A method and system for detecting pla2r1 and kim-1 markers based on raman spectroscopy

Abstract

The present application relates to the technical field of biomedical detection and spectral analysis, and particularly relates to a PLA2R1 and KIM-1 marker detection method and system based on Raman spectrum. The detection method disclosed in the present application integrates PLA2R1 related antibody markers and KIM-1 related markers into the same Raman spectrum detection framework, synchronously obtains immune abnormality and kidney injury state information, and effectively avoids the information limitation and result fragmentation of single index detection. The combined detection of PLA2R1 reflecting glomerular injury and KIM-1 reflecting renal tubular injury is not a simple index superposition, but provides a more comprehensive and dynamic kidney injury detection perspective, significantly enhancing the accuracy of the detection result, the risk stratification ability and the reliability of the treatment monitoring.

Description

Technical Field

[0001] This invention relates to the field of biomedical detection and spectral analysis technology, and in particular to a method and system for detecting PLA2R1 and KIM-1 markers based on Raman spectroscopy. Background Technology

[0002] Nephritis is a clinical syndrome characterized by pathological damage to the glomeruli, tubulointerstitium, or renal vessels, and its occurrence and development are closely related to dysregulation of the body's immune response. In clinical auxiliary diagnosis, anti-glomerular basement membrane antibodies, anti-neutrophil cytoplasmic antibodies, antinuclear antibodies, anti-double-stranded DNA antibodies, anti-C1q antibodies, and anti-PLA2R antibodies correspond to serological markers of different types of nephritis or related immune abnormalities. Combined detection of these multiple antibody indicators helps to more comprehensively assess pathological subtypes, evaluate disease activity, and predict relapse risk, thereby improving overall clinical decision-making capabilities.

[0003] Existing methods for detecting nephritis-related antibodies mainly include enzyme-linked immunosorbent assay (ELISA), chemiluminescent immunoassay, immunofluorescence assay, Western blotting, and other immunoassay techniques. While these methods are widely used and technically mature in clinical practice, they still have the following limitations: First, most antibody tests employ a decentralized detection model, with the detection process and reagent system being fragmented, making it difficult to achieve combined analysis of multiple antibodies within a unified technical framework. Second, quantitative results from different detection platforms often exhibit systematic biases, reducing the reliability of combined interpretation of multiple indicators. Third, the complex matrix of samples such as serum, plasma, or urine, rich in proteins, metabolites, and thresholds, makes it difficult to fully capture the dynamic relationships between multiple antibodies, resulting in the ineffective utilization of the comprehensive information contained in the antibody profile.

[0004] Raman spectroscopy can characterize the vibrational energy level structure of molecules, offering significant advantages such as non-destructive testing, fingerprint identification, and simultaneous multi-component analysis. Enhanced Raman spectroscopy, represented by surface-enhanced Raman spectroscopy, utilizes plasmonic nanostructures made of noble metals such as gold and silver to significantly improve the scattering cross-section and spectral response intensity of analyte molecules, opening up new technical pathways for the detection of low-abundance biomarkers in complex biological samples. However, antibodies, as large protein molecules, have a small intrinsic Raman scattering cross-section, resulting in weak signals during direct detection. Furthermore, their spectral profile is easily affected by multiple factors, including sample matrix background, protein conformational changes, non-specific adsorption, and substrate homogeneity. Directly applying conventional Raman detection methods to the detection of nephritis antibodies will struggle to obtain stable signals, resolvable spectra, and results suitable for multi-antibody combined analysis. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for detecting PLA2R1 and KIM-1 biomarkers based on Raman spectroscopy. This method aims to solve the problems of scattered information of single indicators, difficulty in simultaneously assessing immune abnormalities and kidney damage status, insufficient consistency of results of multiple indicators, and strong interference from complex sample backgrounds in existing detection methods for nephritis biomarkers.

