WO2023016135A1 - 生物样品中细菌鉴定及抗生素敏感性测试的分析方法 - Google Patents

生物样品中细菌鉴定及抗生素敏感性测试的分析方法 Download PDF

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WO2023016135A1
WO2023016135A1 PCT/CN2022/103304 CN2022103304W WO2023016135A1 WO 2023016135 A1 WO2023016135 A1 WO 2023016135A1 CN 2022103304 W CN2022103304 W CN 2022103304W WO 2023016135 A1 WO2023016135 A1 WO 2023016135A1
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bacteria
biological sample
bacterial
capillary
bacterium
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French (fr)
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黄光明
詹柳娟
侯壮豪
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中国科学技术大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
    • G01N1/30—Staining; Impregnating ; Fixation; Dehydration; Multistep processes for preparing samples of tissue, cell or nucleic acid material and the like for analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
    • G01N1/34—Purifying; Cleaning

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  • the present invention relates to the field of biology. Specifically, the present invention relates to analytical methods for bacterial identification and antibiotic susceptibility testing in biological samples.
  • the culture method is currently the most commonly used method for the detection of bacteria in biological samples.
  • the density of bacteria is increased by long-term culture, so that the bacteria can be identified and subsequently analyzed.
  • the culture method takes a long time, and the detection results cannot be obtained in time, which has a large lag, and the culture method cannot be used for detection of some living non-culturable bacteria. Therefore, in order to solve these problems, researchers have developed many culture-free methods to realize the rapid detection of bacteria in biological samples.
  • the fluorescence-based method can detect a single bacterium in a biological sample, which has high sensitivity but requires the information of known bacteria to target the bacteria, so the fluorescence-based method cannot accurately judge unknown bacteria.
  • Mass spectrometry is a non-targeted analysis method that enables simultaneous analysis of multiple metabolites based on the difference in mass-to-charge ratio of various analytes. Since mass spectrometry does not require labels and does not need to know the information of analytes in advance, it can quickly identify various unknown components and is widely used in the analysis of unknown biological samples. However, the small size of individual bacteria and the low content of metabolites have brought great challenges to the analysis of individual bacteria by mass spectrometry. At present, mass spectrometry is still based on culture in the detection of bacteria. After incubating a single bacterium into a colony, mass spectrometry is performed. The sensitivity of mass spectrometry analysis cannot reach the sensitivity of single bacterium analysis. Therefore, in order to perform mass spectrometry analysis on a single bacterium, it is necessary to develop a highly sensitive mass spectrometry method.
  • the purpose of the present invention is to provide a method for rapid and accurate identification of a single living bacterium in a biological sample directly without incubation.
  • the present invention proposes a single live bacteria mass spectrometry analysis method, through in-situ extraction, extracts the metabolites of a single live bacteria in a biological sample and performs mass spectrometry detection to realize the detection of a single live bacteria in a biological sample Identification and antibiotic susceptibility testing, biological sample pretreatment plus single live bacteria sampling and mass spectrometry detection process takes less than 1 hour, meeting the needs of rapid and accurate bacterial analysis in actual biological samples.
  • the invention provides a method for identifying bacteria in a biological sample, which is characterized in that it includes the steps of staining and fixing the bacteria in the biological sample, and performing mass spectrometry detection after extracting the metabolites of the fixed single live bacteria.
  • the metabolites of immobilized single living bacteria are extracted by capillary.
  • the capillary is pre-drawn prior to use.
  • the immobilization is by electrostatic interaction or covalent binding
  • immobilized slides include, but are not limited to, polylysine slides, polyethyleneimine slides, gelatin slides N-(2-aminoethyl)-3-aminopropyltrimethoxysilane glass slides and 3-aminopropyltriethoxysilane glass slides.
  • the fixed slide is selected from polylysine slides, polyethyleneimine slides, gelatin slides N-(2-aminoethyl)-3-aminopropyl 3-Aminopropyltriethoxysilane glass slides and 3-aminopropyltriethoxysilane glass slides.
  • a step of sedimenting large interfering substances in the biological sample by low speed centrifugation is included.
  • the method for identifying bacteria in a biological sample comprises the following steps:
  • Step (1) Settling the large particle interfering substances in the biological sample by low-speed centrifugation, retaining the upper layer solution containing bacteria, and obtaining the bacteria in the biological sample;
  • Step (2) Stain the upper layer solution obtained in step (1) for bacterial staining, locate the bacteria, and centrifuge at high speed after staining to wash away the staining agent;
  • Step (3) drop the stained bacterial solution obtained in step (2) on the glass slide, and capture the bacterial sample in the fixation solution by electrostatic adsorption or covalent binding;
  • Step (4) Inject the extraction solution into the capillary, and extract the single living bacteria fixed in the step (3) under a microscope by means of in-situ extraction;
  • Step (5) inserting an electrode into the capillary in step (4), applying a voltage for electrospray, and analyzing the extracted bacterial metabolites by mass spectrometry.
  • the biological sample includes, but is not limited to, human whole blood, rabbit whole blood, urine samples, saliva, cerebrospinal fluid, alveolar lavage fluid, abscesses, bacterially infected cell solutions, and bacterially infected tissue suspensions.
  • the biological sample is selected from the group consisting of human whole blood, rabbit whole blood, urine sample, saliva, cerebrospinal fluid, alveolar lavage fluid, abscess, bacterially infected cell solution, and bacterially infected tissue suspension.
  • the bacteria include, but are not limited to, Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Acinetobacter baumannii.
  • the bacterium is selected from Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Acinetobacter baumannii.
  • the rotational speed of low-speed centrifugation is 2000 rpm-4000 rpm, and the centrifugation time is 5 min-10 min.
  • the rotation speed of high-speed centrifugation is 10000 rpm-14000 rpm, and the centrifugation time is 5 min-10 min.
  • the stains include, but are not limited to, Acridine Orange, Fluorescein Diacetate, Syto 9, Syto 64, hoechst 33258, and DAPI.
  • the stain is selected from Acridine Orange, Fluorescein Diacetate, Syto 9, Syto 64, hoechst 33258, and DAPI.
  • the dyeing time is 10 min-30 min, the dye concentration is 1 ⁇ M-10 ⁇ M, and the dyeing is protected from light.
  • the bacterial solution interacts with the glass slide for 10-30 minutes.
  • the capillary tip opening is less than 2 ⁇ m, preferably between 500 nm and 1 ⁇ m;
  • the extraction solution is selected from one or more of the group consisting of but not limited to water, methanol, ethanol, acetonitrile, acetone, chloroform, formic acid, acetic acid and trifluoroacetic acid.
