A method for screening a specific peptide segment marker and application thereof

Abstract

The application relates to the field of biological medicine, and particularly discloses a method for screening specific peptide segment markers and application thereof. First, a target sample is pretreated by using a magnetic metal organic framework material, then serum peptide fingerprint is extracted by directly performing MALDI-TOF / TOF MS analysis without any elution process. The mass spectrum is subjected to peak extraction and normalization, and is subjected to orthogonal partial least squares discriminant analysis to screen characteristic polypeptide markers. Then, through integration of a machine learning algorithm, nano-LC-MS / MS and Uniprot database searching, serum peptides closely related to Alzheimer's disease are determined, and a diagnosis model is established. The sample pretreatment method synthesized by the application greatly simplifies the diagnosis process, and has the advantages of high throughput, accuracy and low cost, and has great application prospect in large-scale clinical diagnosis of complex diseases.

Description

Technical Field

[0001] This application relates to the field of biomedicine, and more specifically, to a method for screening specific peptide biomarkers and its application. Background Technology

[0002] Endogenous peptides are mainly composed of protein degradation fragments, gene-encoded proteins, and gene-independent enzymes, and are widely present in human body fluids such as serum, plasma, and urine. Their expression levels in vivo can reflect the physiological and pathological state of the body, and they have broad clinical application prospects as biomarkers. Peptidomics analysis is a comprehensive study and analysis of endogenous peptides in biological samples, which can directly reflect an individual's physiological and pathological state from the level of small molecule peptides, and has important clinical significance. For example, changes in the Aβ42 / Aβ40 level in human plasma samples can show pathological changes of amyloid protein deposition before obvious plaque pathological changes appear in brain imaging. Peptides (amino acids 151-166) gradually increase in patients with Alzheimer's disease, mild cognitive impairment, and subjective cognitive decline as the severity of cognitive impairment increases. Therefore, monitoring the expression levels of endogenous peptides in diseases has great potential for establishing diagnostic methods for Alzheimer's disease.

[0003] Magnetic porous materials possess advantages such as ease of separation, flexible modification, large specific surface area, and high stability, offering certain advantages in extracting low-abundance endogenous peptides from complex biological samples. Notably, metal-organic frameworks (MOFs) porous materials formed by the self-assembly of inorganic metal centers and organic ligands have been widely applied in the life sciences and have demonstrated excellent performance in peptide extraction as an emerging type of porous material. Among them, the copper-containing MOF HKUST-1 can chelate with carboxyl and amino groups on peptides through copper ions, exhibiting a strong enrichment capacity for endogenous peptides. Matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a rapid, high-throughput, high-resolution, and high-sensitivity technique. To date, researchers have focused on combining magnetic porous materials with MALDI-TOF MS for peptidomics analysis. By comparing peptidomics information between disease patients and healthy individuals, differential expression patterns can be revealed, aiming to discover biomarkers for complex diseases. Therefore, it is necessary to combine the advantages of abundant active metal sites and easy modification of magnetic metal-organic frameworks to prepare magnetic biomaterials that can efficiently extract endogenous peptides from biological samples, and to use MALDI-TOF MS technology to facilitate rapid and accurate diagnosis and classification of specific diseases.

[0004] Alzheimer's disease is a devastating and progressive neurodegenerative disease characterized by obvious clinical manifestations such as memory impairment and cognitive dysfunction, which usually only appear in the later stages of the disease. This disease progresses slowly, is irreversible, and its pathogenesis is not yet fully understood; currently, there is still no effective cure. Therefore, early detection and accurate diagnosis of Alzheimer's disease are crucial. However, current clinical diagnostic methods for Alzheimer's disease mostly involve invasive procedures such as lumbar puncture for cerebrospinal fluid analysis or expensive positron emission tomography (PET), the sampling process of which is extremely painful and prone to infection. Therefore, the discovery of serum biomarkers for diagnosing Alzheimer's disease is essential for early diagnosis and timely intervention in the disease's progression. Summary of the Invention

[0005] The purpose of this invention is to provide a method for screening specific peptide biomarkers and its application.