[0006] This invention provides a method for detecting PLA2R1 and KIM-1 markers based on Raman spectroscopy, comprising:

[0007] An in vitro biological sample is obtained, and the in vitro biological sample is pretreated to obtain the sample to be tested; Raman or enhanced Raman spectral intensity information of PLA2R1-related antibody markers and KIM-1-related markers in the sample to be tested is collected to obtain spectral intensity information; The spectral intensity information is preprocessed to extract the spectral features of PLA2R1-related antibody markers, the spectral features of KIM-1-related markers, background correction features, and the relative relationship between the two markers. Based on the obtained spectral characteristics of the PLA2R1-related antibody biomarkers, the spectral characteristics of the KIM-1-related biomarkers, the background correction characteristics, and the relative relationship between the two biomarkers, a PLA2R1 / KIM-1 joint feature matrix is ​​constructed. Based on the PLA2R1 / KIM-1 joint feature matrix, the in vitro detection results of relevant biomarkers are output.

[0008] Furthermore, the PLA2R1-related antibody biomarkers include at least one of anti-PLA2R1 antibody, PLA2R1-related immune complex, and PLA2R1 recognition-related signals.

[0009] Furthermore, KIM-1-related biomarkers include at least one of KIM-1 protein, KIM-1-related binding signals, and KIM-1 recognition complex.

[0010] Furthermore, the spectral characteristics of the PLA2R1-related antibody biomarker or the KIM-1-related biomarker include at least one of the following: peak position, peak intensity, peak area, peak area ratio, characteristic band intensity vector, or spectral variables after dimensionality reduction.

[0011] Furthermore, the relative relationship features include at least one of the following: intensity ratio, peak area ratio, difference value, combined trend, or normalized combination features between the spectral features of PLA2R1-related antibody markers and the spectral features of KIM-1-related kidney injury markers.

[0012] Furthermore, the background correction features include at least one of baseline intensity, peak height, net peak area, and peak intensity after background correction.

[0013] Furthermore, the PLA2R1 / KIM-1 joint feature matrix obtains detection results through threshold discrimination model, standard curve model, multiple linear regression model, partial least squares regression model, support vector machine model, random forest model or neural network model.

[0014] Furthermore, the detection results include at least one of the following: sample information, PLA2R1-related antibody marker detection results, KIM-1-related marker detection results, combined analysis results of both, or risk stratification results.

[0015] Furthermore, the ex vivo biological sample includes one or more of serum, plasma, urine, whole blood, and tissue extract.

[0016] This invention also discloses a detection system for PLA2R1 and KIM-1 markers based on Raman spectroscopy, comprising: The sample acquisition unit is used to acquire in vitro biological samples and preprocess the in vitro biological samples to obtain the sample to be tested. The spectral acquisition unit is used to acquire Raman or enhanced Raman spectral intensity information of PLA2R1-related antibody markers and KIM-1-related markers in the sample to be tested, and obtain spectral intensity information. The feature extraction unit is used to perform spectral preprocessing on the spectral intensity information to extract the spectral features of PLA2R1-related antibody markers, the spectral features of KIM-1-related markers, background correction features, and the relative relationship features between the two markers. The data processing unit constructs a PLA2R1 / KIM-1 joint feature matrix based on the obtained spectral features of the PLA2R1-related antibody markers, the spectral features of the KIM-1-related markers, the background correction features, and the relative relationship features between the two markers. The result output unit outputs the in vitro detection results of relevant biomarkers based on the PLA2R1 / KIM-1 joint feature matrix.

[0017] In summary, the beneficial effects of the detection method for PLA2R1 and KIM-1 markers based on Raman spectroscopy provided by this invention are as follows: (1) Integrating PLA2R1-related antibody biomarkers and KIM-1-related biomarkers into the same Raman spectroscopy detection framework allows for the simultaneous acquisition of information on immune abnormalities and renal injury status, effectively avoiding the limitations and fragmented results of single-indicator detection. The combined detection of PLA2R1, which reflects glomerular injury, and KIM-1, which reflects tubular injury, is not simply an aggregation of indicators, but rather provides a more comprehensive and dynamic perspective on renal injury detection, significantly enhancing the accuracy of detection results, risk stratification capabilities, and the reliability of treatment monitoring.

