Real-time quantitative detection method for next-generation probiotics on basis of flow cytometry and use thereof

Abstract

Provided in the present application are a real-time quantitative detection method for next-generation probiotics on the basis of flow cytometry and the use thereof. The next-generation probiotics comprise Akkermansia muciniphila and / or Bacteroides fragilis. In the present application, flow cytometry is used in the detection of next-generation probiotics to provide quantitative data of viable and non-viable cells in real time, thereby rapidly completing the high-throughput detection of viable cells in a sample while reducing the risk of contamination during the detection process. Moreover, the detection method can also detect next-generation probiotics that are unculturable due to insufficient metabolic activity, offering rapid detection speed and high accuracy.

Description

A Real-Time Quantitative Detection Method for Second-Generation Probiotics Based on Flow Cytometry and Its Application Technical Field

[0001] This application belongs to the field of microbial technology, specifically relating to a real-time quantitative detection method for second-generation probiotics based on flow cytometry and its application. Background Technology

[0002] The gut microbiota is a community of microorganisms residing in the human gut. The human gut contains 40 trillion bacteria, with a total gene count approximately 150 times that of the human genome. This demonstrates that the gut microbiota is an extremely large group, often referred to as the human "second genome," "second brain," or "gut brain." Under normal circumstances, the gut microbiota maintains a dynamic ecological balance with the host and the external environment. However, disruption of this balance can lead to loss of host barrier function, inflammation, and immune dysfunction, thereby inducing host diseases.

[0003] Next-generation probiotics (NGPs) are gut microbiota that can intervene in human diseases. They refer to live microorganisms that, while not yet fully identified, can influence animal health and production characteristics through specific actions when administered in sufficient doses. They are suitable for preventing and treating metabolic disorders or improving host health. NGPs use gut symbiotic bacteria as the main component and are artificially designed into microecological preparations to act on the host and reshape the gut microbiota to exert their specific functions. Currently known second-generation probiotics include *Akkermansia myxophilus* and *Bacteroides fragilis*.

[0004] Akkermansia muciniphila (AkK bacteria) is a common commensal bacterium in the human gut. It survives by relying on mucin in the intestinal mucus layer, primarily colonizing the outer mucus layer of the gastrointestinal tract. It uses gastrointestinal mucin as its carbon and nitrogen source for growth, and its consumption of mucin and the regeneration of mucin by goblet cells maintain a dynamic balance, thus keeping the mucus layer stable. Over the past decade, increasing research has demonstrated that patients with diabetes, cardiovascular disease, diseased bowel disease, and neurological disorders have lower abundance of AkK bacteria in their gastrointestinal tract. This suggests that AkK bacteria may become a promising next-generation probiotic for clinical applications, especially in the prevention and treatment of diabetes, obesity, and cancer.

[0005] Bacteroides fragilis is a symbiotic bacterium that colonizes the mammalian gut and is essential for maintaining normal host function. The probiotic effect of its secreted PSA is widely recognized, making Bacteroides fragilis a promising potential probiotic strain. Multiple studies have further revealed its close association with inflammatory bowel disease and immune-mediated diseases. Furthermore, compared to other anaerobic bacteria in the gastrointestinal tract, Bacteroides fragilis exhibits the highest antibiotic resistance and the most diverse antibiotic resistance mechanisms. This not only makes treating infections caused by Bacteroides fragilis difficult but also suggests that it could serve as a reservoir of antibiotic resistance genes, leading to their transfer to other normal bacterial flora through transposon integration and genetic element integration.

[0006] In 2022, the Chinese Institute of Food Science and Technology published "T / CIFST 009-2022 General Rules for Probiotics for Food Use," which sets forth basic requirements for probiotics used as food ingredients, including strain level requirements, production process requirements, technical requirements, storage and transportation, application in food, and labeling. It also requires that the live bacteria count of probiotics be ≥1.0 × 10⁻⁶. 8 CFU / g(mL), where CFU stands for colony-forming unit, which refers to the colony formed by the growth and reproduction of a single bacterial cell or multiple bacterial cells aggregated on a solid culture medium in bacterial culture. It is used to express the number of viable bacteria.

