Three-dimensional dna nano-probe detection method based on surface-enhanced raman spectroscopy

Abstract

This invention relates to the field of chemical detection technology, specifically to a method for detecting three-dimensional DNA nanoprobes based on surface-enhanced Raman spectroscopy. The method includes: acquiring background spectral data, reference spectral data, and target spectral data; acquiring Raman wavelength, resonance wavelength, and characteristic wavelength based on peaks in all reference spectral data; acquiring reconstructed spectral data for each cancer cell tissue slice based on the background and target spectral data; and acquiring the degree of gold nanoparticle binding in the reconstructed spectral data of the cancer cell tissue slices based on the reconstructed spectral data of all cancer cell tissue slices and the target spectral data, thereby obtaining the true three-dimensional DNA nanoprobe content of the cancer cell tissue slices. This invention improves the accuracy of three-dimensional DNA nanoprobe detection by constructing a standardized spectral analysis system, decomposing the characteristic signals of the reconstructed probes, and introducing a gold nanoparticle binding degree correction model.

Description

Technical Field

[0001] This invention relates to the field of chemical detection technology, specifically to a three-dimensional DNA nanoprobe detection method based on surface-enhanced Raman spectroscopy. Background Technology

[0002] Three-dimensional DNA nanoprobes are molecular probes with a three-dimensional structure constructed based on DNA nanotechnology. They are assembled into three-dimensional structures (such as tetrahedrons, cubes, etc.) by precisely designed DNA sequences, and their surfaces are modified with recognition units (such as aptamers, antibodies, etc.), enabling them to specifically recognize and bind to target molecules. However, since Raman scattering is an extremely weak phenomenon, surface-enhanced Raman spectroscopy is used to detect the content of three-dimensional DNA nanoprobes in order to improve detection accuracy.

[0003] Traditional surface-enhanced Raman spectroscopy (SERS) methods typically rely on the premise of good binding between nanoparticles and the analyte, estimating the content of the target molecule through the intensity of a single characteristic peak. However, in practical applications, the degree of binding between DNA nanoprobes and metal nanoparticles is often uncertain. If the binding is not tight or the distribution is uneven, the characteristic peak signal will be weak, resulting in an underestimation of the content. More importantly, it is often difficult to directly determine whether the binding is sufficient during the detection process, which limits the detection accuracy and makes it difficult to accurately detect the content of three-dimensional DNA nanoprobes. Summary of the Invention

[0004] This invention provides a method for detecting three-dimensional DNA nanoprobes based on surface-enhanced Raman spectroscopy, in order to solve the existing problem that traditional surface-enhanced Raman spectroscopy methods cannot accurately detect the content of three-dimensional DNA nanoprobes.

[0005] The three-dimensional DNA nanoprobe detection method based on surface-enhanced Raman spectroscopy of the present invention adopts the following technical solution:

[0006] Includes the following steps:

[0007] Based on cancer cell tissue sections, three-dimensional DNA nanoprobe solutions, and gold nanoparticle concentration solutions, several background spectral data, several reference spectral data, and several target spectral data were obtained.

[0008] Based on the peaks in all reference spectral data, several Raman wavelengths are obtained and divided into resonance wavelengths and characteristic wavelengths. A Raman matrix is ​​constructed based on the background spectral data and the target spectral data. The Raman matrix is ​​decomposed to obtain the base spectrum matrix and the weight matrix. The probe characterization factor of each row in the base spectrum matrix is ​​obtained based on the base spectrum matrix and the weight matrix. The baseline row set is selected from the base spectrum matrix and reconstructed by combining the weight matrix to obtain the reconstructed spectral data of each cancer cell tissue slice.

[0009] Based on the amplitude of the reconstructed spectral data of all cancer cell tissue sections and the target spectral data at each characteristic wavelength, the confidence level of each characteristic wavelength is obtained; based on the amplitude of the reconstructed spectral data of all cancer cell tissue sections and the target spectral data at the resonance wavelength, combined with the confidence level of each characteristic wavelength, the degree of gold nanoparticle binding in the reconstructed spectral data of the cancer cell tissue sections is obtained.

[0010] The true three-dimensional DNA nanoprobe content of cancer cell tissue sections was obtained by determining the degree of gold nanoparticle binding based on the reconstructed spectral data of cancer cell tissue sections.

[0011] Preferably, the specific method for obtaining several background spectral data, several reference spectral data, and several target spectral data based on cancer cell tissue slices, three-dimensional DNA nanoprobe solution, and gold nanoparticle concentration solution includes:

[0012] Preset number of cancer cell tissue slices , concentration of three-dimensional DNA nanoprobe solution Concentration of nano-gold particles in solution Initiation ratio of probe nano-gold mixture Probe nano-gold mixing termination ratio Probe nanogold hybrid iteration step size ;

[0013] preparation We analyzed cancer cell tissue slices and used a Raman spectrometer to obtain the Raman spectral data for each cancer cell tissue slice, which were recorded as background spectral data.

[0014] Preparation concentration is A molar concentration of three-dimensional DNA nanoprobe solution with a concentration of A molar concentration solution of gold nanoparticles; the gold nanoparticle concentration solution is mixed with a three-dimensional DNA nanoprobe solution at... The gold nanoparticle concentration solution and the three-dimensional DNA nanoprobe solution were mixed in a certain proportion to obtain the first mixed solution; the concentration of the gold nanoparticles was then mixed with that of the three-dimensional DNA nanoprobe solution at a certain ratio to obtain the first mixed solution. The solutions were mixed in a specific ratio to obtain a second mixed solution, and so on, until the concentration of the gold nanoparticles and the three-dimensional DNA nanoprobe solution reached a certain ratio. The solutions were mixed in the specified proportions to obtain the final mixed solution; Raman spectra of each mixed solution were acquired using a Raman spectrometer and recorded as reference spectra.

[0015] Each cancer cell tissue slice was bound to a three-dimensional DNA nanoprobe solution, and Raman spectroscopy was used to acquire the Raman spectral data of each cancer cell tissue slice bound to the three-dimensional DNA nanoprobe solution. These data were recorded as the target spectral data.

