Method for analyzing target molecules on surfaces of particles and application of method

Abstract

The invention provides a particle surface target molecule analysis method and application thereof, and the analysis method comprises the following steps: mixing metal-containing nanoparticles containing functional molecules on the surface with to-be-detected particles for incubation, and then carrying out single-particle inductively coupled plasma mass spectrometry detection, and analyzing and calculating the target molecule distribution condition on the surface of the to-be-detected particle according to a detection result. The method provided by the invention realizes quantitative analysis on target molecules such as biomarkers on the particle surface on a single particle level, and can effectively quantify the number concentration of marked particles, the content of particles with a specific copy number, the number concentration and the total concentration of detected particles; 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 particles and its application, and in particular to an analysis method for target molecules on the surface of particles that is effective at a single particle level and its application. Background Art

[0002] With the development of bio-nanotechnology, micro / nanoparticles are increasingly widely used in the field of biomedicine. By modifying the surface of magnetic beads and other particles with functional molecules, the purpose of immunoadsorption, separation and purification can be achieved; the surface can also be modified with molecules that can specifically bind to antigens to achieve the stable existence and targeted drug delivery of these nanoparticles in the body. Since micro / nanoparticle-biomolecule complexes interact with cells and biological barriers, and may be accompanied by participation in different biological pathways, it is necessary to clarify whether the different copy numbers of biomolecules on micro / nanoparticles affect their activity and pathways in the body.

[0003] Currently, the technologies that can quantify the functional molecules on the surface of particles include: Differential Centrifugal Sedimentation (DCS), Isothermal Titration Calorimetry (ITC), Mass Spectrometry (MS) and Enzyme Linked Immunosorbent Assay (ELISA). Among them, DCS realizes the overall average quantification of the functional molecules on the surface of the particles to be tested by different sedimentation rates in the density gradient liquid. It is currently mostly used in simplified models with uniform particle size such as polystyrene microspheres; ITC calculates the number of target molecules on the surface of particles by the change in reaction heat, and can also only obtain the overall average value. Due to the low sensitivity of heat change, a large amount of reactants is often required; mass spectrometry is to strip off the functional molecules on the surface of a large number of particles and then conduct quantitative analysis; ELISA connects antigens or antibodies with a certain enzyme to form enzyme-labeled antigens or enzyme-labeled antibodies, and maintains its 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 molecules to be tested in the sample, and the overall number of target molecules and the average number on each particle surface can be measured. The above four technologies can only be analyzed as a whole to obtain the average number of target molecules on each particle surface. Flow cytometry is a common characterization method in the biological field. Conventional flow cytometry can measure particles larger than 300nm, but smaller particles cannot be observed based on forward angle scattering, so this tool cannot directly detect nanoparticles with smaller particle sizes. 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 particles to be tested.

[0004] In order to better understand the mechanism of nanoparticles regulating life activities, expand their application scope and achieve more controllable surface modification, it is not enough to simply conduct qualitative or overall quantitative analysis. Therefore, it is urgent to develop a method for quantitative analysis of target molecules on the particle surface at the single particle 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 particles and its application, and in particular to provide a method for analyzing target molecules on the surface of particles 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 micro / nanoparticles at the single particle 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 measured micro / nanoparticles; and realizes high-throughput testing, collecting signals of tens of thousands of measured 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 particles, the method comprising the following steps:

[0008] Metal-containing Nanoparticles (MCNPs) with functional molecules on their surfaces are mixed and incubated with the particles to be tested, 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 particles to be tested based on the test results;

[0009] The particles to be tested include any one of polymer microspheres, metal particles, inorganic microspheres, micelles, carbon dots or magnetic beads, or a combination of at least two thereof;

[0010] The surface of the particle to be tested contains at least two target molecules;

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

[0012] The above method constructs MCNP tags to specifically label target molecules on the surface of micro / nanoparticles, and uses single-particle inductively coupled plasma mass spectrometry to achieve quantitative analysis of target molecules on the surface of micro / nanoparticles 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 measured micro / nanoparticles.

[0013] Preferably, the particle size of the particles to be tested is 1-50000 nm.

[0014] Preferably, the MCNPs containing functional molecules on the surface are constructed by a method comprising the following steps:

[0015] 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.

[0016] 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.

[0017] Preferably, the incubated mixture is separated before the spICP-MS detection, so that the free MCNPs in the system are separated from the aggregates of metal nanoparticles and particles to be tested.