  • the extraction solution is one or more selected from the group consisting of water, methanol, ethanol, acetonitrile, acetone, chloroform, formic acid, acetic acid and trifluoroacetic acid.
  • the method further comprises the step of establishing a comparison library of bacteria.
  • establishing a bacterial comparison library comprises the steps of:
  • the present invention provides a method for testing the antibiotic susceptibility of bacteria in a biological sample, which is characterized in that it includes the above method, and optionally further includes the step of incubating the bacteria in the biological sample with antibiotics.
  • the method for testing the antibiotic susceptibility of bacteria in a biological sample comprises the following steps:
  • Step (1) Settling the large particle interfering substances in the biological sample by low-speed centrifugation, retaining the upper layer solution containing bacteria, and obtaining the bacteria in the biological sample;
  • Step (2) adding different concentrations of antibiotics to the supernatant solution containing bacteria and incubating;
  • Step (3) Centrifuge the incubated bacterial solution, discard the supernatant, and resuspend the precipitate in deionized water;
  • Step (4) staining the bacterial solution obtained in step (3), positioning the bacteria, and centrifuging at high speed after staining to wash away the staining agent;
  • Step (5) drop the stained bacterial solution obtained in step (4) on the glass slide, and capture the bacterial sample in the fixation solution by electrostatic adsorption or covalent binding;
  • Step (6) Inject the extraction solution into the capillary, and extract the single live bacteria fixed in the step (5) under a microscope by means of in-situ extraction;
  • Step (7) inserting electrodes into the capillary in step (6), applying a voltage for electrospray, and analyzing the extracted bacterial metabolites by mass spectrometry.
  • t-SNE t-distributed stochastic neighbor embedding
  • t-SNE is a machine learning algorithm for dimensionality reduction, which was proposed by Laurens van der Maaten and Geoffrey Hinton in 2008.
  • t-SNE is a nonlinear dimensionality reduction algorithm, which is very suitable for reducing the dimensionality of high-dimensional data to 2D or 3D for visualization.
  • t-SNE analysis can be performed on a large number of high-dimensional bacterial mass spectrometry data, the bacterial mass spectrometry data can be processed in batches, the significance index can be automatically derived, and the bacterial mass spectrometry data with differences can be distinguished and effective. Typing.
  • the speed is fast, no incubation is required, and a single live bacterium in a bacterial infection biological sample can be analyzed.
  • the pretreatment of the biological sample plus the sampling of a single live bacterium and the mass spectrometry detection process are less than 1 hour;
  • Non-target detection which can accurately analyze unknown bacteria, identify different bacteria and analyze the metabolic response of bacteria after antibiotic stimulation according to the difference of metabolites.
  • Fig. 1 is a single live bacteria mass spectrometry flow chart
  • Fig. 2 is the flow chart of the sorting and capture immobilization of bacteria in whole blood according to an embodiment of the present invention
  • 3 is a bright field image and a fluorescence image after the bacteria are captured and fixed according to an embodiment of the present invention
  • Fig. 4 is the in situ extraction diagram of a single living bacterium according to an embodiment of the present invention.
  • Figure 5 is a single bacterial metabolite mass spectrogram according to an embodiment of the present invention.
  • Fig. 6 is the metabolite typing diagram of Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus according to an embodiment of the present invention
  • Fig. 7 is according to the embodiment of the present invention Escherichia coli, Acinetobacter baumannii, Staphylococcus aureus identification accuracy rate neural network test graph;
  • Figure 8 is a diagram of bacterial species identification according to one embodiment of the present invention.
  • Figure 9 is a diagram of bacterial species identification according to one embodiment of the present invention.
  • Fig. 10 is a bacterial species identification diagram according to one embodiment of the present invention.
  • Figure 11 is a diagram of bacterial species identification according to one embodiment of the present invention.
  • Fig. 12 is a response diagram of a single Escherichia coli metabolite change after being stimulated by different antibiotic drugs of Escherichia coli according to an embodiment of the present invention
  • Fig. 13 is a response diagram of changes in individual E. coli metabolites after stimulation with different concentrations of kanamycin in E. coli according to an embodiment of the present invention.
  • the detection and identification of a single live bacterium in the whole blood of embodiment 1 comprises the following steps:
  • step (2) Put the capillary drawn in step (2) into the deionized water (containing 1% formic acid) solution, take out the capillary and fix it on the micro-manipulation platform, position the capillary under the microscope to contact the bacteria, and push out the solution in the capillary by the syringe pump After extracting the bacterial metabolites for 1 second, suck back the solution, and the in-situ extraction of a single living bacterium is shown in Figure 4;
  • Table 1 is a set of metabolites detected by the three bacteria Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus. According to the measured metabolites, the three bacteria were analyzed by dimensionality reduction and visualization (t-SNE), Fig.
  • t-SNE 6 is the high-dimensional data dimensionality reduction and visualization (t-SNE) analysis of metabolite fingerprints of three bacteria, Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus. Typing, building a database for the identification of unknown bacteria.
  • Table 1 List of metabolites detected by Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus
  • Fig. 7 is the neural network test chart of the identification accuracy of three kinds of bacteria, and the bacterial metabolite database established in step (5) has an accuracy rate of 90% for identification of bacteria.
  • step (4) Input the bacterial metabolite fingerprints obtained in step (4) into the bacterial database established in step (5), and the bacteria will be classified into different types according to the detected metabolite differences.
  • the identification of bacteria species detected in step (4) is shown in Figure 8, and the detected bacteria points fall within the range of Acinetobacter baumannii in the database, indicating that the bacteria detected in step (4) are Acinetobacter baumannii.
  • the detection and identification of a single live bacterium in the cell of embodiment 2 bacterial infection comprises the following steps:
  • step (1) Add 10 ⁇ L of 100 ⁇ M Acridine Orange stain to the bacterial solution obtained in step (1) for staining for 15 minutes, centrifuge the stained solution at 10,000 rpm for 8 minutes, wash off the stain, and resuspend the precipitate in 100 ⁇ L of deionized water. Drop the resuspended bacterial solution on the poly-lysine glass slide and let it stand for 30 minutes to allow the bacteria to adsorb and fix on the poly-lysine glass slide, rinse with deionized water, and dry the poly-lysine slide at room temperature. slides, and observe the location of the bacteria under a fluorescent microscope.