[0016] Preferably, the specific method for obtaining several Raman wavelengths based on the peaks in all reference spectral data and dividing the Raman wavelengths into resonance wavelengths and characteristic wavelengths includes:

[0017] Preset a wavelength offset range For any two peaks in any two reference spectral data, if the absolute value of the difference between the corresponding wavelengths of the two peaks in the two reference spectral data is less than... Then, the two peaks in the two reference spectral data are classified into the same Raman peak group to obtain several Raman peak groups; for any Raman peak group, the mean of the wavelengths corresponding to all peaks in the Raman peak group is taken as the corresponding wavelength of the Raman peak group, and the corresponding wavelengths of all Raman peak groups are recorded as Raman wavelengths.

[0018] The ratio of the mean amplitude of all peaks in the Raman peak group to the mean amplitude of all peaks in all Raman peak groups is multiplied by the standard deviation of the amplitude of all peaks in the Raman peak group, and the product is taken as the resonance degree of the Raman peak group. The resonance degree of all Raman peak groups is obtained, and the Raman peak group with the highest resonance degree is recorded as the resonance peak group. The remaining Raman peak groups are recorded as characteristic peak groups. The mean wavelength of all peaks in the resonance peak group is taken as the resonance wavelength. The mean wavelength of all peaks in the characteristic peak group is taken as the characteristic wavelength.

[0019] Preferably, the specific method for constructing the Raman matrix based on the background spectral data and the target spectral data is as follows:

[0020] Build a An empty matrix, the The number of cancer cell tissue slices, the The number of data points in the spectral data is equal to the number of data points in the spectral data. Each background spectral data or each target spectral data is placed as a row in an empty matrix to construct the Raman matrix.

[0021] Preferably, the method for obtaining the fundamental spectrum matrix and weight matrix by decomposing the Raman matrix, and obtaining the probe characterization factor for each row of the fundamental spectrum matrix based on the fundamental spectrum matrix and weight matrix, includes the following specific methods:

[0022] The Raman matrix is ​​decomposed by NMF to obtain the basis matrix and coefficient matrix of the Raman matrix, which are denoted as the basis matrix and weight matrix, respectively. The corresponding element of each element in the basis matrix in the weight matrix is ​​used as the weight of each element in the basis matrix.

[0023] For the th in the fundamental spectrum matrix In the middle of the line The nth element in the Raman matrix The cancer cell tissue slices corresponding to the row are denoted as feature slices, and the Raman matrix is ​​divided into two parts except for the first part. The cancer cell tissue slices corresponding to other rows outside the designated row are the spectral data of the feature slices. The corresponding row index is denoted as the feature row index. The first cell in the base spectrum matrix located at the feature row index is... The element, as the th element in the fundamental spectrum matrix In the middle of the line The corresponding element of each element;

[0024] Based on the first row of all rows in the fundamental spectrum matrix The element and its corresponding weight, all Raman wavelengths, and the element in the fundamental spectrum matrix. The amplitude and corresponding wavelength of each element in the row are used to obtain the first element in the fundamental spectrum matrix. The probe characterization factor of the row.

[0025] Preferably, the step of obtaining the first fundamental spectrum matrix is... The probe characterization factors for the row include the following specific methods:

[0026]

[0027] In the formula, Represents the first in the fundamental spectrum matrix The probe characterization factor of the row; Indicates the column number in the fundamental spectrum matrix; Indicates the number of Raman wavelengths; Represents the first in the fundamental spectrum matrix In the middle of the line The weight of each element; Represents the first in the fundamental spectrum matrix In the middle of the line The weight of each element corresponding to another element; Represents the first in the fundamental spectrum matrix In the middle of the line The magnitude of each element; Represents the first in the fundamental spectrum matrix In the middle of the line Each element corresponds to a wavelength; Indicates the first One Raman wavelength; This represents the function that takes the absolute value. Indicates traversal The result of the operation is taken by the function that minimizes the value.

[0028] Preferably, the specific method for obtaining the probe characterization factor of each row in the fundamental spectrum matrix based on the fundamental spectrum matrix and the weight matrix, selecting a set of baseline rows from the fundamental spectrum matrix, and reconstructing the set of baseline rows in combination with the weight matrix to obtain the reconstructed spectral data of each cancer cell tissue slice includes:

[0029] Based on the probe characterization factor of each row in the Raman matrix, all elements in the Raman matrix are divided into two classes by the maximum inter-class variance method, resulting in two row sets; the row set with the largest mean of probe characterization factors of all rows in the row set is denoted as the baseline row set.

[0030] Based on the weights corresponding to each element in each row of the baseline row set, the baseline row set is reconstructed to obtain the reconstructed spectral data of each cancer cell tissue slice.

[0031] Preferably, the method for obtaining the confidence level of each characteristic wavelength based on the amplitude of the reconstructed spectral data of all cancer cell tissue slices and the target spectral data at each characteristic wavelength includes:

[0032]

[0033] In the formula, Indicates the first Reconstruction confidence at each characteristic wavelength; This indicates the amount of reconstructed spectral data from cancer cell tissue sections; Indicates the number of characteristic wavelengths; Indicates the first Reconstructed spectral data of cancer cell tissue slices in the first... Amplitude at each characteristic wavelength; Indicates the first The target spectral data corresponding to the cancer cell tissue slice in the _th Amplitude at each characteristic wavelength; Indicates the first Reconstructed spectral data of cancer cell tissue slices in the first... Amplitude at each characteristic wavelength; Indicates the first The target spectral data corresponding to the cancer cell tissue slice in the _th Amplitude at each characteristic wavelength; Indicates traversal The result is then used to take the mean function.

