Method of detecting neurodegenerative disease

Abstract

The present disclosure relates generally to the field of neurology. In particular, the present disclosure relates to a method of detecting a neurodegenerative disease in a subject and a method of treatment thereof. The method comprises detecting the level of exosome-bound aggregation biomarkers in a sample obtained from the subject, wherein an increase in the level of exosome-bound aggregation biomarkers compared to a reference indicates that the subject has a neurodegenerative disease. Also described are methods for detecting a subject at risk of developing an amyloidosis or neurodegenerative disease, methods for detecting and treating an amyloidosis or neurodegenerative disease in a subject, and methods for determining the aggregation status of biomarkers in a sample.

Description

[0001] This application is a divisional application of Chinese invention patent application filed on January 30, 2020, with application number 202080012032.7 and invention title "Method for Detecting Neurodegenerative Diseases".

[0002] Cross-reference to related applications

[0003] This application claims priority to Singapore Provisional Application No. 10201900940Y, filed on January 31, 2019, entitled “Method for Detecting Neurodegenerative Diseases”. Technical Field

[0004] This disclosure generally relates to the field of neurology. Specifically, this disclosure relates to methods for detecting amyloidosis or neurodegenerative diseases in individuals, as well as methods for monitoring and treating individuals. Background Technology

[0005] Surface plasmon resonance (SPR) sensing is a widely used technique in laboratories for characterizing interactions between biomolecules, such as antibody-antigen interactions. This technique is typically based on immobilizing ligand-capturing molecules on a metal surface and measuring the change in refractive index as the ligand binds to the capturing molecule. It is a label-free technique, eliminating the need for specialized tags or dyes to sensitively measure molecular interactions. It is currently being developed for laboratory diagnosis of patients with various diseases, such as dementia, hepatitis, diabetes, and cancer.

[0006] Dementia is a public health crisis of the 21st century. Alzheimer's disease (AD) is the most common severe form of dementia, characterized by progressive loss of memory and cognitive function. Affected individuals exhibit significant limitations in self-care, social, and occupational functioning. However, molecular features of AD may emerge and develop long before these comprehensive clinical symptoms appear. These include extracellular amyloid-β (Aβ) plaques and intracellular tau neurofibrillary tangles. Due to its complex and progressive neuropathology, early detection and intervention are considered crucial for the success of disease-modifying therapies.

[0007] However, current AD diagnosis and disease surveillance are subjective and late-stage. They are achieved through the use of published standard clinical and neuropsychological assessments. These methods lack sensitivity and specificity, especially in the early stages when symptoms are subtle and significantly overlap with a variety of other diseases. New molecular diagnostic analyses are being developed, including cerebrospinal fluid measurements and brain amyloid plaque imaging via positron emission tomography (PET); however, these tests face limitations because they either require invasive lumbar punctures or are too expensive for wider clinical adoption. Therefore, there is considerable interest in searching for serum biomarkers for AD to assist in early diagnosis and disease surveillance.

[0008] Therefore, it is necessary to overcome or at least mitigate one or more of the above problems. Summary of the Invention

[0009] This article discloses a method for detecting amyloidosis or neurodegenerative diseases in individuals, as well as a method for monitoring and treating individuals.

[0010] In one aspect, a method for detecting amyloidosis or neurodegenerative disease in an individual is provided, the method comprising detecting the level of an exosome-bound aggregated biomarker in a sample obtained from the individual, wherein an increase in the level of the exosome-bound aggregated biomarker compared to a reference indicates that the individual has a neurodegenerative disease.

[0011] In one aspect, a method is provided for detecting the risk of an individual developing amyloidosis or a neurodegenerative disease, the method comprising detecting the level of an exosome-bound aggregated biomarker in a sample obtained from the individual, wherein an increase in the level of the exosome-bound aggregated biomarker compared to a reference indicates that the individual has a neurodegenerative disease.

[0012] In one aspect, a method for detecting and treating amyloidosis or neurodegenerative diseases in an individual is provided, the method comprising:

[0013] a) Detect the level of exosome-bound aggregation biomarkers in samples obtained from individuals, wherein an increase in the level of exosome-bound aggregation biomarkers compared with a reference indicates that the individual has a neurodegenerative disease;

[0014] b) Treat individuals with neurodegenerative diseases.

[0015] In one aspect, a method is provided for determining the aggregation state of biomarkers in a sample, the method comprising detecting the level of exosome-bound biomarkers in the sample, wherein an increase in the level of exosome-bound biomarkers compared to a reference indicates the degree of biomarker aggregation. Attached Figure Description

[0016] Some embodiments of the invention will now be described by way of non-limiting example only, with reference to the accompanying drawings, in which:

[0017] Figure 1: APEX platform for analyzing Aβ binding of circulating exosomes

[0018] (a) Exosome binding to Aβ protein. Aβ protein, a major component of amyloid plaques found in AD brain pathology, is released into the extracellular space. Exosomes are nanoscale extracellular membrane vesicles actively secreted by mammalian cells. Exosomes can bind to released Aβ protein via their surface glycoproteins and glycolipids. (b) Transmission electron micrograph of Aβ bound to exosomes. Exosomes from neuronal cells (SH-SY5Y) were treated with Aβ42 aggregates and labeled with gold nanoparticles (10 nm) using an Aβ42-specific antibody. The nanoparticles are shown as clumps (indicated by red arrows). (c) Schematic diagram of APEX assay. For sensitive analysis at the nanoscale, exosomes were first immunocaptured onto a plasma nanosensor (before amplification). Insoluble optical deposits were locally formed on the sensor-bound exosomes by in situ enzymatic amplification (after amplification). This deposition was spatially defined as a molecular colocalization assay and altered the refractive index that enhanced the SPR signal. Please note that, to complement enzymatic amplification, the nanosensors are backlit (away from enzyme activity) to achieve analytical stability. Deposition causes a redshift in the transmitted light through the nanosensors. (d) Representative schematic diagram of the transmission spectral changes with APEX amplification. Specific exosome binding (before) and subsequent amplified analysis (after) are monitored via the APEX platform as transmission spectral shift (Δλ), au in arbitrary units. (e) Measurement of exosome binding Aβ in blood samples from patients with diseases such as Alzheimer's disease (AD), mild cognitive impairment (MCI), and no cognitive impairment (NCI) in a control group using the APEX platform. Blood measurements were correlated with corresponding PET imaging of cerebral amyloid plaque deposition. (f) Photograph of the APEX microarray. Each sensor chip contains 6 × 10 sensing elements composed of a uniformly fabricated plasmonic lattice for multiple measurements. For information on sensor fabrication, characterization, and design optimization, please refer to [link to relevant documentation]. Figure 7-10 .

[0019] Figure 2 APEX signal amplification and multiple analysis

[0020] (a) Stepwise changes in APEX transmission spectrum. We performed a series of operations, namely antibody conjugation (anti-CD63) to the sensor, exosome binding, enzyme labeling, and enzymatic deposition, and monitored the resulting spectral shifts. While enzyme labeling did not cause any significant changes, deposition formation did (****P<0.0001, ns non-significant, Student's t-test). (b) Comparison of APEX signal amplification and 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 were normalized to pre-signal amplification. The insert (right) shows SEM images of exosomes bound to the sensor before and after APEX amplification. (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 backlighting. Backlighting minimizes direct incident light on enzyme activity (occurring at the top of the sensor). Arrows indicate the direction of incident light. (d) Real-time sensing plot of APEX amplification kinetics. Different concentrations of optical substrate (3,3'-diaminobenzidine tetrahydrochloride; high: 1 mg / ml, low: 0.01 mg / ml) were used to monitor amplification efficiency. All data were normalized against a negative control using an IgG isotope control antibody. (e) Comparison of detection sensitivity for 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 assays for measuring target proteins. Detection methods were developed for amyloid β (Aβ42), amyloid precursor protein (APP), α-synuclein (α-syn), close homolog of LI (CHL1), insulin receptor substrate 1 (IRS-1), neural cell adhesion molecule (NCAM), and tau protein. All assays demonstrated specific detection. Thermographic signals were measured (rows) and normalized. All measurements were performed in triplicate, and data are shown as mean ± sd in a, b, d, and e.

[0021] Figure 3: Preferential binding between Aβ aggregates and exosomes

[0022] (a) Schematic diagram of Aβ protein aggregation. We varied the degree of clustering and used a filtering method to prepare small and large Aβ42 aggregates separately. (b) Characterization of Aβ protein aggregates. (left) Transmission electron microscopy shows the spherical morphology of the prepared Aβ42 aggregates. (right) Dynamic light scattering analysis confirmed the single-peak size distribution of the different sizes of the formulations. (c) Schematic diagram of exosome-Aβ binding analysis. Aβ42 aggregates (small and large) were immobilized on APEX sensors and treated with an equal concentration of exosomes from neuronal cells (SH-SY5Y) to determine binding kinetics. All exosome binding data were normalized to the surface area of ​​the corresponding Aβ42 aggregates immobilized on the sensor (see Methods). (d) Real-time sensor plot of exosome binding kinetics. Compared with similar-sized bovine serum albumin (BSA) control aggregates (see Methods). Figure 13 Compared to the smaller Aβ42 aggregates (left), exosomes exhibited stronger binding affinity to the larger Aβ42 aggregates (right). Importantly, exosomes showed a stronger affinity for the larger Aβ42 aggregates (right) compared to the smaller Aβ42 aggregates (left). Note the proportional differences on the y-axis. All binding affinity (KD) was determined by normalizing exosome binding data and relative to the BSA control. KD(small) / KD(large) = 5.27. (e) Different bindings of various extracellular vesicles to Aβ42 aggregates. Vesicles were derived from different cellular sources, namely neurons, glial cells, endothelial cells, monocytes, erythrocytes, platelets, and epithelial cells, and were used at equal concentrations for binding analysis. Using the APEX platform, we first measured the direct binding of vesicles to the Aβ42 functionalized sensor (direct). Next, for each cell source, we labeled the binding vesicles for either a source-specific marker (cell-source specific marker) or a pan-exosomal marker (i.e., CD63, pan-exosomal marker) and measured the associated APEX signal amplification. All measurements were performed relative to an IgG isotope control antibody and in triplicate. Data are shown in e as mean ± sd.

[0023] Figure 4 Clinical relevance of circulating exosome binding to Aβ and brain imaging

[0024] (a) Representative reconstructed PET brain images from clinical individuals showing increased amyloid plaque burden. Normalized uptake ratio (SUVR) of specific brain regions, normalized relative to mean cerebellar gray matter intensity, was used to determine amyloid plaque burden. (b) Correlation between different circulating Aβ42 populations and overall mean PET brain imaging (n = 72). Using APEX analysis, we measured the individual signals of exosome-bound Aβ42 (left), unbound Aβ42 (middle), and total Aβ42 (right) in blood samples from Alzheimer's disease (AD), mild cognitive impairment (MCI), and non-cognitive impairment (NCI) controls, vascular dementia (VaD), and vascular mild cognitive impairment (VMCI) patients. When correlated with overall imaging data of amyloid plaques, the signal of unbound Aβ42 (center, R) was significantly higher than that of unbound Aβ42 (center, R). 2 =0.0193) or total Aβ42 (right, R 2 =0.1471) compared to exosome-bound Aβ42 (left, R) 2 =0.9002) Measurements showed the best correlation. (c) Analysis of different circulating Aβ42 groups in distinguishing different clinical groups (n=84). Only APEX measurements of circulating exosome-bound Aβ42 (left) could distinguish the AD clinical group (AD and MCI) from other normal (NCI) and clinical controls (VaD, VMCI, and acute stroke). Measurements of unbound Aβ42 (center) and total Aβ42 (right) showed no statistical significance between different clinical groups (**P<0.01, ****P<0.0001, ns, not significant, Student's t-test). All measurements were performed relative to IgG isotype control antibody and in triplicate. Data are shown as mean ± SD in bc.

[0025] Figure 5 Characterization of extracellular vesicles detached from neurons.

[0026] (a) Scanning electron micrograph of a neuronal cell (SH-SY5Y) showing the massive release of nanoscale extracellular vesicles from the cell. (b) High-magnification image of the released vesicles. (c) Single-peak size distribution of extracellular vesicles determined by nanoparticle tracking analysis, showing an average diameter of ~150 nm. (d) Western blot analysis of vesicle lysates. Vesicles were lysed and immunoblotted to obtain exosome markers (LAMP1, ALIX, HSP90, HSP70, CD63, Flotillin1, TSG101), neuronal markers (NCAM), and negative markers including lipoprotein (APOE) and other compartment markers (calcinin, GRP94). (e) Transmission electron micrographs of dual immunolabeling with gold nanoparticles of different sizes (CD63, 20 nm; Aβ42, 5 nm) confirmed the co-localization of the two markers on the same vesicle, indicating the presence of exosome-bound Aβ.

[0027] Figure 6 APEX scaled-up products

[0028] Scanning electron micrographs of the APEX sensor: (a) before magnification, showing exosomes captured on the sensor by an anti-CD63 antibody; and (b) after magnification, showing localized growth of insoluble optical deposits from a soluble substrate (3,3'-diaminobenzidine tetrahydrochloride). The resulting APEX signal amplification is closely related to the increase in localized deposit coverage.

[0029] Figure 7 Mass production of APEX microarray sensors.

