Method for analyzing target molecules on surface of biological particle membrane and application of method

Abstract

The invention provides an analysis method for target molecules on the surface of a bio-particle membrane and application of the analysis method, and the analysis method comprises the following steps: mixing and incubating metal-containing nano-particles with to-be-detected bio-particles, separating free metal-containing nanoparticles in the system from metal nanoparticle-to-be-detected biological particle aggregate, performing single-particle inductively coupled plasma mass spectrometry detection, analyzing according to a detection result, and calculating the distribution condition of target molecules on the surface of a to-be-detected biological particle film. According to the method provided by the invention, quantitative analysis on target molecules such as biomarkers on the surface of a biological particle film on a single particle layer is realized, and the number concentration of marked particles, the content of particles with a specific copy number, the number concentration and the total concentration of detected biological particles can be effectively quantified; high-throughput testing is achieved, signals of tens of thousands of tested nano-particles are collected within a few minutes, and reliable statistical distribution information is obtained.

Description

Technical Field

[0001] The present invention belongs to the field of analysis and detection, and specifically relates to an analysis method for target molecules on the surface of biological particle membranes and its application, and especially relates to an analysis method for target molecules on the surface of biological particle membranes that is effective at a single particle level and its application. Background Art

[0002] The study of target molecules or structures on the surface of biological membranes is of great significance to biology. For example, cell membrane biomarkers play an important role in cell functions and interactions. Membrane proteins participate in important processes such as cell signal transduction, material transport and recognition. Glycolipids and glycoproteins are also widely present on cell membranes, and they play an important role in cell recognition, adhesion, signal transduction and membrane stability. Abnormal expression of glycoproteins is closely related to the occurrence and development of various diseases. The identification and detection of tumor cell membrane surface markers are of great significance for the diagnosis and treatment of tumor diseases. Biomarkers of extracellular vesicles secreted by tumor cells can also define a variety of human cancers. For substances such as exosomes, it has a relatively stable phospholipid bilayer structure and can also be used as a natural drug carrier to protect drug activity. In addition, it can also be combined with cell line selection, culture medium selection and gene modification to regulate the expression of a substance on exosomes to improve drug loading performance. Stem cell-derived exosomes can promote the regeneration and repair of damaged tissues, have a strong immune function regulation effect, and are easy to specifically target and accumulate in pathological related sites, showing superiority in the treatment of diabetes, cardiovascular system diseases, kidney damage and other aspects. By studying the distribution, structure and function of target molecules on the membrane surface, researchers can gain a deeper understanding of or reveal the mechanism of signal transduction in vivo, the occurrence and development of related diseases, and develop corresponding diagnostic and treatment methods.

[0003] At present, the characterization methods for target molecules or structures on the surface of biological sample membranes include: Enzyme Linked Immunosorbent Assay (ELISA), Mass Spectrometry (MS), and Western Blot (WB). Among them, ELISA connects antigens or antibodies to certain enzymes to form enzyme-labeled antigens or enzyme-labeled antibodies, and maintains their immunoreactivity. By binding the enzyme-labeled antigens or enzyme-labeled antibodies to the target molecules on the surface of the particles, after adding the substrate of the enzyme reaction, the substrate is catalyzed by the enzyme to become a colored product. The amount of the product is directly related to the amount of the target molecule tested in the sample, and the overall number of target molecules and the average number on the surface of each particle can be measured; mass spectrometry is to strip off the functional molecules on the surface of a large number of nanoparticles, and then identify the analyzed substances and quantitatively analyze them; WB separates protein samples by polyacrylamide gel electrophoresis and labels them with corresponding antibodies to identify whether they contain a certain protein, and judge the content difference between different samples by the depth of color. The above technologies can only be used for qualitative, quantitative or semi-quantitative analysis of them as a whole. Flow cytometry is a common characterization method in the biological field. Conventional flow cytometers can measure particles larger than 300nm, but smaller particles cannot be observed based on forward angle scattering. Therefore, this tool cannot directly detect nanoparticles with smaller particle sizes such as exosomes. The invention of nanoflow cytometry has introduced a new high-sensitivity tool for the detection of nanoparticles, with a size detection limit of 7-1000nm, which makes up for the shortcomings of cell flow cytometry. However, nanoflow cytometry can only obtain a qualitative result of whether the particle is positive, and cannot perform quantitative analysis on the biological particles to be tested.

