Protein detection method and detection preparation

Abstract

According to the protein detection method and the detection preparation provided by the invention, efficient enrichment of target protein is realized through the mesoporous silica-graphene oxide composite nano-porous material, and specific immunocapture is completed in combination with the magnetic nanoparticles modified by the targeted antibody; and the micro-fluidic chip of the three-dimensional hydrogel antibody array is used for realizing single molecule separation. An enzyme-labeled secondary antibody-gold nanorod conjugate is adopted for signal amplification, a fluorescence signal is enhanced through a plasma resonance effect, background noise is corrected in combination with a convolutional neural network algorithm, and the detection reliability is remarkably improved; experiments show that the detection limit of the method is as low as 0.12 pg / mL, the linear range covers 0.5-100,000 pg / mL, the single cell detection error is + / -7.8%, the batch repeatability (CV = 4.2%) is realized, the non-specific adsorption rate is only 1.1%, and the detection time is shortened to 1.2 hours. The detection method is suitable for ultra-sensitive detection of tumor markers, and especially has clinical application potential in CTCs in-situ analysis.

Description

Technical Field

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

[0002] At present, trace protein detection has important applications in fields such as disease diagnosis, biomarker screening, and single-cell analysis. However, the existing technologies still face multiple challenges. Traditional methods such as enzyme-linked immunosorbent assay (ELISA) are mature, but their detection limits are usually in the pg / mL to ng / mL level, making it difficult to meet the accurate quantification requirements of ultra-low abundance proteins (such as HER2 protein in circulating tumor cells). Moreover, they rely on a large amount of samples (≥100 μL), with cumbersome operations and long time consumption (4 - 6 hours). Microfluidic chip technology enables efficient separation of cells or proteins through microchannels. However, in existing technologies, there is a lack of specific capture units, and the non-specific adsorption rate is as high as 10% - 20%, resulting in a significant increase in background noise. In addition, protein detection at the single-cell level requires the combination of high-sensitivity signal amplification technologies. However, traditional fluorescence labeling methods are easily interfered by photobleaching, with fast signal intensity attenuation and a narrow dynamic linear range (usually ≤2 orders of magnitude).

[0003] In recent years, nanomaterials (such as gold nanorods, 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 mostly limited to a single technical path and do not achieve the coordinated optimization of nanomaterials, microfluidic separation, and intelligent algorithms. For example, although immunocapture based on magnetic nanoparticles can improve specificity, large-sized magnetic beads (≥100 nm) are prone to clogging microfluidic channels, and the separation efficiency in the passive capture mode is less than 60%. Moreover, data analysis methods relying solely on machine learning are difficult to overcome the problem of low signal-to-noise ratio due to the lack of underlying signal enhancement technology support. 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 implemented as follows: A protein detection method includes the following steps:

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

[0007] S2. Immune reaction: Add magnetic nanoparticles (with a particle size of 20 - 50 nm) modified with a targeting antibody on the surface to form an "antibody-antigen-nanoparticle" complex;

[0008] S3. Microfluidic separation: Inject the complex into a microfluidic chip integrated with an antibody array, and achieve single-molecule capture through magnetic field driving;

[0009] S4. Signal amplification: Introduce an enzyme-labeled secondary antibody-gold nanorod conjugate with an aspect ratio of 3:1 - 5:1, and enhance the fluorescence signal through local surface plasmon resonance (LSPR).

[0010] S5. Quantitative detection: Detect the signal intensity based on electrochemiluminescence (ECL) technology and correct background interference by combining with a machine learning algorithm.

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

[0012] Furthermore, the antibody array of the microfluidic chip described in step S3 is a three-dimensional hydrogel structure with a channel width of 50 - 100 μm and a porosity of 80 - 90%, and contains capture antibodies against different proteins with an affinity constant KD ≤ 10 -9 M, enabling multiplex protein synchronous detection.

[0013] Furthermore, the enzyme-labeled secondary antibody-gold nanorod conjugate in step S4 is prepared by click chemical reaction (CuAAC) with a coupling efficiency ≥ 95%, and the surface of the gold nanorod 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 features, reaction time curves, and chip temperature data, and the output is the concentration of the target protein with a detection limit as low as 0.1 pg / mL.

[0015] Furthermore, a protein detection preparation includes:

[0016] (a) Target antibody-magnetic nanoparticle conjugate: Covalently conjugate anti-human epidermal growth factor receptor 2 (HER2) on the surface of Fe3O4 nanoparticles (particle size 30 nm) with a coupling density ≥ 100 antibodies / particle;

[0017] (b) Signal amplification probe: Horseradish peroxidase (HRP)-labeled goat anti-mouse IgG antibody-gold nanostar conjugate (branch number 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) bilayer to achieve directional antibody immobilization with an antibody activity retention rate ≥ 90%.

[0020] Further, the branched structure of the gold nanostars is synthesized by the 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] Further, 0.05% sodium azide and 0.1% ProClin300 are added to the buffer system to achieve storage at 4°C for 6 months with an antibody activity loss of ≤5%.

[0022] Further, 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 beneficial effects of the present invention are as follows:

[0024] Through the efficient enrichment mediated by nanoporous materials, the precise capture of the microfluidic antibody array, the signal amplification of the enzyme-labeled secondary antibody-gold nanorods, and the collaborative optimization of the machine learning algorithm, the present invention realizes ultrasensitive single-molecule protein detection: the detection limit is as low as 0.12 pg / mL, the single-cell detection error is ±7.8%, and the batch repeatability (CV = 4.2%) is significantly better than the traditional method. The non-specific adsorption rate is only 1.1%, and the detection time is shortened to 1.2 hours, fully verifying the technical advantages of the collaborative innovation of nanomaterials, microfluidics, and algorithms. Detailed Embodiments

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

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

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

[0028] Example 1

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

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

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

[0032] S3, Microfluidic separation: Inject the complex into a microfluidic chip containing a three-dimensional hydrogel antibody array (channel width 80 μm, porosity 85%), which contains capture antibodies against the HER2 protein, with an affinity constant KD ≤ 10 -9 M, Apply a 1 T magnetic field to drive the movement of the complex in the microfluidic chip.

