A method for analyzing a target molecule on a particle surface and use thereof

Abstract

The application provides a particle surface target molecule analysis method and application thereof, and the analysis method comprises the following steps: mixing and incubating metal-containing nanoparticles containing functional molecules on the surface with particles to be detected, then performing single particle inductively coupled plasma mass spectrometry detection, and analyzing and calculating the surface target molecule distribution of the particles to be detected according to the detection result. The method provided by the application realizes quantitative analysis of target molecules such as biomarkers on the surface of particles at the single particle level, can effectively quantify the number concentration of labeled particles, the content of particles with specific copy number, the number concentration and the total number concentration of measured particles, and realizes high-throughput testing, in which signals of tens of thousands of measured nanoparticles are collected within a few minutes, and reliable statistical distribution information is obtained.

Description

Technical Field

[0001] This invention belongs to the field of analytical detection, specifically relating to an analytical method for target molecules on particle surfaces and its application, and more particularly to an effective analytical method for target molecules on particle surfaces at the single-particle level and its application. Background Technology

[0002] With the development of bionanotechnology, micro / nanoparticles are increasingly widely used in the biomedical field. By modifying the surface of particles such as magnetic beads with functional molecules, purposes such as immunoadsorption and separation purification can be achieved. Furthermore, molecules that can specifically bind to antigens can be modified on the surface, enabling these nanoparticles to exist stably in vivo and for targeted drug delivery. Since micro / nanoparticle-biomolecule complexes interact with cells and biological barriers, and may participate in different biological pathways, it is necessary to clarify whether different biomolecule copy numbers on micro / nanoparticles affect their activity and pathways in vivo.

[0003] Currently, techniques for quantifying functional molecules on particle surfaces include: Differential Centrifugal Sedimentation (DCS), Isothermal Titration Calorimetry (ITC), Mass Spectrometry (MS), and Enzyme-Linked Immunosorbent Assay (ELISA). DCS (Distributed Density Sedimentation) achieves overall average quantification of functional molecules on the surface of test particles by varying sedimentation rates in a density gradient solution. It is currently mostly used in simplified models with uniform particle size, such as polystyrene microspheres. ITC (Integrated Thermal Chromatography) calculates the number of target molecules on the particle surface by changing the heat of reaction, but it also only yields an overall average value. Due to its low sensitivity to heat changes, it often requires large amounts of reactants. Mass spectrometry (MS) involves stripping functional molecules from a large number of particle surfaces and then quantifying them. ELISA (Enzyme-Labeled Immunosorbent Assay) conjugates antigens or antibodies with an enzyme, maintaining their immunoreactivity. By binding the enzyme-labeled antigen or antibody to the target molecules on the particle surface, and then adding the substrate for the enzyme reaction, the substrate is catalyzed by the enzyme to become a colored product. The amount of product is directly related to the amount of the target molecules in the sample, allowing for the determination of both the overall number of target molecules and the average number on each particle surface. All four techniques only provide an overall analysis, yielding an average number of target molecules on each particle surface. Flow cytometry is a common characterization technique in the biological field. Conventional flow cytometers can measure particles larger than 300 nm, but smaller particles cannot be observed based on forward scattering. Therefore, this tool cannot directly detect small nanoparticles. The invention of nanoflow cytometry has introduced a new, highly sensitive tool for nanoparticle detection, with a size detection limit of 7-1000 nm, effectively compensating for the limitations of cell flow cytometry. However, nanoflow cytometry can only provide a qualitative result—whether the particle is positive—and cannot provide a good quantitative analysis of the particle being tested.

[0004] To better understand the mechanisms by which nanoparticles regulate biological activities, expand their applications, and achieve more controllable surface modification, qualitative or overall quantitative analysis alone is insufficient. Therefore, there is an urgent need to develop a method for quantitative analysis of target molecules on the surface of nanoparticles at the single-particle level. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide an analytical method for target molecules on particle surfaces and its applications, particularly an effective analytical method for target molecules on particle surfaces at the single-particle level. The method provided by this invention enables quantitative analysis of target molecules on the surface of micro / nanoparticles at the single-particle level, effectively quantifying the number concentration of labeled particles, the content of particles with specific copy numbers, the number concentration, and the total concentration of the measured micro / nanoparticles. Furthermore, it achieves high-throughput testing, acquiring signals from tens of thousands of measured nanoparticles within minutes to obtain reliable statistical distribution information.