[0004] In order to better understand the mechanism of biological particles regulating life activities, expand their application scope and achieve more controllable surface modification, it is not enough to simply conduct qualitative, overall quantitative or semi-quantitative analysis, especially for biological systems with strong heterogeneity, such as cells, synaptosomes, exosomes, bacteria, viruses, liposomes, etc. Therefore, it is urgent to develop a method for the identification and quantitative analysis of target molecules on the membrane surface at the single event level. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method for analyzing target molecules on the surface of biological particle membranes and its application, and in particular to provide a method for analyzing target molecules on the surface of biological particle membranes that is effective at the single particle level and its application. The method provided by the present invention realizes quantitative analysis of target molecules on the surface of biological particle membranes at the single particle (single event) level, and can effectively quantify the number concentration of labeled particles, the content and number concentration of particles with a specific copy number, and the total number concentration of tested biological particles; and realizes high-throughput testing, collecting signals of tens of thousands of tested nanoparticles within a few minutes, and obtaining reliable statistical distribution information.

[0006] In order to achieve the purpose of the invention, the present invention adopts the following technical solutions:

[0007] In one aspect, the present invention provides a method for analyzing target molecules on the surface of biological particle membranes, the method comprising the following steps:

[0008] Metal-containing Nanoparticles (MCNPs) with functional molecules on their surfaces are mixed and incubated with the biological particles to be tested, and then the free metal-containing nanoparticles and the metal-containing nanoparticle-biological particle aggregates in the system are separated, and then single particle inductively coupled plasma mass spectrometry (spICP-MS) is performed to analyze and calculate the distribution of target molecules on the surface of the biological particle film to be tested based on the test results;

[0009] The biological particles to be detected include any one of cells, bacteria, fungi, synaptosomes, migratory bodies, extracellular vesicles, viruses or liposomes, or a combination of at least two of them.

[0010] The surface of the biological particle membrane to be detected contains at least two target molecules.

[0011] The functional molecule specifically binds to the target molecule.

[0012] The above method constructs an MCNP tag to specifically label the target molecules on the surface of the biological particle membrane, and uses single-particle inductively coupled plasma mass spectrometry to achieve quantitative analysis of the target molecules on the surface of the biological particle membrane at the single-particle level. It can effectively quantify the number concentration of labeled particles, the content and number concentration of particles with a specific copy number, and the total number concentration of the tested biological particles; and through the separation process, it can reduce the free MCNPs in the test sample, increase the probability of detecting the metal nanoparticle-test biological particle aggregates, shorten the detection time, greatly improve the efficiency of collecting effective data, improve the accuracy of detection, and avoid signal distortion.

[0013] Among the biological particles to be tested, for naturally occurring or secreted particles, methods including but not limited to gene modification, cell line selection, body fluid selection, culture medium selection, etc. can be adopted to obtain particles with target molecules of different copy numbers and / or distributions. In order to obtain relatively pure biological particles to be tested, a separation and purification method can be used as an auxiliary method. The separation and purification methods include but are not limited to immunoaffinity adsorption, ultracentrifugation, density gradient centrifugation, differential centrifugation, field flow separation, ultrafiltration, size exclusion chromatography, flow cytometer sorting, etc.

[0014] Preferably, the particle size of the biological particles to be detected is 1-50000 nm.

[0015] Preferably, the metal-containing nanoparticles having functional molecules on the surface are constructed by a method comprising the following steps:

[0016] The functional molecule is combined with the MCNP through click chemistry surface binding technology or a surface modification method based on coordination / ligand recognition or ligand exchange or a method based on a cross-linking agent to construct the MCNP with the functional molecule on the surface.

[0017] Alternatively, the MCNPs containing functional molecules on the surface can also be obtained commercially, and the present application does not impose too many restrictions on their sources.

[0018] Preferably, the separation method comprises any one of density gradient centrifugation, membrane filtration, field flow separation, differential centrifugation, immunoaffinity separation, microfluidic separation, ultrafiltration, size exclusion chromatography or flow cytometry sorting.

