阅读说明：本技术 检测神经变性疾病的方法 (Method for detecting neurodegenerative diseases ) 是由 邵慧琳 Z·J·C·林 张岩 L-H·C·陈 骆子品 于 2020-01-30 设计创作，主要内容包括：本公开一般涉及神经病学领域。具体地,本公开涉及一种检测受试者中神经变性疾病的方法及其治疗方法。所述方法包括检测获自受试者的样品中外泌体结合的聚集生物标志物的水平,其中与参照相比外泌体结合的聚集生物标志物的水平增加表明受试者患有神经变性疾病。还描述了用于检测有患淀粉样变性或神经变性疾病风险的受试者的方法、用于检测和治疗受试者的淀粉样变性或神经变性疾病的方法、以及测定样品中生物标志物聚集状态的方法。(The present disclosure relates generally to the field of neurology. In particular, the present disclosure relates to a method of detecting neurodegenerative disease in a subject and methods of treatment thereof. The method comprises detecting the level of exosome-bound aggregating biomarker in a sample obtained from the subject, wherein an increased level of exosome-bound aggregating biomarker compared to a reference indicates that the subject has a neurodegenerative disease. Also described are methods for detecting a subject at risk for an amyloidosis or neurodegenerative disease, methods for detecting and treating an amyloidosis or neurodegenerative disease in a subject, and methods of determining the aggregation state of a biomarker in a sample.)

1. A method of detecting a neurodegenerative disease or amyloidosis in a subject, comprising detecting the level of exosome-bound aggregated biomarker in a sample obtained from the subject, wherein an increase in the level of exosome-bound aggregated biomarker compared to a reference indicates that the subject has a neurodegenerative disease or amyloidosis.

2. The method of claim 1, wherein the biomarker is selected from the group consisting of a β, APP, a-Syn, Tau, APOE, SOD1, TDP-43, basoon, and/or fibronectin.

3. The method according to claim 1, wherein the method comprises detecting the level of a molecular subtype of the exosome-bound biomarker.

4. The method of claim 3, wherein the molecular subtype of A β is A β 42, A β 40, A β 39 or A β 38.

5. The method of claim 1, wherein the biomarker is a pre-fibril aggregate.

6. The method of any one of claims 1-5, wherein the method further comprises detecting an exosome biomarker selected from the group consisting of CD63, CD9, CD81, ALIX, TSG101, Flotilin-1, Flotilin-2, LAMP-1, HSP70, HSP90, RNA and DNA, wherein the exosome biomarker is co-localized with a biomarker to which the exosome binds.

7. The method of any one of claims 1-6, wherein the method further comprises detecting a neuronal biomarker selected from the group consisting of NCAM, L1CAM, CHL-1, and IRS-1, wherein the neuronal biomarker is co-localized with the exosome-binding biomarker.

8. The method of any one of claims 1-7, wherein the neurodegenerative disease is selected from Alzheimer's disease, mild cognitive impairment, dementia, vascular mild cognitive impairment, Parkinson's disease, amyotrophic lateral sclerosis, multiple sclerosis, progressive supranuclear palsy, and/or tauopathy.

9. The method of any one of claims 1-7, wherein the neurodegenerative disease is selected from alzheimer's disease and mild cognitive impairment.

10. The method of any one of claims 1-9, wherein the method comprises treating the subject with a neurodegenerative disease or amyloidosis.

11. The method of any one of claims 1-10, wherein the sample is a tissue biopsy sample, blood, plasma, serum, or cerebrospinal fluid.

12. The method of any one of claims 1-11, wherein the method is further associated with a brain imaging study.

13. A method of detecting a subject at risk of having a neurodegenerative disease or amyloidosis, comprising detecting the level of exosome-bound aggregated biomarker in a sample obtained from the subject, wherein an increase in the level of exosome-bound aggregated biomarker compared to a reference indicates that the subject has a neurodegenerative disease or amyloidosis.

14. A method of detecting and treating a neurodegenerative disease or amyloidosis in a subject, the method comprising: a) detecting the level of exosome-bound aggregation biomarker in a sample obtained from the subject, wherein an increased level of exosome-bound aggregation biomarker compared to a reference indicates that the subject has a neurodegenerative disease or amyloidosis; and b) treating the subject with a neurodegenerative disease or amyloidosis.

15. A method of determining the aggregation state of a biomarker in a sample, the method comprising detecting the level of an exosome-bound biomarker in the sample, wherein an increased level of exosome-bound biomarker compared to a reference indicates the extent of aggregation of the biomarker.

16. The method of claim 15, wherein the method comprises the step of contacting the sample with a population of exosomes prior to the step of detecting a level of an exosome-bound biomarker.

17. The method of claim 15 or 16, wherein the aggregating biomarker in the sample preferentially binds exosomes as compared to a non-aggregating biomarker.

18. The method of claim 17, wherein the sample is obtained from a subject.

19. The method of claim 18, wherein an increased degree of aggregation of the biomarker compared to a reference is indicative of a neurodegenerative disease or amyloidosis in the subject.

20. The method of claim 19, wherein the method comprises treating the subject with a neurodegenerative disease or amyloidosis.

21. The method of claim 20, wherein the treatment comprises administering to the subject a therapeutically effective amount of one or more drugs or a combination thereof.

22. The method of claim 21, wherein the drug is selected from the group consisting of a cholinesterase inhibitor, an NMDA receptor antagonist, a combination of a cholinesterase inhibitor and an NMDA receptor antagonist, a BACE1 inhibitor, an antibody, a protein aggregation inhibitor, a proteasome inhibitor, a small molecule, gene therapy, an anti-tau drug, or a combination thereof.

Technical Field

The present disclosure relates generally to the field of neurology. In particular, the disclosure relates to methods of detecting amyloidosis or neurodegenerative disease in a subject and methods of monitoring and treating a subject.

Background

Surface Plasmon Resonance (SPR) sensing is a widely used technique in the laboratory to characterize interactions between biomolecules, such as antibody-antigen interactions. This technique is generally based on immobilizing ligand capture molecules on a metal surface and measuring the change in refractive index upon binding of the ligand to the capture molecules. This technique is a label-free technique that does not require the use of specialized labels or dyes to sensitively measure interactions between molecules. Are currently being developed for laboratory diagnosis of patients suffering from different diseases, such as dementia, hepatitis, diabetes and cancer.

Dementia is a public health crisis in the 21 st century. Alzheimer's Disease (AD) is the most common form of severe dementia, characterized by gradual loss of memory and cognitive function. Infected individuals exhibit significant limitations in self-care, social and occupational function. However, the molecular characteristics of AD may manifest and develop as early as before these overall clinical symptoms appear. These include extracellular amyloid beta (a β) plaques and intracellular tau neurofibrillary tangles. Due to complex and progressive neuropathology, early detection and intervention is considered critical to the success of disease modifying therapies.

However, current AD diagnosis and disease monitoring is subjective and late. They are achieved by using published standard clinical and neuropsychological assessments. These methods lack sensitivity and specificity, particularly in the early stages, with subtle symptoms and significant overlap with various other diseases. New molecular diagnostic assays are being developed, including cerebrospinal fluid measurements and brain amyloid plaque imaging by Positron Emission Tomography (PET); however, these tests face limitations because they either require invasive lumbar puncture or are too expensive for more widespread clinical use. Therefore, there is a great interest in finding serological biomarkers for AD to aid in early diagnosis and disease monitoring.

Accordingly, there is a need to overcome or at least alleviate one or more of the above-mentioned problems.

Disclosure of Invention

Disclosed herein are methods of detecting an amyloidogenic or neurodegenerative disease in a subject and methods of monitoring and treating a subject.

In one aspect, a method of detecting an amyloidosis or a neurodegenerative disease in an individual is provided, the method comprising detecting a level of exosome-bound aggregated biomarker in a sample obtained from the individual, wherein an increase in the level of exosome-bound aggregated biomarker compared to a reference indicates that the individual has a neurodegenerative disease.

In one aspect, there is provided a method of detecting a risk of an individual developing an amyloidosis or a neurodegenerative disease, the method comprising detecting a level of an aggregate biomarker bound to exosomes in a sample obtained from the individual, wherein an increased level of the aggregate biomarker bound to exosomes as compared to a reference indicates that the individual has a neurodegenerative disease.

In one aspect, there is provided a method of detecting and treating an amyloidosis or neurodegenerative disease in a subject, the method comprising:

a) detecting the level of exosome-bound aggregating biomarker in a sample obtained from the individual, wherein an increased level of exosome-bound aggregating biomarker compared to a reference indicates that the individual has a neurodegenerative disease;

b) treating an individual having a neurodegenerative disease.

In one aspect, there is provided a method of determining the aggregation state of a biomarker in a sample, the method comprising detecting the level of exosome-bound biomarker in the sample, wherein an increase in the level of exosome-bound biomarker compared to a reference indicates the extent of aggregation of the biomarker.

Drawings

Some embodiments of the invention will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which:

FIG. 1: APEX platform for analysis of circulating exosome-bound Abeta

(a) Exosomes bind to the a β protein. The main component of a β protein found in AD brain pathology is released into the extracellular space. Exosomes are nanoscale extracellular membrane vesicles actively secreted by mammalian cells. Exosomes may be bound to released a β protein by their surface glycoproteins and glycolipids. (b) Transmission electron micrographs of exosome-bound Α β. Exosomes from neuronal cells (SH-SY5Y) were treated with Α β 42 aggregates and labeled with gold nanoparticles (10nm) by Α β 42-specific antibodies. The nanoparticles are shown as block dots (indicated by red arrows). (c) Schematic diagram of APEX assay. For sensitive analysis on the nanometer scale, exosomes are first immuno-captured onto plasmonic nanosensors (before amplification). By in situ enzymatic amplification, insoluble optical deposits (after amplification) are locally formed on the sensor-bound exosomes. This deposition is spatially defined as a molecular co-localization analysis and changes the refractive index of the SPR signal enhancement. Note that to supplement enzymatic amplification, the nanosensors are backlit (away from enzyme activity) to achieve analytical stability. The deposition causes a red shift in the transmitted light through the nanosensor. (d) Representative schematic of the transmission spectrum change with APEX amplification. Specific exosome binding (before) and subsequent amplification analysis (after) was monitored as transmission spectral shifts (Δ λ), a.u. arbitrary units by the APEX platform. (e) Exosome-bound a β was measured in blood samples of patients with diseases such as Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and cognitive impairment free (NCI) controls using the APEX platform. Blood measurements were correlated with corresponding PET imaging of brain amyloid plaque deposits. (f) Photograph of APEX microarray. Each sensor chip contains 6 x 10 sensor elements, consisting of a uniformly fabricated plasmonic lattice, for multiple measurements. For information on sensor fabrication, characterization, and design optimization, see fig. 7-10, respectively.

FIG. 2: APEX Signal amplification and multiplex analysis

(a) Stepwise APEX transmission spectrum changes. We performed a series of manipulations, antibody coupling (anti-CD 63) to the sensor, exosome binding, enzyme labeling and enzymatic deposition, and monitored the resulting spectral shifts. Although the enzyme labeling did not cause any significant change, the formation of deposits resulted (. x.p < 0.0001, n.s. non-significant, student's t-test). (b) APEX signal amplification and comparison of optical deposition area coverage. The increase in area coverage was determined by Scanning Electron Microscopy (SEM) analysis (×) p < 0.0001, student's t-test). All data are normalized to data before signal amplification. Insert (right) shows SEM images of sensor-bound exosomes before and after APEX magnification. (c) Finite difference time domain simulation with backlighting. The APEX sensor design, but not the gold-plated glass design, is capable of generating an enhanced electromagnetic field through a backlight. Backlighting can minimize direct incident light on the enzyme activity (occurring on top of the sensor). The arrows indicate the direction of the incident light. (d) APEX amplifies the real-time sensorgram of kinetics. Different concentrations of optical substrate (3, 3' -diaminobenzidine tetrahydrochloride; high: 1mg/ml, low: 0.01mg/ml) were used to monitor the amplification efficiency. All data were normalized to negative controls, using IgG isotope control antibodies. (e) Detection sensitivity comparisons of APEX, ELISA and Western blot. The APEX detection limits (dashed lines) before and after amplification were determined by titrating known amounts of exosomes and measuring their CD63 signals. (f) Specificity of APEX assay for measuring target proteins. Assays were developed for amyloid beta (A β 42), Amyloid Precursor Protein (APP), α -synuclein (α -syn), the close homolog of LI (CHL1), insulin receptor substrate 1(IRS-1), Neural Cell Adhesion Molecule (NCAM), and tau protein. All assays demonstrated specific detection. The heat map signals were normalized by assay (row). All measurements were performed in triplicate and data are shown as mean ± sd at a, b, d and e.

FIG. 3: preferential binding between Ap aggregates and exosomes

(a) Schematic representation of A beta protein aggregation. We varied the degree of clustering and used a filtering method to prepare small and large Α β 42 aggregates, respectively. (b) Characterization of A β protein aggregates. The (left) transmission electron micrograph shows the spherical morphology of the prepared Α β 42 aggregates. Dynamic light scattering analysis (right) confirmed a monomodal size distribution of the different sized formulations. (c) Schematic representation of exosome- Α β binding assay. Α β 42 aggregates (small and large) were immobilized on APEX sensors and treated with equal concentrations of exosomes from neuronal cells (SH-SY5Y) to determine binding kinetics. All exosome binding data were normalized to the corresponding Α β 42 aggregate surface area immobilized on the sensor (see methods for details). (d) Real-time sensorgram of exosome binding kinetics. Exosomes bound more strongly to Α β 42 aggregates than similarly sized Bovine Serum Albumin (BSA) control aggregates (see fig. 13). Importantly, exosomes exhibit stronger affinity for larger Α β 42 aggregates (right) compared to their binding affinity for smaller Α β 42 aggregates (left). Note the difference in scale on the y-axis. All binding affinities (KD) were determined from normalized exosome binding data and relative to BSA controls. KD (small)/KD (large) 5.27. (e) Different binding of various extracellular vesicles to Α β 42 aggregates. Vesicles were derived from different cell sources, i.e. neurons, glial cells, endothelial cells, monocytes, erythrocytes, platelets and epithelial cells, respectively, and were used in equal concentrations for binding assays. Using the APEX platform, we first measured direct binding of vesicles to the a β 42 functionalized sensor (direct). Next, for each cell source, we labeled the binding vesicles for the source-specific marker (cell source-specific marker) or pan-exosome marker (i.e. CD63, pan-exosome marker) and measured the associated APEX signal amplification. All measurements were performed in triplicate against IgG isotope control antibody. Data are shown as mean ± sd in e.