[0007] Currently, there is a lack of detection methods for second-generation probiotics. In traditional culture and counting, some damaged cells have insufficient metabolic activity, so they cannot be counted, and the accuracy needs to be improved. In addition, some second-generation probiotics have high requirements for the culture environment, and the detection results are delayed.

[0008] In summary, developing an efficient and accurate detection method for second-generation probiotics and applying it to real-time monitoring of their processing and production has become an urgent problem to be solved in this field.

[0009] Summary of the Invention

[0010] This application provides a real-time quantitative detection method for second-generation probiotics based on flow cytometry and its application. By pretreating and staining the second-generation probiotics, this method can obtain viable cell count data and cell state ratios within one minute of flow cytometry input, providing real-time quantitative detection data and quality assessment for rapid analysis during the production process.

[0011] In a first aspect, this application provides a real-time quantitative detection method for second-generation probiotics based on flow cytometry, wherein the second-generation probiotics include *Ackermania myxophilus* and / or *Bacteroides fragilis*. Preferably, the real-time quantitative detection method for second-generation probiotics based on flow cytometry includes: pretreating and staining the second-generation probiotics to obtain a sample to be tested; placing the sample to be tested into a flow cytometer for detection; obtaining the detection results; and performing data analysis on the detection results.

[0012] Preferably, the pretreatment includes: mixing the second-generation probiotics with a diluent to obtain diluted second-generation probiotics.

[0013] Preferably, the diluent comprises peptone and inorganic salts.

[0014] The diluent provided in this application can be used to adjust the concentration of the sample to be tested and to protect the bacteria. It is suitable for flow cytometry detection of second-generation probiotics, especially for detecting anaerobic and / or facultative anaerobic bacteria in second-generation probiotics.

[0015] Preferably, the peptone includes any one or a combination of at least two of casein peptone, yeast peptone, or soy peptone.

[0016] Preferably, the inorganic salt includes any one or a combination of at least two of sodium chloride, sodium bicarbonate, disodium hydrogen phosphate, or potassium dihydrogen phosphate.

[0017] Preferably, the concentration of peptone in the diluent is 0.8-1.2 g / L (e.g., 0.8 g / L, 0.9 g / L, 1.0 g / L, 1.1 g / L, or 1.2 g / L, etc.), and the concentration of inorganic salts is 7-10 g / L (e.g., 7 g / L, 8 g / L, 8.5 g / L, 9 g / L, or 10 g / L, etc.).

[0018] Preferably, the diluent comprises 1.0 g / L casein peptone and 8.5 g / L sodium chloride.

[0019] Preferably, the staining includes: staining particles with a particle number of 2 × 10 3 -2×10 4 / μL (e.g. 2×10⁻⁶) 3 / μL, 4×10 3 / μL, 6×10 3 / μL, 8×10 3 / μL, 1×10 4 / μL, 1.2×10 4 / μL, 1.4×10 4 / μL, 1.6×10 4 / μL, 1.8×10 4 / μL or 2×10 4The diluted second-generation probiotics (e.g., per μL) were mixed with the staining agent, vortexed, and then incubated in the dark for 10-20 minutes (e.g., 10 min, 13 min, 15 min, 17 min, or 20 min).

[0020] Preferably, the staining agent includes any one or a combination of at least two of SYTO 9, SYTO 11, SYTO 16 or propidium iodide.

[0021] Preferably, the concentration ratio of SYTO 9 to propidium iodide in the staining agent is 1:(0.8-1.2) (e.g., 1:0.8, 1:0.9, 1:1, 1:1.1 or 1:1.2, etc.).

[0022] Preferably, the volume ratio of the dye to the diluted second-generation probiotics is 1:(800-1200) (e.g., 1:800, 1:900, 1:1000, 1:1100, or 1:1200, etc.).