[0034] Preferably, the specific method for obtaining the degree of gold nanoparticle binding in the reconstructed spectral data of cancer cell tissue sections based on the amplitude of the reconstructed spectral data of all cancer cell tissue sections and the target spectral data at the resonance wavelength, combined with the confidence level of each characteristic wavelength, includes:

[0035] For the The characteristic wavelength, for the _th ... The amplitude of the reconstructed spectral data of the first cancer cell tissue slice at the resonance wavelength is compared with that of the second. The ratio of amplitudes at each characteristic wavelength is normalized to its maximum value, and the result of the maximum value normalization is taken as the first value. Reconstructed spectral data of cancer cell tissue slices in the first... The binding coefficient of gold nanoparticles at each characteristic wavelength;

[0036] For the first The reconstruction confidence at the i-th characteristic wavelength is linearly normalized, and the result of the linear normalization is compared with the i-th characteristic wavelength. Reconstructed spectral data of cancer cell tissue slices in the first... The product of the nano-gold binding coefficients at each characteristic wavelength is used as the product of the first characteristic wavelength. Reconstructed spectral data of cancer cell tissue slices in the first... The nano-gold binding factor with the characteristic wavelength will bind the first... The reconstructed spectral data of a cancer cell tissue slice are summed at all characteristic wavelengths for the gold nanoparticle binding factor, as the first... The degree of gold nanoparticle binding in reconstructed spectral data of a cancer cell tissue slice.

[0037] Preferably, the method for obtaining the true three-dimensional DNA nanoprobe content of cancer cell tissue slices based on the degree of gold nanoparticle binding from the reconstructed spectral data of cancer cell tissue slices includes:

[0038]

[0039] In the formula, Indicates the first The actual three-dimensional DNA nanoprobe content of a cancer cell tissue slice; To assess existing parameters in the three-dimensional DNA nanoprobe content of cancer cell tissue sections using traditional regression equations; This also provides an existing parameter for evaluating the content of three-dimensional DNA nanoprobes in cancer cell tissue sections using traditional regression equations; Indicates the first The degree of gold nanoparticle binding in reconstructed spectral data of a cancer cell tissue slice; Indicates the first The amplitude of the reconstructed spectral data of a cancer cell tissue slice at the resonance wavelength.

[0040] The beneficial effects of the technical solution of this invention are as follows: This application obtains several background spectral data, several reference spectral data, and several target spectral data based on cancer cell tissue slices, three-dimensional DNA nanoprobe solutions, and nanogold particle concentration solutions. To correct the signal deviation caused by uneven binding of the probe and nanogold, a standardized spectral analysis system needs to be established. First, a Raman wavelength set is constructed based on the reference spectrum as a unified benchmark, and the resonance wavelength characterizing the strongest enhancement ability and the characteristic wavelength reflecting the probe characteristics are distinguished from it. Subsequently, by constructing and decomposing the Raman matrix, the fundamental spectral matrix representing the basic spectral mode and its weights are obtained, and then the probe characterization factor is calculated to quantify the correlation between each mode and the probe component. Finally, high-factor rows are selected to form a benchmark row set, and pure spectral data that suppresses background interference is reconstructed accordingly, providing a reliable signal basis for subsequent quantitative analysis.

[0041] To assess and ensure the reliability of the reconstructed signal, a reconstruction confidence level is introduced to measure the signal fidelity of each characteristic wavelength during the reconstruction process, identifying stable and reliable probe characteristic peaks. The nano-gold binding coefficient is obtained by calculating the ratio of resonance to characteristic wavelength amplitude, and a nano-gold binding factor is obtained by weighting with the confidence level, comprehensively reflecting the enhancement efficiency and signal reliability at a specific wavelength. The sum of the binding factors of each characteristic peak yields the nano-gold binding degree of the sample, effectively quantifying the overall binding efficiency. Combining confidence level and binding degree not only distinguishes between the true signal and background interference but also corrects for underestimation of content due to differences in binding efficiency, providing a crucial basis for the accurate calculation of the final true probe content. Based on the nano-gold binding degree of the reconstructed spectral data of cancer cell tissue sections, the true three-dimensional DNA nanoprobe content of the cancer cell tissue sections is obtained. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating the steps of the three-dimensional DNA nanoprobe detection method based on surface-enhanced Raman spectroscopy of the present invention. Detailed Implementation

[0044] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the three-dimensional DNA nanoprobe detection method based on surface-enhanced Raman spectroscopy proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0046] The following description, in conjunction with the accompanying drawings, details the specific scheme of the three-dimensional DNA nanoprobe detection method based on surface-enhanced Raman spectroscopy provided by this invention.

[0047] Please see Figure 1 The diagram illustrates a flowchart of a three-dimensional DNA nanoprobe detection method based on surface-enhanced Raman spectroscopy provided by an embodiment of the present invention. The method includes the following steps:

[0048] Step S001: Based on cancer cell tissue slices, three-dimensional DNA nanoprobe solution, and gold nanoparticle concentration solution, obtain some background spectral data, some reference spectral data, and some target spectral data.

[0049] It should be noted that this embodiment is a three-dimensional DNA nanoprobe detection method based on surface-enhanced Raman spectroscopy. Its purpose is to accurately estimate the true content of three-dimensional DNA nanoprobes in complex tissue backgrounds. To accurately estimate the true content of three-dimensional DNA nanoprobes, cancer cell tissue slices, three-dimensional DNA nanoprobe solutions, and gold nanoparticle concentration solutions are first prepared and the corresponding Raman spectral data are obtained.

[0050] Specifically, the preset number of cancer cell tissue slices , concentration of three-dimensional DNA nanoprobe solution Concentration of nano-gold particles in solution Initiation ratio of probe nano-gold mixture Probe nano-gold mixing termination ratio Probe nanogold hybrid iteration step size The , , , , as well as The specific value can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, it is used as... , , , , , Let's take an example to illustrate;

[0051] preparation We analyzed cancer cell tissue slices and used a Raman spectrometer to obtain the Raman spectral data for each cancer cell tissue slice, which were recorded as background spectral data.

[0052] Preparation concentration is A molar concentration of three-dimensional DNA nanoprobe solution with a concentration of A molar concentration solution of gold nanoparticles; the gold nanoparticle concentration solution is mixed with a three-dimensional DNA nanoprobe solution at... The gold nanoparticle concentration solution and the three-dimensional DNA nanoprobe solution were mixed in a certain proportion to obtain the first mixed solution; the concentration of the gold nanoparticles was then mixed with that of the three-dimensional DNA nanoprobe solution at a certain ratio to obtain the first mixed solution. The solutions were mixed in a specific ratio to obtain a second mixed solution, and so on, until the concentration of the gold nanoparticles and the three-dimensional DNA nanoprobe solution reached a certain ratio. The solutions were mixed in the specified proportions to obtain the final mixed solution; Raman spectra of each mixed solution were acquired using a Raman spectrometer and recorded as reference spectra.

