Aptamer sensing method based on matrix-target overall unit pattern recognition and application

Abstract

The invention discloses an aptamer sensing method based on matrix-target overall unit pattern recognition and application, the method is a biosensing method capable of synchronously resisting matrix interference and recognizing a target in a real sample, and the failure bottleneck of a traditional aptamer sensor in actual sample detection is broken through. A traditional'key and lock 'high-specificity sensing mode is abandoned, a plurality of aptamer sequences with different affinity are selected as recognition elements, a detection target and a sample matrix are integrally used as'recognition units', and a matrix mode of an artificial simulation olfaction system is adopted for sensing. According to response signals of different units, characteristic fingerprints of a target in different sample matrixes are obtained, and blind sample classification and target identification and detection under different matrix effects are realized through a linear discriminant analysis machine learning method.

Description

Technical Field

[0001] The present invention belongs to the field of biochemistry and sensor intersection technology, and specifically relates to an aptamer sensing method and application based on "matrix-target" overall unit pattern recognition. Background Art

[0002] Aptamers, oligonucleotide sequences derived through systematic evolution of ligands by exponential enrichment, offer numerous advantages over antibodies. They can be chemically synthesized in vitro and stored in a stable powder form. However, most aptamers are currently still in the laboratory stage, and their practical applications are limited to the same breadth as antibodies. A major limitation to their development is the complex matrix effect, which influences the spatial structure and conformation of the aptamer, thereby impairing target binding in practical applications.

[0003] Currently, traditional aptamer sensors use a "key-lock" principle, offering high specificity but being susceptible to matrix interference and limited to a single target, significantly interfering with the analysis and classification of actual samples. Array sensors, however, are based on a pattern recognition strategy and employ a multi-point, multi-information acquisition model. They can respond differently to a variety of analytes and, through specific models and cross-reactions, achieve classification and identification, as well as predictions for unknown samples. Summary of the Invention

[0004] To address the above-mentioned technical problems, the present invention provides an aptamer sensing method and application based on "matrix-target" overall unit pattern recognition. This method can simultaneously resist matrix interference and identify targets in real samples as a biosensing method, breaking through the failure bottleneck of traditional aptamer sensors in actual sample detection. Abandoning the traditional "key and lock" high-specificity sensing mode, multiple aptamer sequences with different affinities are selected as recognition elements, with the detection target and sample matrix as the "recognition unit". Matrix pattern sensing is adopted to artificially simulate the olfactory system. Based on the response signals of "different units", the characteristic fingerprint of the target in different sample matrices is obtained. Through the "linear discriminant analysis" machine learning method, blind sample classification, target identification and detection under different matrix effects are achieved.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: an aptamer sensing method based on "matrix-target" overall unit pattern recognition, At least three aptamers are used as probes, and the Apt-AuNPs colorimetric method or enzyme-linked immunosorbent assay is used to detect the absorbance value I of at least two test sample matrices. The absorbance value II of the matrix solution to which the target is added is then detected. The difference between the absorbance value I and the absorbance value II is used as the response signal of the array sensing experiment and the fingerprint of the colorimetric response. The data is processed using linear discriminant analysis technology, and the data is subjected to regression analysis using software to complete the model construction of the test samples.

[0006] Furthermore, the following steps are included: (1) Microplate preparation: Immobilize the target lactoferrin on the microplate; (2) Preparation of detection probes: at least two biotin-labeled sequences are mixed and incubated with an equal volume of SA-HRP enzyme to obtain detection probe solutions, wherein the biotin-labeled sequence is a sequence that specifically recognizes lactoferrin; (3) Aptamer sensor affinity detection; (4) Processing of test samples For the treatment of the sample to be tested, the sample to be tested is weighed and dissolved, stirred and mixed, centrifuged at low temperature, and the layer where the test object is located is aspirated to obtain the matrix solution of the sample to be tested and stored for future use; (5) Sample matrix array sensing detection steps: Add 80 nmol / L lactoferrin to at least two sample matrix solutions and mix well with the detection probe solution from the second step, incubate, add the mixed solution to the microwells, incubate, pour out, rinse with buffer, and pat dry; add a colorimetric substance and incubate, then add a stop reaction solution; measure the absorbance at 450 nm using a microplate reader, and define the difference between the absorbance of the sample matrix solution in the previous step as the blank matrix solution and the absorbance measured in this step as the response signal S, S = A blank – A conc , as the fingerprint of the system; (6) Linear discriminant analysis technology was used to statistically analyze the results and quantitatively identify the response patterns of targets in the sample matrix on the array sensor.

