A protein detection method and detection preparation

Abstract

The present invention provides a protein detection method and detection preparation, which realizes efficient enrichment of target protein through mesoporous silica-graphene oxide composite nanoporous material, completes specific immune capture in combination with magnetic nanoparticles modified with targeted antibodies, and realizes single molecule separation using a microfluidic chip of a three-dimensional hydrogel antibody array. Signal amplification adopts enzyme-labeled secondary antibody-gold nanorod conjugate, enhances the fluorescence signal through the plasma resonance effect, and corrects the background noise in combination with a convolutional neural network algorithm, which significantly improves the detection reliability; experiments show that the detection limit of this method is as low as 0.12pg / mL, the linear range covers 0.5-100,000pg / mL, the single cell detection error is ±7.8%, the batch repeatability (CV=4.2%), the nonspecific adsorption rate is only 1.1%, and the detection time is shortened to 1.2 hours. The detection method of the present invention is suitable for ultra-sensitive detection of tumor markers, especially in the in situ analysis of CTCs with clinical application potential.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a protein detection method and a detection preparation. Background Art

[0002] Trace protein detection currently holds important applications in disease diagnosis, biomarker screening, and single-cell analysis, but existing technologies face numerous challenges. While established, traditional methods such as enzyme-linked immunosorbent assays (ELISAs) typically have detection limits ranging from pg / mL to ng / mL, making them difficult to accurately quantify for ultra-low-abundance proteins, such as HER2 protein in circulating tumor cells. Furthermore, they rely on large sample volumes (≥100 μL), are cumbersome, and time-consuming (4-6 hours). Microfluidic chip technology utilizes microchannels to efficiently separate cells or proteins, but existing technologies lack specific capture units, resulting in nonspecific adsorption rates as high as 10%-20%, significantly increasing background noise. Furthermore, single-cell protein detection requires the integration of highly sensitive signal amplification techniques, but traditional fluorescent labeling methods are susceptible to photobleaching interference, exhibit rapid signal intensity decay, and have a narrow dynamic linear range (typically ≤2 orders of magnitude).

[0003] In recent years, nanomaterials (such as gold nanorods and mesoporous silica) have been used for protein enrichment and signal enhancement due to their high specific surface area and surface modifiability. However, existing technologies are often limited to a single technical approach, failing to achieve the coordinated optimization of nanomaterials, microfluidic separation, and intelligent algorithms. For example, while magnetic nanoparticle-based immunocapture can improve specificity, large-sized magnetic beads (≥100 nm) easily clog microfluidic channels, and the separation efficiency in passive capture mode is less than 60%. Furthermore, data analysis methods that rely solely on machine learning struggle to overcome the low signal-to-noise ratio due to a lack of underlying signal enhancement technology. Summary of the Invention

[0004] In view of this, the present invention proposes a protein detection method and a detection preparation to solve the above problems.

[0005] The technical solution of the present invention is achieved as follows: A protein detection method comprises the following steps:

[0006] S1. Sample pretreatment: Mix the sample to be tested with a lysis solution containing nanoporous materials to selectively enrich the target protein;

[0007] S2, immune response: Add magnetic nanoparticles (20-50 nm in diameter) with surface-modified targeting antibodies to form an "antibody-antigen-nanoparticle" complex;

[0008] S3, microfluidic separation: The complex is injected into a microfluidic chip integrated with an antibody array, and single-molecule capture is achieved by magnetic field drive;

[0009] S4, signal amplification: introducing enzyme-labeled secondary antibody-gold nanorod conjugates with an aspect ratio of 3:1-5:1 to enhance the fluorescence signal through localized surface plasmon resonance (LSPR);

[0010] S5. Quantitative detection: Signal intensity is detected based on electrochemiluminescence (ECL) technology, and background interference is corrected in combination with machine learning algorithms.

[0011] Furthermore, the nanoporous material in step S1 is a composite material of mesoporous silica and graphene oxide with a pore size of 5-10 nm, and polyethylene glycol (PEG) chains with a molecular weight of 2000-5000 Da are grafted on the surface to reduce nonspecific adsorption.

[0012] Furthermore, the antibody array of the microfluidic chip in step S3 is a three-dimensional hydrogel structure with a channel width of 50-100 μm and a porosity of 80-90%, containing capture antibodies targeting different proteins with an affinity constant KD ≤ 10 -9 M, can realize the simultaneous detection of multiple proteins.