[0006] To achieve this objective, the present invention adopts the following technical solution:

[0007] On one hand, 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 surface are mixed and incubated with the target particles, followed by single-particle inductively coupled plasma mass spectrometry (spICP-MS) detection. The distribution of target molecules on the surface of the target particles is analyzed and calculated based on the detection results.

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

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

[0011] The functional molecule binds specifically 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 of particles with a specific copy number, the number concentration, and the total concentration of the measured micro / nanoparticles.

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

[0014] Preferably, the MCNP containing functional molecules on its surface is constructed by a method comprising the following steps:

[0015] Functional molecules are combined with MCNPs through click chemistry surface bonding technology, surface modification methods based on coordination / ligand recognition or ligand exchange, or methods based on crosslinking agents to construct MCNPs with functional molecules on their surfaces.

[0016] Alternatively, the MCNP containing functional molecules on its surface can also be obtained commercially, and this application does not impose too many restrictions on its source.

[0017] Preferably, before performing spICP-MS detection, the incubated mixture is further separated to separate the free MCNP in the system from the aggregate of metal nanoparticles-test particles.

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

[0019] Preferably, the separation method includes any one of density gradient centrifugation, membrane filtration, field flow separation, differential centrifugation, immunoaffinity separation, microfluidic separation, ultrafiltration, size exclusion chromatography, or flow cytometry.

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

[0021] Preferably, the total concentration of metal nanoparticles, analyte aggregates, and free MCNPs in the mixture is diluted to the range applicable to the spICP-MS.

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

[0023] 1.1 The particle size and mass distribution of MCNP tags were obtained by electron microscopy or spICP-MS characterization. First, a finite mixing distribution was used. Describes the particle size distribution of free MCNP tags, where x d Let X be a random variable representing particle size. d The values ​​of ψ1 are given by: ψ1 = (π1, ..., π) g h1, ...h g f1, ..., f g ) represents the parameters of the mixture model, where π j For content, h j Let fj be the parameter of a single distribution, and fj be the probability density function of that single distribution. Preferably, a single Gaussian distribution can be used for monodistributed MCNP tags, i.e., g = 1, π j =1,f j (x d hj Let N(x) be a Gaussian distribution. d The particle size distribution g(x) is calculated using the cubic function relationship between nanoparticle mass and particle size. d ;ψ1) and mass distribution g(x m ;ψ2)(x m For mass random variable X m The values ​​of ψ1 and σ1 are converted between each other through particle density, meaning that knowing one allows us to obtain the other. Next, the parameter ψ1 in the particle size distribution (or mass distribution) is estimated. Preferably, for monodisperse MCNP tags, the Gaussian distribution parameters μ and σ are estimated. The particle size distribution parameters can be estimated by statistically analyzing the particle size of the MCNP tags using electron microscopy, or by obtaining the mass distribution parameters from the spICP-MS signal. This is achieved by deconvolving the spICP-MS signal with a pre-calibrated instrument response function (IRF). Since a simulated spICP-MS signal can be obtained by convolving the assumed mass distribution (or the mass distribution derived from the assumed particle size distribution) with IRF, the parameters of the assumed mass distribution (or particle size distribution) can be estimated using data fitting or statistical estimation methods based on the measured spICP-MS signal, thus obtaining the mass distribution (or particle size distribution) and its parameter values. For parameter estimation methods of finite mixture parameter distribution models, please refer to Chinese Patent [A method for quantitatively measuring the content and concentration of particulate 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 The mass distribution of free MCNP tags (known by parameters in step 1.1) is extended to construct a finite mixture distribution model of the components and their contents in a mixture consisting of metal nanoparticles, aggregates of test particles, and free MCNPs. Where p j (x m h j This refers to the mass distribution of free MCNP tags or each aggregate component, specifically the mass distribution of each MCNP tag aggregate (aggregated on the surface of the test particle) classified by its aggregation number j, ranging from 1 to n, with a content of π. j The mass distribution of each component can be represented by the mass distribution of the free metal-containing nanoparticle tag obtained in 1.1, i.e. Where j represents the cluster number, k represents the particle number in the cluster, and the parameter ψ2 has been estimated in step 1.1. The parametric distribution can be easily transformed into the form of a nonparametric kernel density distribution, i.e., the kernel density distribution is obtained by sampling random variables and estimating the kernel density. For the convenience of model representation and parameter estimation, the finite mixture distribution model q(x) is used. m Both ψ2) can be modeled using a kernel density finite mixture distribution model (denoted as FMKD or FMKDE), that is, p j (x m h j (Takes the form of kernel density function) in, K is the kernel estimate of the probability density function of the j-th component; K() is the kernel function; k is a sample of observed values ​​for a certain component. The number of observations in h; j For bandwidth. Although the distribution descriptions here all use mass distribution, similarly, particle size distribution and mass distribution can be converted to each other, and particle size distribution can also be used for description.

