An internal reference protein and application thereof in preparation of reagent for detecting neurodegenerative disease

Abstract

The application belongs to the technical field of molecular biology, and relates to application of transferrin (TF) as an internal reference protein in preparation of a reagent for detecting exosome proteins of a neurodegenerative disease. It is verified through experiments that TF can maintain stable expression levels in exosomes derived from nerve cells, brain tissues, plasma and serum of subjects with neurodegenerative diseases, the neurodegenerative diseases including Alzheimer's disease, mild cognitive impairment, amyotrophic lateral sclerosis, Parkinson's disease or Huntington's disease, the coefficient of variation of expression of the TF is significantly lower than that of a commonly used exosome marker, the expression level of the TF has a stronger correlation with the total protein amount of the exosome, and the expression level of the TF is not affected by external factors such as cell inflammatory stress. This shows that the TF has superior performance as an internal reference protein, can help more accurately reflect the total amount of a sample, and provides a more stable and reliable standardized tool for the field of exosome research.

Description

Technical Field

[0001] This invention belongs to the field of molecular biology technology, and in particular relates to an internal reference protein and its application in the preparation of reagents for detecting neurodegenerative diseases. Background Technology

[0002] Alzheimer's disease (AD) is a common neurodegenerative disease, and early diagnosis is crucial for disease intervention. Exosomes (extracellular vesicles, EVs), as important carriers of intercellular communication, are widely present in bodily fluids such as blood and cerebrospinal fluid. They encapsulate biomolecules such as proteins and nucleic acids derived from blastocysts and can reflect the physiological or pathological state of the central nervous system, thus being considered a highly promising source of liquid biopsy biomarkers.

[0003] In exosome proteomics research and clinical testing, reliable quantitative standards (i.e., internal controls) are essential for data normalization to correct for differences in sample extraction efficiency, loading errors, and instrument fluctuations. Currently, commonly used quantitative strategies include: normalization based on total protein concentration, normalization based on exogenous additives, and normalization based on housekeeping proteins.

[0004] However, the aforementioned existing technologies all have significant limitations in practical applications. First, exosome extraction is often contaminated with plasma proteins (such as albumin and lipoproteins), causing the total protein concentration measured by the BCA method to fail to accurately reflect the actual level of exosomes. Second, exogenous additives can only correct for errors in the detection process and cannot reflect the differences in extraction and recovery rates of the biological samples themselves. More critically, the expression levels of cell housekeeping proteins (such as GAPDH and ACTB) or universal exosome markers (such as CD9, CD63, ALIX, and TSG101) commonly used as internal controls often fluctuate significantly due to cell type, cellular physiological state, and pathological environment (such as neuroinflammation and oxidative stress). For example, in the context of AD-related neuroinflammation, the expression levels of some traditional markers are not constant. If they continue to be used as internal controls, it will lead to deviations in the quantitative results of the target biomarkers, seriously affecting the accuracy and reproducibility of diagnosis. Therefore, finding an exosome internal control protein that maintains highly stable expression in different nerve cell sources, different body fluid samples, and different pathological states is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes an internal reference protein and its application in the preparation of reagents for detecting neurodegenerative diseases.

[0006] The technical solution of this invention is as follows:

[0007] The application of transferrin as an internal reference protein in the preparation of reagents for detecting exosomal proteins in neurodegenerative diseases, wherein the transferrin has the UniProt number P02787.

[0008] Furthermore, the neurodegenerative diseases include Alzheimer's disease, mild cognitive impairment, amyotrophic lateral sclerosis (ALS), Parkinson's disease, and Huntington's disease.

[0009] Furthermore, the exosomes are derived from biological samples of the subject, including plasma, serum, cerebrospinal fluid, brain tissue homogenate, and nerve cell culture supernatant.

[0010] A method for detecting the relative abundance of a target protein in exosomes of neurodegenerative diseases, comprising the following steps:

[0011] S1. Obtain the expression signal value of the target protein in the exosome sample to be tested;

[0012] S2. Obtain the expression signal value of transferrin, the internal reference protein, in the exosome sample to be tested;

[0013] S3. Using the transferrin expression signal value as a standard, the expression signal value of the target protein is corrected to obtain the relative abundance of the target protein;

[0014] The transferrin in question is designated as UniProt P02787.

