Method, system, composition, and reagent kit for the diagnosis and differential diagnosis of Alzheimer's disease based on spatial transcriptomics in the human hippocampal region.

Abstract

To provide a method, system, composition, and reagent kit for the diagnosis and differential diagnosis of Alzheimer's disease based on spatial transcriptomics in the human brain hippocampus, which enables differential diagnosis of Alzheimer's disease by detecting one or more of CCK, Neurogranin, and PMP2 carried by plasma-derived extracellular vesicles (EVs). [Solution] This nanoflow cytometry technology focuses on the clinical and research challenges of early and differential diagnosis of cognitive impairments associated with Alzheimer's disease (AD). By utilizing world-leading spatial transcriptomics and single-cell sequencing, along with novel nanoflow cytometry technology, it enables highly sensitive and high-throughput detection of central nervous system-derived EVs (EVs) present in peripheral blood. Offering the advantages of rapid and low-cost detection, and utilizing resources derived from Chinese brains, newly discovered central nervous system brain regions, and cell-specific EV markers, it enables rapid and highly efficient early and differential diagnosis of AD-related cognitive impairments, providing new technological means and methods for precise diagnostic work related to the clinical application and large-scale screening of AD cognitive impairments in clinical settings.

Description

[Technical Field]

[0001] The present invention relates to the technical field of precise diagnosis of Alzheimer's disease, and more particularly to a method, system, composition, and reagent kit for the diagnosis and differential diagnosis of Alzheimer's disease based on spatial transcriptomics in the human hippocampus region. In particular, the present invention relates to a method, system, composition, and reagent kit that enables the diagnosis and differential diagnosis of Alzheimer's disease by combining research data and screening using spatial transcriptomics / single-cell sequencing in the human hippocampus region with exosome detection technology. [Background technology]

[0002] Traditionally, early diagnostic techniques for Alzheimer's disease (AD) have not been well established, and related products have been limited. In clinical practice, there is a lack of early diagnostic methods and criteria with high specificity and sensitivity. By the time AD patients are definitively diagnosed based on clinical symptoms and imaging indicators, irreversible neuronal damage has already progressed, and the disease has reached the mid-to-late stage, thus missing the optimal timing for intervention. Furthermore, there is a lack of diagnostic means to rapidly and efficiently differentiate between AD and non-AD-type cognitive impairment. Moreover, the pathogenesis of AD is not yet fully understood, and in clinical practice, there are no drugs that are effective in treating the cause, and treatment is currently limited to symptomatic relief.

[0003] While imaging diagnostics have traditionally shown some usefulness in diagnosing Alzheimer's disease (AD), the costs of these tests are extremely high, the necessary equipment is limited to large general hospitals, the testing process is complex and time-consuming, and interpretation by skilled radiologists is essential, resulting in a significant economic burden on patients and society. Compared to the high costs and complex processes of imaging diagnostics, biomarker testing for AD can be performed at low cost and in a short time, and biomarkers related to cerebrospinal fluid (CSF) have shown some clinical usefulness as an auxiliary diagnostic tool for AD. However, collecting CSF requires advanced skills from medical professionals, and patients have low tolerance for the invasive and risky procedure of lumbar puncture, making it difficult to implement in clinical diagnosis, especially screening.

[0004] As mentioned above, the early diagnosis and differential diagnosis of cognitive impairment associated with Alzheimer's disease (AD) have long been technical challenges in clinical practice and research, and have been a bottleneck in research. In contrast, peripheral blood-derived biomarkers for AD diagnosis have significant advantages, including being minimally invasive, highly tolerable by patients, easy to operate, standardizable, low-cost, and highly adaptable. Therefore, this technology has extremely high practical value in clinical applications and the field of public health. [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] The present invention aims to overcome the limitations inherent in the prior art by providing a method, system, composition, and reagent kit that enable the diagnosis and differential diagnosis of Alzheimer's disease based on screening of research data obtained by spatial transcriptomics / single-cell sequencing in the human brain hippocampus region.