[0007] Furthermore, the biotin-labeled sequence in step (2) refers to a sequence that can specifically recognize the target, which can be the following four: Seq 1.1: 5'- TGG TGC TGC CCC TAG TCT CCG GCT GAT AGC TGC TTC TTG G -3'; Seq 1.2: 5'- AGG GCA GCG TTG TGT ACC GCA TCA TTC GTT CAG TTA CTC G -3'; Seq 1.3: 5'- GAC ATC TGG GCA GCT CCA AGA AGT AGC GTC CTC TCG CCG C -3'; Seq 2.7: 5'- GGG ACG GGC ATG GGC ACT GGG ATC ACC TCA AGC GGC ATG C -3'.

[0008] Furthermore, the fixation method in step (1) is as follows: lactoferrin dilution is added to the microplate, coated overnight, poured out and washed with PBS-T buffer and patted dry; then BSA solution is added for blocking, and after the blocking is completed at room temperature, poured out and rinsed with PBS-T buffer and patted dry, and stored at 4°C after vacuuming for later use.

[0009] Furthermore, the affinity detection method in step (3) is as follows: lactoferrin standard solutions of different concentrations are mixed with the detection probe solution and incubated. After the incubation is completed, the mixed solution is added to the microwell, and the incubation is continued. Then, the solution is poured out and washed with PBS-T buffer and patted dry; the aptamer sensor is obtained; TMB is added for incubation, and the solution is then added to stop the reaction; and the absorbance is measured at 450 nm using an enzyme reader.

[0010] An aptamer sensing method based on "substrate-target" overall unit pattern recognition is characterized in that a test solution containing β-LG and four different aptamer solutions are mixed and incubated at room temperature, the mixed solution is added to a microwell containing an AuNPs solution, mixed and incubated, and then a NaCl solution is added; after 5 minutes, the absorbance of the mixed solution at 520 nm and 620 nm is recorded using a microplate reader, and K is defined as OD 620nm / OD 520nm ΔK = K - K0 was calculated using the K value for the test solution containing β-LG and the K0 value for the blank solution without β-LG. Data were processed using SPSS Statistics 27 and Origin 2024. Regression analysis was performed using SPSS software, and the presence of multicollinearity in the independent variables was confirmed by analyzing the VIF value. A model was constructed for the training samples, and the data were analyzed using LDA. Predictions were also made for blind samples.

[0011] Furthermore, the sequences used for the four different aptamers were: β-1: 5′-AGCAGCACAGAGGTCAGATGTTCGGCCTTTGCGTTAACGAACTTCTAGCTATGCGGCGTACCTATGCGTGCTACCGTGAA-3′; β-4: 5'-CGACGATCGGACCGCAGTACCCACCCACCAGCCCCAACATCATGCCCATCCGTGTGTG-3'; β-6: 5'-GGGGTTGGGGTATGTATGGGGTTGGGGGGTTGGGGTATGTATGGGGTTGGGG-3'; β-7: 5'-ATACCAGCTTATTCAATTCGACGATCGGACCGCAGTACCCACCCACCAGCCCCAACATCATGCCCATCCGTGTGTGAGATAGTAAGTGCAATCT-3'.

[0012] An aptamer sensing method based on "substrate-target" overall unit pattern recognition is applied to the detection of proteins and small molecule compounds.