[0018] (2) By extracting the spectral features of PLA2R1-related antibody markers, the spectral features of KIM-1-related markers, the background correction features, and the relative relationship features between the two markers, a PLA2R1 / KIM-1 joint feature matrix is ​​constructed, realizing the leap from single-index detection to multi-dimensional joint detection, and significantly reducing problems such as background interference, weak signals, and insufficient explanatory power of single peak positions.

[0019] (3) Compared with the detection of a single biomarker, this invention makes full use of the intensity ratio, peak area ratio, difference value and joint change trend between the two to construct a comprehensive detection system that reflects the correlation between immune abnormality biomarkers and kidney injury biomarkers, which has significant synergistic detection advantages. Attached Figure Description

[0020] Figure 1 A flowchart illustrating a method for detecting PLA2R1 and KIM-1 markers based on Raman spectroscopy, provided in this embodiment of the invention; Figure 2 This is a schematic diagram of the PLA2R1 / KIM-1 joint feature matrix constructed in an embodiment of the present invention; Figure 3 This is a schematic block diagram of a detection system for PLA2R1 and KIM-1 markers based on Raman spectroscopy, provided as an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0022] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0023] See Figure 1 , Figure 1 The present invention provides a flowchart of a method for detecting PLA2R1 and KIM-1 markers based on Raman spectroscopy, which specifically includes steps S10-S50.

[0024] S10. Obtain an isolated biological sample and preprocess the isolated biological sample to obtain the sample to be tested.

[0025] Specifically, in step S10, the ex vivo biological sample includes one or more of serum, plasma, urine, whole blood, and tissue extract.

[0026] Ex vivo biological samples can be serum, plasma, urine, whole blood, tissue extracts, or other ex vivo biological samples containing nephritis-related markers.

[0027] Pretreatment steps may include one or more of the following: centrifugation, dilution, filtration, buffer conditioning, quantitative sample aliquoting, background interference reduction, or target marker enrichment. Pretreatment of ex vivo biological samples reduces the impact of proteins, salts, metabolites, and non-target components in complex biological samples on subsequent Raman spectroscopy acquisition.

[0028] S20. Collect the Raman or enhanced Raman spectral intensity information of PLA2R1-related antibody markers and KIM-1-related markers in the sample to be tested, and obtain the spectral intensity information.

[0029] Specifically, in step S20, the PLA2R1-related antibody markers include at least one of anti-PLA2R1 antibody, PLA2R1-related immune complex, and PLA2R1 recognition-related signals.

[0030] PLA2R1-associated immune complexes include antigen-antibody complexes and components of the complement system that are subsequently activated. PLA2R1 recognition-related signals refer to a series of signaling pathways triggered intracellularly after PLA2R1 binds to its ligand.

[0031] KIM-1 related biomarkers include at least one of KIM-1 protein, KIM-1 related binding signals, and KIM-1 recognition complex.

[0032] KIM-1 stands for Kidney Injury Molecule-1. It is a transmembrane glycoprotein. When renal tubular epithelial cells are subjected to ischemic or toxic damage, KIM-1 is highly expressed on the cell membrane surface, and its extracellular segment can be cleaved and released into the urine and blood.

[0033] The KIM-1-related binding signal is a response signal that can be detected by instruments when the KIM-1 protein binds specifically to its recognition molecules (such as anti-KIM-1 antibodies or aptamers) in an antigen-antibody specific binding process.

[0034] The KIM-1 recognizer is a multi-molecular assembly whose core function is to achieve specific recognition and signal output of the KIM-1 protein.

[0035] This invention does not limit the specific identification molecules, labeling methods, or sample carrier structures. As long as spectral intensity information related to the above-mentioned markers can be obtained through Raman or enhanced Raman spectroscopy, and a joint feature matrix can be further constructed for in vitro detection result output, it should fall within the protection scope of this invention.

[0036] S30. Perform spectral preprocessing on the spectral intensity information to extract the spectral features of PLA2R1-related antibody markers, the spectral features of KIM-1-related markers, background correction features, and the relative relationship features between the two markers.

