CN118671353A - Biomarker for AIS diagnosis and application thereof - Google Patents

Abstract

The invention relates to the technical field of biomarkers, and discloses a biomarker for AIS diagnosis, which comprises one or more of CILP-1 and ACTC, and a screening method for the biomarker for AIS diagnosis, wherein the screening method specifically comprises the following steps: obtaining a plasma sample of an AIS patient and a plasma sample of healthy people as comparison; obtaining AIS patient muscle tissue; for plasma exosome enriching; identifying the form and the particle size of the plasma exosomes; identifying exosome marker proteins; extracting exosome protein; the exosome proteins are cleaved into peptide fragments. The invention adopts CILP-1 and ACTC as biomarkers for early diagnosis of plasma extracellular vesicles of AIS patients, has less influence by plasma high kurtosis protein, and solves the problems that the prior art adopts plasma proteomics to screen differential proteins and biomarkers, the sources of plasma proteins are complex, and a large amount of high kurtosis proteins exist in the plasma, which possibly affects the detection quality of low-abundance proteins.

Description

Biomarkers for AIS diagnosis and their applications

Technical Field

The present invention relates to the technical field of biomarkers, and in particular to biomarkers for AIS diagnosis and applications thereof.

Background Art

Adolescent idiopathic scoliosis (AIS) is the most common three-dimensional structural deformity during adolescence, with a global incidence of 1%-4%. The incidence rate in girls is higher than that in boys. AIS is the abnormal growth and development of multiple vertebral structures in the three-dimensional space of the spine, including flat back deformity in the sagittal plane, scoliosis in the coronal plane, and rotational deformity in the axial position. Unlike neuromuscular scoliosis and congenital scoliosis with clear etiologies, the pathogenesis of AIS patients still needs to be further clarified. Current research tends to show that AIS is caused by the combined effects of multiple internal factors and external environmental changes. In order to develop more effective prognostic and treatment methods, it is currently very necessary to study unexplored areas. Further clarifying the etiology of AIS will provide a more reliable basis for clinical diagnosis and treatment.

To elucidate the pathogenesis of AIS, researchers have adopted a variety of research methods and techniques to explore the abnormalities in AIS patients, including whole genome sequencing and family genetic inheritance, hormone level measurement, proteomics, metabolomics, biomechanics, finite element analysis and animal models. Abnormalities in muscle fiber type, extracellular matrix and muscle structure have been confirmed in the paraspinal muscles of AIS. In a recent study, after temporary paralysis of the psoas major muscle, the degree of scoliosis and vertebral rotation in AIS patients showed varying degrees of relief. It is reported that the muscle strength of AIS patients is weaker than that of healthy people of similar age and gender. Pulmonary function studies have shown that the respiratory muscle strength of AIS patients is weakened. The imbalance of the posterior paraspinal muscles (including multiple muscles and erector spinae) that provide dynamic stability to the spine is speculated to be the cause and/or progression of AIS spinal deformity. This suggests that muscles attached to the vertebral body may be one of the important factors involved in the etiology of AIS and the severity of scoliosis progression. At present, the research on the paraspinal muscles of AIS mainly includes abnormal muscle gene expression levels, histopathological changes, imaging changes and changes in electromyography.

At present, the study of paraspinal muscles in AIS mainly relies on muscle biopsy. However, due to ethical concerns, muscle biopsy samples from healthy controls or patients with mild to moderate AIS are very difficult to obtain. Therefore, the use of these samples and intraoperative muscle pathology samples from patients with severe AIS for comparison is an obstacle to the development of related mechanisms and diagnostic work. Therefore, it is best to use less invasive tests to reveal muscle-related changes at an earlier time point and for longitudinal studies. Blood samples are samples for systematic phenotypic studies and can help doctors discover biomarkers for early diagnosis.

Advanced metabolomics and proteomics platforms enable researchers to quantify a large number of small molecules, peptides and proteins in the circulation, thereby discovering a large number of predictive and prognostic indicators and metabolic pathways related to the disease. At present, the progress of "omics" research has enabled researchers to quantify a large number of proteins/peptides and metabolites in the blood circulation in a non-targeted and unbiased manner, thereby identifying AIS-related predictive and prognostic biomarkers. Therefore, it is very important to use plasma proteomics to screen early biomarkers of the disease and study the pathogenesis of the disease.

In recent years, extracellular vesicles (EVs) have become an important tool for finding biomarkers for different diseases and exploring pathogenic factors involved in the development of diseases [17-20]. EVs exist in different biological fluids, such as blood, urine, saliva, bile and ascites. EVs are lipid bilayer spheres with a diameter of 40 to more than 1000 nm produced by different types of cells, containing specific proteins, lipids, different types of RNA, DNA and metabolites. At present, the most common EVs studied are exosomes, whose particle size ranges from 40 to 300 nm [21]. The components contained in these exosomes may carry biomarkers of diseases and play an important role in the pathophysiological process of human diseases. Exosomes have a membrane structure, so the contents in their cavity are not degraded by extracellular proteases and are highly stable under storage conditions [16].

Based on this characteristic of exosomes, the exosome proteins detected by mass spectrometry will be less affected by high-abundance plasma proteins and will be easier to detect relatively more low-abundance protein species. Therefore, the use of exosomes for biomarker detection of AIS may have great potential. Biological fluids (blood, urine, saliva, etc.) are widely used in clinical pathological diagnosis and disease monitoring because of the ease of sample acquisition and simple collection. However, there is currently no relevant research on plasma exosome proteins in AIS patients, and the most commonly used biochemical factors in clinical plasma exosomes for evaluating AIS patients have not yet been discovered.

