Peripheral blood M protein heavy chain N-glycosylation discriminant marker acquisition method and application

Abstract

The present application relates to a kind of peripheral blood M protein heavy chain N-glycosylation discriminant marker acquisition method and application, belong to biomedical detection technical field, solve the problem that present technique is difficult to utilize peripheral blood to non-invasive distinction of plasma cell disease, and lack stable and efficient M protein heavy chain N-glycopeptide enrichment means and systemic glycosylation marker system, cannot satisfy one of clinical efficient auxiliary identification needs etc.Provided by the present application, the acquisition method includes: step one, cut M protein heavy chain band in plasma and carry out in-gel trypsin enzymolysis, obtain heavy chain peptide segment mixture;Step two, utilize Fe3O4@PANI magnetic bead suspension to enrich heavy chain N glycopeptide in heavy chain peptide segment mixture, obtain heavy chain N-glycopeptide enrichment product.The acquisition method described in the present application improves the selectivity and enrichment efficiency of low-abundance N-glycopeptide, helps to improve the problems such as blood matrix complexity and weak glycopeptide signal, provides reliable support for stable detection and large-scale analysis of clinical samples.

Description

Technical Field

[0001] This invention relates to the field of biomedical detection technology, and in particular to a method for obtaining and applying a discriminant marker for N-glycosylation of peripheral blood M protein heavy chain. Background Technology

[0002] Monoclonal immunoglobulin-related diseases include systemic light chain amyloidosis (AL), light chain deposition disease (LCDD), and monoclonal gammaglobulinopathy of undetermined significance (MGUS). All three can present with monoclonal M protein abnormalities, but their organ involvement patterns, clinical manifestations, and prognoses differ significantly. AL is characterized by multi-organ involvement, particularly cardiac involvement; LCDD shows significant renal targeting; and MGUS is typically a relatively stable prodromal state. Current clinical detection methods include serum protein electrophoresis, immunofixation electrophoresis, serum free light chain detection, bone marrow examination, and histopathological evaluation. These methods are valuable for M protein screening and disease monitoring, but they still have limitations in non-invasive molecular-assisted identification based on peripheral blood samples.

[0003] The constant region of immunoglobulin heavy chains contains relatively conserved N-glycosylation sites. Heavy chain N-glycosylation modifications are reflected not only in differences in the expression of individual glycopeptide peaks but also in changes in the relative composition of different glycoforms at the same glycosylation site. Studies have shown a close correlation between abnormal M protein glycosylation and the clinical phenotype, prognosis, and treatment response of plasma cell diseases. Therefore, constructing an auxiliary identification index system based on the M protein heavy chain N-glycosylation characteristics has a promising molecular basis and application prospects.

[0004] However, blood sample matrices are complex, and the abundance of target glycopeptides is relatively low, making stable extraction, systematic annotation, differential screening, and ratio construction of heavy chain N-glycopeptides technically challenging. Current technologies lack an integrated approach to constructing a combination of discriminant indicators based on the intensity ratio of N-glycosylation characteristic peaks and glycoform peaks at the same site in peripheral blood monoclonal M protein heavy chains, and to using this combination for assisted identification of AL, LCDD, and MGUS. Summary of the Invention

[0005] Based on the above analysis, the present invention aims to provide a method and application for obtaining N-glycosylation discriminant markers of peripheral blood M protein heavy chain, thereby solving one of the problems in the prior art, such as the difficulty in non-invasively distinguishing plasma cell diseases using peripheral blood, and the lack of stable and efficient methods for enriching M protein heavy chain N-glycopeptides and a systematic glycosylation marker system, which fails to meet the needs of efficient clinical auxiliary identification.

[0006] The first aspect of this invention provides a method for obtaining a combination of discriminant indicators for N-glycosylation of the heavy chain of peripheral blood monoclonal M protein, comprising the following steps: Step 1: Cut the heavy chain band of M protein from the plasma and perform intragel trypsin digestion to obtain a mixture of heavy chain peptides; Step 2: Enrich the heavy chain N in the heavy chain peptide mixture using Fe3O4@PANI magnetic bead suspension. Glycopeptides were extracted to obtain a heavy chain N-glycopeptide enrichment product.

[0007] Furthermore, the acquisition method further includes step three: detecting the heavy chain N-glycopeptide enriched product using Fourier transform ion cyclotron resonance mass spectrometry, extracting single isotope peaks with a signal-to-noise ratio > 3.0, and performing glycopeptide composition prediction and base peak normalization.

[0008] Furthermore, the acquisition method also includes step four: establishing heavy chain N. Glycopeptide database and database of peak intensity ratios of N-glycosylated glycans at the same site.

[0009] Furthermore, in the acquisition method described above, the established heavy chain N The glycopeptide database contains 6 immunoglobulin subclasses and 65 heavy chain N-type proteins. Glycopeptides and 14 core N The glycosylation ratio was verified by 30 QC samples, and the RSD of all glycosylation ratios was <20%.

[0010] Furthermore, the acquisition method also includes step five: statistical difference screening and OPLS. DA analysis and ROC evaluation were used to obtain heavy chain N with disease differentiation efficacy. Glycosylation markers.

[0011] Furthermore, the method further includes, prior to step one, an immunoprecipitation extraction of monoclonal M protein in plasma using a κ / λ mixed affinity matrix to obtain an eluent containing the M protein heavy chain.

[0012] Furthermore, in the acquisition method, N in step two Glycopeptide enrichment was performed using 80% acetonitrile as the enrichment buffer.

[0013] Furthermore, in the acquisition method, N in step two Glycopeptide enrichment was performed using 0.1% ammonia solution as the eluent.

