Autoantibody spectrum detection platform based on liquid phase chip technology and application

Abstract

The invention relates to the technical field of liquid phase chips, in particular to an autoantibody spectrum detection platform based on a liquid phase chip technology, and the method comprises the following steps: preparation and functionalization of quantum dot encoding microspheres; the core-shell structure quantum dots are used as coding elements, and fluorescence coding combination of more than 1000 is realized by regulating and controlling the size, 3-10nm, Mn < 2 + > doping and spatial distribution of the quantum dots. The method has the advantages of super multi-target detection, super sensitivity, strong anti-interference capability and intelligent analysis, in the actual process, the core-shell structure quantum dots are adopted as coding elements, more than 1000 fluorescent coding combinations are realized by regulating and controlling the size, doping and spatial distribution of the quantum dots, high-density coding is realized by utilizing three-parameter coding and an orthogonal decoupling algorithm, and the method is suitable for large-scale industrial production. More types of autoantibodies can be detected at the same time, so that the detection flux is improved; the surfaces of the quantum dots are coated with silicon dioxide layers, and the fluorescence intensity is attenuated by lt under continuous laser irradiation; and the stability of a fluorescence signal in the detection process is ensured.

Description

Technical Field

[0001] The present invention relates to the field of liquid chip technology, and in particular to an autoantibody spectrum detection platform and application based on liquid chip technology. Background Art

[0002] Liquid chip technology, also known as suspension array technology, is a high-throughput multiplex detection technology based on a microsphere suspension system that combines flow cytometry, fluorescence encoding and molecular biology methods.

[0003] Traditional detection methods can often only detect one item at a time, and the number of codes in liquid chip technology is limited. For example, early liquid chip technology can reach up to 100 types. The detection throughput is significantly lower, and it is difficult to meet the needs of detecting more types of autoantibodies at the same time; secondly, the sensitivity of existing detection methods is relatively low. For example, the minimum detection concentration of some traditional methods is high, and extremely low concentrations of autoantibodies cannot be detected; furthermore, some will have problems with unstable fluorescence signals, such as rapid decay of fluorescence intensity under continuous laser irradiation, which affects the accuracy and reliability of the test results; existing detection methods do not adequately deal with nonspecific adsorption and interference of large molecular proteins in samples, resulting in deviations in test results. For low-concentration samples, possible effective concentration methods cannot increase the antibody concentration to within the detection sensitivity range, affecting the accuracy of the test; finally, the lack of advanced signal processing algorithms makes it difficult to effectively remove noise interference in the signal, resulting in a low signal-to-noise ratio, which affects the accuracy of the test results.

[0004] Therefore, there is an urgent need for an autoantibody spectrum detection platform and application based on liquid phase chip technology to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide an autoantibody spectrum detection platform and application based on liquid chip technology, which has the advantages of ultra-multi-target detection, ultra-sensitivity, strong anti-interference ability, and intelligent analysis, and solves the problems raised by the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an autoantibody spectrum detection platform based on liquid phase chip technology, wherein the method comprises the following steps: S1: Preparation and functionalization of quantum dot-encoded microspheres.

[0007] S1.1: Using core-shell quantum dots as coding elements, by adjusting the size of quantum dots, 3-10nm, doping Mn 2+ and spatial distribution, achieving more than 1000 fluorescence coding combinations; using the emission wavelength (520-700nm), fluorescence lifetime (10-100 ns) and intensity ratio (I 520 / I 620) three-parameter encoding, achieving high-density encoding through an orthogonal decoupling algorithm; carboxylated polystyrene microspheres (5μm in diameter) are surface-modified with polyethylene glycol (PEG) to reduce nonspecific adsorption while retaining carboxyl groups (-COOH) for antigen coupling; the quantum dots are surface-coated with a silica layer (5nm thick) to significantly enhance resistance to photobleaching (fluorescence intensity decay <5% / h under continuous laser irradiation).