[0018] The above separation process can reduce the free MCNPs in the test sample, increase the probability of detecting the metal nanoparticle-test particle aggregates, shorten the detection time, greatly improve the collection efficiency of effective data, improve the accuracy of detection, and avoid signal distortion.

[0019] 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.

[0020] Preferably, the incubation mixture is diluted before the spICP-MS detection.

[0021] Preferably, the dilution is performed until the total concentration of the metal nanoparticle-test particle aggregates and free MCNPs in the mixture reaches the applicable range of the spICP-MS.

[0022] Preferably, the specific steps and algorithms for analyzing and calculating the test results are as follows:

[0023] 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 π j 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 ;hj ) is taken as Gaussian distribution N(x d ; μ, σ), the distribution parameters are μ, σ. 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.

[0024] 1.2 Expand the mass distribution of free MCNP tags (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 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 particle to be tested) classified by aggregation number, with aggregation number j ranging from 1 to n and content π j The 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.

[0025] 1.3 Estimate the content parameters of various aggregate components classified by aggregation number in the aggregates containing metal nanoparticles-test particles, that is, 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.

[0026] 1.4 The number of MCNP tag aggregation is positively correlated with the number of target molecules on the surface of the particle 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.

[0027] 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:

[0028] signal=f1(maSS)+N(signal; 0, f2(mass))

[0029] 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.

[0030] The idea of ​​data analysis and calculation of the present invention is as follows:

[0031]

[0032] Where n is the number of target molecules on the surface of the particle to be tested, I is the overall signal intensity of all MCNPs on the surface of the particle 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.

[0033] On the other hand, the present invention also provides the use of the above-mentioned analytical method in the surface detection of micron particles and / or nanoparticles.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention provides an analysis method for target molecules on the surface of particles. Functionalized MCNP tags are constructed to specifically label target molecules on the surface of micro / nanoparticles, 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 overall metal signal intensity on the surface of the particle to be tested can be converted into the number of MCNPs, thereby enabling high-throughput quantitative analysis of 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 micro / nanoparticles to be tested. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The signal intensity distribution diagram of gold nanoparticle label (AuNP@SA) modified with streptavidin under spICP-MS.

[0037] Figure 2 This is a transmission electron microscopy image of the polystyrene microspheres (PS@Bio_200nm) with biotin modified on the surface before being labeled in Example 1.

[0038] Figure 3 This is a transmission electron microscopy image of the polystyrene microspheres (PS@Bio_200nm) with biotin modified on the surface after being labeled in Example 1.

[0039] Figure 4 This is the distribution diagram of AuNP signal intensity in the incubation solution (PS@Bio_200nm+AuNP@SA) under spICP-MS.

[0040] Figure 5This is the distribution diagram of the copy number of biotin molecules on the surface of PS@Bio_200nm.

[0041] Figure 6 This is a transmission electron microscopy image of the polystyrene microspheres (PS@Bio_500nm) with biotin modified on the surface before being labeled in Example 2.

[0042] Figure 7 This is a transmission electron microscopy image of the polystyrene microspheres (PS@Bio_500nm) with biotin modified on the surface after being labeled in Example 2.

[0043] Figure 8 This is the distribution diagram of AuNP signal intensity in the incubation solution (PS@Bio_500nm+AuNP@SA) under spICP-MS.

[0044] Fig. 9 This is the distribution diagram of the copy number of biotin molecules on the surface of PS@Bio_500nm.

[0045] Fig.10 The distribution diagram of AuNP signal intensity after co-incubation of AuNP@SA and unmodified biotin PS (500 nm) microspheres in the negative control group 1 under spICP-MS.

[0046] Fig.11 The distribution diagram of AuNP signal intensity after mixing AuNP@SA with water in the negative control group 1 under spICP-MS.

[0047] Fig.12 The distribution diagram of AuNP signal intensity after co-incubation of AuNPs and PS@Bio_500nm in the negative control group 2 under spICP-MS.

[0048] Fig.13 The distribution diagram of AuNP signal intensity after AuNPs were mixed with water in the negative control group 2 under spICP-MS. DETAILED DESCRIPTION

[0049] 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.

[0050] Example 1

[0051] This example provides a method for quantifying the copy number of biotin molecules on the surface of 200 nm PS@Bio microspheres at the single particle level.