[0006] Firstly, this application proposes a method for screening specific peptide biomarkers, employing the following technical solution:

[0007] A method for screening specific peptide biomarkers includes the following steps:

[0008] Step 1: Mix the magnetic metal-organic framework material with deionized water to prepare a material dispersion. Add the material dispersion and the target sample to deionized water for incubation. After incubation, discard the supernatant to obtain the material enriched into peptides. Redisperse the material enriched into peptides in deionized water to obtain a dispersion.

[0009] Step 2: Mix the dispersion obtained in Step 1 with the matrix and apply it to the target for MALDI-TOF / TOF MS analysis;

[0010] Step 3: Extract and normalize the peaks of the MALDI-TOF / TOF MS mass spectrum obtained in Step 2, and perform orthogonal partial least squares discriminant analysis and principal component analysis to select characteristic peptide biomarkers.

[0011] Step 4: Elute the material enriched with peptides in Step 1 with elution buffer, freeze-dry the eluent and analyze it with nano-LC-MS / MS to identify the peptides.

[0012] Step 5: Match the mass-to-charge ratio of the characteristic peptide marker obtained in Step 3 with the peptide identified by nano-LC-MS / MS analysis in Step 4 to determine the amino acid sequence of the characteristic peptide marker.

[0013] Preferably, the synthesis steps of the magnetic metal-organic framework material used in step 1 are as follows:

[0014] Step ①: Dissolve ferric chloride hexahydrate in ethylene glycol until the solution is clear and transparent, then add anhydrous sodium acetate, stir thoroughly and sonicate, then transfer to a reaction vessel and heat at 100-450℃ for 10-20 hours. After the reaction is complete, let the reaction vessel cool to room temperature, wash the obtained product thoroughly with deionized water and anhydrous ethanol, and dry under vacuum at 40-75℃.

[0015] The mass-to-volume ratio of ferric chloride hexahydrate, ethylene glycol, and anhydrous sodium acetate is 1.35 g: 75 mL: 3.6 g;

[0016] Step 2: Disperse the product obtained in Step 1 with dopamine hydrochloride in Tris buffer, sonicate for 5 min to homogenize, stir at room temperature for 9 h, wash the product thoroughly with deionized water and anhydrous ethanol, and vacuum dry the product at 40-75℃.

[0017] The Tris buffer solution is a buffer solution containing tris(hydroxymethyl)aminomethane, ethanol, and deionized water in a mass-to-volume ratio of 0.05 g: 40 mL: 40 mL.

[0018] The mass-to-volume ratio of the product obtained in step ①, dopamine hydrochloride, and Tris buffer is 120 mg: 320 mg: 80 mL.

[0019] Step ③: The product obtained in step ②, copper acetate and pyromellitic acid are uniformly dispersed in N,N-dimethylformamide. After being sonicated, the mixture is stirred continuously at 70°C for 45 minutes. The product is thoroughly washed with N,N-dimethylformamide and anhydrous ethanol and then vacuum dried at 40-75°C.

[0020] The product obtained in step ② has a mass-to-volume ratio of copper acetate, trimesic acid, and N,N-dimethylformamide of 100 mg:160 mg:168 mg:80 mL.

[0021] Preferably, the target sample in step 1 is serum.

[0022] Preferably, step 1 is as follows: the magnetic metal-organic framework material and deionized water are mixed at a weight-to-volume ratio of 20g:1L to prepare a material dispersion. 20μL of the material dispersion and 2μL of the target sample are added to 250μL of deionized water and incubated at 37°C for 60 minutes. Then, with the help of a magnet, the supernatant is discarded to obtain the material enriched to peptides. The material enriched to peptides is then redispersed in 40μL of deionized water to obtain a dispersion.

[0023] Preferably, step 2 is: mixing 1 μL of the dispersion obtained in step 1 with 1 μL of matrix for target application, allowing it to dry naturally, and then performing MALDI-TOF / TOF MS analysis;

[0024] The matrix is ​​a 20 mg / mL 2,5-dihydroxybenzoic acid buffer solution containing acetonitrile, water, and trifluoroacetic acid in a volume ratio of 50:49.9:0.1.

[0025] Preferably, the elution buffer in step 4 is a buffer solution containing concentrated ammonia and deionized water in a volume ratio of 1:33.45.