[0053] Each cancer cell tissue slice was bound to a three-dimensional DNA nanoprobe solution, and Raman spectroscopy was used to acquire the Raman spectral data of each cancer cell tissue slice bound to the three-dimensional DNA nanoprobe solution. These data were recorded as the target spectral data.

[0054] It should be noted that background spectral data is used to characterize the background Raman signal of cancer cell tissue sections without probe modification, including the inherent responses of intracellular proteins, lipids, and nucleic acids. Background spectral data serves as a reference baseline for subsequent decomposition and reconstruction, helping to distinguish the target signal from background interference. Reference spectral data is obtained by preparing mixed solutions of three-dimensional DNA nanoprobes and gold nanoparticles with different binding degrees, reflecting the enhancement pattern and characteristic peak changes of the probes under surface plasmon resonance, preparing for accurate three-dimensional DNA nanoprobe content analysis. Target spectral data corresponds to actual tissue sections labeled with three-dimensional DNA nanoprobes and modified with gold nanoparticles; this is the final target for content analysis. By comparing with background and reference spectral data, the true contribution of the probe signal in the target spectral data can be effectively separated and corrected.

[0055] At this point, we have obtained the background spectral data, reference spectral data, and target spectral data.

[0056] Step S002: Based on the peaks in all reference spectral data, obtain several Raman wavelengths and divide the Raman wavelengths into resonance wavelengths and characteristic wavelengths; construct a Raman matrix based on the background spectral data and target spectral data; decompose the Raman matrix to obtain the base spectrum matrix and weight matrix; obtain the probe characterization factor of each row in the base spectrum matrix based on the base spectrum matrix and weight matrix; select the benchmark row set from the base spectrum matrix; reconstruct the benchmark row set by combining the weight matrix to obtain the reconstructed spectral data of each cancer cell tissue slice.

[0057] It is important to note that, to accurately quantify the actual binding efficiency of the three-dimensional DNA nanoprobes and gold nanoparticles and correct signal deviations caused by uneven binding, a standardized spectral analysis system must first be established. Therefore, a Raman wavelength set is constructed using stably occurring peaks in the reference spectral data as the signal comparison benchmark for all samples. The wavelength with the most significant enhancement effect is defined as the resonance wavelength to characterize the strongest enhancement capability; the remaining stably occurring wavelengths are used as characteristic wavelengths to track the specific signal of the probe itself. Simultaneously, the purpose of constructing the Raman matrix is ​​to uniformly represent the spectral data of multiple cancer cell tissue slices in a two-dimensional numerical form, allowing it to be decomposed into a base spectrum matrix and its corresponding weight matrix. Each row in the base spectrum matrix can be considered a basic spectral mode, and the amplitude of the base spectrum at the characteristic wavelength, combined with its weight in different samples, forms the probe characterization factor of that base spectrum, used to measure the degree to which the base spectrum characterizes the components of the three-dimensional DNA nanoprobes. Finally, to obtain pure signals that can be used for precise quantification, the rows with the highest probe characterization factors are identified as the benchmark row set. This set represents high-confidence probe signals selected through data-driven screening. Based on this set, the original spectra of each slice are reconstructed. The resulting reconstructed spectral data suppresses background interference and non-specific adsorption to the greatest extent, providing a reliable basis for subsequent evaluation of the degree of gold nanoparticle binding and calculation of the true probe content.

[0058] Preferably, in a specific embodiment of the present invention, a wavelength offset range is preset. The The specific value can be set according to the actual situation. This embodiment does not make a hard requirement. In this embodiment, it is used as... Taking this as an example, for any two peaks in any two reference spectral data, if the absolute value of the difference between the corresponding wavelengths of the two peaks in the two reference spectral data is less than... Then, the two peaks in the two reference spectral data are classified into the same Raman peak group to obtain several Raman peak groups; for any Raman peak group, the mean of the wavelengths corresponding to all peaks in the Raman peak group is taken as the corresponding wavelength of the Raman peak group, and the corresponding wavelengths of all Raman peak groups are recorded as Raman wavelengths.

[0059] Furthermore, the ratio of the mean amplitude of all peaks in the Raman peak group to the mean amplitude of all peaks in all Raman peak groups is multiplied by the standard deviation of the amplitude of all peaks in the Raman peak group, and the product is taken as the resonance degree of the Raman peak group. The resonance degree of all Raman peak groups is obtained, and the Raman peak group with the highest resonance degree is recorded as the resonance peak group. The remaining Raman peak groups are recorded as characteristic peak groups. The mean wavelength of all peaks in the resonance peak group is taken as the resonance wavelength. The mean wavelength of all peaks in the characteristic peak group is taken as the characteristic wavelength.

[0060] It should be noted that, in order to accurately quantify the actual binding efficiency of the three-dimensional DNA nanoprobe and the gold nanoparticles and correct the signal deviation caused by uneven binding, it is necessary to construct a Raman wavelength set based on the stably occurring peaks in the spectral data as a benchmark for signal comparison of all samples; the wavelength with the most significant enhancement effect is defined as the resonance wavelength to characterize the strongest enhancement ability; the other stably occurring wavelengths are used as characteristic wavelengths to track the specific signal of the probe itself.

[0061] It should be further explained that, in order to uniformly represent the spectral data of multiple cancer cell tissue slices in a two-dimensional numerical form, it is decomposed into several base spectra and their corresponding weights. Each row in the base spectrum matrix can be regarded as a basic spectral mode, and the amplitude of the base spectrum at the characteristic wavelength, combined with its weight in different samples, forms the probe characterization factor of that base spectrum, which is used to measure the degree of characterization of the three-dimensional DNA nanoprobe components by that base spectrum.