[0023] Too much or too little dye can lead to large differences between parallel samples. The preferred volume ratio of dye to diluted second-generation probiotics is 1:1000.

[0024] Preferably, the second-generation probiotic real-time quantitative detection method based on flow cytometry further includes the step of creating an on-machine data analysis template.

[0025] Preferably, the data analysis template includes a live cell signal template and a dead cell signal template.

[0026] Preferably, the method for preparing the live cell signal template includes: immediately after activating the second-generation probiotics, using a SYTO 9 single staining machine, adjusting the voltage of the B525-A channel until the fluorescence signal is concentrated in the center or near the center of the X-axis FSC and Y-axis SSC flow cytometry, and obtaining the first voltage parameter and the first image linearity coefficient.

[0027] Preferably, the method for preparing the dead cell signal template includes: treating second-generation probiotics with isopropanol to disrupt cell membranes and then staining them with SYTO 9 and propidium iodide. After adjusting the voltage of the B585-A channel, the fluorescence signal is concentrated in the center or near the center of the X-axis FSC and Y-axis SSC flow cytometry, thereby obtaining the second voltage parameter and the second image linearity coefficient.

[0028] Preferably, when using SYTO 9 and propidium iodide for co-staining, the concentration ratio of SYTO 9 to propidium iodide is 1:(0.8-1.2) (e.g., 1:0.8, 1:0.9, 1:1, 1:1.1 or 1:1.2, etc.).

[0029] Preferably, the step of placing the sample to be tested into a flow cytometer includes: excitation with a 488nm argon ion laser configured in the flow cytometer, selecting a low speed for sample detection; receiving the SYTO 9 fluorescence signal using the B525-A channel, adjusting the channel voltage to a first voltage parameter and the image to a first image linearity coefficient; receiving the propidium iodide fluorescence signal using the B585-A channel, adjusting the channel voltage to a second voltage parameter and the image to a second image linearity coefficient.

[0030] This application uses a low-speed mode for sample testing, resulting in a smaller standard deviation across three replicates and higher testing stability.

[0031] Preferably, the data analysis includes: adjusting the compensation of the B525-A and B585-A channels to separate the corresponding positive and negative clusters; removing the blank fluorescence background; comparing the data with the live cell signal template and the dead cell signal template respectively; determining the location of the live and dead cell clusters; and calculating the live bacteria count (AFU) = N × a × 1000 based on the number of particles displayed by the device, where N is the result of the live bacteria concentration measured by the instrument, particle count / μL, and a is the sample dilution factor.

[0032] The final result is presented in the following format: the third significant digit is rounded to the nearest whole number, leaving the first two significant digits, and the remaining digits are replaced with zero or expressed as an exponent.

[0033] Preferably, the real-time quantitative detection method for second-generation probiotics based on flow cytometry includes the following steps:

[0034] (1) The second-generation probiotics are mixed with a diluent to obtain diluted second-generation probiotics, wherein the diluent includes peptone and inorganic salts;

[0035] (2) Mix the diluted second-generation probiotics described in step (1) with the staining agent to obtain the sample to be tested. The staining agent includes SYTO 9 and propidium iodide.

[0036] (3) Place the sample to be tested described in step (2) into a flow cytometer for detection, receive the fluorescence signals of SYTO 9 and propidium iodide, and obtain the detection results; and

[0037] (4) Calculate the number of live bacteria and the ratio of dead bacteria of the second-generation probiotics based on the test results described in step (3).

[0038] Preferably, the second-generation probiotic real-time quantitative detection method based on flow cytometry is performed under anaerobic conditions.

[0039] Secondly, this application provides a real-time quantitative detection device for second-generation probiotics, which is used to perform the steps in the flow cytometry-based real-time quantitative detection method for second-generation probiotics described in the first aspect.

[0040] Preferably, the real-time quantitative detection device includes a sample detection unit and a data analysis unit.

[0041] Preferably, the sample detection unit is used to perform the following: pre-treating and staining the second-generation probiotics to obtain a sample to be tested, placing the sample to be tested into a flow cytometer for detection, and obtaining the detection result.