Summary of the invention

The purpose of the present invention is to solve the problems existing in the prior art and to propose biomarkers for AIS diagnosis and their applications.

In order to achieve the above object, the present invention adopts the following technical solutions:

Biomarkers for the diagnosis of AIS include one or more of CILP-1 and ACTC.

Use of proteins as biomarkers for AIS diagnosis, wherein the proteins include one or more of CILP-1 and ACTC.

A method for screening biomarkers for AIS diagnosis, the screening method specifically comprising the following steps:

Obtain plasma samples from AIS patients and healthy people for comparison;

Obtain muscle tissue from AIS patients;

Enrichment of plasma exosomes;

Identify the morphology and particle size of plasma exosomes;

Identify exosome marker proteins;

Extracting exosomal proteins;

Cleave exosomal proteins into peptide fragments;

Liquid phase separation and mass spectrometry detection technology to qualitatively and quantitatively analyze proteins in samples;

Search and identify protein databases based on mass spectrometry data;

Identification of differentially expressed proteins and screening of diagnostic markers.

Preferably, the specific process of obtaining muscle tissue of AIS patients is as follows: a piece of deep multifidus muscle tissue of each side (convex side and concave side) of the apical vertebra of scoliosis of intraoperative AIS patients (n=10) is obtained, the size of which is about 1 cm×1 cm×1 cm, and the muscle tissue is placed in 4% paraformaldehyde and fixed for 24 hours for paraffin sectioning.

Preferably, the specific process of enriching plasma exosomes is: removing cell debris, microvesicles and apoptotic bodies in 700 μL plasma through a 0.2 μm filter membrane; adding 10 mg of TiO2 microspheres, incubating at 4°C with shaking for 5 minutes; washing 3 times with 500 μL PBS, centrifuging, removing the supernatant and collecting the TiO2 microsphere precipitate, the surface of which is enriched with plasma exosomes.

Preferably, the specific process of identifying the morphology and particle size of plasma exosomes is:

Transmission electron microscopy was used to identify the morphology of exosomes: PBS solution containing exosomes (20 μL) was dropped onto a copper mesh formvar carbon support film (200 mesh) and allowed to stand for 10 min to fully precipitate the exosomes. Saturated uranyl acetate ethanol solution was added for counterstaining for 1 min, dried at room temperature, and the morphology of exosomes was observed using a transmission electron microscope;

Exosome particle size and concentration detection: ZetaView nanoparticle tracking instrument was used to detect the particle size and concentration of exosomes. The enriched exosomes were diluted 5000 times with PBS buffer, the instrument was calibrated with polystyrene particles with a particle size of about 100 nm, the sample pool was cleaned, and the samples were tested on the machine (temperature was 23°C);

Identification of exosome marker proteins (Western-blot): RPIA lysis buffer was added to the enriched exosomes for protein extraction and the total protein concentration was quantified by Nanodrop2000. 10 μg of protein was prepared with loading buffer and then boiled for denaturation for SDS-PAGE electrophoresis. The membrane was then transferred to a PVDF membrane and blocked on a shaker at room temperature for 1 hour. The membrane was incubated with primary antibodies of three exosome marker proteins (CD9, CD63, TSG101) and incubated overnight at 4°C. After washing the primary antibody with TBST, the membrane was incubated with the secondary antibody on a shaker at room temperature for 1 hour, and the membrane was washed again with TBST and developed with a developer.

Preferably, the specific process of cutting the exosome protein into peptide segments is:

The precipitate obtained above was taken and lysed by adding 50ul of 2% sodium dodecyl sulfate (SDS), vortexed and ultrasonicated on ice for 30min;

Filter-Assisted Sample Preparation (FASP) was used to cut exosome proteins into peptide fragments;

A new 30KD ultrafiltration tube was washed with 400 μL UA (8 M) (14000 g, centrifugation at room temperature for 10 min);

The lysed liquid was transferred to a washed 30KD ultrafiltration tube and centrifuged at 14000g for 15 min at room temperature; 200 μL UA (8M) was added and centrifuged at 14000g for 15 min at room temperature;

Add 200 μL DTT (10 mM), place in a 37°C incubator for 4 h; centrifuge at 14,000 g for 15 min at room temperature; add 200 μL UA (8 M), centrifuge at 14,000 g for 15 min at room temperature;

Add 200 μL IAA (50 mM) and incubate at room temperature in the dark for 40 min;

Add 200 μL NH4HCO3 (50 mM), centrifuge at 14000 x g for 10 min at room temperature, repeat twice;

Add 200 μL NH4HCO3 (50 mM), mix well by pipetting, add 1 μg protease, and place in a 37°C incubator; after 12 hours, add 1 μg trypsin enzyme and place in a 37°C incubator; take out after 4 hours;

After protease hydrolysis into peptides, centrifuge at 14000g for 10 minutes at room temperature (from this step, the centrifuged liquid needs to be retained); add 200μL of water, centrifuge at 14000g for 10 minutes at room temperature, repeat once; transfer the centrifuged liquid to a 1.5mL centrifuge tube and heat dry. Redissolve with 0.1% FA for exosome identification, peptide concentration detection and mass spectrometry analysis.