[0030] All APEX sensors were fabricated on 8-inch silicon (Si) wafers. The fabrication steps included: (1) preparing a 10 nm silicon dioxide (SiO2) layer by thermal oxidation and depositing a 145 nm silicon nitride (Si3N4) layer on the wafer by low-pressure chemical vapor deposition (LPCVD). (2) after applying photoresist, deep ultraviolet (DUV) lithography was performed to define a nanopore array pattern in the photoresist. This pattern was transferred onto the Si3N4 film by reactive ion etching (RLE). (3) after removing the photoresist, a thin SiO2 protective layer (100 nm) was deposited on the front side of the wafer using plasma-enhanced chemical vapor deposition (PECVD). For light energy transmission, photoresist was spin-coated on the back side of the wafer; photolithography was used to define the sensing area. (4) Si3N4 and SiO2 were etched by RLE etching followed by etching with potassium hydroxide (KOH) and tetramethylammonium hydroxide (TMAH) of Si. (5) After etching, the protective SiO2 layer is removed using diluted hydrogen fluoride (DHF) (1:100). (6) Ti / Au (10nm / 100nm) is deposited on the Si3N4 film.

[0031] Figure 8 Characterization of APEX microarray sensors.

[0032] (a) An image of an 8-inch wafer shows the large-scale fabrication of the APEX microarray sensor chip. Each wafer consists of >2000 sensing elements. (b) A scanning electron micrograph of highly uniform nanopores in the APEX sensor. An inset shows a magnified view of the nanopore lattice.

[0033] Figure 9 : Gradual spectral changes.

[0034] The APEX sensors were conjugated to (a) an anti-CD63 antibody for exosome capture or (b) an isotopic control antibody. All sensors were treated with an isotopic concentration of exosomes from a neuronal cell line (SH-SY5Y) prior to APEX amplification. While the sensors exhibited surface functionalization (antibody conjugation) of similar magnitude to the antibodies, only the anti-CD63 functionalized sensors showed significant spectral shifts associated with exosome binding and APEX amplification, respectively. Note that in the control sensors, APEX amplification caused negligible spectral changes in the absence of exosome binding, au in arbitrary units.

[0035] Figure 10 Optimization of APEX sensor performance.

[0036] (a) Comparison of sensor performance with backlighting. We compared the SPR transmission intensity, full width at half maximum (FWHM) of the spectral peaks, and detection sensitivity of different APEX sensors with different nanopore diameters. All sensors were back-illuminated to supplement APEX enzymatic amplification. The optimized APEX design had a nanopore diameter of 230 nm, patterned at a regular periodicity of 450 nm in a 100 nm thick gold layer suspended on a silicon nitride film. This bilayer plasmonic structure supported SPR excitation via back-illumination. (b) Transmission spectral changes of the optimized sensor with increased refractive index. Increased refractive index caused changes in the transmission spectrum and shifted the resonance peak to a longer wavelength. (c) Spectral shift showed a linear correlation with increased refractive index. (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 results showed the following coefficients of variation: between groups = 2.76%, within groups = 4.14%, total = 4.59%. All measurements were performed in triplicate or more, and the data are shown in (a) as mean ± sd, au in any unit, ns, not significant, Student's t-test.

[0037] Figure 11 APEX workflow and scaling efficiency.

[0038] (a) APEX workflow for detecting proteins (extravesicular and intravesicular) and miRNAs. (b) APEX amplification efficiency for different molecular targets. APEX signals were obtained for the following targets: extravesicular proteins, Aβ42 protein; intravesicular proteins, heat shock protein 90; miRNAs, miRNA-9. All signals were normalized to the signals before the addition of optical substrates to determine the amplification. Measurements were performed in triplicate, and the data are shown in (b) as mean ± SD.

[0039] Figure 12 : A fibrous structure assembled from large Aβ aggregates.

[0040] Amyloid fibrils were observed after incubating the prepared large Aβ42 aggregates for 2 hours. The formed structures were immunolabeled with gold nanoparticles (15 nm) using anti-Aβ42 antibody and characterized by transmission electron microscopy.

[0041] Figure 13 Preparation of BSA control aggregates.

[0042] (a) Schematic diagram of BSA protein aggregates. We varied the heating duration 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 showed a unimodal size distribution. The small aggregates had a diameter of ~15 nm, and the large aggregates had a diameter of ~100 nm.

[0043] Figure 14: Extracellular vesicles isolated from various cell sources.

[0044] 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). All vesicles were characterized using nanoparticle tracking analysis.

[0045] Figure 15 APEX measurements of different cyclic Aβ groups.

[0046] (a) APEX assay configuration for characterizing different circulating Aβ groups in clinical plasma samples. Exosome-bound Aβ42 and total Aβ42 groups were measured from native plasma, while unbound Aβ42 groups were detected from plasma filtrate. (b) Incubation of fibronectin with exosome-bound Aβ42 resulted in negligible changes in the APEX signal. (c) Negligible signals were observed in negative controls (APOE lipoprotein and Aβ42 protein, human serum albumin / HSA), indicating that the APEX assay is specific for exosome-bound Aβ42. All measurements were performed in triplicate, and data are presented as mean ± SD.

[0047] Figure 16 Characterization of Aβ group in clinical samples.

[0048] (a) Exosome-bound Aβ42 cluster. We enriched Aβ42 directly from native plasma samples and measured the relative levels of colocalization signals for exosome markers (CD63, CD81, and CD9) and neuronal markers (NCAM, L1CAM, and CHL-1) within the captured Aβ42. All markers were detectable, with CD63 being the most highly expressed marker in the clinical samples tested. (b) Plasma filtrate for characterizing unbound Aβ42 cluster. To assess unbound Aβ42 cluster, we prepared vesicle-free plasma filtrate using membrane filtration (cutoff size = 50 nm, Nucleopore, Whatman). The filtrate showed negligible vesicle counts, as determined by nanoparticle tracking analysis. (c) The plasma filtrate also showed negligible signals for exosome markers (CD63) and neuronal markers (NCAM), demonstrating effective removal of exosomes by filtration. Healthy controls with Alzheimer's disease (AD), mild cognitive impairment (MCI), and no cognitive impairment (NCI). All measurements were performed in triplicate, and data are presented as mean ± sd.

[0049] Figure 17 The correlation between exosome-bound Aβ42 and regional brain amyloid load.

[0050] We identified specific brain regions for imaging SUVR, namely the cingulate gyrus affected early in AD and the occipital region affected late in AD. APEX measurements of exosome-bound Aβ42 were compared with imaging data from regions affected early in AD (a, R). 2=0.8808) showed better concordance with imaging data of late-stage AD-affected areas (b, R = 0.6863). Alzheimer's disease (AD, n = 17), mild cognitive impairment (MCI, n = 18), vascular dementia (VaD, n = 9), vascular mild cognitive impairment (VMCI, n = 12), and healthy controls without cognitive impairment (NCI, n = 16). All measurements were performed in triplicate, and data are presented as mean ± SD.

[0051] Figure 18 Comparison of PET imaging in clinical individuals with different diagnoses.

[0052] 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). The normalized uptake ratio (SUVR) of total mean plaque deposition distinguished the AD clinical group (AD and MCI) from other healthy individuals (NCI) and clinical controls (VaD and VMCI) (**P<0.01, ****P<0.0001, Student's t-test).

[0053] Figure 19: Extracellular vesicles in clinical samples.

[0054] (a) Representative analysis of extracellular vesicles, measured by nanoparticle tracking analysis, from blood samples of individuals with different clinical diagnoses (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 differences in vesicle size and concentration were found among samples with different clinical diagnoses (ns, not significant, Student's t-test).

[0055] Figure 20 A comparison of APEX detection technology, sensor design, and manufacturing.

[0056] Figure 21 (a) Inhibition of amyloid aggregation. (b) Dynamic light scattering analysis confirmed the unimodal size distribution and the size differences of amyloid when incubated with and without inhibitors. (c) Real-time sensing plot of exosome binding kinetics. Exosomes showed a stronger affinity for larger Aβ42 aggregates (untreated) compared to smaller Aβ42 aggregates (treated with inhibitors).

[0057] Figure 22A real-time sensory plot of exosome binding kinetics is shown. Exosomes bind more strongly to amyloid proteins (e.g., Aβ, APP, α-Syn, IRS-1, Tau, APOE, SOD1, TDP-43, bassoon, fibronectin) compared to similar-sized bovine serum albumin (BSA) control aggregates.

[0058] Figure 23 The specificity of the APEX assays for measuring target miRNA molecules was demonstrated. Assays for miR-9, miR-15b, miR-29b, miR-29c, miR-107, miR-146a, and miR-181c were developed. All assays demonstrated specific detection. The heatmap signals were measured and normalized (rows). Detailed Implementation

[0059] This article discloses a method for detecting neurodegenerative diseases and its treatment methods.

[0060] 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 produces surface plasmon resonance.

[0061] In one implementation, the design of a multilayer structured material (conductive metal and supporting substrate) enables plasmon coupling. This design supports bidirectional excitation of surface plasmon resonances, where SPR performance from bidirectional illumination (from top or bottom) is comparable.

[0062] As used herein, the term "conductive layer" can refer to a conductive material that exhibits surface plasmon resonance when excited by electromagnetic energy, such as light waves. A conductive material can refer to, for example, a metallic conductive material. Such a metallic conductive material can 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, zinc aluminum oxide, zinc gallium oxide, titanium nitride, and graphene. In one embodiment, the conductive material is gold, silver, aluminum, sodium, indium, or titanium. The metal can be in its bare form or coated with a layer of additional protective and reinforcing materials.

[0063] A conductive material can be "optically observable" when it exhibits significant scattering intensity in an optical region (ultraviolet-visible-infrared spectrum) encompassing wavelengths from approximately 100 nm to 3000 nm. A conductive material can be "visible to the naked eye" when it exhibits significant scattering intensity in a wavelength band (visible spectrum) from approximately 380 nm to 750 nm.

[0064] In one implementation, the membrane support layer is a structured membrane support layer.

[0065] In one embodiment, the film support layer is silicon nitride or sodium dioxide. Other support materials include substrates that can be patterned to form coupled multilayer plasma structures.

[0066] The diameter and periodicity of multiple pores extending through the conductive layer and the film support layer can be changed to achieve different resonant wavelengths and evanescent wave penetration.

[0067] Multiple apertures include symmetrical circular apertures, spatially anisotropic shapes such as ellipses and slits, and also include any aperture in the shape of a triangle, square, rectangle, or polygon. Combinations of aperture shapes can also be used. Apertures can have a wavelength of approximately 1500 nm or less, approximately 1400 nm or less, approximately 1300 nm or less, approximately 1200 nm or less, approximately 1100 nm or less, approximately 1000 nm or less, approximately 900 nm or less, approximately 800 nm or less, approximately 700 nm or less, approximately 600 nm or less, approximately 500 nm or less, approximately 450 nm or less, approximately 400 nm or less, approximately 350 nm or less, approximately 300 nm or less, approximately 250 nm or less, approximately 240 nm or less, approximately 230 nm or less, approximately 220 nm or less, approximately 210 nm or less. Sizes or diameters of approximately 10 nm or smaller, approximately 200 nm or smaller, approximately 190 nm or smaller, approximately 180 nm or smaller, approximately 170 nm or smaller, approximately 160 nm or smaller, approximately 150 nm or smaller, approximately 140 nm or smaller, approximately 130 nm or smaller, approximately 120 nm or smaller, approximately 110 nm or smaller, approximately 100 nm or smaller, approximately 90 nm or smaller, approximately 80 nm or smaller, approximately 70 nm or smaller, approximately 60 nm or smaller, approximately 50 nm or smaller, approximately 40 nm or smaller, approximately 30 nm or smaller, approximately 20 nm or smaller, or approximately 10 nm or smaller.

[0068] In one embodiment, the aperture may have a size or diameter of about 150 nm to about 450 nm. In one embodiment, the size or diameter of the aperture is selected from 150 nm, 160 nm, 170 nm, 180 nm, 190 nm, 200 nm, 210 nm, 220 nm, 230 nm, 240 nm, 250 nm, 260 nm, 270 nm, 280 nm, 290 nm, 300 nm, 310 nm, 320 nm, 330 nm, 340 nm, 350 nm, 360 nm, 370 nm, 380 nm, 390 nm, 400 nm, 4200 nm, 430 nm, 440 nm, 450 nm, or any combination thereof. In one embodiment, the aperture is a small aperture and has a diameter of 230 nm.

[0069] The term "periodicity" can refer to the recurrence or repetition of holes positioned on a sensor chip at regular intervals. Therefore, the term "periodic" refers to a regular, predefined pattern of holes relative to each other.

[0070] Surface plasmon resonance sensor chips can include a periodic array of apertures. Regular periodicity allows for tight control over the resonant wavelength and the transmission of evanescent waves. In one embodiment, the apertures have a periodicity of approximately 250 nm to approximately 650 nm. In one embodiment, the aperture has 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 both. In one embodiment, the aperture has a periodicity of 450nm.

[0071] 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 recognized molecule.

[0072] In one embodiment, a conductive layer and a film support layer are disposed on a substrate having voids formed therein in regions adjacent to a plurality of holes, such that surface plasmon resonance can be generated by irradiating the conductive layer and / or the film support layer in any direction.

[0073] In one embodiment, the sensor chip includes a first recognition molecule immobilized on the surface of a conductive layer. The first recognition molecule can be immobilized on the surface using techniques known in the art. For example, the first recognition molecule can be adsorbed onto the surface. Alternatively, the surface can be coated with a layer of streptavidin or avidin before immobilizing the first recognition molecule. The first recognition molecule can be biotinylated and immobilized on the surface via streptavidin-biotin conjugation. In one embodiment, the surface can be incubated with polyethylene glycol (PEG) molecules. The surface can be incubated with active (carboxylated) thiol-PEG. The surface can then be activated and conjugated with the first recognition molecule by crosslinking with carbodiimide in an excess NHS / EDC mixture dissolved in MES buffer. In an alternative embodiment, the surface can 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 binding. The surface can then be activated by crosslinking with carbodiimide in an excess of NHS / EDC mixture dissolved in MES buffer and conjugated to the first recognition molecule.