FIG. 4: clinical relevance of circulating exosome binding a β to brain imaging

(a) Representative reconstructed PET brain imaging from clinical individuals showed increased brain amyloid plaque burden. The normalized uptake ratio SUVR for a particular brain region is normalized to the mean cerebellar gray matter intensity to determine brain amyloid plaque burden. (b) Correlation of different circulating Α β 42 populations with ensemble averaged PET brain imaging (n-72). Using the APEX assay, we measured the respective signals for exosome-bound a β 42 (left), unbound a β 42 (middle) and total a β 42 (right) in blood samples of Alzheimer's Disease (AD) patients, Mild Cognitive Impairment (MCI) patients and non-cognitive impairment (NCI) control individuals, vascular dementia (VaD) patients and Vascular Mild Cognitive Impairment (VMCI) patients. When correlated with global imaging data of brain amyloid plaques, to unbound a β 42 population (center, R)²0.0193) or total Α β 42 (right, R)²0.1471) compared to exosome-bound a β 42 (left, R)²0.9002) showed the best correlation. (c) Analysis of different circulating a β 42 populations in differentiating different clinical groups (n ═ 84). Only APEX measurements of circulating exosome-bound a β 42 (left) can distinguish AD clinics (AD and MCI) from other Normal (NCI) and clinical controls (VaD, VMCI and acute stroke). Is not knottedNeither the combined Α β 42 measurements (center) nor the total Α β 42 measurements (right) showed any statistical significance between the different clinical groups (× P < 0.01, × P < 0.0001, n.s., not significant, student's t-test). All measurements were performed in triplicate against an IgG isotype control antibody. Data are shown as mean ± sd in b-c.

FIG. 5: characterization of extracellular vesicles shed by neuronal cells.

(a) Scanning electron micrographs of neuronal cells (SH-SY5Y) showed massive release of nanoscale extracellular vesicles from the cells. (b) High magnification images of the released vesicles. (c) The monomodal size distribution of extracellular vesicles determined by nanoparticle tracking analysis showed an average diameter of-150 nm. (d) Western blot analysis of vesicle lysates. The vesicles were lysed and immunoblotted to obtain exosome markers (LAMP1, ALIX, HSP90, HSP70, CD63, Flotillin1, TSG101), neuronal markers (NCAM), as well as negative markers including lipoproteins (APOE) and other membrane compartment markers (calnexin, GRP 94). (e) Transmission electron micrographs doubly immunolabeled with gold nanoparticles of different sizes (CD63, 20 nm; Abeta 42, 5nm) confirmed the co-localization of the two markers on the same vesicle, indicating the presence of exosome-bound Abeta.

FIG. 6: APEX amplification product

Scanning electron micrographs of APEX sensors (a) exosomes were captured onto the sensor by anti-CD 63 antibody before amplification, and (b) after amplification, local growth of insoluble optical deposits from soluble substrate (3, 3' -diaminobenzidine tetrahydrochloride) was shown. The amplification of the APEX signal generated is closely related to the increase of the coverage area of the local deposit.

FIG. 7: large scale fabrication of APEX microarray sensors.

All APEX sensors were fabricated on 8 inch silicon (Si) wafers. The manufacturing steps comprise: (1) preparation of 10nm Silica (SiO) by thermal Oxidation₂) Layer and depositing 145nm silicon nitride (Si) on the wafer by Low Pressure Chemical Vapor Deposition (LPCVD)₃N₄). (2) After coating the photoresist, Deep Ultraviolet (DUV) lithography is performed to define an array of nanopores in the photoresistColumn pattern. The pattern is transferred to Si by reactive ion etching (RLE)₃N₄On the membrane. (3) After removing the photoresist, a thin layer of SiO is deposited on the front side of the wafer using Plasma Enhanced Chemical Vapor Deposition (PECVD)₂Protective layer (100 nm). For optical energy transmission, the back side of the wafer is spin coated with photoresist; the sensing region is defined using a lithographic method. (4) Etching Si by etching with RLE, followed by etching with potassium hydroxide (KOH) and tetramethylammonium hydroxide (TMAH) of Si₃N₄And SiO₂. (5) After etching, the protective SiO was removed using Dilute Hydrogen Fluoride (DHF) (1: 100)₂And (3) a layer. (6) Deposition of Ti/Au (10nm/100nm) on Si₃N₄On the membrane.

FIG. 8: characterization of APEX microarray sensors.

(a) Photographs of 8-inch wafers show mass production of APEX microarray sensor chips. Each wafer consists of > 2000 sensing elements. (b) Scanning electron micrographs of highly uniform nanopores in an APEX sensor. The insertions show a magnified view of the nanopore lattice.

FIG. 9: a gradual spectral change.

The APEX sensor was conjugated to either (a) an anti-CD 63 antibody for exosome capture or (b) an isotope control antibody. All sensors were treated with equal concentrations of exosomes from the neuronal cell line (SH-SY5Y) prior to APEX amplification. While the sensor showed a similar degree of surface functionalization as the antibody (antibody coupling), only the anti-CD 63 functionalized sensor showed significant spectral shifts associated with exosome binding and APEX amplification, respectively. Note that in the control sensor, APEX amplification caused negligible spectral changes, a.u. arbitrary units, in the absence of exosome binding.

FIG. 10: and (4) optimizing the performance of the APEX sensor.

(a) Comparison of sensor performance with backlight. We compared SPR transmission intensity, full width at half maximum (FWHM) of spectral peaks and detection sensitivity for different APEX sensors with different nanopore diameters. All sensors were back-illuminated to complement APEX enzymatic amplification. The optimized APEX design has nanopores 230nm in diameter and is periodically patterned at 450nm in a 100nm thick gold layer suspended on a silicon nitride film. This bilayer plasmon structure supports SPR excitation by backside illumination. (b) Optimizing the transmission spectral variation of the sensor for increased refractive index. The increase in refractive index causes a change in the transmission spectrum and shifts the resonance peak to longer wavelengths. (c) The spectral shift shows a linear dependence on the refractive index increase. (d) APEX reproducibility and repeatability. APEX enzymatic amplification was performed on the same sample and measurements were taken between different users, sensor chips and measurement times. The measurements showed the following analytical coefficient of variation: the total content between groups was 2.76%, the total content within groups was 4.14%, and the total content was 4.59%. All measurements were performed in triplicate or more and the data are shown in (a) as mean ± sd, a.u. arbitrary units, n.s., insignificant, student's t-test.

FIG. 11: APEX workflow and amplification efficiency.

(a) APEX workflow for detection of proteins (extracapsular and intracapsular) and mirnas. (b) APEX amplification efficiency for different molecular targets. The APEX signals were obtained for the following targets: extravesicular protein, a β 42 protein; an intra-vesicular protein, heat shock protein 90; miRNA, miRNA-9. All signals were normalized to the signal before the addition of the optical substrate to determine the magnification. Measurements were performed in triplicate and data are shown as mean ± sd in (b).

FIG. 12: fibrous structures assembled from large Α β aggregates.

Amyloid fibrils were observed after 2 hours incubation of the prepared large Α β 42 aggregates. The resulting structures were immunolabeled with gold nanoparticles (15nm) by anti-a β 42 antibody and characterized by transmission electron microscopy.

FIG. 13: preparation of BSA control aggregates.

(a) Schematic representation of BSA protein aggregates. We varied the duration of heating to prepare small and large BSA control aggregates, respectively. (b) Characterization of BSA protein aggregates. The hydrodynamic diameter of the BSA aggregates was determined by dynamic light scattering analysis. Both aggregates show a monomodal size distribution. The diameter of the small aggregates is-15 nm, and the diameter of the large aggregates is-100 nm.

FIG. 14: extracellular vesicles isolated from various cell sources.

Extracellular vesicles were obtained from (a) neurons (SH-SY5Y), (b) glial cells (GLI36), (c) endothelial cells (HUVEC), (d) monocytes (THP-1), (e) erythrocytes, (f) platelets, (g) epithelial cells of prostate origin (PC-3) and (h) epithelial cells of ovarian origin (SK-OV-3), respectively. All vesicles were characterized using nanoparticle tracking analysis.

FIG. 15: APEX measurement of different circulating Α β populations.

(a) APEX assay configuration for characterizing different circulating a β populations in clinical plasma samples. Exosome-bound Α β 42 and total Α β 42 populations were measured from native plasma, whereas unbound Α β 42 populations were detected from plasma filtrate. (b) Incubation of fibronectin with exosome-bound a β 42 resulted in negligible changes in APEX signal. (c) The negative control (APOE lipoprotein with a β 42 protein, human serum albumin/HSA) showed negligible signal, indicating that the APEX assay is specific for exosome-bound a β 42. All measurements were performed in triplicate and the data are shown as mean ± sd.

FIG. 16: characterization of a β population in clinical samples.

(a) Exosome-bound abeta 42 population. We directly enriched a β 42 from native plasma samples and measured the relative levels of co-localization signals of exosome markers (CD63, CD81 and CD9) and neuronal markers (NCAM, L1CAM and CHL-1) in captured a β 42. All markers could be detected, CD63 being the most highly expressed marker in the tested clinical samples. (b) Plasma filtrate used to characterize the unbound Α β 42 population. To evaluate the unbound Α β 42 population, we prepared a vesicle-free plasma filtrate using membrane filtration (cut-off size 50nm, nucleocore, Whatman). The filtrate showed negligible vesicle counts as determined by nanoparticle tracking analysis. (c) The plasma filtrate also showed negligible signals for the exosome marker (CD63) and the neuronal marker (NCAM), demonstrating that exosomes were effectively removed by filtration. Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and healthy controls without cognitive impairment (NCI). All measurements were performed in triplicate and the data are shown as mean ± sd.

FIG. 17: correlation of exosome-bound a β 42 with regional brain amyloid burden.

We determined imaging SUVR for specific brain regions, namely the cingulate gyrus region affected early by AD and the occipital region affected late by AD. APEX measurement of exosome-bound Α β 42 and imaging data of early AD-affected areas (a, R)²0.8808) showed better consistency than the imaging data of the late AD affected area (b, R0.6863). Alzheimer's disease (AD, n-17), mild cognitive impairment (MCI, n-18), vascular dementia (VaD, n-9), vascular mild cognitive impairment (VMCI, n-12), healthy control without cognitive impairment (NCI, n-16). All measurements were performed in triplicate and the data are shown as mean ± sd.

FIG. 18: comparison of PET imaging in clinical individuals of different diagnoses.

PET imaging of brain amyloid plaque burden was performed in patients with different clinical diagnoses (n-72): AD (n-17), MCI (n-18), NCI (n-16), VaD (n-9), and VMCI (n-12). Normalized uptake value ratios (SUVR) of overall mean plaque deposits can distinguish AD clinical groups (AD and MCI) from other healthy individuals (NCI) and clinical controls (VaD and VMCI) (. P < 0.01,. P < 0.0001, student's t-test).

FIG. 19: extracellular vesicles in clinical samples.

(a) Representative analysis of extracellular vesicles, measured by nanoparticle tracking analysis, blood samples from different clinically diagnosed individuals (AD 17, MCI 18, NCI 16, VaD 9, VMCI 12, acute stroke 12). Comparison of (b) vesicle size and (c) vesicle concentration from clinical blood samples (n ═ 84). Note that no significant difference in vesicle size and concentration was found in the different clinically diagnosed samples (n.s., not significant, student's t-test).

FIG. 20: APEX detection technique, comparison of sensor design and fabrication.

Fig. 21 shows (a) inhibition of amyloid aggregation. (b) Dynamic light scattering analysis confirmed the unimodal size distribution and the size difference when amyloid was incubated with and without inhibitor. (c) Real-time sensorgram of exosome binding kinetics. Exosomes exhibit stronger affinity for larger Α β 42 aggregates (untreated) compared to smaller Α β 42 aggregates (treated with inhibitors).

Figure 22 shows a real-time sensorgram of exosome binding kinetics. Exosomes bind more strongly to amyloid proteins (e.g., Α β, APP, a-Syn, IRS-1, Tau, APOE, SOD1, TDP-43, basoon, fibronectin) than similarly sized Bovine Serum Albumin (BSA) control aggregates.

Figure 23 shows the specificity of APEX assays used to measure target miRNA molecules. Assays for miR-9, miR-15b, miR-29c, miR-107, miR-146a and miR-181c are developed. All assays demonstrated specific detection. The heat map signals were normalized by assay (row).

Detailed Description

Disclosed herein are methods of detecting neurodegenerative diseases and methods of treatment thereof.

In one aspect, a sensor chip is provided comprising a conductive layer on a membrane support layer, wherein a plurality of holes extend through the conductive layer and the membrane support layer and are arranged such that illumination of the conductive layer and/or the membrane support layer generates surface plasmon resonances.

In one embodiment, the multilayer structured material (conductive metal and support substrate) is designed to enable plasma coupling. This design can support bi-directional excitation of surface plasmon resonances where SPR performance from bi-directional illumination (from top or from bottom) is comparable.

As used herein, the term "conductive layer" may be a conductive material that exhibits surface plasmon resonance when excited by electromagnetic energy, such as light waves. The conductive material may refer to, for example, a metallic conductive material. Such metallic conductive materials may be any metal including noble metals, alkali metals, transition metals, and alloys. Examples of conductive materials include, but are not limited to, gold, rhodium, palladium, silver, platinum, osmium, iridium, titanium, aluminum, copper, lithium, sodium, potassium, nickel, metal alloys, indium tin oxide, aluminum zinc oxide, gallium zinc oxide, titanium nitride, and graphene. In one embodiment, the conductive material is gold, silver, aluminum, sodium, indium, or titanium. The metal may be in its bare form or coated with additional layers of protective and reinforcing materials.