[0062] Preferably, in a specific embodiment of the present invention, a An empty matrix, the The number of cancer cell tissue slices (each cancer cell tissue slice has background spectral data and target spectral data), the The number of data points in the spectral data is equal to the number of data points in the spectral data. Each background spectral data or each target spectral data is placed as a row in an empty matrix to construct the Raman matrix.

[0063] Furthermore, the Raman matrix is ​​decomposed using NMF (Non-negative Matrix Factorization) to obtain the basis matrix and coefficient matrix of the Raman matrix, denoted as the basis spectrum matrix and weight matrix, respectively. Since NMF decomposition is a well-known existing technique, it will not be elaborated upon in this embodiment. The corresponding element in the weight matrix for each element in the basis spectrum matrix (each element in the basis spectrum matrix corresponds to a data point in the spectral data, so the element has the amplitude of the corresponding data point and the wavelength of the corresponding data point, i.e., the horizontal coordinate of the data point in the spectral data) is used as the weight of each element in the basis spectrum matrix (each element in the basis spectrum matrix has a corresponding element in the weight matrix).

[0064] For the th in the fundamental spectrum matrix In the middle of the line The nth element in the Raman matrix The cancer cell tissue slices corresponding to the row are denoted as feature slices, and the Raman matrix is ​​divided into two parts except for the first part. The row index corresponding to the spectral data of the cancer cell tissue slices in other rows outside the feature slice is denoted as the feature row index. The first row in the base spectrum matrix located at the feature row index is... The element, as the th element in the fundamental spectrum matrix In the middle of the line The corresponding element of each element;

[0065] Based on the first row of all rows in the fundamental spectrum matrix The element and its corresponding weight, all Raman wavelengths, and the element in the fundamental spectrum matrix. The amplitude and corresponding wavelength of each element in the row are used to obtain the first element in the fundamental spectrum matrix. The probe characterization factor of the row is calculated using the following formula:

[0066]

[0067] In the formula, Represents the first in the fundamental spectrum matrix The probe characterization factor of the row; Indicates the column number in the fundamental spectrum matrix; Indicates the number of Raman wavelengths; Represents the first in the fundamental spectrum matrix In the middle of the line The weight of each element; Represents the first in the fundamental spectrum matrix In the middle of the line The weight of each element corresponding to another element; Represents the first in the fundamental spectrum matrix In the middle of the line The magnitude of each element; Represents the first in the fundamental spectrum matrix In the middle of the line Each element corresponds to a wavelength; Indicates the first One Raman wavelength; This represents the function that takes the absolute value. Indicates traversal The result of the operation is taken by the function that minimizes the value.

[0068] It should be noted that the first [unit] in the fundamental spectrum matrix... The probe characterization factor in the row represents the number of probes in the fundamental spectrum matrix. The degree of performance of the three-dimensional DNA nanoprobe in the matrix; if the first row in the matrix represents the degree of performance of the three-dimensional DNA nanoprobe; Since the row represents the actual signal generated after probe binding, the weight of its corresponding element should be significantly higher than its own weight. The larger the value, the more it indicates that the first element in the fundamental spectrum matrix is... The rows represent the actual signals generated after the probes bind; This indicates signal quality inefficiency. The larger the value, the higher the value of the spectral matrix. The row contains many signals with high amplitudes but far from the standard Raman peak position, which are likely invalid noise; conversely, if the value is small, it proves that the main signal energy is concentrated on the true characteristic peaks, and therefore the characterization of the probe by this row is more reliable; therefore The larger the value, the more significant the first element in the fundamental spectrum matrix. The line can exhibit the characteristics of three-dimensional DNA nanoprobes. After obtaining the first [missing information] in the fundamental spectrum matrix... After determining the probe characterization factors, high-confidence probe signals can be screened based on these factors. The original spectra of each slice are then reconstructed based on this set. The resulting reconstructed spectral data suppresses background interference and non-specific adsorption to the greatest extent, providing a reliable basis for subsequent evaluation of the degree of gold nanoparticle binding and calculation of the true probe content.

[0069] Preferably, in a specific embodiment of the present invention, the probe characterization factor of each row in the Raman matrix is ​​obtained. Based on the probe characterization factor of each row in the Raman matrix, all elements in the Raman matrix are divided into two classes by the maximum inter-class variance method to obtain two row sets. Since the maximum inter-class variance method is a well-known prior art, it will not be described in detail in this embodiment. The row set with the largest mean of probe characterization factors of all rows in the row set is denoted as the baseline row set.

[0070] Furthermore, based on the weights corresponding to each element in each row of the baseline row set, the baseline row set is reconstructed to obtain the reconstructed spectral data of each cancer cell tissue slice.

[0071] Thus, reconstructed spectral data of cancer cell tissue sections were obtained.

[0072] Step S003: Based on the amplitude of the reconstructed spectral data of all cancer cell tissue slices and the target spectral data at each characteristic wavelength, obtain the confidence level of each characteristic wavelength; based on the amplitude of the reconstructed spectral data of all cancer cell tissue slices and the target spectral data at the resonance wavelength, and combined with the confidence level of each characteristic wavelength, obtain the degree of gold nanoparticle binding in the reconstructed spectral data of the cancer cell tissue slices.

[0073] It should be noted that, to avoid the distortion caused by the spectral decomposition and reconstruction process in step S002 affecting the content estimation, the reliability of the reconstructed spectrum needs to be measured at the characteristic wavelengths. Therefore, the reconstruction confidence is obtained by comparing the relative amplitudes of the reconstructed spectrum and the original spectrum at each characteristic wavelength, which quantifies the degree to which the signal at that wavelength retains its original structure during reconstruction. A high reconstruction confidence indicates that the characteristic peak can still stably represent the probe signal after reconstruction; conversely, it indicates that the peak may be affected by background or noise interference. Furthermore, to truly reflect the enhancement effect between the probe and the gold nanoparticles, a proportional relationship needs to be established between the resonance wavelength and the characteristic wavelengths. The ratio of the amplitude of the reconstructed spectrum at the resonance wavelength to each characteristic wavelength is used as the gold nanoparticle binding coefficient, and weighted by the reconstruction confidence of that characteristic wavelength to obtain the gold nanoparticle binding factor, thereby comprehensively reflecting the reliability and effectiveness of a certain characteristic peak under the enhancement effect. The binding factors of all characteristic peaks are then summed to obtain the degree of gold nanoparticle binding in the sample, serving as a quantitative indicator of overall binding efficiency. The reconstructed confidence level ensures the reliability of the peak-level signal, while the degree of gold nanoparticle binding further reveals the overall enhancement efficiency. The combination of these two factors not only distinguishes the probe signal from background components but also corrects the underestimation caused by differences in binding efficiency, providing a solid basis for subsequent correction of the regression equation and accurate calculation of the true probe content.