[0026] Preferably, the specific conditions for the MALDI-TOF / TOF MS analysis in step 2 are as follows: a Bruker UltrafleXtreme MALDI-TOF / TOF mass spectrometer is used, employing a 355 nm Nd:YAG laser source with a laser frequency of 2000 Hz and an accelerating voltage of 20 kV, wherein the voltage at ion source 1 is 20 kV and the voltage at ion source 2 is 17.6 kV; the acquisition mode is reflectance cation mode, and the acquisition range is 700-5000 Da; mass spectrometry data are obtained from Flexcontrol 3.4 and exported from Flexanalysis 3.4.

[0027] Preferably, the specific conditions for nano-LC-MS / MS analysis in step 4 are as follows: An EASY-nLC 1000 liquid chromatograph (Thermo Fisher Scientific) coupled with an Orbitrap Fusion mass spectrometer (Thermo Fisher Scientific) is used. Phases A and B of the liquid chromatography are water containing 0.1% formic acid and acetonitrile containing 0.1% formic acid, respectively. The eluent obtained in step 1 is lyophilized and redissolved in phase A. A linear gradient is applied (5% A - 30% B) for 50 min, and the sample is loaded onto an analytical column (Thermo Scientific Acclaim PepMap C18, 75 μm × 25 cm) for analysis. The voltage of the electrospray ionization is 2.3 kJ / mL. kV, the scanning range of the parent ion in the first-order spectrum is m / z=350-1600, with a resolution of 60000 (m / z=200). The second-order spectrum is obtained through high-energy collisional dissociation, with a resolution of 15000 and m / z=200. The high-energy collisional dissociation mode is selected to sequentially fragment parent ions with charges of +2, +3, and +4. The normalized collision energy is 28%.

[0028] Tandem mass spectrometry was performed using Proteome Discoverer (Thermo Fisher Scientific, version 2.4.0.305) and the Uniprot-SwissProt database (taxonomic: Homo sapiens, 20386 entries) was searched. The mass tolerance of the parent ion was set to 10 ppm and the mass tolerance of the fragment ion was set to 0.020 Da.

[0029] Preferably, the conditions for selecting the characteristic polypeptide marker in step 3 are VIP value > 2, P value < 0.05, and FC value > 2 or < 0.5.

[0030] Preferably, in step 5, the peptide matching condition is that the mass-to-charge ratio of the characteristic peptide marker selected by MALDI-TOF / TOF MS analysis and the mass-to-charge ratio of the peptide identified by nano-LC-MS / MS analysis are consistent within 100 ppm.

[0031] Preferably, in step 5, the MALDI-TOF / TOF from step 3 is used. The mass-to-charge ratio of the MS characteristic peptide markers matched the peptides identified by nano-LC-MS / MS in step 4, determining the amino acid sequences of 15 characteristic peptide markers: m / z value 4215.41 corresponds to HNVYINGITYTPVSSTNEKDMYSFLEDMGLKAFTNSK, m / z value 2884.77 corresponds to DQTVSDNELQEMSNQGSKYVNKEIQ, m / z value 2704.61 corresponds to REKPRVQEKQHPVPPPAQNQNQV, m / z value 2930.55 corresponds to SSSYSKQFTSSTSYNRGDSTFESKSY, m / z value 4192.66 corresponds to LPAVDEKLRDLYSKSTAAM(+15.99)STYTGIFTDQVLSVLKGEE, m / z value 2955.34 corresponds to GPRRYTIAALLSPYSYSTTAVVTNPKE, m / z value 3605.0 1 corresponds to VSETESRGSESGIFTNTKESSSHHPGIAEFPSRG, m / z value is 2510.05; FTSSTSYNRGDSTFESKSYKMA, m / z value is 2784.80; ILRQQQHLFGSNVTDCSGNFCLFR, m / z value is 2541.30; DAHKSEVAHRFKDLGEENFKAL, m / z value is 2680.93; ADRSGKDGVMEMNSIEPAKETTTNV, m / z value is 3314.60; SVPPSASHVAPTETFTYEWTVPKEVGPTNAD, m / z value is 2379.03; SYSKQFTSSTSYNRGDSTFES, m / z value is 2328.42; SQEEEKTEALTSAKRYIETD, m / z value is 2354.68; DNELQEMSNQGSKYVNKEIQ.