Preferably, the specific process of the liquid phase separation and the mass spectrometry detection technology for qualitative and quantitative analysis of proteins in the sample is: the sample is first separated online by nanoliter liquid phase and then detected, and the data is collected in a data independent acquisition (DIA) scanning mode. The DIA technology first uses conventional Data dependent Acquisition (DDA) mass spectrometry detection technology to analyze and establish a spectrum library, and then uses the DIA method to collect mass spectrum data of the sample to be tested, and after comparing the DIA data with the DDA spectrum library information, qualitative and quantitative analysis of the protein in the sample is performed;

The specifications of the pre-column and analytical column of the EASY-nLC 1200 liquid phase system are as follows:

Pre-column: 3 μm particle size C18 packing, 2 cm × 100 μm inner diameter;

Analytical column: 1.9 μm particle size C18 packing, 30 cm × 150 μm inner diameter;

Mobile phase A: 0.1% formic acid, flow rate 600 nl/min;

Mobile phase B: acetonitrile and 0.1% formic acid (volume ratio 1000:1);

The elution gradient for peptide separation was as follows: 0-6 min, 7-12% B; 6-72 min, 12-30% B; 72-94 min, 30-42% B; 94-95 min, 42-95% B and 95-100 min, 95% B;

The mass spectrometer was performed using the Q-Exactive HFX ^TM system (Thermo Fisher Scientific). The spectral library was established using the Data dependent Acquisition (DDA) mode with the following conditions: the scanning mode was positive ion scanning mode, the primary mass spectrometer was detected by Orbitrap, the resolution was m/z 120,000@200, the maximum injection time was 80 ms, and the scanning range was 400-1400; the secondary scanning resolution was set to 15,000@m/z200, the starting scanning mass (fixed first mass) was 120 m/z, the maximum ion injection time was 45 ms, the HCD relative collision energy was 27%, and 18 s dynamic exclusion was used during data acquisition;

Sample mass spectrometry acquisition: Serum/plasma mass spectrometry data were acquired in Data Independent Acquisition (DIA) mode, with positive ion scanning mode, and Orbitrap detection for primary mass spectrometry, with a resolution of m/z 60,000@200, a maximum injection time of 80 ms, and a scanning range of 400-1200; the DIA scanning resolution was set to 30,000@m/z 200, the starting scanning mass (Fixed first mass) was 200 m/z, and the maximum ion injection time was 45 ms; the DIA isolation window for each acquisition was 25 m/z, the number of isolation windows was 32, and the HCD relative collision energy was 27%.

Preferably, the specific process of searching the protein database based on mass spectrometry data is as follows: the mass spectrometry data is obtained by using the Spectronaut 14.5 200813.47784 search engine to establish a spectrum library and perform DIA data search, and various parameters for database search are set in the Spectronaut software template: library spectrum library is established, the human protein sequence database of the Uniprot database is selected in "Protein Database"; Trypsin/p is selected in "Enzymes/cleavagerule"; Specific is selected in "Digest Type"; 2 is selected in "Missed Cleavages"; 20ppm is filled in "Precursor Mass Tolerance"; Acetyl (Protein N-term), Oxidation (M) are selected in "Variable Modifications"; Carbamidomethyl (C) is selected in "Fixed Modifications", the library spectrum library established above is selected for DIA data search, and "BGSFactory Settings (default)" is selected for parameter setting.

Preferably, the differential protein identification and screening of diagnostic markers include: using R language to perform machine learning analysis, which is specifically described as follows:

Construction of protein physiological range: Since the abundance, abundance span and discreteness of different proteins are significantly different, the upper and lower limits of the normal histone-specific physiological range are determined using machine learning algorithms based on the data characteristics of different protein abundances, and the physiological fluctuation range of normal serum proteins is established;

Identification of individual and group differential proteins: All proteins identified in a sample are compared with the physiological range of the protein one by one. If the abundance of a protein in a sample exceeds the upper limit of the physiological range of the protein or is lower than the lower limit of the physiological range, the protein is defined as abnormal in the sample. This method is used to detect all abnormal proteins in the sample. By comparing with the physiological range of the protein, the abnormal protein information of the sample is integrated to construct the abnormal protein information association matrix of the group, and more than 90% of the samples are defined as abnormal as the standard for detecting abnormal proteins at the group level.

Compared with the prior art, the present invention has the following beneficial effects:

The present invention uses CILP-1 and ACTC as biomarkers for early diagnosis of plasma extracellular vesicles in AIS patients, which are less affected by plasma high-abundance proteins. This solves the problem that the prior art uses plasma proteomics to screen differential proteins and biomarkers, but the source of plasma proteins is complex and there are a large number of high-abundance proteins in plasma, which may affect the detection quality of low-abundance proteins.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is a schematic diagram of differentially expressed proteins identified in the plasma exosome proteome between the AIS group and the control group;

Figure 1: A. Volcano plot of differentially expressed proteins, red represents proteins significantly increased by more than 1.5 times in the AIS group compared with the control group, blue represents proteins decreased by less than 1.5 times (p<0.05), the horizontal axis is log2 (AIS follow-up group/control group), and the vertical axis is -log10 (pvalue);

B. Two-dimensional principal component analysis of AIS group and control group, PC1 = 25.8%, PC2 = 15.9%;

C. Heat map analysis of differential protein abundance in plasma exosomes of AIS patients and control groups. Darker red represents higher protein abundance, and darker blue represents lower protein abundance.