[0008] S1.2: Specific antigens were covalently linked to the microsphere surface via EDC / NHS chemistry with a coupling density of 10 5 -10 6 For each antigen, a "click chemistry" strategy was used to introduce an azide group on the antigen surface, which reacted with the alkyne group on the microsphere surface through CuAAC to form a 1,2,3-triazole ring, thereby achieving directional arrangement of the antigen and improving the antibody binding efficiency. The antigen coupling efficiency (>95%) and activity (retaining more than 80% of the antigen binding ability) were verified by flow cytometry.

[0009] S2: Reaction between sample and microspheres.

[0010] S2.1: Nanomagnetic beads are used to adsorb immunoglobulin G in the sample to reduce nonspecific adsorption, and interference from large molecular proteins is removed through ultrafiltration membranes. Low-concentration samples are concentrated by vacuum centrifugation to increase the antibody concentration to within the detection sensitivity range.

[0011] S2.2: 50 μL reaction solution contains 10 5 The cells were mixed with coded microspheres, 10 μL of sample, and blocking agent; incubated at 37°C for 30 minutes, and antigen-antibody binding was promoted by a microfluidic oscillator; and antigen-antibody affinity constants were measured by surface plasmon resonance technology to ensure specificity.

[0012] S3: Surface-enhanced Raman scattering labeling and signal enhancement.

[0013] S3.1: Au@Ag NSs (80 nm in diameter) were surface-modified with Raman reporter molecules (e.g., 4-mercaptobenzoic acid, 4-MBA) to generate a characteristic Raman peak (1076 cm-1) under 633 nm laser excitation. -1 The tip structure on the surface of the nanostar produces a "hotspot effect", which enhances the Raman signal by 10 7 The detection limit was 0.1 fg / mL.

[0014] S3.2: Connect the anti-human IgG secondary antibody to the SERS nanostars through the biotin-streptavidin system to ensure that each antigen-antibody complex is bound to 5-10 nanostars; confirm the uniform distribution of nanostars on the surface of the complex by transmission electron microscopy.

[0015] S4: Multi-dimensional signal detection and analysis.

[0016] S4.1: Fluorescence detection is used to identify the coded information of the microspheres to determine the type of autoantibody being detected; Raman detection is used to quantitatively analyze the concentration of autoantibodies.

[0017] S4.2: Construct a convolutional neural network with fluorescence-Raman dual-modal signal images as input and antibody type and concentration as output; use a wavelet transform denoising algorithm to increase the signal-to-noise ratio from 10:1 to 1000:1.

[0018] S5: The detected signal data is transmitted to the computer system and processed and analyzed by professional software. The software will calculate the concentration of various autoantibodies in the sample based on the preset standard curve and algorithm, and generate a detailed test report.

[0019] The application of the autoantibody spectrum detection platform based on liquid phase chip technology includes early screening and diagnosis of autoimmune diseases, disease typing and precise treatment guidance, and identification of severe infection and autoimmune overlap syndrome.

[0020] Early screening and diagnosis of autoimmune diseases: patients with positive family history and unexplained fever / rash; abnormal antibody profile detection 1-2 years before symptoms appear.

[0021] Disease classification and precise treatment guidance: autoimmune disease classification; prediction of biological agent efficacy.

[0022] Differentiation between severe infection and autoimmune overlap syndrome: Differentiating the causes of fever between infection and autoimmune disease.

[0023] Furthermore, as a preferred embodiment of the present invention, in step S1.1, the core-shell structure quantum dots refer to CdSe and ZnS.

[0024] Furthermore, as a preferred embodiment of the present invention, in step S1.1, high-density encoding = emission wavelength*fluorescence lifetime*intensity ratio.

[0025] Furthermore, as a preferred embodiment of the present invention, in step S2.2, the frequency of the microfluidic oscillator is 2 Hz and the amplitude is 1 mm.

[0026] Furthermore, as a preferred embodiment of the present invention, in step S3.2, the nanostar spacing is less than 20 nm.

[0027] Furthermore, as a preferred embodiment of the present invention, in step S4.1, fluorescence detection uses a 405 nm laser to excite quantum dots, and the fluorescence coding is analyzed by a spectrometer with a resolution of 0.5 nm.

[0028] Furthermore, as a preferred embodiment of the present invention, in step S4.1, a 633 nm laser is used to excite the SERS signal, and the Raman spectrum is collected through a confocal microscope with a numerical aperture of 0.9.