[0042] Preferably, the data analysis unit is used to perform the following: data analysis on the detection results.

[0043] Thirdly, this application provides the application of the real-time quantitative detection method for second-generation probiotics based on flow cytometry as described in the first aspect and / or the real-time quantitative detection device for second-generation probiotics as described in the second aspect in the development of second-generation probiotic products and / or the development of second-generation probiotic production processes.

[0044] This application addresses the analysis of the ratio of live to dead cells in second-generation probiotics, which can be applied to the fermentation process and the tracking of product stability during the shelf life, as well as to the evaluation of culture purity. It provides data support for the control of fermentation process parameters and product quality control, and is of great significance to the industrialization of second-generation probiotics.

[0045] Other specific point values ​​within the range of the above values ​​can be selected, and will not be elaborated on here.

[0046] Compared with the prior art, this application has the following beneficial effects:

[0047] This application applies flow cytometry to the detection of second-generation probiotics, providing real-time quantitative data on live and dead cells. It enables rapid, high-throughput detection of live cell counts in samples while reducing the risk of contamination during the detection process. It can also detect second-generation probiotics that cannot be cultured using traditional methods due to insufficient metabolic activity. The detection speed is fast and the accuracy is high. Furthermore, since most second-generation probiotics are anaerobic bacteria, traditional culture and detection processes are time-consuming and require a high degree of anaerobic activity. This application utilizes an anaerobic workstation to provide an anaerobic environment for sample processing, reducing the loss of live bacteria due to environmental factors during sample processing. The live-to-dead cell ratio analysis in this application can be applied to fermentation processes and product shelf-life stability tracking, which is of great significance for the industrialization of second-generation probiotics. Attached Figure Description

[0048] Figure 1 shows the flow cytometry results of blank samples in Example 1.

[0049] Figure 2 is a template diagram of dead cell signals from flow cytometry in Example 1.

[0050] Figure 3 shows the flow cytometry results of Example 1.

[0051] Figure 4 shows the flow cytometry results of Example 11.

[0052] Figure 5 shows the flow cytometry results of Example 12. Detailed Implementation

[0053] To further illustrate the technical means and effects adopted in this application, the following description, in conjunction with embodiments and accompanying drawings, will provide further details. It is understood that the specific embodiments described herein are merely for explaining this application and not for limiting it.

[0054] Where specific techniques or conditions are not specified in the examples, they shall be performed in accordance with the techniques or conditions described in the literature in this field, or in accordance with the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased through legitimate channels.

[0055] The strains, reagents, and instruments used in the following examples:

[0056] The strains involved in the following examples:

[0057] Akkermansia muciniphila Akk11: The depositary institution is the China Center for Type Culture Collection, accession number CCTCC NO: M 2024119, the deposit date is January 15, 2024, and the deposit address is Wuhan University, No. 299 Bayi Road, Wuchang District, Wuhan City, Hubei Province.

[0058] Bacteroides fragilis: purchased from Guangdong Provincial Microbial Culture Collection Center, ATCC 25285.

[0059] Casein peptone: purchased from Qingdao Haibo, product code HB8271.

[0060] SYTO 9 dye and PI dye: derived from the LIVE / DEAD BacLight bacterial activity assay kit, purchased from Thermo Fisher Scientific, product code L7012.

[0061] Flow cytometer: purchased from Beckman Coulter, product model B53013.

[0062] Example 1

[0063] This embodiment provides a method for real-time quantitative detection of Akkermansia myxophilus based on flow cytometry, the method comprising the following steps:

[0064] (1) Reagent preparation:

[0065] Diluent: 1.0 g / L casein peptone, 8.5 g / L sodium chloride, sterilized by moist heat at 121℃ for 15 min and then dispensed for use.

[0066] Mixed dyes: In an anaerobic workstation, take 1 μL of SYTO 9 and 1 μL of PI dye into a 1.5 mL centrifuge tube, vortex to mix, and then centrifuge briefly for later use.