[0025] 1.3 Estimate the content parameters of various aggregate components classified by aggregation number in the metal nanoparticle-analyte aggregate, i.e. π in j Parameters. Through the convolutional transformation of the instrument response function IRF, the mass distribution can be transformed into the signal distribution q(x). s ;ψ3), x s Let X be a random variable of the particle signal. s The value of ψ3 is a parameter of the signal distribution mixture model. For a description of the signal distribution FMKDE model and parameter estimation method, please refer to Chinese Patent [A Method for Quantitatively Measuring the Content and Concentration of Particulate Components in a Mixture, 202210773566.2]. The parameter π obtained from the data analysis results... j Let j = 1, ..., n be the content of the free tag or aggregate component to be determined. The number concentration can be further obtained by measuring the transmission efficiency of the instrument. For the conversion from content to number concentration, please refer to Chinese Patent [A method for quantitatively measuring the content and concentration of particulate components in a mixture, 202210773566.2] or other published spICP-MS number concentration calibration methods.

[0026] 1.4 The number of MCNP tag clusters 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 transformation function that converts the mass distribution of particles into the distribution of the instrument output signal. Experimental characterization revealed that for monodisperse particles, the output signal of spICP-MS can be obtained using a traditional Gaussian convolution transform. However, the Gaussian broadening differs for monodisperse particles of different sizes, and the convolution transform is difficult for non-monodisperse particles. Further experimental characterization and analysis revealed that, to address the issue of varying Gaussian broadening, the functional form of the IRF can be constructed as follows:

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

[0029] Here, random variable f1(mass) represents the quantitative transformation relationship between the quality distribution and the signal distribution, and random variable N(signal; 0, f2(mass)) represents that the convolution function is a Gaussian distribution or a composite distribution. The broadening parameter is determined by another functional form of the quality distribution, f2(mass), i.e., the standard deviation of the Gaussian distribution is a function of the quality distribution. Preferably, f1 and f2 can be represented by linear functions, i.e., f1(mass) = a1 + a2·mass, f2(mass) = a3 + a4·mass. The above functional forms of IRF are valid under a wide range of experimental conditions. Because the Gaussian convolution and deconvolution techniques involved in traditional signal transformation can only be used when the Gaussian broadening takes a fixed value, equivalent to the case where a4 = 0 and a3 is a constant, this is insufficient for describing the instrument response function of spICP-MS. It will introduce a large bias when the particle size is not monodisperse, so traditional Gaussian convolution or deconvolution methods cannot be used for signal processing. This invention describes signal convolution transformation using the summation of random variables. Gaussian convolution corresponds to the Gaussian distribution or its composite distribution in the latter half of the instrument response function. The variable variance Gaussian broadening phenomenon can be easily described using a variable variance Gaussian composite distribution. This invention solves the technical problem of signal transformation when Gaussian broadening during convolution and deconvolution is not constant. Furthermore, the linear functional relationship used in the construction of the IRF function form can be further extended to a nonlinear relationship. Functional forms such as power functions, exponential functions, logarithmic functions, and their reciprocals, which are applicable in nonlinear fitting, can all be applied here to make it effective over a wider quality range. The aforementioned linear functional relationship is a preferred result of this invention and should not be considered a specific limitation on the functional form of the IRF. By using statistical information on the particle size distribution of several particle size standards, the parameter values ​​in the IRF can be estimated, thereby completing the calibration process of the instrument response function (IRF).

[0030] The data analysis and calculation approach of this invention is as follows:

[0031]

[0032] In the formula, n is the number of target molecules on the surface of the particle to be tested, and I is the overall signal intensity of all MCNPs on the surface of the particle to be tested. MCNP The signal strength of a single MCNP. Since the measured spICP-MS signal strength is a random variable, I... MCNP It is a distribution function. The formula above provides a calculation approach, but it cannot be directly used for calculation. If we use I... MCNP Calculating the average signal strength of n only yields the average value of n or a scaling transformation of the distribution of I, but not the true distribution of n. The algorithm provided in this invention overcomes this difficulty by using a distribution function and its parameter estimation based on a finite mixture model.