[0029] Beneficial effects: The technical solution of the present application has the following technical effects: the present invention has the advantages of super-multi-target detection, ultra-sensitivity, strong anti-interference ability, and intelligent analysis. In the actual process, core-shell structure quantum dots are used as coding elements. By regulating the size, doping and spatial distribution of quantum dots, more than 1,000 fluorescence coding combinations are achieved. Three-parameter coding and orthogonal decoupling algorithm are used to achieve high-density coding, which can simultaneously detect more types of autoantibodies and improve the detection throughput; the surface of the quantum dots is coated with a silica layer, and under continuous laser irradiation, the fluorescence intensity decays by <5% / h, which ensures the stability of the fluorescence signal during the detection process and improves the reliability of the detection results; the specific antigen is covalently linked to the surface of the microspheres by EDC / NHS chemistry, and the coupling density is 10 5 -10 6 The method uses a "click chemistry" strategy to achieve directional arrangement of antigens. Flow cytometry verification shows that the antigen coupling efficiency is >95%, and more than 80% of the antigen binding capacity is retained, which can improve the antibody binding efficiency and enhance the sensitivity and specificity of the test. Nanomagnetic beads are used to adsorb immunoglobulin G in the sample to reduce nonspecific adsorption. The interference of large molecular proteins is removed by ultrafiltration membrane. Low-concentration samples are concentrated by vacuum centrifugation to increase the antibody concentration to within the detection sensitivity range, which can effectively improve the accuracy of the test and is particularly suitable for the detection of low-concentration samples. A microfluidic oscillator is used to promote antigen-antibody binding, and surface plasmon resonance technology is used to determine the antigen-antibody affinity constant to ensure the specificity of the reaction and make the test results more accurate and reliable. The surface of gold-silver core-shell nanostars is used to modify the Raman reporter molecules. The tip structure of the nanostar surface produces a "hotspot effect", which enhances the Raman signal by 10 7 times, with a detection limit of 0.1 fg / mL, which improves the sensitivity of detection and can detect extremely low concentrations of autoantibodies; the anti-human IgG secondary antibody is connected to the SERS nanostar through a biotin-streptavidin system to ensure that each antigen-antibody complex is bound to 5-10 nanostars, and transmission electron microscopy is used to confirm that the nanostars are evenly distributed on the surface of the complex, ensuring the consistency and stability of signal enhancement; fluorescence detection is used to identify the microsphere encoding information to determine the type of autoantibody, and Raman detection is used to quantitatively analyze the concentration of autoantibodies, realizing multi-dimensional signal detection and analysis, and improving the accuracy and comprehensiveness of detection; a convolutional neural network is constructed to process the fluorescence-Raman dual-modal signal image, and a wavelet transform denoising algorithm is used to increase the signal-to-noise ratio from 10:1 to 1000:1, effectively removing noise interference and improving the accuracy and efficiency of signal processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a flowchart of the present invention; Figure 2 This is a comparison chart of the detection sensitivity of the anti-Sm antibody of the present invention; Figure 3 This is a differential diagnosis diagram for infection and autoimmune disease of the present invention. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. In order to better understand the technical content of the present invention, specific embodiments are cited and explained in conjunction with the drawings as follows. Various aspects of the present invention are described in this disclosure with reference to the drawings, which show many illustrative embodiments. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any of many ways. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0032] As attached Figure 1 To the attached Figure 3 As shown: This embodiment provides an autoantibody spectrum detection platform based on liquid phase chip technology, and the method includes the following steps: S1: Preparation and functionalization of quantum dot-encoded microspheres.

[0033] S1.1: Using core-shell quantum dots as coding elements, by adjusting the size of quantum dots, 3-10nm, doping Mn 2+ and spatial distribution, achieving more than 1000 fluorescence coding combinations; using the emission wavelength (520-700nm), fluorescence lifetime (10-100 ns) and intensity ratio (I 520 / I 620 ) three-parameter encoding, achieving high-density encoding through an orthogonal decoupling algorithm; carboxylated polystyrene microspheres (5μm in diameter) are surface-modified with polyethylene glycol (PEG) to reduce nonspecific adsorption while retaining carboxyl groups (-COOH) for antigen coupling; the quantum dots are surface-coated with a silica layer (5nm thick) to significantly enhance resistance to photobleaching (fluorescence intensity decay <5% / h under continuous laser irradiation).