[0074] The term "recognition molecule" can refer to a molecule that can specifically bind to an analyte. Recognition molecules can be antibodies, nucleic acids, peptides, aptamers, small molecules, or other synthetic reagents.

[0075] The term "analyte" refers to a substance present in a sample to be detected or measured on a sensor chip. An analyte can include cells, viruses, nucleic acids, lipids, proteins, peptides, glycopeptides, nanovesicles, microvesicles, exosomes, extracellular vesicles, sugars, metabolites, or combinations thereof, or a tissue state. An analyte can be, for example, a peptide or nucleic acid (e.g., miRNA) biomarker that is bound to or associated with an exosome. For example, an analyte can also be a complex between a cell and a protein or a protein and a nucleic acid.

[0076] In one embodiment, the first recognition molecule is an antibody or a fragment thereof. For example, the antibody may be an antibody that recognizes, for instance, a pan-exosomal marker or a marker associated with or bound to exosomes. 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 cell origin-specific markers such as CHL1, L1CAM, or NCAM. The antibody may also recognize biomarkers associated with or bound to exosomes. For example, the antibody may be an anti-Aβ antibody that recognizes Aβ, or an antibody that recognizes APP, α-syn, or Tau that binds to or is associated with exosomes.

[0077] As used herein, the term "antibody" includes, but is not limited to, synthetic antibodies, monoclonal antibodies, recombinant 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-idiotype (anti-Id) antibodies, as well as epitope-binding fragments of any of the above antibodies. The antibodies described herein may be monospecific, bispecific, trispecific, or have higher multispecificity. Multispecific antibodies may be specific to different epitopes of a peptide, or specific to both the peptide and a heterologous epitope, such as a heterologous peptide or a solid support material.

[0078] The terms "protein" and "peptide" are used interchangeably and refer to any polymer of amino acids (dipeptides or polypeptides) linked by peptide bonds or modified peptide bonds. Polypeptides with fewer than about 10-20 amino acid residues are generally referred to as "peptides". The polypeptides of this invention may contain non-peptide components, such as carbohydrate groups. Carbohydrates and other non-peptide substituents may be added to the polypeptide by the cell that produces the polypeptide and will vary depending on the cell type. Polypeptides are defined herein according to their amino acid backbone structure; substituents such as carbohydrate groups are generally not specified but may still be present.

[0079] As described herein, "nucleic acid" can be RNA or DNA, and can be single-stranded or double-stranded. It can be, for example, nucleic acids encoding proteins of interest, polynucleotides, oligonucleotides, nucleic acid analogs such as peptide-nucleic acid (PNA), pseudo-complementary PNA (pc-PNA), locked nucleic acid (LNA), etc. Such nucleic acid sequences include, for example, but not limited to, nucleic acid sequences encoding proteins, such as those acting as transcriptional repressors, antisense molecules, ribozymes, and small repressive nucleic acid sequences, such as, but not limited to, RNAi, shRNAi, siRNA, microRNAi (mRNAi), antisense oligonucleotides, etc.

[0080] As used herein, "nanovesicle" can refer to a naturally occurring or synthetic vesicle that includes an internal cavity. Nanovesicles may include a lipid bilayer surrounding 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, air sacs, matrix vesicles, or multivesicle bodies. Nanovesicles may have a size of approximately 1000 nm or smaller, approximately 900 nm or smaller, approximately 800 nm or smaller, approximately 700 nm or smaller, approximately 600 nm or smaller, approximately 500 nm or smaller, approximately 450 nm or smaller, approximately 400 nm or smaller, approximately 350 nm or smaller, approximately 300 nm or smaller, approximately 250 nm or smaller, approximately 240 nm or smaller, approximately 230 nm or smaller, approximately 220 nm or smaller, approximately 210 nm or smaller, approximately 200 nm or smaller, or approximately 190 nm or smaller. Small, approximately 180nm or smaller, approximately 170nm or smaller, approximately 160nm or smaller, approximately 150nm or smaller, approximately 140nm or smaller, approximately 130nm or smaller, approximately 120nm or smaller, approximately 110nm or smaller, approximately 100nm or smaller, approximately 90nm or smaller, approximately 80nm or smaller, approximately 70nm or smaller, approximately 60nm or smaller, approximately 50nm or smaller, approximately 40nm or smaller, approximately 30nm or smaller, approximately 20nm or smaller, or approximately 10nm or smaller.

[0081] Exosomes are nanovesicles, also known in the art as extracellular vesicles, microvesicles, or microparticles. These vesicles detach from eukaryotic cells or fall from the plasma membrane to the extracellular space. These membrane vesicles vary in size, ranging from about 10 nm to about 5000 nm in diameter. Small vesicles (about 10 to 1000 nm in diameter, preferably 30 to 100 nm) released via exocytosis of intracellular multivesicular bodies are referred to in the art as "exosomes". The methods and compositions described herein are equally applicable to other vesicles of all sizes.

[0082] The term "sample" refers to any sample in which an analyte or the analyte being tested is present. Such samples include those derived from or containing cells, organisms (bacteria, viruses), lysed cells or organisms, cell extracts, nuclear extracts, components of cells or organisms, extracellular fluid, culture media from which cells or organisms are cultured in vitro, blood, plasma, serum, gastrointestinal secretions, urine, ascites, homogenates of tissues or tumors, synovial fluid, feces, saliva, sputum, cystic fluid, amniotic fluid, cerebrospinal fluid, peritoneal fluid, bronchoalveolar lavage fluid, semen, lymph, tears, pleural effusion, nipple aspiration, breast milk, skin secretions, respiratory secretions, intestinal secretions, and urogenital secretions, as well as prostatic fluid. Samples can be viral or bacterial samples, samples obtained from environmental sources such as sewage, air, or soil samples, and samples from the food industry. A sample can be a biological sample, meaning it originates from or is obtained from a living organism. An organism can be in vivo (e.g., a whole organism) or in vitro (e.g., cells or organs grown in a culture). A “biosample” also refers to cells or cell populations or a quantity of tissue or fluid derived from an individual. In most cases, the sample has been removed from the individual, but the term “biosample” can also refer to cells or tissue analyzed in vivo, i.e., not removed from the individual. Typically, a “biosample” will contain cells from the individual, but the term can also refer to non-cellular biological material, such as non-cellular portions of blood, saliva, or urine. Biosamples can originate from resection of primary, secondary, or metastatic tumors, bronchoscopic biopsy, or core needle biopsy, or from cell masses from pleural effusion. Fine-needle aspiration of biosamples is also useful. In one embodiment, the biosample is primary ascites cells. Biosamples also include explants derived from patient tissue and primary and / or transformed cell cultures. Biosamples can be provided by removing cell samples from an individual, but can also be provided 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 outcomes, may also be used. Biological samples include, but are not limited to, tissue biopsies, abrasions (e.g., cheek abrasions), whole blood, plasma, serum, urine, saliva, cell cultures, or cerebrospinal fluid. Samples analyzed by the compositions and methods described herein may have been treated to purify or enrich the exosomes contained therein. In one embodiment, the sample is blood.

[0083] In one aspect, an imaging system is provided that includes 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.

[0084] In one aspect, a kit comprising a sensor chip as defined herein is also provided. The kit may also include a second recognition molecule specific to the captured analyte or an analyte associated with a captured analyte on the surface of the sensor chip. The kit may contain one or more second recognition molecules, each specific to one or more analytes, thereby enabling the detection of one or more analytes.

[0085] The second recognition molecule can allow for 1) signal amplification, 2) colocalization analysis (e.g., detection of different targets found simultaneously in the same vesicle), and 3) differentiation of analyte subgroups based on molecular and tissue differences.

[0086] The second recognition molecule can be coupled to the signal amplification portion, wherein the signal amplification portion is capable of inducing the formation of insoluble aggregates with increased optical density relative to the captured analyte and increasing the measured surface plasmon resonance signal. For example, the kit may contain a second recognition molecule as an antibody (e.g., an antibody specific to Aβ42). The second recognition molecule can be conjugated to horseradish peroxidase. The kit may also contain an enzyme substrate. Thus, horseradish peroxidase is capable of inducing the formation of insoluble aggregates with increased optical density relative to the analyte captured on the sensor chip surface.

[0087] The term "signal amplifying molecule" can refer to a molecule capable of inducing the formation of insoluble aggregates on the surface of a sensor chip, thereby increasing the optical density relative to the captured analyte. When a second recognition molecule binds to the analyte on the sensor chip surface, this results in a larger change in the transmission wavelength (spectral shift) or a change in transmission intensity, thus contributing to improved sensitivity of the sensor chip. An "signal amplifying molecule" can be, for example, an enzyme such as horseradish peroxidase, which reacts with its substrate to form insoluble aggregates on the sensor chip surface. A "signal amplifying molecule" can also be a second antibody that binds to a second recognition molecule on the sensor chip surface and forms aggregates. The second antibody can be further conjugated to an enzyme, gold particles, or a macromolecule that helps form larger aggregates to increase optical density.

[0088] In one embodiment, the present invention relates to a highly sensitive analytical platform, amplified plasma exosome (APEX), for the direct detection of exosome-bound amyloid β (Aβ) from blood samples of Alzheimer's disease (AD) patients. The analytical method can utilize transmission surface plasmon resonance (SPR) and in-situ enzymatic conversion of optical products to achieve multiple population analysis. APEX technology enables multiparameter in-situ analysis of exosome contents (e.g., proteins and miRNAs). The APEX platform can be used to measure different circulating Aβ populations (exosome-bound, unbound, and total) and different tissue states of circulating Aβ, and correlate these blood measurements with PET imaging of cerebral amyloid plaque burden.

[0089] In one aspect, a method for manufacturing a sensor chip is provided, the method comprising the following steps:

[0090] a) Provide a top membrane support layer;

[0091] b) Deposit a conductive layer on the top film support layer;

[0092] c) Forming a plurality of pores extending through the membrane support layer, the pores 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.

[0093] In some implementations, the method further includes:

[0094] A top support layer and a bottom support layer are coated on the upper and lower surfaces of a silicon substrate;

[0095] A layer of photoresist is provided on the top film support layer, and

[0096] Multiple holes are defined in the photoresist using deep ultraviolet lithography (DUV), and the pattern of the multiple holes is transferred to the top film support layer by reactive ion etching (RIE).

[0097] In some implementations, the method further includes the following steps:

[0098] Remove the photoresist on the top film support layer and coat the surface of the top film support layer with a silicon dioxide protective layer;

[0099] A layer of photoresist is coated on the bottom film support layer;

[0100] The sensing area is defined in the photoresist by photolithography; and the pattern of the sensing area is transferred to the base film support layer by reactive ion etching (RIE).

[0101] The pattern of the sensing area is transferred to the silicon substrate;

[0102] The protective layer on the surface of the top film support layer is removed with diluted hydrogen fluoride; and a conductive layer is deposited on the top film support layer.

[0103] As used herein, the term “sensing region” refers to a region in a sensor chip that includes multiple holes arranged such that irradiation of the plasma layer and / or film support layer produces surface plasmon resonance.

[0104] As used herein, the term "resist" refers to a thin layer used to transfer an image or pattern onto a substrate on which it is deposited. Resist can be patterned using photolithography to form a (sub)micron-scale temporary mask that protects selected areas of the underlying substrate during subsequent processing steps, typically etching. The material used to prepare the thin layer (typically a viscous solution) is also included in the term resist. Resist is typically a mixture of polymers or their precursors and other small molecules (e.g., photoacid generators) specifically formulated for a particular photolithography technique. For example, the resist used during photolithography is called a "photoresist." The resist used in electron beam lithography is called an "electron beam resist."

[0105] In one aspect, a method for detecting an analyte in a sample is provided, the method comprising:

[0106] a) Capturing the analyte onto the surface of the sensor chip as defined herein; and

[0107] b) Detect the binding of a second recognition molecule to the analyte trapped on the surface of the sensor chip, wherein the second recognition molecule is specific to the analyte, and an increase in the binding of the second recognition molecule compared to a control sample indicates the presence of the analyte in the sample.

[0108] This method may include detecting the binding of one or more second recognition molecules (sequentially or simultaneously) that are specific to one or more analytes. This allows for the detection, quantification, and analysis of the tissue state (e.g., co-localization) of multiple analytes or biomarkers in a sample. Each analyte can be recognized by different sets of first and second recognition molecules. The first and second recognition molecules may recognize the same analyte or different analytes, respectively. Different combinations of first and second recognition molecules can allow for the detection of analyte co-localization. This allows for the simultaneous detection of multiple analytes and can also allow for the detection of the co-localization of these molecules.

[0109] In one embodiment, the first recognition molecule is an antibody that recognizes Aβ42 and the second recognition molecule is another antibody that recognizes Aβ42.

[0110] In one implementation, the first recognition molecule is an antibody that recognizes Aβ42, the second recognition molecule is an antibody that recognizes CD63, and the co-localization of Aβ42 and CD63 is detected.

