI transit, transit time variation, and GI transit survivability across multiple complex product formulations would be of interest.
Both of the stressors examined can lead to cell injury, loss in viability, and death. However, the pH buffering capacity, product composition, transit time, and volume of gastric juice are also affected by recent food consumption and composition [49]. Different strains also have differences in bile tolerance due to the individual species’ ability to express the bile salt hydrolase enzyme [50]. LAB strains that express more bile salt hydrolase can survive longer in bile salts. Hydrolysed bile salts are a key feature of probiotic bacteria; however, not all probiotics possess this capability to the same degree [5,51]. LAB strains can also possess other abilities to cope with bile exposure, such as active efflux of bile salts and changes in the cell wall and cell membrane composition, which may have downstream effects when assessing cell viability, a question which is worthy of further investigation.
In this work, flow cytometry was used to count the proportion of viable bacterial cells remaining following two different challenges. As the ability to tolerate each of these stressors is likely to differ to some extent between strains with the mixture [38], it would be useful to be able to determine the strain level composition of the surviving population. This could be achieved by using cell sorting to separate viable and non-viable populations following stress assays followed by 16S rRNA amplicon sequencing, which would aid in identification of strains that were more or less tolerant of exposure to such stressors. While the in vitro method used here to simulate stomach digestion and intestinal challenges conform with standard assays used for probiotic research [38,47,48], as discussed above, they have inherent limitations, offering only limited similarity to the complex GI transit of the human body [19,52]. The human digestive system is a dynamic process where there is continuous flow of fluids and enzymes, and varying concentrations of pH and bile salts, throughout the GI tract, which is generally difficult to maintain in an in vitro system [19,52]. Since the in vitro method is not a true representation of the human GI tract system, it is possible that the impact was either milder or more detrimental than what may occur in vivo.
There are many commercial cell viability dyes suitable for flow cytometry applications, with dyes that indicate membrane integrity, such as the SYTOXTM Green dye used in our study, among the most common methods of viability assessment. SYTOXTM Green dye stains nucleic acids in membrane compromised cells, while combinations of other nucleic acid dyes such as SYTO 9/PI allow the detection of total cells (SYTO 9) and enhance the discrimination of membrane compromised cells using PI [53]. Yet, these nucleic acid dyes do not distinguish between metabolically active and dormant cells or damaged cells with intact membranes. While the staining approach used here does not directly identify damaged cells, histogram plotting of the intensity of the fluorescence signal from the SYTOX dye when applied to the probiotic product (Figure 1F) revealed some continuity between the main peaks corresponding to membrane intact and membrane compromised cells, which might indicate the presence of membrane damaged cells. Further flow cytometric examination using additional dyes could be employed to explore the additional information on the physiological status of mixed-species probiotics that can be determined. Additional combinations of fluorescent dyes compatible with flow cytometry [30,54], could be trialled to gain more information about the physiological and metabolic state of cells in probiotic mixtures. Dyes such as CTC, which when reduced by dehydrogenases form fluorescent membrane-impermeant formazan, have been considered useful in providing an indication of actively respiring cells, as absence of formazan production indicates a lack of esterases or dehydrogenases, which could be indicative of dead or damaged cells [32]. It may also be possible to fluorescently tag antibodies specific to particular bacterial strains with viability stains to enable specific enumeration of viable cell numbers of each strain within a mixed population [40]. However, for complex probiotic products, establishing such assays would require considerable set up time and cost, which are likely to make this a less convenient approach for routine testing by probiotic industries.
Probiotic products are increasing in complexity and there are a range of products on the market that combine live probiotic bacteria with other ingredients, including prebiotic fibre and other active but non-living ingredients. For these, flow cytometry enumeration requires an additional processing step to remove large particles to prevent clogging of flow cytometer tubing. Here, we showed that filtration (using 100 µm cell strainers) was successful in preparing such a product for flow cytometry, having a minimal impact on viable cell counts. Such processing is likely to be a necessary step for flow cytometric cell enumeration of similar products containing prebiotic fibres, but independent validation for different products using plate-count-based comparisons is required to ensure there is not a higher proportion of fibre-adherent cells in some products.
Flow cytometry provides various advantages over the standard plate count method. We have shown that flow cytometry can differentiate yeast and bacterial populations and rapidly enumerate viable cell numbers within complex mixtures in a matter of hours, while the traditional plate count method requires 48–72 h to grow visible colonies, providing counts of only culturable cells [40]. Culture-based approaches may also be more variable in assessing stress tolerance depending on recovery times used in assays prior to plating, as stressed cells may transfer to different non-culturable states, possibly resulting in the measured CFU differing to varying degrees from the actual viable cell count [19]. Furthermore, competition among mixed bacterial populations for nutrient sources can lead to outgrowth of certain microorganisms over others, likely leading to undercounting of slower growing or condition-sensitive organisms [25]. In addition to successfully overcoming known plate count limitations, flow cytometry may provide information on various aspects of cell physiology. The ability of flow cytometry to enumerate cells in probiotic mixtures at any stage in the product formulation process has the potential to provide valuable information on the impact of different manufacturing steps or compare alternative techniques for use in product preparation, such as spray drying and freeze drying, on the viability of probiotic microorganisms.
It should be noted that a known issue with flow cytometry is the presence of background noise, which can be especially problematic when analysing relatively small bacterial cells or complex mixtures [27]. While background noise can in some cases be excluded via threshold setting or gating, this is not always possible as the noise may overlap with populations of interest. In such cases, subtracting the average counts for repeated blank measurements from the cell counts of interest, as carried out in this study, has been considered an acceptable approach to overcome this issue [55]. Another option may be to use two fluorescent dyes in combination, which has the potential to reduce the amount of background noise that is inseparable from the signal indicative of stained cells. Additionally, while performing established assays with this technique is much faster than plate counting, it should be acknowledged that the design of reliable flow cytometric assays can take time. These initial steps require use of appropriate controls to optimise instrument settings and enable appropriate gates to be established to count subpopulations of interest.
While successful use of flow cytometry requires one to first establish reproducible protocols and optimise instrument settings for a particular probiotic product, once this has been achieved, the same protocol can subsequently be implemented as a routine, rapid enumeration technique. This has numerous possible applications, such as assessing viable cell counts across different batches of a product or to determine how different stressors impact the product. While some members of the probiotic industry are trialling flow cytometry [30,36], this technique has not yet been adopted by the regulatory bodies in most of countries, including Australia, for their product assessment activities. However, this may change in the future, as this technique shows considerable potential for providing more accurate measurements of viable cells in probiotic products and is already a regulated requirement for other industries such as the dairy industry [27].