[0034] Furthermore, in step S1.1, the core-shell structure quantum dots refer to CdSe and ZnS.

[0035] Furthermore, in step S1.1, high-density encoding = emission wavelength * fluorescence lifetime * intensity ratio.

[0036] S1.2: Specific antigens were covalently linked to the microsphere surface via EDC / NHS chemistry with a coupling density of 10 5 -10 6 For each antigen, a "click chemistry" strategy was used to introduce an azide group on the antigen surface, which reacted with the alkyne group on the microsphere surface through CuAAC to form a 1,2,3-triazole ring, thereby achieving directional arrangement of the antigen and improving the antibody binding efficiency. The antigen coupling efficiency (>95%) and activity (retaining more than 80% of the antigen binding ability) were verified by flow cytometry.

[0037] S2: Reaction between sample and microspheres.

[0038] S2.1: Nanomagnetic beads are used to adsorb immunoglobulin G in the sample to reduce nonspecific adsorption, and interference from large molecular proteins is removed through ultrafiltration membranes. Low-concentration samples are concentrated by vacuum centrifugation to increase the antibody concentration to within the detection sensitivity range.

[0039] S2.2: 50 μL reaction solution contains 10 5 The cells were mixed with coded microspheres, 10 μL of sample, and blocking agent; incubated at 37°C for 30 minutes, and antigen-antibody binding was promoted by a microfluidic oscillator; and antigen-antibody affinity constants were measured by surface plasmon resonance technology to ensure specificity.

[0040] Furthermore, in step S2.2, the frequency of the microfluidic oscillator is 2 Hz and the amplitude is 1 mm.

[0041] S3: Surface-enhanced Raman scattering labeling and signal enhancement.

[0042] S3.1: Au@Ag NSs (80 nm in diameter) were surface-modified with Raman reporter molecules (e.g., 4-mercaptobenzoic acid, 4-MBA) to generate a characteristic Raman peak (1076 cm-1) under 633 nm laser excitation. -1 The tip structure on the surface of the nanostar produces a "hotspot effect", which enhances the Raman signal by 10 7 The detection limit was 0.1 fg / mL.

[0043] S3.2: Connect the anti-human IgG secondary antibody to the SERS nanostars through the biotin-streptavidin system to ensure that each antigen-antibody complex is bound to 5-10 nanostars; confirm the uniform distribution of nanostars on the surface of the complex by transmission electron microscopy.

[0044] Furthermore, in step S3.2, the nanostar spacing is less than 20 nm.

[0045] S4: Multi-dimensional signal detection and analysis.

[0046] S4.1: Fluorescence detection is used to identify the coded information of the microspheres to determine the type of autoantibody being detected; Raman detection is used to quantitatively analyze the concentration of autoantibodies.

[0047] Furthermore, in step S4.1, fluorescence detection uses a 405 nm laser to excite the quantum dots, and the fluorescence code is analyzed by a spectrometer with a resolution of 0.5 nm.

[0048] Furthermore, in step S4.1, a 633 nm laser is used to excite the SERS signal, and the Raman spectrum is collected through a confocal microscope with a numerical aperture of 0.9.

[0049] S4.2: Construct a convolutional neural network with fluorescence-Raman dual-modal signal images as input and antibody type and concentration as output; use a wavelet transform denoising algorithm to increase the signal-to-noise ratio from 10:1 to 1000:1.

[0050] S5: The detected signal data is transmitted to the computer system and processed and analyzed by professional software. The software will calculate the concentration of various autoantibodies in the sample based on the preset standard curve and algorithm, and generate a detailed test report.

[0051] The application of the autoantibody spectrum detection platform based on liquid phase chip technology includes early screening and diagnosis of autoimmune diseases, disease typing and precise treatment guidance, and identification of severe infection and autoimmune overlap syndrome.