[0067] (2) Sample pretreatment:

[0068] In the anaerobic workstation, weigh 1g of Akkermansia myxophilus powder, add it to 99mL of diluent, mix thoroughly, and then dilute.

[0069] (3) Sample staining:

[0070] Absorb 10 -4 Add the prepared mixed dye to the gradient sample homogenate. The volume ratio of the mixed dye to the sample is 1:1000. Vortex mix and then centrifuge briefly. Incubate in the dark for 15 minutes.

[0071] (4) Equipment calibration:

[0072] During sample incubation, the equipment is turned on and cleaned. After the process is completed, 2 mL of deionized water is taken and tested to confirm that there is no abnormality in the background signal of the channel. If the background signal is too strong, the equipment is cleaned with deionized water again.

[0073] (5) Preparation of blank control samples:

[0074] Sterilized diluent was used for proportional staining and instrument testing, serving as a reagent background blank.

[0075] (6) Create a template for computer-based data analysis:

[0076] The method for preparing the live cell signal template includes: immediately after activating Akkermansia mycotoxin, using a SYTO 9 single staining machine, adjusting the voltage of the B525-A channel until the fluorescence signal is concentrated in the center or near the center of the X-axis FSC and Y-axis SSC flow cytometry, and obtaining the first voltage parameter and the first image linearity coefficient.

[0077] The method for creating the dead cell signal template includes: treating second-generation probiotics with isopropanol to destroy cell membranes and then staining them with SYTO 9 and propidium iodide. After adjusting the voltage of the B585-A channel, the fluorescence signal is concentrated in the center or near the center of the X-axis FSC and Y-axis SSC flow cytometry, and the second voltage parameter and the second image linear coefficient are obtained.

[0078] (7) Sample loading:

[0079] After incubation, the samples were analyzed by flow cytometry. The flow cytometer was equipped with a 488nm argon ion laser for excitation. The channel voltage was adjusted to the template voltage and the image was adjusted to the template linearity coefficient. The SYTO 9 fluorescence signal was received by the B525-A channel and the PI fluorescence signal was received by the B585-A channel. The samples were analyzed at a low speed. Each sample was repeated three times.

[0080] (8) Data Analysis:

[0081] Adjust the compensation of channels B525 and B585 to separate the corresponding positive and negative groups. After removing the blank fluorescence background, compare them with the dead and live signal templates respectively, and then apply appropriate gating. Calculate the viable cell count (AFU) based on the particle count displayed by the device: N × a × 1000, where N is the instrument's viable cell concentration (particles / μL), a is the sample dilution concentration, and 1000 is the volume unit conversion factor. The final result is reported in the following format: round the third significant figure to the nearest hundredth, retaining the first two significant figures, and replacing other digits with zeros or expressing them in exponential form.

[0082] Example 2

[0083] This embodiment provides a method for real-time quantitative detection of Akkermansia myxophilus based on flow cytometry. The only difference from Embodiment 1 is that a medium flow rate is selected for sample testing in step (7).

[0084] Example 3

[0085] This embodiment provides a method for real-time quantitative detection of Akkermansia myxophilus based on flow cytometry. The only difference from Embodiment 1 is that in step (7), high-speed sample loading is selected for detection.

[0086] Example 4

[0087] This embodiment provides a method for real-time quantitative detection of Akkermansia myxophilus based on flow cytometry. The only difference from Example 1 is that the volume ratio of the mixed dye to the sample in step (3) is 1:700.

[0088] Example 5

[0089] This embodiment provides a method for real-time quantitative detection of Akkermansia myxophilus based on flow cytometry. The only difference from Example 1 is that the volume ratio of the mixed dye to the sample in step (3) is 1:1300.

[0090] Example 6

[0091] This embodiment provides a method for real-time quantitative detection of Akkermansia myxophilus based on flow cytometry. The only difference from Embodiment 1 is that the casein peptone in the diluent is replaced with an equal amount of soybean peptone.