[0019] Preferably, the incubated mixture is diluted before the single particle inductively coupled plasma mass spectrometry detection.

[0020] Preferably, the dilution is performed until the total concentration of the metal-containing nanoparticle-test biological particle aggregates and free metal-containing nanoparticles in the mixture reaches the applicable range of the single particle inductively coupled plasma mass spectrometry.

[0021] Preferably, the specific steps and algorithms for analyzing and calculating the test results are as follows:

[0022] 1.1 Obtain the particle size distribution and mass distribution of MCNP tags by electron microscopy or spICP-MS. First, use finite mixture distribution ) describes the particle size distribution of free MCNP tags, where x d is the particle size random variable X d The value of ψ1=(π1,...,π g , h1, ...h g , f1, ..., f g ) are the parameters of the mixed model, where πj is the content, h j is the parameter of a single distribution, and fj is the probability density function of a single distribution. Preferably, a single Gaussian distribution is used for monodisperse MCNP tags, that is, g = 1, π j =1,f j (x d ;h j ) is taken as Gaussian distribution N(x d ; μ, σ), the distribution parameters are μ, v. Using the cubic function relationship between the mass and particle size of nanoparticles, the particle size distribution g(x d ;ψ1) and mass distribution g(x m ;ψ2)(x m is the mass random variable X m The values ​​of) are converted to each other through the particle density, that is, knowing one of the two can obtain the other. Secondly, the parameter ψ1 in the particle size distribution (or mass distribution) is estimated. Preferably, for the monodisperse MCNP tag, the Gaussian distribution parameters μ, σ are estimated accordingly. The particle size of the MCNP tag can be statistically analyzed by electron microscopy to obtain an estimate of the particle size distribution parameters, or the mass distribution parameters can be obtained by the spICP-MS signal, that is, the spICP-MS signal is deconvoluted by the instrument response function IRF calibrated in advance to obtain the mass distribution parameters: because the assumed mass distribution (or the mass distribution transformed from the assumed particle size distribution) is convoluted with IRF to obtain a simulated spICP-MS signal, the parameters of the assumed mass distribution (or particle size distribution) can be estimated by using the measured spICP-MS signal using data fitting or statistical estimation methods to obtain the mass distribution (or particle size distribution) and its parameter values. For parameter estimation methods of the finite mixture parameter distribution model, please refer to Chinese patent [A method for quantitatively measuring the content and concentration of particle components in a mixture, 202210773566.2], or general data fitting or statistical estimation methods such as nonlinear least squares fitting algorithm or expectation-maximization (EM) algorithm.

[0023] 1.2 Expand the mass distribution of free MCNP labels (the parameters are known in step 1.1) to construct a finite mixture distribution model of the components and their contents of a mixture consisting of metal nanoparticles-test biological particle aggregates and free MCNPs where p j (x m ;h j ) is the mass distribution of free MCNP tags or each aggregate component, that is, the mass distribution of each MCNP tag aggregate (aggregated on the surface of the bioparticle membrane to be tested) classified by aggregation number, with aggregation number j ranging from 1 to n and content π jThe mass distribution of each component can be represented by the mass distribution of the free metal nanoparticle-containing tags obtained in 1.1, that is, Where j represents the number of aggregates, k represents the number of particles in the aggregate, and the parameter ψ2 has been estimated in step 1.1. The parametric distribution can be easily transformed into a non-parametric kernel density distribution, that is, the kernel density distribution is obtained by random variable sampling and kernel density estimation. For the convenience of model representation and parameter estimation, the finite mixture distribution model q(x m ; ψ2) can adopt the kernel density finite mixture distribution model (denoted as FMKD or FMKDE), that is, p j (x m ;h j ) is taken as the kernel density function in, is the kernel estimate of the probability density function of the jth component; K() is the kernel function; k is the observed value sample of a component The number of observations in h j Although the distribution description here adopts mass distribution, similarly, particle size distribution and mass distribution can be converted into each other, and can also be described by particle size distribution.