5. Conclusions

Flow cytometry analysis has the potential to provide very detailed information about all cells within both simple and complex samples. However, certain knowledge of the cell population is required to be able to achieve an experimental design that will yield reliable information and reproducible results [56]. Here, we undertook to compare plate counting and flow cytometry approaches for enumeration of viable cells in a probiotic product that contains a complex mixture of bacterial strains, yeast strains, and other active ingredients. For this, we developed a methodology for processing the probiotic capsule to allow flow cytometric analyses, along with instrument protocols and gating strategies to separate out subpopulations and enumerate viable cell fractions. This protocol has the potential to be applied to investigate how a range of different factors affect bacterial viability, from the impact of different manufacturing steps through to exploring batch variability, or shelf life, as product viability and composition can change within a probiotic product over time [57]. This could also be used to examine other factors of high interest to the probiotic industry, such as site-specific activity and lysis along the GI tract.
Flow cytometry was found to have advantages over plate counting in terms of speed, reproducibility, and sensitivity, likely due to the capacity to detect cells in the VBNC state. There are some acknowledged limitations, however, with flow cytometry requiring large particulates in samples, such as probiotic fibres, to be removed prior to analysis. This may, in some instances, affect viable cell counts, either due to high rates of fibre attachment or the potential that removal of additional active ingredients alters viability, possibly due to the protective effects of these additional components of the formulation [57]. Despite such issues, we conclude that flow cytometry has the potential to be used as a routine enumeration technique for rapid and accurate viability assessment for probiotics, especially for mixed-species probiotic products and, as such, is likely to be increasingly used in the coming years.