[0052] Early screening and diagnosis of autoimmune diseases Application scenarios: Patients with positive family history and unexplained fever / rash; abnormal antibody profile detected 1-2 years before symptoms appear.

[0053] Implementation plan: 1. Sample collection: Collect serum / plasma samples (200 μL) from patients and transport them to the laboratory at low temperature.

[0054] Automated detection: A microfluidic chip was used to complete sample pretreatment (magnetic bead adsorption of IgG, ultrafiltration concentration), microsphere reaction (incubation at 37°C for 30 min), and SERS labeling (15 min).

[0055] Signals are collected synchronously through a dual-modal optical system (fluorescence + Raman), and AI algorithms analyze data in real time.

[0056] Report generation: Outputs antibody spectrum heat map and clinical recommendations (such as "anti-dsDNA antibodies are positive, referral to rheumatology and immunology department is recommended").

[0057] Clinical value: In the diagnosis of SLE, the sensitivity of anti-Sm antibody detection is 3 times higher than that of traditional ELISA (92% vs. 68%).

[0058] The detection time is shortened to 2 hours (traditional methods take 2-3 days).

[0059] Disease classification and precise treatment guidance Application scenarios: Autoimmune disease classification; prediction of biological agent efficacy.

[0060] Implementation plan: 1. Multi-target detection: A single test covers 1,000 antibodies, including autoantibodies, anti-drug antibodies and cytokines.

[0061] Dynamic monitoring: Detect the antibody spectrum before treatment and at 1 month, 3 months, and 6 months after treatment, and draw the efficacy curve.

[0062] AI-assisted decision-making: Input antibody spectrum data and combine it with the patient's genotype (such as HLA-DRB1*0301) to generate a personalized treatment plan.

[0063] Example: If anti-dsDNA antibodies continue to rise and anti-C1q antibodies are positive, it is recommended to increase the dose of immunosuppressants.

[0064] Clinical value: In rheumatoid arthritis, combined detection of anti-CCP and anti-MCV antibodies can increase the accuracy of predicting disease activity to 95%.

[0065] Reduce the rate of ineffective treatment (e.g., 30% of SLE patients fail treatment due to antibody spectrum mismatch).

[0066] Differentiation between severe infection and autoimmune overlap syndrome Application scenario: Differentiating the causes of fever in infection (sepsis) and autoimmune disease (adult-onset Still's disease).

[0067] Implementation plan: 1. Combined testing: Simultaneous detection of infection markers (such as PCT, IL-6) and autoantibodies (such as anti-ferritin antibodies).

[0068] Multimodal analysis: Fluorescence signals analyze antibody types, Raman signals quantify antibody concentrations, and comprehensive judgments are made based on infection marker levels.

[0069] Differential diagnosis model: Build a machine learning model (such as random forest), input the antibody spectrum and infection indicators, and output the differential diagnosis probability.

[0070] Clinical value: Among patients with fever awaiting investigation, the accuracy of differential diagnosis reached 90% (traditional methods were only 65%).

[0071] Avoid excessive immunosuppressive therapy caused by misdiagnosis (such as misdiagnosing infection as SLE and using hormones).