[0033] On the other hand, the present invention also provides the application of the analytical methods described above in the detection of micron-sized and / or nano-sized particle surfaces.

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

[0035] This invention provides an analytical method for target molecules on particle surfaces. By constructing functionalized MCNP tags to specifically label target molecules on the surface of micro / nanoparticles, and using single-particle inductively coupled plasma mass spectrometry, quantitative analysis of target molecules on the surface of micro / nanoparticles is achieved at the single-particle level. Through the development of a specific algorithm, the overall metal signal intensity of the particle surface can be converted into the number of MCNPs, thereby enabling high-throughput quantitative analysis of the number concentration of labeled particles, the content of particles with a specific copy number, the number concentration, and the total concentration of the measured micro / nanoparticles. Attached Figure Description

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

[0037] Figure 2 This is a transmission electron microscope image of the surface-modified biotin polystyrene microspheres (PS@Bio_200nm) in Example 1 before they were labeled.

[0038] Figure 3 This is a transmission electron microscope image of the biotin-modified polystyrene microspheres (PS@Bio_200nm) labeled in Example 1.

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

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

[0041] Figure 6 This is a transmission electron microscope image of the surface-modified biotin polystyrene microspheres (PS@Bio_500nm) in Example 2 before they were labeled.

[0042] Figure 7 This is a transmission electron microscope image of the biotin-modified polystyrene microspheres (PS@Bio_500nm) labeled in Example 2.

[0043] Figure 8 The image shows the distribution of AuNP signal intensity in the incubation medium (PS@Bio_500nm+AuNP@SA) under spICP-MS.

[0044] Figure 9 This is a distribution diagram of the biotin molecule copy number on the PS@Bio_500nm surface.

[0045] Figure 10 The distribution of AuNP signal intensity after co-incubation of AuNP@SA with unmodified biotinylated PS (500 nm) microspheres in negative control group 1 under spICP-MS is shown.

[0046] Figure 11 The distribution of AuNP signal intensity in negative control group 1 after mixing with water is shown in spICP-MS.

[0047] Figure 12 The distribution of AuNP signal intensity after co-incubation with PS@Bio_500nm in negative control group 2 under spICP-MS is shown.

[0048] Figure 13 This is a distribution diagram of AuNP signal intensity after mixing AuNPs with water in negative control group 2, as shown by spICP-MS. Detailed Implementation

[0049] The technical solution of the present invention will be further illustrated below through specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the present invention and should not be construed as limiting the invention in any way.

[0050] Example 1

[0051] This embodiment 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 tag (AuNP@SA) and spICP-MS measurement

[0053] Streptavidin-modified gold nanoparticles (AuNP@SA), 30 nm in size, with a mass concentration of 0.1 mg / mL, were dispersed in a buffer solution of Na-Hydroxy-Ethylpiperazine-N'-Ethane-Sulfanic Acid (HEPES, pH 7.2-7.4) and were ordered from Anhui Zesheng Technology Co., Ltd. Before spICP-MS analysis, the purchased AuNP@SA was diluted to a concentration of 1.5 × 10⁻⁶. 5 The particle count is approximately [value missing] / 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, with an average signal strength of 19.8.

[0054] (ii) Biotin-modified polystyrene microspheres labeled with MCNP tags (PS@Bio)

[0055] Biotin-modified polystyrene microspheres (PS@Bio) with an average particle size of 200 nm and a mass concentration of 10 mg / mL were dispersed in a buffer solution of Na-Hydroxy-Ethylpiperazine-N'-EthaneSulfanic Acid (HEPES, pH 7.2-7.4) and were ordered from Anhui Zesheng Technology Co., Ltd. The purchased PS@Bio was diluted 1000-fold before use. A mixture of PS@Bio and AuNP@SA at a volume ratio 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 nanoparticle-tag to be tested from the free tags in solution

[0057] Because the number concentration ratio of the MCNP tag to the polystyrene microspheres prepared in this embodiment is relatively small, the aggregates and free tag mixture formed during incubation can be directly analyzed and detected by spICP-MS without separation. The morphology images of PS@Bio_200nm before and after labeling under a transmission electron microscope are as follows. Figure 2 and Figure 3 .