[0015] Furthermore, the method for obtaining the expression signal value in steps S1 and S2 is selected from liquid chromatography-mass spectrometry, enzyme-linked immunosorbent assay, immunoblotting, and dot blot.

[0016] Further, the correction described in step S3 involves calculating the ratio of the target protein expression signal value to the transferrin expression signal value.

[0017] Furthermore, the exosome sample is placed in a pathological environment stimulated by inflammatory factors, including IL-1β or IL-6.

[0018] An exosome detection kit for the auxiliary diagnosis of Alzheimer's disease includes: a reagent for detecting transferrin expression in a subject's biological sample, wherein the transferrin has the UniProt number P02787.

[0019] Compared with the prior art, the present invention has at least the following advantages:

[0020] This invention relates to the application of transferrin as an internal control protein in the preparation of reagents for detecting exosomal proteins in neurodegenerative diseases. Experimental verification shows that transferrin maintains stable expression levels in exosomes derived from nerve cells, brain tissue, plasma, and serum of subjects with neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, amyotrophic lateral sclerosis (ALS), Parkinson's disease, or Huntington's disease. The coefficient of variation for transferrin expression is significantly lower than that of commonly used exosomal markers. The expression level of transferrin (TF) shows a stronger correlation with the total protein content of exosomes and is unaffected by external factors such as cellular inflammatory stress. This indicates that TF has superior performance as an internal control protein, helping to more accurately reflect the total sample loading amount and providing a more stable and reliable standardized tool for the field of exosome research. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0022] Figure 1 This is a bar chart showing the number of quantitative proteins in each group of samples based on LFQ data statistics in Example 1;

[0023] Figure 2 This is a Venn diagram showing the screening of highly stable proteins from four types of nerve cell-derived exosomes in Example 1;

[0024] Figure 3 This is a graph showing the expression difference of the candidate internal reference protein between the AD group and the CN group in Example 1;

[0025] Figure 4 This is a comparison chart of the coefficient of variation (CV) values ​​of the candidate internal control protein in Example 1 with those of the traditional internal control and biomarker proteins;

[0026] Figure 5 This is a comparison chart of the expression stability of candidate proteins and traditional internal controls and biomarkers among different samples, based on the dataset PXD037708 in Example 2.

[0027] Figure 6 This is the probability density distribution of the relative stability index (R value) of the candidate internal reference protein calculated based on the dataset PXD037708 in Example 2;

[0028] Figure 7 This is the abundance distribution histogram of candidate proteins in the full protein spectrum analyzed based on the dataset PXD037708 in Example 2;

[0029] Figure 8 This is a scatter plot showing the differences in candidate protein expression among all samples based on dataset PXD037708 in Example 2.

[0030] Figure 9 This is a comparison of the expression stability of candidate internal control proteins and traditional internal controls and biomarkers between the disease group and the normal group based on the PXD024216 dataset in Example 2.

[0031] Figure 10 This is the probability density distribution diagram of the candidate protein R value analyzed based on the dataset PXD024216 in Example 2;

[0032] Figure 11 This is the abundance distribution histogram of candidate internal reference proteins in the full protein profile of the sample, based on the PXD024216 dataset, in Example 2.

[0033] Figure 12 This is a scatter plot showing the differences in candidate protein expression under different disease states (AD, MCI, Control) based on the dataset PXD024216 in Example 2.

[0034] Figure 13 This is a comparison of the expression stability of candidate internal control proteins, traditional internal controls, and biomarkers between the amyotrophic lateral sclerosis (ALS) disease group and the normal group, based on the PXD036652 dataset analysis in Example 2.

[0035] Figure 14 This is the probability density distribution of the candidate internal reference protein R value calculated based on the dataset PXD036652 in Example 2;

[0036] Figure 15 This is the abundance distribution histogram of candidate internal reference proteins in the full protein profile of the sample, based on the PXD036652 dataset, as shown in Example 2.

[0037] Figure 16 This is a scatter plot showing the expression differences between the ALS disease group and the normal group based on the dataset PXD036652 in Example 2.