[0013] Furthermore, the enzyme-labeled secondary antibody-gold nanorod conjugate described in step S4 is prepared by click chemistry reaction (CuAAC), with a coupling efficiency of ≥95%, and the surface of the gold nanorods is modified with bovine serum albumin (BSA) to enhance stability.

[0014] Furthermore, the machine learning algorithm described in step S4 is a signal processing model based on a convolutional neural network (CNN). The input parameters include ECL spectral characteristics, reaction time curve and chip temperature data, and the output is the target protein concentration. The detection limit is as low as 0.1 pg / mL.

[0015] Furthermore, a protein detection preparation comprises:

[0016] (a) Targeted antibody-magnetic nanoparticle conjugate: Anti-human epidermal growth factor receptor 2 (HER2) was covalently coupled to the surface of Fe3O4 nanoparticles (particle size 30 nm), with a coupling density of ≥100 antibodies / particle;

[0017] (b) Signal amplification probe: horseradish peroxidase (HRP)-labeled goat anti-mouse IgG antibody-gold nanostar conjugate (number of branches 4-6, particle size 60-80 nm);

[0018] (c) Buffer system: a stable solution containing 20 mM Tris-HCl (pH 7.4), 0.1% Tween-20, 5% trehalose, and 1 mM EDTA.

[0019] Furthermore, the surface of the magnetic nanoparticles is modified with a dopamine-polyethylene glycol (DA-PEG) amphiphilic layer to achieve directional fixation of antibodies, and the antibody activity retention rate is ≥90%.

[0020] Furthermore, the branched structure of the gold nanostar is synthesized by a seed-mediated method, and the surface plasmon resonance (SPR) peak is located at 700-800 nm, forming a resonance energy transfer (CRET) system with the chemiluminescence spectrum of HRP (λmax=425 nm).

[0021] Furthermore, by adding 0.05% sodium azide and 0.1% ProClin300 to the buffer system, the antibody activity loss was ≤5% when stored at 4°C for 6 months.

[0022] Furthermore, the preparation is used for in situ detection of HER2 protein in circulating tumor cells (CTCs), and in combination with the described method, quantitative analysis of HER2 copy number (≤100 molecules) in single CTCs is achieved.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] This method achieves ultrasensitive single-molecule protein detection through efficient enrichment mediated by nanoporous materials, precise capture using a microfluidic antibody array, signal amplification using enzyme-labeled secondary antibodies and gold nanorods, and collaborative optimization using a machine learning algorithm. The method achieves a detection limit as low as 0.12 pg / mL, a single-cell detection error of ±7.8%, and batch reproducibility (CV = 4.2%) significantly superior to traditional methods. The nonspecific adsorption rate is only 1.1%, and the detection time is shortened to 1.2 hours, fully demonstrating the technological advantages of the collaborative innovation of nanomaterials, microfluidics, and algorithms. DETAILED DESCRIPTION

[0025] In order to better understand the technical content of the present invention, specific examples are provided below to further illustrate the present invention.

[0026] Unless otherwise specified, the experimental methods used in the examples of the present invention are all conventional methods.

[0027] Unless otherwise specified, the materials, reagents, etc. used in the examples of the present invention can be obtained from commercial sources.

[0028] Example 1

[0029] A protein detection method comprises the following experimental steps:

[0030] S1. Sample pretreatment: Serum (1 mL) from HER2-positive breast cancer patients was mixed with mesoporous silica-graphene oxide composite nanoporous material (50 μL, pore size 5-10 nm, surface grafted with PEG 3000 Da) and vortexed for 10 minutes to selectively capture HER2 protein.

[0031] S2. Immune response: Add HER2-targeting magnetic nanoparticles (particle size 25 nm, antibody density 120 antibodies / particle, 50 μL) and incubate with rotation at 4°C for 1 hour to form a complex.

[0032] S3. Microfluidic separation: The complex was injected into a microfluidic chip (channel width 80 μm, porosity 85%) containing a three-dimensional hydrogel antibody array, which contained a capture antibody targeting the HER2 protein with an affinity constant KD ≤ 10 -9 M, a 1T magnetic field is applied to drive the complex to move in the microfluidic chip.

[0033] S4. Signal amplification: Add enzyme-labeled secondary antibody-gold nanorod conjugate (HRP-goat anti-mouse IgG, surface-modified with BSA, gold nanorod aspect ratio 4:1), react in the dark for 30 minutes, and the ECL signal is enhanced 20 times.

[0034] S5. Quantitative detection: An electrochemiluminescence analyzer (detection wavelength 620 nm) combined with a convolutional neural network algorithm is used for analysis. The input parameters include ECL spectrum, reaction time curve, and temperature data, and the output is HER2 concentration (0.5 pg / mL-100 ng / mL).

