An analytical method for target molecules on the surface of biological particle membranes and its application.

Abstract

This invention provides an analytical method for target molecules on the surface of bioparticle membranes and its application. The analytical method includes the following steps: mixing and incubating metal-containing nanoparticles with functional molecules on their surface with bioparticles to be tested; then separating the free metal-containing nanoparticles from the aggregates of metal-containing nanoparticles and bioparticles to be tested in the system; followed by single-particle inductively coupled plasma mass spectrometry (ICP-MS) detection; and analyzing and calculating the distribution of target molecules on the surface of the bioparticle membrane based on the detection results. The method provided by this invention enables quantitative analysis of target molecules, such as biomarkers, on the surface of bioparticle membranes at the single-particle level. It can effectively quantify the number concentration of labeled particles, the content of particles with specific copy numbers, the number concentration, and the total concentration of the tested bioparticles. Furthermore, it achieves high-throughput testing, acquiring signals from tens of thousands of tested nanoparticles within minutes to obtain reliable statistical distribution information.

Description

Technical Field

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

[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 a crucial role in cell function and interactions. Membrane proteins participate in important processes such as cell signaling, substance transport, and recognition. Glycolipids and glycoproteins are also widely present on cell membranes, playing important roles 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 various human cancers. Exosomes, with their relatively stable phospholipid bilayer structure, can also serve as natural drug carriers, protecting drug activity. Furthermore, their drug-carrying capacity can be improved by regulating the expression of certain substances on exosomes through cell line selection, culture medium selection, and gene modification. Stem cell-derived exosomes can promote the regeneration and repair of damaged tissues, have strong immunomodulatory effects, and readily accumulate specifically at pathological sites, demonstrating superiority in the treatment of diabetes, cardiovascular diseases, and kidney injury. By studying the distribution, structure, and function of target molecules on the membrane surface, researchers can gain a deeper understanding or reveal the mechanisms of signal transduction in organisms, the occurrence and development of related diseases, and develop corresponding diagnostic and treatment methods.

[0003] Currently, methods for characterizing target molecules or structures on the surface of biological sample membranes include: Enzyme-Linked Immunosorbent Assay (ELISA), Mass Spectrometry (MS), and Western Blot (WB). ELISA involves linking antigens or antibodies to an enzyme to form enzyme-labeled antigens or antibodies, maintaining their immunoreactivity. By binding the enzyme-labeled antigen or antibody to the target molecule on the particle surface, and adding a substrate, 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 molecule in the sample, allowing for the determination of the total number of target molecules and the average number per particle surface. Mass spectrometry involves stripping functional molecules from the surface of numerous nanoparticles, then identifying and quantifying the analyte. WB uses polyacrylamide gel electrophoresis to separate protein samples and labels them with corresponding antibodies to identify the presence of a specific protein. The intensity of the color development indicates the content difference between different samples. These techniques can only provide qualitative, quantitative, or semi-quantitative analysis of the overall sample. 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 such as exosomes. The invention of nanoflow cytometry has introduced a new, highly sensitive tool for the detection of nanoparticles, 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 biological particles being tested.

[0004] To better understand the mechanisms by which bioparticles regulate life activities, expand their applications, and achieve more controllable surface modification, qualitative, overall quantitative, or semi-quantitative analysis alone is insufficient, especially for highly heterogeneous biological systems such as cells, synaptic bodies, exosomes, bacteria, viruses, and liposomes. Therefore, there is an urgent need to develop a method for the identification and quantitative analysis of target molecules on membrane surfaces at the single-event 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 the surface of bioparticle membranes and its applications, particularly an effective method for analyzing target molecules on the surface of bioparticle membranes at the single-particle level. The method provided by this invention enables quantitative analysis of target molecules on the surface of bioparticle membranes at the single-particle (single-event) 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 tested bioparticles. Furthermore, it achieves high-throughput testing, acquiring signals from tens of thousands of tested nanoparticles within minutes to obtain reliable statistical distribution information.

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

[0007] On one hand, 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 surface were mixed and incubated with the target biological particles. Then, the free metal-containing nanoparticles in the system were separated from the metal-containing nanoparticle-target biological particle aggregates. After that, single-particle inductively coupled plasma mass spectrometry (spICP-MS) was used for detection. The distribution of target molecules on the surface of the target biological particle membrane was analyzed and calculated based on the detection results.