[0037] More specifically, in step S30, the steps of spectral preprocessing of the spectral intensity information include dark noise correction, baseline correction, background subtraction, smoothing filtering, wavenumber calibration, normalization processing, and abnormal spectrum removal.

[0038] By performing spectral preprocessing on the spectral intensity information, the impact of different sample batches, acquisition conditions, and complex backgrounds on the analysis of characteristic peak intensity can be reduced.

[0039] The spectral characteristics of the PLA2R1-related antibody biomarker or the KIM-1-related biomarker include at least one of the following: peak position, peak intensity, peak area, peak area ratio, characteristic band intensity vector, or spectral variables after dimensionality reduction.

[0040] The relative relationship features include at least one of the following: intensity ratio, peak area ratio, difference value, combined trend, or normalized combination features between the spectral features of PLA2R1-related antibody markers and the spectral features of KIM-1-related markers.

[0041] The background correction features include at least one of baseline intensity, peak height, net peak area, and peak intensity after background correction.

[0042] S40. Construct a PLA2R1 / KIM-1 joint feature matrix based on the obtained spectral features of the PLA2R1-related antibody markers, the spectral features of the KIM-1-related markers, the background correction features, and the relative relationship features between the two markers.

[0043] S50. Based on the PLA2R1 / KIM-1 joint feature matrix, output the in vitro detection results of relevant biomarkers.

[0044] More specifically, in step S50, the PLA2R1 / KIM-1 joint feature matrix obtains the detection result through a threshold discrimination model, a standard curve model, a multiple linear regression model, a partial least squares regression model, a support vector machine model, a random forest model, or a neural network model.

[0045] The test results include at least one of the following: sample information, PLA2R1-related antibody biomarker test results, KIM-1-related biomarker test results, combined analysis results of both, or risk stratification results.

[0046] For example, such as Figure 3 As shown, Raman or enhanced Raman spectral intensity information of PLA2R1-related antibody markers, KIM-1-related markers, and background or blank control samples were collected respectively.

[0047] After baseline correction and normalization, the acquired spectral intensity information is used to extract the intensities of multiple representative characteristic peaks within a preset wavenumber range. For example, intensities of approximately 610 cm⁻¹ can be extracted. -1 1003cm -1 1245cm -1 1360cm -1 1585cm -1 Spectral intensity or peak area near the isowavenumber is used as a candidate characteristic variable.

[0048] The wavenumber positions mentioned above are only used to illustrate the feature extraction method and do not constitute a limitation on the scope of protection of this invention; those skilled in the art can select other feature peaks or feature bands with discriminative significance based on actual samples and detection data.

[0049] The characteristic peak intensity corresponding to PLA2R1-related antibody biomarkers can be denoted as I. PLA2R1 610、I PLA2R1 ,1360, etc.; the characteristic peak intensity corresponding to KIM-1 related markers can be denoted as I. KIM-1 610、I KIM-1 ,1360, etc. Combined with background or blank control spectra, calculate background correction characteristics such as baseline intensity, peak height, net peak area, and peak intensity after background correction.

[0050] Calculate the relative characteristics between PLA2R1-related antibody biomarkers and KIM-1-related biomarkers. For example, calculate the intensity ratio, peak area ratio, and difference value of the two at the same characteristic peak position, or calculate the normalized combination characteristics between different bands.

[0051] The detection results obtained based on the spectral characteristics of the obtained PLA2R1-related antibody markers, the spectral characteristics of the KIM-1-related markers, the background correction characteristics, and the relative relationship between the two markers no longer depend solely on the intensity of a single peak, but comprehensively reflect PLA2R1-related immune abnormality information, KIM-1-related kidney injury information, and the relative relationship between the two.

[0052] The PLA2R1 / KIM-1 joint feature matrix can be constructed according to the sample number. Each row of the matrix corresponds to a test sample, and each column corresponds to a feature variable. The feature variables include PLA2R1 feature peak intensity, KIM-1 feature peak intensity, background correction feature, intensity ratio feature, and difference feature, etc.