[0014] A second aspect of the present invention also provides a heavy chain N obtained using the above method. Application of glycosylation discrimination index combination in the preparation of plasma cell disease auxiliary identification products.

[0015] Furthermore, in the aforementioned applications, the plasma cell diseases include light chain amyloidosis (AL), light chain deposition disease (LCDD), and monoclonal gammaglobulinosis of unknown significance (MGUS).

[0016] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. The method for obtaining the combination of N-glycosylation discriminant indicators of peripheral blood monoclonal M protein heavy chain described in this invention establishes an integrated detection process from peripheral blood samples to M protein heavy chain N-glycosylation markers. Relying on Fe3O4@PANI magnetic nanomaterials and an optimized enrichment-elution system, it improves the selectivity and enrichment efficiency of low-abundance N-glycopeptides, which helps to improve problems such as complex blood matrix and weak glycopeptide signals, and provides reliable support for stable detection and large-scale analysis of clinical samples.

[0017] 2. The method for obtaining the combination of N-glycosylation discrimination indicators for peripheral blood monoclonal M protein heavy chains described in this invention constructs a standardized database containing 6 immunoglobulin subclasses, 65 heavy chain N-glycopeptides, and 14 core glycoform ratios. Validated with 30 QC samples, the RSD of the glycoform ratios is less than 20%, indicating that the method has good precision and reproducibility. Furthermore, the introduction of the peak intensity ratio of glycoforms at the same site as a core indicator helps improve the anti-interference ability and quantitative stability of the biomarkers, and reduces the influence of sample and operational factors on the detection results.

[0018] 3. This invention utilizes the method for obtaining the peripheral blood monoclonal M protein heavy chain N-glycosylation discriminant index combination in the preparation of plasma cell disease auxiliary identification products. The N-glycosylation discriminant index combination screened by this technology has good auxiliary identification efficacy for AL, LCDD, and MGUS, with some indicators reaching an AUC of 1.00. It can effectively distinguish between depositional diseases and MGUS precursor states, providing a novel molecular reference for early, non-invasive disease risk stratification in clinical practice. Furthermore, the heavy chain N-glycosylation profiles of bone marrow and peripheral blood have high consistency, and peripheral blood alone can largely reflect the glycosylation characteristics of bone marrow-derived proteins, which helps improve the non-invasiveness of the test and patient compliance.