[0074] Preferably, in a specific embodiment of the present invention, for the first Based on the reconstructed spectral data of each cancer cell tissue slice and its corresponding target spectral data, the characteristic wavelength is determined at the 1st spectral point. Each characteristic wavelength and except The amplitude values ​​at all characteristic wavelengths other than the first characteristic wavelength are obtained. The reconstruction confidence of each characteristic wavelength is calculated using the following formula:

[0075]

[0076] In the formula, Indicates the first Reconstruction confidence at each characteristic wavelength; This indicates the amount of reconstructed spectral data from cancer cell tissue sections; Indicates the number of characteristic wavelengths; Indicates the first Reconstructed spectral data of cancer cell tissue slices in the first... Amplitude at each characteristic wavelength; Indicates the first The target spectral data corresponding to the cancer cell tissue slice in the _th Amplitude at each characteristic wavelength; Indicates the first Reconstructed spectral data of cancer cell tissue slices in the first... Amplitude at each characteristic wavelength; Indicates the first The target spectral data corresponding to the cancer cell tissue slice in the _th Amplitude at each characteristic wavelength; Indicates traversal The result is then used to take the mean function.

[0077] It should be noted that the reconstruction confidence level at the characteristic wavelength represents the stability and reliability of the characteristic wavelength in the reconstructed spectrum; ideally, the reconstruction effect of a characteristic wavelength should be consistent across all samples, rather than fluctuating drastically with different samples. A larger value indicates that the reconstruction effect should be consistent across all samples. The larger the value, the more likely it is to be the first The higher the reconstruction confidence at each characteristic wavelength, the better. Further, by combining the proportional relationship between the resonance wavelength and the characteristic wavelength, the degree of gold nanoparticle binding in the reconstructed spectral data of cancer cell tissue sections can be obtained, providing a solid basis for subsequent correction of the regression equation and accurate calculation of the true probe content.

[0078] Preferably, in a specific embodiment of the present invention, for the first The characteristic wavelength, for the _th ... The amplitude of the reconstructed spectral data of the first cancer cell tissue slice at the resonance wavelength is compared with that of the second. The ratio of amplitudes at each characteristic wavelength is normalized to its maximum value (the normalization range covers all reconstructed spectral data at all characteristic wavelengths), and the result of the maximum value normalization is used as the first value. Reconstructed spectral data of cancer cell tissue slices in the first... The binding coefficient of gold nanoparticles at each characteristic wavelength;

[0079] Furthermore, regarding the first The reconstruction confidence at each characteristic wavelength is linearly normalized (the normalization range is the reconstruction confidence at all characteristic wavelengths), and the result of the linear normalization is compared with the first characteristic wavelength. Reconstructed spectral data of cancer cell tissue slices in the first... The product of the nano-gold binding coefficients at each characteristic wavelength is used as the product of the first characteristic wavelength. Reconstructed spectral data of cancer cell tissue slices in the first... The nano-gold binding factor with the characteristic wavelength will bind the first... The reconstructed spectral data of a cancer cell tissue slice are summed at all characteristic wavelengths for the gold nanoparticle binding factor, as the first... The degree of gold nanoparticle binding in reconstructed spectral data of a cancer cell tissue slice.

[0080] Thus, the degree of gold nanoparticle binding was obtained from the reconstructed spectral data of cancer cell tissue sections.

[0081] Step S004: Based on the degree of gold nanoparticle binding in the reconstructed spectral data of cancer cell tissue sections, obtain the true three-dimensional DNA nanoprobe content of the cancer cell tissue sections.

[0082] It should be noted that after obtaining the degree of gold nanoparticle binding in the reconstructed spectral data of cancer cell tissue slices through step S003, the traditional linear regression evaluation equation can be modified based on the degree of gold nanoparticle binding in the reconstructed spectral data of cancer cell tissue slices to accurately obtain the true three-dimensional DNA nanoprobe content of cancer cell tissue slices.

[0083] Preferably, in a specific embodiment of the present invention, for the first The first cancer cell tissue slice, according to the first The degree of gold nanoparticle binding in the reconstructed spectral data of the first cancer cell tissue slice was used to correct the linear regression assessment of the three-dimensional DNA nanoprobe content, resulting in the... The specific formula for calculating the true three-dimensional DNA nanoprobe content of a cancer cell tissue slice is as follows:

[0084]

[0085] In the formula, Indicates the first The actual three-dimensional DNA nanoprobe content of a cancer cell tissue slice; Existing parameters in the regression equation for assessing the content of three-dimensional DNA nanoprobes in cancer cell tissue sections using traditional regression equations; This also provides existing parameters in the regression equation for assessing the content of three-dimensional DNA nanoprobes in cancer cell tissue sections using traditional regression equations; Indicates the first The degree of gold nanoparticle binding in reconstructed spectral data of a cancer cell tissue slice; Indicates the first The amplitude of the reconstructed spectral data of a cancer cell tissue slice at the resonance wavelength.

[0086] It should be noted that the traditional regression equation for assessing the content of three-dimensional DNA nanoprobes in cancer cell tissue sections is: ;in and Both can be obtained through traditional linear regression analysis, while This means that the higher the binding efficiency of the probe to the gold nanoparticles, the stronger the signal conversion ability, and therefore, under the same signal ( The higher the actual probe content, the better.