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

[0010] The surface of the bioparticle membrane 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 bioparticle membranes and uses single-particle inductively coupled plasma mass spectrometry to achieve quantitative analysis of target molecules on the surface of bioparticle membranes at the single-particle level. It can effectively quantify the number concentration, content of particles with specific copy numbers, number concentration, and total concentration of the tested bioparticles. Furthermore, the separation process can reduce free MCNPs in the test sample, increase the probability of detecting aggregates of metal nanoparticles and tested bioparticles, shorten the detection time, greatly improve the efficiency of effective data acquisition, improve the accuracy of detection, and avoid signal distortion.

[0013] For naturally occurring or secreted biological particles, methods including, but not limited to, gene modification, cell line selection, body fluid selection, and culture medium selection can be used to obtain particles with different copy numbers and / or distributions of target molecules. In order to obtain relatively pure biological particles, separation and purification methods can be used as an aid. 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, and flow cytometry sorting.

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

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

[0016] 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.

[0017] 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.

[0018] 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.

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

[0020] Preferably, the total concentration of the metal nanoparticles, the target biological particle aggregates, and the free metal nanoparticles in the diluted 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 detection results are as follows:

[0022] 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 πj 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 h j Let N(x) be a Gaussian distribution. d The particle size distribution g(x, σ) 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.

[0023] 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, bioparticle aggregates to be tested, 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 bioparticle membrane) classified by its aggregation number j, ranging from 1 to n, with a content of π. jThe 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.

[0024] 1.3 Estimate the content parameters of various aggregate components classified by aggregation number in the metal nanoparticle-analyte bioparticle 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.

[0025] 1.4 The number of MCNP tag aggregations is positively correlated with the number of target molecules on the surface of the bioparticle membrane. 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 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:

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

[0028] 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).

[0029] The data analysis and calculation approach of this invention is as follows:

[0030]

[0031] In the formula, n is the number of target molecules on the surface of the bioparticle membrane to be tested, and I is the overall signal intensity of all MCNPs on the surface of the bioparticle membrane 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.

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

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

[0034] This invention provides an analytical method for target molecules on the surface of bioparticle membranes. By constructing functionalized MCNP tags to specifically label target molecules on the surface of bioparticle membranes, and utilizing single-particle inductively coupled plasma mass spectrometry (ICP-MS), 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 on the surface of the bioparticle membrane can be converted into the number of MCNPs, 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 tested bioparticles. Simultaneously, the separation process reduces free MCNPs in the test sample, increases the probability of detecting metal-containing nanoparticle-test bioparticle aggregates, shortens detection time, greatly improves the efficiency of effective data acquisition, enhances detection accuracy, and avoids signal distortion. Attached Figure Description

[0035] Figure 1 The signal intensity distribution of AuNPs on spICP-MS is shown.

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

[0037] Figure 3 This is a morphological image of exosomes under a transmission electron microscope.

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

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

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

[0041] Figure 7 This is a distribution map of CD63 copy number in the exosome as a whole.

[0042] Figure 8 The signal intensity distribution of AuNP@Apt_CD81 after aptor modification is shown on spICP-MS.

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

[0044] Figure 10 The signal intensity distribution of L12 on spICP-MS is shown.

[0045] Figure 11 This is a distribution map of CD81 copy number in the exosomes. Detailed Implementation

[0046] 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.

[0047] Example 1

[0048] This embodiment provides a method for quantifying the copy number of CD63 protein on the surface of exosomes at the single-event level. The specific steps are as follows:

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

[0050] 1. Prepare a phosphate-binding buffer solution containing 0.55 mM MgCl2 (Phosphate Buffer Saline, PBS, 10 mM, pH 7.4).

[0051] 2. Dissolve the thiol-modified CD63 aptamer in an appropriate amount of binding buffer to a final concentration of 100 μM. Take 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 approximately 3 × 10⁻⁶) 11The AuNP@Apt_CD63 particles (purchased from BBI Solutions) were added to the pre-treated aptamers and frozen at -20°C for 2 hours, then thawed at room temperature. The mixture was centrifuged at 13000 rpm for 20 minutes to remove unbound aptamers. AuNP@Apt_CD63 was resuspended in PBS buffer, washed five times by cyclic centrifugation, and finally dispersed in 500 μL of PBS for later use.

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

[0053] 3. spICP-MS analysis was performed on gold nanoparticles (AuNPs) before and after aptamer modification, and the signal intensity distributions obtained were as follows: Figure 1 and Figure 2 This proves that AuNPs do not aggregate due to modification of the aptamer.