[0019] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0020] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Figure 1 For monoclonal M protein heavy chain N A flowchart illustrating the method for obtaining combinations of glycosylation discrimination indicators; Figure 2 This is a schematic diagram of the N-glycosylation site and corresponding glycoform of the M protein IgG1 heavy chain. Figure 3 This is a schematic diagram of the N-glycosylation site and corresponding glycoform of the IgG2 heavy chain of the M protein. Figure 4 This is a schematic diagram of the N-glycosylation modification sites and corresponding glycans in the heavy chain of the M protein. Figure 4 A is a schematic diagram of the N-glycosylation modification sites and corresponding glycan structures of the heavy chain of IgA2 type M protein. Figure 4 B is a schematic diagram of the N-glycosylation modification sites and corresponding glycan structures of the heavy chain of IgM type M protein; Figure 5 This is a schematic diagram of the N-glycosylation sites and corresponding glycoforms of the IgD / IgE heavy chain of the M protein. Figure 5 A is a schematic diagram of the N-glycosylation site and corresponding glycoform structure of the IgD heavy chain of the M protein. Figure 5 B is a schematic diagram of the N-glycosylation site and corresponding glycoform structure of the gE heavy chain of the M protein. Figure 6 For core N Schematic diagram of glycosylated sugar composition; Figure 7 This is a combined graph of OPLS-DA model results for AL-PB and MGUS-PB samples, where... Figure 7 A represents the OPLS-DA scatter plot. Figure 7 B is a bar chart of the fitting parameters of the OPLS-DA model. Figure 7 C is the histogram of the OPLS-DA permutation test; Figure 8 This is a combined graph of OPLS-DA model results for LCDD-PB and MGUS-PB samples, where... Figure 8 A represents the OPLS-DA scatter plot. Figure 8 B is a bar chart of the fitting parameters of the OPLS-DA model. Figure 8 C is the histogram of the OPLS-DA permutation test; Figure 9 This is a combined graph of OPLS-DA model results for AL-PB and LCDD-PB samples, where... Figure 9 A represents the OPLS-DA scatter plot. Figure 9 B is a bar chart of the fitting parameters of the OPLS-DA model. Figure 9C is the histogram of the OPLS-DA permutation test; Figure 10 This is a composite image showing the N-glycopeptide screening results of the differences between the AL-PB and MGUS-PB groups. Figure 10 A represents the differential heavy chain N-glycopeptide volcano plot between AL-PB and MGUS-PB groups. Figure 10 B is a heatmap showing the difference in expression between the AL-PB and MGUS-PB groups; Figure 11 This is a composite image showing the differential N-glycopeptide screening results between the LCDD-PB and MGUS-PB groups. Figure 11 A is the volcano diagram of the differentially expressed heavy chain N-glycopeptides between LCDD-PB and MGUS-PB groups. Figure 11 B is a heatmap of the difference in expression between the LCDD-PB and MGUS-PB groups; Figure 12 This is a composite image showing the differential N-glycopeptide screening results between the AL-PB and LCDD-PB groups. Figure 12 A represents the volcano diagram of differentially expressed heavy chain N-glycopeptides between AL-PB and LCDD-PB groups. Figure 12 B is a heatmap of the difference in expression between the AL-PB and LCDD-PB groups; Figure 13 This is a composite graph showing the results of key indicators for screening N-glycopeptides that differentiate between AL-PB and MGUS-PB. Figure 13 A is the VIP sequencing diagram of the differentially expressed heavy chain N-glycopeptides of AL-PB and MGUS-PB. Figure 13 B is a Venn diagram showing the screening conditions for differentially expressed heavy chain N-glycopeptides between AL-PB and MGUS-PB; Figure 14 This is a composite graph showing the results of key indicators for screening N-glycopeptides that differentiate between LCDD-PB and MGUS-PB. Figure 14 A is the VIP sequencing diagram of the differentially expressed heavy chain N-glycopeptides of LCDD-PB and MGUS-PB. Figure 14 B is a Venn diagram showing the screening conditions for differentially expressed heavy chain N-glycopeptides between LCDD-PB and MGUS-PB. Figure 15 This is a composite graph showing the results of key indicators for screening N-glycopeptides that differentiate between AL-PB and LCDD-PB. Figure 15 A is the VIP sequencing diagram of the differentially expressed heavy chain N-glycopeptides of AL-PB and LCDD-PB. Figure 15 B is a Venn diagram showing the screening conditions for differentially expressed heavy chain N-glycopeptides between AL-PB and LCDD-PB; Figure 16 This is a composite plot of the glycoform structure, expression box plot, and ROC curve of the representative heavy chain N-sugar characteristic peaks of AL-PB and MGUS-PB. Figure 16A represents the combined plot of the glycoform structure, expression box plot, and ROC curve at m / z 3217.2571. Figure 16 B represents the combined plot of the glycoform structure (m / z 3116.3430), expression box plot, and ROC curve. Figure 16 C represents the glycoform structure at m / z 2561.0290, along with a combination of box plots and ROC curves. Figure 16 D represents the combined plot of the glycoform structure, expression box plot, and ROC curve (m / z 3055.2043). Figure 16 E represents the combined graph of glycoform structure, expression box plot, and ROC curve (m / z 3071.1992). Figure 16 F represents the combined plot of glycoform structure (m / z 3100.3480), expression box plot, and ROC curve. Figure 16 G represents the glycoform structure at m / z 3347.2950, ​​along with its expression box plot and ROC curve combination. Figure 17 The diagram shows the glycoform structure, expression box plot, and ROC curve combination of the representative heavy chain N-sugar characteristic peaks of LCDD-PB and MGUS-PB. Figure 17 A represents the combined plot of the glycoform structure, expression box plot, and ROC curve at m / z 3941.1362. Figure 17 B represents the glycoform structure at m / z 2455.9982, along with a combination of box plots and ROC curves. Figure 17 C represents the glycoform structure at m / z 2983.1832, along with a combination of box plots and ROC curves. Figure 17 D represents the combined plot of glycoform structure, expression box plot, and ROC curve (m / z 2398.9770). Figure 17 E represents the combined graph of glycoform structure, expression box plot, and ROC curve (m / z 3116.3430). Figure 17 F represents the combined plot of glycoform structure, expression box plot, and ROC curve (m / z 2593.0190). Figure 17 G represents the glycoform structure at m / z 2958.1515, and the combined box plot and ROC curve are presented. Figure 17 H represents the glycoform structure (m / z 3217.2571), along with its expression box plot and ROC curve combination. Figure 17 I represents the combined plot of glycoform structure, expression box plot, and ROC curve at m / z 2812.0936; Figure 18 The glycoform structure, expression box plot and ROC curve combination diagram of the characteristic peaks of N-sugar in the representative heavy chain of AL-PB and LCDD-PB; Figure 18 A represents the combined plot of glycoform structure, expression box plot, and ROC curve for m / z 2561.0290. Figure 18 B represents the combined plot of the glycoform structure (m / z 3071.1992), expression box plot, and ROC curve. Figure 18 C represents the glycoform structure at m / z 2891.2680, along with a combination of box plots and ROC curves. Figure 18 D represents the glycoform structure at m / z 2659.0775, along with a combination of box plots and ROC curves. Figure 18 E is 3227.2890 m / z. Combined plot of glycoform structure, expression box plot, and ROC curve. Figure 19 This is a volcano plot combination of the intensity ratios of differentially expressed heavy chain N-glycopeptides among the three groups: AL-PB, LCDD-PB, and MGUS-PB. Figure 19 A represents a volcano plot combination of the intensity ratios of AL-PB and MGUS-PB differentially expressed heavy chain N-glycopeptides. Figure 19 B represents a volcano plot combination of the intensity ratios of differentially expressed heavy chain N-glycopeptides between LCDD-PB and MGUS-PB. Figure 19 C represents the volcano plot combination of the intensity ratios of AL-PB and LCDD-PB differentially heavy chain N-glycopeptides; Figure 20 A composite plot of glycoform structure, expression box plot, and ROC curve for the intensity ratio of differentially expressed heavy chain N-glycopeptides between AL-PB and MGUS-PB. Figure 20 A represents a combination of box plots and ROC curves showing the glycoform structure, expression, and expression of G1-G2F1N2 / G2F1N2S1. Figure 20 B is a composite graph of the G2-G2N3 / G2F1N3 glycoform structure, expression box plot, and ROC curve. Figure 20 C represents a combination of the G2-G1F1N2 / G1F1N2S1 glycoform structure, expression box plot, and ROC curve. Figure 20 D is a composite graph of the G2-G1N1 / G1F1S1 glycoform structure, expression box plot, and ROC curve. Figure 20 E represents a combination of box plots and ROC curves showing the glycoform structure and expression of G2-G1N1 / G1F1N1. Figure 20 F represents a combination of D-G1F1N3 / G2F1N3 glycoform structure, expression box plot, and ROC curve; Figure 21 The graph shows the glycoform structure, expression box plot, and ROC curve combination of the markers for the intensity ratio of differentially expressed heavy chain N-glycopeptides between LCDD-PB and MGUS-PB. Figure 21 A represents a combination of the G2-N2 / F1N2 glycoform structure, expression box plot, and ROC curve. Figure 21 B represents a combination of the G2-G1F1N2 / G1F1N2S1 glycoform structure, expression box plot, and ROC curve. Figure 21 C represents a combination of the glycoform structure, expression box plot, and ROC curve of G1-G2F1N2 / G2F1N2S1. Figure 21D is a combination of box plot and ROC curve of G2-G2N3 / G2F1N3 glycoform structure; Figure 22 The glycoform structure, expression box plot, and ROC curve composite plot of the differential heavy chain N-glycopeptide intensity ratio markers between AL-PB and LCDD-PB are shown below. Figure 22 A represents a combination of the G2-N3 / G1N3 glycoform structure, expression box plot, and ROC curve. Figure 22 B is a composite graph of the G2-G2N3 / G2F1N3 glycoform structure, expression box plot, and ROC curve. Figure 22 C represents a combination of the G1-N2 / F1N2 glycoform structure, expression box plot, and ROC curve. Figure 22 D represents a combination of the glycoform structure, expression box plot, and ROC curve of G2-G1F1N2 / G1F1N2S1. Figure 22 E represents a combination of box plots and ROC curves showing the glycoform structure and expression of G2-G1N1 / G1F1N1. Figure 22 F represents a combination of box plots and ROC curves showing the glycoform structure, expression, and expression of D-G1F1N3 / G2F1N3. Figure 22 G represents a combination of the G1-G2N2 / G2N2S1 glycoform structure, expression box plot, and ROC curve. Figure 22 H represents a combination of the G2-N2 / F1N2 glycoform structure, expression box plot, and ROC curve; Figure 23 A composite diagram of OPLS-DA model analysis for AL-bone marrow (BM) and AL-peripheral blood (PB) samples, in which... Figure 23 A is the scatter plot of OPLS-DA for AL-BM and AL-PB samples. Figure 23 B is a bar chart showing the fitting parameters of the OPLS-DA model for the AL-BM and AL-PB samples. Figure 23 C is the permutation test histogram of the OPLS-DA model for AL-BM and AL-PB samples; Figure 24 This is a composite diagram of OPLS-DA model analysis for LCDD-bone marrow (BM) and LCDD-peripheral blood (PB) samples. Figure 24 A is the scatter plot of OPLS-DA for LCDD-BM and LCDD-PB samples. Figure 24 B is a bar chart showing the fitting parameters of the OPLS-DA model for LCDD-BM and LCDD-PB samples. Figure 24 C is the permutation test histogram of the OPLS-DA model for LCDD-BM and LCDD-PB samples; Figure 25 This is a composite diagram of OPLS-DA model analysis for MGUS-bone marrow (BM) and MGUS-peripheral blood (PB) samples, where... Figure 25A is the scatter plot of OPLS-DA for MGUS-BM and MGUS-PB samples. Figure 25 B is a bar chart showing the fitting parameters of the OPLS-DA model for the MGUS-BM and MGUS-PB samples. Figure 25 C is the histogram of the permutation test of the OPLS-DA model for samples MGUS-BM and MGUS-PB. Detailed Implementation The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention / utility model to illustrate the principles of the present invention / utility model, but are not intended to limit the scope of the present invention / utility model.