[0053] The PLA2R1 / KIM-1 joint feature matrix can be used as input to threshold discrimination models, standard curve models, multiple linear regression models, partial least squares regression models, support vector machine models, random forest models, or neural network models to achieve joint discrimination and risk assessment.

[0054] The PLA2R1 / KIM-1 joint feature matrix outputs a comprehensive score or discrimination probability, which is then categorized into different levels such as negative, suspicious, positive, or high-risk. For example, when the comprehensive score falls within a first preset range, a negative or low-risk result can be output; when it falls within a second preset range, a suspicious or intermediate-risk result can be output; and when it falls within a third preset range, a positive or high-risk result can be output. These threshold ranges can be set and optimized based on the sample size, clinical reference results, or model training results.

[0055] This invention does not directly apply mature Raman detection methods to the detection of a single nephritis biomarker, nor does it simply perform parallel detections of PLA2R1 and KIM-1. Instead, it incorporates PLA2R1-related immune abnormality information, KIM-1-related kidney injury information, background correction information for complex samples, and the relative relationship between the two into a single joint feature matrix for analysis. This approach overcomes the problems of weak biomarker signals, overlapping background peaks, insufficient interpretability of single indicators, and insufficient consistency of results from multiple detection items in complex biological samples, thereby achieving a comprehensive detection effect superior to single biomarker detection or simple spectral acquisition.

[0056] This method allows for the detection of ex vivo samples collected from the same subject at different time points, and the comparison of the PLA2R1 / KIM-1 Raman combined feature matrices at different time points to obtain dynamic trends. By analyzing the synchronous increase, decrease, or separation trends of PLA2R1-related spectral features and KIM-1-related spectral features, it can be used for observation of treatment response, monitoring of disease progression, or assessment of relapse risk.

[0057] It should be noted that the PLA2R1-related antibody biomarkers detected by the present invention may include one or a combination of anti-PLA2R1 antibodies, PLA2R1-related immune complexes, and PLA2R1 recognition-related signals; KIM-1-related biomarkers may include one or a combination of KIM-1 protein, KIM-1-related binding signals, and KIM-1 recognition complexes.

[0058] This invention does not limit the specific identification molecules, labeling methods, or sample carrier structures. As long as spectral intensity information related to the above-mentioned markers can be obtained through Raman or enhanced Raman spectroscopy, and a joint feature matrix can be further constructed for in vitro detection result output, it should fall within the protection scope of this invention.

[0059] Another embodiment of the present invention provides a detection system 100 for PLA2R1 and KIM-1 markers based on Raman spectroscopy, which can realize the detection method described above, such as... Figure 3 As shown, the detection system 100 includes: The sample acquisition unit 101 is used to acquire in vitro biological samples and preprocess the in vitro biological samples to obtain the sample to be tested. The spectral acquisition unit 102 is used to acquire Raman or enhanced Raman spectral intensity information of PLA2R1-related antibody markers and KIM-1-related markers in the sample to be tested, and obtain spectral intensity information. The feature extraction unit 103 is used to perform spectral preprocessing on the spectral intensity information to extract the spectral features of PLA2R1-related antibody markers, the spectral features of KIM-1-related markers, background correction features, and the relative relationship features between the two markers. Data processing unit 104 constructs a PLA2R1 / KIM-1 joint feature matrix based on the obtained spectral features of the PLA2R1-related antibody markers, the spectral features of the KIM-1-related markers, the background correction features, and the relative relationship features between the two markers; The result output unit 105 outputs the in vitro detection results of relevant biomarkers based on the PLA2R1 / KIM-1 joint feature matrix.

[0060] The detection system 100 provided in this invention performs joint analysis on the spectral characteristics, background correction characteristics, and relative relationship characteristics between PLA2R1 and KIM-1 nephritis-related biomarkers in isolated biological samples. This enables a method and system for the lineage-based in vitro detection of nephritis-related antibodies, assisted screening, disease progression observation, and relapse risk assessment. This improves the consistency, anti-interference ability, and result interpretation of the joint detection of nephritis-related antibodies in complex biological samples.