[0072] It should be noted that, in this document, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0073] While the present invention has been disclosed above with reference to preferred embodiments, this is not intended to limit the present invention. Persons skilled in the art will readily appreciate that various modifications and variations can be made without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. An autoantibody spectrum detection platform based on liquid phase chip technology, characterized by: The method comprises the following steps: S1: Preparation and functionalization of quantum dot-encoded microspheres; S1.1: Using core-shell quantum dots as coding elements, by adjusting the size of quantum dots, 3-10nm, doping Mn 2+ and spatial distribution, achieving more than 1,000 fluorescence coding combinations; using the three-parameter encoding of quantum dots, namely emission wavelength, fluorescence lifetime and intensity ratio, to achieve high-density encoding through orthogonal decoupling algorithm; using carboxylated polystyrene microspheres, the surface is modified with polyethylene glycol to reduce nonspecific adsorption while retaining the carboxyl group for antigen coupling; the surface of the quantum dots is coated with a silica layer; S1.2: Specific antigens were covalently linked to the microsphere surface via EDC / NHS chemistry with a coupling density of 10 5 -10 6 For each antigen, a "click chemistry" strategy was used to introduce an azide group on the antigen surface. This reacted with the alkyne group on the microsphere surface through CuAAC to form a 1,2,3-triazole ring, achieving directional arrangement of the antigen and improving antibody binding efficiency. Flow cytometry was used to verify the antigen coupling efficiency and activity. S2: reaction between sample and microspheres; S2.1: Nanomagnetic beads are used to absorb immunoglobulin G in the sample, and interference from large molecular proteins is removed through an ultrafiltration membrane. Low-concentration samples are concentrated by vacuum centrifugation. S2.2: 50 μL reaction solution contains 10 5 The cells were then incubated with 10 μL of sample and blocking agent at 37°C for 30 min, and antigen-antibody binding was promoted by microfluidic oscillator. Surface plasmon resonance was used to determine the antigen-antibody affinity constant. S3: Surface-enhanced Raman scattering labeling and signal enhancement; S3.1: Using gold-silver core-shell nanostars, the surface is modified with Raman reporter molecules, which produce characteristic Raman peaks under 633nm laser excitation; the tip structure on the surface of the nanostar produces a "hotspot effect", which enhances the Raman signal by 10 7 times, and the detection limit reached 0.1 fg / mL; S3.2: Link the anti-human IgG secondary antibody to the SERS nanostars using a biotin-streptavidin system, ensuring that 5-10 nanostars are bound to each antigen-antibody complex. Confirm the uniform distribution of the nanostars on the surface of the complex using transmission electron microscopy. S4: Multi-dimensional signal detection and analysis; S4.1: Fluorescence detection is used to identify the encoded information of the microspheres; Raman detection is used to quantify the concentration of autoantibodies; S4.2: Construct a convolutional neural network with fluorescence-Raman dual-modal signal images as input and antibody type and concentration as output. Use a wavelet transform denoising algorithm to improve the signal-to-noise ratio from 10:1 to 1000:

1. S5: The detected signal data is transmitted to the computer system and processed and analyzed by professional software. The software will calculate the concentration of various autoantibodies in the sample based on the preset standard curve and algorithm, and generate a detailed test report.

2. Application of an autoantibody spectrum detection platform based on liquid phase chip technology, characterized by: Including early screening and diagnosis of autoimmune diseases, disease classification and precise treatment guidance, and identification of severe infection and autoimmune overlap syndrome; Early screening and diagnosis of autoimmune diseases: patients with positive family history and unexplained fever / rash; abnormal antibody profile detection 1-2 years before symptoms appear; Disease classification and precise treatment guidance: autoimmune disease classification; prediction of biological agent efficacy; Differentiation between severe infection and autoimmune overlap syndrome: Differentiating the causes of fever between infection and autoimmune disease.

3. The autoantibody spectrum detection platform based on liquid phase chip technology according to claim 1, characterized in that: In the step S1.1, the core-shell structure quantum dots refer to CdSe and ZnS.

4. The autoantibody spectrum detection platform based on liquid phase chip technology according to claim 1, characterized in that: In step S1.1, high-density encoding = emission wavelength * fluorescence lifetime * intensity ratio.

5. The autoantibody spectrum detection platform based on liquid phase chip technology according to claim 1, characterized in that: In step S2.2, the frequency of the microfluidic oscillator is 2 Hz and the amplitude is 1 mm.

6. The autoantibody spectrum detection platform based on liquid phase chip technology according to claim 1, characterized in that: In step S3.2, the distance between nanostars is less than 20 nm.

7. The autoantibody spectrum detection platform based on liquid phase chip technology according to claim 1, characterized in that: In step S4.1, fluorescence detection uses a 405 nm laser to excite quantum dots, and a spectrometer with a resolution of 0.5 nm is used to analyze the fluorescence code.

8. The autoantibody spectrum detection platform based on liquid phase chip technology according to claim 1, characterized in that: In step S4.1, a 633 nm laser is used to excite the SERS signal, and the Raman spectrum is collected through a confocal microscope with a numerical aperture of 0.9.

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