[0021] The main instruments, reagents and materials used in the implementation of this invention are shown in Tables 1 and 2.

[0022] Table 1 Experimental Instruments

[0023] Table 2 Main experimental reagents and materials

[0024] The first aspect of this invention provides a method for obtaining a combination of discriminant indicators for N-glycosylation of the heavy chain of peripheral blood monoclonal M protein, comprising the following steps: Step 1: Cut the heavy chain band of M protein from the plasma and perform intragel trypsin digestion to obtain a mixture of heavy chain peptides; Step 2: Enrich the heavy chain N in the heavy chain peptide mixture using Fe3O4@PANI magnetic bead suspension. Glycopeptides were extracted to obtain a heavy chain N-glycopeptide enrichment product.

[0025] Furthermore, the acquisition method further includes step three: detecting the heavy chain N-glycopeptide enriched product using Fourier transform ion cyclotron resonance mass spectrometry, extracting single isotope peaks with a signal-to-noise ratio > 3.0, and performing glycopeptide composition prediction and base peak normalization.

[0026] Furthermore, the acquisition method also includes step four: establishing heavy chain N. Glycopeptide database and database of peak intensity ratios of N-glycosylated glycans at the same site.

[0027] Furthermore, in the acquisition method described above, the established heavy chain N The glycopeptide database contains 6 immunoglobulin subclasses and 65 heavy chain N-type proteins. Glycopeptides and 14 core N The glycosylation ratio was verified by 30 QC samples, and the RSD of all glycosylation ratios was <20%.

[0028] Furthermore, the acquisition method also includes step five: statistical difference screening and OPLS. DA analysis and ROC evaluation were used to obtain heavy chain N with disease differentiation efficacy. Glycosylation markers.

[0029] Furthermore, the method further includes, prior to step one, an immunoprecipitation extraction of monoclonal M protein in plasma using a κ / λ mixed affinity matrix to obtain an eluent containing the M protein heavy chain.

[0030] Furthermore, in the acquisition method, N in step two Glycopeptide enrichment was performed using 80% acetonitrile as the enrichment buffer.