[0006] This application aims to achieve early diagnosis and differential diagnosis of Alzheimer's disease (AD) by screening candidate targets using a combination of spatial transcriptomics / single-cell sequencing data from the human brain and an exosome database, and by detecting brain region-specific extracellular vesicles (EVs) of nervous system origin present in peripheral blood. This application has the advantages of being minimally invasive, highly tolerable by patients, cost-effective, and easy to disseminate, and is expected to be developed for clinical application and large-scale screening. The main biomarkers in this application are differential markers with disease specificity and brain region specificity discovered by spatial transcriptomics of the human brain, directly reflecting specific neuropathological changes in the central nervous system associated with AD, and enabling the diagnosis and differential diagnosis of AD. This application includes all differential genes (A2M, ABCA2, ABI1, AC009879.3, AC091167.2, AC092143.1, AC093330.1, APOE, AC093512.2, ACADVL, ACAT2, ACTB, ACTG1, ACTR1A, ADD3, ADGRB3, ADGRG1, ADIRF, AEBP1, AHNAK, C9orf16, AHSA1, AL049839.2, AL121594.1, AL365205.1, ANGPTL4, ANK2, AQP1, AP2M1, C3, AP2S1, APLNR, APOC1, ARL1, ARL6IP1, ARPC2, ARPC5L, CALM1, AT) included in spatial transcriptomics / single-cell sequencing data from the human hippocampus region. F4, CABP7, CADM2, ATP1A1, ATP1B1, BHLHE22, ATP2A2, ATP5F1A, ATP5F1B, CALM3, ATP5F1D, ATP5F1E, CADM3, ATP5MC1, ATP5MC2, ATP5MC3, ATP5MF, CACYBP, ATP5MG, ATP5MPL, CALM2, ATP5PB, ATP6AP2, ATP 6V0E2, ATP6V1D, ATP6V1E1, ATP6V1F, ATP6V1H, BEX4, ATP8A1, AUTS2, AUXG01000058.1, B2M, B4GAT1, BAIAP3, BCYRN1, BEX1, C1orf216, BEX2, BEX3, BRK1, C1QB, BSCL2, BTBD3, BTF3, CALY, CAMK1, CAMK2B,CAMK2N1, CAPS, CBR1, CCT5, CCT8, CD14, CD151, CD44, CD59, CD63, CD74, CEB PD, CELF4, CELSR2, CHGB, CHI3L1, CFL1, CHCHD2, CELF2, CD81, CD9, CDK2AP1 CDKN1A, CIRBP, CLSTN1, CLSTN2, CNN3, CKB, COMT, CLDN5, CLU, CNBP, CLASP 2. CLDN11, CNDP2, COPA, DLG2, COX4I1, COX6C, COX5A, COX5B, COX6A1, COX6B1 