Method for detecting neurodegenerative disease

Abstract

To provide a method for detecting a neurodegenerative disease of a subject, and a method for treating the subject.SOLUTION: A method includes a step of detecting a level of exosome-associated coagulation biomarkers in a specimen collected from a subject. In the method, an increased level of exosome-associated coagulation biomarkers compared to a reference level indicates that the subject is suffering from a neurodegenerative disease.SELECTED DRAWING: None

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to Singapore Provisional Patent Application No. 10201900940Y, entitled METHOD OF DETECTING A NEURODEGENERATIVE DISEASE, filed on January 31, 2019.

[0002] Field The present disclosure relates generally to the field of neurology. In particular, the present disclosure relates to methods for detecting amyloidosis or neurodegenerative disease in a subject, as well as methods for monitoring and treating the subject. [Background technology]

[0003] background Surface Plasmon Resonance (SPR) sensing is a technique widely used in laboratories to characterize interactions between biomolecules, for example, between antibodies and antigens. The technique is typically based on immobilizing a ligand capture molecule on a metal surface and measuring the change in refractive index when the ligand binds to the capture molecule. The technique is label-free, meaning that it does not require the use of special tags or dyes for sensitive measurement of molecular interactions. It is currently being developed for use in laboratories for the diagnosis of patients with various pathologies, such as dementia, hepatitis, diabetes, and cancer.

[0004] Dementia is a public health crisis of the 21st century. The most common form of severe dementia, Alzheimer's disease (AD), is characterized by progressive loss of memory and cognitive function. Affected individuals experience significant limitations in self-care, social and occupational functioning. However, molecular hallmarks of AD can become evident and develop long before these full-blown clinical symptoms appear. These include extracellular amyloid-β (Aβ) plaques and intracellular tau neurofibrillary tangles. Because this neuropathology is complex and progressive, early detection and intervention are believed to be essential for successful disease-modifying treatment.

[0005] However, current AD diagnosis and disease monitoring are subjective and late-stage. They are based on clinical and neuropsychological assessments using published criteria. Such approaches lack sensitivity and specificity, especially in the early stages when symptoms are subtle and overlap significantly with various other disorders. Progress is being made in the development of new molecular diagnostic assays, such as cerebrospinal fluid measurements and brain amyloid plaque imaging by positron emission tomography (PET), but these tests face limitations because they require invasive lumbar punctures or are too expensive for widespread clinical application. As a result, there is great interest in finding serum biomarkers of AD to aid in early diagnosis and disease monitoring.

[0006] There is therefore a need to overcome, or at least mitigate, one or more of the above problems. Summary of the Invention

[0007] Disclosed herein are methods for detecting amyloidosis or a neurodegenerative disease in a subject, as well as methods for monitoring and treating the subject.

[0008] In one aspect, a method of detecting amyloidosis or a neurodegenerative disease in a subject is provided, the method comprising: detecting a level of an exosome-bound aggregation biomarker in a sample obtained from the subject; wherein an increased level of the exosome-bound aggregation biomarker compared to the reference indicates that the subject is afflicted with a neurodegenerative disease.

[0009] In one aspect, a method is provided for detecting a subject at risk for developing an amyloidosis or neurodegenerative disease, the method comprising: detecting a level of an exosome-bound aggregation biomarker in a sample obtained from the subject; wherein an increased level of the exosome-bound aggregation biomarker compared to the reference indicates that the subject is afflicted with a neurodegenerative disease.

[0010] In one aspect, a method of detecting and treating amyloidosis or a neurodegenerative disease in a subject is provided, the method comprising the steps of: (a) detecting a level of an exosome-bound aggregation biomarker in a sample obtained from the subject, wherein an increased level of the exosome-bound aggregation biomarker compared to a reference indicates that the subject is afflicted with a neurodegenerative disease; and (b) treating a subject suffering from a neurodegenerative disease. Includes.