Conductive materials may be "optically observable" when the conductive material exhibits significant scattering intensity in the optical region (ultraviolet-visible-infrared spectrum) including wavelengths of about 100nm (nm) to 3000 nm. The conductive material may be "visible to the naked eye" when the conductive material exhibits significant scattering intensity in a wavelength band (i.e., visible spectrum) of about 380nm to 750 nm.

In one embodiment, the membrane support layer is a structured membrane support layer.

In one embodiment, the membrane support layer is silicon nitride or sodium dioxide. Other support materials include substrates that can be patterned to form coupled multi-layer plasma structures.

The diameter and periodicity of the plurality of holes extending through the conductive layer and the membrane support layer may be varied to achieve different resonant wavelengths and penetration of evanescent waves.

The plurality of holes include symmetrical round holes, spatially anisotropic shapes such as ovals, slits, and also include any holes of triangular, square, rectangular or polygonal shape. Combinations of different shaped apertures may also be used. The pores may have a size of about 1500nm or less, about 1400nm or less, about 1300nm or less, about 1200nm or less, about 1100nm or less, about 1000nm or less, about 900nm or less, about 800nm or less, about 700nm or less, about 600nm or less, about 500nm or less, about 450nm or less, about 400nm or less, about 350nm or less, about 300nm or less, about 250nm or less, about 240nm or less, about 230nm or less, about 220nm or less, about 210nm or less, about 200nm or less, about 190nm or less, about 180nm or less, about 170nm or less, about 160nm or less, about 150nm or less, about 140nm or less, about 130nm or less, about 120nm or less, about 110nm or less, about 100nm or less, about 90nm or less, about 70nm or less, about 50nm or less, about 60nm or less, about 100nm or less, about 200nm or less, or a, A size or diameter of about 40nm or less, about 30nm or less, about 20nm or less, or about 10nm or less.

In one embodiment, the pores may have a size or diameter of about 150nm to about 450 nm. In one embodiment, the size or diameter of the pores is selected from 150nm, 160nm, 170nm, 180nm, 190nm, 200nm, 210nm, 220nm, 230nm, 240nm, 250nm, 260nm, 270nm, 280nm, 290nm, 300nm, 310nm, 320nm, 330nm, 340nm, 350nm, 360nm, 370nm, 380nm, 390nm, 400nm, 4200nm, 430nm, 440nm, 450nm, or between any two. In one embodiment, the pores are small and have a diameter of 230 nm.

The term "periodic" may refer to the positioning of the holes on the sensor chip recurring or repeating at regular intervals. Thus, the term "periodic" refers to a regular predefined pattern of holes relative to each other.

The surface plasmon resonance sensor chip may comprise a periodic array of holes. The regular periodicity may allow for tight control of the resonance wavelength and penetration of the evanescent wave. In one embodiment, the pores have a periodicity of about 250nm to about 650 nm. In one embodiment, the pores have a periodicity selected from 250nm, 260nm, 270nm, 280nm, 290nm, 300nm, 310nm, 320nm, 330nm, 340nm, 350nm, 360nm, 370nm, 380nm, 390nm, 400nm, 410nm, 420nm, 430nm, 440nm, 450nm, 460nm, 470nm, 480nm, 490nm, 500nm, 510nm, 5300nm, 540nm, 550nm, 560nm, 570nm, 580nm, 590nm, 600nm, 610nm, 620nm, 630nm, 640nm, and 650nm, or between. In one embodiment, the pores have a periodicity of 450 nm.

In one embodiment, the apertures are arranged such that the decay length of the surface plasmon resonance generated upon irradiation is approximately equal to the diameter of the target of the first recognition molecule.

In one embodiment, the conductive layer and the film support layer are disposed on a substrate having voids formed therein in regions adjacent to the plurality of apertures to enable irradiation of the conductive layer and/or the film support layer in either direction to generate surface plasmon resonance.

In one embodiment, the sensor chip comprises a first recognition molecule immobilized on the surface of the conductive layer. The first recognition molecule may be immobilized on the surface using techniques well known in the art. For example, the first recognition molecule may be adsorbed onto the surface. Alternatively, the surface may be coated with a layer of streptavidin or avidin prior to immobilization of the first recognition molecule. The first recognition molecule may be biotinylated by streptavidin-biotin conjugation and immobilized on a surface. In one embodiment, the surface may be incubated with polyethylene glycol (PEG) molecules. The surface can be incubated with active (carboxylated) thiol-PEG. The surface can then be activated by carbodiimide cross-linking in excess NHS/EDC mixture dissolved in MES buffer and conjugated to the first recognition molecule. In an alternative embodiment, the surface may be incubated with a mixture of polyethylene glycol (PEG) containing long active (carboxylated) thiol-PEG and short inactive methylated thiol-PEG. The ratio of long active (carboxylated) thiol-PEG to short inactive methylated thiol-PEG can be optimized to achieve maximum functional conjugation. The surface can then be activated by carbodiimide cross-linking in excess NHS/EDC mixture dissolved in MES buffer and conjugated to the first recognition molecule.

The term "recognition molecule" may refer to a molecule capable of specifically binding to an analyte. The "recognition molecule" may be an antibody, nucleic acid, peptide, aptamer, small molecule, or other synthetic agent.

The term "analyte" refers to a substance present in a sample to be detected or measured on a sensor chip. An "analyte" may include a cell, virus, nucleic acid, lipid, protein, peptide, glycopeptide, nanovesicle, microvesicle, exosome, extracellular vesicle, sugar, metabolite, or a combination or tissue state thereof. An "analyte" may be, for example, a peptide or nucleic acid (e.g., miRNA) biomarker that binds or associates with an exosome. For example, an "analyte" may also be a complex between a cell and a protein or a protein and a nucleic acid.

In one embodiment, the first recognition molecule is an antibody or fragment thereof. For example, the antibody can be, e.g., an antibody that recognizes a pan-exosome marker or a marker associated or bound to an exosome. For example, the antibody may be an antibody specific for CD63, CD9, or CD81, which are abundant and characteristic in exosomes. The antibody may also be specific for a marker specific for cellular origin, such as CHL1, L1CAM, or NCAM. The antibody may also recognize a biomarker associated with or binding to an exosome. For example, the antibody may be an anti-a β antibody that recognizes a β, or an antibody that recognizes APP, a-syn, or Tau that binds or associates with an exosome.

As used herein, the term "antibody" includes, but is not limited to, synthetic antibodies, monoclonal antibodies, recombinantly produced antibodies, multispecific antibodies (including bispecific antibodies), human antibodies, humanized antibodies, chimeric antibodies, single chain fv (scfv), Fab fragments, F (ab') fragments, disulfide linked fv (sdFv), including bispecific sdFv, and anti-idiotypic (anti-Id) antibodies, as well as epitope-binding fragments of any of the foregoing. The antibodies provided herein can be monospecific, bispecific, trispecific, or more multispecific. Multispecific antibodies may be specific for different epitopes of a polypeptide, or may be specific for a polypeptide as well as for heterologous epitopes such as heterologous polypeptides or solid support materials.

The terms "protein" and "polypeptide" are used interchangeably and refer to any polymer of amino acids (dipeptides or polypeptides) linked by peptide bonds or modified peptide bonds. Polypeptides of less than about 10-20 amino acid residues are commonly referred to as "peptides". The polypeptides of the invention may comprise non-peptide components, such as carbohydrate groups. Carbohydrates and other non-peptide substituents may be added to the polypeptide by the cell producing the polypeptide and will vary with the type of cell. Polypeptides are defined herein in terms of their amino acid backbone structure; substituents such as carbohydrate groups are generally not specified but may still be present.

As described herein, a "nucleic acid" may be RNA or DNA, and may be single-stranded or double-stranded, and may be, for example, a nucleic acid encoding a protein of interest, a polynucleotide, an oligonucleotide, a nucleic acid analog such as a peptide-nucleic acid (PNA), a pseudo-complementary PNA (pc-PNA), a Locked Nucleic Acid (LNA), or the like. Such nucleic acid sequences include, for example, but are not limited to, nucleic acid sequences encoding proteins, e.g., acting as transcriptional repressors, antisense molecules, ribozymes, small inhibitory nucleic acid sequences, e.g., but not limited to, RNAi, shRNAi, siRNA, microRNAi (mnrnai), antisense oligonucleotides, and the like.

As used herein, "nanovesicles" may refer to naturally occurring or synthetic vesicles that include cavities within the interior. The nanovesicles may include a lipid bilayer membrane enclosing the contents of the internal cavity. Nanovesicles may include liposomes, exosomes, extracellular vesicles, microvesicles, apoptotic vesicles (or apoptotic bodies), vacuoles, lysosomes, transport vesicles, secretory vesicles, balloons, matrix vesicles, or multivesicular bodies. Nanovesicles may have a size of about 1000nm or less, about 900nm or less, about 800nm or less, about 700nm or less, about 600nm or less, about 500nm or less, about 450nm or less, about 400nm or less, about 350nm or less, about 300nm or less, about 250nm or less, about 240nm or less, about 230nm or less, about 220nm or less, about 210nm or less, about 200nm or less, about 190nm or less, about 180nm or less, about 170nm or less, about 160nm or less, about 150nm or less, about 140nm or less, about 130nm or less, about 120nm or less, about 110nm or less, about 100nm or less, about 90nm or less, about 80nm or less, about 70nm or less, about 60nm or less, about 50nm or less, about 40nm or less, about 30nm or less, about 20nm or less, or about 10nm or less.

An exosome is a nanovesicle, also known in the art as an extracellular vesicle, microvesicle, or microparticle. These vesicles are shed outside the cell by eukaryotic cells, or from the plasma membrane. These membrane vesicles vary in size, ranging in diameter from about 10nm to about 5000 nm. Vesicles (about 10 to 1000nm in diameter, preferably 30 to 100nm) released by exocytosis of intracellular multivesicules are known in the art as "exosomes". The methods and compositions described herein are equally applicable to other vesicles of all sizes.

The term "sample" refers to any sample that contains the analyte or analytes being tested for the presence. Such samples include samples derived from or containing cells, organisms (bacteria, viruses), lysed cells or organisms, cell extracts, nuclear extracts, components of cells or organisms, extracellular fluids, media in which cells or organisms are cultured in vitro, blood, plasma, serum, gastrointestinal secretions, urine, ascites, homogenates of tissues or tumors, synovial fluid, stool, saliva, sputum, cyst fluid, amniotic fluid, cerebrospinal fluid, peritoneal fluid, lung lavage fluid, semen, lymph fluid, tears, pleural fluid, nipple aspirates, breast milk, skin exocrine, respiratory exocrine, intestinal exocrine, and urogenital exocrine, as well as prostatic fluid. The sample may be a viral or bacterial sample, a sample obtained from an environmental source, such as a contaminated water body, air sample or soil sample, and a food industry sample. The sample may be a biological sample, which refers to the fact that it originates from or is obtained from a living organism. The organism may be in vivo (e.g., whole organism) or may be in vitro (e.g., cells or organs grown in culture). "biological sample" also refers to a cell or group of cells or an amount of tissue or fluid from an individual. In most cases, the sample has been removed from the individual, but the term "biological sample" may also refer to cells or tissue analyzed in vivo, i.e., not removed from the individual. Typically, a "biological sample" will comprise cells from an individual, but the term may also refer to non-cellular biological material, such as non-cellular portions of blood, saliva or urine. The biological sample may be from the resection of a primary, secondary or metastatic tumor, a bronchoscopic or hollow needle biopsy, or from a cell mass in the pleural fluid. In addition, fine needle aspiration of biological samples is also useful. In one embodiment, the biological sample is primary ascites cells. Biological samples also include explants and primary and/or transformed cell cultures derived from patient tissue. Biological samples can be provided by removing a cell sample from an individual, but can also be achieved by using previously isolated cells or cell extracts (e.g., isolated by another person, at another time, and/or for another purpose). Archival tissues, such as those with a history of treatment or outcome, may also be used. Biological samples include, but are not limited to, tissue biopsy samples, abrasions (e.g., buccal abrasions), whole blood, plasma, serum, urine, saliva, cell cultures, or cerebrospinal fluid. The sample analyzed by the compositions and methods described herein may have been processed for purification or enrichment of exosomes contained therein. In one embodiment, the sample is blood.

In one aspect, there is provided an imaging system comprising a light source, a detector and a sensor chip as defined herein, wherein the detector is positioned to detect light generated by the light source and transmitted through the sensor chip.

In one aspect, there is also provided a kit comprising a sensor chip as defined herein. The kit may further comprise a second recognition molecule specific for the captured analyte or an analyte associated with the captured analyte on the sensor chip surface. The kit may comprise one or more second recognition molecules, each specific for one or more analytes, such that the one or more analytes may be detected.

The second recognition molecule may allow 1) signal amplification, 2) co-localization analysis (e.g., detection of different targets found simultaneously in the same vesicle), and 3) differentiation of subpopulations of analytes based on molecular and tissue differences.

The second recognition molecule can be coupled to a signal amplification portion, and wherein the signal amplification portion is capable of inducing the formation of insoluble aggregates having an increased optical density relative to the captured analyte and increasing the measured surface plasmon resonance signal. For example, the kit may comprise a second recognition molecule that is an antibody (e.g., an antibody specific for a β 42). The second recognition molecule may be conjugated to horseradish peroxidase. The kit may further comprise an enzyme substrate. Thus, horseradish peroxidase is able to induce the formation of insoluble aggregates with increased optical density relative to the analyte captured on the sensor chip surface.

The term "signal amplification molecule" may refer to a molecule capable of inducing the formation of insoluble aggregates on the surface of the sensor chip and thereby increasing the optical density relative to the captured analyte. This may lead to a larger change in the transmission wavelength (spectral shift) or a change in the transmission intensity when the second recognition molecule binds to the analyte on the surface of the sensor chip, thereby contributing to an increase in the sensitivity of the sensor chip. The "signal amplification molecule" may be, for example, an enzyme such as horseradish peroxidase, which reacts with the enzyme substrate to form insoluble aggregates on the surface of the sensor chip. The "signal-amplifying molecule" may also be a second antibody which binds to a second recognition molecule on the surface of the sensor chip and forms an aggregate. The second antibody may be further conjugated to an enzyme, gold particle, or macromolecule that facilitates the formation of larger aggregates to increase optical density.