COX7A2, COX7C, CRYAB, CSMD1, CSMD3, CYCS, DBI, CSRP1, CST3, CTSB, CTSD CTXND1, CX3CL1, DGKG, DHCR24, DHCR7, DKK3, DCLK1, DDIT4, DEGS1, DNAJA1. DNAJA4, DNPH1, DSE, DTD1, DUSP1, DYNLL1, EEF1A1, EEF2, EEF1G, EFHD1, EID 1. EIF4A1, EIF4A2, EIF5, ELOB, ENC1, ENO1, ENO2, FBXO2, EPDR1, EPHA4, EPHA 5. ERBIN, ERC2, ERH, FABP3, FABP5, FAM107A, FAM162A, FAM3C, FAU, FBXW5, F EZ1, FHL1, FIS1, FKBP1B, FKBP2, FKBP5, FOS, FSCN1, FTH1, FTL, FTX, GALNT1 5, FXYD6, GABARAP, GABARAPL1, GABARAPL2, GADD45B, GAPDH, GDF1, GDI1, GL S、GLUL、GNAI2、GNAO1、GNAS、GNB2、GOT1、GPM6B、GPRC5B、GRB2、GRIA1、GSN、G STP1, GUK1, H2AFZ, H3F3B, HACD3, HINT1, HLA-B, HLA-DRB1, HMGCR, HMGCS1 HNRNPC, HNRNPDL, HNRNPK, HOPX, HS6ST3, HSP90AA1, HSP90AB1, HSPA4, HSPA 8. HSPB1, HSPB8, HSPE1, HSPH1, HTRA1, ICAM5, ID4, IDI1, ISCU, ISG15, ITGA V, IDS, ITGB4, ITM2B, ITM2C, JUN, ITPKB, IGFBP5, IGFBP7, IRS2, JUNB, JUND.KCNIP4、KCNMB4、KIAA0408、KIF1A、KIF1B、KLF9、KLHL2、LARP6、LDHA、LDHB、 LGALS1、MAPK8IP3、MIR7-3HG、MICAL2、MGST3、MAP4K4、LINC00844、MARCKSL 1、MFSD6、MFSD4A、LMO4、LRP1B、LSAMP、MAL、MALAT1、MAOB、MGST1、MAP1LC3A 、MDH2、MAP1LC3B、MAP2K1、MDH1、MEG3、MLLT11、MMD、MRFAP1、MRPL41、MRPL51 、MRPS21、MSMO1、MT1E、MT2A、MTATP6P1、MTCH1、MT-CO2、MT-CO3、MT-CYB、MT LN、MT-ND1、MT-ND2、MT-ND4、MT-ND6、MTPN、MT-RNR1、MT-RNR2、MYL12B、NDU FA11、MYL6、NDUFA13、NAA60、NDUFA4、NACA、NAP1L5、NAPB、NCAM2、NCDN、NDR G1、NDRG3、NDRG4、NDUFA1、NDUFA8、NDUFB8、NDUFAB1、NDUFS6、NDUFAF8、NDUF B4、NDUFV1、NEAT1、NECAB1、NENF、NLRP1、NME1、NME1-NME2、NOP56、NORAD、N PC2、NPM1、NPTN、NPTXR、NRIP3、NRN1、NRXN1、NRXN3、NTRK2、NUCKS1、NUPR1、 OAZ1、OLFM1、OXCT1、PREPL、PAK3、PARK7、PCDH8、PCDH9、PCP4、PDHA1、PEBP1 、PEX5L、PFKP、PGAM1、PGK1、PGRMC1、PHC2、PHPT1、PI4KA、PIP4K2A、PITHD1、P KM、PLCB1、PLEC、PLEKHA2、PLLP、PRDX5、POLR2I、PPIA、PRDX1、PPP2CA、PPP2 R2B、PPP3CA、PPP3R1、PPT1、PREX1、PSMA7、PRKAR1B、PRKCA、PRXL2A、PSMA4、 PSAP、PSMB4、PSMB5、PSMB7、PSMC3、PSMD1、PSMD8、PTMA、PTMS、PTP4A2、PTPR D、PTTG1IP、QDPR、QKI、RAB31、RABAC1、RACK1、RPL18A、RAMP1、RAN、RANGAP1、RASD1、RASGRP1、RBM3、RBX1、RFK、RGMA、RGS14、RPL18、RHBDD2、RNASE1、RNA SET2、RPL10、RPL17-C18orf32、RPL10A、RPL11、RPL13、RPL13A、RPL15、RPL17 -C18orf32、RPL19、RPL23、RPL21、RPL27A、RPL27、RPL28、RPL24、RPL26、RPL 