[0011] In one aspect, a method for determining the aggregation state of a biomarker in a sample is provided, the method comprising: Detecting the level of exosome-bound biomarkers in the sample. and an increased level of the exosome-bound biomarker compared to the baseline is indicative of the degree of aggregation of the biomarker. [Brief description of the drawings]

[0012] Some aspects of the invention will now be described, by way of non-limiting examples only, with reference to the accompanying drawings, in which:

[0013] [Figure 1-1]Figure 1: APEX platform for analysis of circulating exosome-bound Aβ. (a) Exosomes bind Aβ protein. Aβ protein, the main component of amyloid plaques found in AD brain pathology, is released into the extracellular space. Exosomes are nanoscale extracellular membrane vesicles that are actively secreted by mammalian cells. Exosomes can bind released Aβ protein by glycoproteins and glycolipids on their surface. (b) Transmission electron micrograph of exosome-bound Aβ. Exosomes derived from neuronal cells (SH-SY5Y) were treated with Aβ42 aggregates and labeled with gold nanoparticles (10 nm) via an Aβ42-specific antibody. The nanoparticles are visible as black dots (indicated by red arrows). (c) Schematic of the APEX assay. To enable sensitive nanoscale profiling, exosomes are first immunocaptured on a plasmonic nanosensor (before amplification). In situ enzymatic amplification localizes insoluble photoattachments onto the sensor-bound exosomes (post-amplification). This attachment is spatially defined for molecular colocalization analysis and changes the refractive index for SPR signal enhancement. Note that to complement the enzymatic amplification, the nanosensor is back-illuminated (away from the enzymatic activity) to achieve analytical stability. This attachment results in a red shift of the light transmitted through the nanosensor. (d) Representative schematic of the transmission spectrum change due to APEX amplification. Specific exosome binding (before) and subsequent amplification profiling (after) were observed by the APEX platform as a transmission spectrum shift (Δλ). au: arbitrary units. (e) Exosome-bound Aβ was measured in blood samples from Alzheimer's disease (AD), mild cognitive impairment (MCI) patients, and non-cognitively impaired (NCI) controls using the APEX platform. The blood measurements were correlated with corresponding PET imaging of brain amyloid plaque deposits. (f) Photograph of the APEX microarray. Each sensor chip contains 6 x 10 sensing elements made of uniformly fabricated plasmonic gratings for multiplexed measurements. See Figures 7-10 for sensor fabrication, characterization, and design optimization, respectively. [Figure 1-2] See description of Figure 1-1. [Diagram 2]APEX signal amplification and multiplex profiling. (a) Stepwise APEX transmission spectral change. A series of manipulations, namely antibody (anti-CD63) binding to the sensor, exosome binding, enzyme labeling, and enzymatic attachment, were performed and the resulting spectral shifts were observed. Enzyme labeling did not result in significant changes, whereas attachment formation resulted in approximately 400% signal enhancement (****P < 0.0001, ns: not significant, Student's t test). (b) Comparison of APEX signal amplification with area coverage of photoattachment. 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 that before signal amplification. The inset (right) shows SEM images of sensor-bound exosomes before and after APEX amplification. (c) Finite-difference time-domain simulation with backside illumination. The APEX sensor design allows for the generation of enhanced electromagnetic fields by backside illumination, whereas the gold-loaded glass design does not. Backside illumination minimizes direct exposure of enzymatic activity (occurring on the top surface of the sensor) to incident light. The arrow indicates the incident direction of illumination. (d) Real-time sensorgram of APEX amplification kinetics. Amplification efficiency was monitored using different concentrations of photosubstrate (3,3'-diaminobenzidine tetrahydrochloride; high: 1 mg / ml, low: 0.01 mg / ml). All data were normalized to a negative control performed with an IgG isotope control antibody. (e) Comparison of detection sensitivity of APEX, ELISA, and Western blotting. APEX detection limit (dotted