[0087] Furthermore, the true three-dimensional DNA nanoprobe content of all cancer cell tissue sections was obtained, thereby accurately detecting the true three-dimensional DNA nanoprobe content of all cancer cell tissue sections.

[0088] This concludes the embodiment.

[0089] 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A three-dimensional DNA nanoprobe detection method based on surface-enhanced Raman spectroscopy, characterized in that, The method includes the following steps: Based on cancer cell tissue sections, three-dimensional DNA nanoprobe solutions, and gold nanoparticle concentration solutions, several background spectral data, several reference spectral data, and several target spectral data were obtained. Based on the peaks in all reference spectral data, several Raman wavelengths are obtained and divided into resonance wavelengths and characteristic wavelengths. A Raman matrix is ​​constructed based on the background spectral data and the target spectral data. The Raman matrix is ​​decomposed to obtain the base spectrum matrix and the weight matrix. The probe characterization factor of each row in the base spectrum matrix is ​​obtained based on the base spectrum matrix and the weight matrix. The baseline row set is selected from the base spectrum matrix and reconstructed by combining the weight matrix to obtain the reconstructed spectral data of each cancer cell tissue slice. Based on the amplitude of the reconstructed spectral data of all cancer cell tissue sections and the target spectral data at each characteristic wavelength, the confidence level of each characteristic wavelength is obtained; based on the amplitude of the reconstructed spectral data of all cancer cell tissue sections and the target spectral data at the resonance wavelength, combined with the confidence level of each characteristic wavelength, the degree of gold nanoparticle binding in the reconstructed spectral data of the cancer cell tissue sections is obtained. The true three-dimensional DNA nanoprobe content of cancer cell tissue sections was obtained by determining the degree of gold nanoparticle binding based on the reconstructed spectral data of cancer cell tissue sections.

2. The method for detecting three-dimensional DNA nanoprobes based on surface-enhanced Raman spectroscopy according to claim 1, characterized in that, The specific method for obtaining background spectral data, reference spectral data, and target spectral data based on cancer cell tissue slices, three-dimensional DNA nanoprobe solution, and gold nanoparticle concentration solution includes: Preset number of cancer cell tissue slices , concentration of three-dimensional DNA nanoprobe solution Concentration of nano-gold particles in solution Initial mixing ratio of gold nanoparticle probes Nano-gold probe mixing termination ratio Probe nanogold hybrid iteration step size ; preparation We analyzed cancer cell tissue slices and used a Raman spectrometer to obtain the Raman spectral data for each cancer cell tissue slice, which were recorded as background spectral data. Preparation concentration is A molar concentration of three-dimensional DNA nanoprobe solution with a concentration of A molar concentration solution of gold nanoparticles; the gold nanoparticle concentration solution is mixed with a three-dimensional DNA nanoprobe solution at... The gold nanoparticle concentration solution and the three-dimensional DNA nanoprobe solution were mixed in a certain proportion to obtain the first mixed solution; the concentration of the gold nanoparticles was then mixed with that of the three-dimensional DNA nanoprobe solution at a certain ratio to obtain the first mixed solution. The solutions were mixed in a specific ratio to obtain a second mixed solution, and so on, until the concentration of the gold nanoparticles and the three-dimensional DNA nanoprobe solution reached a certain ratio. The solutions are mixed in the specified proportions to obtain the final mixed solution. Raman spectral data for each mixed solution were acquired using a Raman spectrometer and recorded as each reference spectral data. Each cancer cell tissue slice was combined with a three-dimensional DNA nanoprobe solution, and Raman spectroscopy was used to acquire the Raman spectral data of each cancer cell tissue slice combined with the three-dimensional DNA nanoprobe solution. These data were recorded as the target spectral data.

3. The three-dimensional DNA nanoprobe detection method based on surface-enhanced Raman spectroscopy according to claim 1, characterized in that, The specific method for obtaining several Raman wavelengths based on the peaks in all reference spectral data and dividing the Raman wavelengths into resonance wavelengths and characteristic wavelengths includes: Preset a wavelength offset range For any two peaks in any two reference spectral data, if the absolute value of the difference between the corresponding wavelengths of the two peaks in the two reference spectral data is less than... Then, the two peaks in the two reference spectral data are classified into the same Raman peak group to obtain several Raman peak groups; for any Raman peak group, the mean of the wavelengths corresponding to all peaks in the Raman peak group is taken as the corresponding wavelength of the Raman peak group, and the corresponding wavelengths of all Raman peak groups are recorded as Raman wavelengths. The ratio of the mean amplitude of all peaks in the Raman peak group to the mean amplitude of all peaks in all Raman peak groups is multiplied by the standard deviation of the amplitude of all peaks in the Raman peak group, and the product is taken as the resonance degree of the Raman peak group. The resonance degree of all Raman peak groups is obtained, and the Raman peak group with the highest resonance degree is recorded as the resonance peak group. The remaining Raman peak groups are recorded as characteristic peak groups. The mean wavelength of all peaks in the resonance peak group is taken as the resonance wavelength. The mean wavelength of all peaks in the characteristic peak group is taken as the characteristic wavelength.

4. The method for detecting three-dimensional DNA nanoprobes based on surface-enhanced Raman spectroscopy according to claim 1, characterized in that, The specific method for constructing the Raman matrix based on background spectral data and target spectral data is as follows: Build a An empty matrix, the The number of cancer cell tissue slices, the The number of data points in the spectral data is equal to the number of data points in the spectral data. Each background spectral data or each target spectral data is placed as a row in an empty matrix to construct the Raman matrix.