3、RPL30、RPL31、RPL34、RPL35、RPL35A、RPL36、RPL37A、RPL38、RPL4、RPL41、 RPL5、RPL8、RPL9、RPLP0、RPLP1、RPS10、RPS11、RPS12、RPS13、RPS14、RPS15 、RPS15A、RPS16、RPS19、RPS2、RPS20、RPS21、RPS23、RPS27、RPS27A、RPS29、R PS3、RPS3A、RPS4X、RPS6、RPS7、RPS8、RPSA、RTN3、RTN4、RYR2、S100A10、S10 0A6、SAP18、SAT1、SCAMP5、SCD、SCG3、SCGN、SCOC、SCRG1、SDHA、SEC61B、SELE NOP, SELENOW, SEM1, SEMA3B, SEMA5A, SERF2, SERINC1, SHTN1, SIK3, SKP1, SLC22A17, SLC24A3, SLC25A3, SLC25A4, SLC2A3, SLC38A2, SLC44A1, SLC7A11 SLIT1、SNRPN、SOD1、SOD2、SORBS1、SPARC、SPARCL1、SPOCK1、SPP1、STXBP1、 SUB1、SUMO2、SUN2、SUSD4、SYNE1、SYNM、SYS1-DBNDD2、SYT11、SYT13、TAGLN3 、TALDO1、TBCA、TCEAL2、TCEAL3、TCEAL4、TCEAL5、TCF4、TCP1、TENM2、TF、TH Y1、TIMP2、TIMP3、TKT、TM2D3、TM9SF2、TMA7、TMBIM1、TMEM130、TMEM35A、TMS B10、TMSB4X、TMTC1、TNRC6C、TOLLIP、TOMM34、TOMM7、TPI1、TPPP、TPT1、TRIM 2、TRMT112、TSC22D1、TSPAN7、TSPYL4、TUBA1A、TUBA4A、TUBB2A、TXN、TXNIP、TXNL1, UBB, UBC, UBE2N, UBL5, UBQLN1, UCHL1, UQCR10, UQCR11, UQCRB, UQCRH, UQCRQ, USP11, USP22, VAMP2, VAPA, VDAC2, VIM, V STM2L, WBP2, XRCC6, YJEFN3, YWHAB, YWHAG, YWHAH, YWHAQ, YWHAZ, ZCCHC12, ZCCHC24, ZFAND5, ZFP36, ZFP36L1, ZFP36L2, ZMAT2) The exosome database was integrated and screened to identify genes that show significant changes during the onset and progression of AD (AK5, ALDOC, AMER, APC2, APOD, ATP1A3, BAALC, BCAN, C1orf61, CABP1, CACNG3, CACNG8, CAMKV, CCK, CHN1, CNKSR2, CHN1, CNDP1, CNTNAP4, CREG2, CTXN1, DLGAP1, DNAJC6, ENHO, GABRA5, GAD1, G AP43, GFAP, GNG3, GPM6A, GRIK2, GRIN1, GRIN2A, GRIN2B, HEPACAM, HPCA, KCNJ10, KCNQ3, KCTD16, KIF5C, LAMP5, MAP2, MBP, MOBP , MTURN, MLC1, MT3, MTURN, NEFL, NEFM, NEUROD2, NKX6-2, OMG, NPTX1, NRGN, OLIG1, OPALIN, PAQR6, PDYN, PHYHIP, PIANP, PLANP, Identify PLEKHB1, PLP1, PMP2, PMP22, PNMA2, POLR2F, PPKCG, PTPRZ1, RAB3A, RGS4, RTN1, S100B, SCN2B, SEZ6L, SLC1A2, SLC17A7, SLC1A3, SLC24A2, SLC2A1, SLC4A10, SLC6A7, SNAP25, SNCB, STMN4, STMN2, SULT4A1, SYN2, SYNPR, SYT1, TTC9B, TUBB2B, TUBB4A). These data are highly reliable and contribute to improving the accuracy of diagnosis and differential diagnosis. [Means for solving the problem]