NC: control group, S: AIS group;

Figure 2 is a schematic diagram of GO analysis and KEGG analysis of differentially expressed proteins in plasma exosomes between the AIS group and the control group;

In Figure 2:

A. GO analysis of all differentially expressed proteins in plasma exosomes of the AIS group;

B. KEGG analysis of all differentially expressed proteins in plasma exosomes of AIS group;

FIG3 is a schematic diagram of multiple models analyzing differential proteins between the AIS group and the control group and their ROC diagnostic analysis;

In Figure 3: A. 25 differentially expressed proteins obtained by using multiple models, where selectedfrequency represents the frequency (threshold) selected by the model. The higher the frequency, the better the protein's ability to discriminate between the two groups.

B. Analysis of ROC curve on the efficiency of 25 differentially expressed proteins in diagnosing AIS;

Figure 4 is a schematic diagram of differentially expressed proteins identified in plasma exosome proteomes between the AIS follow-up group and the control group;

Figure 4: A. Volcano plot of differentially expressed proteins, red represents proteins significantly increased by more than 1.5 times in the AIS follow-up group compared with the control group, blue represents proteins decreased by less than 1.5 times (p<0.05), the horizontal axis is log2 (AIS follow-up group/control group), and the vertical axis is -log10 (pvalue);

B. Two-dimensional principal component analysis of the AIS follow-up group and the control group, NC: control group, S1: AIS follow-up group, PC1 = 24.6%, PC2 = 16.3%;

C. Heat map analysis of differential protein abundance in plasma exosomes of AIS patients and control group. Darker red represents higher protein abundance, and darker blue represents lower protein abundance; NC: control group, S1: AIS follow-up group;

Figure 5 is a schematic diagram of GO analysis and KEGG analysis of differentially expressed proteins in plasma exosomes between the AIS follow-up group and the control group;

Figure 5: A. GO analysis of all differential proteins in plasma exosomes of the AIS follow-up group;

B. KEGG analysis of all differential proteins in plasma exosomes of the AIS follow-up group;

Figure 6 is a heat map of the expression of 25 differentially expressed proteins in the control group and AIS group screened by machine learning;

In Figure 6: A. Expression of 25 differentially expressed proteins in the AIS follow-up group, red represents abnormal up-regulation of protein expression, and blue represents abnormal down-regulation of protein expression;

B. Heat map of the expression of 25 differentially expressed proteins in the control group, AIS follow-up group, and AIS surgery group. Red represents up-regulated expression, blue represents down-regulated expression, and the legend on the right represents the multiple of expression difference;

FIG7 is a schematic diagram of the ROC curves and expression levels of ACTC and CILP-1 in the AIS follow-up group and the control group;

In Figure 7: A. ROC curve of ACTC for diagnosis of AIS follow-up group, the area under the curve is 1;

B. Expression levels of ACTC in the control group and AIS follow-up group;

C. ROC curve of CILP-1 for diagnosis of AIS follow-up group, with an area under the curve of 0.85;

D. Expression levels of CILP-1 in the control group and AIS follow-up group;

FIG8 is a schematic diagram of the ROC curves and expression levels of ACTC and CILP-1 in AIS patients (follow-up group and surgery group) and the control group;

In Figure 8: A. ROC curve of ACTC for diagnosis of AIS patients, with an area under the curve of 1;

B. Expression levels of ACTC in the control group and AIS group;

C. ROC curve of CILP-1 for the diagnosis of AIS patients, with an area under the curve of 0.817;

D. The expression level of CILP-1 in the control group and AIS group.

DETAILED DESCRIPTION

The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

Biomarkers for the diagnosis of AIS include one or more of CILP-1 and ACTC.

Use of proteins as biomarkers for AIS diagnosis, wherein the proteins include one or more of CILP-1 and ACTC.

A method for screening biomarkers for AIS diagnosis, the screening method specifically comprising the following steps:

Obtain plasma samples from AIS patients and healthy people for comparison;

Obtain muscle tissue from AIS patients;

Enrichment of plasma exosomes;

Identify the morphology and particle size of plasma exosomes;

Identify exosome marker proteins;

Extracting exosomal proteins;

Cleave exosomal proteins into peptide fragments;

Liquid phase separation and mass spectrometry detection technology to qualitatively and quantitatively analyze proteins in samples;

Search and identify protein databases based on mass spectrometry data;

Identification of differentially expressed proteins and screening of diagnostic markers.

In this embodiment, the specific process of obtaining muscle tissue of AIS patients is as follows: a piece of deep multifidus muscle tissue of each side (convex side and concave side) of the apical vertebra of scoliosis of intraoperative AIS patients (n=10) is obtained, the size of which is about 1 cm×1 cm×1 cm, and the muscle tissue is placed in 4% paraformaldehyde and fixed for 24 hours for paraffin sectioning.

In this embodiment, the specific process of enriching plasma exosomes is as follows: removing cell debris, microvesicles, apoptotic bodies, etc. in 700 μL of plasma through a 0.2 μm filter membrane; adding 10 mg of TiO2 microspheres, incubating at 4°C with shaking for 5 minutes; washing 3 times with 500 μL PBS, centrifuging, removing the supernatant and collecting the TiO2 microsphere precipitate, the surface of which is enriched with plasma exosomes.