[0013] The present invention designs a pattern recognition-based colorimetric array aptamer sensor. Building on the enzyme-linked colorimetric aptamer sensor, it extracts multidimensional signals through direct competition to identify targets, sample matrices, and their mixtures, thereby avoiding matrix interference. This invention utilizes SA-HRP conjugated to a biotin-labeled sequence via a streptavidin-biotin reaction to form a detection probe. When the target lactoferrin is absent from the system, the detection probe specifically recognizes and binds to lactoferrin in the microplate. Due to the high level of HRP enzyme in the detection probe retained within the wells, the solution color changes to blue upon addition of TMB, and to yellow upon addition of H2SO4. When lactoferrin is present, the lactoferrin in the sample solution competes with the lactoferrin on the microplate for binding to the detection probe. Due to the low or no HRP enzyme in the detection probe retained within the wells, the solution color changes to light blue or colorless upon addition of TMB, and to light yellow or colorless upon addition of H2SO4. The concentration of lactoferrin in the sample is inversely proportional to the depth of color development. For validation, a pattern recognition model was created using 17 dairy matrix samples. Lactoferrin was used as the target protein for recognition, and four sequences with different affinities were selected as recognition probes. The color change of TMB (3,3',5,5'-tetramethylbenzidine) catalyzed by horseradish peroxidase was used as the response signal. Supervised analysis using linear discriminant analysis (LDA) in SPSS was used to identify lactoferrin in different dairy matrices, predict the identity of unknown samples, and predict the identity of unknown samples using LDA machine learning.

[0014] We have also proposed a novel pattern recognition-based array sensing strategy for β-LG detection. This strategy utilizes a previously reported β-LG nucleic acid aptamer as the DNA recognition unit and the colorimetric reaction of gold nanoparticles as the sensing principle, enabling target recognition by discriminating between different matrix interferences. As a proof-of-concept experiment, 15 samples were selected as pattern recognition matrices, with β-LG as the target protein. AuNPs and β-LG competed for the limited aptamer, as the aptamer readily binds to β-LG, leaving the remaining unbound aptamer coated on the surface of the AuNPs. In the presence of a high-concentration salt solution, the addition of varying concentrations of β-LG resulted in varying degrees of aggregation of the AuNPs, leading to a visible color change. Since aptamers are subject to varying matrix interferences when recognizing β-LG in samples, we recognized the model protein and matrix as a single entity, enabling protein detection while also mitigating matrix interference in aptamer sensors. By quantitatively analyzing the colorimetric signal and establishing a fingerprint using linear discriminant analysis (LDA), the proposed strategy enables accurate identification of β-LG in complex matrices and prediction of unknown samples. The proposed strategy is simple, sensitive, and exhibits high resistance to interference in complex biological environments, demonstrating excellent recognition efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The response pattern diagram of the four sequences to lactoferrin standard solutions with different concentrations; Figure 2 The response pattern diagram of the four sequences to 17 kinds of milk powder matrices; Figure 3 This is a diagram of the color changes after adding different matrices in the present invention; Figure 4 It is the LDA linear discriminant analysis result diagram of the present invention; Figure 5 A. Response diagram of four aptamers to 15 sample patterns; B. 2D-LDA plotted by two typical factors, all error bars are standard deviations of five tests. DETAILED DESCRIPTION To further illustrate the present invention, specific examples are provided below. It should be understood that these examples are only used to help understand the present invention and do not constitute a limitation of the scope of the present invention. In addition, it should be understood that after studying the present invention, those skilled in the art can make various improvements or adjustments, and these changes should also be considered to be within the scope of protection of the present invention.

[0016] Before introducing the specific embodiments of the present invention, it should be clear that the scope of protection of the present invention is not limited to the specific embodiments described below. At the same time, the terms used in the present invention are for the convenience of describing specific embodiments and are not used to limit the scope of the invention. Unless otherwise defined, all technical terms in the present invention should be interpreted according to the common understanding of those skilled in the art. For experimental methods not specifically listed in the examples, they should generally be operated according to conventional conditions or the recommended conditions of the equipment manufacturer. In addition to the specific methods, equipment and materials used in the examples, the implementation of the present invention can also be achieved by using existing technical solutions that are similar or equivalent to the methods, equipment and materials described in the examples.