  • step (3) Put the capillary tube drawn in step (3) into the solution of deionized water (containing 1% formic acid), take out the capillary tube and fix it on the micro-operation platform, position the capillary tube under the microscope to contact the bacteria, and the syringe pump pushes out the capillary tube.
  • the solution extracts a single live bacterial metabolite for 1 second and then sucks it back into the solution;
  • Table 1 is a set of metabolites detected by the three bacteria Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus. According to the measured metabolites, the three bacteria were analyzed by dimensionality reduction and visualization (t-SNE), Fig.
  • t-SNE 6 is the high-dimensional data dimensionality reduction and visualization (t-SNE) analysis of metabolite fingerprints of three bacteria, Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus. Typing, building a database for the identification of unknown bacteria.
  • Fig. 7 is the neural network test chart of the identification accuracy of three kinds of bacteria, and the bacterial metabolite database established in step (6) has an accuracy rate of 90% for identification of bacteria.
  • step (5) Input the bacterial metabolite fingerprints in step (5) into the bacterial database established in step (6), and the bacteria will be classified into different types according to the detected metabolite differences.
  • the identification of bacteria species detected in step (5) is shown in Figure 9, and the detected bacteria points fall within the range of Escherichia coli in the database, indicating that the bacteria detected in step (5) are Escherichia coli.
  • the detection and identification of a single live bacterium in the bacterial infection tissue of embodiment 3 comprises the following steps:
  • Wipe the infected tissue with a sterile cotton swab put the swab into 1mL deionized water to elute the bacteria, and keep the eluate containing the bacteria.
  • step (1) Add 10 ⁇ L of 1 mM Acridine Orange stain to the bacterial solution obtained in step (1) for staining for 15 minutes, centrifuge the stained solution at 10,000 rpm for 8 minutes, wash off the stain, and resuspend the precipitate in 100 ⁇ L of deionized water. Drop the resuspended bacterial solution on the poly-lysine glass slide and let it stand for 30 minutes to allow the bacteria to adsorb and fix on the poly-lysine glass slide, rinse with deionized water, and dry the poly-lysine slide at room temperature. slides, and observe the location of the bacteria under a fluorescent microscope.
  • step (3) Put the capillary tube drawn in step (3) into the solution of deionized water (containing 1% formic acid), take out the capillary tube and fix it on the micro-operation platform, position the capillary tube under the microscope to contact the bacteria, and the syringe pump pushes out the capillary tube.
  • the solution extracts a single live bacterial metabolite for 1 second and then sucks it back into the solution;
  • Table 1 is a set of metabolites detected by the three bacteria Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus. According to the measured metabolites, the three bacteria were analyzed by dimensionality reduction and visualization (t-SNE), Fig.
  • t-SNE 6 is the high-dimensional data dimensionality reduction and visualization (t-SNE) analysis of metabolite fingerprints of three bacteria, Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus. Typing, building a database for the identification of unknown bacteria.
  • Fig. 7 is the neural network test chart of the identification accuracy of three kinds of bacteria, and the bacterial metabolite database established in step (6) has an accuracy rate of 90% for identification of bacteria.
  • step (5) Input the bacterial metabolite fingerprints in step (5) into the bacterial database established in step (6), and the bacteria will be classified into different types according to the detected metabolite differences.
  • the identification of bacteria species detected in step (5) is shown in Figure 10, and the detected bacteria points fall within the range of Staphylococcus aureus in the database, indicating that the bacteria detected in step (5) are Staphylococcus aureus.
  • the detection and identification of a single live bacterium in the urine of embodiment 4 comprises the following steps:
  • step (2) Put the capillary drawn in step (2) into the deionized water (containing 1% formic acid) solution, take out the capillary and fix it on the micro-operation platform, position the capillary under the microscope to contact the bacteria, and push out the solution in the capillary by the syringe pump After extracting bacterial metabolites for 1 second, suck back the solution;
  • deionized water containing 1% formic acid
  • Table 1 is a set of metabolites detected by the three bacteria Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus. According to the measured metabolites, the three bacteria were analyzed by dimensionality reduction and visualization (t-SNE), Fig.
  • t-SNE 6 is the high-dimensional data dimensionality reduction and visualization (t-SNE) analysis of metabolite fingerprints of three bacteria, Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus. Typing, building a database for the identification of unknown bacteria.
  • Fig. 7 is the neural network test chart of the identification accuracy of three kinds of bacteria, and the bacterial metabolite database established in step (5) has an accuracy rate of 90% for identification of bacteria.
  • step (4) Input the bacterial metabolite fingerprints in step (4) into the bacterial database established in step (5), and determine the bacterial species according to the detected metabolite differences.
  • the identification of bacteria species detected in step (4) is shown in Figure 11, and the detected bacteria points fall within the range of Acinetobacter baumannii in the database, indicating that the bacteria detected in step (4) are Acinetobacter baumannii.
  • Single viable bacteria antibiotic susceptibility test in the whole blood of embodiment 5, comprises the following steps:
  • step (3) Put the capillary tube drawn in step (3) into the solution of deionized water (containing 1% formic acid), take out the capillary tube and fix it on the micro-operation platform, position the capillary tube under the microscope to contact the bacteria, and the syringe pump pushes out the capillary tube.
  • the solution extracts bacterial metabolites for 1 second and sucks them back into the solution;
  • Figure 12 is the response graph of the change of a single bacterial metabolite alanylthreonine after different antibiotic drugs stimulated Escherichia coli, and the sensitive antibiotic kanamycin After incubation with antibiotics and norfloxacin, the bacterial alanylthreonine signal was significantly reduced, while incubation without antibiotics did not cause significant changes in the bacterial alanylthreonine signal.
  • Figure 13 is a graph showing the change response of a single E. coli metabolite alanyl threonine after different concentrations of kanamycin stimulated E. coli.