Author Contributions

Conceptualization, S.G.T., I.T.P., A.K.C. and H.K.A.H.G.; methodology, S.G.T., H.K.A.H.G. and J.V.P.; validation, J.V.P.; formal analysis, J.V.P., S.G.T. and H.K.A.H.G.; investigation, J.V.P.; writing—original draft preparation, J.V.P.; writing—review and editing, J.V.P., S.G.T., H.K.A.H.G., I.T.P., A.K.C. and E.H.; supervision, S.G.T., I.T.P., H.K.A.H.G., A.K.C. and E.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Australian Research Council Industrial Transformation Training Centre for Facilitated Advancement of Australia’s Bioactives (Grant IC210100040).

Data Availability Statement

Not applicable.

Conflicts of Interest

E.H. is the Managing Director (Australia and New Zealand) and a Senior Advisor (Global) at Factors Group Australia Pty Ltd. All other authors declare that they have no competing interest.

References

  1. Hemarajata, P.; Versalovic, J. Effects of probiotics on gut microbiota: Mechanisms of intestinal immunomodulation and neuromodulation. Therap. Adv. Gastroenterol. 2013, 6, 39–51. [Google Scholar] [CrossRef] [PubMed]
  2. Pandey, K.; Naik, S.; Vakil, B. Probiotics, prebiotics and synbiotics- a review. Food Sci. Technol. 2015, 52, 7577–7587. [Google Scholar] [CrossRef] [PubMed]
  3. Nazir, Y.; Hussain, S.A.; Abdul Hamid, A.; Song, Y. Probiotics and their potential preventive and therapeutic role for cancer, high serum cholesterol, and allergic and HIV diseases. BioMed Res. Int. 2018, 2018, 3428437. [Google Scholar] [CrossRef] [PubMed]
  4. Fallico, V.; Rea, M.; Stanton, C.; Ilestam, N.; McKinney, J. Next-generation multiparameter flow cytometry assay improves the assessment of oxidative stress in probiotics. Food Microbiol. 2020, 91, 103501. [Google Scholar] [CrossRef]
  5. FAO/WHO. “Probiotics in food. Health and nutritional properties and guidelines for evaluation,” FAO. Food Nutr. Paper 2002, 85, 1–56. [Google Scholar]
  6. O’Shea, E.F.; Cotter, P.D.; Stanton, C.; Ross, R.P.; Hill, C. Production of bioactive substances by intestinal bacteria as a basis for explaining probiotic mechanisms: Bacteriocins and conjugated linoleic acid. Int. J. Food Microbiol. 2012, 152, 189–205. [Google Scholar] [CrossRef]
  7. Williams, N.T. Probiotics. Am. J. Health-Syst. Pharm. 2010, 67, 449–458. [Google Scholar] [CrossRef]
  8. Di Cerbo, A.; Palmieri, B. The market of probiotics. Pak. J. Pharm Sci. 2015, 28, 2199–2206. [Google Scholar]
  9. Stanton, C.; Gardiner, G.; Meehan, H.; Collins, K.; Fitzgerald, G.; Lynch, P.B.; Ross, R.P. Market potential for probiotics. Am. J. Clin. Nutr. 2001, 73, 476s–483s. [Google Scholar] [CrossRef]
  10. Reid, G. The importance of guidelines in the development and application of probiotics. Curr. Pharm. Des. 2005, 11, 11–16. [Google Scholar] [CrossRef]
  11. Campana, R.; van Hemert, S.; Baffone, W. Strain-specific probiotic properties of lactic acid bacteria and their interference with human intestinal pathogens invasion. Gut Pathog. 2017, 9, 1–12. [Google Scholar] [CrossRef] [PubMed]
  12. Soccol, C.R.; de Souza Vandenberghe, L.P.; Spier, M.R.; Medeiros, A.P.; Yamaguishi, C.T.; De Dea Lindner, J.; Pandey, A.; Thomaz-Soccol, V. The potential of probiotics: A review. Food Technol. Biotechnol. 2010, 48, 413–434. [Google Scholar]
  13. Sen, S.; Mansell, T.J. Yeasts as probiotics: Mechanisms, outcomes, and future potential. Fungal Genet. Biol. 2020, 137, 103333. [Google Scholar] [CrossRef] [PubMed]