[0031] Furthermore, in the acquisition method, N in step two Glycopeptide enrichment was performed using 0.1% ammonia solution as the eluent.

[0032] A second aspect of the present invention also provides a heavy chain N obtained using the above method. Application of glycosylation discrimination index combination in the preparation of plasma cell disease auxiliary identification products.

[0033] Furthermore, in the aforementioned applications, the plasma cell diseases include light chain amyloidosis (AL), light chain deposition disease (LCDD), and monoclonal gammaglobulinosis of unknown significance (MGUS).

[0034] Example 1: Method for obtaining a combination of discriminant indicators for N-glycosylation of peripheral blood M protein heavy chain 1. Clinical Sample Sources and Preservation Clinical samples were obtained from Peking University First Hospital. With the patients' consent and signed informed consent forms, the research was approved by the Biomedical Research Ethics Committee of Peking University First Hospital and complied with the Declaration of Helsinki. AL and LCDD patients were diagnosed by histopathology between January 2011 and June 2025, while MGUS was diagnosed by clinical and laboratory tests during the same period.

[0035] Sample composition: AL group: 94 cases of peripheral blood and 126 cases of bone marrow LCDD group: 13 cases from peripheral blood, 22 cases from bone marrow. MGUS group: 5 cases of peripheral blood and 48 cases of bone marrow Immediately after sample collection, the samples were centrifuged at 3000×g and 4℃ for 10 min to separate serum / plasma. The samples were then aliquoted and stored at -80℃.

[0036] 2. Preparation of Fe3O4@PANI magnetic nanomaterials 2.1 Weigh out 1.35g of ferric chloride The hexahydrate was dissolved in 75 mL of ethylene glycol. After stirring until dissolved, 3.6 g of sodium acetate was added, and stirring was continued until dissolved.

[0037] 2.2 The solution obtained in step 2.1 was transferred into a reaction vessel, heated at 200°C for 16 hours, and naturally cooled to obtain Fe3O4. The solution was washed alternately with ethanol and ultrapure water and dried at 60°C.

[0038] 2.3 Take 30 mL of 0.1% (m / v) hydrochloric acid and add 0.3 mL of aniline. Stir in an ice bath for 10 min, then add 300 mg of Fe3O4 obtained in step 2.2 and stir for another 10 min.

[0039] 2.4 Slowly add 1 mL of 0.38 g / L ammonium persulfate solution to the mixture obtained in step 2.3, stir in an ice bath for 4 h to obtain Fe3O4@PANI.

[0040] 2.5 Wash the Fe3O4@PANI prepared in step 2.4 with acetonitrile / ultrapure water alternately, and dry at 60℃ for later use.

[0041] 3. Plasma sample processing and preparation of mixed QC samples Plasma samples were thawed naturally at room temperature. 20 μL of plasma from each patient was collected, mixed in equal volumes, and a pooled quality control (QC) sample was prepared. This QC sample was used for enrichment condition optimization, elution condition optimization, database establishment, and methodological repeatability validation. Throughout the entire testing process, one QC sample was inserted for every 10 test samples processed to monitor method stability and batch-to-batch consistency.

[0042] 4. Extraction of M protein 4.1 Take 10 μL of plasma from step 1 and dilute it with 180 μL of 1×PBS buffer.

[0043] 4.2 Mix the κ light chain and λ light chain specific affinity matrices in equal volumes at a ratio of 1:1 to obtain a mixed matrix. Add 10 μL of the mixed matrix to the diluted plasma obtained in step 4.1 and incubate at room temperature for 45-60 min by rotation.

[0044] 4.3 Centrifuge at 5000 r / min for 5 min, discard the supernatant, and wash 3 times each with PBS buffer and ultrapure water, 200 μL each time.

[0045] 4.4 Add 40 μL of 40 mM TCEP formic acid solution (prepared with 5% formic acid), mix by rotation for 10-20 min, centrifuge at 15000×g and 4℃ for 5 min, collect the supernatant to obtain the eluent containing M protein.

[0046] 5. M protein purification verification 5.1 Take 10 μL of M protein elution buffer, add 10 μL of 2×SDS-PAGE loading buffer, heat at 99℃ for 5 min to denature, and after cooling, take 20 μL of sample and load it.

[0047] 5.2 Add all the samples obtained in step 5.1 to the 4% to 12% gradient precast gel electrophoresis lanes, first electrophore at 60V constant voltage for 45 min, and then electrophore at 120V constant voltage for 1 h.

[0048] 5.3 Stain with Coomassie Brilliant Blue rapid staining solution for 10 min, then destain with ultrapure water until the background is clear.

[0049] 5.4 Western blot detection: The protein separated in step 5.2 was transferred to a PVDF membrane and incubated with anti-κ light chain antibody, anti-λ light chain antibody and anti-human immunoglobulin heavy chain antibody, respectively. Then, it was incubated with the corresponding secondary antibodies. ECL colorimetric imaging was used to interpret the purification results of M protein.

[0050] 5.5 Protein quantification: The remaining M protein eluent was used to determine the protein concentration using the BCA method, which served as the basis for subsequent in-gel digestion and sample loading control.

[0051] 6. Setting up positive and negative controls Positive control: Trastuzumab, diluted to 1 mg / mL, processed according to the sample procedure to verify the reliability of the procedure.

[0052] Negative control: Polyclonal immunoglobulin from plasma of healthy individuals was processed in parallel according to the sample procedure to assess the impact of polyclonal background.