[0017] Example 1 (1) Microplate preparation: Lactoferrin was diluted to 200 nmol / L, 100 μL was added to the microplate, coated overnight at 4°C, poured out and washed three times with 300 μL of 0.05% PBS-T buffer and patted dry; Add 200 μL of 2% BSA solution for blocking, block at room temperature for 1 h, pour out and wash three times with 0.05% PBS-T buffer and pat dry, vacuum and store at 4°C for later use.

[0018] (2) Preparation of detection probes Mix 50 μL of 50 nmol / L biotin-labeled sequence with an equal volume of 1:4000 diluted SA-HRP enzyme and incubate for 10 min to obtain the detection probe solution; The biotin-labeled sequences are four sequences that can specifically recognize lactoferrin: Seq 1.1: 5'- TGG TGC TGC CCC TAG TCT CCG GCT GAT AGC TGC TTC TTG G -3'; Seq 1.2: 5'- AGG GCA GCG TTG TGT ACC GCA TCA TTC GTT CAG TTA CTC G -3'; Seq 1.3: 5'- GAC ATC TGG GCA GCT CCA AGA AGT AGC GTC CTC TCG CCG C -3'; Seq 2.7: 5'- GGG ACG GGC ATG GGC ACT GGG ATC ACC TCA AGC GGC ATG C -3'; (3) Detection process Lactoferrin standard solutions of varying concentrations (0, 20, 40, 60, 80, 100, 120, 140, 160, 180, and 200 nmol / L) were mixed with the detection probe solution and incubated for 10 minutes. 100 μL of the mixed solution was then added to the microwells and incubated for 30 minutes. The mixture was then decanted and washed three times with 300 μL of 0.05% PBS-T buffer and patted dry to obtain the aptamer sensor. 100 μL of TMB was added and incubated for 10 minutes. The reaction was then stopped by adding 100 μL of 2 mol / L H₂SO₄ solution. The absorbance was measured at 450 nm using a microplate reader within 5 minutes. The absorbance for Seq1.1 was 20.7 nM, Seq1.2 was 584.1 nM, Seq1.3 was 64.9 nM, and Seq2.7 was 208.6 nM. These results indicate that the adaptor has good affinity.

[0019] (4) Treatment of the sample to be tested (blank matrix) For the treatment of the dairy products to be tested, we weighed an appropriate amount of milk powder and dissolved it in acetic acid (HAc) solution, stirred and mixed it, and centrifuged it at 8000 r / min at low temperature for 10 min to obtain three layers of samples, namely the fat layer, the clear liquid layer and the protein precipitation layer from top to bottom; the middle clear liquid layer was aspirated and passed through a 0.22 μm cellulose acetate (CA) membrane to obtain a blank matrix solution, which was stored at 4°C for later use.

[0020] (5) The sample matrix array sensing detection step: Seventeen types of milk powder were prepared into 80 nmol / L lactoferrin matrix solutions, which were then mixed with the detection probe solutions made from the four sequences and incubated for 10 minutes. 100 μL of the mixed solution was added to the microwells and incubated for 30 minutes. The solution was then poured out and washed three times with 300 μL of 0.05% PBS-T buffer and patted dry. 100 μL of TMB was added and incubated for 10 minutes, followed by the addition of 100 μL of 2 mol / L H2SO4 solution to stop the reaction. The absorbance was measured at 450 nm using a microplate reader within 5 minutes. The difference between the absorbance of the blank sample and the measured absorbance was defined as the response signal S (S = A blank – A conc ), as the fingerprint of the system, we get Figure 2 and Figure 3 .

[0021] (6) The results were analyzed using the statistical analysis technique - linear discriminant analysis (LDA), and the response spectra of the target protein (lactoferrin) in different types of milk powder matrices on the array sensor were quantitatively identified. The 17 types of milk powder matrices were tested 5 times on the array sensor, and the factor weights of the three sensor units were 83.88%, 13.59%, and 2.53%, respectively. The 85 training samples composed of 17 types of milk powder matrices can be clearly divided into 17 groups. According to the Jackknifed classification matrix results, the classification accuracy of all 17 types of milk powder matrices reached 100%. The scores of the two most significant factors were plotted to obtain the attached Figure 4 .