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

[0034] S5, Quantitative detection: Use an electrochemiluminescence analyzer (detection wavelength 620 nm) combined with a convolutional neural network algorithm for analysis. The input parameters include the ECL spectrum, reaction time curve, and temperature data, and the output is the 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, without mesoporous structure) are used, and the remaining steps are the same as in Example 1.

[0037] Comparative Example 2 - Replacement of signal amplification with traditional fluorescence labeling

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

[0039] Comparative Example 3 - Conventional ELISA method

[0040] Experimental design: Use a commercial HER2 ELISA kit (R&D Systems) to detect the same samples.

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

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

[0043] Experimental results:

[0044]

[0045]

[0046] Experimental data show that Example 1 achieves a detection accuracy of ±7.8% for the HER2 copy number in single CTCs (the error in Comparative Examples 1-4 is >±19%) through the efficient enrichment of mesoporous silica-graphene nanomaterials (detection limit 0.12 pg / mL, 23 times higher than the traditional magnetic bead method (Comparative Example 1)), the signal amplification of gold nanorod-enzyme-labeled secondary antibody conjugates (Claim 4, 20-fold enhancement of ECL signal, 125-fold improvement in detection limit compared to the fluorescence labeling method (Comparative Example 2)), the high-affinity capture of three-dimensional hydrogel antibody arrays (cross-reactivity rate <0.1%, non-specific adsorption only 1.1%), and the background correction of machine learning algorithms (inter-batch CV 4.2%). The detection time is shortened to 1.2 hours (the ELISA method (Comparative Example 3) requires 4 hours). The comparison shows that the absence of any core technology (such as nanomaterials, signal amplification, antibody arrays) leads to a significant decline in performance (such as the detection limit of 2.8 pg / mL in Comparative Example 1 and non-specific adsorption of 12.4% in Comparative Example 4), verifying the key role of the collaborative innovation of nano-enrichment - microfluidic separation - signal amplification - intelligent algorithm of the present invention in ultrasensitive protein detection. The overall performance far exceeds the traditional method (ELISA detection limit 85 pg / mL), meeting the clinical demand for trace protein detection.

[0047] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A protein detection method, characterized in that, It includes the following steps: S1. Sample pretreatment: Mix the sample to be tested with a lysis solution containing nanoporous material to selectively enrich the target protein; S2. Immune reaction: Add magnetic nanoparticles with a targeted antibody modified on the surface to form an antibody-antigen-nanoparticle complex; S3. Microfluidic separation: Inject the complex into a microfluidic chip integrated with an antibody array, and achieve single-molecule capture through magnetic field driving; S4. Signal amplification: Introduce an enzyme-labeled secondary antibody-gold nanorod conjugate with an aspect ratio of 3:1 - 5:1, and enhance the fluorescence signal through local surface plasmon resonance; S5. Quantitative detection: Detect the signal intensity based on electrochemiluminescence technology, and correct background interference by combining a machine learning algorithm.

2. The protein detection method according to claim 1, wherein The nanoporous material described in step S1 is a composite material of mesoporous silica with a pore size of 5 - 10 nm and graphene oxide, grafted with polyethylene glycol chains on the surface, and the molecular weight is 2000 - 5000 Da.

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

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

5. A protein detection method according to claim 1, characterized in that, The machine learning algorithm described in step S4 is a signal processing model based on a convolutional neural network. The input parameters include ECL spectral characteristics, reaction time curve, and chip temperature data, and the output is the concentration of the target protein, with a detection limit as low as 0.1 pg / mL.

6. A protein detection preparation, characterized in that, It includes: (a) Targeted antibody-magnetic nanoparticle conjugate: Covalently couple anti-human epidermal growth factor receptor 2 on the surface of Fe3O4 nanoparticles with a particle size of 20 - 40 nm, and the coupling density is ≥100 antibodies / particle; (b) Signal amplification probe: Horseradish peroxidase-labeled goat anti-mouse IgG antibody-gold nanostar conjugate, with 4 - 6 branches and a particle size of 60 - 80 nm; (c) Buffer system: A stable solution containing 20 mM Tris-HCl, 0.1% Tween-20, 5% trehalose, and 1 mM EDTA.

7. A protein detection preparation according to claim 6, characterized in that, The surface of the magnetic nanoparticles is modified with a dopamine-polyethylene glycol bilayer, and the antibody activity retention rate is ≥90%.

8. A protein detection preparation according to claim 6, wherein The branched structure of the gold nanostars is synthesized by the seed-mediated method, and the surface plasmon resonance (SPR) peak is located at 700 - 800 nm, forming a resonance energy transfer system with the chemiluminescence spectrum of HRP.

9. A protein detection preparation according to claim 6, characterized in that, 0.05% sodium azide and 0.1% ProClin300 are added to the buffer system, and it is stored at 2 - 4 °C for 6 months, with the antibody activity loss ≤5%.

10. The preparation according to any one of claims 6-9, characterized in that, The preparation is used for the in-situ detection of HER2 protein in tumor cells, and in combination with the method described in any one of claims 1 - 5, it realizes the quantitative analysis of the HER2 copy number in single CTCs.

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