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Abstract

鉴定生物样品中的细菌的方法，以及生物样品中细菌的抗生素敏感性的测试方法，包括如下步骤：步骤(1)离心分离生物样品中的细菌；步骤(2)对步骤(1)中获得的细菌进行染色；步骤(3)通过静电吸附作用固定步骤(2)中染色后的细菌；步骤(4)毛细管尖端注入萃取溶液后，通过原位萃取的方式萃取单个活细菌的代谢物；步骤(5)在步骤(4)中获得的毛细管内插入电极施加电压，进行质谱分析。无需依赖孵育，可实现对初期细菌感染生物样品中细菌种类鉴定及抗生素敏感性测试，具有快速，高灵敏度，非靶向分析等优点，应用前景好。

Description

生物样品中细菌鉴定及抗生素敏感性测试的分析方法 技术领域
本发明涉及生物领域。具体的，本发明涉及生物样品中细菌鉴定及抗生素敏感性测试的分析方法。
背景技术
许多不同领域都需要检测或鉴定细菌，快速准确的细菌鉴定在医学、生物学等领域至关重要。部分生物样品在细菌感染初期，细菌含量较低，如血液感染初期，每毫升血液中只有1-1000个细菌，且随着抗生素的过度使用及滥用，越来越多的细菌发生变异，这对快速准确的细菌检测提出了更为严峻的要求。
培养法是目前生物样品中细菌检测最常用的手段，通过长时间培养的方式来增加细菌的密度，从而对细菌进行鉴定及后续分析。但培养法耗时长，无法及时得到检测结果，具有较大的滞后性，且对于一些活的非可培养状态的细菌无法应用培养法来进行检测。因此，为了解决这些问题，研究人员开发了许多无需培养的方式，以实现生物样品中细菌的快速检测。例如基于荧光的方式可以对生物样品中的单个细菌进行检测，灵敏度高但需要已知细菌的信息对细菌进行靶向标记，所以基于荧光的方式无法准确的对未知细菌做出判断。
质谱分析是一种非靶向的分析方式，依据各种分析物的质荷比差异，实现多种代谢物的同时分析。由于质谱法无需标记，不需要事先获知分析物的信息，因此，能够对各种未知成分进行快速鉴定，被广泛应用于未知生物样品的分析。然而，单个细菌体积极小，且代谢物含量低，这给质谱用于单个细菌的分析带来了巨大的挑战。目前质谱分析在细菌检测方面依旧基于培养，将单个细菌孵育成菌落后再进行质谱分析，质谱分析方式的灵敏度还无法达到单个细菌分析的灵敏度。因此想要对单个细菌进行质谱分析，就需要发展一种高灵敏的质谱分析方式。
发明内容
本发明的目的是提供一种无需孵育直接对生物样品中单个活细菌进行快速准确鉴定的方式。针对现存检测方法所存在的不足，本发明提出了单个活细菌质谱分析方法，通过原位萃取的方式，对生物样品中单个活细菌的代谢物萃取后进行质谱检测，实现生物样品中单个活细菌的鉴定及抗生素敏感性测试，生物样品前处理加单个活细菌取样及质谱检测过程小于1小时，满足在实际生物样品中快速准确细菌分析的需求。
本发明的技术方案如下：
本发明提供鉴定生物样品中的细菌的方法，其特征在于，包括对生物样品中的细菌进行染色和固定，并对固定的单个活细菌的代谢物萃取后进行质谱检测的步骤。
在一些实施方案中，通过毛细管对固定的单个活细菌的代谢物进行萃取。
在一些实施方案中，在使用毛细管前对其进行预拉制。
在一些实施方案中，所述固定通过静电相互作用或共价结合作用进行，固定的载玻片包括但不限于多聚赖氨酸载玻片、聚乙烯亚胺载玻片、明胶载玻片N-(2-氨基乙基)-3-氨基丙基三甲氧基硅烷载玻片和3-氨基丙基三乙氧基硅烷载玻片。
在一些实施方案中，所述固定的载玻片选自多聚赖氨酸载玻片、聚乙烯亚胺载玻片、明胶载玻片N-(2-氨基乙基)-3-氨基丙基三甲氧基硅烷载玻片和3-氨基丙基三乙氧基硅烷载玻片。
在一些实施方案中，在对细菌染色前，包括通过低速离心沉降生物样品中的大颗粒干扰物质的步骤。
在一些实施方案中，所述的鉴定生物样品中的细菌的方法，包括以下步骤：
步骤(1)：通过低速离心沉降生物样品中的大颗粒干扰物质，保留含有细菌的上层溶液，获取生物样品中的细菌；
步骤(2)：将步骤(1)获得的上层溶液进行细菌染色，定位细菌，染色后高速离心以洗去染色剂；
步骤(3)：将步骤(2)获得的染色后的细菌溶液滴在载玻片上，通过静电吸附或共价结合作用捕捉固定溶液中的细菌样本；
步骤(4)：在毛细管内注入萃取溶液，通过原位萃取的方式对步骤(3)中固定的单个活细菌在显微镜下进行萃取；
步骤(5)：在步骤(4)的毛细管中插入电极，施加电压进行电喷雾，通过质谱对萃取到的细菌代谢物进行分析。
在一些实施方案中，所述生物样品包括但不限于人全血、兔全血、尿样、唾液、脑脊液、肺泡灌洗液、脓肿、细菌感染的细胞溶液和细菌感染的组织悬液。
在一些实施方案中，所述生物样品选自人全血、兔全血、尿样、唾液、脑脊液、肺泡灌洗液、脓肿、细菌感染的细胞溶液和细菌感染的组织悬液。
在一些实施方案中，所述细菌包括但不限于大肠杆菌、金黄色葡萄球菌、绿脓杆菌和鲍曼不动杆菌。
在一些实施方案中，所述细菌选自大肠杆菌、金黄色葡萄球菌、绿脓杆菌和鲍曼不动杆菌。
在一些实施方案中，低速离心的转速为2000rpm-4000rpm，离心时间为5min-10min。
在一些实施方案中，高速离心的转速为10000rpm-14000rpm，离心时间为5min-10min。
在一些实施方案中，所述染色剂包括但不限于Acridine Orange、Fluorescein Diacetate、Syto 9、Syto 64、hoechst 33258和DAPI。
在一些实施方案中，所述染色剂选自Acridine Orange、Fluorescein Diacetate、Syto 9、Syto 64、hoechst 33258和DAPI。
在一些实施方案中，染色时间为10min-30min，染色剂浓度为1μM-10μM，避光染色。
在一些实施方案中，细菌溶液与载玻片的作用时间为10-30min。
在一些实施方案中，毛细管尖端开口小于2μm，优选为500nm-1μm；
在一些实施方案中，所述萃取溶液选包括但不限于水、甲醇、乙醇、乙腈、丙酮、氯仿、甲酸、乙酸和三氟乙酸组成的组中的一种或多种。
在一些实施方案中，所述萃取溶液选自由水、甲醇、乙醇、乙腈、丙酮、氯仿、甲酸、乙酸和三氟乙酸组成的组中的一种或多种。
在一些实施方案中，所述方法还包括建立细菌对比库的步骤。
在一些实施方案中，建立细菌对比库包括以下步骤：
a.对一种以上单个参照活细菌代谢物进行质谱分析；