  14. Swanson, K.S.; Gibson, G.R.; Hutkins, R.; Reimer, R.A.; Reid, G.; Verbeke, K.; Scott, K.P.; Holscher, H.D.; Azad, M.B.; Delzenne, N.M.; et al. The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of synbiotics. Nat. Rev. Gastroenterol. Hepatol. 2020, 17, 687–701. [Google Scholar] [CrossRef]
  15. Markowiak, P.; Slizewska, K. Effects of probiotics, prebiotics, and synbiotics on human health. Nutrients 2017, 9, 1021. [Google Scholar] [CrossRef]
  16. Kechagia, M.; Basoulis, D.; Konstantopoulou, S.; Dimitriadi, D.; Gyftopoulou, K.; Skarmoutsou, N.; Fakiri, E.M. Health benefits of probiotics: A review. Int. Sch. Res. Notices 2013, 2013, 481651. [Google Scholar] [CrossRef]
  17. da Silva, M.N.; Tagliapietra, B.L.; do Amaral Flores, V.; dos Santos Richards, N.S.P. In vitro test to evaluate survival in the gastrointestinal tract of commercial probiotics. Curr. Res. Food Sci. 2021, 4, 320–325. [Google Scholar] [CrossRef]
  18. Galdeano, C.M.; Perdigon, G. Role of viability of probiotic strains in their persistence in the gut and in mucosal immune stimulation. J. Appl. Microbiol. 2004, 97, 673–681. [Google Scholar] [CrossRef]
  19. Wendel, U. Assessing Viability and Stress Tolerance of Probiotics- A Review. Front Microbiol. 2021, 12, 818468. [Google Scholar] [CrossRef]
  20. Judkins, T.C.; Archer, D.L.; Kramer, D.C.; Solch, R.J. Probiotics, nutrition, and the small intestine. Curr. Gastroenterol. Rep. 2020, 22, 1–8. [Google Scholar] [CrossRef]
  21. Lahtinen, S.J. Probiotic viability–does it matter? Microb. Ecol. Health Dis. 2012, 23, 18567. [Google Scholar] [CrossRef] [PubMed]
  22. Diaz, M.; Herrero, M.; García, L.A.; Quiros, C. Application of flow cytometry to industrial microbial bioprocesses. Biochem. Eng. J. 2010, 48, 385–407. [Google Scholar] [CrossRef]
  23. Sielatycka, K.; Juzwa, W.; Sliwa-Dominiak, J.; Kaczmarczyk, M.; Loniewski, I.; Marlicz, W. Multiparameter flow cytometric enumeration of probiotic-containing commercial powders. Innov. Food Sci. Emerg. Technol. 2021, 68, 102598. [Google Scholar] [CrossRef]
  24. 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. [Google Scholar] [CrossRef]
  25. Davis, C. Enumeration of probiotic strains: Review of culture-dependent and alternative techniques to quantify viable bacteria. J. Microbiol. Methods. 2014, 103, 9–17. [Google Scholar] [CrossRef]
  26. Hansen, S.J.; Tang, P.; Kiefer, A.; Galles, K.; Wong, C.; Morovic, W. Droplet digital PCR is an improved alternative method for high-quality enumeration of viable probiotic strains. Front. Microbiol. 2020, 10, 3025. [Google Scholar] [CrossRef]
  27. Wang, X.; Howe, S.; Deng, F.; Zhao, J. Current Applications of Absolute Bacterial Quantification in Microbiome Studies and Decision-Making Regarding Different Biological Questions. Microorganisms 2021, 9, 1797. [Google Scholar] [CrossRef]
  28. Tkacz, A.; Hortala, M.; Poole, P.S. Absolute quantitation of microbiota abundance in environmental samples. Microbiome 2018, 6, 110. [Google Scholar] [CrossRef]
  29. Tracey, H.; Coates, N.; Hulme, E.; John, D.; Michael, D.R.; Plummer, S.F. Insights into the enumeration of mixtures of probiotic bacteria by flow cytometry. BMC Microbiol. 2023, 23, 48. [Google Scholar] [CrossRef]
  30. Michelutti, L.; Bulfoni, M.; Nencioni, E. A novel pharmaceutical approach for the analytical validation of probiotic bacterial count by flow cytometry. J. Microbio. Methods 2020, 170, 105834. [Google Scholar] [CrossRef]