[0052] (I) Construction of MCNP tags (AuNP@SA) and spICP-MS measurements

[0053] Streptavidin-modified gold nanoparticles (AuNP@SA), 30 nm, mass concentration 0.1 mg / mL, dispersed in a buffer solution of hydroxyethylpiperazine-N'-ethanesulfonic acid (HEPES, pH 7.2-7.4), were ordered from Anhui Zesheng Technology Co., Ltd. Before spICP-MS detection, the purchased AuNP@SA was diluted a certain multiple to make its number concentration between 1.5×10 5 particles / mL, and its signal intensity distribution under spICP-MS is as follows Figure 1 , the signal strength is distributed in the range of 0-300, and the average signal strength is 19.8.

[0054] (II) Labeling of biotin-modified polystyrene microspheres (PS@Bio) with MCNP tags

[0055] Biotin-modified polystyrene microspheres PS@Bio have an average particle size of 200 nm and a mass concentration of 10 mg / mL, dispersed in a buffer solution of hydroxyethylpiperazine-N'-EthaneSulfanic Acid (HEPES, pH 7.2-7.4), ordered from Anhui Zesheng Technology Co., Ltd. The purchased PS@Bio was diluted 1000 times before use. A mixed system with a volume ratio of PS@Bio:AuNP@SA of 1000:43 was prepared and incubated at 37°C and 100 rpm for 2 hours to fully form PS@Bio_200nm-AuNP@SA aggregates.

[0056] (III) Separation of the aggregates of the nanoparticles-labels to be tested and the free labels in the solution

[0057] Because the number concentration ratio of the MCNP label and the polystyrene microspheres prepared in this example is relatively small, the mixed system of aggregates and free labels formed by incubation can be directly analyzed and detected by spICP-MS without separation. The morphology of PS@Bio_200nm before and after labeling under transmission electron microscope is as follows: Figure 2 and Figure 3 .

[0058] (IV) Measuring information of aggregates and free labels using spICP-MS

[0059] After the incubation liquid was diluted 1500 times, the total particle number concentration in the suspension reached 1.5×10 5Particles / mL, followed by spICP-MS analysis and detection, the typical instrument test parameters are nebulizer flow rate of 1.06L / min, auxiliary gas flow rate of 1.2L / min, plasma gas flow rate of 18L / min, ICP power of 1600W, injection flow rate of 0.305mL / min, residence time of 100μs, instrument calibrated transmission efficiency of 6.48%, scanning time of 200s, according to the specific instrument model and sample properties, the instrument test parameters can vary. The signal intensity distribution of the suspension under spICP-MS is shown in the figure. Figure 4 As shown, its signal strength is distributed in the range of 0-2000.

[0060] (V) Quantitative analysis of the number and concentration of MCNP labels on the surface of the nanoparticles to be tested

[0061] From the signal intensity distribution data on spICP-MS, it can be seen that the number of modified biotin molecules on the 200nm PS microspheres is relatively small, resulting in the AuNP@SA tag on the PS@Bio-AuNP@SA aggregate not forming an independent peak under spICP-MS relative to the free AuNP@SA tag, and only existing in the form of a tail. The intensity signal of the aggregate is converted into the number of AuNP@SA tags it contains using the algorithm provided in the inventive content section of the present invention, which can be further converted into information on the number of biotin copies on the surface of functional nanoparticles, that is, the number of copies of biotin molecules at the level of a single polystyrene microsphere, and the distribution of the number of copies of biotin molecules on the entire polystyrene microsphere sample. The method of the present invention gives a distribution of the number of copies of biotin molecules on the surface of PS@Bio microspheres between 3 and 75, and the specific distribution is as follows: Figure 5 As shown, it can be seen that the copy number distribution does not form an independent peak, but is in the form of a decay function, corresponding to the tailing form of the original signal distribution. The slightly prominent small peak in the copy number distribution corresponds to the weak bulge in the signal tailing peak, showing that the algorithm in the present invention has good aggregation number resolution and weakens the resolution reduction phenomenon caused by signal convolution. The average copy number of biotin on the surface of PS@Bio_200nm microspheres is 15.

[0062] (VI) Determine that the particles to be tested are saturated with markers

[0063] By adding an excess of AuNP@SA labels to the co-incubation solution, it was found that the distribution of the number of labels on the PS@Bio-AuNP@SA aggregates did not change significantly, and it can be considered that the target molecules on the surface of the nanoparticles to be tested have been saturated with labels.