[0111] The "binding" of the second recognition molecule to the analyte trapped on the surface of the sensor chip can be detected by a spectral shift (a change in transmission wavelength) or a change in transmission intensity at a fixed wavelength. For example, the analyte trapped on the surface of the sensor chip will have an initial reference wavelength. When binding to the second recognition molecule, the transmission wavelength can be shifted to a longer wavelength.

[0112] Changes in the transmission resonance wavelength (or spectral shift (Δλ)) or the transmission intensity at a fixed wavelength in a sample can be compared with changes observed in a control sample. This can be used, for example, to determine whether the binding of a second recognition molecule to the captured analyte has increased.

[0113] Compared to the control sample, the "increased binding of the second recognition molecule" in the sample can be determined by comparing the changes in spectral shift or transmission intensity at a fixed wavelength between the sample and the control sample when the second recognition molecule is bound. An increase in spectral shift or transmission intensity indicates increased binding of the second recognition molecule to the analyte.

[0114] In one implementation, an increase in spectral shift or transmission intensity can refer to an increase of 1.2 times or more between an individual and a control. The term can also refer to values ​​selected from 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, and 46 times. The increases were 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, 61 times, 62 times, 63 times, 64 times, 65 times, 66 times, 67 times, 68 times, 69 times, 70 times, 71 times, 72 times, 73 times, 74 times, 75 times, 76 times, 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.

[0115] The second recognition molecule can be an analyte-specific recognition molecule. The second recognition molecule can be coupled to the signal amplification section, wherein the signal amplification section is capable of inducing the formation of insoluble aggregates with increased optical density relative to the captured analyte. For example, the analyte captured on the surface of a sensor chip will have an initial reference wavelength. When the second recognition molecule is combined, the transmission wavelength can shift to a longer wavelength. When the second recognition molecule is coupled to the signal amplification section, the transmission wavelength can shift to even longer wavelengths due to the increased optical density.

[0116] The second recognition molecule can be coupled to the signal amplification section. Alternatively, the second recognition molecule can be coupled to the signal amplification section.

[0117] The signal amplification portion can be an enzyme. In one embodiment, the signal amplification portion is an enzyme. This enzyme can be horseradish peroxidase (HRP), alkaline phosphatase, glucose oxidase, β-lactamase, or β-galactosidase, or an enzymatic fragment thereof. In one embodiment, the enzyme is horseradish peroxidase. In one embodiment, a first biorecognition molecule is fused to the signal amplification portion. For example, the first biorecognition molecule can be an antibody covalently fused to horseradish peroxidase, the horseradish peroxidase being covalently linked to the antibody using techniques well known in the art.

[0118] This method may also involve contacting the enzyme with an enzyme substrate. The enzyme substrate can be a substrate that forms an insoluble product in the presence of the enzyme or under enzymatic catalysis. For horseradish peroxidase (HRP), preparations such as 3-amino-9-ethylcarbazole, 3,3',5,5'-tetramethylbenzidine, or chloronaphthol, 4-chloro-1-naphthol can be used. These substrates can be converted into insoluble products during the enzymatic reaction of HRP.

[0119] In one embodiment, the enzyme substrate is 3,3'-diaminobenzidine tetrahydrochloride.

[0120] In another embodiment, the signal amplification portion can be a second antibody capable of binding to the second recognition molecule. The binding of the second antibody to the second recognition molecule can induce the formation of insoluble aggregates.

[0121] In one embodiment, a first recognition molecule is immobilized on the surface of a surface plasmon resonance sensor chip, wherein the first recognition molecule is capable of capturing analytes on the surface of the sensor chip. The analyte may be an exosome-binding or exosome-associated biomarker. The analyte may be an exosome-binding aggregation biomarker. The first recognition molecule may be specific to the analyte.

[0122] In one implementation, the first recognition molecule is an antibody. For example, the antibody may be an antibody that recognizes a pan-exosomal marker or a marker associated with or bound to exosomes. For instance, the antibody may be specific to CD63, CD9, or CD81, which are abundant and characteristic in exosomes. The antibody may also be specific to cell-derived specific markers such as CHL1, L1CAM, or NCAM. The antibody may also recognize biomarkers associated with or bound to exosomes. For example, the antibody may be an anti-Aβ antibody that recognizes Aβ, or an antibody that recognizes APP, α-syn, or Tau that binds to or is associated with exosomes.

[0123] The term "control sample" refers to a sample that does not contain the analyte. A "control sample" can be used to compare with a sample to determine whether the sample contains the analyte of interest.

[0124] As used herein, the term "biomarker" should be understood as a reagent or entity whose presence or level is associated with an event of interest. Biomarkers can be cells, proteins, nucleic acids, peptides, glycopeptides, exosomes, or combinations thereof. For example, a biomarker is Aβ42 or Tau peptide, the presence or level of which indicates whether an individual has neurodegenerative disease or amyloidosis or is at risk of developing neurodegenerative disease or amyloidosis. In another embodiment, the biomarker is exosome-bound Aβ42 or Tau peptide, the presence or level of which indicates whether an individual has neurodegenerative disease or amyloidosis or is at risk of developing neurodegenerative disease or amyloidosis. In some embodiments, the biomarker is an exosome-associated biomarker.

[0125] In one implementation, a sensor chip as defined herein is provided for use in detecting analytes.

[0126] In one aspect, a method for detecting neurodegenerative diseases or amyloidosis in an individual is provided, the method comprising:

[0127] a) Contact the sample with the surface of the sensor chip as defined herein; and

[0128] b) Detect the binding of the second recognition molecule to the analyte trapped on the surface of the sensor chip;

[0129] The second recognition molecule is specific to the analyte, and increased binding of the second recognition molecule compared to the control individual indicates that the individual has a neurodegenerative disease or amyloidosis.

[0130] The term “individual” refers to any animal, including any vertebrate or mammal, especially a human, and may also be referred to as, for example, an individual or a patient.

[0131] The term "control individual" refers to an individual who is known not to have neurodegenerative diseases or amyloidosis, or who is not at risk of developing neurodegenerative diseases or amyloidosis. A "control individual" can also be a healthy individual. A "control individual" can be an individual without cognitive impairment (NCI). This term includes samples obtained from control individuals.

[0132] In one embodiment, the biomarker is an exosome-binding or exosome-associated biomarker. In one embodiment, the biomarker is an exosome-binding aggregation biomarker. The biomarker may be selected from, but is not limited to, Aβ, APP, α-Syn, Tau, APOE, SOD1, TDP-43, bassoon, and fibronectin. In one embodiment, Aβ is Aβ42. In another embodiment, Aβ is Aβ40. In some embodiments, the molecular subtype of Aβ is Aβ42, Aβ40, Aβ39, or Aβ38. In one embodiment, the biomarker is Tau. In some embodiments, the biomarker is an exosome biomarker selected from CD63, CD9, CD81, ALIX, TSG101, Flotilin-1, Flotilin-2, LAMP-1, HSP70, HSP90, RNA, and DNA.

[0133] Neurodegenerative diseases can 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 Tau proteinosis.

[0134] The method may also include treating individuals found to have neurodegenerative diseases or amyloidosis.

[0135] As used herein, the term “treatment” can mean (1) preventing or delaying the onset of one or more symptoms of a disease; (2) suppressing the development of a disease or one or more symptoms of a disease; (3) alleviating a disease, i.e., the disappearance of at least one or more symptoms of a disease; and / or (4) reducing the severity of one or more symptoms of a disease.

[0136] In one implementation, the term "treatment" refers to the administration of a drug to slow the progression of neurodegenerative diseases or amyloidosis.

[0137] This article provides a method for detecting neurodegenerative diseases or amyloidosis in an individual, the method comprising detecting the level of exosome-bound biomarkers in a sample obtained from the individual, wherein an increase in the level of exosome-bound biomarkers compared to a reference indicates that the individual has neurodegenerative diseases or amyloidosis.

[0138] In one aspect, a method for detecting neurodegenerative diseases or amyloidosis in an individual is provided, the method comprising detecting the level of an exosome-bound aggregated biomarker in a sample obtained from the individual, wherein an increase in the level of the exosome-bound aggregated biomarker compared to a reference indicates that the individual has neurodegenerative diseases or amyloidosis.

[0139] This method may include detecting biomarkers that bind to one or more exosomes in a sample. This allows for the detection of the co-localization or presence of multiple biomarkers on the same exosome.

[0140] Biomarkers may be selected from, but are not limited to, Aβ, APP, α-Syn, Tau, APOE, SOD1, TDP-43, bassoon, and / or fibronectin.

[0141] This method may include detecting the level of molecular subtypes of exosome-bound biomarkers.

[0142] In one implementation, Aβ is Aβ42 or Aβ40. In some implementations, Aβ is Aβ42, Aβ40, Aβ39, or Aβ38.

[0143] In one embodiment, Aβ is a prefibrillary aggregate. Prefibrillary Aβ aggregates have been found to preferentially bind to exosomes. In one embodiment, the method defined herein includes detecting exosomes that bind to prefibrillary Aβ aggregates.

[0144] In one implementation, the biomarker for aggregation is a prefibrillary aggregate. The biomarker for aggregation may be a prefibrillary aggregate of Aβ. Alternatively, the biomarker for aggregation may be a prefibrillary aggregate of APP, α-Syn, or Tau.

[0145] In one implementation scheme, the reference is a control individual.

[0146] In one embodiment, an alternative method for measuring exosome binding to determine the prefibrillary tissue state of protein aggregates is provided, wherein an increase in the level of protein aggregates in the prefibrillary tissue state, compared to a control, indicates that an individual has a neurodegenerative disease or amyloidosis.

[0147] In one embodiment, the method further includes detecting exosomal biomarkers selected from CD63, CD9, CD81, ALIX, TSG101, Flotilin-1, Flotilin-2, LAMP-1, HSP70, HSP90, RNA, and DNA, wherein the exosomal biomarkers colocalize with exosome-bound biomarkers.

[0148] In one embodiment, the method further includes detecting neuronal biomarkers selected from NCAM, L1CAM, CHL-1, and IRS-1, wherein the neuronal biomarkers colocalize with exosome-bound biomarkers.

[0149] In one embodiment, a method is provided for measuring different tissue and molecular subpopulations of biomarkers. For example, the method may include measuring exosome-bound biomarkers, free (unbound) biomarkers, and total (both exosome-bound and unbound) biomarkers. The method may also include measuring the relative concentrations of different biomarkers to better predict disease.

[0150] In one implementation, 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 Tau proteinosis.

[0151] In one implementation, the neurodegenerative disease is selected from Alzheimer's disease and mild cognitive impairment.

[0152] This method may also include treating individuals with neurodegenerative diseases or amyloidosis.

[0153] This method can be further correlated with brain imaging studies, such as PET imaging. This includes correlation with imaging of specific brain regions. In one embodiment, a method is provided for identifying and measuring circulating biomarkers associated with brain imaging (PET). In another embodiment, a method is provided for identifying and measuring circulating biomarkers associated with imaging of specific brain regions (PET).

[0154] This invention is based on the following findings: 1) Biomarkers (including different co-localized biomarkers) can be used to measure and characterize different molecular, biophysical, and tissue subpopulations of circulating Aβ; 2) Aβ bound to circulating exosomes in the blood can be closely correlated with PET imaging of Aβ deposition (overall average) in different patient populations; 3) Aβ bound to circulating exosomes in the blood can be closely correlated with PET imaging of Aβ (early AD area, cingulate gyrus area) in different patient populations; 4) Aβ bound to circulating exosomes can distinguish clinical subgroups (such as Alzheimer's disease, mild cognitive impairment, no cognitive impairment, vascular dementia, vascular mild cognitive impairment, and acute stroke).

[0155] This method may include administering a therapeutically effective amount of a drug to an individual in need of treatment. For example, the drug may be 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, such as a combination of donepezil and memantine. The drug may be a BACE1 inhibitor, such as AZD3293, or an antibody, such as an anti-amyloid antibody, such as aducanumab. 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 drugs described herein, or a combination of two or more of the drugs described herein, may be administered to the individual in need of treatment.

[0156] In some embodiments, molecules that effectively reduce the amount of amyloid aggregates may be potential candidates for treatment. Therefore, in some embodiments, the method may include administering to an individual in need of treatment one or more of the following molecules (medications) or a combination of two or more of the following molecules (medications): methyl thiocyanate, colorless methyl thiocyanate, bis(hydromethanesulfonate), curcumin, acid fuchsin, epigallocatechin gallate, saffron aldehyde, Congo red, apigenin, sky blue C, basic blue 41, (trans, trans)-1-bromo-2,5-bis-(3-hydroxycarbonyl-4-hydroxy)styrene (BSB), Chicago sky blue 6B, cyclodextrin, donomycin hydrochloride, dimethyl yellow, direct red 80, 2,2-dihydroxybenzophenone, Hexadecyltrimethylammonium bromide (C16), heme chloride, heme, indomethacin, jujube, resorcinol blue, methylchloroflavone sulfosalicylate, melatonin, myricetin, 1,2-naphthoquinone, nordihydroguaiaric acid, R()-normorphine hydrobromide, orange G, o-vanillin (2-hydroxy-3-methoxybenzaldehyde), phenazine, phthalocyanine, rifamycin SV, phenol red, hydropyridine tetracycline, quinacrine mustard dihydrochloride, thioflavin S, ThT and trimethyl(tetradecyl)ammonium bromide (C17), diallyl tartrate, eosin Y, fenofibrate, new copper base, nystatin, octadecyl sulfate and rhodamine B.