[0032] Secondly, this application provides the application of the above method in the preparation of Alzheimer's disease diagnostic kits.

[0033] Thirdly, this application provides an Alzheimer's disease diagnostic kit, including reagents for detecting 15 specific peptide markers;

[0034] The amino acid sequences of the 15 specific peptide markers are shown in SEQ ID NO: 1-15.

[0035] SEQ ID NO: 1: HNVYINGITYTPVSSTNEKDMYSFLEDMGLKAFTNSK,

[0036] SEQ ID NO: 2: DQTVSDNELQEMSNQGSKYVNKEIQ,

[0037] SEQ ID NO: 3: REKPRVQEKQHPVPPPAQNQNQV,

[0038] SEQ ID NO: 4: SSSYSKQFTSSTSYNRGDSTFESKSY,

[0039] SEQ ID NO: 5: LPAVDEKLRDLYSKSTAAM(+15.99)STYTGIFTDQVLSVLKGEE,

[0040] SEQ ID NO: 6: GPRRYTIAALLSPYSYSTTAVVTNPKE,

[0041] SEQ ID NO: 7: VSETESRGSESGIFTNTKESSSHPGIAEFPSRG,

[0042] SEQ ID NO: 8: FTSSTSYNRGDSTFESKSYKMA,

[0043] SEQ ID NO: 9: LRQQQHLFGSNVTDCSGNFCLFR,

[0044] SEQ ID NO: 10:DAHKSEVAHRFKDLGEENFKAL,

[0045] SEQ ID NO: 11: ADRSGKDGVMEMNSIEPAKETTTNV,

[0046] SEQ ID NO: 12: SVPPSASHVAPTETFTYEWTVPKEVGPTNAD,

[0047] SEQ ID NO: 13: SYSKQFTSSTSYNRGDSTFES,

[0048] SEQ ID NO: 14: SQEEEKTEALTSAKRYIETD,

[0049] SEQ ID NO: 15: DNELQEMSNQGSKYVNKEIQ.

[0050] This application has the following beneficial effects:

[0051] 1. The magnetic metal-organic framework material proposed in this invention contains abundant active metal sites, has a large specific surface area, good magnetic responsiveness, and strong chelation with endogenous peptides. Therefore, the method of this invention can more sensitively and extensively separate and enrich endogenous peptides.

[0052] 2. The method of this invention uses machine learning algorithms to analyze the expression differences of endogenous peptides between healthy individuals and Alzheimer's disease patients, thereby screening characteristic peptide biomarkers. Combined with nano-LC MS / MS, it can identify endogenous hydrophilic peptides on a large scale and conduct in-depth analysis of biological functions.

[0053] 3. In the method of the present invention, multiple machine learning diagnostic models are established by screening out polypeptide biomarkers for the preparation of Alzheimer's disease diagnostic kits.

[0054] In summary, the magnetic metal-organic framework material prepared by this invention possesses high peptide affinity, a unique porous structure, and excellent magnetic responsiveness. It can be successfully used to specifically separate and enrich endogenous peptides from the serum of Alzheimer's disease patients and healthy individuals, and 15 characteristic hydrophilic peptides were screened as potential biomarkers for Alzheimer's disease. Based on machine learning algorithms, a highly accurate diagnosis of Alzheimer's disease patients was successfully achieved, indicating its great application prospects in large-scale population screening and disease diagnosis. Attached Figure Description

[0055] Figure 1 Scanning electron microscope image of the magnetic metal-organic framework material of Example 1;

[0056] Figure 2 This is a transmission electron microscope image of the magnetic metal-organic framework material of Example 1;

[0057] Figure 3 The X-ray diffraction pattern of the magnetic metal-organic framework material in Example 1 is shown below.

[0058] Figure 4 The nitrogen adsorption isotherm and pore size distribution diagram of the magnetic metal-organic framework material in Example 1 are shown.

[0059] Figure 5The Fourier transform infrared spectrum of the magnetic metal-organic framework material in Example 1 is shown below.

[0060] Figure 6 This is a mass spectrum of the magnetic metal-organic framework material used in Example 2 to separate and enrich endogenous peptides in serum.