[0038] Figure 17 This is a microscopic morphological image of SH-SY5Y cells after 24 hours of stimulation with IL-1β and IL-6 in Example 3;

[0039] Figure 18 This is a statistical chart of the exosome particle diameters of each treatment group detected by nanoflow cytometry in Example 3;

[0040] Figure 19 This is a statistical graph of exosome particle concentrations in each treatment group detected by nanoflow cytometry in Example 3;

[0041] Figure 20 This is a statistical chart of the total exosome protein concentrations in each treatment group detected by the BCA method in Example 3;

[0042] Figure 21 This is a Dot Blot analysis of the imaging results of commonly used markers in exosomes of each treatment group in Example 3;

[0043] Figure 22 This is a Dot Blot image showing the imaging results of transferrin in exosomes of each treatment group in Example 3;

[0044] Figure 23 This is a linear correlation fitting diagram of the relative signal gray values ​​of transferrin and traditional markers (ALIX, TSG101) with the total protein concentration of exosomes in Example 3. Detailed Implementation

[0045] The present invention will now be described in further detail. It should be noted that the following specific embodiments are only used to further illustrate the present invention and should not be construed as limiting the scope of protection of the present invention. Those skilled in the art can make some non-essential improvements and adjustments to the present invention based on the above application content.

[0046] This invention provides a general and / or specific description of the materials and experimental methods used in the experiments. Unless otherwise specified, all experimental or testing methods are conventional methods; all reagents or instruments used, unless otherwise specified, are commercially available conventional products prepared or used using conventional methods.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art.

[0048] The collection, processing, and use of clinical samples involved in this application were all subject to written informed consent from the subjects or their legal guardians. The research protocol and sample collection procedures have been reviewed and approved by the Ethics Committee of the PLA Army Characteristic Medical Center, and the experimental process complies with ethical standards. The cell lines used in this application were purchased from legitimate commercial channels or cell banks; all publicly available database data cited are publicly accessible resources, and the acquisition and use of the data comply with the database's usage agreements and relevant laws and regulations.

[0049] Source of materials:

[0050] Antibody:

[0051] Proteintech 17435-1-AP-100ul Transferrin / TF Polyclonal antibody;

[0052] Abcam Anti-ALIX antibody [EPR23653-32] Anti-TSG101 antibody [EPR7130(B)];

[0053] Peroxidase-conjugated AffiniPure® Goat Anti-Rabbit IgG (H+L);

[0054] Reagents:

[0055] Thermo Fisher Scientific Fetal Bovine Serum, Premium Plus A5669701-500ml;

[0056] Thermo Fisher Scientific DMEM, high in sugar, containing pyruvate C11995500BT-500 ml;

[0057] Thermo Fisher Scientific Trypsin 0.25% EDTA 25200056-100 mL;

[0058] Thermo Fisher Scientific dual anti-penicillin 10000U / ml and streptomycin 10000ug / ml, 100x 15140122-100 mL;

[0059] Thermo Fisher Human IL-6 Recombinant Protein 200-06-20;

[0060] Thermo Fisher Human IL-1 beta Recombinant Protein 200-01B-10UG.

[0061] Example 1: Screening of internal reference proteins based on multicellular exosomal proteomics

[0062] This embodiment provides a method for screening universal internal reference proteins in exosomals using proteomics, and the specific steps are as follows.

[0063] S1. Sample Preparation and Exosome Enrichment: Induced pluripotent stem cells (hiPSCs) were collected from 3 Alzheimer's disease (AD) patients and 3 cognitively normal (CN) controls. These hiPSCs were differentiated into four types of neural cells: neurons (iNeu), astrocytes (iAst), microglia (iMic), and oligodendrocytes (iOligo). Cell culture was performed using high-glucose DMEM medium (Thermo Fisher Scientific, C11995500BT) containing 10% fetal bovine serum (Thermo Fisher Scientific, A5669701) and 1% penicillin-dextrin antibiotics (Thermo Fisher Scientific, 15140122). Cell passage was performed using 0.25% trypsin (Thermo Fisher Scientific, 25200056). The culture supernatant of the cells was collected. Three biological replicates were established for each cell line. Exosomes (EVs) were enriched using the following procedure.