[0035] Comparative Example 1 - Traditional magnetic bead method without nanoporous material

[0036] In step S1, ordinary superparamagnetic Fe3O4 nanoparticles (particle size 500 nm, no mesoporous structure) are used, and the remaining steps are the same as in Example 1.

[0037] Comparative Example 2 - Traditional Fluorescent Labeling Alternative Signal Amplification

[0038] In step S4, FITC-labeled secondary antibody was used instead (without gold nanorods and enzyme-catalyzed amplification), and the rest was the same as in Example 1.

[0039] Comparative Example 3-Conventional ELISA method

[0040] Experimental Design: The same samples were tested using a commercial HER2 ELISA kit (R&D Systems).

[0041] Comparative Example 4 - Multichannel competition without antibody array

[0042] In step S3, a common PDMS chip (without an antibody array) is used, and the complex is captured by passive diffusion.

[0043] Experimental results:

[0044]

[0045] Experimental data showed that Example 1 achieved a detection accuracy of ±7.8% for the HER2 copy number in a single CTC (the errors of Comparative Examples 1-4 were all >±19%) through efficient enrichment of mesoporous silica-graphene nanomaterials (detection limit of 0.12 pg / mL, a 23-fold increase over the traditional magnetic bead method (Comparative Example 1)), signal amplification of gold nanorod-enzyme-labeled secondary antibody conjugates (ECL signal enhancement of 20-fold, detection limit increased by 125-fold compared to the fluorescent labeling method (Comparative Example 2)), high-affinity capture of the three-dimensional hydrogel antibody array (cross-reaction rate <0.1%, nonspecific adsorption only 1.1%), and background correction of the machine learning algorithm (inter-batch CV 4.2%). The detection time was shortened to 1.2 hours (the ELISA method (Comparative Example 3) required 4 hours). Comparison shows that the lack of any core technology (such as nanomaterials, signal amplification, and antibody arrays) leads to a significant decline in performance (for example, the detection limit of comparative example 1 is 2.8 pg / mL, and the nonspecific adsorption of comparative example 4 is 12.4%), verifying the key role of the collaborative innovation of nanoenrichment-microfluidic separation-signal amplification-intelligent algorithm in the ultra-sensitive protein detection of the present invention. The overall performance far exceeds that of traditional methods (ELISA detection limit is 85 pg / mL), meeting the clinical needs of trace protein detection.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A protein detection method, characterized in that: The following steps are involved: S1. Sample pretreatment: The sample to be tested is mixed with a mesoporous silica-graphene oxide composite nanoporous material. The nanoporous material has a pore size of 5-10 nm and is grafted with polyethylene glycol chains with a molecular weight of 2000-5000 Da to selectively enrich the target protein. S2, immune response: adding magnetic nanoparticles with surface modified with targeting antibodies to form antibody-antigen-nanoparticle complexes; S3, microfluidic separation: The complex is injected into a microfluidic chip integrated with a three-dimensional hydrogel antibody array, and single-molecule capture is achieved by magnetic field drive; S4, signal amplification: adding an enzyme-labeled secondary antibody-gold nanorod conjugate, the gold nanorod aspect ratio is 3:1-5:1, the enzyme-labeled secondary antibody is HRP-goat anti-mouse IgG, and the surface of the enzyme-labeled secondary antibody-gold nanorod conjugate is modified with BSA, and the fluorescence signal is enhanced by localized surface plasmon resonance; S5. Quantitative detection: Signal intensity is detected based on electrochemiluminescence technology, and background interference is corrected in combination with a machine learning algorithm. The machine learning algorithm is a signal processing model based on a convolutional neural network algorithm. The input parameters include ECL spectral characteristics, reaction time curve, and chip temperature data. The output is the target protein concentration, with a detection limit of 0.12 pg / mL.

2. A protein detection method according to claim 1, characterized in that, The antibody array of the microfluidic chip in step S3 is a three-dimensional hydrogel structure with a channel width of 50-100 μm and a porosity of 80-90%. It contains capture antibodies targeting different proteins with an affinity constant KD ≤ 10 -9 M.

3. A protein detection method according to claim 1, characterized in that: The enzyme-labeled secondary antibody-gold nanorod conjugate described in step S4 is prepared by a click chemistry reaction, with a coupling efficiency of ≥95%, and bovine serum albumin is modified on the surface of the gold nanorods.

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