  31. Doherty, S.B.; Wang, L.; Ross, R.P.; Stanton, C.; Fitzgerald, G.F.; Brodkorb, A. Use of viability staining in combination with flow cytometry for rapid viability assessment of Lactobacillus rhamnosus GG in complex protein matrices. J. Microbiol. Methods 2010, 82, 301–310. [Google Scholar] [CrossRef] [PubMed]
  32. Buysschaert, B.; Byloos, B.; Leys, N.; Van Houdt, R.; Boon, N. Reevaluating multicolor flow cytometry to assess microbial viability. Appl. Microbiol. Biotechnol. 2016, 100, 9037–9051. [Google Scholar] [CrossRef] [PubMed]
  33. Fiore, W.; Arioli, S.; Guglielmetti, S. The neglected microbial components of commercial probiotic formulations. Microorganisms 2020, 8, 1177. [Google Scholar] [CrossRef] [PubMed]
  34. Massicotte, R.; Mafu, A.A.; Ahmad, D.; Deshaies, F.; Pichette, G.; Belhumeur, P. Comparison between flow cytometry and traditional culture methods for efficacy assessment of six disinfectant agents against nosocomial bacterial species. Front. Microbiol. 2017, 8, 112. [Google Scholar] [CrossRef] [PubMed]
  35. Ou, F.; McGoverin, C.; Swift, S.; Vanholsbeeck, F. Absolute bacterial cell enumeration using flow cytometry. J. Appl. Microbiol. 2017, 123, 464–477. [Google Scholar] [CrossRef]
  36. Bunthof, C.J.; Bloemen, K.; Breeuwer, P.; Rombouts, F.M.; Abee, T. Flow cytometric assessment of viability of lactic acid bacteria. Appl. Environ. Microbiol. 2001, 67, 2326–2335. [Google Scholar] [CrossRef]
  37. He, S.; Hong, X.; Huang, T.; Zhang, W.; Zhou, Y.; Wu, L.; Yan, X. Rapid quantification of live/dead lactic acid bacteria in probiotic products using high-sensitivity flow cytometry. Methods Appl. Fluoresc. 2017, 5, 024002. [Google Scholar] [CrossRef]
  38. Shehata, M.G.; El Sohaimy, S.A.; El-Sahn, M.A.; Youssef, M.M. Screening of isolated potential probiotic lactic acid bacteria for cholesterol lowering property and bile salt hydrolase activity. Ann. Agric. Sci. 2016, 61, 65–75. [Google Scholar] [CrossRef]
  39. Amor, K.B.; Breeuwer, P.; Verbaarschot, P.; Rombouts, F.M.; Akkermans, A.D.; De Vos, W.M.; Abee, T. Multiparametric flow cytometry and cell sorting for the assessment of viable, injured, and dead Bifidobacterium cells during bile salt stress. Appl. Environ. Microbiol. 2002, 68, 5209–5216. [Google Scholar] [CrossRef]
  40. Chiron, C.; Tompkins, T.A.; Burguiere, P. Flow cytometry: A versatile technology for specific quantification and viability assessment of micro-organisms in multistrain probiotic products. J. Appl. Microbiol. 2018, 124, 572–584. [Google Scholar] [CrossRef]
  41. Sohrabvandi, S.; Mortazavian, A.M.; Dolatkhahnejad, M.R.; Monfared, A.B. Suitability of MRS-bile agar for the selective enumeration of mixed probiotic bacteria in presence of mesophilic lactic acid cultures and yoghurt bacteria. Iran. J. Biotechnol. 2012, 10, 16–21. [Google Scholar]
  42. Mainville, I.; Arcand, Y.; Farnworth, E.R. A dynamic model that simulates the human upper gastrointestinal tract for the study of probiotics. Int. J. Food Microbiol. 2005, 99, 287–296. [Google Scholar] [CrossRef] [PubMed]
  43. Matouskova, P.; Hoova, J.; Rysavka, P.; Marova, I. Stress effect of food matrices on viability of probiotic cells during model digestion. Microorganisms 2021, 9, 1625. [Google Scholar] [CrossRef] [PubMed]