line) before and after amplification was determined by titration of known amounts of exosomes and measuring their CD63 signal. (f) Target protein measurement specificity of the APEX assay. Assays were developed for amyloid beta (Aβ42), amyloid precursor protein (APP), alpha-synuclein (α-syn), close homolog of L1 (CHL1), insulin receptor substrate 1 (IRS-1), neural cell adhesion molecule (NCAM), and tau proteins. Specific detection was demonstrated in all assays. Heatmap signals were normalized across assays (rows). All measurements were performed in triplicate, and in a, b, d, and e data are presented as mean ± standard deviation. [Figure 3-1] Figure 3: Preferential binding of Aβ aggregates to exosomes. (a) Schematic of Aβ protein aggregation. Small and large Aβ42 aggregates were prepared using a filtration method with different degrees of clustering, respectively. (b) Characterization of Aβ protein aggregates. (Left) Transmission electron micrographs showed the spherical morphology of the prepared Aβ42 aggregates. (Right) Dynamic light scattering analysis confirmed the unimodal size distribution of the different size preparations. (c) Schematic of exosome-Aβ binding analysis. Aβ42 aggregates (small vs. large) were immobilized on the APEX sensor and treated with equal concentrations of neuronal cell (SH-SY5Y)-derived exosomes to determine the binding kinetics. All exosome binding data were normalized to the respective Aβ42 aggregate surface area immobilized on the sensor (see Methods for details). (d) Real-time sensorgram of exosome binding kinetics. Exosomes bound Aβ42 aggregates more strongly compared to similarly sized bovine serum albumin (BSA) control aggregates (see Figure 13). Importantly, exosomes demonstrated much stronger binding affinity to larger Aβ42 aggregates (right) compared to smaller Aβ42 aggregates (left). Note the difference in scale on the y-axis. All binding affinities (KD) were determined from normalized exosome binding data and relative to the BSA control. KD(small) / KD(large)=5.27. (e) Differential binding of various extracellular vesicles to Aβ42 aggregates. Vesicles were derived from different cellular origins, i.e., neurons, glial cells, endothelial cells, monocytes, erythrocytes, platelets, and epithelial cells, respectively, and were used in the binding analysis at equal concentrations. First, the direct binding of each vesicle to the Aβ42-functionalized sensor was measured using the APEX platform (Direct). For each cell origin, we then labeled bound vesicles with an origin-specific marker (cell origin specific marker) or a pan-exosomal marker (i.e., CD63, a pan-exosomal marker) and measured the associated APEX signal amplification. All measurements were relative to an IgG isotope control antibody and performed in triplicate. In e, data are shown as mean ± standard deviation. [Figure 3-2] See description of Figure 3-1. [Figure 3-3]See description of Figure 3-1. [Figure 4] Clinical correlation of circulating exosome-bound Aβ with brain imaging. (a) Representative reconstructed PET brain images from clinical subjects showing increasing cerebral amyloid plaque burden. Cerebral amyloid plaque burden was determined by normalizing the standardized uptake value ratio (SUVR) of specific brain regions relative to the mean cerebellar gray matter intensity. (b) Correlation of different circulating Aβ42 populations with global average PET brain imaging (n = 72). Using the APEX assay, we measured signals from exosome-bound Aβ42 (left), unbound Aβ42 (center), and total Aβ42 (right) in blood samples from patients with Alzheimer's disease (AD), mild cognitive impairment (MCI), and control subjects with no cognitive impairment (NCI), vascular dementia (VaD), and vascular mild cognitive impairment (VMCI), respectively. Correlation with global imaging data of brain amyloid plaques demonstrated that measurements of exosome-bound Aβ42 correlated best (left, R2 = 0.9002) compared to measurements of unbound Aβ42 populations (middle, R2 = 0.0193) or total Aβ42 (right, R2 = 0.1471). (c) Analysis of different circulating Aβ42 populations in distinguishing between different clinical groups (n = 84). Only APEX measurements of circulating exosome-bound Aβ42 (left) were able to distinguish between AD clinical groups (AD and MCI) and also between other healthy controls (NCI) and clinical controls (VaD, VMCI, and acute