[0007] To achieve the above objective, we provide a composition that enables the diagnosis and differential diagnosis of Alzheimer's disease, developed based on spatial transcriptomics / single-cell sequencing in the human brain hippocampus region. The composition comprises plasma-derived extracellular vesicles (EVs) carrying one or more of the following: CCK, Neurogranin (NRGN), and PMP2.

[0008] Preferably, the CCK, Neurogranin, and PMP2 carried by the plasma-derived extracellular vesicles (EVs) are concentrated by PEG8000 precipitation.

[0009] This invention relates to Zenon, a reagent for labeling antibodies. TM Alexa Fluor TM We offer a detection reagent kit containing the 647 Rabbit IgG Labeling Kit, lipid probes, and antibodies; The lipid probe has the TTTTTTTTTTTTTTTTTTTTTTTTTTTTTT sequence shown in SEQ ID NO: 1, with 5'6-CY3 modified at the 5' end and 3'Cholesteryl modified at the 3' end, and the antibody contains one or more antibodies against CCK, Neurogranin, and PMP2.

[0010] This invention provides the use of CCK protein in the differential diagnosis of Alzheimer's disease and non-AD type dementia.

[0011] The present invention provides a method for detecting positive proteins CCK, Neurogranin, and PMP2, comprising the following steps: The process involves labeling antibodies against CCK, Neurogranin, and PMP2 using the Alexa Fluro fluorescent labeling kit, thoroughly mixing the labeled antibodies with blocked exosomes, incubating overnight at 4°C in the dark, adding a lipid probe, and then incubating again at 4°C in the dark. The process involves adding PFA to the labeled sample, mixing thoroughly, and then incubating at room temperature under light-shielded conditions. The process involves diluting the sample to an appropriate concentration with PBS, loading it into the instrument, and detecting the number of particles at less than 10,000 particles / second using CytoFLEX or a similar nanoflow cytometry technique, and A step of detecting lipid probe-positive extracellular vesicles (EVs) using the VSSC mode of CytoFLEX or a similar nanoflow cytometry technique, and obtaining the positive rate of protein labeling in the extracellular vesicles (EVs),

[0012] Preferably, the method described above is used for differential diagnosis purposes, not for the purpose of diagnosing or treating diseases. [Effects of the Invention]

[0013] The core detection technology employed in this application is nanoflow cytometry, which enables highly sensitive and high-throughput detection of central nervous system-derived EVs in peripheral blood. This technology offers the advantages of speed and low cost, providing technical support for clinical applications and large-scale screening. This application focuses on the clinical and research challenges of early and differential diagnosis of AD-related cognitive impairment. By utilizing world-leading spatial transcriptomics and single-cell sequencing, as well as novel nanoflow cytometry technology, resources derived from Chinese brains, and newly discovered central nervous system brain region and cell-specific EV markers, rapid and highly efficient early and differential diagnosis of AD-related cognitive impairment becomes possible, providing new technological means and methods for AD diagnostic work in clinical settings. [Brief explanation of the drawing]

[0014] [Figure 1] This is an identification image of plasma EVs extracted in Example 1 of the present invention (observation by transmission electron microscopy shows that the EVs have a bilayer structure and their size is within the particle size range of EVs). [Figure 2] This graph shows the particle size distribution and concentration analysis results of EVs concentrated by PEG8000 precipitation in Example 1 of the present invention (the horizontal axis shows the particle size distribution, and the vertical axis shows the concentration of EVs). [Figure 3] This graph shows the results of detecting the specificity and stability of plasma EVs extracted in Example 1 of the present invention using nanoflow cytometry technology (Figure A shows that the proportion of EVs containing CCK, Neurogranin, and PMP2 in peripheral plasma is significantly higher compared to the same-type IgG control group. Figures B and C show the stability of the labeling efficacy of EVs with CCK, Neurogranin, and PMP2 antibodies at different dilution ratios (B) and different incubation times (C), respectively). [Figure 4] This figure shows the diagnostic ability screening for differentiating several potential biomarkers according to the present invention (Figures A-B show statistical analysis and ROC results for CCK protein detection, Figures C-D show statistical analysis and ROC results for Neurogranin protein detection, Figures E-F show statistical analysis and ROC results for PMP2 protein detection, Figures G-H show statistical analysis and ROC results for CREG2 protein detection, Figures I-J show statistical analysis and ROC results for CPLX2 protein detection, Figures K-L show statistical analysis and ROC results for NEFM protein detection, Figures M-N show statistical analysis and ROC results for STMN4 protein detection, and Figures O-P show statistical analysis and ROC results for STMN protein detection). [Figure 5]It is an analysis diagram of the verification cohort results of Example 3 of the present invention (as shown in Figure 5A, compared with the NC group, the ratio of CCK protein-positive EVs in the plasma of the AD group to the total number of EVs significantly decreased (****, p<0.0001). Compared with the NAD group, the ratio of CCK protein-positive EVs in the plasma of the AD group to the total number of EVs also significantly decreased (***, p<0.001). Furthermore, compared with the NC group, the ratio of CCK protein-positive EVs in the plasma of the NAD group to the total number of EVs also significantly decreased (**, p<0.01). As shown in Figure 5B, compared with the NC group, the ratio of Neurogranin protein-positive EVs in the plasma of the AD group to the total number of EVs significantly decreased (****, p<0.0001). Compared with the NC group, the ratio of Neurogranin protein-positive EVs in the plasma of the NAD group to the total number of EVs also significantly decreased (***, p<0.001). As shown in Figure 5C, compared with the NC group, the ratio of PMP2 protein-positive EVs in the plasma of the AD group to the total number of EVs significantly decreased (****, p<0.0001). Compared with the NC group, the ratio of PMP2 protein-positive EVs in the plasma of the NAD group to the total number of EVs also significantly decreased (**, p<0.01). As shown in Figure 5D, as a result of integrating and analyzing the data of CCK, Neurogranin, and PMP2 by logistic regression analysis using the Enter method, AUC = 0.92 (NC VS AD). As shown in Figure 5E, as a result of integrating and analyzing the data of CCK, Neurogranin, and PMP2 by logistic regression analysis using the Enter method, AUC = 0.83 (AD VS NAD).).