  44. Broeckx, G.; Vandenheuvel, D.; Claes, I.J.; Lebeer, S.; Kiekens, F. Drying techniques of probiotic bacteria as an important step towards the development of novel pharmabiotics. Int. J. Pharm. 2016, 505, 303–318. [Google Scholar] [CrossRef] [PubMed]
  45. Saarela, M.; Virkajarvi, I.; Alakomi, H.L.; Mattila-Sandholm, T.; Vaari, A.; Suomalainen, T.; Matto, J. Influence of fermentation time, cryoprotectant and neutralization of cell concentrate on freeze-drying survival, storage stability, and acid and bile exposure of Bifidobacterium animalis ssp. lactis cells produced without milk-based ingredients. J. Appl. Microbiol. 2005, 99, 1330–1339. [Google Scholar] [CrossRef]
  46. Sabikhi, L.; Babu, R.; Thompkinson, D.K.; Kapila, S. Resistance of microencapsulated Lactobacillus acidophilus LA1 to processing treatments and simulated gut conditions. Food Bioproc. Technol. 2010, 3, 586–593. [Google Scholar] [CrossRef]
  47. Ortakci, F.A.T.İ.H.; Sert, S. Stability of free and encapsulated Lactobacillus acidophilus ATCC 4356 in yogurt and in an artificial human gastric digestion system. J. Dairy Sci. 2012, 95, 6918–6925. [Google Scholar] [CrossRef]
  48. Devarajan, A.; Mudgil, P.; Aldhaheri, F.; Hamed, F.; Dhital, S.; Maqsood, S. Camel milk-derived probiotic strains encapsulated in camel casein and gelatin complex microcapsules: Stability against thermal challenge and simulated gastrointestinal digestion conditions. J. Dairy Sci. 2022, 105, 1862–1877. [Google Scholar] [CrossRef]
  49. Fredua-Agyeman, M.; Gaisford, S. Comparative survival of commercial probiotic formulations: Tests in biorelevant gastric fluids and real-time measurements using microcalorimetry. Benef. Microbes 2015, 6, 141–151. [Google Scholar] [CrossRef]
  50. Bustos, A.Y.; Saavedra, L.; de Valdez, G.F.; Raya, R.R.; Taranto, M.P. Relationship between bile salt hydrolase activity, changes in the internal pH and tolerance to bile acids in lactic acid bacteria. Biotechnol. Lett. 2012, 34, 1511–1518. [Google Scholar] [CrossRef]
  51. Ruiz, L.; Margolles, A.; Sanchez, B. Bile resistance mechanisms in Lactobacillus and Bifidobacterium. Front. Microbiol. 2013, 4, 396. [Google Scholar] [CrossRef] [PubMed]
  52. Dupont, D.; Alric, M.; Blanquet-Diot, S.; Bornhorst, G.; Cueva, C.; Deglaire, A.; Denis, S.; Ferrua, M.; Havenaar, R.; Lelieveld, J.; et al. Can dynamic in vitro digestion systems mimic the physiological reality? Crit. Rev. Food Sci. Nutri. 2019, 59, 1546–1562. [Google Scholar] [CrossRef] [PubMed]
  53. Stiefel, P.; Schmidt-Emrich, S.; Maniura-Weber, K.; Ren, Q. Critical aspects of using bacterial cell viability assays with the fluorophores SYTO9 and propidium iodide. BMC Microbiol. 2015, 15, 36. [Google Scholar] [CrossRef]
  54. Bensch, G.; Ruger, M.; Wassermann, M.; Weinholz, S.; Reichl, U.; Cordes, C. Flow cytometric viability assessment of lactic acid bacteria starter cultures produced by fluidized bed drying. Appl. Microbiol. Biotechnol. 2014, 98, 4897–4909. [Google Scholar] [CrossRef] [PubMed]
  55. Harhala, M.; Gembara, K.; Miernikiewicz, P.; Owczarek, B.; Kazmierczak, Z.; Majewska, J.; Nelson, D.C.; Dabrowska, K. DNA dye Sytox Green in detection of bacteriolytic activity: High speed, precision and sensitivity demonstrated with endolysins. Front. Microbiol. 2021, 12, 3129. [Google Scholar] [CrossRef] [PubMed]
  56. Olszewska, M.A.; Nynca, A.; Bialobrzewski, I.; Kocot, A.M.; Laguna, J. Assessment of the bacterial viability of chlorine-and quaternary ammonium compounds-treated Lactobacillus cells via a multi-method approach. J. Appl. Microbiol. 2019, 126, 1070–1080. [Google Scholar] [CrossRef]