In one embodiment, the present invention relates to a highly sensitive assay platform, Amplified Plasma Exosomes (APEX), for detecting exosome-bound amyloid β (Α β) directly from blood samples of Alzheimer's Disease (AD) patients. The assay methods can utilize transmission Surface Plasmon Resonance (SPR) and in situ enzymatic conversion of optical products to achieve multiplexed population analysis. APEX technology can enable multi-parameter in situ analysis of exosome contents (e.g., proteins and mirnas). The APEX platform can be used to measure different circulating Α β populations (exosome-bound, unbound and total) and different tissue states of circulating Α β and correlate these blood measurements with PET imaging of brain amyloid plaque burden.

In one aspect, there is provided a method of manufacturing a sensor chip, the method comprising the steps of:

a) providing a top film support layer;

b) depositing a conductive layer on the top film support layer;

c) a plurality of apertures are formed extending through the membrane support layer, the apertures also extending through the conductive layer and being arranged such that irradiation of the conductive layer and/or the top membrane support layer produces surface plasmon resonance.

In some embodiments, the method further comprises:

coating a top film supporting layer and a bottom film supporting layer on the upper surface and the lower surface of a silicon substrate;

providing a layer of photoresist on the top film support layer, and

a plurality of holes are defined in the photoresist by deep ultraviolet lithography (DUV), and the pattern of the plurality of holes is transferred to the top film support layer by Reactive Ion Etching (RIE).

In some embodiments, the method further comprises the steps of:

removing the photoresist on the top film supporting layer, and coating a silicon dioxide protective layer on the surface of the top film supporting layer;

coating a layer of photoresist on the bottom film supporting layer;

defining a sensing region in the photoresist by photolithography; and transferring the pattern of the sensing region to the base film support layer by Reactive Ion Etching (RIE);

transferring the pattern of the sensing region to a silicon substrate;

removing the protective layer on the surface of the top film support layer with diluted hydrogen fluoride; and

a conductive layer is deposited on the top film support layer.

As used herein, the term "sensing region" refers to a region of the sensor chip that includes a plurality of holes arranged such that irradiation of the plasmonic layer and/or the membrane support layer produces surface plasmon resonances.

As used herein, the term "resist" refers to a thin layer used to transfer an image or pattern to a substrate on which it is deposited. The resist may be patterned by photolithography to form a (sub-) micron-scale temporary mask that protects selected areas of the underlying substrate during subsequent processing steps, typically etching. Materials used for preparing thin layers, usually viscous solutions, are also encompassed by the term resist. Resists are typically mixtures of polymers or their precursors and other small molecules (e.g., photoacid generators) that are specifically formulated for a particular lithographic technique. For example, a resist used during photolithography is referred to as a "photoresist". Resists used in electron beam lithography processes are referred to as "electron beam resists".

In one aspect, there is provided a method of detecting an analyte in a sample, the method comprising:

a) capturing an analyte onto a surface of a sensor chip as defined herein; and

b) detecting binding of a second recognition molecule to the captured analyte on the surface of the sensor chip, wherein the second recognition molecule is specific for the analyte, wherein an increase in binding of the second recognition molecule compared to the control sample indicates the presence of the analyte in the sample.

The method may comprise detecting binding of one or more second recognition molecules (sequentially or simultaneously) specific for the one or more analytes. This allows for the detection, quantification, and analysis of the tissue status (e.g., co-localization) of multiple analytes or biomarkers in a sample. Each analyte may be recognized by a different set of first recognition molecules and second recognition molecules. The first recognition molecule and the second recognition molecule may recognize the same analyte or different analytes, respectively. Different combinations of the first recognition molecule and the second recognition molecule may allow for detection of co-localization of the analytes. This may allow for the simultaneous detection of multiple analytes and may allow for the detection of co-localization of these molecules.

In one embodiment, the first recognition molecule is an antibody that recognizes a β 42 and the second recognition molecule is another antibody that recognizes a β 42.

In one embodiment, the first recognition molecule is an antibody that recognizes a β 42 and the second recognition molecule is an antibody that recognizes CD63, and co-localization of a β 42 and CD63 is detected.

The "binding" of the second recognition molecule to the analyte captured on the surface of the sensor chip can be detected by a spectral shift (change in transmission wavelength) or a change in transmission intensity at a fixed wavelength. For example, an analyte captured on the surface of the sensor chip will have an initial reference wavelength. Upon binding of the second recognition molecule, the transmission wavelength may be shifted to a longer wavelength.

The change in transmission resonance wavelength (or spectral shift (Δ λ)) or the change in transmission intensity at a fixed wavelength in the sample can be compared to the change observed in the control sample. This can be used, for example, to determine whether the binding of the second recognition molecule to the captured analyte is increased.

The "increased binding of the second recognition molecule" in the sample compared to the control sample can be determined by comparing the change in spectral shift between the sample and the control sample when the second recognition molecule is bound or the change in transmitted intensity at a fixed wavelength. An increase in the spectral shift or change in the transmitted intensity may indicate an increase in binding of the second recognition molecule to the analyte.

In one embodiment, an increase in spectral shift or change in transmitted intensity can refer to a 1.2-fold or greater increase between an individual and a control individual. The term may also refer to a nucleic acid molecule selected from the group consisting of 1.1 times, 1.3 times, 1.4 times, 1.5 times, 1.6 times, 1.7 times, 1.8 times, 1.9 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times, 11 times, 12 times, 13 times, 14 times, 15 times, 16 times, 17 times, 18 times, 19 times, 20 times, 21 times, 22 times, 23 times, 24 times, 25 times, 26 times, 27 times, 28 times, 29 times, 30 times, 31 times, 32 times, 33 times, 34 times, 35 times, 36 times, 37 times, 38 times, 39 times, 40 times, 41 times, 42 times, 43 times, 44 times, 45 times, 46 times, 47 times, 48 times, 49 times, 50 times, 51 times, 52 times, 53 times, 54 times, 55 times, 56 times, 57 times, 58 times, 59 times, 62 times, 65 times, 64 times, 71 times, 74 times, 71 times, 73 times, 74 times, 65 times, 66 times, 73 times, 72 times, 6 times, 6 times, and so, 77 times, 78 times, 79 times, 80 times, 81 times, 82 times, 83 times, 84 times, 85 times, 86 times, 87 times, 88 times, 89 times, 90 times, 91 times, 92 times, 93 times, 94 times, 95 times, 96 times, 97 times, 98 times, 99 times and 100 times.

The second recognition molecule may be a recognition molecule specific for the analyte. The second recognition molecule may be coupled to a signal amplification portion, and wherein the signal amplification portion is capable of inducing the formation of insoluble aggregates having an increased optical density relative to the captured analyte. For example, an analyte captured on the surface of the sensor chip will have an initial reference wavelength. Upon binding of the second recognition molecule, the transmission wavelength may be shifted to a longer wavelength. When the second recognition molecule is coupled to the signal amplification section, the transmitted wavelength can be shifted to even longer wavelengths due to the increase in optical density.

The second recognition molecule may be coupled to a signal amplification portion. Alternatively, the second recognition molecule may be coupled to a signal amplification portion.

The signal amplification part may be an enzyme. In one embodiment, the signal amplification portion is an enzyme. The enzyme may be horseradish peroxidase (HRP), alkaline phosphatase, glucose oxidase, beta-lactamase or beta-galactosidase, or enzymatic fragments thereof. In one embodiment, the enzyme is horseradish peroxidase. In one embodiment, the first biological recognition molecule is fused to the signal amplification portion. For example, the first biorecognition molecule can be an antibody covalently fused to horseradish peroxidase, which is covalently linked to the antibody using techniques well known in the art.

The method may further comprise contacting the enzyme with an enzyme substrate. The enzyme substrate may be a substrate which in the presence of the enzyme or enzymatically forms an insoluble product. As horseradish peroxidase (HRP), preparations such as 3-amino-9-ethylcarbazole, 3',5,5' -tetramethylbenzidine or chloronaphthol, 4-chloro-l-naphthol, etc. can be used. These substrates can become insoluble products in the enzymatic reaction of HRP.

In one embodiment, the enzyme substrate is 3,3' -diaminobenzidine tetrahydrochloride.

In another embodiment, the signal amplification portion may be a second antibody capable of binding to a second recognition molecule. Binding of the second antibody to the second recognition molecule induces the formation of insoluble aggregates.

In one embodiment, the first recognition molecule is immobilized on the surface of the surface plasmon resonance sensor chip, wherein the first recognition molecule is capable of capturing an analyte on the surface of the sensor chip. The analyte may be an exosome-bound or exosome-associated biomarker. The analyte may be an exosome-bound aggregation biomarker. The first recognition molecule may be specific for the analyte.

In one embodiment, the first recognition molecule is an antibody. For example, the antibody may be an antibody that recognizes a pan-exosome marker or a marker associated with or binding to an exosome. For example, the antibody may be an antibody specific for CD63, CD9, or CD81, CD63, CD9, or CD81 being abundant and characteristic in exosomes. The antibody may also be specific for a cell-derived specific marker such as CHL1, L1CAM or NCAM. The antibody may also recognize a biomarker associated with or binding to an exosome. For example, the antibody may be an anti-a β antibody that recognizes a β, or an antibody that recognizes APP, a-syn, or Tau that binds or is associated with an exosome.

The term "control sample" refers to a sample that does not contain an analyte. A "control sample" can be used as a comparison to a sample to determine whether the sample contains an analyte of interest.

As used herein, the term "biomarker" is understood to be an agent or entity whose presence or level is correlated with an event of interest. The biomarker may be a cell, a protein, a nucleic acid, a peptide, a glycopeptide, an exosome, or a combination thereof. For example, the biomarker is a β 42 or Tau peptide, the presence or level of which indicates whether the subject has or is at risk of developing a neurodegenerative disease or amyloidosis. In another embodiment, the biomarker is exosome-bound Α β 42 or Tau peptide, the presence or level of which indicates whether the individual has or is at risk of developing a neurodegenerative disease or amyloidosis. In some embodiments, the biomarker is an exosome-associated biomarker.

In one embodiment, there is provided the use of a sensor chip as defined herein for detecting an analyte.

In one aspect, there is provided a method of detecting a neurodegenerative disease or amyloidosis in a subject, the method comprising:

a) contacting a sample with a surface of a sensor chip as defined herein; and

b) detecting binding of the second recognition molecule to the analyte captured on the surface of the sensor chip;

wherein the second recognition molecule is specific for the analyte, wherein increased binding of the second recognition molecule as compared to a control individual indicates that the individual has a neurodegenerative disease or amyloidosis.

The term "individual" refers to any animal, including any vertebrate or mammal, in particular a human, and may also be referred to as e.g. an individual or a patient.

The term "control subject" refers to a subject known not to have a neurodegenerative disease or amyloidosis or to be at risk of having a neurodegenerative disease or amyloidosis. The "control individual" may also be a healthy individual. The "control individual" may be an individual without cognitive impairment (NCI). The term includes samples obtained from control individuals.

In one embodiment, the biomarker is an exosome-bound or exosome-associated biomarker. In one embodiment, the biomarker is an exosome-bound aggregate biomarker. Biomarkers can be selected from, but are not limited to, A β, APP, a-Syn, Tau, APOE, SOD1, TDP-43, basnoon, and fibronectin. In one embodiment, a β is a β 42. In another embodiment, a β is a β 40. In some embodiments, the molecular subtype of a β is a β 42, a β 40, a β 39, or a β 38. In one embodiment, the biomarker is Tau. In some embodiments, the biomarker is an exosome biomarker selected from the group consisting of CD63, CD9, CD81, ALIX, TSG101, Flotilin-1, Flotilin-2, LAMP-1, HSP70, HSP90, RNA, and DNA.

The neurodegenerative disease may be selected from alzheimer's disease, mild cognitive impairment, vascular dementia, vascular mild cognitive impairment, parkinson's disease, amyotrophic lateral sclerosis, multiple sclerosis, progressive supranuclear palsy and/or tauopathies.

The method may also include treating a subject found to have a neurodegenerative disease or amyloidosis.

As used herein, the term "treating" can refer to (1) preventing or delaying the appearance of one or more symptoms of a disease; (2) inhibiting the development of a disease or one or more symptoms of a disease; (3) remission, i.e., causing regression of the disease or at least one or more symptoms of the disease; and/or (4) cause a reduction in the severity of one or more symptoms of the disease.

In one embodiment, the term "treating" refers to administering a drug to slow the progression of a neurodegenerative disease or amyloidosis.

Provided herein is a method of detecting a neurodegenerative disease or amyloidosis in an individual, the method comprising detecting the level of an exosome-bound biomarker in a sample obtained from the individual, wherein an increase in the level of exosome-bound biomarker compared to a reference indicates that the individual has a neurodegenerative disease or amyloidosis.

In one aspect, a method of detecting a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising detecting a level of exosome-bound aggregated biomarker in a sample obtained from the subject, wherein an increase in the level of exosome-bound aggregated biomarker compared to a reference indicates that the subject has a neurodegenerative disease or amyloidosis.

The method may comprise detecting one or more exosome-bound biomarkers in the sample. This allows for the detection of co-localization or presence of multiple biomarkers present on the same exosome.

Biomarkers can be selected from, but are not limited to, A β, APP, a-Syn, Tau, APOE, SOD1, TDP-43, basnoon, and/or fibronectin.

The method may comprise detecting the level of a molecular subtype of the exosome-bound biomarker.

In one embodiment, a β is a β 42 or a β 40. In some embodiments, a β is a β 42, a β 40, a β 39, or a β 38.

In one embodiment, a β is a pre-fibril aggregate. It was found that the pre-fibrillar a β aggregates preferentially bind to exosomes. In one embodiment, the method as defined herein comprises detecting exosomes bound to pre-fibrillar a β aggregates.