[0058] (iv) Utilizing spICP-MS to measure information from aggregates and free tags

[0059] After the co-incubation liquid was diluted 1500 times, the total particle number concentration in the suspension reached 1.5 × 10⁻⁶. 5The sample concentration was approximately 1000 μL / mL, followed by spICP-MS analysis. Typical instrument parameters were: nebulizer flow rate 1.06 L / min, auxiliary gas flow rate 1.2 L / min, plasma gas flow rate 18 L / min, ICP power 1600 W, injection flow rate 0.305 mL / min, residence time 100 μs, instrument-calibrated transfer efficiency 6.48%, and scan time 200 s. These parameters may vary depending on the specific instrument model and sample properties. The signal intensity distribution of the suspension under spICP-MS is shown below. 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 tags on the surface of the nanoparticles to be tested.

[0061] Signal intensity distribution data from spICP-MS shows that the number of biotin molecules modified on the 200 nm PS microspheres is relatively small. This results in the AuNP@SA tag on the PS@Bio-AuNP@SA aggregates not forming an independent peak in the spICP-MS signal compared to the free AuNP@SA tag, but only exhibiting a tail-like appearance. Using the algorithm provided in the invention's description, the intensity signal of the aggregates is converted into the number of AuNP@SA tags it contains, which can be further converted into information on the biotin copy number on the surface of the functional nanoparticles, i.e., the copy number of biotin molecules at the level of a single polystyrene microsphere, and the distribution of biotin molecule copy number across the entire polystyrene microsphere sample. The method of this invention provides a biotin molecule copy number distribution between 3 and 75 on the surface of PS@Bio microspheres, as shown in the following figure. Figure 5 As shown, the copy number distribution does not form an independent peak, but rather exhibits a decaying function, corresponding to the tailing pattern of the original signal distribution. The slightly prominent small peaks in the copy number distribution correspond to the weak bulges in the signal tail peaks, demonstrating that the algorithm in this invention has excellent aggregation resolution and mitigates the resolution reduction caused by signal convolution. The average biotin copy number on the surface of PS@Bio_200nm microspheres is 15.

[0062] (vi) Determine if the test particles are saturated with the label.

[0063] By adding an excess of AuNP@SA tags to the co-incubation solution, it was found that the tag number distribution on the PS@Bio-AuNP@SA aggregates did not change significantly, which 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 from spICP-MS, the total particle number concentration of the diluted suspension is 120422 particles / mL, of which the proportion of PS@Bio_200nm-AuNP@SA aggregates is 0.491. Therefore, the number concentration of the original PS microspheres is: 120422 × 0.491 × 1500 × (1000 + 43) / 1000 × 1000 = 9.25 × 10⁻⁶ 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 [link to relevant documentation]. Figure 5 Its number concentration can be obtained by multiplying the proportion of existence by the total concentration.

[0065] Example 2

[0066] This embodiment 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 tag (AuNP@SA) and spICP-MS measurement

[0068] Same as Example 1.

[0069] (ii) Biotin-modified polystyrene microspheres labeled with MCNP tags (PS@Bio)

[0070] Biotin-modified polystyrene microspheres (PS@Bio) with an average particle size of 500 nm and a mass concentration of 10 mg / mL were dispersed in a buffer solution of Na-Hydroxy-Ethylpiperazine-N'-EthaneSulfanic Acid (HEPES, pH 7.2-7.4) and were ordered from Anhui Zesheng Technology Co., Ltd. The purchased PS@Bio was diluted 500-fold before use. A PS@Bio:AuNP@SA mixture with a volume ratio 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 nanoparticle-tag to be tested from the free tags in solution

[0072] Because the number concentration ratio of the MCNP tag to the polystyrene microspheres prepared in this embodiment is relatively small, the aggregates and free tag mixture formed during incubation can be directly analyzed and detected by spICP-MS without separation. The morphology images of PS@Bio_500nm before and after labeling under a transmission electron microscope are as follows. Figure 6 and Figure 7 .