  57. Hill, C.; Guarner, F.; Reid, G.; Gibson, G.R.; Merenstein, D.J.; Pot, B.; Morelli, L.; Canani, R.B.; Flint, H.J.; Salminen, S.; et al. Expert consensus document. The International Scientific Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic. Nat. Rev. Gastroenterol. Hepatol. 2014, 11, 506–514. [Google Scholar] [CrossRef]
Applmicrobiol 03 00074 g001
Figure 1. Representative flow cytometric plots of cells populations for heat-killed control populations (A–C) and the complete probiotic product (D–F), all analysed following SYTOXTM Green viability dye staining. In panels (A,D), gating of total bacterial and yeast cells is shown. In plots (B,E), the green gates indicate membrane comprised bacterial cells and the brown gates indicate membrane compromised yeast cells. The coloured histogram peaks in (C,F) show that cells located within the “membrane compromised” gates had the expected high FITC-A intensity and are distinct populations, separate from the membrane intact populations (grey peak in (F), absent in the dead control in plot (C)).
Figure 1. Representative flow cytometric plots of cells populations for heat-killed control populations (A–C) and the complete probiotic product (D–F), all analysed following SYTOXTM Green viability dye staining. In panels (A,D), gating of total bacterial and yeast cells is shown. In plots (B,E), the green gates indicate membrane comprised bacterial cells and the brown gates indicate membrane compromised yeast cells. The coloured histogram peaks in (C,F) show that cells located within the “membrane compromised” gates had the expected high FITC-A intensity and are distinct populations, separate from the membrane intact populations (grey peak in (F), absent in the dead control in plot (C)).
Applmicrobiol 03 00074 g001
Table 1. The viable cell counts of three multi-species probiotic capsules determined using the standard plate count method.
Table 1. The viable cell counts of three multi-species probiotic capsules determined using the standard plate count method.
Samples(Mean ± SD)
CFU/CapsuleLog10 CFU/Capsule
Capsule 13.4 ± 0.5 × 1099.5 ± 0.09
Capsule 23.2 ± 0.1 × 1099.5 ± 0.01
Capsule 33.1 ± 0.2 × 1099.5 ± 0.02
Shown as mean ± SD; each capsule analysis was carried out in technical triplicate.
Table 2. Plate-count-based assessment of the effect of filtration (100 µm) on viable cells counts for the multi-species probiotic product suspension.
Table 2. Plate-count-based assessment of the effect of filtration (100 µm) on viable cells counts for the multi-species probiotic product suspension.
Viable Cell Counts (Mean ± SD)
CFU/CapsuleLog10 CFU/Capsule
Filtrate cell count2.2 ± 0.1 × 1099.3 ± 0.02
Capsule cell count
(unfiltered)		2.3 ± 0.1 × 1099.4 ± 0.02
Shown as mean ± SD; analyses were carried out in technical triplicate. CFU—colony forming units.
Table 3. The average viable cell counts per capsule of the multi-species probiotic product determined by flow cytometry and plate count methods.
Table 3. The average viable cell counts per capsule of the multi-species probiotic product determined by flow cytometry and plate count methods.
Viable Cell Counts (Mean ± SD)
Flow cytometry6.4 ± 0.4 × 109 cells/capsule9.8 ± 0.03
Log10 cells/capsule
Plate count2.0 ± 0.1 × 109 CFU/capsule9.3 ± 0.03
Log10 CFU/capsule