[0053] The results showed that the M protein heavy chain N-glycopeptide profile was significantly different from that of the polyclonal immunoglobulin heavy chain, but highly similar to that of the trastuzumab heavy chain glycopeptide profile.

[0054] 7. In-gel enzymatic hydrolysis 7.1 Based on the Coomassie Brilliant Blue staining results, combined with the molecular weight position of the pre-stained protein marker and the Western blot verification results, the M protein heavy chain band was cut from the region corresponding to approximately 50 kDa, and then minced into approximately 1 mm³ gel particles before being transferred to centrifuge tubes.

[0055] 7.2 Add 400 μL of 50% acetonitrile to the centrifuge tube from step 7.1 and decolorize for 10-20 min. Centrifuge and discard the supernatant. Repeat the decolorization process once.

[0056] 7.3 Add 200 μL of 100% acetonitrile to the centrifuge tube from step 7.2 to dehydrate until the gel particles turn white and harden.

[0057] 7.4 Add 10 μL of sequencing-grade trypsin solution to the centrifuge tube from step 7.3, incubate at 4°C for 30 min, then add 200 μL of 25 mM ammonium bicarbonate solution and incubate at 37°C for 12-15 h.

[0058] 7.5 After the enzymatic hydrolysis is completed, place it on ice for 5 minutes to terminate the enzymatic hydrolysis reaction. Collect the supernatant and residual enzymatic hydrolysate in the colloidal particles, centrifuge at 15000×g and 4℃ for 5 minutes, transfer the supernatant, freeze dry under vacuum and store at -80℃.

[0059] 8. Enrichment of N-glycopeptides 8.1 Weigh 20 mg of the Fe3O4@PANI magnetic nanomaterial prepared in step 2, add 10 mL of acetonitrile, and ultrasonically disperse for 30 s to prepare a 2 mg / mL magnetic bead dispersion, which should be prepared and used immediately. Take 80 μL of the magnetic bead dispersion for each sample for N-glycopeptide enrichment, which is equivalent to using 0.16 mg of Fe3O4@PANI magnetic nanomaterial per sample.

[0060] 8.2 Add 80 μL of magnetic bead dispersion to the lyophilized enzymatic hydrolysis sample prepared in step 7.5, vortex for 20 s, and shake at room temperature in the dark for 1 h.

[0061] 8.3 Perform magnetic separation for 3 min, discard the supernatant, and wash 3 times with 100 μL of 80% acetonitrile.

[0062] 8.4 Add 80 μL of 0.1% ammonia solution as elution buffer, elute by shaking at room temperature in the dark for 40 min, centrifuge at 5000 r / min for 2 min, collect the supernatant by magnetic separation, and freeze-dry under vacuum for storage.

[0063] like Figure 1 As shown, the method of the present invention establishes a complete technical process from sample processing, M protein extraction, in-gel enzymatic digestion, N-glycopeptide enrichment to mass spectrometry detection and data analysis.

[0064] 9. Optimization of enrichment and elution conditions Enrichment buffer: Optimized by mixing QC samples, 80% acetonitrile was determined to be the best.

[0065] Eluent: Compared with 0.2% TFA, 1% FA, ultrapure water, 5mM NH4HCO3, and 0.1% NH3 H2O was used to determine that 0.1% ammonia solution was the most effective.

[0066] 10. Mass spectrometry detection 10.1 Add 10 μL of 0.1% formic acid to the lyophilized N-glycopeptide sample obtained in step 8.4 for reconstitution, and vortex for 20 s.

[0067] 10.2 Take 1 μL of the sample prepared in step 10.1 and drop it onto the MTP AnchorChip™ target plate. Add 1 μL of CHCA matrix solution (10 mg / mL, ACN / MeOH / 0.1% TFA=49.95 / 49.95 / 0.1 (v / v / v)), mix well, and air dry at room temperature.

[0068] 10.3 Positive ion reflectance mode detection was performed using a 7T SolariX XR FTICR-MS (Bruker Daltonics). The laser power was set to 50%–60%, and 30 laser shots were accumulated for each sample, with an acquisition range of m / z 500–5000. External standard calibration for mass spectrometry was performed using Bruker Calibration Mix, with a resolution set to 200,000 (m / z 400).

[0069] 11. Data Processing and Normalization 11.1 Data Analysis 4.4 The software processes the raw data and extracts the quality and signal strength of single isotope peaks with a signal-to-noise ratio > 3.0.

[0070] 11.2 GlycoMod Tool predicts the composition and structure of glycopeptidase.

[0071] 11.3 The intensity of the highest signal peak in each mass spectrum was set to 1, and the intensities of the remaining peaks in the same mass spectrum were normalized to obtain the relative abundance of each glycopeptide.

[0072] 12. Heavy chain N-glycopeptide database and methodological stability 12.1 Mixed QC samples identified 6 immunoglobulin subclasses and 65 heavy chain N-glycopeptides, covering multiple modification types. Among them, the structure of the IgG1 type heavy chain N-glycopeptide is shown below. Figure 2 As shown, IgG2 type Figure 3 As shown, IgA / IgM type Figure 4 As shown, IgD / IgE type Figure 5 As shown.

[0073] 12.2 The method stability was validated using 30 QC samples. The results showed good intra-day and inter-day repeatability, and the measured m / z of the glycopeptide peaks were in high agreement with the theoretical m / z. The RSDs of each core heavy chain N-glycopeptide and glycoform ratio were 0.35%–4.86%, all below 20%, meeting the analytical quality control requirements.