In this embodiment, the specific process of identifying the morphology and particle size of plasma exosomes is as follows:

Transmission electron microscopy was used to identify the morphology of exosomes: PBS solution containing exosomes (20 μL) was dropped onto a copper mesh formvar carbon support film (200 mesh) and allowed to stand for 10 min to fully precipitate the exosomes. Saturated uranyl acetate ethanol solution was added for counterstaining for 1 min, dried at room temperature, and the morphology of exosomes was observed using a transmission electron microscope;

Exosome particle size and concentration detection: ZetaView nanoparticle tracking instrument was used to detect the particle size and concentration of exosomes. The enriched exosomes were diluted 5000 times with PBS buffer, the instrument was calibrated with polystyrene particles with a particle size of about 100 nm, the sample pool was cleaned, and the samples were tested on the machine (temperature was 23°C);

Identification of exosome marker proteins (Western-blot): RPIA lysis buffer was added to the enriched exosomes for protein extraction and the total protein concentration was quantified by Nanodrop2000. 10 μg of protein was prepared with loading buffer and then boiled for denaturation for SDS-PAGE electrophoresis. The membrane was then transferred to a PVDF membrane and blocked on a shaker at room temperature for 1 hour. The membrane was incubated with primary antibodies of three exosome marker proteins (CD9, CD63, TSG101) and incubated overnight at 4°C. After washing the primary antibody with TBST, the membrane was incubated with the secondary antibody on a shaker at room temperature for 1 hour, and the membrane was washed again with TBST and developed with a developer.

In this embodiment, the specific process of cutting the exosome protein into peptide segments is:

The precipitate obtained above was taken and lysed by adding 50ul of 2% sodium dodecyl sulfate (SDS), vortexed and ultrasonicated on ice for 30min;

Filter-Assisted Sample Preparation (FASP) was used to cut exosome proteins into peptide fragments;

A new 30KD ultrafiltration tube was washed with 400 μL UA (8 M) (14000 g, centrifugation at room temperature for 10 min);

The lysed liquid was transferred to a washed 30KD ultrafiltration tube and centrifuged at 14000g for 15 min at room temperature; 200 μL UA (8M) was added and centrifuged at 14000g for 15 min at room temperature;

Add 200 μL DTT (10 mM), place in a 37°C incubator for 4 h; centrifuge at 14,000 g for 15 min at room temperature; add 200 μL UA (8 M), centrifuge at 14,000 g for 15 min at room temperature;

Add 200 μL IAA (50 mM) and incubate at room temperature in the dark for 40 min;

Add 200 μL NH4HCO3 (50 mM), centrifuge at 14000 x g for 10 min at room temperature, repeat twice;

Add 200 μL NH4HCO3 (50 mM), mix well by pipetting, add 1 μg protease, and place in a 37°C incubator; after 12 hours, add 1 μg trypsin enzyme and place in a 37°C incubator; take out after 4 hours;

After protease hydrolysis into peptides, centrifuge at 14000g for 10 minutes at room temperature (from this step, the centrifuged liquid needs to be retained); add 200μL of water, centrifuge at 14000g for 10 minutes at room temperature, repeat once; transfer the centrifuged liquid to a 1.5mL centrifuge tube and heat dry. Redissolve with 0.1% FA for exosome identification, peptide concentration detection and mass spectrometry analysis.

In this embodiment, the specific process of the liquid phase separation and mass spectrometry detection technology for qualitative and quantitative analysis of proteins in the sample is as follows: the sample is first separated online by nanoliter liquid phase and then detected, and the data is collected in a data independent acquisition (DIA) scanning mode. The DIA technology first uses conventional Datadependent Acquisition (DDA) mass spectrometry detection technology to analyze and establish a spectrum library, and then uses the DIA method to collect mass spectrum data of the sample to be tested, and qualitatively and quantitatively analyzes the protein in the sample after comparing the DIA data with the spectrum library information of DDA;

The specifications of the pre-column and analytical column of the EASY-nLC 1200 liquid phase system are as follows:

Pre-column: 3 μm particle size C18 packing, 2 cm × 100 μm inner diameter;

Analytical column: 1.9 μm particle size C18 packing, 30 cm × 150 μm inner diameter;

Mobile phase A: 0.1% formic acid, flow rate 600 nl/min;

Mobile phase B: acetonitrile and 0.1% formic acid (volume ratio 1000:1);

The elution gradient for peptide separation was as follows: 0-6 min, 7-12% B; 6-72 min, 12-30% B; 72-94 min, 30-42% B; 94-95 min, 42-95% B and 95-100 min, 95% B;

The mass spectrometer was performed using the Q-Exactive HFXTM system (Thermo Fisher Scientific). The spectral library was established using the Data dependent Acquisition (DDA) mode with the following conditions: the scanning mode was positive ion scanning mode, the primary mass spectrometer was detected by Orbitrap, the resolution was m/z 120,000@200, the maximum injection time was 80 ms, and the scanning range was 400-1400; the secondary scanning resolution was set to 15,000@m/z200, the starting scanning mass (fixed first mass) was 120 m/z, the maximum ion injection time was 45 ms, the HCD relative collision energy was 27%, and 18 s dynamic exclusion was used during data acquisition;

Sample mass spectrometry acquisition: Serum/plasma mass spectrometry data were acquired in Data Independent Acquisition (DIA) mode, with positive ion scanning mode, and Orbitrap detection for primary mass spectrometry, with a resolution of m/z 60,000@200, a maximum injection time of 80 ms, and a scanning range of 400-1200; the DIA scanning resolution was set to 30,000@m/z 200, the starting scanning mass (Fixed first mass) was 200 m/z, and the maximum ion injection time was 45 ms; the DIA isolation window for each acquisition was 25 m/z, the number of isolation windows was 32, and the HCD relative collision energy was 27%.