[0061] Figure 6 (a) is a representative mass spectrum of endogenous polypeptides enriched in the serum of healthy individuals in this material;

[0062] Figure 6 (b) is a representative mass spectrum of endogenous polypeptides in the serum of Alzheimer's patients enriched in this material;

[0063] Figure 7 This is the orthogonal partial least squares discriminant analysis model based on the training set in Example 3;

[0064] Figure 8 The heatmap of 15 feature peptides based on the training set in Example 3;

[0065] Figure 9 This is the principal component analysis model of 15 characteristic peptides from all samples in Example 4;

[0066] Figure 10 The machine learning model constructed based on 15 feature peptides from the training set in Example 5;

[0067] Figure 11 This is the machine learning model constructed based on three feature peptides from the training set in Example 6. Detailed Implementation

[0068] This invention utilizes the interaction between magnetic metal-organic framework materials and endogenous peptides to achieve the enrichment and analysis of serum endogenous peptides. The following description, in conjunction with the accompanying drawings and examples, provides a more detailed explanation of this application.

[0069] Example 1

[0070] The synthesis steps for magnetic metal-organic framework materials are as follows:

[0071] Step ①: 1.35 g FeCl3·6H2O was magnetically stirred in 75 mL of ethylene glycol until the solid was completely dissolved. Then, 3.6 g sodium acetate was added, and the mixture was thoroughly stirred and sonicated before being transferred to a hydrothermal reactor. The reactor was heated at 200°C for 16 hours. After the reactor cooled, the product was washed three times with deionized water and ethanol, respectively, and then dried under vacuum at 50°C.

[0072] Step ②: Disperse 120 mg of the product obtained in step ① and 320 mg of dopamine hydrochloride evenly in a mixed solution containing 0.05 g of tris(hydroxymethyl)aminomethane in 40 mL of ethanol and 40 mL of deionized water. Stir at room temperature for 9 hours while sonicating until homogeneous. After the reaction is complete, wash the product thoroughly with deionized water and anhydrous ethanol. Dry the product under vacuum at 50 °C.

[0073] Step ③: 100 mg of the product obtained in Step ②, 160 mg of copper acetate and 168 mg of trimesic acid were uniformly dispersed in 80 mL of N,N-dimethylformamide. After continuous sonication until homogeneous, the mixture was stirred continuously at 70 °C for 45 minutes. After the reaction was completed, the product was washed three times with N,N-dimethylformamide and anhydrous ethanol, respectively, and then dried under vacuum at 50 °C. The magnetic metal-organic framework material was obtained and named Mag HKUST-1.

[0074] Scanning electron microscope images of magnetic metal-organic framework materials, such as Figure 1 As shown; transmission electron microscope images of magnetic metal-organic framework materials, as shown. Figure 2 As shown; X-ray diffraction pattern of magnetic metal-organic framework material as shown in the figure. Figure 3 As shown; nitrogen adsorption isotherms and pore size distribution diagrams of magnetic metal-organic framework materials are as follows. Figure 4 As shown; the Fourier transform infrared spectrum of the magnetic metal-organic framework material is as follows. Figure 5 As shown.

[0075] Analysis results: From Figures 1 to 5 It can be seen that the magnetic metal-organic framework material exhibits a uniform spherical morphology and core-shell structure, with a rough geometric crystal shape observed on the surface, and has a diameter of 126.9 μm. 2 g -1 and 0.27 cm 3 g -1 Specific surface area and porosity.

[0076] Example 2

[0077] The magnetic metal-organic framework material obtained in Example 1 was used as a solid-phase adsorbent for the separation and enrichment of endogenous peptides in serum samples from 110 Alzheimer's patients and 120 healthy individuals. The steps are as follows:

[0078] (1) The serum of 110 Alzheimer’s patients and 120 healthy controls were randomly divided into training set and test set in a ratio of 7:3, corresponding to 161 and 69 samples respectively.

[0079] (2) Take 2 mg of the magnetic metal-organic framework material obtained in Example 1 and mix it with 100 μL of deionized water to prepare a material dispersion. Take 20 μL of the material dispersion and 2 μL of serum and add them to 250 μL of deionized water. Incubate at 37°C for 60 minutes. Then, with the help of a magnet, discard the supernatant and redisperse the material enriched in peptides into 40 μL of deionized water to obtain a dispersion.