[0074] Example 2: Construction and Quality Control Validation of Heavy Chain N-Glycoform Ratio Database Based on a database of 65 heavy chain N-glycopeptides, the peak intensity ratio of different glycoforms at the same glycosylation site was calculated and defined as the glycoform ratio. Specifically, heavy chain N-glycopeptides with the same peptide sequence and the same N-glycosylation site but different glycan structures were defined as glycoforms at the same site. The signal intensity of each heavy chain N-glycopeptide was compared with the base peak signal intensity of the same mass spectrum, and the peak intensity ratios between glycoforms at the same site were further calculated to construct a glycoform ratio database.

[0075] A database of 14 core glycoform ratios was constructed, covering fucosylation, sialylation, and galactosylation modifications, such as... Figure 6 As shown in Table 3, the RSD statistics of the ratios of 14 core sugar types in 30 QC samples are as follows.

[0076] The entire process of QC sample verification showed that the RSD of all sugar form ratios was <20%, which met the analytical quality requirements.

[0077] Table 3. RSD statistics of 14 core glycoform ratios in 30 QC samples.

[0078] Example 3: AL vs. MGUS Auxiliary Differentiation Based on Peripheral Blood Samples Peripheral blood samples were collected from 94 cases of AL and 5 cases of MGUS. Experiments and data processing were performed according to the procedure in Example 1. Benjamini-Hochberg corrected Mann-Whitney U test, OPLS-DA, and ROC analysis were used to screen differential biomarkers. The OPLS-DA model employed 7-fold cross-validation and underwent 1000 permutation tests. The screening criteria for differential biomarkers were: VIP > 1.0, corrected P < 0.05, AUC ≥ 0.70, and candidate peaks were selected based on |log2FC| > 0.25 in the volcano plot. ROC curves were calculated using 95% confidence intervals, and the optimal cutoff value was determined using the Youden index.

[0079] OPLS-DA results showed that AL-PB and MGUS-PB were clearly distinguishable, with model R²Y=0.734 and Q²=0.37, both P<0.001, indicating that heavy chain N-glycosylation features have good global discriminative ability. The combined OPLS-DA model results are shown in the figure below. Figure 7 As shown. Volcano plot and expression heatmap of differentially expressed glycopeptide biomarkers are shown below. Figure 10 As shown, the VIP ranking chart and filtering results are as follows: Figure 13 As shown.

[0080] Characteristic peaks of N-glycans in the core heavy chain (VIP>1.5, corrected P<0.05, AUC≥0.7): The values ​​were 3217.3249, 2561.1468, 3071.4303, 3347.5541, 3116.3919, 3055.4325, and 3100.4036, with 3217.3249 and 2561.1468 both having an AUC of 0.96. The corresponding biomarker validation chart is shown below. Figure 16 As shown.

[0081] Indicators of peak intensity ratio of glycoforms at the same site: There are six types, including G1-G2F1N2 / G2F1N2S1 and G2-G2N3 / G2F1N3, among which the AUC of the first two is 0.97. The corresponding ratio marker verification graph is shown below. Figure 20 As shown.

[0082] Volcano diagram of differential glycopeptide marker distribution between AL-PB and MGUS-PB as shown in the figure. Figure 10 As shown in Table 4, the differential markers of the core heavy chain N-glycans between AL-PB and MGUS-PB are shown in Table 5, and the differential heavy chain N-glycan peptide intensity ratio markers between AL-PB and MGUS-PB are shown in Table 5.

[0083] Table 4. Differential markers of N-glycans in the core heavy chain of AL-PB and MGUS-PB

[0084] Table 5. Indicators of differential heavy chain N-glycopeptide strength ratios between AL-PB and MGUS-PB

[0085] Example 4: LCDD and MGUS-assisted identification based on peripheral blood samples Peripheral blood samples were collected from 13 LCDD patients and 5 MGUS patients. The experiments and data processing were carried out according to the procedure in Example 1, and the statistical analysis was performed in the same way as in Example 3.

[0086] OPLS-DA results: LCDD-PB and MGUS-PB are clearly separated, R²Y=0.884, Q²=0.652, P=0.041, P=0.005. The combined OPLS-DA model results are shown in the figure below. Figure 8 As shown. Volcano plot and expression heatmap of differentially expressed glycopeptide biomarkers are shown below. Figure 11 As shown, the VIP sorting chart and the Venn diagram for filtering conditions are as follows: Figure 14 As shown.

[0087] Characteristic peaks of N-sugars in the core heavy chain: The values ​​were 2941.2234, 2983.3177, 3116.3919, 2958.2555, 2812.2697, 2456.0925, 2399.0580, 2593.0873, and 3217.3249, with 2941.2234 having an AUC of 0.94. The corresponding biomarker validation chart is shown below. Figure 17 As shown.

[0088] Indicators of peak intensity ratio of glycoforms at the same site: G2-N2 / F1N2, G1-G2F1N2 / G2F1N2S1, G2-G2N3 / G2F1N3, G2-G1F1N2 / G1F1N2S1, where the AUC of G2-N2 / F1N2 is 1.00. The corresponding ratio marker verification chart is shown below. Figure 21 As shown.

[0089] Volcano diagram showing the distribution of differentially expressed glycopeptide biomarkers between LCDD-PB and MGUS-PB. Figure 11 As shown in Table 6, the differential markers of the core heavy chain N-sugar between LCDD-PB and MGUS-PB are differentiating markers.

[0090] Table 6. Differential markers of N-glycans in the core heavy chain of LCDD-PB and MGUS-PB

[0091] Example 5: AL and LCDD-assisted identification based on peripheral blood samples Peripheral blood was collected from 94 cases of AL and 13 cases of LCDD. The experiment and data processing were completed according to the procedure in Example 1, and the statistical analysis was performed in the same way as in Example 3.