[0022] In order to further test the performance of the sensor by identifying unknown samples, 34 types of milk powder were identified according to the method of Example 1. The results showed that the recognition rate of the array sensor for the 34 blind samples reached 100%, as shown in Table 1.

[0023] Table 1 Responses of the array sensor to 34 unknown samples of milk powder matrix and identification results by LDA

[0024] Example 2 1. Materials, Reagents, and Instruments β-Lactoglobulin (β-lg, from bovine milk, purity ≥90%) and chloroauric acid (HAuCl4·3H2O) were purchased from Sigma-Aldrich Co., Ltd. (Beijing, China). Samples were purchased from local supermarkets; their specific brands are provided in the supplementary material. Sodium chloride, HCl, and NaOH were all analytically pure and purchased from Sinopharm Chemical Reagent Co., Ltd. (Beijing, China). L-Ascorbic acid was purchased from Shanghai McLean Biochemical Technology Co., Ltd. (Shanghai, China). The four β-LG aptamer (ssDNA) dry powders were purchased from Sangon Biotechnology Co., Ltd. (Shanghai, China), namely: 5′-AGCAGCACAGAGGTCAGATGTTCGGCCTTTGCGTTAACGAACTTCTAGCTATGCGGCGTACCTATGCGTGCTACCGTGAA-3′ (β-1); 5′-CGACGATCGGACCGCAGTACCCACCCACCAGCCCCAACATCATGCCCATCCGTGTGTG-3′ (β-4); 5′-GGGGTTGGGGTATGTATGGGGTTGGGGGGGTTGGGGTATGTATGGGGTTGGGG-3′ (β-6); and 5′-ATACCAGCTTATTCAATTCGACGATCGGACCGCAGTACCCACCCACCAGCCCCAACATCATGCCCATCCGTGTGTGAGATAGTAAGTGCAATCT-3′ (β-7).

[0025] 2. Apt-AuNPs colorimetric detection of β-LG A test solution containing 40 nM β-LG and four different aptamer solutions (500 nM) were mixed at a 1:1 ratio at room temperature for 10 minutes. 80 μL of the mixture was added to a microwell containing 100 μL of AuNPs solution, mixed thoroughly, and incubated for 5 minutes. Then, 10 μL of NaCl solution (700 mM) was added. After 5 minutes, the absorbance of the mixed solution at 520 nm and 620 nm was measured using a microplate reader. K = OD 620nm / OD 520nm The ΔK was calculated by comparing the test solution containing β-LG (K) and the blank solution without β-LG (K0) (ΔK = K-K0).

[0026] 3. Sample Pretreatment Weigh 500 mg / 1 mL of sample into a 50 mL centrifuge tube. Dissolve the sample in 10 mL of ultrapure water, heat in a 40°C water bath for 30 minutes, and then centrifuge at 8000 rpm for 10 minutes in a high-speed refrigerated centrifuge. Cool in an ice bath for 15-20 minutes, and skim off any suspended solids. Acidify the solution to pH 4.6 with 2 M HCl. Allow to settle to precipitate casein. After 20 minutes, centrifuge at 8000 rpm for 10 minutes. Collect the supernatant, filter through a 0.22 μm cellulose acetate membrane, and neutralize with 1 M NaOH. Dilute the solution with water in the appropriate proportions and store in a refrigerator at 4°C until needed. Prepare the sample matrix spiked with β-LG by adding a 40 nM β-LG standard.

[0027] 4. Identification of β-LG in Different Sample Matrices Four aptamers were used as probes in a colorimetric assay using Apt-AuNPs to detect 15 sample matrices with or without β-LG (40 nM). All experiments were repeated five times. The absorbance difference (ΔK = K - K0) was used as the response signal and colorimetric fingerprint of the array sensing experiment. Data were processed using SPSS Statistics 27 and Origin 2024. Regression analysis was performed using SPSS software, and VIF (Variance Inflation Factor) was used to determine whether the independent variables contained multicollinearity. Models were constructed for the training samples, and the data were analyzed using LDA, with predictions performed on blind samples.