[0024] 1.3 Estimate the content parameters of various aggregate components classified by aggregation number in the metal nanoparticle-test biological particle aggregates, namely π j Parameters. Through the convolution transformation of the instrument response function IRF, the mass distribution can be transformed into the signal distribution q(x s ; ψ3), x s is the particle signal random variable X s The value of ψ3 is the parameter of the signal distribution mixed model. For the description of the signal distribution FMKDE model and the parameter estimation method, please refer to the Chinese patent [A method for quantitatively measuring the content and concentration of particle components in a mixture, 202210773566.2]. The parameter π obtained by data analysis results j , j = 1, ..., n is the content of the free label or aggregate component to be determined. The number concentration can be further obtained by the transmission efficiency of the instrument. For the conversion from content to number concentration, please refer to the Chinese patent [A method for quantitatively measuring the content and concentration of particle components in a mixture, 202210773566.2] or other published spICP-MS number concentration calibration methods.

[0025] 1.4 The aggregation number of MCNP tags is positively correlated with the number of target molecules on the surface of the bioparticle membrane to be tested. Under saturated labeling conditions, the two are the same or very close, and the former can be used as the result of quantitative analysis of the latter.

[0026] 1.5 Calibration method of instrument response function IRF. The instrument response function is a conversion function that converts the mass distribution of particles into the distribution of instrument output signals. Experimental characterization found that for monodisperse particles, the output signal of spICP-MS can be obtained using traditional Gaussian convolution transformation, but the Gaussian broadening of monodisperse particles of different particle sizes is different, and there are difficulties in convolution transformation for non-monodisperse particles. Further experimental characterization and analysis found that in order to solve the problem of different Gaussian broadening, the functional form of IRF can be constructed as follows:

[0027] signal=f1(mass)+N(signal; 0, f2(mass))

[0028] Wherein, the random variable f1(mass) represents the quantitative transformation relationship between the mass distribution and the signal distribution, the random variable N(signal; 0, f2(mass)) represents that the convolution function is a Gaussian distribution or its composite distribution, and the broadening parameter is determined by another function form f2(mass) of the mass distribution, that is, the standard deviation of the Gaussian distribution is a function of the mass distribution. Preferably, f1 and f2 can be represented by linear functions, that is, f1(mass)=a1+a2·mass, f2(mass)=a3+a4·mass, and the function form of the above IRF is valid under a wide range of experimental conditions. Because the Gaussian convolution and deconvolution techniques involved in traditional signal transformation can only be used for the case where the Gaussian broadening takes a fixed value, which is equivalent to the case where a4=0 and a3 is a constant here, this is not enough to describe the instrument response function of spICP-MS, and will introduce a large deviation when the particle size is not monodisperse, so it is impossible to use traditional Gaussian convolution or deconvolution methods for signal processing. The present invention describes the signal convolution transformation by the way of random variable summation, and Gaussian convolution corresponds to the Gaussian distribution or its composite distribution of the latter half of the instrument response function, and the Gaussian broadening phenomenon of variable variance can be easily described by the Gaussian composite distribution of variable variance. The present invention solves the technical difficulty of signal transformation when the Gaussian broadening of the convolution and deconvolution process is not a constant. In addition, the linear function relationship adopted in the IRF function form construction can be further expanded to a nonlinear relationship, such as power function, exponential function, logarithmic function, and its reciprocal, etc., which can be adopted in nonlinear fitting, can be applied here, so that it can be effectively applied in a wider mass range, and the above-mentioned linear function relationship is the preferred result of the present invention, and should not be regarded as a specific restriction to the function form of IRF. Through the statistical information of the particle size distribution of several particle size standards, the parameter value in IRF can be estimated, thereby completing the calibration process of the instrument response function IRF.

[0029] The idea of ​​data analysis and calculation of the present invention is as follows:

[0030]

[0031] Where n is the number of target molecules on the surface of the bioparticle membrane to be tested, I is the overall signal intensity of all MCNPs on the surface of the bioparticle membrane to be tested, and I MCNP is the signal intensity of a single MCNP. Since the measured spICP-MS signal intensity is a random variable, I MCNP is a distribution function. The above formula provides a calculation idea, but it cannot be used directly for calculation. MCNP If the average signal strength is calculated, only the average value of n or the scaling transformation of the distribution of I can be obtained, but the real distribution of n cannot be obtained. The algorithm provided by the present invention is based on the distribution function described by the finite mixture model and its parameter estimation, which overcomes this difficulty.