[0092] OPLS-DA results: AL-PB and LCDD-PB are partially separated, R²Y=0.528, Q²=0.285, both P<0.001. The combined OPLS-DA model results are shown in the figure below. Figure 9 As shown. Volcano plot and expression heatmap of differentially expressed glycopeptide biomarkers are shown below. Figure 12 As shown, the VIP sorting chart and the Venn diagram for filtering conditions are as follows: Figure 15 As shown.

[0093] Representative heavy chain N-sugar characteristic peaks: The values ​​were 2561.1468, 2897.1965, 3227.2621, 3071.4303, and 3097.4066, with the AUC of 3071.4303 being 0.90 and the AUC of 3097.4066 being 0.88. The corresponding biomarker validation chart is shown below. Figure 18 As shown.

[0094] Indicators of peak intensity ratio of glycoforms at the same site: Eight types were identified, including DD-G1F1N3 / G2F1N3, G2-G1N1 / G1F1N1, and G1-G2N2 / G2N2S1. Among them, D-G1F1N3 / G2F1N3 had an AUC of 0.88. The corresponding ratio marker verification chart is shown below. Figure 22 As shown in the figure. The distribution of differential glycoform ratio markers between AL-PB and LCDD-PB is as follows. Figure 19 As shown in Table 7, the representative heavy chain N-glycan differential markers between AL-PB and LCDD-PB are shown in Table 8, and the differential heavy chain N-glycan peptide intensity ratio markers between AL-PB and LCDD-PB are shown in Table 8.

[0095] Table 7. Representative heavy chain N-glycan differential markers between AL-PB and LCDD-PB

[0096] Table 8. Indicators of differential heavy chain N-glycopeptide strength ratios between AL-PB and LCDD-PB

[0097] Example 6: Consistency Analysis of Heavy Chain N-glycosylation Characteristics in Bone Marrow and Peripheral Blood To assess the relevance of peripheral blood samples to the heavy chain glycoprofiles of bone marrow-derived proteins, OPLS-DA comparative analyses were performed on AL-BM versus AL-PB, LCDD-BM versus LCDD-PB, and MGUS-BM versus MGUS-PB. The extraction, purification, intragel digestion, and N-glycopeptide enrichment procedures for M proteins in bone marrow samples were consistent with those for peripheral blood samples.

[0098] The results showed that in the AL group, AL-BM and AL-PB overlapped significantly in the score plot (R²Y=0.14, Q²=0.0832); in the LCDD group, LCDD-BM and LCDD-PB showed some offset, but the elliptical regions of the two groups still overlapped considerably (R²Y=0.743, Q²=-0.017); in the MGUS group, MGUS-BM and MGUS-PB also showed significant overlap as the main characteristic (R²Y=0.244, Q²=-0.168). The combined OPLS-DA model results for the three groups of bone marrow and peripheral blood samples are shown in the figure below. Figure 23 , such as 24 and Figure 25 As shown in the figure. In summary, the bone marrow and peripheral blood show a high degree of overlap in their heavy chain N-glycan profiles, suggesting that peripheral blood can, to some extent, reflect the heavy chain glycan profile characteristics of bone marrow-derived tissue.

[0099] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for obtaining a discriminant marker for N-glycosylation of peripheral blood M protein heavy chain, characterized in that, Includes the following steps: Step 1: Cut the heavy chain band of M protein from the plasma and perform intragel trypsin digestion to obtain a mixture of heavy chain peptides; Step 2: Enrich the heavy chain N in the heavy chain peptide mixture using Fe3O4@PANI magnetic bead suspension. Glycopeptides were extracted to obtain a heavy chain N-glycopeptide enrichment product.

2. The acquisition method according to claim 1, characterized in that, The method also includes step three, which involves detecting the enriched heavy chain N-glycopeptide product using Fourier transform ion cyclotron resonance mass spectrometry, extracting single isotope peaks with a signal-to-noise ratio > 3.0, and performing glycopeptide composition prediction and base peak normalization.

3. The acquisition method according to claim 2, characterized in that, It also includes step four, establishing heavy chain N. Glycopeptide database and database of peak intensity ratios of N-glycosylated glycans at the same site.

4. The acquisition method according to claim 3, characterized in that, The established heavy chain N The glycopeptide database contains 6 immunoglobulin subclasses and 65 heavy chain N-type proteins. Glycopeptides and 14 core N The glycosylation ratio was verified by 30 QC samples, and the RSD of all glycosylation ratios was <20%.

5. The acquisition method according to claim 3, characterized in that, It also includes step five, statistical difference screening, and OPLS. DA analysis and ROC evaluation were used to obtain heavy chain N with disease differentiation efficacy. Glycosylation markers.

6. The acquisition method according to claim 1, characterized in that, Before step one, the method also includes an immunoprecipitation extraction of monoclonal M protein in plasma using a κ / λ mixed affinity matrix to obtain an eluent containing the M protein heavy chain.

7. The acquisition method according to claim 1, characterized in that, In step two, N Glycopeptide enrichment was performed using 80% acetonitrile as the enrichment buffer.

8. The acquisition method according to claim 1, characterized in that, In step two, N Glycopeptide enrichment was performed using 0.1% ammonia solution as the eluent.

9. A method using claim 1 8. Heavy chain N obtained by any of the methods described in the above 8 Application of glycosylation discrimination index combination in the preparation of plasma cell disease auxiliary identification products.

10. The application according to claim 9, characterized in that, The plasma cell diseases include light chain amyloidosis (AL), light chain deposition disease (LCDD), and monoclonal gammaglobulinosis of unknown significance (MGUS).

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