[0064] According to the number concentration information on spICP-MS, the total particle number concentration of the suspension after dilution is 120422 Particles / mL, of which the proportion of PS@Bio_200nm-AuNP@SA aggregates is 0.491. Then the number concentration of the original PS microspheres is: 120422×0.491×1500×(1000+43) / 1000×1000=9.25×10 10 Particles / mL. The proportion of particles containing a specific copy number of the target molecule can be obtained from the copy number distribution, see Figure 5 , and its number concentration can be obtained by multiplying the existence ratio by the total number concentration.

[0065] Example 2

[0066] This example provides a method for quantifying the copy number of biotin molecules on the surface of 500 nm PS@Bio microspheres at the single particle level.

[0067] (I) Construction of MCNP tags (AuNP@SA) and spICP-MS measurements

[0068] Same as Example 1.

[0069] (II) Labeling of biotin-modified polystyrene microspheres (PS@Bio) with MCNP tags

[0070] Biotin-modified polystyrene microspheres PS@Bio have an average particle size of 500 nm and a mass concentration of 10 mg / mL, dispersed in a buffer solution of hydroxyethylpiperazine-N'-EthaneSulfanic Acid (HEPES, pH 7.2-7.4), ordered from Anhui Zesheng Technology Co., Ltd. The purchased PS@Bio was diluted 500 times before use. A mixed system with a volume ratio of PS@Bio:AuNP@SA of 500:42 was prepared and incubated at 37°C and 100 rpm for 2 hours to fully form PS@Bio-AuNP@SA aggregates.

[0071] (III) Separation of the aggregates of the nanoparticles-labels to be tested and the free labels in the solution

[0072] Because the number concentration ratio of the MCNP label and the polystyrene microspheres prepared in this example is relatively small, the mixed system of aggregates and free labels formed by incubation can be directly analyzed and detected by spICP-MS without separation. The morphology of PS@Bio_500nm before and after labeling under transmission electron microscope is as follows: Figure 6 and Figure 7 .

[0073] (IV) Measuring information of aggregates and free labels using spICP-MS

[0074] After the incubation liquid was diluted 2700 times, the total particle number concentration in the suspension reached 1.5×10 5 Particles / mL, followed by spICP-MS analysis and detection, the typical instrument test parameters are nebulizer flow rate of 1.06L / min, auxiliary gas flow rate of 1.2L / min, plasma gas flow rate of 18L / min, ICP power of 1600W, injection flow rate of 0.305mL / min, residence time of 100μs, instrument calibrated transmission efficiency of 6.48%, scan time of 200s, which can vary according to the specific instrument model and sample properties. The signal intensity distribution of the suspension under spICP-MS is shown in the figure. Figure 8 As shown, the signal intensity of the MCNP tags on the PS@Bio-AuNP@SA aggregates ranges from 200 to 2600.

[0075] (V) Quantitative analysis of the number and concentration of MCNP labels on the surface of the nanoparticles to be tested

[0076] The algorithm provided in the summary of the invention is used to convert the intensity signal of the aggregate into the number of MCNP tags it contains, which can be further converted into information on the number of biotin copies on the surface of functional nanoparticles, that is, the number of copies of biotin molecules at the level of a single polystyrene microsphere, and the distribution of the number of copies of biotin molecules on the entire polystyrene microsphere sample. The method of the present invention gives the distribution of the number of copies of biotin molecules on the surface of PS@Bio microspheres between 3 and 100. The specific distribution is as follows: Fig. 9 As shown, it can be seen that the copy number distribution corresponds to the independent peak form of the original signal distribution, and there is also a main peak. On the left side of the main peak, there is a small peak structure for the copy number distribution less than 15, corresponding to the weak tailing phenomenon in the signal intensity, showing that the algorithm in the present invention has good aggregation number resolution and can detect subtle signal changes. The average copy number of biotin on the surface of PS@Bio_500nm microspheres is 38.

[0077] (VI) Determine that the particles to be tested are saturated with markers

[0078] By adding an excess of AuNP@SA labels to the co-incubation solution, it was found that the distribution of the number of labels on the PS@Bio-AuNP@SA aggregates did not change significantly, and it can be considered that the target molecules on the surface of the nanoparticles to be tested have been saturated with labels.