[0157] An increase in the level of exosome-bound biomarkers can be defined as an increase of 1.2-fold or more between the individual and the control. The term "increased level" can also refer to an increase selected from the following groups: 1.1-fold, 1.3-fold, 1.4-fold, 1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold, 1.9-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 11-fold, 12-fold, 13-fold, 14-fold, 15-fold, 16-fold, 17-fold, 18-fold, 19-fold, 20-fold, 21-fold, 22-fold, 23-fold, 24-fold, 25-fold, 26-fold, 27-fold, 28-fold, 29-fold, 30-fold, 31-fold, 32-fold, 33-fold, 34-fold, 35-fold, 36-fold, 37-fold, 38-fold, 39-fold, 40-fold, 41-fold, 42-fold, 43-fold, 44-fold. 45x, 46x, 47x, 48x, 49x, 50x, 51x, 52x, 53x, 54x, 55x, 56x, 57x, 58x, 59x, 60x, 61x, 62x, 63x, 64x, 65x, 66x, 67x, 68x, 69x, 70x, 71x, 72x, 73x, 74x, 75x, 76x, 77x, 78x, 79x, 80x, 81x, 82x, 83x, 84x, 85x, 86x, 87x, 88x, 89x, 90x, 91x, 92x, 93x, 94x, 95x, 96x, 97x, 98x, 99x, 100x.

[0158] In one aspect, a method is provided for detecting individuals at risk of developing a neurodegenerative disease, the method comprising detecting the level of an exosome-bound aggregated biomarker in a sample obtained from the individual, wherein an increase in the level of the exosome-bound aggregated biomarker compared to a reference indicates that the individual has a neurodegenerative disease.

[0159] In one aspect, a method for detecting and treating an individual's neurodegenerative disease or amyloidosis is provided, the method comprising:

[0160] a) Detect the levels of exosome-bound aggregation biomarkers in samples obtained from individuals, where an increase in the level of exosome-bound aggregation biomarkers compared to a reference indicates that the individual has neurodegenerative diseases or amyloidosis; and

[0161] b) Treat individuals with neurodegenerative diseases or amyloidosis.

[0162] In one aspect, a method for treating an individual's neurodegenerative disease or amyloidosis is provided, the method comprising:

[0163] a) Detect the levels of exosome-bound aggregation biomarkers in samples obtained from individuals, where an increase in the level of exosome-bound aggregation biomarkers compared to a reference indicates that the individual has neurodegenerative diseases or amyloidosis; and

[0164] b) Treat individuals with neurodegenerative diseases or amyloidosis.

[0165] In one embodiment, a method is provided for detecting and slowing the progression of an individual's neurodegenerative disease or amyloidosis, the method comprising:

[0166] a) Detect the levels of exosome-bound aggregation biomarkers in samples obtained from individuals, where an increase in the level of exosome-bound aggregation biomarkers compared to a reference indicates that the individual has neurodegenerative diseases or amyloidosis; and

[0167] b) Treat individuals with neurodegenerative diseases or amyloidosis.

[0168] In one aspect, a method is provided for determining the aggregation state of biomarkers in a sample, the method comprising detecting the level of exosome-bound biomarkers in the sample, wherein an increase in the level of exosome-bound biomarkers compared to a reference indicates the degree of biomarker aggregation.

[0169] This method may include a step of contacting the sample with a population of exosomes prior to the step of detecting the level of exosome-bound biomarkers.

[0170] In one implementation, biomarkers that aggregate in the sample preferentially bind to exosomes compared to non-aggregated biomarkers.

[0171] Samples can be obtained from individuals.

[0172] In one implementation, an increased degree of clustering of biomarkers compared to a reference indicates that an individual has a neurodegenerative disease or amyloidosis.

[0173] This method can further include treating individuals with neurodegenerative diseases or amyloidosis.

[0174] In one embodiment, a method is provided for determining the aggregation state of a biomarker in a sample, the method comprising contacting the sample with a population of exosomes and detecting the level of exosome-bound biomarkers in the sample, wherein an increase in the level of exosome-bound biomarkers compared to a reference indicates the degree of biomarker aggregation.

[0175] Those skilled in the art will understand that, in addition to those specifically described, the invention described herein is susceptible to variations and modifications. It should be understood that the invention includes all such variations and modifications falling within its spirit and scope. The invention also includes all steps, features, combinations, and compounds individually or collectively mentioned or indicated in this specification, as well as any and all combinations of any two or more of said steps or features.

[0176] In this specification and the following claims, unless the context otherwise requires, the word “comprising” and variations such as “including” and “covering” shall be understood to imply inclusion of the stated integer or step or group of integers or steps, but not to exclude any other integer or step or group of integers or steps.

[0177] References to any prior publications (or information derived therefrom) or any known matters in this specification are not to be construed as, nor should be construed as, an endorsement or permission or any form of implication, which constitute part of the general knowledge in the field of effort covered by this specification.

[0178] Some embodiments of the 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.

[0179] Example

[0180] References to any prior publications (or information derived therefrom) or any known matters in this specification are not to be construed as, nor should be construed as, an endorsement or permission or any form of implication, which constitute part of the general knowledge in the field of effort covered by this specification.

[0181] Materials and methods

[0182] Cell Culture. Human cell lines SH-SY5Y (neurons), HUVEC (umbilical vein endothelial cells), THP-1 (monocytes), PC-3 (prostate epithelium), and SK-OV-3 (ovarian epithelium) were obtained from the American Center for Type Culture Collection. GLI36 (GLI) and SK-OV-3 were grown in Dulbecco modified essential medium (DMEM, Gibco). SH-SY5Y was cultured in Dulbecco 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 media, respectively. All media except EGM-2 supplemented with 5% fetal bovine serum (FBS) were supplemented with 10% FBS and penicillin-streptomycin.

[0183] Exosome isolation and quantification. Cells from passages 1–15 were cultured for 48 hours in vesicle-depleted medium (containing 5% depleted FBS) before vesicle collection. All exosome-containing media were filtered through a 0.2 μm membrane filter (Millipore), separated by differential centrifugation (first at 10,000 g, then at 100,000 g), and used for exosome analysis using the APEX platform. To isolate exosomes from hemocytocytes and platelets, hemocytocytes were derived from blood fractionation, and platelets from platelet-rich plasma. These components were washed in HEPES-buffered saline and incubated at 37 °C with 2 mM calcium chloride and 2 μM calcium ionophore (A23187) to stimulate exosome production. All vesicles were then collected as previously described. For independent quantification of exosome concentration, a nanoparticle tracking analysis (NTA) system (NS300, Nanosight) was used. The exosome concentration was adjusted to obtain approximately 50 vesicles per field of view for optimal counting. To maintain consistency, all NTA measurements were performed using the same system settings.

[0184] APEX sensor fabrication. The APEX sensor is fabricated on an 8-inch silicon (Si) wafer. In short, a 10 nm silicon dioxide (SiO2) layer is prepared by thermal oxidation, and a 145 nm silicon nitride (Si3N4) layer is deposited on the wafer via low-pressure chemical vapor deposition (LPCVD). After photoresist is applied, deep ultraviolet (DUV) lithography is performed to define a nanopore array pattern in the resist. This pattern is transferred onto the Si3N4 film by reactive ion etching (RIE). After removing the photoresist, a thin SiO2 protective layer (100 nm) is deposited on the front side of the wafer using plasma-enhanced chemical vapor deposition (PECVD). For light energy transmission, photoresist is spin-coated on the back side of the wafer; photolithography is used to define the sensing area. The Si3N4 and SiO2 are etched with RIE, then etched with potassium hydroxide (KOH) and tetramethylammonium hydroxide (TMAH). After etching, the protective SiO2 layer is removed using diluted hydrogen fluoride (DHF) (1:100). Finally, Ti / Au (10 nm / 100 nm) was deposited onto the Si3N4 film. All nanopore sizes and sensor uniformity were characterized by scanning electron microscopy (JEOL 6701).

[0185] Channel Assembly. Standard soft lithography was used to fabricate the multi-channel flow cell. SU-8 negative resist (SU8-2025, Microchem) was used to prepare the mold. The photoresist was spin-coated onto the Si wafer at 2000 rpm for 30 seconds and baked at 65°C and 95°C for 2 minutes and 5 minutes, respectively. After UV exposure, the resist was baked again before development with stirring. The developed mold was chemically treated with trichlorosilane vapor in a desiccator for 15 minutes before subsequent use. Polydimethylsiloxane polymer (PDMS) and crosslinking agent were mixed in a 10:1 ratio and cast onto the SU-8 mold. After curing at 65°C for 4 hours, the PDMS layer was cut from the mold and assembled onto the APEX sensor. All inlets and outlets were fabricated using 1.1 mm biopsy perforators for sample handling.

[0186] Optical setup and spectral analysis. A halogen tungsten lamp (Stocker Yale Inc.) was used to illuminate the APEX sensor through a 10X microscope objective. Transmitted light was collected by optical fiber and fed into a spectrometer (Ocean Optics). All measurements were performed at room temperature in a closed chamber to eliminate ambient light interference. Transmitted light intensity was digitally recorded as counts relative to wavelength (330 nm–1600 nm). For spectral analysis, transmission peaks were fitted using a local regression method, and spectral peaks were determined using a custom R program. All fittings were performed locally. That is, for a fit at point x, points near x were used, weighted by their distance from x. This method eliminates variations in results caused by the number of data points and the data range being analyzed, compared to fitting with multi-order polynomial curves. When determining the optimal sensor geometry ( Figure 10 Spectral variations were used to quantify peak transmission intensity, peak shape (full width at half maximum, FWHM), and detection sensitivity in response to refractive index changes. Measured transmission spectra demonstrated homogeneity across different sensors, with sd at the baseline spectral peak position of 0.03 nm. All spectral shifts (Δλ) were determined as variations in the transmission spectral peaks and calculated relative to appropriate control experiments (see below).

[0187] Sensor surface functionalization. To impart molecular specificity to the APEX sensor, the fabricated Au surface was first incubated for 2 hours at room temperature with a mixture of polyethylene glycol (PEG) containing long-active (carboxylated) thiol-PEG and short-active methylated thiol-PEG (Thermo Scientific) (1:3 active: inactive, 10 mM in PBS). After washing, the surface was activated by carbodiimide crosslinking in a mixture of excess NHS / EDC dissolved in MES buffer and conjugated to specific probes and ligands (e.g., antibodies and Aβ42 aggregates). Information on all probes can be found in Table 1. Excess unbound probes were removed by washing with PBS. The conjugated sensor was stored in PBS at 4 °C for subsequent use. Spectroscopic monitoring of all sensor surface modifications was performed to ensure uniform functionalization.

[0188] Table 1. List of markers and probes used in the analysis.

[0189]

[0190]

[0191]

[0192]

[0193] APEX signal amplification. To establish APEX amplification, enzymatic growth of insoluble optical products for signal enhancement was incorporated, and the optical substrate concentration and reaction duration were optimized to establish a platform. Briefly, exosomes were incubated with a CD63-functionalized APEX sensor (BD Biosciences) for 10 min. Bound vesicles were then labeled with a biotinylated anti-CD63 antibody (Ancell, 10 min). As a control, an equal volume of a biotinylated IgG isotope control antibody (Biolegend) was used on bound vesicles to determine amplification efficiency. After washing away unbound antibody, highly sensitive horseradish peroxidase conjugated with neutral avidin (Thermo Scientific) was allowed to react with bound vesicles before introducing different concentrations of 3,3'-diaminobenzidine tetrahydrochloride (Life Technologies) as the optical substrate. Real-time spectral changes were monitored to determine the optimal substrate concentration and reaction duration. Optimized conditions were determined to be 1 mg / mL for 3 min. All flow rates used for incubation and washing were maintained at 3 μl / min and 10 μl / min, respectively. The localized deposition of insoluble optical products was confirmed by scanning electron microscopy. This optimized workflow is as follows: Figure 11 As shown in a.

[0194] Using these conditions, known amounts of exosomes were further titrated and their associated APEX signals were measured. The APEX detection limit was determined to be the lowest target concentration that could produce a detection signal of 3x (sd of the background signal from the control).

[0195] APEX protein assay. All sensor surfaces were blocked with 2% w / v bovine serum albumin (BSA) to reduce nonspecific protein binding. Exosomes were introduced onto the functionalized sensors and incubated at room temperature for 10 min to capture the exosomes, followed by washing with PBS to remove unbound material. For extravesicular protein targets, as described above, exosomes were directly labeled with detection antibodies for APEX amplification. For intravesicular protein targets, exosomes underwent additional immobilization and permeabilization (eBioscience) before labeling with detection antibodies. Spectroscopic measurements were performed before and after APEX amplification and analyzed using a custom-designed R program.

[0196] APEX miRNA detection. The APEX sensor was functionalized with p19 protein (New England Biolabs) via its chitin-binding domain and blocked with 2% w / v BSA. For miRNA detection, exosome lysates were incubated with biotinylated RNA probes (350 nM) for 15 min to hybridize with the target miRNA strand. The mixture was introduced onto the functionalized sensor in binding buffer (1x p19 binding buffer, pH 7.0, 40 U RNase inhibitor, 0.1 mg / mL BSA) to achieve p19 capture of the hybridized miRNA target / RNA probe duplex. Highly sensitive horseradish peroxidase conjugated with neutral avidin (Thermo Scientific) was introduced into the bound biotinylated duplex for APEX amplification. Spectroscopic measurements were performed and analyzed using a custom-designed R program.