In this embodiment, the specific process of searching the protein database based on mass spectrometry data is as follows: the obtained mass spectrometry data is used to establish a spectrum library and perform DIA data search using the Spectronaut 14.5 200813.47784 search engine, and various parameters for database search are set in the Spectronaut software template: library spectrum library is established, the human protein sequence database of the Uniprot database is selected in "Protein Database"; Trypsin/p is selected in "Enzymes/cleavagerule"; Specific is selected in "Digest Type"; 2 is selected in "Missed Cleavages"; 20ppm is filled in "Precursor Mass Tolerance"; Acetyl (Protein N-term), Oxidation (M) are selected in "Variable Modifications"; Carbamidomethyl (C) is selected in "Fixed Modifications", the library spectrum library established above is selected for DIA data search, and "BGSFactory Settings (default)" is selected for parameter setting.

In this embodiment, the differential protein identification and screening of diagnostic markers include: using R language to perform machine learning analysis, which is specifically described as follows:

Construction of protein physiological range: Since the abundance, abundance span and discreteness of different proteins are significantly different, the upper and lower limits of the normal histone-specific physiological range are determined using machine learning algorithms based on the data characteristics of different protein abundances, and the physiological fluctuation range of normal serum proteins is established;

Identification of individual and group differential proteins: All proteins identified in a sample are compared with the physiological range of the protein one by one. If the abundance of a protein in a sample exceeds the upper limit of the physiological range of the protein or is lower than the lower limit of the physiological range, the protein is defined as abnormal in the sample. This method is used to detect all abnormal proteins in the sample. By comparing with the physiological range of the protein, the abnormal protein information of the sample is integrated to construct the abnormal protein information association matrix of the group, and more than 90% of the samples are defined as abnormal as the standard for detecting abnormal proteins at the group level.

Number of exosome proteins identified in each group of samples:

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify exosome proteins in plasma samples of AIS patients and the control group. After searching the database, the number of proteins enriched in the plasma samples of each group was identified: the average number of proteins identified in the control group (n=20), AIS follow-up group (n=11) and surgery group (n=10) was 1398.6±262.89, 1112.09±313.85 and 1288.9±269.24, respectively. A total of 2437 proteins were identified in all samples.

Bioinformatics analysis of differentially expressed proteins between the AIS group and the control group (using PCA analysis, cluster analysis, GO analysis, and KEGG analysis methods):

For the 2437 proteins identified, the AIS group (n=21) and the control group (n=20) were first compared, and the protein expression ratio between the two groups was >1.5 or <0.67 as the standard for screening differential proteins. Compared with the control group, a total of 319 differential proteins were identified in the AIS group, of which 254 proteins were downregulated and 65 proteins were upregulated (:1A). The results of PCA analysis of these differential proteins are shown in Figure 1B. PCA analysis failed to distinguish the two groups well. The heat map analysis of the differential protein abundance between the AIS group and the control group is shown in Figure 1C.

GO analysis was performed on 319 differentially expressed proteins between the two groups, and a total of 2349 GO functional annotations were obtained, which were involved in 1537 biological processes (BP), 381 cellular components (CC) and 429 molecular functions (MF). As shown in Figure 2A, the biological processes involved in these differentially expressed proteins include initial immune response, complement activation, platelet degradation, receptor-mediated endocytosis, extracellular matrix organization and immune response, etc.; the cellular components are mainly cell exosomes, cell membrane, cytoplasm and extracellular regions, etc.; the molecular functions mainly include ATP binding, metal ion binding, DNA binding, serine-like endonuclease activity, cell-to-cell adhesion including cadherin binding, antigen binding, calcium ion binding, actin binding and ubiquitin protein ligase binding, etc. KEGG pathway analysis showed that the differentially expressed proteins were involved in 16 signaling pathways, including extracellular matrix receptor interaction, complement and coagulation pathways, etc. (Figure 2B).

The differential proteins between the AIS group and the control group were analyzed by multi-model analysis to obtain 25 representative proteins:

The 2437 proteins identified were first analyzed by unidimensional and multidimensional statistics, and then analyzed by partial least-squares regression analysis (PLS-DA), support vector machine model (SVM) and random forest (RF) model to obtain 25 differential proteins between the AIS group and the control group. The ROC analysis of these 25 differential proteins including CILP-1 showed an area under the curve of 0.969 (Figure 3).

Bioinformatics analysis of differentially expressed proteins between the AIS follow-up group and the control group (using PCA analysis, cluster analysis, GO analysis, and KEGG analysis methods):

The proteins identified in plasma exosomes of the AIS follow-up group and the control group were compared, and the ratio >1.5 or <0.67 was selected as the standard. Compared with the control group, there were 398 differentially expressed proteins in the AIS follow-up group, of which 356 proteins were down-regulated and 42 proteins were up-regulated (Figure 4A). Principal component analysis (PCA) was performed on these differentially expressed proteins, and the results are shown in Figure 4B. Cluster analysis was performed on the differentially expressed protein abundance between the AIS follow-up group and the control group, and the heat map results are shown in Figure 4C;

GO analysis was performed on 398 differentially expressed proteins between the two groups, and a total of 2662 GO functional annotations were obtained (Figure 5A), which were involved in 1741 biological processes (BP), 430 cellular components (CC), and 491 molecular functions (MF). The biological processes, cellular components, and molecular functions involved in these differentially expressed proteins were similar to the differentially expressed proteins in the AIS group as a whole and the control group. KEGG pathway analysis showed that the differentially expressed proteins were involved in 15 signaling pathways, which were also similar to the differences between the AIS group as a whole and the control group (Figure 5B).

Machine learning screening of differentially expressed proteins between the AIS follow-up group and the control group:

To further explore the pathogenesis of AIS and disease markers for early diagnosis of AIS, we used machine learning methods to screen differentially expressed proteins. From 2437 proteins, we obtained 25 proteins that were differentially expressed in more than 72% of the samples between the AIS follow-up group and the control group.