[0032] On the other hand, the present invention also provides the application of the above-mentioned analytical method in the detection of biological particle membrane surface.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention provides an analysis method for target molecules on the surface of biological particle membranes. Functionalized MCNP tags are constructed to specifically label target molecules on the surface of biological particle membranes, and single-particle inductively coupled plasma mass spectrometry is used to achieve quantitative analysis of target molecules on the surface of micro / nanoparticles at a single particle level. Through the development of a specific algorithm, the metal signal intensity of the entire surface of the biological particle membrane to be tested can be converted into the number of MCNPs, so that high-throughput quantitative analysis can be performed on the number concentration of labeled particles, the content and number concentration of particles with a specific copy number, and the total number concentration of biological particles to be tested; at the same time, the separation process can reduce free MCNPs in the test sample, increase the probability of detecting metal nanoparticle-biological particle aggregates to be detected, shorten the detection time, greatly improve the collection efficiency of effective data, improve the accuracy of detection, and avoid signal distortion. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is the signal intensity distribution diagram of AuNPs on spICP-MS.

[0036] Figure 2 This is the signal intensity distribution diagram of AuNP@Apt_CD63 after aptamer modification on spICP-MS.

[0037] Figure 3 The morphology of exosomes under transmission electron microscopy.

[0038] Figure 4 This is the morphology of the exosomes labeled with AuNP@Apt_CD63 under a transmission electron microscope in Example 1.

[0039] Figure 5 This is the signal intensity distribution diagram of L8, L9, and L10 on spICP-MS.

[0040] Figure 6 The copy number distribution diagram of exosomal CD63 in each layer of L8, L9, and L10.

[0041] Figure 7 This is the distribution diagram of CD63 copy number in the whole exosomes.

[0042] Figure 8 This is the signal intensity distribution diagram of AuNP@Apt_CD81 after aptamer modification on spICP-MS.

[0043] Fig. 9 This is the morphology of the exosomes labeled with AuNP@Apt_CD81 under a transmission electron microscope in Example 2.

[0044] Fig.10 This is the signal intensity distribution diagram of L12 on spICP-MS.

[0045] Fig.11 This is the CD81 copy number distribution diagram of the entire exosome. DETAILED DESCRIPTION

[0046] The technical solution of the present invention is further described below by specific implementation methods. It should be understood by those skilled in the art that the embodiments are only to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0047] Example 1

[0048] This example provides a method for quantifying the number of CD63 protein copies on the surface of exosome membranes at a single event level, and the specific steps are as follows:

[0049] (A) Construction of MCNP tag (AuNP@Apt_CD63) and spICP-MS measurement

[0050] 1. Prepare phosphate buffer saline (PBS, 10 mM, pH 7.4) containing 0.55 mM MgCl2.

[0051] 2. Dissolve the thiol-modified CD63 aptamer in an appropriate amount of binding buffer solution to a final concentration of 100 μM. Take out 30 μL and heat it at 95°C for 5 min, then cool it on ice for 10 min. Add 1 mL of gold nanoparticles (20 nm, number concentration of about 3×10 11Particles / mL, purchased from BBI Solutions) were added to the aptamers treated as above, frozen at -20°C for 2h, and then thawed at room temperature. The aptamers that were not bound were removed by centrifugation at 13000rpm for 20min, and AuNP@Apt_CD63 was resuspended in PBS buffer solution, washed by centrifugation for 5 times, and finally dispersed in 500μL PBS for later use.

[0052] The base sequence of the aptamer used is: CACCCCACCTCGCTCCCGTGACACTAATG CTATTTTTT-SH.

[0053] 3. Gold nanoparticles (AuNPs) were subjected to spICP-MS analysis before and after modification with aptamers, and the obtained signal intensity distributions were Figure 1 and Figure 2 It can be proved that AuNPs did not aggregate due to modification with aptamers.