[0197] Enzyme-linked immunosorbent assay (ELISA). Capture antibody (5 μg / ml) was adsorbed onto the ELISA plate (Thermo Scientific) and blocked with Superblock (Thermo Scientific) before incubation with the sample. After washing with PBST (PBS containing 0.05% Tween 20), detection antibody (2 μg / ml) was added and incubated at room temperature for 2 hours. After incubation with a secondary antibody (Thermo Scientific) conjugated with horseradish peroxidase and a chemiluminescent substrate (Thermo Scientific), the chemiluminescence intensity was measured for protein detection (Tecan).

[0198] Western blotting. Exosomes separated by ultracentrifugation were lysed in radioimmunoprecipitation assay (RIPA) buffer containing protease inhibitors (ThermoScientific) and quantified using a dicaprylic 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 targeting 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 the neuronal marker NCAM (R&D Systems). Enhanced chemiluminescence was used for immunoassay after incubation with a secondary antibody conjugated to horseradish peroxidase (CellSignaling) (Thermo Scientific).

[0199] Protein aggregation. Lyophilized NH4OH-treated Aβ42 protein (rPeptide) was resuspended in NaOH (60 mM, 4 °C), sonicated, and the pH adjusted to 7.4 in PBS34. The protein was immediately filtered through a 0.2 μm Millipore membrane filter, and the filtrate was used as smaller Aβ42 aggregates. To prepare larger Aβ42 aggregates, the protein was treated as described above and incubated with stirring for 1 hour to induce further aggregation, followed by filtration through a 0.2 μm Millipore membrane filter. The filtrate was used as large Aβ aggregates. To prepare BSA aggregates of similar size as a control, 2% w / v BSA was dissolved in PBS and heated at 80 °C for 1 hour and 2 hours to induce small and large control aggregates, respectively.

[0200] Aggregate particle size determination. The hydrodynamic diameters of Aβ42 and BSA aggregates were determined by dynamic light scattering analysis (Zetasizer Nano ZSP, Malvern). Three x 14 measurements were performed at 4 °C. Z-mean diameter and polydispersity were analyzed. For each measurement, the autocorrelation function and polydispersity index were monitored to ensure the quality of the samples used for size determination.

[0201] Characterization of exosome-Aβ binding. As previously described, the prepared protein aggregates (Aβ42 and BSA controls) were used for surface functionalization of the APEX sensor via EDC / NHS coupling. 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 total exosome-bound protein surface area (details below) in order to normalize the binding affinity. After surface functionalization with protein aggregates, exosomes (10... 10 A sample ( / ml) 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 sensing map. Exosome binding kinetics and binding affinity of protein aggregates of different sizes were determined.

[0202] To illustrate the differences in protein aggregate size and the SPR-related exponential decay in sensitivity (with increasing distance from the sensing surface), the following equation is used.

[0203]

[0204] To calculate the total surface area of ​​the 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, which is a constant in this case, l d is the attenuation length, which is set to 200nm in the current sensor design, and r is the radius of the bound protein aggregate.

[0205] All protein aggregates approximate spheres, as shown in transmission electron microscopy (TEM images). Figure 3b The equations (left and right) support the data, and their r values ​​are determined by dynamic light scattering analysis. The above equations are used to determine the number of protein aggregates bound to the sensor and their respective total surface areas to estimate the number of available binding sites for interaction with exosomes. All exosome binding data (Δλ) are normalized relative to their respective protein binding sites. Normalized Aβ42 binding data are performed relative to a similarly sized BSA control and fitted to determine the binding affinity constant KD.

[0206] Scanning electron microscopy. All samples were fixed with half-strength Kanovsky 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 subsequently coated with gold (Leica) before imaging with a scanning electron microscope (JEOL 6701).

[0207] Transmission electron microscopy. Exosomes were immunolabeled with gold nanoparticles (15 nm, Ted Pella), fixed with 2% paraformaldehyde, and transferred onto a copper grid (Ted Pella). The bound vesicles were washed and contrast-stained with a mixture of uranium oxalate and methylcellulose. The dried samples were imaged using a transmission electron microscope (JEOL 2200FS).

[0208] Clinical Sample Collection. This study was approved by the Institutional Review Boards of NUH and NUS (2015 / 00441, 2015 / 00406, and 2016 / 01201). All individuals were recruited with informed consent in accordance with the protocols approved by the Institutional Review Boards. All recruited individuals underwent several neuropsychological assessments at the National University Hospital (NUH, Singapore), including the Mini Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCa), and the Vascular Dementia Database (VDB) cognitive assessment. Clinical diagnoses of AD, MCI, or NCI were derived through neuropsychological assessments combined with evaluations of clinical characteristics and blood surveys. Clinical diagnoses of VaD and VMCI were derived through a combination of neuropsychological assessments, clinical history of stroke, and the extent of cerebrovascular disease observed by magnetic resonance imaging (MRI). All clinical assessments and classifications were performed according to published criteria 46–48 and were independent of APEX measurements. Acute stroke plasma samples were collected from patients diagnosed with stroke within 24 hours of admission. Longitudinal plasma samples were collected from patients during a one-year follow-up period without PET brain imaging. For plasma collection, venous blood (5 ml) was drawn from individuals, placed in EDTA tubes, and processed immediately before injection of PET radiotracers (if applicable). Briefly, all blood samples were centrifuged at 400 g (4 °C) for 10 minutes. Plasma was transferred without disturbing the erythrocyte sedimentation rate (ESR) brown layer and centrifuged again at 1,100 g (4 °C) for 10 minutes. All plasma samples were delabeled and stored at -80 °C before measurement using the APEX platform. All APEX measurements were performed without prior knowledge of PET imaging results and clinical diagnosis.

[0209] Clinical APEX measurement. (Based on supplementary information) Figure 15The assay configuration outlined in section a measures all plasma samples. In short, to measure exosome-bound Aβ42 groups, we directly use native plasma samples, measuring Aβ42 capture and CD63 detection (Aβ42+CD63+) without any vesicle purification or separation. To demonstrate the presence of unbound Aβ42 groups in the plasma samples, we use size exclusion filtration (cutoff size = 50 nm, Whatman) to remove large-sized residues. This is necessary because the assay configuration based on Aβ42 capture and Aβ42 detection cannot distinguish between unbound Aβ42 and total Aβ42. To measure unbound Aβ, we evaluate the plasma filtrate by Aβ42 capture and Aβ42 detection (Aβ42+Aβ42+). Note that this filtration is only used to demonstrate the presence of unbound Aβ42 groups; in a clinical setting, only the more reflective exosome-bound Aβ42 will be measured directly from native plasma samples, which is unnecessary in a clinical setting. To measure total Aβ42, we evaluated native plasma samples directly via Aβ42 capture and Aβ42 detection (Aβ42+Aβ42+). For all measurements, we used 5% BSA as a 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 relative to this IgG control to illustrate the non-specific binding of the sample-matched sensor.

[0210] Positron emission tomography (PET) imaging. Individuals were scanned using a Siemens 3TBiograph mMR system (Siemens Healthineers) after blood draw to acquire PET and MR images simultaneously. PET data were acquired 40–70 minutes after intravenous infusion of 370 MBq of 11C-Pittsburgh compound B (PiB). MR data were acquired using a 12-channel head receiver coil, including ultrashort echo time (UTE) images for PET attenuation correction and T1-weighted magnetization preparation gradient echo (MPRAGE) images (1 mm isotropic resolution, TI / TE / TR = 900 / 3.05 / 1950 ms).

[0211] PET data analysis. T1-weighted MPRAGE images were processed using Freesurfer (5.3.0) to generate cortical segmentation for PET data analysis. PET images were reconstructed using the Ordinary Poisson Ordered Subset Expectation-Maximization (OP-OSEM) algorithm and smoothed using a 4mm Gaussian filter. Data were attenuated using a UTE-based μ-map. The resulting attenuated-corrected normalized uptake value (SUV) images were then co-registered with the MPRAGE images using the Advanced Normalization Tool (ANT), and individual-specific Freesurfer segmentation was used to calculate the normalized uptake value ratio (SUVR) relative to the mean cerebellar gray matter intensity. The mean SUVR for specific regions was calculated, and the overall mean SUVR for each patient was calculated by averaging the SUVRs of all brain regions.

[0212] Statistical analysis. All measurements were performed in triplicate, and data are presented as mean ± sd. Significance was determined using a two-tailed Student's t-test. For between-sample comparisons, each pair of samples was tested, and the resulting p-values ​​were adjusted for multiple hypothesis testing using Bonferroni correction. A p-value < 0.05 after adjustment was considered significant. One-way pairwise ANOVA was used to determine the analytical and biological coefficients of change (i.e., within-group, between-group, and population). For clinical studies, linear regression was used to determine goodness of fit (R²). All statistical analyses were performed using R-package (version 3.4.2) and Graphpad Prism 7.

[0213] Example 1

[0214] Magnified plasma analysis of exosome-bound Aβ

[0215] One of the earliest pathological markers of Alzheimer's disease (AD) is the deposition of Aβ in the brain. These plaques are formed by the aggregation of abnormal amyloid protein fragments, primarily the hydrophobic splice variant Aβ42. Aβ proteins are released into the extracellular space and can circulate in the bloodstream. Also found in the extracellular space are exosomes, nanoscale membrane vesicles secreted by mammalian cells via the fusion of multivesicular endosomes with the plasma membrane. During exosome biogenesis, glycoproteins and glycolipids are incorporated into the invaginated plasma membrane and classified into newly formed exosomes10,11. Through these surface markers, exosomes can associate with and bind to extracellular Aβ proteins. Figure 1a Multimodal characterization of extracellular vesicles derived from neurons (SH-SY5Y cells) confirmed their exosome morphology, size distribution, and molecular composition. Figure 5 ).

[0216] Transmission electron microscopy analysis of the vesicles further revealed their ability to bind to Aβ42 protein aggregates. Figure 1b and Figure 5 ).

[0217] To assess exosome Aβ binding, the APEX platform was developed for amplified, multi-parameter analysis of exosome molecular colocalization. This system measures transmitted SPR via a periodic plasma nanopore array, patterns are formed in a bilayer photonic structure, and in-situ enzymatic transformation is used to rapidly grow insoluble optical products on bound exosomes. Figure 1c To complement APEX enzymatic deposition (occurring at the top of the sensor), size-matched plasmonic nanopores are patterned in a coupled bilayer photonic system to allow for back-side illumination (away from enzyme activity). Figure 20 This enhances SPR measurements. The resulting enzymatic deposition not only stably alters the refractive index of the amplified SPR signal, as evidenced by the redshift in the transmission spectrum (spectral shift Δλ), Figure 1d However, it is also spatially defined as molecular co-localization analysis. APEX scanning electron micrographs of exosomes bound to the sensor before and after magnification confirmed the localized growth of optically deposited material after enzymatic transformation. Figure 6 ).

[0218] Therefore, using the developed APEX platform, the binding of Aβ protein to exosomes can be measured directly from clinical blood samples of AD patients and control individuals, and the measurement results can be correlated with PET imaging of overall and regional brain plaque deposition. Figure 1e For high-throughput, multi-path clinical analysis, advanced manufacturing methods (i.e., deep ultraviolet lithography) are employed. Figure 7 Sensor microarrays are fabricated on 8-inch wafers; each wafer can accommodate more than 40 microarray chips with >2000 sensing elements. Figure 8 a). Figure 1f This image shows a photograph of the APEX microarray chip developed for parallel measurements in this study. A scanning electron micrograph of the developed sensor reveals highly uniform fabrication. Figure 8 b).

[0219] Optimized signal amplification for multiple analysis

[0220] Enzyme-catalyzed APEX amplification was developed for the first time. A series of sensor functionalizations were performed, including antibody conjugation, exosome binding, enzyme labeling, and optical product amplification. The stepwise total spectral shift (cumulative Δλ) was measured. Figure 2a). The sensor was functionalized using an antibody against CD63, a type III lysosomal membrane protein abundant in exosomes, which is characteristic of exosomes for capturing vesicles derived from neuronal cells (SHSY5Y). To facilitate the local deposition of the insoluble optical product, horseradish peroxidase was incorporated as a cascade enzyme to catalyze the conversion of its soluble substrate (3,3'-diaminobenzidine tetrahydrochloride). The sensor-bound vesicles were enzyme-labeled with another anti-CD63 antibody. While enzyme labeling did not cause any significant spectral changes, the formation of the optical product resulted in approximately 400% signal enhancement. In contrast, a control experiment using an IgG isotope control antibody showed minimal background change ( Figure 9 Importantly, this SPR signal amplification is closely correlated with an increase in the area coverage of highly localized optical deposits, as confirmed by scanning electron microscopy. Figure 2 b).

[0221] To complement enzymatic amplification (which occurs at the top of the sensor), the APEX sensor design was optimized to improve its analytical performance and stability. This was achieved in contrast to the existing gold-plated glass design that only supports frontal illumination. Figure 20 In contrast, APEX's bilayer plasma structure enables SPR excitation via back-side illumination. Figure 2 c). The new optimized design not only exhibits strong transmission SPR through back illumination ( Figure 10 It also exhibits analytical stability. Figure 10 d), this may be due to reduced direct incident light on enzyme activity (i.e., temperature fluctuations). An APEX assay was further established by optimizing the enzyme substrate concentration and reaction duration. Figure 2 d). By monitoring real-time spectral changes related to different substrate concentrations through constant back-side illumination, it was found that a large amount of signal amplification could be completed in <10 minutes, thus enabling the entire APEX workflow to be completed in <1 hour.