b.根据所测得的代谢物对一种以上参照活细菌进行降维与可视化分析，并根据所测得的单个活细菌代谢物对不同细菌进行分型，为未知细菌的鉴定建立对比库。
另一方面，本发明提供生物样品中细菌的抗生素敏感性的测试方法，其特征在于，包括上述的方法，任选地，还包括将生物样品中的细菌与抗生素进行孵育的步骤。
在一些实施方案中，生物样品中细菌的抗生素敏感性的测试方法，包括以下步骤：
步骤(1)：通过低速离心沉降生物样品中的大颗粒干扰物质，保留含有细菌的上层溶液，获取生物样品中的细菌；
步骤(2)：在含有细菌的上层溶液加入不同浓度的抗生素并进行孵育；
步骤(3)：对孵育后的细菌溶液进行离心，弃去上清，将沉淀重悬于去离子水中；
步骤(4)：将步骤(3)获得的细菌溶液进行细菌染色，定位细菌，染色后高速离心以洗去染色剂；
步骤(5)：将步骤(4)获得的染色后的细菌溶液滴在载玻片上，通过静电吸附或共价结合作用捕捉固定溶液中的细菌样本；
步骤(6)：在毛细管内注入萃取溶液，通过原位萃取的方式对步骤(5)中固定的单个活细菌在显微镜下进行萃取；
步骤(7)：在步骤(6)的毛细管中插入电极，施加电压进行电喷雾，通过质谱对萃取到的细菌代谢物进行分析。
定义
t-SNE(t-distributed stochastic neighbor embedding)是用于降维的一种机器学习算法，是由Laurens van der Maaten和Geoffrey Hinton在2008年提出来。此外，t-SNE是一种非线性降维算法，非常适用于高维数据降维到2维或者3维，进行可视化。通过编程的MATLAB运行脚本，可以对大量高维的细菌质谱数据进行t-SNE分析，可批量化处理细菌质谱数据，可自动导出显著性指标，可区分具有差异的细菌质谱数据，并进行有效的分型。
本发明具有以下有益效果：
(1)速度快，无需孵育，可以对细菌感染生物样本中的单个活细菌进行分析，生物样品前处理加单个活细菌取样及质谱检测过程小于1小时；
(2)灵敏度高，可以对单个活细菌的代谢物进行质谱分析；
(3)非靶向检测，准确对未知细菌进行分析，可以根据代谢物差异，对不同细菌进行鉴定以及分析抗生素刺激后细菌的代谢响应。
附图说明
图1为单个活细菌质谱分析流程图；
图2为根据本发明的实施方案的全血中细菌的分选及捕获固定流程图；
图3为根据本发明的实施方案的细菌捕获固定后的明场图以及荧光图；
图4为根据本发明的实施方案的单个活细菌原位萃取图；
图5为根据本发明的实施方案的单个细菌代谢物质谱图；
图6为根据本发明的实施方案的大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌代谢物分型图；
图7为根据本发明的实施方案的大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌鉴别准确率神经网络测试图；
图8为根据本发明的一个实施方案的细菌种类鉴定图；
图9为根据本发明的一个实施方案的细菌种类鉴定图；
图10为根据本发明的一个实施方案的细菌种类鉴定图；
图11为根据本发明的一个实施方案的细菌种类鉴定图；
图12为根据本发明的实施方案的大肠杆菌不同抗生素药物刺激后，单个大肠杆菌代谢物变化响应图；
图13为根据本发明的一个实施方案的大肠杆菌不同浓度卡那霉素刺激后，单个大肠杆菌代谢物变化响应图。
具体实施方式
为使本发明的目的、技术方案和优点更加清楚明白，以下结合具体实施例，并参照附图，对本发明作进一步的详细说明。
实施例1全血中单个活细菌的检测鉴定，包括以下步骤：
(1)全血中细菌的捕获定位：
取1mL细菌感染的全血置于离心管中，3000rpm离心8min，弃去血细胞沉淀，将含有细菌的500μL上清10000rpm离心8min，弃去上清，沉淀重悬于100μL去离子水中，加10μL100μM Acridine Orange染色剂染色15min，染色后的溶液10000rpm离心8min，洗去染色剂，沉淀重悬于100μL去离子水中。将重悬后的细菌溶液滴在多聚赖氨酸载玻片上，静置30min，使细菌吸附固定在多聚赖氨酸载玻片上，去离子水冲洗，室温下干燥多聚赖氨酸载玻片，在荧光显微镜下观察细菌的位置如图3所示。
(2)毛细管制备：
使用P1000拉针仪，拉制尖端直径为1μm毛细管；
(3)原位萃取单个活细菌代谢物：
将步骤(2)中拉制好的毛细管伸入去离子水(含1％甲酸)溶液中，拿出毛细管固定于微操平台上，显微镜下将毛细管定位接触细菌，注射泵推出毛细管中的溶液萃取细菌代谢物1s后吸回溶液，单个活细菌的原位萃取如图4所示；
(4)质谱分析：
将萃取后的毛细管置于质谱入口前端，施加电压，采集质谱图如图5所示；
(5)细菌对比库的建立：
在多聚赖氨酸载玻片表面滴加1μL浓度为10 5个/mL纯细菌溶液，静置30min使细菌吸附在多聚赖氨酸载玻片表面，洗涤，室温下干燥，去离子水(含1％甲酸)萃取单个活细菌代谢物后进行质谱分析。表1为三种细菌大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌检测到的代谢物集合，根据所测到的代谢物对三种细菌进行降维与可视化(t-SNE)分析，图6为三种细菌大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌代谢物指纹图谱的高维数据降维与可视化(t-SNE)分析，所测得的单细菌代谢物可以对不同细菌进行分型，为未知细菌的鉴定建立数据库。
表1大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌检测到的代谢物的列表
Figure PCTCN2022103304-appb-000001
(6)细菌种类鉴别准确率测试：
为了验证步骤(5)中建立的细菌代谢物数据库对细菌种类鉴定的准确率，以大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌测得的代谢物作为测试集，进行神经网络测试，图7为三种细菌鉴别准确率神经网络测试图，步骤(5)中建立的细菌代谢物数据库对细菌鉴别的准确率为90％。
(7)细菌种类判断
将步骤(4)获得的细菌代谢物指纹图谱输入步骤(5)建立的细菌数据库中，根据检测到的代谢物差异细菌会被分到不同类型。步骤(4)检测到的细菌种类鉴定如图8所示，检测到的细菌点落在数据库中鲍曼不动杆菌的范围，说明步骤(4)中所检测的细菌为鲍曼不动杆菌。