In one embodiment, the aggregated biomarker is a pre-fibril aggregate. The aggregated biomarker may be a pre-fibril aggregate of a β. Alternatively, the aggregated biomarker may be a pre-fibril aggregate of APP, a-Syn or Tau.

In one embodiment, the reference is a control individual.

In one embodiment, an alternative method of measuring exosome binding to determine the pre-fibrillar tissue state of a protein aggregate is provided, wherein an increase in the level of protein aggregates in the pre-fibrillar tissue state as compared to a control indicates that the individual has a neurodegenerative disease or amyloidosis.

In one embodiment, the method further comprises detecting an exosome biomarker selected from the group consisting of CD63, CD9, CD81, ALIX, TSG101, Flotilin-1, Flotilin-2, LAMP-1, HSP70, HSP90, RNA and DNA, wherein the exosome biomarker is co-localized with the exosome-binding biomarker.

In one embodiment, the method further comprises detecting a neuronal biomarker selected from the group consisting of NCAM, L1CAM, CHL-1, and IRS-1, wherein the neuronal biomarker is co-localized with an exosome-bound biomarker.

In one embodiment, a method of measuring different tissues and sub-populations of molecules of a biomarker is provided. For example, the method may include measuring exosome-bound biomarkers, free (unbound) biomarkers, and total (exosome-bound and unbound) biomarkers. The method may further comprise measuring the relative concentration of different biomarkers to better predict disease.

In one embodiment, the neurodegenerative disease is selected from alzheimer's disease, mild cognitive impairment, vascular dementia, vascular mild cognitive impairment, parkinson's disease, amyotrophic lateral sclerosis, multiple sclerosis, progressive supranuclear palsy and/or tauopathies.

In one embodiment, the neurodegenerative disease is selected from alzheimer's disease and mild cognitive impairment.

The method may also include treating a subject having a neurodegenerative disease or amyloidosis.

The method may further be relevant for brain imaging studies, such as PET imaging. This includes correlation with imaging of specific brain regions. In one embodiment, a method of identifying and measuring circulating biomarkers associated with brain imaging (PET) is provided. In one embodiment, a method of identifying and measuring circulating biomarkers associated with imaging of specific brain regions (PET) is provided.

The present invention is based on the following findings: 1) biomarkers (including co-localized different markers) can be used to measure and characterize different molecular, biophysical, and tissue subpopulations of circulating a β; 2) circulating exosome-bound Α β in blood may be closely correlated with PET imaging of Α β deposits (ensemble-averaged) for different patient populations; 3) circulating exosome-bound Α β in blood can be closely related to PET imaging of Α β (early AD zone, cingulate gyrus zone) in different patient populations; 4) circulating exosome-bound Α β may distinguish clinical subgroups (such as alzheimer's disease, mild cognitive impairment, no cognitive impairment, vascular dementia, vascular mild cognitive impairment and acute stroke).

The method may comprise administering to an individual in need of treatment a therapeutically effective amount of a drug. For example, the drug may be, for example, a cholinesterase inhibitor, such as donepezil, rivastigmine or galantamine. The drug may also be an NMDA receptor antagonist, such as memantine. The drug may be a combination of a cholinesterase inhibitor and an NMDA receptor antagonist, for example a combination of donepezil and memantine. The drug may be a BACE1 inhibitor, such as AZD3293, or an antibody, e.g. an anti-amyloid antibody, such as aducarinumab. The drug may also be an anti-tau drug, such as TRx0237 (LMTX). In some embodiments, a therapeutically effective amount of one or more of the agents described herein, or a combination of two or more of the agents described herein, may be administered to an individual in need of treatment.

In some embodiments, a molecule effective to reduce the amount of amyloid aggregates may be a potential candidate for treatment. Thus, in some embodiments, the method may comprise administering to an individual in need of treatment one or more of the following molecules (drugs) or a combination of two or more of the following molecules (drugs): methioninium chloride, leucomethioninium, bis (hydromethanesulfonate), curcumin, acid fuchsin, epigallocatechin gallate, safraninal, congo red, apigenin, sky blue C, basic blue 41, (trans ) -l-bromo-2, 5-bis- (3-hydroxycarbonyl-4-hydroxy) styrylbenzene (BSB), Chicago sky blue 6B, -cyclodextrin, daunomycin hydrochloride, dimethyl yellow, direct Red 80, 2-dihydroxybenzophenone, cetyl trimethyl ammonium bromide (Cl6), hemin, heme, indomethacin, juglone, resorcinol blue, methylchloroflavin sulfosalicylate, melatonin, myricetin, 1, 2-naphthoquinone, nordihydroguaiaretic acid, R () -normorphine hydrobromide, dihydrogenbromide, dihydrogenquinone, and pharmaceutically acceptable salts thereof, Orange G, o-vanillin (2-hydroxy-3-methoxybenzaldehyde), phenazine, phthalocyanine, rifamycin SV, phenol red, hydropyricycline, quinacrine mustard dihydrochloride, thioflavin S, ThT, and trimethyl (tetradecyl) ammonium bromide (C17), diallyl tartar, eosin Y, fenofibrate, neocupine, nystatin, octadecyl sulfate, and rhodamine B.

An increase in the level of an exosome-bound biomarker may refer to a 1.2-fold or greater increase in level between an individual and a control individual. The term "increased level" may also refer to an increase selected from the group consisting of: 1.1 times, 1.3 times, 1.4 times, 1.5 times, 1.6 times, 1.7 times, 1.8 times, 1.9 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times, 11 times, 12 times, 13 times, 14 times, 15 times, 16 times, 17 times, 18 times, 19 times, 20 times, 21 times, 22 times, 23 times, 24 times, 25 times, 26 times, 27 times, 28 times, 29 times, 30 times, 31 times, 32 times, 33 times, 34 times, 35 times, 36 times, 37 times, 38 times, 39 times, 40 times, 41 times, 42 times, 43 times, 44 times, 45 times, 46 times, 47 times, 48 times, 49 times, 50 times, 51 times, 52 times, 53 times, 54 times, 55 times, 56 times, 57 times, 58 times, 59 times, 60 times, 63 times, 65 times, 76 times, 78 times, 73 times, 72 times, 73 times, 72 times, 9 times, 9 times, and the like, 81 times, 82 times, 83 times, 84 times, 85 times, 86 times, 87 times, 88 times, 89 times, 90 times, 91 times, 92 times, 93 times, 94 times, 95 times, 96 times, 97 times, 98 times, 99 times, 100 times.

In one aspect, a method of detecting an individual at risk of suffering from a neurodegenerative disease is provided, the method comprising detecting a level of exosome-bound aggregated biomarker in a sample obtained from the individual, wherein an increased level of exosome-bound aggregated biomarker compared to a reference indicates that the individual suffers from a neurodegenerative disease.

In one aspect, there is provided a method of detecting and treating a neurodegenerative disease or amyloidosis in a subject, the method comprising:

a) detecting the level of exosome-bound aggregation biomarker in a sample obtained from the individual, wherein an increased level of exosome-bound aggregation biomarker compared to a reference indicates that the individual has a neurodegenerative disease or amyloidosis; and

b) treating a subject having a neurodegenerative disease or amyloidosis.

In one aspect, there is provided a method of treating a neurodegenerative disease or amyloidosis in a subject, the method comprising:

a) detecting the level of exosome-bound aggregation biomarker in a sample obtained from the individual, wherein an increased level of exosome-bound aggregation biomarker compared to a reference indicates that the individual has a neurodegenerative disease or amyloidosis; and

b) treating a subject having a neurodegenerative disease or amyloidosis.

In one embodiment, a method of detecting and slowing the progression of a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising:

a) detecting the level of exosome-bound aggregation biomarker in a sample obtained from the individual, wherein an increased level of exosome-bound aggregation biomarker compared to a reference indicates that the individual has a neurodegenerative disease or amyloidosis; and

b) treating a subject having a neurodegenerative disease or amyloidosis.

In one aspect, a method of determining the aggregation state of a biomarker in a sample is provided, the method comprising detecting the level of exosome-bound biomarker in the sample, wherein an increase in the level of exosome-bound biomarker compared to a reference indicates the extent of aggregation of the biomarker.

The method may comprise the step of contacting the sample with a population of exosomes prior to the step of detecting the level of an exosome-bound biomarker.

In one embodiment, the aggregated biomarker in the sample preferentially binds to exosomes compared to a non-aggregated biomarker.

The sample may be obtained from an individual.

In one embodiment, an increase in the degree of aggregation of the biomarker compared to a reference indicates that the individual has a neurodegenerative disease or amyloidosis.

The method may further comprise treating a subject having a neurodegenerative disease or amyloidosis.

In one embodiment, a method of determining the aggregation state of a biomarker in a sample is provided, the method comprising contacting the sample with a population of exosomes and detecting the level of exosome-bound biomarker in the sample, wherein an increased level of exosome-bound biomarker compared to a reference indicates the extent of aggregation of the biomarker.

Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. It is to be understood that the invention includes all such variations and modifications which fall within the spirit and scope. The invention also includes all of the steps, features, combinations and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any two or more of said steps or features.

In this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" and "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

Certain embodiments of the present invention will now be described with reference to the following examples, which are for illustrative purposes only and are not intended to limit the general scope described above.

Examples

The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

Materials and methods

And (5) culturing the cells. The human cell lines SH-SY5Y (neurons), HUVEC (umbilical vein endothelium), THP-1 (monocytes), PC-3 (prostate epithelium) and SK-OV-3 (ovarian epithelium) were obtained from the American type culture Collection. GLI36(glia) and SK-OV-3 were grown in Dulbecco's modified essential Medium (DMEM, Gibco). SH-SY5Y in Dulbecco's modified Eagle Medium: nutrient mixture F-12 medium (DMEM/F12 Gibco). PC-3, THP-1 and HUVEC were grown in F-12K, RPMI-1640 and EGM-2 medium, respectively. All other media, except EGM-2 supplemented with 5% Fetal Bovine Serum (FBS), were supplemented with 10% FBS and penicillin-streptomycin.

Exosome isolation and quantification. Cells from passages 1-15 were cultured in vesicle-depleted medium (containing 5% depleted FBS) for 48 hours prior to vesicle collection. All media containing exosomes were filtered through 0.2 micron membrane filters (Millipore), separated by differential centrifugation (first at 10,000g, then at 100,000g), and used for exosome analysis using the APEX platform. To separate exosomes from blood cells and platelets, the blood cells are from blood fractionation and the platelets are from platelet rich plasma. These components were washed in HEPES buffered saline and incubated with 2mM calcium chloride and 2 μ M calcium ionophore (a23187) at 37 ℃ to stimulate exosome production. All vesicles were then collected as described previously. For independent quantification of exosome concentrations, a Nanoparticle Tracking Analysis (NTA) system (NS300, Nanosight) was used. Exosome concentrations were adjusted to obtain about 50 vesicles in the field to achieve optimal counts. To maintain consistency, all NTA measurements are done using the same system setup.

APEX sensor manufacture. APEX sensors were fabricated on 8 inch silicon (Si) wafers. Briefly, 10nm Silica (SiO) was prepared by thermal oxidation₂) Layer and Low Pressure Chemical Vapor Deposition (LPCVD) on the waferDeposition of 145nm silicon nitride (Si)₃N₄). After the photoresist is applied, Deep Ultraviolet (DUV) lithography is performed to define the nanopore array pattern in the resist. The pattern is transferred to Si by Reactive Ion Etching (RIE)₃N₄On the membrane. After removing the photoresist, a thin layer of SiO is deposited on the front side of the wafer using Plasma Enhanced Chemical Vapor Deposition (PECVD)₂Protective layer (100 nm). For optical energy transmission, the back side of the wafer is spin coated with photoresist; the sensing region is defined using a lithographic method. Si₃N₄And SiO₂Etching with RIE followed by etching with potassium hydroxide (KOH) and tetramethylammonium hydroxide (TMAH) of Si. After etching, the protective SiO is removed using Dilute Hydrogen Fluoride (DHF) (1: 100)₂And (3) a layer. Finally, Ti/Au (10nm/100nm) is deposited on Si₃N₄On the membrane. All nanopore sizes and sensor uniformity were characterized by scanning electron microscopy (JEOL 6701).

And (6) assembling the channel. Standard soft lithography is used to fabricate multi-channel flow cells. SU-8 negative resist (SU8-2025, Microchem) was used to prepare the mold. The photoresist was spin coated onto the Si wafer at 2000rpm for 30 seconds and baked at 65 ℃ and 95 ℃ for 2 minutes and 5 minutes, respectively. After uv exposure, the resist was baked again before development with agitation. Prior to subsequent use, the developed molds were chemically treated with trichlorosilane vapor in a desiccator for 15 minutes. Polydimethylsiloxane Polymer (PDMS) and crosslinker were mixed in a ratio of 10: 1 and cast onto SU-8 molds. After curing at 65 ℃ for 4 hours, the PDMS layer was cut out of the mold and assembled onto the APEX sensor. All inlets and outlets were made using 1.1 mm biopsy punches for sample processing.

Optical setup and spectral analysis. Tungsten halogen lamps (stocker yale Inc.) were used to illuminate the APEX sensor through a 10X microscope objective. The transmitted light was collected by an optical fiber and sent to a spectrometer (Ocean Optics). All measurements were performed in closed boxes at room temperature to eliminate ambient light interference. The transmitted light intensity is digitally recorded in counts relative to wavelength (330nm-1600 nm). For spectral analysis, the spectral peaks were determined using a custom R program by fitting the transmission peaks using a local regression method. All fits are done proximally. That is, for a fit at point x, the fit is made using points near x, weighted by their distance from x. This method can eliminate variation in results due to the number of data points and the range of data analyzed, as compared to fitting through a multi-order polynomial curve. In determining the optimal sensor geometry (fig. 10), the spectral changes in response to refractive index changes that quantify peak transmission intensity, peak shape (full width at half maximum, FWHM), and detection sensitivity, respectively, are used. The measured transmission spectrum demonstrated uniformity between the different sensors, with s.d. at the baseline spectral peak position of 0.03 nm. All spectral shifts (Δ λ) were determined as changes in the transmission spectrum peaks and calculated relative to appropriate control experiments (see below for details).