[0092] Example 7

[0093] This embodiment provides a method for real-time quantitative detection of Akkermansia myxophilus based on flow cytometry. The only difference from Example 1 is that the casein peptone in the diluent is replaced with an equal amount of yeast peptone.

[0094] Example 8

[0095] This embodiment provides a method for real-time quantitative detection of Akkermansia myxophilus based on flow cytometry. The only difference from Example 1 is that the diluent consists of 1.0 g / L casein peptone, 8.5 g / L sodium chloride, and 1.0 g / L trehalose.

[0096] Example 9

[0097] This embodiment provides a method for real-time quantitative detection of Akkermansia myxophilus based on flow cytometry. The only difference from Example 1 is that the diluent consists of 1.0 g / L casein peptone, 8.5 g / L sodium chloride, and 1.0 g / L sucrose.

[0098] Example 10

[0099] This embodiment provides a method for real-time quantitative detection of Akkermansia myxophilus based on flow cytometry. The only difference from Example 1 is that the diluent consists of 1.0 g / L casein peptone, 8.5 g / L sodium chloride, and 0.5 g / L sodium glutamate.

[0100] Example 11

[0101] This embodiment provides a method for detecting Akkermansia myxophilus based on flow cytometry. The only difference from Embodiment 1 is that before step (2), Akkermansia myxophilus powder is placed in a 37°C incubator for 4 weeks and then taken out.

[0102] Example 12

[0103] This embodiment provides a method for real-time quantitative detection of Bacteroides fragilis based on flow cytometry. The only difference from Embodiment 1 is that the sample to be detected is Bacteroides fragilis.

[0104] Comparative Example 1

[0105] This comparative example provides a method for determining the viable count of Akkermansia muciniphila using a culture counting method, the method comprising the following steps:

[0106] (1) Preparation of culture medium:

[0107] Add 2 g / L of mucin and 10 g / L of agar powder to commercial brain and heart infusion broth to obtain Akkermansia myxophilus culture medium.

[0108] (2) Training and Result Statistics

[0109] Sample dilution was performed in the anaerobic workstation, and 10 μL of sample was taken. -8 1 mL of the sample homogenate was placed in a sterile petri dish, 15 mL of culture medium was poured in, and the mixture was anaerobically incubated at 37°C for 96 h. The samples were counted manually after the incubation period.

[0110] Test Example 1

[0111] This test case uses the methods provided in Examples 1-10 to detect the number of viable bacteria of the same Akkermansia myxophilus. Each group is tested in triplicate, and the mean and standard deviation of the three experimental results are calculated. The results are shown in Table 1.

[0112] Table 1

[0113] From Table 1, we can conclude that:

[0114] (1) Comparing Example 1 with Examples 2 and 3, it can be seen that Example 2 and 3 selected medium speed and high speed for sample testing, respectively. The standard deviation of the three repetitions was greater than that of Example 1, which selected low speed. Therefore, in order to improve the stability of the test, it is preferred to perform sample testing at low speed.

[0115] (2) Comparing Example 1 with Examples 4 and 5, it can be seen that too much or too little dye can lead to large differences between parallel samples;

[0116] (3) Comparing Example 1 with Examples 6 and 7, it can be seen that casein peptone has the best protective effect on bacterial cells and the smallest standard deviation of the test results;

[0117] (4) Comparing Example 1 with Examples 8-10, it can be seen that adding other components to the original diluent composed of casein peptone and sodium chloride will reduce the stability of the detection method.

[0118] Test Example 2

[0119] This test case uses the methods provided in Examples 1 and 11 to detect the number of the same Akkermansia myxophilus. The test results of the blank sample in Example 1 are shown in Figure 1, the dead cell signal template is shown in Figure 2, the test results of Example 1 are shown in Figure 3, and the test results of Example 11 are shown in Figure 4.