[0061] Finally, it should be noted that any modification or equivalent substitution of some or all of the technical features based on the technical solutions of this invention and the embodiments thereof, which do not depart from the corresponding technical solutions of this invention, shall fall within the patent scope of this invention and the embodiments thereof.

Claims

1. A method for detecting PLA2R1 and KIM-1 markers based on Raman spectroscopy, characterized in that, include: An in vitro biological sample is obtained, and the in vitro biological sample is pretreated to obtain the sample to be tested; Raman or enhanced Raman spectral intensity information of PLA2R1-related antibody markers and KIM-1-related markers in the sample to be tested is collected to obtain spectral intensity information; The spectral intensity information is preprocessed to extract the spectral features of PLA2R1-related antibody markers, the spectral features of KIM-1-related markers, background correction features, and the relative relationship between the two markers. Based on the obtained spectral characteristics of the PLA2R1-related antibody biomarkers, the spectral characteristics of the KIM-1-related biomarkers, the background correction characteristics, and the relative relationship between the two biomarkers, a PLA2R1 / KIM-1 joint feature matrix is ​​constructed. Based on the PLA2R1 / KIM-1 joint feature matrix, the in vitro detection results of relevant biomarkers are output.

2. The detection method according to claim 1, characterized in that, The PLA2R1-related antibody biomarkers include at least one of anti-PLA2R1 antibody, PLA2R1-related immune complex, and PLA2R1 recognition-related signals.

3. The detection method according to claim 1, characterized in that, KIM-1 related biomarkers include at least one of KIM-1 protein, KIM-1 related binding signals, and KIM-1 recognition complex.

4. The detection method according to claim 1, characterized in that, The spectral characteristics of the PLA2R1-related antibody biomarker or the KIM-1-related biomarker include at least one of the following: peak position, peak intensity, peak area, peak area ratio, characteristic band intensity vector, or spectral variables after dimensionality reduction.

5. The detection method according to claim 1, characterized in that, The relative relationship features include at least one of the following: intensity ratio, peak area ratio, difference value, combined trend, or normalized combination features between the spectral features of PLA2R1-related antibody markers and the spectral features of KIM-1-related markers.

6. The detection method according to claim 1, wherein the background correction feature includes at least one of baseline intensity, peak height, net peak area, and peak intensity after background correction.

7. The detection method according to claim 1, wherein the PLA2R1 / KIM-1 joint feature matrix obtains the detection result through a threshold discrimination model, a standard curve model, a multiple linear regression model, a partial least squares regression model, a support vector machine model, a random forest model, or a neural network model.

8. The detection method according to claim 7, wherein the detection result includes at least one of sample information, PLA2R1-related antibody marker detection result, KIM-1-related marker detection result, combined analysis result of both, or risk stratification result.

9. The detection method according to claim 1, wherein the ex vivo biological sample includes one or more of serum, plasma, urine, whole blood, and tissue extract.

10. A detection system for PLA2R1 and KIM-1 markers based on Raman spectroscopy, characterized in that, include: The sample acquisition unit is used to acquire in vitro biological samples and preprocess the in vitro biological samples to obtain the sample to be tested. The spectral acquisition unit is used to acquire Raman or enhanced Raman spectral intensity information of PLA2R1-related antibody markers and KIM-1-related markers in the sample to be tested, and obtain spectral intensity information. The feature extraction unit is used to perform spectral preprocessing on the spectral intensity information to extract the spectral features of PLA2R1-related antibody markers, the spectral features of KIM-1-related markers, background correction features, and the relative relationship features between the two markers. The data processing unit constructs a PLA2R1 / KIM-1 joint feature matrix based on the obtained spectral features of the PLA2R1-related antibody markers, the spectral features of the KIM-1-related markers, the background correction features, and the relative relationship features between the two markers. The result output unit outputs the in vitro detection results of relevant biomarkers based on the PLA2R1 / KIM-1 joint feature matrix.

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