The sensor surface is functionalized. To confer APEX sensor molecule specificity, Au surfaces were fabricated first with a mixture of polyethylene glycol (PEG) containing long active (carboxylated) thiol-PEG and short inactive methylated thiol-PEG (thermo scientific) (1: 3 active: inactive, 10mM in PBS) incubated for 2 hours at room temperature. After washing, the surface was activated by carbodiimide cross-linking in a mixture of excess NHS/EDC dissolved in MES buffer and conjugated with specific probes and ligands (e.g., antibodies and Α β 42 aggregates). All probe information can be found in table 1. Excess unbound probe was removed by PBS washing. The conjugated sensors were stored in PBS at 4 ℃ for subsequent use. All sensor surface modifications were monitored spectroscopically to ensure uniform functionalization.

TABLE 1 list of markers and their probes used in the analysis.

APEX signal amplification. To establish APEX amplification, enzymatic growth of insoluble optical products for signal enhancement was incorporated, and optical substrate concentration and reaction duration were optimized to establish a platform. Briefly, exosomes were incubated with CD63 functionalized APEX sensors (BD Biosciences) for 10 minutes. The bound vesicles were then labeled with biotinylated anti-CD 63 antibody (Ancell, 10 min). As a control experiment, the amplification efficiency was determined using an equal amount of biotinylated IgG isotope control antibody (Biolegend) on the bound vesicles. After washing the unbound antibody, high sensitivity horseradish peroxidase conjugated to neutravidin (Thermo Scientific) was allowed to react with the bound vesicles before different concentrations of 3,3' -diaminobenzidine tetrahydrochloride (Life Technologies) were introduced as optical substrate. The real-time spectral changes are monitored to determine the optimal substrate concentration and reaction duration. The optimal conditions were determined to be 1mg/mL for 3 minutes. All flow rates for incubation and washing were maintained at 3. mu.l/min and 10. mu.l/min, respectively. Local deposition of insoluble optical products was confirmed by scanning electron microscopy. The workflow for such optimization is shown in fig. 11 a.

Using this set of conditions, known amounts of exosomes were further titrated and their associated APEX signals measured. The APEX detection limit was determined as the lowest target concentration that could produce a detection signal of 3x (s.d. of background signal from control).

And (4) detecting the APEX protein. All sensor surfaces were blocked with 2% w/v Bovine Serum Albumin (BSA) to reduce non-specific protein binding. Exosomes were introduced onto functionalized sensors, incubated for 10 minutes at room temperature to capture exosomes and washed with PBS to remove unbound material. For the extravesicular protein target, exosomes were directly labeled with detection antibodies for APEX amplification as described above. For the intra-vesicular protein targets, exosomes were subjected to additional immobilization and permeabilization (eBioscience) prior to labeling with detection antibodies. Spectral measurements were taken before and after APEX amplification and analyzed by custom designed R program.

And (4) detecting the APEX miRNA. The APEX sensor was functionalized with p19 protein (New England Biolabs) via its chitin binding domain and blocked with 2% w/vBSA. For miRNA detection, exosome lysates were incubated with biotinylated RNA probes (350nM) for 15 min to hybridize to the target miRNA strand. The mixture was introduced onto the functionalized sensor in binding buffer (1xp19 binding buffer, ph7.0, 40U RNase inhibitor, 0.1mg/ml sa) to achieve p19 capture of the hybrid miRNA target/RNA probe duplex. High sensitivity horseradish peroxidase coupled to neutravidin (Thermo Scientific) was introduced into the bound biotinylated duplex for APEX amplification. Spectral measurements were performed and analyzed by a custom designed R program.

Enzyme-linked immunosorbent assay (ELISA). The capture antibody (5. mu.g/ml) was adsorbed onto an ELISA plate (Thermo Scientific) and blocked with Superlock (Thermo Scientific) prior to incubation with the sample. After washing with PBST (PBS containing 0.05% Tween 20), detection antibody (2. mu.g/ml) was added and incubated for 2 hours at room temperature. After incubation with a second antibody conjugated to horseradish peroxidase (Thermo Scientific) and a chemiluminescent substrate (Thermo Scientific), the chemiluminescent intensity was measured for protein detection (Tecan).

Western blotting. Exosomes isolated by ultracentrifugation were lysed in radioimmunoprecipitation assay (RIPA) buffer containing protease inhibitors (Thermo Scientific) and quantified using the bicinchoninic acid assay (BCA assay, Thermo Scientific). Protein lysates were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), transferred to polyvinylidene fluoride membranes (PVDF, Invitrogen) and immunoblotted with antibodies against protein markers: HSP90(Cell Signaling), HSP70(BioLegend), Flotillin 1(BD Biosciences), CD63(Santa Cruz), ALIX (Cell Signaling), TSG101(BD Biosciences), LAMP-1(R & D Systems), and neuron-labeled NCAM (R & DSystems). After incubation with horseradish peroxidase-conjugated secondary antibody (CellSignaling), enhanced chemiluminescence was used for immunodetection (Thermo Scientific).

The protein aggregates. Freeze-dried NH₄The OH-treated a β 42 protein (rPeptide) was resuspended in NaOH (60mM, 4 ℃), sonicated and the pH adjusted to pH7.4 in PBS 34. The protein was immediately filtered through a 0.2 micron membrane filter (Millipore) and the filtrate was used as the smaller Α β 42 aggregates. To prepare larger Α β 42 aggregates, the protein was treated as described above and incubated for 1 hour with stirring to induce further aggregation, then filtered through a 0.2 micron membrane filter (Millipore). The filtrate was used as large a β aggregates. To prepare similarly sized BSA aggregates as controls, 2% w/vBSA was dissolved in PBS and heated at 80 ℃ for 1 and 2 hours to induce aggregation of small and large control aggregates, respectively.

And (4) determining the aggregate size. The hydrodynamic diameters of a β 42 and BSA aggregates were determined by dynamic light scattering analysis (Zetasizer Nano ZSP, Malvern). 3x14 measurement runs were performed at 4 ℃. Z-average diameter and polydispersity were analyzed. For each measurement, the autocorrelation function and polydispersity index were monitored to ensure sample quality for sizing.

Characterization of exosome-a β binding. The prepared protein aggregates (a β 42 and BSA control) were used for surface functionalization of APEX sensors by EDC/NHS coupling as described previously. Unbound protein aggregates were washed away with PBS. The amount of bound protein was measured from the resulting transmission spectral shift. This information was used to determine the number of bound protein aggregates and their associated exosomes bound total protein surface area (see below for details) in order to normalize binding affinity. Exosomes (10) are introduced after surface functionalization with protein aggregates¹⁰Ml) was introduced onto the sensor. Spectral changes were measured every 3 seconds for a total duration of 480 seconds to construct a real-time kinetic sensorgram. Determining the exosome binding kinetics and binding affinities of protein aggregates of different sizes.

To account for the size difference of protein aggregates and the exponential decay of sensitivity associated with SPR (with increasing distance from the sensing surface), the following equation was used

To calculate the total surface area of bound aggregates interacting with exosomes: where S is the signal, z is the distance from the sensor surface, E is the electric field at z-0, in this case a constant, l_dIs the decay length, set at 200nm in current sensor designs, and r is the radius of the binding protein aggregate.

All protein aggregates were approximately spherical, supported by transmission electron micrographs (fig. 3b, left), and their r was determined by dynamic light scattering analysis. The above equation is used to determine the number of protein aggregates bound to the sensor and their respective total surface area to estimate the number of available binding sites for interaction with exosomes. All exosome binding data (Δ λ) were normalized to their respective protein binding sites. Normalized Α β 42 binding data were performed relative to similarly sized BSA controls and fitted to determine the binding affinity constant KD.

Scanning electron microscopy. All samples were fixed with half-strength carnofsky fixative and washed twice with PBS. After dehydration in a series of increasing concentrations of ethanol, the samples were transferred for critical drying (Leica) and then sputter coated with gold (Leica) before imaging with a scanning electron microscope (JEOL 6701).

Transmission electron microscopy. Exosomes were immuno-labeled with gold nanoparticles (15nm, Ted Pella), fixed with 2% paraformaldehyde and transferred onto copper grids (Ted Pella). The bound vesicles were washed and counterstained with a mixture of uranyl oxalate and methylcellulose. The dried samples were imaged with a transmission electron microscope (JEOL 2200 FS).

And (4) collecting clinical samples. The study was approved by the NUH and NUS institutional review board (2015/00441, 2015/00406, and 2016/01201). All subjects were enrolled with informed consent according to protocols approved by the institutional review board. All enrolled subjects were subjected to several neuropsychological assessments at national university hospital (NUH, singapore), including mini-mental state examination (MMSE), montreal cognitive assessment (MoCa), and vascular dementia pool (VDB) cognitive assessment. AD. Clinical diagnosis of MCI or NCI is derived by neuropsychological assessment as well as assessment of clinical characteristics and blood surveys. Clinical diagnosis of VaD and VMCI is made by combining neuropsychological assessments, clinical history of stroke, and the extent of cerebrovascular disease observed by Magnetic Resonance Imaging (MRI). All clinical assessments and classifications were made according to published standards 46-48 and were independent of APEX measurements. Acute stroke plasma samples were collected from patients diagnosed with stroke within 24 hours of admission to hospital. Longitudinal plasma samples were collected from patients during a one year follow-up without PET brain imaging. For plasma collection, venous blood (5ml) was drawn from the subjects, placed in EDTA tubes and immediately processed prior to injection of PET radiotracer (as applicable). Briefly, all blood samples were centrifuged at 400g (4 ℃) for 10 minutes. The plasma was transferred without disturbing the buffy coat and centrifuged again at 1,100g (4 ℃) for 10 minutes. All plasma samples were de-labeled and stored at-80 ℃ prior to measurement using the APEX platform. All APEX measurements were made without knowledge of PET imaging results and clinical diagnosis.

Clinical APEX measurements. All plasma samples were measured according to the assay configuration outlined in supplementary fig. 15 a. Briefly, to measure the exosome-bound Α β 42 population, we used the native plasma sample directly, with Α β 42 capture and CD63 detection (Α β 42+ CD63+), without any vesicle purification or isolation. To demonstrate the presence of unbound Α β 42 population in the plasma samples, we removed the large size retentate using size exclusion filtration (cut-off size 50nm, Whatman). This is necessary because the assay configuration based on a β 42 capture and a β 42 detection cannot distinguish between unbound a β 42 and total a β 42. To measure unbound a β, we assessed the plasma filtrate by a β 42 capture and a β 42 detection (a β 42 +). Note that this filtration was only used to demonstrate the presence of the unbound Α β 42 population; it is not necessary in a clinical setting where only the more reflective exosome-bound a β 42 is measured directly from a native plasma sample. To measure total a β 42, we evaluated native plasma samples directly by a β 42 capture and a β 42 detection (a β 42 +). For all measurements, we used 5% BSA as the blocking agent for the APEX sensor. We also included a sample matched negative control in which we incubated the same sample on a control sensor functionalized with an IgG isotype control antibody. All measurements were performed against the IgG control to account for sample-matched non-specific binding.

Positron Emission Tomography (PET) imaging. After blood draw, subjects were scanned using a Siemens 3T Biograph MR system (Siemens healtiers) to obtain PET and MR images simultaneously. PET data were acquired 40-70 minutes after intravenous infusion of 370MBq of 11C-Pittsburgh Compound B (PiB). MR data was acquired using 12-channel head receive coils, including ultra-short echo time (UTE) images and T1 weighted magnetization-prepared gradient echo (MPR AGE) images for PET attenuation correction (1mm isotropic resolution, TI/TE/TR 900/3.05/1950 milliseconds).

And (4) PET data analysis. The T1 weighted MPRAGE images were processed using Freesurfer (5.3.0) to generate cortical segmentations for PET data analysis. The PET image was reconstructed using the ordinary poisson ordered subset expectation maximization (OP-OSEM) algorithm and smoothed using a 4mm gaussian filter. Data was attenuated using UTE-based μ -maps. The resulting attenuation-corrected normalized uptake value (SUV) image is then co-registered with the MPRAGE image using an Advanced Normalization Tool (ANT), and a subject-specific freesource segmentation is used to calculate a normalized uptake value ratio (SUVR) relative to the mean cerebellar grey matter intensity. An average SUVR for a particular region is calculated and an overall average SUVR for each patient is calculated by averaging the SUVRs for all brain regions.

And (5) carrying out statistical analysis. All measurements were performed in triplicate and the data are shown as mean ± s.d. The significance test was performed by a two-tailed student's t-test. For the inter-sample comparison, each pair of samples was tested and the resulting P-values were adjusted for multiple hypothesis testing using Bonferroni correction. The adjusted value of P < 0.05 was determined to be significant. One-way pairwise ANOVA tests were used to determine the analysis and biological coefficients of changes (i.e., intra-, inter-, and overall). For clinical studies, correlation was performed using linear regression to determine goodness of fit (R2). All statistical analyses were performed using R-package (version 3.4.2) and Graphpad Prism 7.

Example 1

Amplified plasma analysis of exosome-bound Ap

One of the earliest pathological hallmarks of AD was brain Α β deposition. These plaques are formed by the aggregation of abnormal amyloid fragments, mainly the hydrophobic splice variant a β 42. The a β protein is released into the extracellular space and can circulate in the blood. Exosomes are also found in the extracellular space as nanoscale membrane vesicles secreted by mammalian cells through the fusion of multivesicular endosomes with the plasma membrane. During this exosome biogenesis, glycoproteins and glycolipids are incorporated into the invaginated plasma membrane and are classified into the newly formed exosomes 10, 11. By these surface markers, exosomes can be associated and bound to extracellular a β proteins (fig. 1 a). Multimodal characterization of extracellular vesicles derived from neuronal sources (SH-SY5Y cells) confirmed their exosome morphology, size distribution and molecular composition (fig. 5).