[0222] Under these optimized conditions, the APEX detection sensitivity was then measured for exosome quantification. Neuron-derived vesicles (SH-SY5Y) were quantified using standard nanoparticle tracking analysis. Titration experiments were performed using an anti-CD63 antibody. Figure 2 e). Optimized APEX amplification was determined to improve detection sensitivity by 10-fold, establishing a limit of detection (LOD) of ~200 exosomes. This observed sensitivity represents the best LOD reported to date for batch exosome measurements, and is 10-fold better than both Western blotting and chemiluminescent ELISA. 5 Double 10 3 times.

[0223] Using the APEX microarray platform, further analyses for the determination of multiple biomarkers associated with neurodegenerative diseases were developed. Specific assays were established for the following protein biomarkers ( Figure 2 f): Amyloid-β (Aβ42), amyloid precursor protein (APP), α-synuclein, L1 close homolog (CHL1), insulin receptor substrate 1 (IRS-1), neural cell adhesion molecule (NCAM), and tau protein. Importantly, further development of the APEX assay workflow is needed. Figure 11 a) This platform demonstrates signal amplification capabilities for detecting extracapsular and intracapsular proteins, as well as exosome miRNAs. Figure 11 (b) All the detection probes used for the determination can be found in Table 1.

[0224] Enhance the binding between Aβ aggregates and exosomes

[0225] Using the developed APEX platform, the binding of exosomes to pathological Aβ proteins of different structural forms was next evaluated. To simulate various stages of amyloid inoculation and fibrosis, Aβ42 aggregates of different sizes, a major component of amyloid plaques, were prepared. The degree of aggregation was varied to form Aβ42 aggregates of different sizes. Figure 3a (See Experimental Methods for details). Their spherical morphology and single-peak size distribution were confirmed by transmission electron microscopy and dynamic light scattering analysis, respectively. Figure 3b Furthermore, it was pointed out that larger Aβ42 aggregates exhibited a strong tendency to form fibrous structures. Figure 12 ).

[0226] To determine the kinetics of exosome-Aβ binding, the prepared Aβ42 aggregates were immobilized on the APEX platform, and the sensor was incubated with an equal concentration of neuron-derived exosomes. Figure 3c In contrast, in the control experiment, similar-sized bovine serum albumin (BSA) aggregates were prepared and characterized. Figure 13 By measuring real-time exosome binding, it was demonstrated that exosomes bound to Aβ42 aggregates more strongly than BSA controls of similar size, regardless of aggregate size. Figure 3d More importantly, the binding affinity of vesicles to smaller Aβ42 aggregates ( Figure 3d Compared to the left, vesicles showed significantly higher binding affinity (>5 times) for larger Aβ42 aggregates. Figure 3d (Right). All affinities were normalized relative to the surface area of ​​the Aβ42 aggregate and compared to their respective BSA controls (see Methods).

[0227] Next, using extracellular vesicles derived from different cell sources, the APEX platform was used to measure their individual binding to larger Aβ42 aggregates. Figure 3e As determined by nanoparticle tracking analysis (Figure 14), vesicles of the same concentration from different cellular sources were incubated with an Aβ42-functionalized sensor. It was noted that among all tested cellular sources, neuronal, erythrocyte, platelet, and epithelial cell-derived vesicles showed stronger binding to Aβ42 aggregates, while glial and endothelial cell-derived vesicles showed negligible binding. A set of specific biomarkers for these respective cellular sources, along with a pan-exosome biomarker (i.e., CD63), were then used for APEX signal amplification of the bound vesicles. CD63 consistently performed signal enhancement in all tested vesicles. Based on this biomarker identification, CD63 was thus used to develop an APEX assay to identify and measure exosome-bound Aβ (defined as Aβ42+CD63+; Figure 15 ).

[0228] Blood exosome-bound Aβ reveals brain plaque burden

[0229] Given the enhanced binding between exosomes and prefibrillary Aβ aggregates (building blocks of amyloid plaques), it is hypothesized that exosome-bound Aβ could serve as a more reflexive circulating biomarker of brain plaque burden. To test this hypothesis, various APEX assays were developed using different antibodies to assess different circulating Aβ42 groups (…) from clinical blood samples. Figure 15 a). Specifically, to characterize the exosome-bound Aβ42 group, the APEX assay was designed to directly enrich Aβ42 from native plasma and measure the relative amount of CD63 associated with the captured Aβ42. This assay configuration not only demonstrates specific detection of the Aβ42+CD63+ group ( Figure 15 (bc) Furthermore, it reflects functional relevance: with enhanced binding between profibrillary Aβ aggregates and exosomes, the associated CD63 signal can be considered a surrogate indicator for measuring the relative amount of profibrillary Aβ42 in total circulating Aβ42. To illustrate the presence of unbound Aβ42 clusters, large-size residues (e.g., exosomes) in the plasma were removed using size exclusion filtration before measuring Aβ42 in the plasma filtrate. Finally, to measure total circulating Aβ42, native plasma was assessed by direct Aβ42 enrichment and Aβ42 detection.

[0230] We then conducted a feasibility clinical study to address the following key questions: (1) whether APEX can measure circulating Aβ42 directly from blood samples, (2) how different blood-derived Aβ42 groups are correlated with brain plaque burden, and (3) whether specific groups of circulating Aβ42 can distinguish different clinical populations.

[0231] To achieve these targets, age-matched individuals (n=84) were recruited, including individuals diagnosed with AD (n=17), individuals with mild cognitive impairment (MCI, n=18), healthy controls without cognitive impairment (NCI, n=16), and clinical controls with vascular dementia (VaD, n=9) and neurovascular damage (i.e., vascular mild cognitive impairment, VMCI, n=12; acute stroke, n=12). All clinical information is shown in Table 2. Blood samples were collected from all recruited individuals for APEX analysis. Except for patients with acute stroke, all individuals also consented to PET imaging of brain amyloid plaques concurrently. Plasma samples were collected immediately prior to the infusion of Pittsburgh compound B (PiB) radiotracer for PET imaging. PET imaging showed a wide range of brain plaque burdens between and within the clinical groups being imaged ( Figure 4 a) and demonstrated changes in brain regions (Table 2), consistent with other published clinical studies.

[0232] Table 2. Clinical information and PET imaging standardized uptake ratio (SUVR).

[0233]

[0234]

[0235] AD: Alzheimer's disease; MCI: Mild cognitive impairment; NCI: No cognitive impairment.

[0236] VaD: Vascular dementia; VMCI: Vascular mild cognitive impairment. The developed APEX assay was used. Figure 11 a) We evaluated different circulating Aβ42 groups in these clinical plasma samples, namely exosome-bound Aβ42, unbound groups, and total circulating Aβ42. Figure 4 b). Exosome-bound Aβ42 clusters showed strong colocalization 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 considerable proportion of the clusters. Figure 16 a). As unbound Aβ42, measured from plasma filtrates, we further characterized these filtrates and confirmed that their vesicle counts were negligible and that they had minimal colocalization signals with exosomes and neuronal markers (a). Figure 16 bc). When associated with overall PET amyloid imaging, it is associated with unbound Aβ42 ( Figure 4 b, middle, R 2 =0.0193) or total Aβ42 ( Figure 4 b, right, R 2Compared to (=0.1471), the exosome-bound Aβ42 measurement showed the best correlation ( Figure 4 b, left, R 2 =0.9002). Interestingly, unlike the poor and negative correlation shown by total Aβ42 measurements (as shown in this study and other published reports), relative CD63 measurements of Aβ42 clusters bound by exosomes showed a high and positive correlation with PET imaging of brain amyloid plaques. We attribute this finding to the similar binding preference of exosomes and PET tracers for Aβ42: (1) exosomes showed enhanced binding to profibrillary Aβ42 aggregates, particularly larger aggregates that readily form profibrillary structures ( Figure 3d (2) PET tracers strongly bound to larger amyloid fibrils but hardly to smaller aggregates. Notably, this superior correlation also demonstrates the specificity of brain regions; exosome-bound Aβ42 measurements showed a higher correlation with the occipital region (a region affected by late-stage AD). Figure 17 b) In contrast, the cingulate gyrus (the area affected by early AD) Figure 17 a) It has a stronger correlation with brain plaque burden.

[0237] In differentiating clinical diagnoses, APEX analysis of only exosome-bound Aβ42, rather than APEX analysis of unbound populations or total Aβ42 populations, showed good specificity. Figure 4 In particular, exosome-bound Aβ42 measurements not only distinguished the AD clinical group (i.e., AD and MCI, P<0.01) but also other healthy and clinical controls (P<0.0001, Student's t-test). In distinguishing between various clinical groups, the specificity of this evidence was comparable to that of PET brain amyloid imaging. Figure 18 On another front, nanoparticle tracking analysis of plasma extracellular vesicles did not show any significant differences in vesicle size or concentration across all clinical groups (Figure 19).

[0238] Example 2

[0239] Alzheimer's disease (AD) is the most common form of severe dementia. Due to its complex and progressive neuropathology, early detection and timely intervention are crucial for the success of disease-modifying therapies. Despite a strong interest in finding serum biomarkers for AD, their development has been hampered by several challenges. First, unlike their counterparts in cerebrospinal fluid, the concentration of pathological AD molecules in circulation is much lower. Plasma Aβ levels tend to be close to the detection limit of conventional ELISA assays; this limitation has led to several conflicting findings in published reports. Second, there is little correlation between plasma Aβ analysis and brain plaque deposition, the earliest pathological marker of AD. One possible reason may stem from the different measurement methods. PET imaging probes, commonly used to determine brain amyloid load, preferentially measure insoluble fibrillary deposits, while conventional ELISA measures soluble Aβ in plasma. Furthermore, previous whole-blood measurements may have masked the potential relevance of blood-based measurements to brain pathology. However, this discrepancy raises a more fundamental question—are there subsets of circulating Aβ proteins that better reflect fibrillary pathology in the brain?

[0240] A dedicated analytical platform (APEX) has been developed for multi-parameter analysis of exosome-bound, unbound, and total Aβ directly derived from plasma to differentiate between different circulating Aβ groups. Specifically, it leverages recent advances in sensor design, device fabrication, and assay development to achieve enhanced optical performance and detection capabilities. Figure 20In sensor design and fabrication, the APEX platform comprises a periodic array of gold nanopores suspended on a patterned silicon nitride membrane, fabricated using deep ultraviolet lithography, a current-generation fabrication method for large-scale, precise nanopatterning. These advancements have enabled APEX technology to achieve 1) improved optical performance (i.e., enhanced transmission intensity for SPR detection via bidirectional light irradiation) and 2) reliable large-scale production. In terms of assay technique, the APEX platform utilizes rapid in-situ enzymatic conversion to achieve highly localized signal amplification. This development not only enables sensitive detection of diverse targets (e.g., intracapsular proteins and RNA targets) but also facilitates exosome colocalization analysis in multi-parameter population studies, as insoluble deposits only form locally when multiple targets are simultaneously present on the exosome. Through these combined advancements, the observed APEX sensitivity is the best reported to date for exosome analysis, exceeding standard ELISA measurements by several orders of magnitude. Using the developed APEX platform, we demonstrated enhanced binding between exosomes and larger prefibrillar pre-Aβ (a key building block of fibrous amyloid plaques). Further identification and quantification of exosome-bound amyloid (CD63+Aβ42+) subsets in clinical plasma samples revealed that these samples were highly correlated with brain amyloid plaque burden in different clinical populations (i.e., AD, MCI, cognitively normal controls, and clinical controls with other neurodegenerative and neurovascular diseases).

[0241] Assessing different circulating Aβ groups could lead to a paradigm shift in AD research and clinical care. A growing body of evidence supports the role of prefibrillary Aβ aggregates as toxic drivers of AD neurodegeneration. Their preferential binding to exosomes, and the recent discovery of exosomal biomarkers enriched in human amyloid plaques, not only reveal potential new mechanisms of plaque seeding but also demonstrate the importance of exosome-bound Aβs as more reflective circulating biomarkers for complex AD pathology. Therefore, this study is expected to complement other preclinical and clinical research in terms of technology development and biomarker improvement. For example, in terms of technology development, while IP mass spectrometry enables unbiased molecular screening and is valuable for biomarker discovery, particularly in detecting different molecular isotypes and variants (e.g., (APP)669-711 and Aβ1-40), APEX technology provides rapid, sensitive readouts from native plasma samples without the extensive sample processing typically required for mass spectrometry measurements, thus making it suitable for targeted clinical measurements. Regarding improvements in biomarkers, as current research has demonstrated, analysis of different circulating Aβ groups can reveal novel correlations previously masked by whole blood measurements and drive future blood-based clinical management of AD. Importantly, with over 400 AD clinical trials currently underway, further exploration is needed to enhance the development of approaches that redefine current standards of patient care. Through additional technological innovations, such as on-chip exosome processing, combined analysis of other AD biomarkers, and longitudinal clinical cohort validation, developed technologies can provide a comprehensive capability to facilitate minimally invasive early detection, molecular stratification, and continuous monitoring—all crucial for the objective evaluation of disease-improving therapies at different stages of clinical trials.

[0242] Example 3

[0243] This example demonstrates that incubating amyloid aggregates with an inhibitor reduces spontaneous protein aggregation.

[0244] method

[0245] Aggregate size determination. The hydrodynamic diameters of amyloid and BSA aggregates were determined by dynamic light scattering analysis (Zetasizer Nano ZSP, Malvern). Three × 14 measurements were performed at 4 °C. Z-mean diameter and polydispersity were analyzed. For each measurement, the autocorrelation function and polydispersity index were monitored to ensure the quality of the samples used for size determination.