[0073] (iv) Utilizing spICP-MS to measure information from aggregates and free tags

[0074] After the co-incubation liquid was diluted 2700 times, the total particle number concentration in the suspension reached 1.5 × 10⁻⁶. 5 The sample concentration was approximately 100 particles / mL, followed by spICP-MS analysis. Typical instrument parameters were: nebulizer flow rate 1.06 L / min, auxiliary gas flow rate 1.2 L / min, plasma gas flow rate 18 L / min, ICP power 1600 W, injection flow rate 0.305 mL / min, residence time 100 μs, instrument-calibrated transfer efficiency 6.48%, and scan time 200 s. These parameters may vary depending on the specific instrument model and sample properties. The signal intensity distribution of the suspension under spICP-MS is shown below. Figure 8 As shown, the signal strength distribution of MCNP tags on the PS@Bio-AuNP@SA aggregate is between 200 and 2600.

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

[0076] The algorithm provided in the description of this invention converts the intensity signal of the aggregate into the number of MCNP tags it contains, which can be further converted into information on the biotin copy number on the surface of functional nanoparticles, i.e., the copy number of biotin molecules at the level of a single polystyrene microsphere, and the distribution of biotin molecule copy number across the entire polystyrene microsphere sample. The method of this invention provides a distribution of biotin molecule copy number on the surface of PS@Bio microspheres between 3 and 100, as shown in the specific distribution below. Figure 9 As shown, the copy number distribution corresponds to the independent peak form of the original signal distribution, and also exhibits a main peak. To the left of the main peak, the copy number distribution less than 15 shows a small peak structure, corresponding to a slight tailing phenomenon in the signal intensity, demonstrating that the algorithm in this invention has good aggregation resolution and can detect subtle signal changes. The average biotin copy number on the surface of PS@Bio_500nm microspheres is 38.

[0077] (vi) Determine if the test particles are saturated with the label.

[0078] By adding an excess of AuNP@SA tags to the co-incubation solution, it was found that the tag number distribution on the PS@Bio-AuNP@SA aggregates did not change significantly, which 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 from spICP-MS, the total particle number concentration of the diluted suspension is 173464 particles / mL, of which the proportion of PS@Bio-AuNP@SA aggregates is 0.144. Therefore, the number concentration of the original PS microspheres is: 173464 × 0.144 × 2700 × (500 + 42) / 500 × 500 = 3.66 × 10⁻⁶ 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 [link to relevant documentation]. Figure 9 Its number concentration can be obtained by multiplying the proportion of existence by the total concentration.

[0080] (vii) Negative control

[0081] To demonstrate that the high-intensity tag signal in the PS@Bio and AuNP@SA mixture under spICP-MS originates from the specific binding between biotin and streptavidin, rather than non-specific adsorption between them, two negative control experiments are introduced here for verification.

[0082] 1. Co-incubation of MCNP tags (AuNP@SA) with unmodified polystyrene microspheres (PS)

[0083] Biotin-free polystyrene microspheres (PS) with an average particle size of 500 nm and a mass concentration of 10 mg / mL, dispersed in HEPES (pH 7.2-7.4) buffer solution, were ordered from Anhui Zesheng Technology Co., Ltd. The purchased PS was diluted 500 times before use. A PS:AuNP@SA mixture with a volume ratio of 500:42 was prepared and incubated at 37℃ and 100 rpm for 2 hours to allow for complete adsorption of PS and AuNP@SA.

[0084] To investigate the influence of the environment on the tag aggregation state, equal amounts of AuNP@SA tags were added to a three-stage water solution with an equal volume of PS, and co-incubated under the same conditions. After incubation, the solution was diluted appropriately to achieve a gold particle number concentration of 1.5 × 10⁻⁶. 5 The particles were approximately [particles / mL], and then detected and analyzed by spICP-MS. The tag signal intensity distributions are shown in [see table]. Figure 10 , 11 No high-intensity aggregate signals were observed 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-fold before use. Additionally, sodium citrate-reduced gold nanoparticles (AuNPs, purchased from BBI Solution) with an average particle size of 30nm were selected as a non-specific binding tag. A PS@Bio:AuNPs mixture with a volume ratio of 500:42 was prepared and incubated at 37°C and 100 rpm for 2 hours to allow for complete adsorption of PS@Bio and AuNPs.

[0087] To investigate the influence of the environment on the aggregation state of AuNPs, equal amounts of AuNPs were added to a volume equal to that of PS@Bio in three solutions and co-incubated under the same conditions. After incubation, the solutions were diluted appropriately to achieve a gold particle number concentration of 1.5 × 10⁻⁶. 5 The AuNPs concentration was approximately 100 particles / mL, and then detected and analyzed by spICP-MS. The signal intensity distribution of AuNPs is shown in the figure below. Figure 12 , 13 No high-intensity aggregate signals were observed in the signal intensity distribution.