[0028] 5. Results and Discussion Design principle of colorimetric array sensor based on Apt-AuNPs We chose a colorimetric method based on unmodified gold nanoparticles as our reaction system. The morphology of the prepared AuNPs was characterized by field emission transmission electron microscopy (HRTEM). The AuNPs are coated with a layer of citrate ions. The presence of high salt concentrations causes aggregation of the AuNPs, resulting in a color change. Single-stranded DNA (ssDNA) can coat the AuNPs, inhibiting this salt-induced aggregation and preventing a color change. However, the presence of an analyte disrupts the direct interaction between the AuNPs and the DNA, causing the AuNPs to lose the single-stranded DNA protection and aggregate and discolor. Different aptamer recognition units can produce different responses to the same sample, and matrix effects can also cause the same aptamer recognition unit to produce different colorimetric signals for different samples. We selected four aptamers with different affinities from a pool of reported β-LG aptamers as recognition units. Predictable differences in affinity make pattern recognition more likely. When the sample matrix containing β-LG is added to AuNPs together with the aptamer, the aptamer will specifically bind to β-LG, and the remaining aptamer will cover the surface of the AuNPs. After the addition of NaCl solution, the amount of remaining aptamer that has not bound to the target varies due to the different amounts of target added, so the AuNPs will aggregate to varying degrees, causing different color changes. Application in actual samples By "training" the database model to discriminate and classify known samples, the response pattern obtains better orthogonality, thereby distinguishing different samples. The typical factor LDA analysis generated by the β-LG added data set shows that when the β-LG addition amount is 40nM, the 75 training samples (15 matrices × 5 parallel groups) can be clearly divided into 15 groups ( Figure 5 B), effectively discriminating between different matrices, provides a reliable method for target identification in complex matrices. In a blinded experiment, 30 samples spiked with β-LG (40 nM) were randomly prepared, with no prior knowledge of the sample's identity. LDA discriminant function was used to predict the identity of the 30 unknown samples, achieving 100% accuracy (Table 2). This demonstrates the reproducibility and generalizability of this method.

[0029] Table 2 Identification and classification of unknown samples using LDA Table1 Identification and classification of unknown samples using LDA

[0030] In summary, we have successfully developed an Apt-AuNPs-based colorimetric array sensor. This method is sensitive, convenient, low-cost, and can be observed with the naked eye. LDA analysis of real-world sample test data yielded a unique fingerprint, successfully grouping 15 sample matrices spiked with 40 nM β-LG with 100% classification accuracy based on the Jackknifed classification matrix. Predictive LDA analysis also achieved 100% grouping accuracy for 30 unknown samples. This unique response pattern not only improves overall analytical performance but also ensures robust target recognition. Furthermore, the function and conformation of the aptamer can be tuned through a variety of innovative approaches, enabling the development of unique recognition elements for array sensors. Apt-AuNPs-based colorimetric array sensors hold great promise for application in food allergen detection.

Claims

1. An aptamer sensing method based on "substrate-target" overall unit pattern recognition, characterized in that: At least three aptamers are used as probes, and the absorbance values ​​I of the matrices of at least two test samples are detected by Apt-AuNPs colorimetry, enzyme-linked immunosorbent assay, or other biosensor methods. The absorbance value II of the matrix solution to which the target is added is then detected. The difference between the absorbance values ​​I and II is used as the response signal of the array sensing experiment and the fingerprint of the colorimetric response. The data is processed using linear discriminant analysis technology, and the data is subjected to regression analysis using software to complete the construction of a model for the test samples, which is then used to detect blind samples.