[0080] (3) Mass spectrometry analysis: Take 1 μL of the dispersion from step (2) and mix it with 1 μL of 20 mg / mL solution. -1 A 2,5-dihydroxybenzoic acid (DHB) matrix (acetonitrile / water / trifluoroacetic acid volume ratio = 50 / 49.9 / 0.1) was used as a target. After natural drying, MALDI-TOF / TOFMS analysis was performed using a Bruker UltrafleXtreme MALDI-TOF / TOF mass spectrometer with a 355 nm Nd:YAG laser source at a frequency of 2000 Hz and an accelerating voltage of 20 kV (20 kV at ion source 1 and 17.6 kV at ion source 2). The acquisition mode was reflectance cation mode, and the acquisition range was 700-5000 Da. Mass spectrometry data were obtained from Flexcontrol 3.4 and exported from Flexanalysis 3.4. The mass spectra are shown below. Figure 6 As shown.

[0081] Analysis results: Figure 6 It can be seen that endogenous peptides in serum are captured by this material, and the serum peptide mass spectra of Alzheimer's patients are significantly different from those of healthy individuals.

[0082] Example 3

[0083] Peak extraction and normalization were performed on the serum endogenous peptide mass spectra obtained in Example 2. Orthogonal partial least squares discriminant analysis was conducted on the training set using Metaboanalyst 5.0 and SIMCA to calculate the VIP value, P value, and FC value of each peptide, and characteristic peptide biomarkers were screened. The steps are as follows:

[0084] (1) Peak extraction and normalization of serum hydrophilic peptide mass spectra were performed using the R packages MALDIquant, MALDIquantForeign and limma.

[0085] (2) Orthogonal partial least squares discriminant analysis and principal component analysis were performed on the training set and validation set respectively using Metaboanalyst 5.0 and SIMCA to select characteristic peptide biomarkers. Specifically, Metaboanalyst 5.0 and SIMCA were used to calculate the VIP value, P value and FC value of each peptide, and peptides with VIP value > 2, P value < 0.05 and FC value > 2 or < 0.5 were selected as characteristic peptide biomarkers.

[0086] (3) Create heatmaps of the training set based on characteristic polypeptide markers.

[0087] Orthogonal partial least squares discriminant analysis model based on training set, such as Figure 7 As shown; heatmaps of 15 feature peptides based on the training set are as follows. Figure 8 As shown.

[0088] Analysis results: Figure 7 It can be seen that the orthogonal partial least squares discriminant analysis model shows good separation of serum endogenous peptides from Alzheimer's patients and healthy individuals. Figure 8 It can be seen that the 15 characteristic polypeptide biomarkers play an important role in distinguishing Alzheimer's patients from healthy individuals.

[0089] Example 4

[0090] Principal component analysis was performed on all samples using the 15 characteristic polypeptide biomarkers obtained in Example 3.

[0091] Principal component analysis model based on 15 characteristic peptides from all samples, as follows: Figure 9 As shown.

[0092] Analysis results: Figure 9 It can be seen that, using principal component analysis, an unsupervised machine learning algorithm, the 15 characteristic peptide biomarkers are capable of distinguishing Alzheimer's patients from healthy individuals.

[0093] Example 5

[0094] The 15 characteristic polypeptide biomarkers obtained in Example 3 were used to establish a machine learning diagnostic model for the training set.

[0095] A machine learning model built based on 15 feature peptides from the training set is as follows: Figure 10 As shown.

[0096] Analysis results: Figure 10 It can be seen that, under the model established by six machine learning algorithms, namely Random Forest (RF), Logistic Regression (LR), Neural Network (NN), Support Vector Machine (SVM), Naive Bayes (NB), and k-Nearest Neighbors (kNN), the 15 characteristic peptide biomarkers have the ability to diagnose Alzheimer's disease patients.

[0097] Example 6

[0098] The three highest-scoring characteristic peptide markers (m / z values ​​of 4215.4, 2884.77, and 2704.61) from the 15 characteristic peptide markers obtained in Example 3 were used to establish the six machine learning diagnostic models in Example 6 on the training set.

[0099] A machine learning model built based on three feature peptides from the training set, such as... Figure 11 As shown.