[0079] According to the number concentration information on spICP-MS, the total particle number concentration of the suspension after dilution is 173464 Particles / mL, of which the proportion of PS@Bio-AuNP@SA aggregates is 0.144. Then the number concentration of the original PS microspheres is: 173464×0.144×2700×(500+42) / 500×500=3.66×10 10 Particles / mL. The proportion of particles containing a specific copy number of the target molecule can be obtained from the copy number distribution, see Fig. 9 , and its number concentration can be obtained by multiplying the existence ratio by the total number concentration.

[0080] (VII) Negative control

[0081] In order to prove that the high-intensity labeling signal in the PS@Bio and AuNP@SA mixture under spICP-MS comes from the specific binding between biotin and streptavidin rather than the nonspecific adsorption between each other, two sets of negative control experiments were introduced here for verification.

[0082] 1. MCNP tag (AuNP@SA) co-incubated with unmodified polystyrene microspheres (PS)

[0083] The average particle size of the non-biotin-modified polystyrene microspheres (PS) was 500 nm, the mass concentration was 10 mg / mL, dispersed in a HEPES (pH 7.2-7.4) buffer solution, and ordered from Anhui Zesheng Technology Co., Ltd. The purchased PS was diluted 500 times before use. A mixed system with a volume ratio of PS:AuNP@SA of 500:42 was prepared and incubated at 37°C and 100 rpm for 2 h to allow PS and AuNP@SA to be fully adsorbed.

[0084] In order to investigate the effect of the environment on the aggregation state of the tags, an equal amount of AuNP@SA tags was added to the same volume of water as the above PS and incubated under the same conditions. After incubation, the solution was diluted appropriately to make the number of gold particles in the incubation solution between 1.5×10 5 particles / mL, and then detected and analyzed under spICP-MS, the label signal intensity distribution is shown in Fig.10 , 11 , no high-intensity aggregate signals appeared in the signal intensity distribution.

[0085] 2. Co-incubation of unmodified MCNP particles (AuNPs) with biotin-modified polystyrene microspheres (PS@Bio)

[0086] The aforementioned 500nm PS@Bio was diluted 500 times before use, and sodium citrate-reduced gold nanoparticles (AuNPs, purchased from BBI Solution) with an average particle size of 30nm were selected as non-specific binding labels. A mixed system with a volume ratio of PS@Bio:AuNPs of 500:42 was prepared and incubated at 37°C and 100rpm for 2h to allow PS@Bio and AuNPs to be fully adsorbed.

[0087] In order to investigate the effect of the environment on the aggregation state of AuNPs, an equal amount of AuNPs was added to the same volume of water as the above PS@Bio and incubated under the same conditions. After incubation, the solution was diluted appropriately to make the number of gold particles in the incubation solution between 1.5×10 5 particles / mL, and then detected and analyzed under spICP-MS, the AuNPs signal intensity distribution is shown in Fig.12 , 13 , and no high-intensity aggregate signals appeared in the signal intensity distribution.

[0088] The above two groups of negative control experiments show that there is no high-intensity signal in the AuNP@SA-PS and AuNPs-PS@Bio systems under the spICP-MS analysis conditions, which further proves that the high-intensity signal in the AuNP@SA-PS@Bio co-incubation system comes from the specific binding between biotin and streptavidin, rather than the nonspecific adsorption between each other. The number of biotin molecules on the PS microspheres obtained by this method is authentic and reliable.

[0089] 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 particles and can effectively analyze the distribution of target molecules on the surface of particles.

[0090] The applicant declares that the present invention illustrates the analysis method of the target molecule on the particle surface and its application 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.

[0091] 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.

[0092] 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 particle surfaces, 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 particles to be tested, 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 particles to be tested based on the test results; The particles to be tested include any one of polymer microspheres, metal particles, inorganic microspheres, micelles, carbon dots or magnetic beads, or a combination of at least two thereof; The surface of the particle to be tested 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 particles to be tested 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 any one of claims 1 to 3, characterized in that Before the single particle inductively coupled plasma mass spectrometry detection, the incubated mixture is separated to separate the free metal-containing nanoparticles and the metal-containing nanoparticle-to-be-detected particle aggregates in the system.

5. The analysis method according to claim 4, 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.

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

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

8. The analysis method according to any one of claims 1 to 7, characterized in that The components and contents of the mixture of the metal-containing nanoparticle-test 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.

9. The analysis method according to claim 8, characterized in that The instrument response function IRF is expressed as the sum of two random variables, namely, sigmal=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).

10. Use of the analytical method according to any one of claims 1 to 9 in surface detection of microparticles and / or nanoparticles.

Images