[0054] (ii) Exosomes were labeled with the MCNP tag AuNP@Apt_CD63

[0055] 1. The exosomes used were obtained from Suzhou Weisierkang Technology Co., Ltd., and were isolated and purified from 293F cell culture medium. The final exosome number concentration was 2.6 × 10⁻⁶. 11 Particles / mL (obtained by NTA testing), with a particle size distribution between 30-200 nm.

[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 As shown.

[0058] (III) Cesium chloride density gradient centrifugation to separate free AuNP@Apt_CD63 tags

[0059] 1. Prepare a cesium chloride solution by dispersing 10 g of cesium chloride in 6 mL of triterpenoid water, resulting in a density of 1.857 g / mL. Then, add 50 μL of the previously incubated exosome-AuNP@Apt_CD63 mixture to the surface of the gradient solution.

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

[0061] 3. To clarify the distribution of exosomes and analyze their presence in more detail, after centrifugation, each 100 μL sample was aliquoted into a centrifuge tube, resulting in 12 layers (layers 1-10 were 100 μL each, layer 11 was 50 μL each, and layer 12 consisted of particles resuspended at the bottom of the centrifuge tube in 200 μL PBS). The layers were numbered from top to bottom as L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, and L12.

[0062] 4. Since cesium chloride affects the ionization efficiency of AuNPs on spICP-MS, the samples were ultrafiltered at 9000 rpm for 10 min before analysis, and the cesium chloride was removed by two cycles. Finally, each layer of sample was brought to a final volume of 250 μL for subsequent analysis.

[0063] (iv) Measure information of separated aggregates and free tags using spICP-MS

[0064] The sample from each layer was diluted by a certain factor to ensure that its number concentration was within the dynamic range of the mass spectrometer, specifically around 2.5 × 10⁻⁶. 5 The concentration of particles was approximately 1000 μL, followed by spICP-MS analysis. The signal intensity distribution of AuNP@Apt_CD63 in each layer was obtained from the spICP-MS signal intensity data. Analysis revealed that exosome-AuNP@Apt_CD63 aggregates mainly appeared in L8, L9, and L10. The signal intensity distribution of the above layers is shown in the figure. Figure 5 .

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

[0066] The signal intensity distribution data from spICP-MS reveals the signal intensity distribution of MCNP tags on exosome-AuNP@Apt_CD63 aggregates. Using the algorithm provided in the invention's description, the intensity signal of the aggregates is converted into the number of MCNP tags they contain, which can be further converted into information on the copy number of CD63 protein on the exosome membrane surface. This allows us to obtain the copy number of CD63 protein at the single exosome vesicle level, as well as the copy number distribution of CD63 protein across the entire exosome sample. The CD63 copy number distribution in each layer is shown below. Figure 6 As shown; summarizing the exosome information from all layers yields the CD63 copy number distribution for the entire sample, which ranges from 3 to 60, as shown. Figure 7 As shown.

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

[0068] Based on the concentration information and dilution factor from spICP-MS, the total number of CD63-positive exosomes in 2.5 μL of exosomes was 113,982,121, with a positive count concentration of 4.559 × 10⁻⁶. 10 Particles / mL.

[0069] Example 2

[0070] This embodiment provides a method for quantifying the copy number of CD81 protein on the surface of exosomes at the single-event level. The specific steps are as follows:

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

[0072] 1. Prepare a phosphate-binding buffer solution containing 0.55 mM MgCl2 (Phosphate Buffer Saline, PBS, 10 mM, pH 7.4).

[0073] 2. Dissolve the thiol-modified CD81 aptamer in an appropriate amount of binding buffer to a final concentration of 100 μM. Take 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 approximately 3 × 10⁻⁶) to the solution. 11 The particles ( / mL, purchased from BBI Solutions) were added to the aptamers treated above, and the mixture was frozen at -20°C for 2 hours, then thawed at room temperature. The mixture was centrifuged at 13000 rpm for 20 minutes to remove unbound aptamers. AuNP@Apt_CD81 was resuspended in PBS buffer, washed 5 times by cyclic centrifugation, and finally dispersed in 500 μL of PBS for later use.

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

[0075] 3. Perform spICP-MS analysis on the prepared AuNP@Apt_CD81 tags to obtain the signal intensity distribution map of the AuNP@Apt_CD81 tags, such as... Figure 8 This proves that AuNPs did not aggregate due to modification of the aptamer.