[0100] Analysis results: Figure 11 It can be seen that a machine learning model built using only three characteristic peptide biomarkers is capable of diagnosing Alzheimer's disease patients.

[0101] Example 7

[0102] The eluent obtained in Example 2 was analyzed by nano-LC-MS / MS to identify the peptide sequence, as follows:

[0103] (1) An EASY-nLC 1000 liquid chromatograph (Thermo Fisher Scientific) coupled with an OrbitrapFusion mass spectrometer (Thermo Fisher Scientific) was used. Phase A and Phase B of the liquid chromatography were water containing 0.1% formic acid and acetonitrile containing 0.1% formic acid, respectively. The eluent obtained in step (1) was lyophilized and redissolved in phase A. A linear gradient was applied, with 5% A-30% B, for 50 min. The eluent was then loaded onto an analytical column (Thermo Scientific Acclaim PepMap C18, 75 μm × 25 cm) for analysis. The voltage of the electrospray ionization was 2.3 kJ / kg. kV, the scanning range of the parent ion in the first-order spectrum is m / z=350-1600, with a resolution of 60000 (m / z=200). The second-order spectrum is obtained through high-energy collisional dissociation, with a resolution of 15000 and m / z=200. The high-energy collisional dissociation mode is selected to sequentially fragment parent ions with charges of +2, +3, and +4. The normalized collision energy is 28%.

[0104] (2) Tandem mass spectrometry was performed using Proteome Discoverer (Thermo Fisher Scientific, version 2.4.0.305) and the Uniprot-SwissProt database (taxonomic: Homo sapiens, 20386 entries) was searched. The mass tolerance of the parent ion was set to 10 ppm and the mass tolerance of the fragment ion was set to 0.020 Da.

[0105] (3) The mass-to-charge ratio of the characteristic polypeptide markers obtained in Example 3 was matched with the polypeptides identified by nano-LC-MS / MS, and the amino acid sequences of 15 characteristic polypeptide markers were determined by the principle of consistency within 100 ppm.

[0106] Analysis results: The amino acid sequences of the 15 characteristic polypeptide markers are as follows:

[0107] A m / z value of 4215.41 corresponds to HNVYINGITYTPVSSTNEKDMYSFLEDMGLKAFTNSK.

[0108] A m / z value of 2884.77 corresponds to DQTVSDNELQEMSNQGSKYVNKEIQ.

[0109] A m / z value of 2704.61 corresponds to REKPRVQEKQHPVPPPAQNQNQV.

[0110] A m / z value of 2930.55 corresponds to SSSYSKQFTSSTSYNRGDSTFESKSY.

[0111] A m / z value of 4192.66 corresponds to LPAVDEKLRDLYSKSTAAM(+15.99)STYTGIFTDQVLSVLKGEE.

[0112] A m / z value of 2955.34 corresponds to GPRRYTIAALLSPYSYSTTAVVTNPKE.

[0113] A m / z value of 3605.01 corresponds to VSETESRGSESGIFTNTKESSSHHPGIAEFPSRG.

[0114] A m / z value of 2510.05 corresponds to FTSTSYNRGDSTFESKSYKMA.

[0115] A m / z value of 2784.80 corresponds to ILRQQQHLFGSNVTDCSGNFCLFR.

[0116] A m / z value of 2541.30 corresponds to DAHKSEVAHRFKDLGEENFKAL.

[0117] A m / z value of 2680.93 corresponds to ADRSGKDGVMEMNSIEPAKETTTNV.

[0118] A m / z value of 3314.60 corresponds to SVPPSASHVAPTETFTYEWTVPKEVGPTNAD.

[0119] A m / z value of 2379.03 corresponds to SYSKQFTSSTSYNRGDSTFES.

[0120] A m / z value of 2328.42 corresponds to SQEEEKTEALTSAKRYIETD.

[0121] A m / z value of 2354.68 corresponds to DNELQEMSNQGSKYVNKEIQ.

[0122] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

Claims

1. An Alzheimer's disease diagnostic kit, characterized in that, Includes reagents for detecting 15 specific peptide markers; The amino acid sequences of the 15 specific peptide markers are shown in SEQ ID NO: 1-15.

Images