[0054] (ii) Labeling exosomes with MCNP-tagged AuNP@Apt_CD63

[0055] 1. The exosomes used were from Suzhou Weiselkang Technology Co., Ltd. and were isolated and purified from 293F cell culture medium. The final exosome concentration was: 2.6×10 11 Particles / mL (obtained by NTA test), with particle size distribution between 30-200nm.

[0056] 2. Add excess AuNP@Apt_CD63 (200 μL) to 2.5 μL of exosomes and incubate at room temperature for 1 h.

[0057] 3. Observe the morphology of exosomes before and after labeling under a transmission electron microscope, such as Figure 3-4 shown.

[0058] (III) Separation of free AuNP@Apt_CD63 tags by cesium chloride density gradient centrifugation

[0059] 1. Prepare cesium chloride solution, disperse 10g cesium chloride in 6mL of tertiary water, with a density of 1.857g / mL. Then add 50μL of the exosome-AuNP@Apt_CD63 mixed system incubated as above on the gradient liquid surface.

[0060] 2. Centrifuge at 9000 rpm for 5 min.

[0061] 3. In order to clarify the distribution of exosomes and analyze the presence of exosomes in more detail, after centrifugation, each 100 μL was dispensed into a centrifuge tube, which was divided into 12 layers (the volume of the 1st to 10th layers of samples was 100 μL, the volume of the 11th layer of samples was 50 μL, and the 12th layer was the particles resuspended in 200 μL PBS at the bottom of the centrifuge tube), numbered from top to bottom as L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, and L12.

[0062] 4. Since cesium chloride will affect the ionization efficiency of AuNPs on spICP-MS, ultrafiltration was performed at 9000 rpm for 10 min before testing on the machine, and the cesium chloride was removed by two cycles. Finally, the volume of each layer of sample was fixed to 250 μL for subsequent analysis.

[0063] (IV) Measuring the information of aggregates and free labels after separation using spICP-MS

[0064] The samples of each layer were diluted by a certain multiple so that their number concentration was within the dynamic range of the mass spectrometer. 5 Particles / mL, and then spICP-MS analysis was performed. According to the signal intensity data on spICP-MS, the signal intensity distribution of AuNP@Apt_CD63 in each layer can be obtained. After analysis, it was found that exosome-AuNP@Apt_CD63 aggregates mainly appeared in L8, L9, and L10. The signal intensity distribution of the above layers is shown in Figure 5 .

[0065] (V) Analysis of the number of target molecule copies on the surface of the nanoparticles to be tested and the number and concentration of positive particles

[0066] The signal intensity distribution data on spICP-MS show that the signal intensity distribution of the MCNP label on the exosome-AuNP@Apt_CD63 aggregates. The algorithm provided in the invention summary section of the present invention is used to convert the intensity signal of the aggregates into the number of MCNP labels contained therein, which can be further converted into information on the number of copies of CD63 protein on the surface of the exosome membrane. The number of copies of CD63 protein at the level of a single exosome vesicle and the distribution of the number of copies of CD63 protein on the entire exosome sample can be obtained. The distribution of the number of copies of CD63 in each layer is shown in FIG. Figure 6 As shown; the exosome information of all layers can be summarized to obtain the distribution of CD63 copy number of the entire sample, which is distributed between 3-60, as shown in Figure 7 shown.

[0067] Note: The signal intensity distributions of all the above spICP-MS were uniformly normalized, and the particle probabilities under different signal intensities are comparable.

[0068] According to the information of number concentration and dilution factor on spICP-MS, the total number of CD63 positive exosome particles in 2.5 μL of exosomes was 113982121, and the positive number concentration was 4.559×10 10 Particles / mL.

[0069] Example 2

[0070] This example provides a method for quantifying the number of CD81 protein copies on the surface of exosome membranes at a single event level, and the specific steps are as follows:

[0071] (A) Construction of MCNP tag (AuNP@Apt_CD81) and spICP-MS measurement

[0072] 1. Prepare phosphate buffer saline (PBS, 10 mM, pH 7.4) containing 0.55 mM MgCl2.