We further analyzed the expression of these 25 proteins in the AIS follow-up group and found that 6 of the 25 proteins (VDCA1, PARVB, KV122, CO4A, GDIB and COPG1) were abnormally downregulated in the AIS follow-up group, and the remaining 19 proteins were abnormally upregulated in the AIS follow-up group. It is worth noting that ACTC was highly expressed in all 11 samples of the AIS follow-up group, while CILP-1 was highly expressed in 8 samples of the AIS follow-up group (Figure 6A). The heat map of the 25 differentially expressed proteins in the control group, AIS follow-up group and AIS surgery group is shown in Figure 6B. It can also be seen that the expression level of ACTC in 21 AIS patient samples was higher than that in the control group, and CILP-1 was also relatively highly expressed in most AIS patients. The area under the ROC curve analysis of ACTC protein in the AIS follow-up group was 1, and the area under the ROC curve of CILP-1 protein in the AIS follow-up group was 0.85 (Figure 7). The area under the ROC curve analysis of ACTC protein as a screening protein for all AIS patients (follow-up group and surgery group) was still 1, while the area under the ROC curve of CILP-1 protein was 0.817 (Figure 8).

The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims (10)

1. A biomarker for the diagnosis of AIS, characterized by comprising one or more of CILP-1 and ACTC. 2. Use of proteins as biomarkers for the diagnosis of AIS, characterized in that the proteins include one or more of CILP-1 and ACTC. 3. A method for screening biomarkers for AIS diagnosis, characterized in that: the screening method specifically comprises the following steps: Obtain plasma samples from AIS patients and healthy people for comparison; Obtain muscle tissue from AIS patients; Enrichment of plasma exosomes; Identify the morphology and particle size of plasma exosomes; Identify exosome marker proteins; Extracting exosomal proteins; Cleave exosomal proteins into peptide fragments; Liquid phase separation and mass spectrometry detection technology to qualitatively and quantitatively analyze proteins in samples; Search and identify protein databases based on mass spectrometry data; Identification of differentially expressed proteins and screening of diagnostic markers. 4. The method for screening biomarkers for AIS diagnosis according to claim 3 is characterized in that: the specific process of obtaining muscle tissue of AIS patients is as follows: a piece of deep multifidus muscle tissue of each side (convex side and concave side) of the apical vertebra of scoliosis of intraoperative AIS patients (n=10) is taken, the size of which is about 1cm×1cm×1cm, and the muscle tissue is placed in 4% paraformaldehyde and fixed for 24 hours for use as paraffin sections. 5. The method for screening biomarkers for AIS diagnosis according to claim 4 is characterized in that: the specific process of enriching plasma exosomes is: removing cell debris, microvesicles and apoptotic bodies in 700 μL plasma through a 0.2 μm filter membrane; adding 10 mg of TiO2 microspheres, incubating at 4°C with shaking for 5 minutes; washing 3 times with 500 μL PBS, centrifuging, removing the supernatant and collecting the TiO2 microsphere precipitate, the surface of which is enriched with plasma exosomes. 6. The method for screening biomarkers for AIS diagnosis according to claim 5, characterized in that: the specific process of identifying the morphology and particle size of plasma exosomes is: Transmission electron microscopy was used to identify the morphology of exosomes: PBS solution containing exosomes (20 μL) was dropped onto a copper mesh formvar carbon support film (200 mesh) and allowed to stand for 10 min to fully precipitate the exosomes. Saturated uranyl acetate ethanol solution was added for counterstaining for 1 min, dried at room temperature, and the morphology of exosomes was observed using a transmission electron microscope; Exosome particle size and concentration detection: ZetaView nanoparticle tracking instrument was used to detect the particle size and concentration of exosomes. The enriched exosomes were diluted 5000 times with PBS buffer, the instrument was calibrated with polystyrene particles with a particle size of about 100 nm, the sample pool was cleaned, and the samples were tested on the machine (temperature was 23°C); Identification of exosome marker proteins (Western-blot): RPIA lysis buffer was added to the enriched exosomes for protein extraction and the total protein concentration was quantified by Nanodrop2000. 10 μg of protein was prepared with loading buffer and then boiled for denaturation for SDS-PAGE electrophoresis. The membrane was then transferred to a PVDF membrane and blocked on a shaker at room temperature for 1 hour. The membrane was incubated with primary antibodies of three exosome marker proteins (CD9, CD63, TSG101) and incubated overnight at 4°C. After washing the primary antibody with TBST, the membrane was incubated with the secondary antibody on a shaker at room temperature for 1 hour, and the membrane was washed again with TBST and developed with a developer. 7. The method for screening biomarkers for AIS diagnosis according to claim 6, characterized in that: the specific process of cutting the exosome protein into peptide segments is: The precipitate obtained above was taken and lysed by adding 50ul of 2% sodium dodecyl sulfate (SDS), vortexed and ultrasonicated on ice for 30min; Filter-Assisted Sample Preparation (FASP) was used to cut exosome proteins into peptide fragments; A new 30KD ultrafiltration tube was washed with 400 μL UA (8 M) (14000 g, centrifugation at room temperature for 10 min); The lysed liquid was transferred to a washed 30KD ultrafiltration tube and centrifuged at 14000g for 15 min at room temperature; 200 μL UA (8M) was added and centrifuged at 14000g for 15 min at room temperature; Add 200 μL DTT (10 mM), place in a 37°C incubator for 4 h; centrifuge at 14,000 g for 15 min at room temperature; add 200 μL UA (8 M), centrifuge at 14,000 g for 15 min at room temperature; Add 200 μL IAA (50 mM) and incubate at room temperature in the dark for 40 min; Add 200 μL NH4HCO3 (50 mM), centrifuge at 14000 x g for 10 min at room temperature, repeat twice; Add 200 μL NH4HCO3 (50 mM), mix well by pipetting, add 1 μg protease, and place in a 37°C incubator; after 12 hours, add 1 μg trypsin enzyme and place in a 37°C incubator; take out after 4 hours; After protease hydrolysis into peptides, centrifuge at 14000g for 10 minutes at room temperature (from this step, the centrifuged liquid needs to be retained); add 200μL of water, centrifuge at 14000g for 10 minutes at room temperature, repeat once; transfer the centrifuged liquid to a 1.5mL centrifuge tube and heat dry. Redissolve with 0.1% FA for exosome identification, peptide concentration detection and mass spectrometry analysis. 