5. The method for detecting three-dimensional DNA nanoprobes based on surface-enhanced Raman spectroscopy according to claim 1, characterized in that, The method for decomposing the Raman matrix to obtain the fundamental spectrum matrix and weight matrix, and obtaining the probe characterization factor for each row of the fundamental spectrum matrix based on the fundamental spectrum matrix and weight matrix, includes the following specific methods: The Raman matrix is ​​decomposed by NMF to obtain the basis matrix and coefficient matrix of the Raman matrix, which are denoted as the basis matrix and weight matrix, respectively. The corresponding element of each element in the basis matrix in the weight matrix is ​​used as the weight of each element in the basis matrix. For the th in the fundamental spectrum matrix In the middle of the line The nth element in the Raman matrix The cancer cell tissue slices corresponding to the row are denoted as feature slices, and the Raman matrix is ​​divided into two parts except for the first part. The cancer cell tissue slices corresponding to other rows outside the designated row are the spectral data of the feature slices, and the corresponding row index is denoted as the feature row index. The cell slice located at the feature row index in the base spectrum matrix is... The element, as the th element in the fundamental spectrum matrix In the middle of the line The corresponding element of each element; Based on the first row of all rows in the fundamental spectrum matrix The element and its corresponding weight, all Raman wavelengths, and the element in the fundamental spectrum matrix. The amplitude and corresponding wavelength of each element in the row are used to obtain the first element in the fundamental spectrum matrix. The probe characterization factor of the row.

6. The method for detecting three-dimensional DNA nanoprobes based on surface-enhanced Raman spectroscopy according to claim 5, characterized in that, The first in the obtained fundamental spectrum matrix The probe characterization factors for the row include the following specific methods: In the formula, Represents the first in the fundamental spectrum matrix The probe characterization factor of the row; Indicates the column number in the fundamental spectrum matrix; Indicates the number of Raman wavelengths; Represents the first in the fundamental spectrum matrix In the middle of the line The weight of each element; Represents the first in the fundamental spectrum matrix In the middle of the line The weight of each element corresponding to another element; Represents the first in the fundamental spectrum matrix In the middle of the line The magnitude of each element; Represents the first in the fundamental spectrum matrix In the middle of the line Each element corresponds to a wavelength; Indicates the first One Raman wavelength; This represents the function that takes the absolute value. Indicates traversal The result of the operation is taken by the function that minimizes the value.

7. The method for detecting three-dimensional DNA nanoprobes based on surface-enhanced Raman spectroscopy according to claim 1, characterized in that, The specific method for obtaining probe characterization factors for each row of the fundamental spectrum matrix based on the fundamental spectrum matrix and the weight matrix, selecting a set of baseline rows from the fundamental spectrum matrix, and reconstructing the set of baseline rows using the weight matrix to obtain the reconstructed spectral data of each cancer cell tissue slice includes: Based on the probe characterization factor of each row in the Raman matrix, all elements in the Raman matrix are divided into two classes by the maximum inter-class variance method, resulting in two row sets; the row set with the largest mean of probe characterization factors of all rows in the row set is denoted as the baseline row set. Based on the weights corresponding to each element in each row of the baseline row set, the baseline row set is reconstructed to obtain the reconstructed spectral data of each cancer cell tissue slice.

8. The method for detecting three-dimensional DNA nanoprobes based on surface-enhanced Raman spectroscopy according to claim 1, characterized in that, The method for obtaining the confidence level of each characteristic wavelength based on the amplitude of the reconstructed spectral data of all cancer cell tissue slices and the target spectral data at each characteristic wavelength includes the following specific methods: In the formula, Indicates the first Reconstruction confidence at each characteristic wavelength; This indicates the amount of reconstructed spectral data from cancer cell tissue sections; Indicates the number of characteristic wavelengths; Indicates the first Reconstructed spectral data of cancer cell tissue slices in the first... Amplitude at each characteristic wavelength; Indicates the first The target spectral data corresponding to the cancer cell tissue slice in the _th Amplitude at each characteristic wavelength; Indicates the first Reconstructed spectral data of cancer cell tissue slices in the first... Amplitude at each characteristic wavelength; Indicates the first The target spectral data corresponding to the cancer cell tissue slice in the _th Amplitude at each characteristic wavelength; Indicates traversal The result is then used to take the mean function.

9. The method for detecting three-dimensional DNA nanoprobes based on surface-enhanced Raman spectroscopy according to claim 1, characterized in that, The method for obtaining the degree of gold nanoparticle binding in the reconstructed spectral data of cancer cell tissue sections based on the amplitude of the reconstructed spectral data of all cancer cell tissue sections and the target spectral data at the resonance wavelength, combined with the confidence level of each characteristic wavelength, includes the following specific methods: For the The characteristic wavelength, for the _th ... The amplitude of the reconstructed spectral data of the first cancer cell tissue slice at the resonance wavelength is compared with that of the second. The ratio of amplitudes at each characteristic wavelength is normalized to its maximum value, and the result of the maximum value normalization is taken as the first value. Reconstructed spectral data of cancer cell tissue slices in the first... The binding coefficient of gold nanoparticles at each characteristic wavelength; For the The reconstruction confidence at the characteristic wavelength is linearly normalized, and the result of the linear normalization is compared with the characteristic wavelength. Reconstructed spectral data of cancer cell tissue slices in the first... The product of the nano-gold binding coefficients at each characteristic wavelength is used as the product of the first characteristic wavelength. Reconstructed spectral data of cancer cell tissue slices in the first... The nano-gold binding factor with the characteristic wavelength will bind the first... The reconstructed spectral data of a cancer cell tissue slice are summed at all characteristic wavelengths for the gold nanoparticle binding factor, as the first... The degree of gold nanoparticle binding in reconstructed spectral data of a cancer cell tissue slice.

10. The method for detecting three-dimensional DNA nanoprobes based on surface-enhanced Raman spectroscopy according to claim 1, characterized in that, The method for obtaining the true three-dimensional DNA nanoprobe content of cancer cell tissue slices based on the degree of gold nanoparticle binding according to the reconstructed spectral data of cancer cell tissue slices includes the following specific methods: In the formula, Indicates the first The actual three-dimensional DNA nanoprobe content of a cancer cell tissue slice; To assess existing parameters in the three-dimensional DNA nanoprobe content of cancer cell tissue sections using traditional regression equations; This also provides a basis for evaluating the content of three-dimensional DNA nanoprobes in cancer cell tissue sections using traditional regression equations; Indicates the first The degree of gold nanoparticle binding in reconstructed spectral data of a cancer cell tissue slice; Indicates the first The amplitude of the reconstructed spectral data of a cancer cell tissue slice at the resonance wavelength.

Images