[0064] S11. Centrifuge the cell culture supernatant at 2000g for 20 minutes at 4°C to remove cells and cell debris.

[0065] S12. The supernatant was filtered through a 0.22 μm filter membrane and then concentrated using an ultrafiltration tube with a molecular weight cutoff of 100 kDa.

[0066] S13. Use exosome extraction reagents and an automated extraction instrument to extract EVs, and resuspend them in PBS.

[0067] S14. Wash and concentrate again using a 100 kDa ultrafiltration tube to obtain purified EVs sample.

[0068] S2. Proteomics Mass Spectrometry Analysis: Proteomics analysis of enriched EVs samples was performed using liquid chromatography-mass spectrometry (LC-MS / MS). An ultra-high performance liquid chromatography (UHPLC) system and a timsTOF Pro mass spectrometer were used. Quantitative data were acquired in data-independent acquisition (DIA) mode, with software default parameters used for retrieval and quantitative calculations. A spectral library was constructed using data-dependent acquisition (DDA) mode as a supplement. The raw mass spectrometry data were analyzed using DIA-NN software (v1.8.1), with the UniProt reference proteome (UP000005640) as the reference database. Intensity-based absolute quantification (iBAQ) and label-free quantification (LFQ) data for each protein were obtained. Figure 1 As shown, a total of 10,826 proteins were quantified in the iBAQ data and 10,804 proteins were quantified in the LFQ data.

[0069] S3. Screening and identification of internal control candidate proteins:

[0070] S31. Stability screening: Based on iBAQ data, proteins with a detection rate of over 70% in all samples (AD and CN groups) for each cell type are screened.

[0071] S32. Low Variability Screening: For a specific cell type (e.g., iNeu), extract the iBAQ value of a certain protein from three biological replicates within that group, and calculate the arithmetic mean of these three values. ) and standard deviation ( Substitute into the formula The CV value of the protein in each cell type was obtained. The above calculation was performed independently for each of the four cell types (iNeu, iAst, iMic, and iOligo), and the protein with the highest CV value in all four cell types (with the least fluctuation within the group, i.e., the most stable expression) was selected as the candidate protein.

[0072] S33, Cross-validation: such as Figure 2 As shown, intersection analysis of the top 1% of proteins by CV value in four different types of neuronal cell-derived exosomes was performed using Venn diagrams, identifying 13 overlapping proteins that showed high stability in neurons, astrocytes, microglia, and oligodendrocytes. This result indicates that the expression stability of these 13 proteins is cell type nonspecific and can overcome the quantitative bias caused by exosome heterogeneity. The 13 candidate internal control proteins include: apolipoprotein A-II (Uniprot ID P02652_APOA2), calmodulin-like protein 5 (Uniprot ID Q9NZT1_CALML5), filaggrin (Uniprot ID P20930_FLG), transferrin (Uniprot ID P02787_TF), α-2-glycoprotein 1 (zinc-binding, Uniprot ID P25311_AZGP1), S100 calcium-binding protein A9 (Uniprot ID IDP06702_S100A9), γ-glutamyl cyclase (Uniprot ID O75223_GGCT), S100 calcium-binding protein A7 (Uniprot ID P31151_S100A7), ferritin heavy chain 1 (Uniprot ID P02794_FTH1), and dermcidin (antimicrobial peptide, Uniprot ID). P81605_DCD), immunoglobulin λ-like polypeptide 5 (Uniprot ID B9A064_IGLL5), keratin 1 (Uniprot ID P04264_KRT1), and amyloid precursor protein (Uniprot ID P05067_APP).

[0073] S34. Differential expression verification: such as Figure 3 As shown, a bubble chart was used to visualize the differential expression of 13 candidate proteins between the AD group and the CN group. The Y-axis of the figure lists the selected candidate proteins, and the X-axis represents different cell types. The results in the figure show that the bubbles corresponding to all candidate proteins are small and uniform, and no bubble features representing significant differences are found. Moreover, the performance of each protein is stable and there are no statistically significant differences, which meets the requirements for the stability of internal reference proteins and ensures that their expression levels are not affected by the AD pathological state.