Modes for Carrying Out the Invention

[0015] To make the object, technical means, and advantages of the present invention clearer, the content of this application will be further described in more detail with specific examples below.

[0016] (Example 1) EVs Concentration by PEG8000 Precipitation 1. Thaw the plasma derived from the subject rapidly at 37°C (within 2 minutes), mix it uniformly by vortexing, 2. Centrifuge the plasma sample (300 μL or more) at 4°C and 2,000 × g for 15 minutes, collect the supernatant, 3. Subsequently, centrifuge the collected supernatant at 4°C and 12,000 × g for 30 minutes, and collect the supernatant again, 4. Pipette 10 μL of the centrifuged plasma into a 1.5 mL centrifuge tube, add 70 μL of PBS and 20 μL of 40% PEG8000 solution, mix well by pipetting, and let it stand at room temperature for 30 minutes, 5. Then, centrifuge at 4°C and 12,000 × g for 20 minutes, 6. Remove the supernatant, add 100 μL of PBS to resuspend, mix well by pipetting, dispense into 10 μL / tube, and store at -80°C.

[0017] (Example 2) 1. Blocking of the sample (1) Transfer 10 μL of the EVs concentrated by PEG8000 precipitation (Example 1) using the above method into a 0.6 mL centrifuge tube, (2) Add 10 μL of 2% BSA solution (filtered through a 0.22 μm filter), mix well, (3) Block at room temperature (25 - 26°C) for 1 hour to remove non-specific binding, (4) Add 10 μL of PBS (filtered through a 0.22 μm filter) to dilute and stop the blocking.

[0018] 2. Labeling of the sample Label the antibodies used for biomarker detection by a labeling method. Using an Alexa Fluor fluorescent labeling reagent kit, label the CCK, Neurogranin, and PMP2 antibodies. The antibody labeling reagent was Zenon purchased from Thermo Fisher Scientific TM Alexa Fluor TMIt was the 647 Rabbit IgG Labeling Kit. The lipid-labeling probe had the sequence TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT shown in SEQ ID NO: 1, with 5`-6-CY3 modified at the 5´ terminus and 3` Cholesteryl modified at the 3´ terminus.

[0019] (1) Dilute 1 μg of the antibody against the target marker with 5 μL of PBS (filtered through a 0.22 μm filter) to 5 μL (0.2 μg / μL). (2) Add 5 μL of Zenon TM Alexa Fluor TM 647 Rabbit IgG Labeling Kit Solution A, mix well, and then incubate for 20 minutes at room temperature (25 - 26°C) in the dark. (3) Add 3 μL (3 μg) of Zenon TM Alexa Fluor TM 647 Rabbit IgG Labeling Kit Solution B (diluted to 1 μg / μL) to the solution from step (2), mix well, and then incubate for 10 minutes at room temperature (25°C) in the dark to quench unbound free fluorescein. (4) Add PBS (filtered through a 0.22 μm filter) and dilute to a total volume of 50 μL. (5) Add 3 μL of the antibody labeled with Zenon TM Alexa Fluor TM 647 Rabbit IgG Labeling Kit to the sample blocked in step 1, mix well, and then incubate overnight at 4°C in the dark.

[0020] 3. Reagent used for probe incubation: A synthetic DNA anchor having the sequence TTTTTTTTTTTTTTTTTTTTTTTTTTTTTT (SEQ ID NO: 1), modified with 5'-6-CY3 at the 5' end and 3'Cholesteryl at the 3' end. The lipid probe can bind to EVs membranes, and particles positively labeled with the probe are considered true EVs, further improving the scientific validity and detection accuracy of EV labeling by this method.