Shown as mean ± SD; analyses were carried out on three capsules (as biological replicates), each in technical triplicate. CFU—colony forming unit.
Table 4. The viable cell counts of six individual capsules of the multi-species probiotic product obtained by flow cytometry and plate count methods.
Table 4. The viable cell counts of six individual capsules of the multi-species probiotic product obtained by flow cytometry and plate count methods.
SamplesViable Cell Counts (Mean ± SD)
Flow CytometryPlate Count
Cells/CapsuleLog10 Cells/CapsuleCFU/CapsuleLog10
CFU/Capsule
Capsule 16.8 ± 0.2 × 1099.8 ± 0.014.5 ± 0.5 × 1077.6 ± 0.05
Capsule 26.1 ± 0.2 × 1099.8 ± 0.015.1 ± 0.2 × 1077.7 ± 0.02
Capsule 35.6 ± 0.1 × 1099.7 ± 0.01 3.9 ± 0.3 × 1077.6 ± 0.03
Capsule 47.1 ± 0.4 × 1099.9 ± 0.024.3 ± 0.4 × 1088.6 ± 0.04
Capsule 56.6 ± 0.1 × 1099.8 ± 0.016.3 ± 0.2 × 1088.8 ± 0.02
Capsule 66.9 ± 0.2 × 1099.8 ± 0.015.4 ± 0.5 × 1088.7 ± 0.04
Shown as mean ± SD; analyses were carried out in technical triplicate for each of the six capsules. CFU—colony forming unit.
Table 5. Impact of simulated gastric juice on the viable cells of multi-species probiotic capsules, determined by flow cytometry and plate count methods.
Table 5. Impact of simulated gastric juice on the viable cells of multi-species probiotic capsules, determined by flow cytometry and plate count methods.
Exposure Time
(Hours)		0.3% Pepsin (pH 2.0)
(Mean ± SD)
Flow CytometryPlate Count
Cells/CapsuleLog10 Cells/CapsuleCFU/CapsuleLog10 CFU/Capsule
08.5 ± 0.2 × 1099.9 ± 0.019.7 ± 0.3 × 1088.8 ± 0.02
1.53.9 ± 0.2 × 1099.6 ± 0.03 9.3 ± 0.3 × 1077.9 ± 0.01
33.9 ± 0.5 × 1099.6 ± 0.069.1 ± 0.8 × 1077.9 ± 0.04
Shown as mean ± SD; each time point was carried out with three biological replicates, and each in technical triplicate.
Table 6. Impact of exposure to bile salts on the viable cells of multi-species probiotic product, determined by flow cytometry and plate count methods.
Table 6. Impact of exposure to bile salts on the viable cells of multi-species probiotic product, determined by flow cytometry and plate count methods.
Exposure Time
(Hours)		0.3% Bile Salts (pH 7.0)
(Mean ± SD)
Flow CytometryPlate Count
Cells/CapsuleLog10 Cells/CapsuleCFU/CapsuleLog10 CFU/Capsule
08.1 ± 0.3 × 1099.9 ± 0.024.4 ± 0.6 × 1088.6 ± 0.07
1.54.6 ± 0.3 × 1099.7 ± 0.021.9 ± 0.2 × 1077.8 ± 0.06
34.8 ± 0.2 × 1099.7 ± 0.021.6 ± 0.1 × 1077.7 ± 0.03
Shown as mean ± SD; each time point was carried out with three biological replicates, and each in technical triplicate.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Pereira, J.V.; Gamage, H.K.A.H.; Cain, A.K.; Hayes, E.; Paulsen, I.T.; Tetu, S.G. High-Throughput Viability Testing of Microbial Communities in a Probiotic Product Using Flow Cytometry. Appl. Microbiol. 2023, 3, 1068-1082. https://doi.org/10.3390/applmicrobiol3030074

AMA Style

Pereira JV, Gamage HKAH, Cain AK, Hayes E, Paulsen IT, Tetu SG. High-Throughput Viability Testing of Microbial Communities in a Probiotic Product Using Flow Cytometry. Applied Microbiology. 2023; 3(3):1068-1082. https://doi.org/10.3390/applmicrobiol3030074

Chicago/Turabian Style

Pereira, Joanna V., Hasinika K. A. H. Gamage, Amy K. Cain, Evan Hayes, Ian T. Paulsen, and Sasha G. Tetu. 2023. "High-Throughput Viability Testing of Microbial Communities in a Probiotic Product Using Flow Cytometry" Applied Microbiology 3, no. 3: 1068-1082. https://doi.org/10.3390/applmicrobiol3030074

Article Metrics

Back to TopTop