[0073] 2. Dissolve the thiol-modified CD81 aptamer in an appropriate amount of binding buffer solution to a final concentration of 100 μM. Take out 30 μL and heat at 95°C for 5 min, then cool on ice for 10 min. Add 1 mL of gold nanoparticles (20 nm, number concentration of about 3×10 11 Particles / mL, purchased from BBI Solutions) were added to the aptamers treated as above, frozen at -20°C for 2h, and then thawed at room temperature. The unbound aptamers were removed by centrifugation at 13000rpm for 20min, and AuNP@Apt_CD81 was resuspended in PBS buffer solution, washed by centrifugation for 5 times, and finally dispersed in 500μL PBS for later use.

[0074] The base sequence of the aptamer used is: CATTGACCATCCGGGTCTATGTTTTTT-SH, which is derived from the patent [A CD81 aptamer and its application, 201911098597.7].

[0075] 3. The prepared AuNP@Apt_CD81 tag was subjected to spICP-MS analysis to obtain the signal intensity distribution diagram of the AuNP@Apt_CD81 tag, as shown in Figure 8 , which proves that AuNPs did not aggregate due to modification with aptamers.

[0076] (ii) Labeling exosomes with MCNP-tagged AuNP@Apt_CD81

[0077] 1. The exosomes used were from Suzhou Weiselkang Technology Co., Ltd. and were isolated and purified from 293F cell culture medium. The final exosome concentration was: 2.6×10 11 Particles / mL (obtained by NTA test), with particle size distribution between 30-200nm.

[0078] 2. Add excess AuNP@Apt_CD81 (200 μL) to 2.5 μL of exosomes and incubate at room temperature for 1 h.

[0079] 3. Observe the morphology of exosomes labeled with AuNP@Apt_CD81 under TEM, such as Fig. 9 shown.

[0080] (III) Separation of free AuNP@Apt_CD81 tags by cesium chloride density gradient centrifugation

[0081] 1. Prepare cesium chloride solution, disperse 10g cesium chloride in 6mL tertiary water, density is 1.857g / mL. Then add 50μL of the exosome-AuNP@Apt_CD81 mixed system incubated as above to the top of the gradient solution.

[0082] 2. Centrifuge at 9000 rpm for 10 min.

[0083] 3. In order to clarify the distribution of exosomes and analyze the presence of exosomes in more detail, after centrifugation, each 100 μL was dispensed into a centrifuge tube, which was divided into 12 layers (the volume of the 1st to 10th layer samples was 100 μL, the volume of the 11th layer sample was 50 μL, and the 12th layer was the particles resuspended in 200 μL PBS at the bottom of the centrifuge tube), numbered from top to bottom as L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, and L12.

[0084] (IV) Measuring the information of aggregates and free labels after separation using spICP-MS

[0085] The samples of each layer were diluted by a certain multiple so that their number concentration was within the dynamic range of the mass spectrometer. 5 Particles / mL, and then spICP-MS analysis was performed. According to the signal intensity data on spICP-MS, the signal intensity distribution of AuNP@Apt_CD81 in each layer can be obtained. After analysis, it was found that the exosome-AuNP@Apt_CD81 aggregates mainly appeared in L12. The signal intensity distribution of L12 is shown in Fig.10 .

[0086] (V) Analysis of the number of target molecule copies on the surface of the nanoparticles to be tested and the number and concentration of positive particles

[0087] From the signal intensity distribution data on spICP-MS, it can be seen that the signal intensity distribution of the MCNP label on the exosome-AuNP@Apt_CD81 aggregates. The algorithm provided in the invention summary of the present invention is used to convert the intensity signal of the aggregate into the number of MCNP labels it contains, which can be further converted into information on the number of copies of CD81 protein on the surface of the exosome membrane. The number of copies of CD81 protein at the level of a single exosome vesicle and the distribution of the number of copies of CD81 protein on the entire exosome sample can be obtained. The number of copies of CD81 on exosomes is distributed between 2-30, such as Fig.11 shown.

[0088] Note: The signal intensity distributions of all the above spICP-MS were uniformly normalized, and the particle probabilities under different signal intensities are comparable.

[0089] According to the information of number concentration and dilution factor on spICP-MS, the total number of CD81 positive exosome particles in 2.5 μL of exosomes was 63,406,645, and the positive number concentration was 2.536×10 10 Particles / mL.