[0120] As shown in Figure 4, the samples treated with heating exhibited clusters of dead bacterial cells in the dead area. The position and shape of the cell clusters were not significantly different from those in the dead bacterial template in Figure 2, and the proportion showed a corresponding trend change. This indicates that the method can effectively distinguish between the number of live and dead bacteria in the sample and can provide the live-to-dead ratio.

[0121] Test Example 3

[0122] This test case used the methods provided in Example 1 and Comparative Example 1 to detect the number of viable bacteria of the same Akkermansia myxophilus. Each group was tested in triplicate, and the results are shown in Table 2.

[0123] Table 2

[0124] Comparing Example 1 with Comparative Example 1, it can be seen that: the traditional culture method of Comparative Example 1 requires an average of 97 hours per sample from sample processing to result output, and the amount of reagents prepared in the early stage is large, the cleaning time of consumables in the later stage is long, and it is labor-intensive. The saturated workload of the same testing personnel is 12 samples / day; while the method adopted in this application can save half the time, the amount of related reagents prepared can be reduced by half, and the data results can be obtained within 1 minute of a single sample being put on the machine.

[0125] The results from traditional culture methods are lower than those from flow cytometry because some cells with insufficient metabolic activity cannot be cultured and therefore cannot be detected by traditional methods. Furthermore, traditional culture methods require manual colony counting and calculation, necessitating manual data input and processing for analysis. Real-time quantitative detection, on the other hand, allows for direct export of data from multiple batches.

[0126] Test Example 4

[0127] This test case used the method provided in Example 12 to detect the number of Bacteroides fragilis, and the results are shown in Figure 5.

[0128] As calculated from Figure 5, the number of viable Bacteroides fragilis particles was 2379.78, the number of dead particles was 593.15, and the number of viable particles was 2.4 × 10⁻⁶. 8 AFU / g.

[0129] In summary, this application provides a real-time quantitative detection method for second-generation probiotics based on flow cytometry, which provides real-time quantitative data of live and dead cells. Compared with traditional culture methods, it greatly improves accuracy and detection speed, and is especially suitable for real-time quantitative detection of anaerobic bacteria. It can be widely applied in the industrialization development of second-generation probiotics.

[0130] The applicant declares that the above description is only a specific implementation of this application, but the protection scope of this application is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application fall within the protection and disclosure scope of this application.

Claims

1. A flow cytometry-based real-time quantitative detection method for second generation probiotics, wherein, The second-generation probiotics include mucinophilic Akkermansia and / or Bacteroides fragilis.

2. The flow cytometry-based real-time quantitative detection method of the second generation probiotics according to claim 1, wherein, The flow cytometry-based real-time quantitative detection method of the second-generation probiotics comprises the following steps: pretreating and staining the second-generation probiotics to obtain a sample to be detected, placing the sample to be detected into a flow cytometer for detection to obtain a detection result, and performing data analysis on the detection result.

3. The flow cytometry-based real-time quantitative detection method of the second generation probiotics according to claim 2, wherein, The pretreatment comprises mixing the second-generation probiotics with a diluent to obtain diluted second-generation probiotics. Preferably, the diluent comprises peptone and inorganic salt. Preferably, the peptone comprises any one or a combination of at least two of casein peptone, yeast peptone or soybean peptone. Preferably, the inorganic salt comprises any one or a combination of at least two of sodium chloride, sodium bicarbonate, disodium hydrogen phosphate or potassium dihydrogen phosphate. Preferably, the concentration of the peptone in the diluent is 0.8-1.2 g / L, and the concentration of the inorganic salt is 7-10 g / L.

4. The flow cytometry-based real-time quantitative detection method of the second generation probiotics according to claim 2, wherein, The staining includes: mixing the diluted second-generation probiotics with a particle number of 2 x 10 3 -2 x 10 4 / μL with a staining agent, vortexing uniformly, and then incubating in the dark for 10-20 min. Preferably, the staining agent comprises any one or a combination of at least two of SYTO 9, SYTO 11, SYTO 16 or propidium iodide. Preferably, the concentration ratio of SYTO 9 to propidium iodide in the staining agent is 1:(0.8-1.2). Preferably, the volume ratio of the staining agent to the diluted second-generation probiotics is 1:(800-1200).