[0021] (1) The following day, add 62 μL of PBS and 5 μL of lipid probe (working solution concentration: 10 μM) (total reaction volume was 100 μL, and the final probe concentration was adjusted to 500 nM), and incubate at 4°C under light-shielded conditions for 1 hour. (2) Add 100 μL of 4% PFA solution (filtered through a 0.22 μm filter) to the labeled sample, mix thoroughly, and incubate at room temperature (25-26°C) in the dark for 20 minutes. (3) Dilute to the appropriate concentration with PBS and load into the instrument. Standard: Measured using a CytoFLEX instrument, the number of particles per second is less than 10,000. 50,000 particles were collected at the lipid probe-positive gate (the total number of particles under these conditions is approximately 100,000; in other words, the particles obtained in the example were screened with a lipid probe, of which approximately 50% are true EVs).

[0022] 4. Measurement Using CytoFLEX VSSC mode, the positive rate of associated protein labels was measured when the lipid probe was positive.

[0023] (Example 3) Verification of the differential diagnostic ability of some of the aforementioned biomarkers in a small cohort. Plasma samples were collected from 15 age- and sex-matched healthy control subjects (NC group), 15 Alzheimer's disease patients (AD group), and 15 non-AD type dementia patients (NAD group), and experiments were conducted using the methods described in Examples 1 and 2 above.

[0024] Using the VSSC mode of CytoFLEX, the number of fluorescently labeled extracellular viable cells (EVs) for each protein—CCK, Neurogranin, PMP2, CREG2, CPLX2, NEFM, STMN4, and STMN2—was measured individually. The discriminative ability of NC, AD, and NAD was evaluated by calculating the ratio of the number of protein-positive EVs to the total number of lipid probe-positively labeled EVs. The results are shown in Figure 4. Compared to the NC group, the proportion of CCK protein-positive EVs in plasma of the AD group relative to the total number of EVs was significantly reduced (****, p<0.0001), and compared to the NC group, the proportion of CCK protein-positive EVs in plasma of the NAD group relative to the total number of EVs was also significantly reduced (****, p<0.0001) (4A), ROC=0.86 (4B). Compared to the NC group, the proportion of Neurogranin protein-positive EVs in plasma of the AD group relative to the total number of EVs was significantly reduced (****, p<0.0001), and compared to the NC group, the proportion of Neurogranin protein-positive EVs in plasma of the NAD group relative to the total number of EVs was significantly reduced (**, p<0.01) (4C), ROC=0.84 (4D). Compared to the NC group, the proportion of PMP2 protein-positive EVs in plasma of the AD group relative to the total number of EVs was significantly reduced (****, p<0.0001), and compared to the NC group, the proportion of PMP2 protein-positive EVs in plasma of the NAD group relative to the total number of EVs was significantly reduced (**, p<0.01) (4E), ROC=0.80 (4F). Compared to the NC group, the proportion of CREG-2 protein-positive EVs in the plasma relative to the total number of EVs was significantly reduced in the AD group (**, p<0.01) (4G), ROC=0.76 (4H). Compared to the NC group, the proportion of CPLX2 protein-positive EVs in plasma of the AD group relative to the total number of EVs was significantly reduced (****, p<0.0001), and compared to the NC group, the proportion of CPLX2 protein-positive EVs in plasma of the NAD group relative to the total number of EVs was also significantly reduced (****, p<0.0001) (4I), ROC=0.79 (4J). Compared to the NC group, there was no significant difference in the proportion of NEFM protein-positive EVs in the plasma of the AD group relative to the total number of EVs (4K), ROC=0.79 (4L). Compared to the NC group, there was no significant difference in the proportion of STMN4 protein-positive EVs in the plasma of the AD group and the NAD group relative to the total number of EVs (4M), ROC=0.69 (4N). Compared to the NC group, there was no significant difference in the proportion of STMN4 protein-positive EVs in the plasma of the AD group and the NAD group relative to the total number of EVs (4M), ROC=0.69 (4N). Compared to the NC group, there was no significant difference in the proportion of STMN2 protein-positive EVs in the plasma of the AD group and the NAD group relative to the total number of EVs (4O), ROC=0.64 (4P).

[0025] (Example 4) Verification of the differential diagnostic ability of the above biomarkers in a standardized clinical cohort. Plasma samples were collected from 66 age- and sex-matched healthy control subjects (NC group), 45 Alzheimer's disease patients (AD group), and 45 non-AD type dementia patients (NAD group), and experiments were conducted using the methods described in Example 1 and Example 2.