实施例2细菌感染的细胞中单个活细菌的检测鉴定，包括以下步骤：
(1)细胞中细菌的分离：
在1mL感染细菌的RAW264.7细胞悬液中加入100μL 10％皂苷，37℃裂解5min，3000rpm离心8min，弃去沉淀，保留含有细菌的上清。
(2)细菌的捕获定位：
在步骤(1)中获得的细菌溶液中加10μL 100μM Acridine Orange染色剂染色15min，染色后的溶液10000rpm离心8min，洗去染色剂，沉淀重悬于100μL去离子水中。将重悬后的细菌溶液滴在多聚赖氨酸载玻片上，静置30min，使细菌吸附固定在多聚赖氨酸载玻片上，去离子水冲洗，室温下干燥多聚赖氨酸载玻片，在荧光显微镜下观察细菌的位置。
(3)毛细管制备：
使用P1000拉针仪，拉制尖端直径为1μm毛细管；
(4)原位萃取单个活细菌代谢物：
将步骤(3)中拉制好的毛细管伸入去离子水(含1％甲酸)的溶液中，拿出毛细管固定于微操平台上，显微镜下将毛细管定位接触细菌，注射泵推出毛细管中的溶液萃取单个活细菌代谢物1s后吸回溶液；
(5)质谱分析：
将萃取后的毛细管置于质谱入口前端，施加电压，采集萃取物的细菌质谱图。
(6)细菌对比库的建立：
在多聚赖氨酸载玻片表面滴加1μL浓度为10 5个/mL纯细菌溶液，静置30min使细菌吸附在多聚赖氨酸载玻片表面，洗涤，室温下干燥，去离子水(含1％甲酸)萃取单个活细菌代谢物后进行质谱分析。表1为三种细菌大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌检测到的代谢物集合，根据所测到的代谢物对三种细菌进行降维与可视化(t-SNE)分析，图6为三种细菌大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌代谢物指纹图谱的高维数据降维与可视化(t-SNE)分析，所测得的单细菌代谢物可以对不同细菌进行分型，为未知细菌的鉴定建立数据库。
(7)细菌种类鉴别准确率测试：
为了验证步骤(6)中建立的细菌代谢物数据库对细菌种类鉴定的准确率，以大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌测得的代谢物作为测试集，进行神经网络测试，图7为三种细菌鉴别准确率神经网络测试图，步骤(6)中建立的细菌代谢物数据库对细菌鉴别的准确率为90％。
(8)细菌种类判断
将步骤(5)细菌代谢物指纹图谱输入步骤(6)建立的细菌数据库中，根据检测到的代谢物差异细菌会被分到不同类型。步骤(5)检测到的细菌种类鉴定如图9所示，检测到的细菌点落在数据库中大肠杆菌的范围，说明步骤(5)中所检测的细菌为大肠杆菌。
实施例3细菌感染组织中单个活细菌的检测鉴定，包括以下步骤：
(1)感染组织中细菌的分离：
用无菌棉签擦拭感染组织，将擦拭后的棉签伸入1mL去离子水中洗脱细菌，保留含有细菌的洗脱液。
(2)细菌的捕获定位：
在步骤(1)中获得的细菌溶液中加10μL 1mM Acridine Orange染色剂染色15min，染色后的溶液10000rpm离心8min，洗去染色剂，沉淀重悬于100μL去离子水中。将重悬后的细菌溶液滴在多聚赖氨酸载玻片上，静置30min，使细菌吸附固定在多聚赖氨酸载玻片上，去离子水冲洗，室温下干燥多聚赖氨酸载玻片，在荧光显微镜下观察细菌的位置。
(3)毛细管制备：
使用P1000拉针仪，拉制尖端直径为1μm毛细管；
(4)原位萃取单个活细菌代谢物：
将步骤(3)中拉制好的毛细管伸入去离子水(含1％甲酸)的溶液中，拿出毛细管固定于微操平台上，显微镜下将毛细管定位接触细菌，注射泵推出毛细管中的溶液萃取单个活细菌代谢物1s后吸回溶液；
(5)质谱分析：
将萃取后的毛细管置于质谱入口前端，施加电压，采集质谱图。
(6)细菌对比库的建立：
在多聚赖氨酸载玻片表面滴加1μL浓度为10 5个/mL纯细菌溶液，静置30min使细菌吸附在多聚赖氨酸载玻片表面，洗涤，室温下干燥，去离子水(含1％甲酸)萃取单个活细菌代谢物后进行质谱分析。表1为三种细菌大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌检测到的代谢物集合，根据所测到的代谢物对三种细菌进行降维与可视化(t-SNE)分析，图6为三种细菌大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌代谢物指纹图谱的高维数据降维与可视化(t-SNE)分析，所测得的单细菌代谢物可以对不同细菌进行分型，为未知细菌的鉴定建立数据库。
(7)细菌种类鉴别准确率测试：
为了验证步骤(6)中建立的细菌代谢物数据库对细菌种类鉴定的准确率，以大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌测得的代谢物作为测试集，进行神经网络测试，图7为三种细菌鉴别准确率神经网络测试图，步骤(6)中建立的细菌代谢物数据库对细菌鉴别的准确率为90％。
(8)细菌种类判断
将步骤(5)细菌代谢物指纹图谱输入步骤(6)建立的细菌数据库中，根据检测到的代谢物差异细菌会被分到不同类型。步骤(5)检测到的细菌种类鉴定如图10所示，检测到的细菌点落在数据库中金黄色葡萄球菌的范围，说明步骤(5)中所检测的细菌为金黄色葡萄球菌。
实施例4尿液中单个活细菌的检测鉴定，包括以下步骤：
(1)尿液中细菌的捕获定位：
取1mL尿路感染的尿液置于离心管中，3000rpm离心8min，弃去大颗粒沉淀，将含有细菌的800μL上清10000rpm离心8min，弃去上清，沉淀重悬于100μL去离子水中，加10μL100μM Acridine Orange染色剂染色15min，染色后的溶液10000rpm离心8min，洗去染色剂，沉淀重悬于100μL去离子水中。将重悬后的细菌溶液滴在多聚赖氨酸载玻片上，静置30min，使细菌吸附固定在多聚赖氨酸载玻片上，去离子水冲洗，室温下干燥多聚赖氨酸载玻片，在荧光显微镜下观察细菌的位置。
(2)毛细管制备：
使用P1000拉针仪，拉制尖端直径为1μm毛细管；
(3)原位萃取单个活细菌代谢物：
将步骤(2)中拉制好的毛细管伸入去离子水(含1％甲酸)溶液中，拿出毛细管固定于微操平台上，显微镜下将毛细管定位接触细菌，注射泵推出毛细管中的溶液萃取细菌代谢物1s后吸回溶液；