[0090] The above content fully proves that the method provided by the present invention can realize the specific detection of target molecules on the surface of biological particle membranes and can effectively analyze the distribution of target molecules on the surface of biological particle membranes.

[0091] The applicant declares that the present invention illustrates the analysis method and application of the target molecules on the surface of the bioparticle membrane of the present invention through the above-mentioned embodiments, but the present invention is not limited to the above-mentioned embodiments, that is, it does not mean that the present invention must rely on the above-mentioned embodiments to be implemented. Those skilled in the art should understand that any improvement of the present invention, equivalent replacement of the raw materials of the product of the present invention, addition of auxiliary components, selection of specific methods, modification of the algorithm of the method of the present invention within a reasonable range, etc., all fall within the protection scope and disclosure scope of the present invention.

[0092] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0093] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

Claims

1. A method for analyzing target molecules on the surface of biological particle membranes, characterized in that: The analytical method comprises the following steps: The metal-containing nanoparticles with functional molecules on the surface are mixed and incubated with the biological particles to be tested, and then the free metal-containing nanoparticles and the metal-containing nanoparticle-biological particle aggregates in the system are separated, and then single-particle inductively coupled plasma mass spectrometry is performed to analyze and calculate the distribution of target molecules on the surface of the biological particle film to be tested according to the detection results; The biological particles to be tested include any one or a combination of at least two of cells, bacteria, fungi, synaptosomes, migratory bodies, extracellular vesicles, viruses or liposomes; The surface of the bioparticle membrane to be detected contains at least two target molecules; The functional molecule specifically binds to the target molecule.

2. The analysis method according to claim 1, characterized in that The particle size of the biological particles to be detected is 1-50000 nm.

3. The analysis method according to claim 1 or 2, characterized in that The metal-containing nanoparticles having functional molecules on the surface are prepared by a method comprising the following steps: The functional molecules are combined with the metal-containing nanoparticles through click chemistry surface binding technology or a surface modification method based on coordination / ligand recognition or ligand exchange or a method based on a cross-linking agent to construct the metal-containing nanoparticles with functional molecules on the surface.

4. The analysis method according to claim 1, characterized in that The separation method comprises any one of density gradient centrifugation, membrane filtration, field flow separation, differential centrifugation, immunoaffinity separation, microfluidic separation, ultrafiltration, size exclusion chromatography or flow cytometer sorting.

5. The analysis method according to any one of claims 1 to 4, characterized in that The incubation mixture is further diluted before the single particle inductively coupled plasma mass spectrometry detection.

6. The analysis method according to claim 5, characterized in that The dilution is performed until the total concentration of the metal nanoparticle-test biological particle aggregates and free metal nanoparticles in the mixture reaches the applicable range of the single particle inductively coupled plasma mass spectrometry.

7. The analysis method according to any one of claims 1 to 6, characterized in that The components and contents of the mixture of the metal-containing nanoparticles-test biological particle aggregates and the free metal-containing nanoparticles are described by a finite mixture model, that is, where p j (x m ;h j ) is the mass distribution of free metal-containing nanoparticles or each aggregate component, with an aggregation number j ranging from 1 to n and a content of π j The mass distribution of each component is represented by the mass distribution of free metal-containing nanoparticles, that is, Where j represents the aggregation number, and k represents the particle number in the aggregate; The parameters in the finite mixture model are estimated by convolution transformation of the instrument response function IRF, that is, the mass distribution is transformed into a signal distribution q(x s ; ψ3), the parameter ψ3 in the signal distribution is estimated through the measured signal.

8. The analysis method according to claim 7, characterized in that The instrument response function IRF is expressed as the sum of two random variables, namely, signal=f1(mass)+N(signal; 0, f2(mass)), wherein the random variable f1(mass) represents the quantitative transformation relationship between the mass distribution and the signal distribution, and the random variable N(signal; 0, f2(mass)) indicates that the convolution function is a Gaussian distribution or its composite distribution, and the broadening parameter is determined by another function form of the mass distribution f2(mass).

9. Use of the analytical method according to any one of claims 1 to 8 in the detection of biological particle membrane surfaces.

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