Transmission electron microscopy analysis of vesicles further revealed their ability to bind to Α β 42 protein aggregates (fig. 1b and fig. 5).

To assess the binding of exosome Α β, the APEX platform was developed for scale-up, multi-parameter analysis of exosome molecular co-localization. The system measures transmission SPR through a periodic plasma nanopore array, forms a pattern in a bilayer photonic structure, and rapidly grows insoluble optical products on bound exosomes using in situ enzymatic conversion (fig. 1 c). To supplement APEX enzymatic deposition (occurring at the top of the sensor), size-matched plasmonic nanopores are patterned in a coupled bilayer photonic system to enhance SPR measurements by back-side illumination (away from enzyme activity, fig. 20). The resulting enzymatic deposition not only stably changes the refractive index of the SPR signal amplification as evidenced by the red shift in the transmission spectrum (spectral shift Δ λ, fig. 1d), but is also spatially defined as a molecular co-localization analysis. Scanning electron micrographs of sensor-bound exosomes before and after APEX magnification confirmed the local growth of optical deposits after enzymatic conversion (figure 6).

Thus, using the developed APEX platform, the binding of a β protein to exosomes can be measured directly from clinical blood samples of AD patients and control subjects and correlated with PET imaging of global and regional brain plaque deposits (fig. 1 e). For high throughput, multiplexed clinical analysis, sensor microarrays were fabricated on 8-inch wafers using advanced fabrication methods (i.e., deep ultraviolet lithography, fig. 7); each wafer can hold 40 more microarray chips with > 2000 sensor elements (fig. 8 a). FIG. 1f shows a photograph of the developed APEX microarray chip used for parallel measurements in this study. Scanning electron micrographs of the developed sensors showed highly uniform fabrication (fig. 8 b).

Optimized signal amplification for multiplex analysis

Enzymatic APEX amplification was developed for the first time. A series of sensor functionalisations, namely antibody coupling, exosome binding, enzyme labelling and optical product amplification were performed and the stepwise total spectral shift (cumulative Δ λ, fig. 2a) was measured. The sensor was functionalized with an antibody directed against CD63, CD63 being a type III lysosomal membrane protein enriched in exosomes, with characteristics of exosomes, to capture vesicles derived from neuronal cells (SHSY 5Y). To promote local deposition of insoluble optical products, horseradish peroxidase was incorporated as a cascade enzyme and used to catalyze the conversion of its soluble substrate (3, 3' -diaminobenzidine tetrahydrochloride). The sensor-bound vesicles are enzymatically labeled with another anti-CD 63 antibody. Although the enzyme label did not cause any significant spectral change, the formation of the optical product resulted in about 400% signal enhancement. In contrast, control experiments using IgG isotope control antibodies showed minimal background change (fig. 9). Importantly, this SPR signal amplification is strongly correlated with an increase in area coverage of highly localized optical deposits, as confirmed by scanning electron microscopy (fig. 2 b).

To complement enzymatic amplification (occurring at the top of the sensor), APEX sensor design was optimized to improve its analytical performance and stability. The double layer plasmonic structure of APEX enables SPR excitation by back side illumination (figure 2c) compared to the established gold-plated glass design (figure 20) which supports only front side illumination. The new optimized design not only shows strong transmission SPR by back-side illumination (fig. 10a-c), but also shows analytical stability (fig. 10d), probably due to reduced direct incident light (i.e. temperature fluctuations) to the enzyme activity. The APEX assay was further established by optimizing enzyme substrate concentration and reaction duration (fig. 2 d). By monitoring the real-time spectral changes associated with different substrate concentrations through constant back-side illumination, it was found that a large amount of signal amplification could be accomplished in < 10 minutes, thereby allowing the entire APEX workflow to be completed in < 1 hour.

Under these optimized conditions, the APEX detection sensitivity was next measured for exosome quantification. Neuronal derived vesicles (SH-SY5Y) were quantified using standard nanoparticle tracking assays. Using the anti-CD 63 antibody, we performed a titration experiment (fig. 2 e). The optimized APEX amplification is determined to improve the detection sensitivity by 10 times and establish detection Limit (LOD) to 200 exosomes. This observed sensitivity is the best LOD reported to date for bulk exosome measurements and is 10 better than western blot and chemiluminescence ELISA, respectively⁵Multiple sum of 10³And (4) doubling.

Assays for determining various markers associated with neurodegenerative diseases were further developed using the microarray APEX platform. A specific assay was established for the following protein markers (fig. 2 f): amyloid beta (A β 42), Amyloid Precursor Protein (APP), α -synuclein (a-syn), the intimate homolog of L1 (CHL1), insulin receptor substrate 1(IRS-1), Neuronal Cell Adhesion Molecule (NCAM), and tau protein. Importantly, this platform demonstrates the signal amplification capability for the detection of extracapsular and intravesicular proteins as well as exosome mirnas (figure 11b) by further developing APEX assay workflow (figure 11 a). All detection probes used for assay development can be found in table 1.

Enhancing binding between A beta aggregates and exosomes

Using the developed APEX platform, exosomes were next evaluated for binding to different structural forms of pathological Α β protein. To mimic the various stages of amyloid seeding and fibrosis, differently sized Α β 42 aggregates were prepared, which are the major components of amyloid plaques. The degree of aggregation was varied to form a β 42 aggregates of different sizes (fig. 3a, see experimental methods for details), their globular morphology and monomodal size distribution being confirmed by transmission electron microscopy and dynamic light scattering analysis, respectively (fig. 3 b). It is further noted that larger a β 42 aggregates show a strong tendency to form fibrous structures (fig. 12).

To determine the kinetics of exosome- Α β binding, prepared Α β 42 aggregates were immobilized on the APEX platform and the sensors were incubated with equal concentrations of neuron-derived exosomes (fig. 3 c). In contrast, in control experiments, similarly sized aggregates of Bovine Serum Albumin (BSA) were prepared and characterized (fig. 13). By measuring real-time exosome binding, it was demonstrated that exosomes bind more strongly to Α β 42 aggregates compared to similarly sized BSA controls, regardless of aggregate size (fig. 3 d). More importantly, vesicles showed significantly higher binding affinity (> 5 fold) for larger a β 42 aggregates compared to the binding affinity of vesicles for smaller a β 42 aggregates (fig. 3d, left) (fig. 3d, right). All affinities were normalized to a β 42 aggregate surface area and against the respective BSA controls (see methods for details).

Next, using extracellular vesicles derived from different cell sources, their respective binding to larger Α β 42 aggregates was measured using the APEX platform (fig. 3 e). Vesicles from different cell sources at the same concentration were incubated with a β 42 functionalized sensors as determined by nanoparticle tracking analysis (fig. 14). It is noted that in all cell sources tested, neurons, erythrocytes, platelets and epithelial cell-derived vesicles were shown to exhibit stronger binding to Α β 42 aggregates, while glial and endothelial cell-derived vesicles showed negligible binding. A panel of specific markers for these respective cell sources, as well as a pan-exosome marker (i.e., CD63), was then used for APEX signal amplification of the bound vesicles. CD63 consistently performed signal enhancement in all tested vesicles. By this marker identification, CD63 was therefore used to develop an APEX assay to identify and measure exosome-bound A β (defined as A β 42+ CD63 +; FIG. 15).

Brain plaque burden revealed by blood exosome-bound abeta

Given the enhanced binding between exosomes and protofibrillar a β aggregates (building blocks of amyloid plaques), it is speculated that exosome-bound a β may serve as a more reflective circulating biomarker for brain plaque burden. To test this hypothesis, different APEX assays were developed with various antibodies to evaluate different circulating Α β 42 populations from clinical blood samples (fig. 15 a). Specifically, to characterize the exosome-bound Α β 42 population, the APEX assay was designed to enrich Α β 42 directly from native plasma and measure the relative amount of CD63 associated with captured Α β 42. This assay configuration not only showed specific detection of the a β 42+ CD63+ population (fig. 15b-c), but also reflected a functional correlation: as the binding between the pro-fibril a β aggregates and exosomes increases, the associated CD63 signal can be considered as a surrogate marker to measure the relative amount of pro-fibril a β 42 in total circulating a β 42. To account for the presence of unbound a β 42 population, large size retentate (e.g., exosomes) in plasma was removed using size exclusion filtration prior to measuring a β 42 in plasma filtrate. Finally, to measure total circulating a β 42, native plasma was assessed by direct a β 42 enrichment and a β 42 detection.

We next performed a feasible clinical study aimed at solving the following key problems: (1) whether APEX can measure circulating a β 42 directly from blood samples, (2) how correlated different blood-borne a β 42 populations are with brain plaque burden, and (3) whether a particular population of circulating a β 42 can distinguish between different clinical populations?

To achieve these targets, age-matched subjects (n-84), subjects diagnosed with AD (n-17), mild cognitive impairment subjects (MCI, n-18), healthy controls without cognitive impairment (NCI, n-16), and clinical controls for vascular dementia (VaD, n-9) and neurovascular impairment (i.e. mild cognitive impairment of blood vessels, VMCI, n-12; acute stroke, n-12) were enrolled. All clinical information is shown in table 2. All subjects were enrolled for blood sampling and APEX analysis. All subjects agreed to simultaneous PET imaging of brain amyloid plaques, except for acute stroke patients. Plasma samples were collected just prior to infusion of pittsburgh compound b (pib) radiotracer for PET imaging. PET imaging showed a wide range of brain plaque burden (fig. 4a) and demonstrated regional brain changes (table 2) between and within the imaged clinical groups, consistent with other published clinical studies.

TABLE 2 clinical information and PET imaging normalized uptake ratio (SUVR).

AD: alzheimer's disease, MCI: mild cognitive impairment, NCI: the cognitive disorder is not caused,

VaD: vascular dementia, VMCI: vascular mild cognitive impairment. Using the developed APEX assay (fig. 11a), we evaluated different circulating Α β 42 populations in these clinical plasma samples, i.e. exosome-bound Α β 42, unbound population and total circulating Α β 42 (fig. 4 b). The exosome-bound Α β 42 population showed strong co-localization signals with exosome markers (i.e., CD63, CD9 and CD81) and neuronal markers (i.e., NCAM, L1CAM and CHL-1), indicating that neuronal exosomes can constitute a significant proportion of the population (fig. 16 a). As unbound a β 42, measured from plasma filtrates, we further characterized these filtrates and confirmed that their vesicle counts were negligible and co-localization signals with exosomes and neuronal markers were minimal (fig. 16 b-c). Unbound A β 42 (FIG. 4b, middle, R) when associated with whole-body PET amyloid imaging²0.0193) or total Α β 42 (fig. 4b, right, R)²0.1471) the exosome-bound Α β 42 measurement showed the best correlation (fig. 4b, left, R)²0.9002). Interestingly, the poor and negative correlation shown by total a β 42 measurements (as shown in this study and other published reports) differed, the phase from the exosome-bound a β 42 populationCD63 measurements showed a high correlation and positive correlation with PET imaging of brain amyloid plaques. We attribute this finding to a similar binding preference of exosomes and PET tracers for Α β 42: (1) exosomes showed enhanced binding to the pre-fibrillar a β 42 aggregates, in particular larger aggregates that can readily form fibrils (fig. 3d), and (2) PET tracers bind strongly to larger amyloid fibrils, but hardly to smaller aggregates. Notably, this superior correlation also demonstrates the specificity of the brain region; exosome-bound Α β 42 measurements showed a stronger correlation of the cingulated zone (early AD affected zone, fig. 17a) with brain plaque load compared to the occipital zone (late AD affected zone, fig. 17 b).

In differentiating clinical diagnosis, only APEX analysis of exosome-bound Α β 42, but not of unbound or total Α β 42 populations, showed good specificity (fig. 4 d). In particular, measurement of exosome-bound Α β 42 can distinguish not only AD clinics (i.e. AD and MCI, P < 0.01), but also other healthy and clinical controls (P < 0.0001, student's t-test). The demonstrated specificity was comparable to that of PET brain amyloid imaging in distinguishing between various clinical groups (fig. 18). In another aspect, nanoparticle trace analysis of plasma extracellular vesicles did not show any significant difference in vesicle size or concentration for all clinical groups (fig. 19).

Example 2

AD is the most common form of severe dementia. Due to its complex and progressive neuropathology, early detection and timely intervention are critical to the success of disease modifying therapies. Despite the intense interest in finding serological biomarkers for AD, their development has been plagued by several challenges. First, unlike the counterpart in cerebrospinal fluid, the concentration of circulating pathological AD molecules is much lower. Plasma Α β levels tend to approach the lower detection limit of conventional ELISA assays; this limitation may lead to several conflicting findings in published reports. Second, there was little correlation between plasma Α β analysis and brain plaque deposition, the earliest pathological hallmark of AD. One possible reason may be from different measurement methods. PET imaging probes, which are commonly used to determine brain amyloid burden, preferentially measure insoluble fibrillar deposits, whereas traditional ELISA measures soluble a β in plasma. Furthermore, previous global blood measurements may mask the potential relevance of blood-based measurements to brain pathology. However, this difference presents a more fundamental problem — whether there is a subpopulation of circulating a β proteins that better reflects the fibrillar pathology in the brain.

A dedicated Analysis Platform (APEX) was developed for multiparameter analysis of exosome-bound, unbound and total Α β directly from plasma to differentiate different circulating Α β populations. In particular, it takes advantage of new advances in sensor design, device manufacturing, and assay development to achieve enhanced optical performance and detection capabilities (fig. 20). In terms of sensor design and fabrication, the APEX platform constitutes a periodic array of gold nanopores, suspended on a patterned silicon nitride film, and fabricated by deep ultraviolet lithography, a state-of-the-art fabrication method for large-scale, precise nanopatterning. These advances have driven APEX technology to achieve 1) improved optical performance (i.e., enhanced transmission intensity to achieve SPR detection by bi-directional light illumination) and 2) reliable mass production. In terms of assay technology, the APEX platform utilizes rapid in situ enzymatic conversion to achieve highly localized amplified signals. This development not only enables sensitive detection of different targets (e.g., intravesicular proteins and RNA targets), but also facilitates exosome co-localization analysis for multi-parameter population studies, since insoluble deposits are locally formed only when multiple targets are present in exosomes simultaneously. With these combined advances, the observed APEX sensitivity is the best reported in exosome analysis to date and exceeds standard ELISA measurements by several orders of magnitude. Using the developed APEX platform, we demonstrated enhanced binding between exosomes and larger pre-fibrillar pre- Α β (the key building block of fibrillar amyloid plaques). The exosome-bound amyloid (CD63+ Α β 42+) subpopulations were further identified and quantified in clinical plasma samples, which were found to be highly correlated with brain amyloid plaque burden in different clinical populations, i.e. AD, MCI, cognitive normal controls and clinical controls with other neurodegenerative and neurovascular diseases.