2. An aptamer sensing method based on "substrate-target" overall unit pattern recognition, characterized in that: The following steps are included: (1) Microplate preparation: Immobilize the target lactoferrin on the microplate; (2) Preparation of detection probes: at least two biotin-labeled sequences are mixed and incubated with an equal volume of SA-HRP enzyme to obtain detection probe solutions, wherein the biotin-labeled sequence is a sequence that specifically recognizes lactoferrin; (3) Aptamer sensor affinity detection; (4) Processing of test samples For the treatment of the sample to be tested, the sample to be tested is weighed and dissolved, stirred and mixed, centrifuged at low temperature, and the layer where the test object is located is aspirated to obtain the matrix solution of the sample to be tested and stored for future use; (5) Sample matrix array sensing detection steps: Adding a certain concentration of lactoferrin to at least two test sample matrix solutions and the detection probe solution in the second step, mixing and incubating, adding the mixed solution to the microwells, incubating, pouring out, adding buffer solution for rinsing, and patting dry; Add colorimetric substance and incubate, then add stop reaction solution; use microplate reader to measure its absorbance at 450nm, and define the difference between the absorbance of the sample matrix solution in the previous step as the blank matrix solution and the absorbance measured in this step as the response signal S, S = A blank – A conc , as the fingerprint of the system; (6) Linear discriminant analysis technology was used to statistically analyze the results, quantitatively identify the response spectrum of targets in the sample matrix on the array sensor, complete the model construction, and then detect the blind samples.

3. The aptamer sensing method based on "matrix-target" overall unit pattern recognition according to claim 2, characterized in that: The biotin-labeled sequence in step (2) refers to a sequence that can specifically recognize the target, and can be the following four: Seq 1.1: SEQ ID NO: 1; Seq 1.2: SEQ ID NO: 2; Seq 1.3: SEQ ID NO: 3; Seq 2.7: SEQ ID NO:

4.

4. The aptamer sensing method based on "matrix-target" overall unit pattern recognition according to claim 2, characterized in that: The fixation method in step (1) is as follows: add the lactoferrin dilution solution into the microplate, coat overnight, pour out and add PBS-T buffer solution to wash and pat dry; then add BSA solution for blocking, pour out and rinse with PBS-T buffer solution after room temperature blocking and pat dry, vacuum and store at 4°C for future use.

5. The aptamer sensing method based on "matrix-target" overall unit pattern recognition according to claim 2, characterized in that: The affinity detection method in step (3) is as follows: mixing lactoferrin standard solutions of different concentrations with the detection probe solution and incubating them. After the incubation is completed, the mixed solution is added to the microwells, and the incubation is continued. Then, the mixture is poured out and washed with PBS-T buffer and patted dry. obtaining an aptamer sensor; TMB was added for incubation, and then the solution was added to stop the reaction; the absorbance was measured at 450 nm using a microplate reader.

6. The aptamer sensing method based on "matrix-target" overall unit pattern recognition according to claim 1, characterized in that: The test solution containing β-LG and four different aptamer solutions were mixed and incubated at room temperature. The mixed solution was added to the microwells containing the AuNPs solution, mixed, incubated, and then NaCl solution was added; After 5 min, the absorbance of the mixed solution at 520 nm and 620 nm was recorded using a microplate reader. K = OD 620nm / OD 520nm ; The test solution K containing β-LG and the blank solution K0 without β-LG were used to calculate ΔK = K-K0; SPSS Statistics 27 and Origin 2024 were used to process the data, and regression analysis was performed using SPSS software. The presence of multicollinearity of the independent variables was confirmed by analyzing the VIF value; The model is constructed for the training samples, the data is analyzed using LDA, and predictions are made for the blind samples.

7. The aptamer sensing method based on "substrate-target" overall unit pattern recognition according to claim 6, characterized in that: The sequences used for the four different aptamers are: β-1: SEQ ID NO: 5; β-4: SEQ ID NO: 6; β-6: SEQ ID NO: 7; β-7: SEQ ID NO:

8.

8. Application of the aptamer sensing method based on "substrate-target" overall unit pattern recognition as claimed in claim 1 in detecting proteins and small molecule compounds.

Images