[0088] The two negative control experiments described above show that, under spICP-MS analysis conditions, neither the AuNP@SA-PS nor AuNPs-PS@Bio systems exhibited high-intensity signals. This further confirms that the high-intensity signals observed in the AuNP@SA-PS@Bio co-incubation system originate from the specific binding between biotin and streptavidin, rather than non-specific adsorption between them. The number of biotin molecules on the PS microspheres obtained using this method is accurate and reliable.

[0089] The above content fully demonstrates that the method provided by the present invention can achieve specific detection of target molecules on the particle surface and can effectively analyze the distribution of target molecules on the particle surface.

[0090] The applicant declares that this invention illustrates the analytical method for target molecules on particle surfaces and its application through the above embodiments, but this invention is not limited to the above embodiments, that is, it does not mean that this invention must rely on the above embodiments to be implemented. Those skilled in the art should understand that any improvements to this invention, equivalent substitutions of raw materials for the products of this invention, addition of auxiliary components, selection of specific methods, and modifications to the algorithm of this invention within a reasonable range all fall within the protection and disclosure scope of this invention.

[0091] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within 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 way without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

Claims

1. A method for analyzing target molecules on the surface of particles, characterized in that, The analytical method includes the following steps: Metal nanoparticles containing functional molecules on their surface are mixed and incubated with the target particles, followed by single-particle inductively coupled plasma mass spectrometry detection. The distribution of target molecules on the surface of the target particles is analyzed and calculated based on the detection results. The particles to be tested include any one or a combination of at least two of polymer microspheres, metal particles, inorganic microspheres, micelles, carbon dots or magnetic beads. The surface of the particle to be tested contains at least two target molecules; The functional molecule specifically binds to the target molecule; The components and their contents of the mixture consisting of metal-containing nanoparticles, aggregates of the test particles, and free metal-containing nanoparticles are described using a finite mixing model, i.e. Where p j (x m h j () represents the mass distribution of free metal-containing nanoparticles or each aggregate component, with the aggregation number j ranging from 1 to n and the content being π. j The mass distribution of each component is represented by the mass distribution of free metal-containing nanoparticles, i.e. Where j represents the cluster number and k represents the particle number in the cluster; The parameters in the finite mixture model are estimated through the convolutional transformation of the instrument response function IRF, that is, the mass distribution is transformed into the signal distribution q(x) through IRF. s ;ψ3), the parameter ψ3 in the signal distribution is estimated by the measured signal.

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

3. The analytical method according to claim 1, characterized in that, The metal-containing nanoparticles with functional molecules on their surface are constructed by a method comprising the following steps: Functional molecules are combined with metal-containing nanoparticles through click chemistry surface bonding technology, surface modification methods based on coordination / ligand recognition or ligand exchange, or methods based on crosslinking agents to construct metal-containing nanoparticles with functional molecules on their surface.

4. The analytical method according to claim 1, characterized in that, Before performing single-particle inductively coupled plasma mass spectrometry (ICP-MS) detection, the incubated mixture is separated to separate the free metal-containing nanoparticles from the aggregates of metal-containing nanoparticles and test particles in the system.

5. The analytical method according to claim 4, characterized in that, The separation method includes any one of density gradient centrifugation, membrane filtration, field flow separation, differential centrifugation, immunoaffinity separation, microfluidic separation, ultrafiltration, size exclusion chromatography, or flow cytometry.

6. The analytical method according to claim 1, characterized in that, Before performing single-particle inductively coupled plasma mass spectrometry (ICP-MS) detection, the incubated mixture is diluted.

7. The analytical method according to claim 6, characterized in that, The total concentration of the metal nanoparticles, the aggregates of the test particles, and the free metal nanoparticles in the mixture is diluted to reach the applicable range of the single-particle inductively coupled plasma mass spectrometry.

8. The analytical method according to claim 1, 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)), where the random variable f1(mass) represents the quantitative transformation relationship between the quality distribution and the signal distribution, and the random variable N(signal; 0, f2(mass)) represents that the convolution function is a Gaussian distribution or a composite distribution, and the broadening parameter is determined by another functional form of the quality distribution, f2(mass).

9. The application of an analytical method according to any one of claims 1-8 in the detection of micron-sized and / or nano-sized particle surfaces.

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