Evaluation of different circulating Α β populations may lead to a shift in paradigm for AD studies and clinical care. There is increasing evidence that protofibril a β aggregates may play a role as toxic drivers of AD neurodegeneration. Their preferential binding to exosomes, and the recent findings of exosome marker enrichment in human amyloid plaques, not only reveal a possible new mechanism of plaque seeding, but also suggest the importance of exosome-bound a β as a more reflective circulating biomarker for complex AD pathology. Thus, it is envisioned that the present study may complement other preclinical and clinical studies in terms of technology development and biomarker improvement. For example, while IP mass spectrometry enables unbiased molecular screening in terms of technology development and is valuable for biomarker discovery, particularly in detecting different molecular isoforms and variants (e.g., (APP) 669-. In terms of biomarker improvement, as demonstrated by current studies, analysis of different circulating Α β populations can reveal new correlations that were previously masked by whole blood measurements and drive future clinical management of blood-based AD. Importantly, there are currently over 400 clinical trials of AD, further envisioned is a method that can be developed to redefine current patient care standards. Through additional technological innovations such as on-chip exosome processing, combinatorial analysis of other AD markers, and longitudinal clinical cohort validation, the developed technology can provide the combined capabilities of facilitating minimally invasive early detection, molecular stratification, and continuous monitoring, all of which are critical for objective assessment of disease-modifying therapies at different stages of clinical trials.

Example 3

This example demonstrates that incubating amyloid aggregates with an inhibitor reduces spontaneous protein aggregation.

Method

And (4) measuring the size of the aggregate. The hydrodynamic diameter of the amyloid and BSA aggregates was determined by dynamic light scattering analysis (Zetasizer Nano ZSP, Malvern). 3X14 measurement runs were carried out at 4 ℃. Z-average diameter and polydispersity were analyzed. For each measurement, both the autocorrelation function and the polydispersity index are monitored to ensure the sample quality for sizing.

The protein aggregates. The lyophilized amyloid was resuspended in NaOH (60mM, 4 ℃), sonicated, and the pH adjusted to pH7.4 in PBS. The protein was immediately filtered through a 0.2 micron membrane filter (Millipore) and the filtrate was used as small initial aggregates. To make larger aggregates, the protein was treated as described above and incubated for 1 hour with stirring to induce further aggregation. For treatment with inhibitors, 10 μ M inhibitor (e.g., methylene blue) was added to the protein prior to incubation. Aggregate size determination was performed at the end of incubation.

And (4) carrying out optical analysis. For experimental analysis, the APEX sensor was back-illuminated through an a x 10 microscope objective using a tungsten halogen lamp (stockkerr yaleinc.). The transmitted light was collected by an optical fiber and sent to a spectrometer (Ocean Optics). All measurements were performed at room temperature in closed boxes to eliminate ambient light interference. The transmitted light intensity is digitally recorded in counts relative to wavelength. For spectral analysis, the spectral peaks were determined using a custom R program by fitting the transmission peaks using a local regression method. All fits are done proximally. That is, for a fit at point x, the fit is made using points near x, weighted by their distance from x. This method can eliminate the variation in results due to the number of data points and the range of data analyzed, as compared to fitting through a multi-order polynomial curve. All spectral shifts (Δ λ) were determined as changes in the transmission spectrum peaks and calculated relative to appropriate control experiments.

Characterization of exosome-protein binding. The prepared protein aggregates (a β 42 and BSA control) were used for surface functionalization on APEX sensors by EDC/NHS coupling as described previously. Unbound protein aggregates were washed away with PBS. The amount of bound protein was measured from the resulting transmission spectral shift. We used this information to determine the number of protein aggregates bound and their associated total protein surface area bound by exosomes (see information below for details) in order to normalize binding affinity. After surface functionalization with protein aggregates, exosomes are introduced onto the sensor. Spectral changes were measured every 3 seconds for a total duration of 480 seconds to construct a real-time kinetic sensorgram. Exosome binding kinetics and binding affinities were determined as protein aggregates of different sizes.

To account for the size difference of protein aggregates and the exponential decay of sensitivity associated with SPR (with increasing distance from the sensor surface), the equation was used

The total surface area of bound aggregates interacting with exosomes was calculated: where S is the signal, z is the distance from the sensor surface, E is the electric field at which z is 0, in this case a constant, l_dIs the decay length, set at 200nm in current sensor designs, and r is the radius of the binding protein aggregate.

All protein aggregates approximated spheres, and their r was determined by dynamic light scattering analysis. We used the above equation to determine the number of protein aggregates bound to the sensor and their respective total surface area to estimate the number of available binding sites for interaction with exosomes. All exosome binding data (Δ λ) were normalized to their respective protein binding sites. Normalized Α β 42 binding data were performed relative to similarly sized BSA controls and fitted to determine the binding affinity constant K_D。

And (4) miRNA analysis. Exosome lysates were incubated with biotinylated RNA probes, followed by capture of RNA duplexes and amplification of the APEX signal on p 19-functionalized APEX sensors.

Results

The pathology of many neurodegenerative diseases (alzheimer's disease, parkinson's disease and amyotrophic lateral sclerosis) involves the aggregation of misfolded amyloid proteins. Therapies targeting protein aggregation involve various strategies for clearing aggregated amyloid, including breaking down amyloid aggregates or inhibiting amyloid aggregation. Molecules that have been shown to be effective in reducing the number of amyloid aggregates and are therefore potential candidates for disease modifying therapies, include methylthioninium chloride, leucomethylthioninium bis (hydrogen methanesulfonate), curcumin, acid fuchsin, epigallocatechin gallate, safranine, congo red, apigenin, azure C, basic blue 41, (trans ) -1-bromo-2, 5-bis- (3-hydroxycarbonyl-4-hydroxy) styrylbenzene (BSB), Chicago azure 6B, -cyclodextrin, daunomycin hydrochloride, dimethylyellow, direct Red 80, 2-dihydroxybenzophenone, hexadecyltrimethylammonium bromide (C16), hemin, heme, indomethacin, juglone, resorcinol blue, methylchloroflavin salicylate, methylchloroflavin, Melatonin, myricetin, 1, 2-naphthoquinone, nordihydroguaiaretic acid, R () -normorphine hydrobromide, orange G, o-vanillin (2-hydroxy-3-methoxybenzaldehyde), phenazine, phthalocyanine, rifamycin SV, phenol red, hydropyracycline, quinacrine mustard dihydrochloride, thioflavin S, ThT and trimethyl (tetradecyl) ammonium bromide (C17), diallyltartaric acid, eosin Y, fenofibrate, neocupine, nystatin, octadecyl sulfate, and rhodamine B.

Since no animal model has been demonstrated to exhibit the exact pathology that reflects the pathology of human brain AD, we used in vitro experiments to simulate the effect of disease modifying treatment in inhibiting amyloid aggregation. The initial size of the amyloid was confirmed by dynamic light scattering analysis, and aliquots of the protein were then incubated with or without inhibitor. In the absence of inhibitor, the size of the protein aggregates increases with increasing incubation time due to spontaneous aggregation of amyloid. However, in the presence of inhibitors, the increase in size due to spontaneous protein aggregation is small, resulting in the formation of smaller protein aggregates.

After incubation, the amyloid is functionalized to the surface of the sensor chip prior to incubation with neuronal exosomes. Neuronal exosomes were observed to bind preferentially to larger protein aggregates, as indicated by the difference in binding affinities, and demonstrated reduced binding to inhibitor-treated smaller protein aggregates. Since the binding of exosomes to proteins can be used as surrogate markers of the biophysical and/or biochemical properties of proteins, which are affected by disease modifying therapies, the APEX platform is able to assess the efficacy of disease modifying therapies.

Having described several example configurations, various modifications, alternative constructions, and equivalents may be used without departing from the spirit of the disclosure. For example, the above-described elements may be components of a larger system. Further, many steps may be taken before, during, or after considering the above-described elements.

All publications, sequence accession numbers, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.

Exemplary embodiments of the present disclosure

In some aspects and embodiments, described herein are:

a method of detecting a neurodegenerative disease or amyloidosis in a subject, comprising detecting the level of an exosome-bound aggregating biomarker in a sample obtained from the subject, wherein an increased level of the exosome-bound aggregating biomarker compared to a reference indicates that the subject has a neurodegenerative disease or amyloidosis.

In some embodiments, the biomarker is selected from the group consisting of A β, APP, a-Syn, Tau, APOE, SOD1, TDP-43, basoon, and/or fibronectin.

In some embodiments, the method comprises detecting the level of a molecular subtype of the exosome-bound biomarker.

In some embodiments, the molecular subtype of a β is a β 42, a β 40, a β 39, or a β 38.

In some embodiments, the biomarker is a pre-fibril aggregate.

In some embodiments, the method further comprises detecting an exosome biomarker selected from the group consisting of CD63, CD9, CD81, ALIX, TSG101, florilin-1, florilin-2, LAMP-1, HSP70, HSP90, RNA, and DNA, wherein the exosome biomarker is co-localized with an exosome-binding biomarker.

In some embodiments, the method further comprises detecting a neuronal biomarker selected from the group consisting of NCAM, L1CAM, CHL-1, and IRS-1, wherein the neuronal biomarker is co-localized with an exosome-binding biomarker.

In some embodiments, the neurodegenerative disease is selected from the group consisting of: alzheimer's disease, mild cognitive impairment, dementia, vascular mild cognitive impairment, Parkinson's disease, amyotrophic lateral sclerosis, multiple sclerosis, progressive supranuclear palsy and/or tauopathies.

In some embodiments, the neurodegenerative disease is selected from the group consisting of alzheimer's disease and mild cognitive impairment.

In some embodiments, the method comprises treating a subject having a neurodegenerative disease or amyloidosis.

In some embodiments, the sample is a tissue biopsy sample, blood, plasma, serum, or cerebrospinal fluid.

In some embodiments, the method is further associated with a brain imaging study.

In another aspect, described herein is a method of detecting a subject at risk of having a neurodegenerative disease or amyloidosis, the method comprising detecting a level of an exosome-bound aggregated biomarker in a sample obtained from the subject, wherein an increased level of the exosome-bound aggregated biomarker compared to a reference indicates that the subject has a neurodegenerative disease or amyloidosis.

In another aspect, described herein is a method of detecting and treating a neurodegenerative disease or amyloidosis in a subject, the method comprising: a) detecting the level of exosome-bound aggregation biomarker in a sample obtained from the subject, wherein an increased level of exosome-bound aggregation biomarker compared to a reference indicates that the subject has a neurodegenerative disease or amyloidosis; b) treating a subject having a neurodegenerative disease or amyloidosis.

In another aspect, described herein is a method of determining the aggregation state of a biomarker in a sample, the method comprising detecting the level of exosome-bound biomarker in the sample, wherein the level of exosome-bound biomarker indicates the extent of aggregation of the biomarker compared to a reference.

In some embodiments, the method comprises the step of contacting the sample with a population of exosomes prior to the step of detecting the level of an exosome-bound biomarker.

In some embodiments, the aggregated biomarker in the sample preferentially binds to exosomes compared to a non-aggregated biomarker.

In some embodiments, the sample is obtained from a subject.

In some embodiments, an increase in the degree of aggregation of the biomarker compared to a reference indicates that the subject has a neurodegenerative disease or amyloidosis.

In some embodiments, the method comprises treating a subject having a neurodegenerative disease or amyloidosis.

In some embodiments, the treatment comprises administering to the subject a therapeutically effective amount of one or more drugs or a combination thereof.

In some embodiments, the drug is selected from a cholinesterase inhibitor, an NMDA receptor antagonist, a combination of a cholinesterase inhibitor and an NMDA receptor antagonist, a BACE1 inhibitor, an antibody, a protein aggregation inhibitor, a proteasome inhibitor, a small molecule, gene therapy, an anti-tau drug, or a combination thereof.

In some embodiments, the cholinesterase inhibitor is donepezil, rivastigmine, or galantamine; the NMDA receptor antagonist is memantine; the BACE1 inhibitor is AZD 3293; the antibody is adocamumab; and/or the anti-tau drug is TRx0237 (LMTX).

In some embodiments, the drug is selected from the group consisting of methionium chloride, leuco methionium bis (hydrogen methanesulfonate), curcumin, acid fuchsin, epigallocatechin gallate, safranal, congo red, apigenin, azure C, basic blue 41, (trans ) -1-bromo-2, 5-bis- (3-hydroxycarbonyl-4-hydroxy) styrylbenzene (BSB), Chicago azure 6B, -cyclodextrin, daunomycin hydrochloride, dimethylyellow, direct Red 80, 2-dihydroxybenzophenone, hexadecyl trimethyl ammonium bromide (C16), heme chloride, heme, indomethacin, juglone, resorcinol blue, methylchloroflavin sulfosalicylate, melatonin, myricetin, 1, 2-naphthoquinone, nordihydroguaiaretic acid, R () -normorphine hydrobromide, indomethacin, and mixtures thereof, Orange G, o-vanillin (2-hydroxy-3-methoxybenzaldehyde), phenazine, phthalocyanine, rifamycin SV, phenol red, hydropyricycline, quinacrine mustard dihydrochloride, thioflavin S, ThT, and trimethyl (tetradecyl) ammonium bromide (C17), diallyl tartar, eosin Y, fenofibrate, neocupine, nystatin, octadecyl sulfate, and/or rhodamine B.