(4)质谱分析：
将萃取后的毛细管置于质谱入口前端，施加电压，采集质谱图；
(5)细菌对比库的建立：
在多聚赖氨酸载玻片表面滴加1μL浓度为10 5个/mL纯细菌溶液，静置30min使细菌吸附在多聚赖氨酸载玻片表面，洗涤，室温下干燥，去离子水(含1％甲酸)萃取单个活细菌代谢物后进行质谱分析。表1为三种细菌大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌检测到的代谢物集合，根据所测到的代谢物对三种细菌进行降维与可视化(t-SNE)分析，图6为三种细菌大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌代谢物指纹图谱的高维数据降维与可视化 (t-SNE)分析，所测得的单细菌代谢物可以对不同细菌进行分型，为未知细菌的鉴定建立数据库。
(6)细菌种类鉴别准确率测试：
为了验证步骤(5)中建立的细菌代谢物数据库对细菌种类鉴定的准确率，以大肠杆菌、鲍曼不动杆菌、金黄色葡萄球菌测得的代谢物作为测试集，进行神经网络测试，图7为三种细菌鉴别准确率神经网络测试图，步骤(5)中建立的细菌代谢物数据库对细菌鉴别的准确率为90％。
(7)细菌种类判断
将步骤(4)细菌代谢物指纹图谱输入步骤(5)建立的细菌数据库中，根据检测到的代谢物差异判断细菌种类。步骤(4)检测到的细菌种类鉴定如图11所示，检测到的细菌点落在数据库中鲍曼不动杆菌的范围，说明步骤(4)中所检测的细菌为鲍曼不动杆菌。
实施例5全血中单个活细菌抗生素敏感性测试，包括以下步骤：
(1)不同抗生素孵育细菌：
取1mL全血置于离心管中，3000rpm离心8min，弃去血细胞沉淀，在含有细菌的500μL上清分别加入10μL 8mg/mL卡那霉素及10μL 2mg/mL诺氟沙星，37℃震荡孵育30min，孵育后，上清10000rpm离心8min，弃去上清，沉淀重悬于100μL去离子水中；不加抗生素孵育的细菌溶液作为参照组，进行上述同样的操作。
(2)不同浓度卡那霉素孵育细菌；
取1mL全血置于离心管中，3000rpm离心8min，弃去血细胞沉淀，在含有细菌的500μL上清分别加入10μL 0.8mg/mL、8mg/mL、80mg/mL卡那霉素，37℃震荡孵育30min，孵育后，上清10000rpm离心8min，弃去上清，沉淀重悬于100μL去离子水中；不加抗生素孵育的细菌溶液作为参照组，进行上述同样的操作。
(3)细菌捕获及定位：
在步骤(1)、(2)中获得的细菌溶液中加10μL 100μM Acridine Orange染色剂染色15min，染色后的溶液10000rpm离心8min，洗去染色剂，沉淀重悬于100μL去离子水中。将重悬后的细菌溶液滴在多聚赖氨酸载玻片上，静置30min，使细菌吸附固定在多聚赖氨酸载玻片上，去离子水冲洗，室温下干燥多聚赖氨酸载玻片，在荧光显微镜下观察细菌的位置。
(4)毛细管制备：
使用P1000拉针仪，拉制尖端直径为1μm毛细管；
(5)原位萃取单个活细菌代谢物：
将步骤(3)中拉制好的毛细管伸入去离子水(含1％甲酸)的溶液中，拿出毛细管固定于微操平台上，显微镜下将毛细管定位接触细菌，注射泵推出毛细管中的溶液萃取细菌代谢物1s后吸回溶液；
(6)质谱分析：
将萃取后的毛细管置于质谱入口前端，施加电压，采集质谱图；
(7)抗生素敏感性判断：
比较不同抗生素孵育后细菌质谱图中代谢物的信号强度变化差异，当细菌受到敏感性抗生素刺激时，单个细菌测到的代谢物肌酐、脯氨酸、肌酸、亮氨酸、亚精胺、组氨酸、精氨酸、乙酰亚精胺、丙氨酰苏氨酸信号强度都会发生显著性变化。以单个细菌中代谢物m/z 191丙氨酰苏氨酸为例，图12为不同抗生素药物刺激大肠杆菌后，单个细菌代谢物丙氨酰苏氨酸 变化响应图，敏感性抗生素卡那霉素及诺氟沙星孵育后，细菌丙氨酰苏氨酸信号显著降低，而不加抗生素孵育则不会导致细菌丙氨酰苏氨酸信号的显著变化。图13为不同浓度卡那霉素刺激大肠杆菌后，单个大肠杆菌代谢物丙氨酰苏氨酸变化响应图，当卡那霉素浓度达到临界抑菌浓度，单个大肠杆菌丙氨酰苏氨酸信号显著降低，显示了活体单细菌质谱能监测单个细菌的代谢响应，可以根据检测到的代谢物信号强度变化判断细菌抗生素敏感性。
以上所述的具体实施例，对本发明的目的、技术方案和有益效果进行了进一步详细说明，应理解的是，以上所述仅为本发明的具体实施例而已，并不用于限制本发明，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 鉴定生物样品中的细菌的方法，其特征在于，包括对生物样品中的细菌进行染色和固定，并对固定的单个活细菌的代谢物萃取后进行质谱检测的步骤，优选地在毛细管内注入萃取溶液，显微镜下将毛细管定位接触单个活细菌，萃取单个活细菌的代谢物。
  2. 根据权利要求1所述的鉴定生物样品中的细菌的方法，其特征在于，所述固定通过静电相互作用或共价结合作用进行，固定的载玻片选自多聚赖氨酸载玻片、聚乙烯亚胺载玻片、明胶载玻片N-(2-氨基乙基)-3-氨基丙基三甲氧基硅烷载玻片和3-氨基丙基三乙氧基硅烷载玻片，任选地，所述细菌与载玻片的作用时间为10-30min。
  3. 根据权利要求1或2所述的鉴定生物样品中的细菌的方法，其特征在于，包括在细菌染色前，通过低速离心沉降生物样品中的大颗粒干扰物质的步骤。
  4. 根据权利要求1-3中任一项所述的鉴定生物样品中的细菌的方法，其特征在于，包括以下步骤：
    步骤(1)：通过低速离心(优选所述低速离心的转速为2000rpm-4000rpm，离心时间为5min-10min)沉降生物样品中的大颗粒干扰物质，保留含有细菌的上层溶液，获取生物样品中的细菌；
    步骤(2)：将步骤(1)获得的上层溶液进行细菌染色，定位细菌，染色后高速离心(优选所述高速离心的转速为10000rpm-14000rpm，离心时间为5min-10min)以洗去染色剂；
    步骤(3)：将步骤(2)获得的染色后的细菌溶液滴在载玻片上，通过静电吸附或共价结合作用捕捉固定溶液中的细菌样本；
    步骤(4)：在毛细管(例如，毛细管尖端开口小于2μm，优选为500nm-1μm)内注入萃取溶液，通过原位萃取的方式对步骤(3)中固定的单个活细菌在显微镜下进行萃取；
    步骤(5)：在步骤(4)的毛细管中插入电极，施加电压进行电喷雾，通过质谱对萃取到的细菌代谢物进行分析。