[0074] S35. Comparison with commonly used internal references: such as... Figure 4 As shown, the CV values ​​of candidate proteins were compared with commonly used intracellular reference proteins (GAPDH, ACTB) and exosome markers (CD9, CD81, ALIX, TSG101). The data showed that the CV values ​​of TF in all four cell types were significantly lower than those of the aforementioned traditional proteins. In particular, in exosomes derived from astrocytes and microglia, traditional markers showed large fluctuations (high CV values), while TF maintained low variability. This indicates that compared with traditional markers, TF can provide smaller data dispersion, introduce less technical error when used as an internal control, and has superior expression stability.

[0075] Example 2: Multi-source sample verification based on public databases

[0076] To verify the stability of the 13 candidate proteins screened in Example 1 across a wider range of sample types, this example uses three independent datasets from the ProteomeXchange public database for reanalysis and verification. The specific steps are as follows.

[0077] S1. Data Source Processing: Three sets of common data were selected, all of which were raw proteomic data in raw and .d formats. Quantitative data were calculated using DIA-NN (versions 1.8 and 2.0) or maxquant software (version 2.6.7.0) with default settings to obtain iBAQ and LFQ data. The FASTA file referenced by the software for database searching was the Homo sapiens reference proteomic sequence data in the UniProt database, UP000005640_9606.fasta. The raw data of the above datasets were reprocessed and quantitatively analyzed, using an evaluation strategy similar to that in Example 1.

[0078] S2. Verification process and results:

[0079] S21, Dataset 1 (PXD037708) Validation: This dataset is derived from exosomes of brain tissue homogenates (frontal lobe, temporal lobe cortex, hippocampus) from AD patients. A total of 6221 proteins were detected, with 6 candidate proteins detected in 70% of the samples. The inter-sample protein expression CV value was calculated based on the iBAQ count. Figure 5 As shown in the figure, proteins with a detection rate of less than 70% are not displayed. In the dataset PXD037708, the CV value of TF protein is only about 0.14, ranking 121st in terms of variability among all 6221 detected proteins (Top 2%), indicating that TF protein has stable expression in different biological samples.

[0080] Furthermore, to eliminate the influence of fluctuations in the overall protein content of the sample, this invention defines and calculates the relative stability index R value, as follows: ;

[0081] Where i and j represent any two different independent samples in dataset 1, Gi and Gj represent the expression levels of the candidate internal reference protein in the i and j samples, and Mi and Mj are the average expression levels of all effective proteins with expression levels greater than 0 in the i and j samples. The closer the R value is to 1, the more stable the expression level of the candidate internal reference protein is relative to the background. Figure 6 The R-value density distribution map shows that the R-values ​​of TF, FTH1, and APP proteins are concentrated among the samples, indicating that their relative expression levels remain constant with the change in total protein content of the samples. Figure 7 This is an expression density distribution map of candidate internal control proteins. The figure shows that TF and FTH1 are medium-to-high abundance proteins in exosomes, and are easily detected by conventional techniques. Figure 8 As shown, the expression level of TF in all subject samples (Pat1-Pat8) showed minimal fluctuations between samples and exhibited extremely high stability across all AD samples.

[0082] S22, Dataset 2 (PXD024216) Validation: This dataset is derived from exosomes in the plasma of Alzheimer's disease (AD), mild cognitive impairment (MCI), and control populations. A total of 976 proteins were detected, with 5 candidate proteins detected in 70% of the samples. Analysis results are as follows... Figure 9-12 As shown, in dataset PXD024216, even when the sample source was expanded to include AD and MCI patients, TF in plasma exosomes also showed extremely low CV values ​​and a stable and concentrated R value distribution, with no significant difference between different disease states (AD vs Normal).

[0083] S23, Dataset 3 (PXD036652) Validation: This dataset is derived from exosomes in the serum of patients with amyotrophic lateral sclerosis (ALS) and control groups. A total of 208 proteins were detected, with 3 candidate proteins detected in 70% of the samples. Analysis results are as follows... Figure 13-16 As shown, TF remains stable in serum exosomes.