5. The flow cytometry-based real-time quantitative detection method of the second generation probiotics according to claim 2, wherein, The flow cytometry-based real-time quantitative detection method of the second-generation probiotics further comprises a step of preparing an on-machine data analysis template. Preferably, the on-machine data analysis template comprises a live cell signal template and a dead cell signal template. Preferably, the method for preparing the live cell signal template comprises the following steps: immediately after the activation treatment of the second-generation probiotics, using SYTO 9 for single staining on the machine, adjusting the B525-A channel voltage, and then collecting the fluorescence signals in the central or near-central region of the X-axis FSC and Y-axis SSC flow chart to obtain first voltage parameters and a first image linear coefficient. Preferably, the method for preparing the dead cell signal template comprises the following steps: using SYTO 9 and propidium iodide for mixed staining on the machine after the second-generation probiotics with damaged cell membranes are treated with isopropanol, adjusting the B585-A channel voltage, and then collecting the fluorescence signals in the central or near-central region of the X-axis FSC and Y-axis SSC flow chart to obtain second voltage parameters and a second image linear coefficient.

6. The flow cytometry-based real-time quantitative detection method of the second generation probiotics according to claim 2, wherein, The placing of the sample to be detected into the flow cytometer for detection comprises the following steps: using the 488 nm argon ion laser configured by the flow cytometer for excitation, selecting low speed for sample on-machine detection; using the B525-A channel to receive the SYTO 9 fluorescence signal, adjusting the channel voltage to the first voltage parameter and the image to the first image linear coefficient; and using the B585-A channel to receive the propidium iodide fluorescence signal, adjusting the channel voltage to the second voltage parameter and the image to the second image linear coefficient.

7. The flow cytometry-based real-time quantitative detection method of the second generation probiotics according to claim 2, wherein, The data analysis includes: adjusting B525-A, B585-A channel compensation to separate the corresponding Yin and Yang groups, comparing with the live cell signal template and the dead cell signal template after removing the blank fluorescence background, determining the live cell and dead cell cluster position, and then gating, calculating the live bacteria number AFU=N x a x 1000 according to the particle number displayed by the equipment, wherein N is the instrument-determined live bacteria concentration result, particle number / μL, and a is the sample dilution multiple.

8. The flow cytometry-based real-time quantitative detection method of probiotics of the second generation according to any one of claims 1-7, wherein, The second-generation probiotic real-time quantitative detection method based on flow cytometry comprises the following steps: (1) mixing the second-generation probiotics with a diluent to obtain diluted second-generation probiotics, wherein the diluent comprises peptone and inorganic salt; (2) mixing the diluted second-generation probiotics in step (1) with a staining agent to obtain a sample to be tested, wherein the staining agent comprises SYTO 9 and propidium iodide; (3) placing the sample to be tested in step (2) into a flow cytometer for detection to receive the fluorescence signals of SYTO 9 and propidium iodide, and obtaining a detection result; and (4) calculating the live bacteria number and the dead / live ratio of the second-generation probiotics according to the detection result in step (3).

9. A second-generation probiotic real-time quantitative detection device for performing the steps in the second-generation probiotic real-time quantitative detection method based on flow cytometry according to any one of claims 1-8. Preferably, the real-time quantitative detection device comprises a sample detection unit and a data analysis unit. Preferably, the sample detection unit is used to perform the steps comprising: pretreating and staining the second-generation probiotics to obtain a sample to be tested, placing the sample to be tested into a flow cytometer for detection, and obtaining a detection result. Preferably, the data analysis unit is used to perform the steps comprising: data analysis on the detection result.

10. Use of the second-generation probiotic real-time quantitative detection method based on flow cytometry according to any one of claims 1-8 and / or the second-generation probiotic real-time quantitative detection device according to claim 9 in the development of second-generation probiotic products and / or the development of second-generation probiotic production processes.

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