[0246] Protein aggregation. Lyophilized amyloid protein was resuspended in NaOH (60 mM, 4 °C), sonicated, and the pH was adjusted to 7.4 in PBS. The protein was immediately filtered through a 0.2 μm Millipore membrane filter, and the filtrate was used as small initial aggregates. To prepare larger aggregates, the protein was treated as described above and incubated with stirring for 1 hour to induce further aggregation. For treatment with an inhibitor, 10 μM of the inhibitor (e.g., methylene blue) was added to the protein before incubation. Aggregate size was determined at the end of incubation.

[0247] Optical Analysis. For experimental analysis, an APEX sensor was back-illuminated using a halogen tungsten lamp (Stocker Yale Inc.) through an a×10 microscope objective. Transmitted light was collected by optical fiber and fed into a spectrometer (Ocean Optics). All measurements were performed at room temperature in a sealed chamber to eliminate ambient light interference. Transmitted light intensity was digitally recorded as a count relative to wavelength. For spectral analysis, transmission peaks were fitted using a local regression method, with spectral peaks determined using a custom R program. All fittings were performed locally. That is, for a fit at point x, points near x were used, weighted by their distance from x. This method eliminates variations in results caused by the number of data points and the data range analyzed, compared to fitting with multi-order polynomial curves. All spectral shifts (Δλ) were determined as variations in the transmission peaks and calculated relative to appropriate control experiments.

[0248] Characterization of exosome-protein binding. As previously described, prepared protein aggregates (Aβ42 and BSA controls) were used for surface functionalization on the APEX sensor via EDC / NHS coupling. Unbound protein aggregates were washed away with PBS. The amount of bound protein was measured from the resulting transmission spectral shifts. We used this information to determine the number of bound protein aggregates and their associated total protein surface area bound by exosomes (see below) in order to normalize binding affinity. After surface functionalization with protein aggregates, exosomes were introduced onto the sensor. Spectral changes were measured every 3 seconds for a total duration of 480 seconds to construct a real-time kinetic sensing plot. Exosome binding kinetics and binding affinity were determined for protein aggregates of different sizes.

[0249] To illustrate the differences in protein aggregate size and the SPR-related exponential decay in sensitivity (with increasing distance from the sensor surface), equations were used.

[0250]

[0251] Calculate the total surface area of ​​the 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 (constant in this case), and l d is the attenuation length, which is set to 200nm in the current sensor design, and r is the radius of the bound protein aggregate.

[0252] All protein aggregates were approximated as spheres, and their 'r' values ​​were determined by dynamic light scattering analysis. We used the above equations to determine the number of protein aggregates bound to the sensor and their respective total surface areas to estimate the number of available binding sites for interaction with exosomes. All exosome binding data (Δλ) were normalized relative to their respective protein binding sites. Normalized Aβ42 binding data were performed against a similarly sized BSA control and fitted to determine the binding affinity constant K. D .

[0253] miRNA analysis. Exosome lysates were incubated with biotinylated RNA probes, and then RNA duplexes were captured and the APEX signal amplified on a p19-functionalized APEX sensor.

[0254] result

[0255] The pathology of many neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis) involves the aggregation of misfolded amyloid proteins. Treatments targeting protein aggregation involve various strategies to clear aggregated amyloid proteins, including breaking down amyloid aggregates or inhibiting their aggregation. Molecules that have been shown to effectively reduce the number of amyloid aggregates and are therefore potential candidates for disease-modifying therapies include methylthionium chloride, colorless methylthionium bis(hydromethanesulfonate), curcumin, acid fuchsin, epigallocatechin gallate, saffron aldehyde, Congo red, apigenin, sky blue C, basic blue 41, (trans,trans)-1-bromo-2,5-bis-(3-hydroxycarbonyl-4-hydroxy)styrene (BSB), Chicago sky blue 6B, cyclodextrin, donomycin hydrochloride, dimethyl yellow, direct red 80, 2,2-dihydroxybenzophenone, and hexadecyltrimethylammonium bromide. (C16), heme chloride, heme, indomethacin, jujube ketone, resorcinol blue, methylchloroflavone sulfosalicylate, melatonin, myricetin, 1,2-naphthoquinone, nordihydroguaiacic acid, R()-normorphine hydrobromide, orange G, o-vanillin (2-hydroxy-3-methoxybenzaldehyde), phenazine, phthalocyanine, rifamycin SV, phenol red, hydropyridine tetracycline, quinacrine mustard dihydrochloride, thioflavin S, ThT and trimethyl(tetradecyl)ammonium bromide (C17), diallyl tartrate, eosin Y, fenofibrate, new copper base, nystatin, octadecyl sulfate and rhodamine B.

[0256] Since no animal model has been shown to accurately reflect the pathology of Alzheimer's disease (AD) in the human brain, we used in vitro experiments to simulate the role of disease-modifying treatments in inhibiting amyloid aggregation. The initial size of amyloid was confirmed by dynamic light scattering analysis, and then aliquots of the protein were incubated with or without an inhibitor. In the absence of an inhibitor, the size of the protein aggregates increased with increasing incubation time due to spontaneous aggregation of amyloid. However, in the presence of an inhibitor, the increase in size due to spontaneous protein aggregation was minimal, resulting in the formation of smaller protein aggregates.

[0257] Following incubation, amyloid protein was functionalized onto the surface of the sensor chip before incubation with neuronal exosomes. Neuronal exosomes were observed to preferentially bind to larger protein aggregates, as indicated by differences in binding affinity, and binding to smaller protein aggregates treated with inhibitors was demonstrated to be reduced. Since exosome binding to proteins can serve as a surrogate indicator of protein biophysical and / or biochemical properties—properties influenced by disease-modifying therapies—the APEX platform is capable of assessing the efficacy of disease-modifying therapies.

[0258] Several example configurations have been described, and various modifications, alternative constructions, and equivalents can be used without departing from the spirit of this disclosure. For example, the aforementioned elements can be components of a larger system. Furthermore, numerous steps can be taken before, during, or after considering the aforementioned elements.

[0259] All publications, serial numbers, patents and patent applications cited herein are incorporated herein by reference in their entirety for all purposes.

[0260] Exemplary implementations of this disclosure

[0261] In some aspects and implementations, this document describes:

[0262] A method for detecting neurodegenerative diseases or amyloidosis in an individual, the method comprising detecting the level of an exosome-bound aggregated biomarker in a sample obtained from the individual, wherein an increase in the level of the exosome-bound aggregated biomarker compared to a reference indicates that the individual has neurodegenerative diseases or amyloidosis.

[0263] In some implementations, the biomarker is selected from the group consisting of Aβ, APP, α-Syn, Tau, APOE, SOD1, TDP-43, bassoon, and / or fibronectin.

[0264] In some implementations, the method includes detecting the level of molecular subtypes of exosome-bound biomarkers.

[0265] In some implementations, the molecular subtypes of Aβ are Aβ42, Aβ40, Aβ39, or Aβ38.

[0266] In some implementations, the biomarker is the profibrillary aggregate.

[0267] In some implementations, the method further includes detecting exosomal biomarkers selected from a group consisting of CD63, CD9, CD81, ALIX, TSG101, Flotilin-1, Flotilin-2, LAMP-1, HSP70, HSP90, RNA, and DNA, wherein the exosomal biomarkers colocalize with exosome-bound biomarkers.

[0268] In some embodiments, the method further includes detecting neuronal biomarkers selected from the group consisting of NCAM, L1CAM, CHL-1, and IRS-1, wherein the neuronal biomarkers colocalize with exosome-bound biomarkers.

[0269] In some implementations, the neurodegenerative diseases are selected from the following group: Alzheimer's disease, mild cognitive impairment, dementia, vascular dementia, vascular mild cognitive impairment, Parkinson's disease, amyotrophic lateral sclerosis, multiple sclerosis, progressive supranuclear palsy, and / or Tau proteinosis.

[0270] In some implementations, neurodegenerative diseases are selected from the group consisting of Alzheimer's disease and mild cognitive impairment.

[0271] In some implementations, the method includes treating individuals with neurodegenerative diseases or amyloidosis.

[0272] In some implementations, the sample is a tissue biopsy, blood, plasma, serum, or cerebrospinal fluid.

[0273] In some implementations, this method is further associated with brain imaging studies.

[0274] In another aspect, this paper describes a method for detecting individuals at risk of developing neurodegenerative diseases or amyloidosis, which includes detecting the level of exosome-bound aggregated biomarkers in samples obtained from the individual, wherein an increase in the level of exosome-bound aggregated biomarkers compared to a reference indicates that the individual has neurodegenerative diseases or amyloidosis.

[0275] In another aspect, this paper describes a method for detecting and treating an individual with neurodegenerative disease or amyloidosis, the method comprising: a) detecting the level of an exosome-bound aggregated biomarker in a sample obtained from the individual, wherein an increase in the level of the exosome-bound aggregated biomarker compared to a reference indicates that the individual has neurodegenerative disease or amyloidosis; b) treating the individual with neurodegenerative disease or amyloidosis.

[0276] In another aspect, this paper describes a method for determining the aggregation state of biomarkers in a sample, the method comprising detecting the level of exosome-bound biomarkers in the sample, wherein the level of exosome-bound biomarkers, compared with a reference, indicates the degree of biomarker aggregation.

[0277] In some implementations, the method includes a step of contacting the sample with a population of exosomes prior to the step of detecting the level of exosome binding biomarkers.

[0278] In some implementations, biomarkers that aggregate in the sample preferentially bind to exosomes compared to non-aggregated biomarkers.

[0279] In some implementations, the samples are obtained from individuals.

[0280] In some implementations, an increased degree of biomarker aggregation compared to a reference indicates that an individual has a neurodegenerative disease or amyloidosis.

[0281] In some implementations, the method includes treating individuals with neurodegenerative diseases or amyloidosis.

[0282] In some implementations, treatment includes administering a therapeutically effective amount of one or more drugs or a combination thereof to an individual.

[0283] In some implementations, the drug is selected from cholinesterase inhibitors, NMDA receptor antagonists, combinations of cholinesterase inhibitors and NMDA receptor antagonists, BACE1 inhibitors, antibodies, protein aggregation inhibitors, proteasome inhibitors, small molecules, gene therapy, anti-tau protein drugs, or combinations thereof.

[0284] In some implementations, the cholinesterase inhibitor is donepezil, rivastigmine, or galantamine; the NMDA receptor antagonist is memantine; the BACE1 inhibitor is AZD3293; the antibody is aducanumab; and / or the anti-tau drug is TRx0237 (LMTX).

[0285] In some implementations, the drug is selected from methylthionium chloride, colorless methylthionium bis(hydromethanesulfonate), curcumin, acid fuchsin, epigallocatechin gallate, saffron aldehyde, Congo red, apigenin, Sky Blue C, Basic Blue 41, (trans, trans)-1-bromo-2,5-bis-(3-hydroxycarbonyl-4-hydroxy)styrene (BSB), Chicago Sky Blue 6B, cyclodextrin, donomycin hydrochloride, dimethyl yellow, direct red 80, 2,2-dihydroxybenzophenone, hexadecyltrimethylammonium bromide (C16), heme chloride, and heme. Indomethacin, jujube ketone, resorcinol blue, methylchloroflavone sulfosalicylate, melatonin, myricetin, 1,2-naphthoquinone, nordihydroguaiac acid, R(n)-normorphine hydrobromide, orange G, o-vanillin (2-hydroxy-3-methoxybenzaldehyde), phenazine, phthalocyanine, rifamycin SV, phenol red, hydropyridine tetracycline, quinacrine mustard dihydrochloride, thioflavin S, ThT and trimethyl(tetradecyl)ammonium bromide (C17), diallyl tartrate, eosin Y, fenofibrate, new copper base, nystatin, octadecyl sulfate and / or rhodamine B.

Claims

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

2. The method according to claim 1, wherein the biomarker is selected from the group consisting of Aβ, APP, α-Syn, Tau, APOE, SOD1, TDP-43, bassoon and / or fibronectin.

3. The method of claim 1, wherein the method includes detecting the level of a molecular subtype of the biomarker bound by the exosome.

4. The method according to claim 3, wherein the molecular subtype of Aβ is Aβ42, Aβ40, Aβ39 or Aβ38.

5. The method according to claim 1, wherein the biomarker is a profibrillary aggregate.

6. The method according to 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 colocalized with a biomarker that binds to the exosome.

7. The method according to any one of claims 1-6, wherein the method further comprises detecting neuronal biomarkers selected from the group consisting of NCAM, L1CAM, CHL-1 and IRS-1, wherein the neuronal biomarkers are colocalized with biomarkers that bind to the exosomes.

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

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

10. The method according to any one of claims 1-9, wherein the method comprises treating the subject suffering from neurodegenerative disease or amyloidosis.

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

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

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

14. A method for detecting and treating neurodegenerative diseases or amyloidosis in a subject, the method comprising: a) Detecting the level of exosome-bound aggregation biomarkers in samples obtained from the subject, wherein an increase in the level of exosome-bound aggregation biomarkers 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 for 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 increase in the level of the exosome-bound biomarker compared to a reference indicates the degree of aggregation of the biomarker.

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

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

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

19. The method of claim 18, wherein an increase in the aggregation of the biomarker compared to a reference indicates neurodegenerative disease or amyloidosis in the subject.

20. The method of claim 19, wherein the method comprises treating the subject suffering from 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 cholinesterase inhibitors, NMDA receptor antagonists, combinations of cholinesterase inhibitors and NMDA receptor antagonists, BACE1 inhibitors, antibodies, protein aggregation inhibitors, proteasome inhibitors, small molecules, gene therapy, anti-tau drugs, or combinations thereof.

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