  5. 根据权利要求1-4中任一项所述的鉴定生物样品中的细菌的方法，其特征在于：所述生物样品选自人全血、兔全血、尿样、唾液、脑脊液、肺泡灌洗液、脓肿、细菌感染的细胞溶液和细菌感染的组织悬液；任选地，所述细菌选自大肠杆菌、金黄色葡萄球菌、绿脓杆菌和鲍曼不动杆菌。
  6. 根据权利要求1-5中任一项所述的鉴定生物样品中的细菌的方法，其特征在于：染色剂选自Acridine Orange、Fluorescein Diacetate、Syto 9、Syto 64、hoechst 33258和DAPI；任选地，染色时间为10min-30min，染色剂浓度为1μM-10μM，避光染色。
  7. 根据权利要求4所述的鉴定生物样品中的细菌的方法，其特征在于：所述萃取溶液选自由水、甲醇、乙醇、乙腈、丙酮、氯仿、甲酸、乙酸和三氟乙酸组成的组中的一种或多种。
  8. 根据权利要求1-7中任一项所述的鉴定生物样品中的细菌的方法，其特征在于：还包括建立细菌对比库的步骤。
  9. 根据权利要求8所述的鉴定生物样品中的细菌的方法，其特征在于：建立细菌对比库包括以下步骤：
    a.对一种以上单个参照活细菌代谢物进行质谱分析；
    b.根据所测得的代谢物对一种以上参照活细菌进行降维与可视化分析，并根据所测得的单个活细菌代谢物对不同细菌进行分型，为未知细菌的鉴定建立对比库。
  10. 生物样品中细菌的抗生素敏感性的测试方法，其特征在于，包括根据权利要求1-9中任一项所述的方法，任选地，还包括将生物样品中的细菌与抗生素进行孵育的步骤。
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Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101122601A (zh) * 2007-09-24 2008-02-13 孙艳萍 一种分离鉴定血液中有核红细胞的方法
WO2011064000A1 (en) * 2009-11-27 2011-06-03 Creative Antibiotics Sweden Ab Method for monitoring, identifying or diagnosing a bacterial infection
CN103245716A (zh) * 2013-05-23 2013-08-14 中国科学院化学研究所 基于小分子代谢物质谱分析的快速高灵敏度微生物鉴定方法
CN103725747A (zh) * 2014-01-26 2014-04-16 佟雪梅 一种细菌对抗生素敏感性的鉴定方法及其设备
CN105969334A (zh) * 2016-05-06 2016-09-28 东南大学 用于检测细菌及真菌死活状态的荧光染色试剂、制备方法及用途
CN108593755A (zh) * 2018-04-28 2018-09-28 清华大学 一种细胞外泌物质采样与原位检测方法及装置
CN110763752A (zh) * 2019-11-27 2020-02-07 清华大学 单细胞萃取电喷雾质谱分析系统和方法
CN112098642A (zh) * 2020-02-13 2020-12-18 普众发现医药科技(上海)有限公司 一种基于高通量抗体芯片的活细胞筛选方法
CN112444582A (zh) * 2021-02-01 2021-03-05 宁波大学 基于液滴萃取的质谱分析方法
CN113640365A (zh) * 2021-08-12 2021-11-12 中国科学技术大学 生物样品中细菌鉴定及抗生素敏感性测试的分析方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111133295A (zh) * 2017-09-08 2020-05-08 普渡研究基金会 拉曼成像技术测定抗生素敏感性的方法
CN112080545A (zh) * 2019-06-14 2020-12-15 中国科学院青岛生物能源与过程研究所 一种单细胞拉曼临床耐药性试剂盒及检测方法
CN111397985B (zh) * 2020-04-14 2021-01-08 清华大学 单细胞质谱分析方法

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101122601A (zh) * 2007-09-24 2008-02-13 孙艳萍 一种分离鉴定血液中有核红细胞的方法
WO2011064000A1 (en) * 2009-11-27 2011-06-03 Creative Antibiotics Sweden Ab Method for monitoring, identifying or diagnosing a bacterial infection
CN103245716A (zh) * 2013-05-23 2013-08-14 中国科学院化学研究所 基于小分子代谢物质谱分析的快速高灵敏度微生物鉴定方法
CN103725747A (zh) * 2014-01-26 2014-04-16 佟雪梅 一种细菌对抗生素敏感性的鉴定方法及其设备
CN105969334A (zh) * 2016-05-06 2016-09-28 东南大学 用于检测细菌及真菌死活状态的荧光染色试剂、制备方法及用途
CN108593755A (zh) * 2018-04-28 2018-09-28 清华大学 一种细胞外泌物质采样与原位检测方法及装置
CN110763752A (zh) * 2019-11-27 2020-02-07 清华大学 单细胞萃取电喷雾质谱分析系统和方法
CN112098642A (zh) * 2020-02-13 2020-12-18 普众发现医药科技(上海)有限公司 一种基于高通量抗体芯片的活细胞筛选方法
CN112444582A (zh) * 2021-02-01 2021-03-05 宁波大学 基于液滴萃取的质谱分析方法
CN113640365A (zh) * 2021-08-12 2021-11-12 中国科学技术大学 生物样品中细菌鉴定及抗生素敏感性测试的分析方法

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