[0084] Example 3: Experimental Functional Verification

[0085] To verify the stability of TF protein under external stimuli, this embodiment conducted cell model experiments and immunoblotting verification.

[0086] The SH-SY5Y human neuroblastoma cell line used in this embodiment is a recognized in vitro model in the field of Alzheimer's disease (AD) mechanism research. According to a systematic review and meta-analysis published by Pinheiro et al. in 2025 (DOI:10.1101 / 2025.10.20.683497), statistical analysis of a large amount of experimental data confirmed that this cell line exhibits significant robustness and reproducibility in mimicking β-amyloid (Aβ)-induced neurotoxicity, synaptic dysfunction, and metabolic activity alterations. Although this cell line originates from neuroblastoma, it retains key pathological response characteristics of human neurons and is therefore widely established as a standardized model for assessing AD-related pathological changes and the stability of biomarkers.

[0087] S1. Cell Model and Treatment: Human neuroblastoma cell line (SH-SY5Y) was used. Three biological replicates were established for each group: a control group, an interleukin-1β (IL-1β, 50 ng / ml, Thermo Fisher Scientific 200-01B-10UG) stimulation group, and an interleukin-6 (IL-6, 100 ng / ml, Thermo Fisher Scientific 200-06-20) stimulation group. Cell supernatant was collected 24 hours after treatment, and EVs were extracted. Figure 17 As shown, after 24 hours of treatment, cells under a 20x microscope exhibited morphological changes due to inflammatory stress. They were more shrunken than the flattened spindle-shaped and irregularly shaped cells after differentiation, with increased vacuolation, retraction of protrusions, and reduced branching. The cells were in a pathophysiological state, confirming the establishment of a cellular inflammation model.

[0088] Characterization and quantification of S2 and EVs:

[0089] S21. EV Particle Size and Concentration Detection: The particle size and concentration of EVs were detected using nanoflow cytometry. The results are as follows: Figure 18 and Figure 19 As shown, there are differences in the diameter and number of exosome particles among different treatment groups.

[0090] S22. Total protein concentration determination: The total protein concentration was determined using the quinoline carboxylic acid (BCA) method. The results are as follows: Figure 20 As shown, there are differences in protein concentration among different treatment groups.

[0091] S23. Dot Blot Validation: Sample preparation involved adding 1 μL of 1% Triton X-100 lysis buffer (diluted with PBS) to 9 μL of EVs sample for each group, with two replicates per group. The samples were repeatedly mixed by pipetting and incubated on ice for 30 minutes, followed by sonication on ice (20% short pulse for 3 seconds, pause for 3 seconds, for a total of 6 times). The experimental procedure involved manually spotting 2 μL of each EV sample and the 0.1% Triton X-100 negative control sample onto a nitrocellulose membrane multiple times, for a total of 10 μL. Each time, the membrane was allowed to dry completely before reapplying at the same spot. After spotting, the membrane was thoroughly air-dried for 20 minutes to fix the proteins. The membrane was then blocked by immersing it in 5% skim milk for 1 hour. Subsequently, the membrane was incubated with primary antibodies: anti-TF (Proteintech 17435-1-AP-100u1 Transferrin / TF Polyclonal antibody), anti-ALIX (Anti-ALIX antibody [EPR23653-32] Anti-TSG101 antibody [EPR7130(B)]), and anti-TSG101 (Peroxidase-conjugated AffiniPure® Goat Anti-Rabbit IgG (H+L)) overnight at 4°C. After washing, the membrane was incubated with secondary antibodies at room temperature for 1 hour, followed by another wash. Finally, chemiluminescence development and analysis were performed. The grayscale values ​​of the developed bands were analyzed using ImageJ software. The results are as follows: Figure 21-22 As shown, in different treatment groups, the colorimetric signal intensity of the anti-TF antibody maintained a good correlation with the amount of sample loaded.