[0076] (ii) Labeling exosomes with the MCNP tag AuNP@Apt_CD81

[0077] 1. The exosomes used were obtained from Suzhou Weisierkang Technology Co., Ltd., and were isolated and purified from 293F cell culture medium. The final exosome number concentration was 2.6 × 10⁻⁶. 11 Particles / mL (obtained by NTA testing), with a particle size distribution between 30-200 nm.

[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... Figure 9 As shown.

[0080] (III) Cesium chloride density gradient centrifugation to separate free AuNP@Apt_CD81 tags

[0081] 1. Prepare a cesium chloride solution by dispersing 10 g of cesium chloride in 6 mL of triterpenoid water, with a density of 1.857 g / mL. Then, add 50 μL of the exosome-AuNP@Apt_CD81 mixture that has been incubated as described above to the top of the gradient solution.

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

[0083] 3. To clarify the distribution of exosomes and analyze their presence in more detail, after centrifugation, each 100 μL sample was aliquoted into a centrifuge tube, resulting in 12 layers (layers 1-10 were 100 μL each, layer 11 was 50 μL each, and layer 12 consisted of particles resuspended at the bottom of the centrifuge tube in 200 μL PBS). The layers were numbered from top to bottom as L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, and L12.

[0084] (iv) Measure information of separated aggregates and free tags using spICP-MS

[0085] The sample from each layer was diluted by a certain factor to ensure that its number concentration was within the dynamic range of the mass spectrometer, specifically around 2.5 × 10⁻⁶. 5 The concentration of particles was approximately 1 mL, followed by spICP-MS analysis. Based on the signal intensity data from spICP-MS, the signal intensity distribution of AuNP@Apt_CD81 in each layer was obtained. Analysis revealed that exosome-AuNP@Apt_CD81 aggregates mainly appeared in L12. The signal intensity distribution in L12 is shown below. Figure 10 .

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

[0087] The signal intensity distribution data from spICP-MS reveals the signal intensity distribution of MCNP tags on exosome-AuNP@Apt_CD81 aggregates. Using the algorithm provided in the invention's description, the intensity signal of the aggregates is converted into the number of MCNP tags they contain, which can be further converted into information on the copy number of CD81 protein on the exosome membrane surface. This allows us to obtain the copy number of CD81 protein at the single exosome vesicle level, as well as the distribution of CD81 protein copy number across the entire exosome sample. The CD81 copy number on exosomes is distributed between 2 and 30. Figure 11 As shown.

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

[0089] Based on the concentration information and dilution factor from spICP-MS, a total of 63,406,645 CD81-positive exosomes were found in 2.5 μL of exosomes, with a positive count concentration of 2.536 × 10⁻⁶. 10 Particles / mL.

[0090] The above content fully demonstrates that the method provided by the present invention can achieve 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 this invention illustrates the analytical method for target molecules on the surface of biological particle membranes and its application through the above embodiments. However, this invention is not limited to the above embodiments, meaning that this invention does not necessarily rely on the above embodiments for implementation. 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 scope all fall within the protection and disclosure scope of this invention.

[0092] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in 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.

[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 describe the various possible combinations separately.

Claims

1. A method for analyzing target molecules on the surface of biological particle membranes, characterized in that, The analytical method includes the following steps: Metal-containing nanoparticles with functional molecules on their surface are mixed and incubated with the target biological particles. Then, the free metal-containing nanoparticles in the system are separated from the metal-containing nanoparticle-target biological particle aggregates. After that, single-particle inductively coupled plasma mass spectrometry is performed. The distribution of target molecules on the surface of the target biological particle membrane is analyzed and calculated based on the detection results. The biological particles to be tested include any one or a combination of at least two of the following: cells, bacteria, fungi, synaptosomes, migratory bodies, extracellular vesicles, viruses, or liposomes. The surface of the bioparticle membrane 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, the bioparticle aggregates to be tested, 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 biological particles to be tested is 1-50000 nm.

3. The analytical method according to claim 1 or 2, 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, 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.

5. 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.

6. The analytical method according to claim 5, characterized in that, The total concentration of the metal nanoparticles, the target biological particle aggregates, and the free metal nanoparticles in the diluted mixture reaches the applicable range of the single-particle inductively coupled plasma mass spectrometry.

7. 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).

8. The application of an analytical method according to any one of claims 1-7 in the detection of biological particle membrane surfaces.

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