[0026] Using the VSSC mode of CytoFLEX, the number of EVs (Exponential Growths) of each protein—CCK, Neurogranin, and PMP2—was measured individually. The discriminative ability of NC, AD, and NAD was evaluated by calculating the ratio of the number of protein-positive EVs to the total number of lipid probe-positively labeled EVs. The results are shown in Figure 5. Compared to the NC group, the proportion of CCK protein-positive EVs in the plasma of the AD group relative to the total number of EVs was significantly reduced (****, p<0.0001). Compared to the NAD group, the proportion of CCK protein-positive EVs in the plasma of the AD group relative to the total number of EVs was also significantly reduced (**, p<0.01). In all of these comparisons, statistically significant differences were observed (Figure 5A). Compared to the NC group, the proportion of Neurogranin protein-positive EVs in the plasma of the AD group relative to the total number of EVs was significantly reduced (****, p<0.0001) (Figure 5B). Compared to the NC group, the proportion of PMP2 protein-positive EVs in the plasma of the AD group relative to the total number of EVs was significantly reduced (****, p<0.0001) (Figure 5C).

[0027] Using logistic regression analysis (Enter method), CCK, Neurogranin, and PMP2 data were used as explanatory variables, resulting in AUC = 0.92 (NC vs. AD) (Figure 5D) and AUC = 0.83 (AD vs. NAD) (Figure 5E). This analysis demonstrated that the proportion of CCK, Neurogranin, and PMP2 protein-positive EVs to the total number of EVs is effective not only for distinguishing between NC and AD patients but also for the differential diagnosis of AD and NAD.

[0028] The foregoing describes only preferred embodiments of this application and does not limit this application in any way. Those skilled in the art can make various changes, improvements, or equivalent modifications based on the technical content disclosed above and known targets. However, as long as they do not deviate from the technical idea of ​​this application, any simple modifications, equivalent modifications, and improvements to the above embodiments based on the technical essence of this application are all covered by the technical means of this application. Finally, it should be noted that the above embodiments are merely examples illustrating the technical means of the present invention and do not limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art will understand that the technical means of the present invention can be replaced with modifications or equivalents without departing from the technical essence and scope of the present invention.

Claims

1. A composition for the diagnosis and differential diagnosis of Alzheimer's disease based on spatial transcriptomics in the human brain hippocampus region, Plasma-derived extracellular vesicles (EVs) carry one or more of the following: CCK, neurogranin, and PMP2. A composition characterized by the following features.

2. The CCK, Neurogranin, and PMP2 carried by the plasma-derived extracellular vesicles (EVs) are concentrated by PEG8000 precipitation. The composition according to claim 1.

3. A detection reagent kit based on the composition described in claim 1 or 2, Zenon reagent for labeling antibodies TM Alexa Fluor TM 647 Rabbit IgG Labeling Kit, lipid probes, and antibodies are included. The lipid probe has the TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT sequence shown in Sequence ID No. 1, and is modified with 5'6-CY3 at the 5' end and 3'Cholesteryl at the 3' end. The aforementioned antibody includes one or more antibodies against CCK, Neurogranin, and PMP2. A detection reagent kit characterized by the following features.

4. The use of CCK protein in the differential diagnosis of Alzheimer's disease and non-AD type dementia.

5. A method for detecting a positive protein based on the composition according to claim 1 or 2, or the detection reagent kit according to claim 3, The process involves labeling antibodies against CCK, Neurogranin, and PMP2 using the Alexa Fluoro fluorescent labeling kit, thoroughly mixing the labeled antibodies with blocked exosomes, incubating overnight at 4°C in the dark, adding a lipid probe, and then incubating again at 4°C in the dark. The process involves adding PFA to the labeled sample, mixing thoroughly, and then incubating at room temperature under light-shielded conditions. The process involves diluting the sample to an appropriate concentration with PBS, loading it into the instrument, and detecting the number of particles at less than 10,000 particles / second using CytoFLEX. The process includes: detecting lipid probe-positive extracellular vesicles (EVs) using the VSSC mode of CytoFLEX, and obtaining the positive rate of protein labeling in the extracellular vesicles (EVs). A method characterized by the following:

6. Used for differential diagnosis, not for the purpose of diagnosing or treating diseases. The method according to feature 4.

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