[0092] S3. Correlation Analysis: Calculate the correlation between the relative gray values ​​of each protein (treatment group / control group) and the relative total protein concentration (treatment group / control group). The results of the linear regression analysis are as follows: Figure 23 As shown, the data points represent the average values ​​of each independent biological replicate sample. A significant and strong positive correlation exists between the gray value of TF protein and the total exosome protein concentration determined by BCA. The fitted curve R... 2 The value is as high as 0.85 (R) 2 The closer the correlation is to 1, the stronger it is. This means that the TF signal can accurately and linearly reflect the total amount of protein loaded onto exosomes; in contrast, ALIX (R 2 <0.01) and TSG101 (R 2 The correlation between the signal gray value (=0.55) and the total protein concentration is weak, and the data points are highly dispersed around the regression line.

[0093] Example 2 demonstrates the universal stability of transferrin (TF) in the human population through large-scale clinical data. The cell model experiment in this example further confirms that the stability of TF stems from its inherent anti-interference ability under the inflammatory stress mechanism of nerve cells. This result proves that even when the cell state is changed due to stimulation by inflammatory factors, the content of TF protein in a single exosome remains stable, and its total amount can accurately reflect the total protein content of exosomes. It is a more reliable internal control protein than commonly used markers ALIX and TSG101.

[0094] Example 4: Method and Effect of Exosomal Target Protein Detection Based on Transferrin (TF) Correction

[0095] The absolute quantitative mass spectrometry (iBAQ) data of exosomes derived from four types of nerve cells (neurons, astrocytes, microglia, and oligodendrocytes) in Example 1 were extracted in the Alzheimer's disease (AD) group and the cognitively normal (CN) group. The data were divided into two groups: the original data group and the corrected data group. The original data group consisted of the log2 transformation of the original iBAQ values ​​of each target protein. The corrected data group used the expression signal value of the internal reference protein transferrin (TF) in the sample as a standard. Based on the original data group, the log2 transformation of the iBAQ values ​​of each target protein in the same sample was subtracted from the log2 transformation of the iBAQ values ​​of the TF protein to obtain the corrected relative abundance of the target proteins.

[0096] After TF protein correction, IBAQ data were analyzed for differences between the AD and normal groups using the rank-sum test. After thresholding (p<0.05, log2(FC)>=1), 1525, 1403, 360, and 1026 differentially expressed proteins were obtained, respectively. After Benjamini-Hochberg correction (a method used to control the false discovery rate in multiple comparisons and reduce false positives), p_adjust was obtained, resulting in 1002, 934, 1391, and 788 differentially expressed proteins with p_adjust<0.05. Therefore, compared to the differentially expressed proteins calculated from iBAQ data without TF protein correction, the false discovery rate and false positive results were reduced after TF protein correction. The number and retention rate of differentially expressed proteins before and after data correction for the four cell types are shown in the table below.

[0097] Table 1. Data before and after correction for four cell types

[0098]

[0099] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. The application of transferrin as an internal control protein in the preparation of reagents for detecting exosomal proteins in neurodegenerative diseases, characterized in that, The transferrin is designated UniProt number P02787; the neurodegenerative disease is Alzheimer's disease.

2. The application according to claim 1, characterized in that, The exosomes are derived from biological samples of the subject, including plasma, serum, cerebrospinal fluid, brain tissue homogenate, or nerve cell culture supernatant.

3. A method for detecting the relative abundance of a target protein in exosomes of neurodegenerative diseases, characterized in that, Includes the following steps: S1. Obtain the expression signal value of the target protein in the exosome sample to be tested; S2. Obtain the expression signal value of transferrin, the internal reference protein, in the exosome sample to be tested; S3. Using the transferrin expression signal value as a standard, the expression signal value of the target protein is corrected to obtain the relative abundance of the target protein; The transferrin in question is designated as UniProt P02787; the neurodegenerative disease is Alzheimer's disease.

4. The detection method according to claim 3, characterized in that, The methods for obtaining expression signal values ​​in steps S1 and S2 are selected from liquid chromatography-mass spectrometry, enzyme-linked immunosorbent assay, immunoblotting, or dot blot.

5. The detection method according to claim 3, characterized in that, The correction described in step S3 is to calculate the ratio of the target protein expression signal value to the transferrin expression signal value.

6. The detection method according to claim 3, characterized in that, The exosome sample was placed in a pathological environment stimulated by inflammatory factors, including IL-1β or IL-6.

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