8. The method for screening biomarkers for AIS diagnosis according to claim 7 is characterized in that: the specific process of the liquid phase separation and the mass spectrometry detection technology for qualitative and quantitative analysis of proteins in the sample is: the sample is first separated online by nanoliter liquid phase and then detected, and the data is collected in a data independent acquisition (DIA) scanning mode. The DIA technology first uses conventional Data dependent Acquisition (DDA) mass spectrometry detection technology to analyze and establish a spectrum library, and then uses the DIA method to collect mass spectrum data of the sample to be tested, and after comparing the DIA data with the DDA spectrum library information, the protein in the sample is qualitatively and quantitatively analyzed; The specifications of the pre-column and analytical column of the EASY-nLC 1200 liquid phase system are as follows: Pre-column: 3 μm particle size C18 packing, 2 cm × 100 μm inner diameter; Analytical column: 1.9 μm particle size C18 packing, 30 cm × 150 μm inner diameter; Mobile phase A: 0.1% formic acid, flow rate 600 nl/min; Mobile phase B: acetonitrile and 0.1% formic acid (volume ratio 1000:1); The elution gradient for peptide separation was as follows: 0-6 min, 7-12% B; 6-72 min, 12-30% B; 72-94 min, 30-42% B; 94-95 min, 42-95% B and 95-100 min, 95% B; The mass spectrometer was performed using the Q-Exactive HFX ^TM system (Thermo Fisher Scientific). The spectral library was established using the Datadependent Acquisition (DDA) mode with the following conditions: the scanning mode was positive ion scanning mode, the primary mass spectrometer was detected by Orbitrap, the resolution was m/z 120,000@200, the maximum injection time was 80 ms, and the scanning range was 400-1400; the secondary scanning resolution was set to 15,000@m/z200, the starting scanning mass (fixed first mass) was 120 m/z, the maximum ion injection time was 45 ms, the HCD relative collision energy was 27%, and 18 s dynamic exclusion was used during data acquisition; Sample mass spectrometry acquisition: Serum/plasma mass spectrometry data were acquired in Data Independent Acquisition (DIA) mode, with positive ion scanning mode, and Orbitrap detection for primary mass spectrometry, with a resolution of m/z 60,000@200, a maximum injection time of 80 ms, and a scanning range of 400-1200; the DIA scanning resolution was set to 30,000@m/z 200, the starting scanning mass (Fixed first mass) was 200 m/z, and the maximum ion injection time was 45 ms; the DIA isolation window for each acquisition was 25 m/z, the number of isolation windows was 32, and the HCD relative collision energy was 27%. 9. The method for screening biomarkers for AIS diagnosis according to claim 8, characterized in that: the specific process of searching and identifying the protein database based on mass spectrometry data is as follows: the mass spectrometry data is used to establish a spectrum library and perform DIA data search using the Spectronaut14.5 200813.47784 search engine, and various parameters for database search are set in the Spectronaut software template: library spectrum library is established, "Protein Database" selects the human protein sequence database of the Uniprot database; Trypsin/p is selected in "Enzymes/cleavage rule"; Specific is selected in "Digest Type"; 2 is selected in "Missed Cleavages"; 20ppm is filled in "Precursor Mass Tolerance"; Acetyl (Protein N-term), Oxidation (M) is selected in "Variable Modifications"; Carbamidomethyl (C) is selected in "Fixed Modifications", the DIA data search selects the above-established library spectrum library, and the parameter setting selects "BGS Factory Settings (default)”. 10. The method for screening biomarkers for AIS diagnosis according to claim 9, characterized in that: the identification of differential proteins and screening of diagnostic markers include: using R language for machine learning analysis, which is specifically described as follows: Construction of protein physiological range: Since the abundance, abundance span and discreteness of different proteins are significantly different, the upper and lower limits of the normal histone-specific physiological range are determined using machine learning algorithms based on the data characteristics of different protein abundances, and the physiological fluctuation range of normal serum proteins is established; Identification of individual and group differential proteins: All proteins identified in a sample are compared with the physiological range of the protein one by one. If the abundance of a protein in a sample exceeds the upper limit of the physiological range of the protein or is lower than the lower limit of the physiological range, the protein is defined as abnormal in the sample. This method is used to detect all abnormal proteins in the sample. By comparing with the physiological range of the protein, the abnormal protein information of the sample is integrated to construct the abnormal protein information association matrix of the group, and more than 90% of the samples are defined as abnormal as the standard for detecting abnormal proteins at the group level.