stroke). Neither measurements of unbound Aβ42 (middle) nor total Aβ42 (right) showed statistical significance between the different clinical groups (**P < 0.01, ****P < 0.0001, ns: not significant, Student's t-test). All measurements were relative to an IgG isotope control antibody and performed in triplicate. In b-c, data are presented as mean ± standard deviation. [Diagram 5]Characterization of extracellular vesicles released from neuronal cells. (a) Scanning electron microscopy image of neuronal cells (SH-SY5Y) showing active release of nanoscale extracellular vesicles from the cells. (b) High magnification image of released vesicles. (c) Unimodal size distribution of extracellular vesicles showing an average diameter of approximately 150 nm as determined by nanoparticle tracking analysis. (d) Western blotting analysis of vesicle lysates. Vesicles were lysed and immunoblotted for exosome markers (LAMP 1, ALIX, HSP90, HSP70, CD63, flotillin 1, TSG101), neuronal marker (NCAM), and negative markers such as lipoprotein (APOE) and markers of other membrane compartments (calnexin, GRP94). (e) Transmission electron micrograph of double immunolabeling with gold nanoparticles of different sizes (CD63, 20 nm; Aβ42, 5 nm) confirming colocalization of both markers on the same vesicles, indicating the presence of exosome-bound Aβ. [Figure 6] APEX amplification products. (a) Scanning electron microscopy images of APEX sensors before and after amplification, where exosomes are captured on the sensor via anti-CD63 antibodies, showing localized growth of insoluble photoattachments from a soluble substrate (3,3'-diaminobenzidine tetrahydrochloride). The resulting APEX signal amplification was highly correlated with the increased areal coverage of localized attachments. [Figure 7]Bulk fabrication of APEX microarray sensors. All APEX sensors were fabricated on 8-inch silicon (Si) wafers. The fabrication process included: (1) a 10 nm silicon dioxide (SiO2) layer was prepared by thermal oxidation, and 145 nm silicon nitride (Si3N4) was deposited on the wafer by low-pressure chemical vapor deposition (LPCVD). (2) After coating with photoresist, deep ultraviolet (DUV) lithography was performed to define a nanohole array pattern in the resist. This pattern was transferred to the Si3N4 membrane by reactive ion etching (RIE). (3) After removing the photoresist, a thin protective layer of SiO2 (100 nm) was deposited on the front side of the wafer by plasma-enhanced chemical vapor deposition (PECVD). Photoresist was spin-coated on the back side of the wafer to allow light transmission; lithography was used to define the sensing area. (4) After etching the Si3N4 and SiO2 by RIE, the Si was etched with potassium hydroxide (KOH) and tetramethylammonium hydroxide (TMAH). (5) After etching, the protective SiO2 layer was removed using diluted hydrogen fluoride (DHF) (1:100). (6) Ti / Au (10 nm / 100 nm) was deposited on the Si3N4 film. [Figure 8] Characterization of APEX microarray sensors. (a) Photograph of an 8-inch wafer showing large-scale fabrication of APEX microarray sensor chips. Each wafer consists of >2000 sensing elements. (b) Scanning electron microscope image of the highly uniform nanoholes of an APEX sensor. The inset is a close-up of the nanohole lattice. [Figure 9]Stepwise spectral changes. APEX sensors were conjugated with either (a) anti-CD63 antibody for exosome capture or (b) isotope control antibody. All sensors were treated with equal concentrations of exosomes from a neuronal cell line (SH-SY5Y) prior to APEX amplification. Although all sensors showed comparable levels of antibody surface functionalization, only anti-CD63 functionalized sensors demonstrated significant spectral shifts associated with exosome binding and APEX amplification, respectively. Note that in control sensors without exosome binding, APEX amplification induced only negligible spectral changes. au: arbitrary units. [Figure 10] Optimization of APEX sensor performance. (a) Comparison of sensor performance with backside illumination. Different APEX sensors with different nanohole diameters were compared in terms of SPR transmission intensity, full width at half maximum (FWHM) of the spectral peak, and detection sensitivity. All sensors were illuminated from the backside to complement APEX enzymatic amplification. The optimal APEX design has a nanohole diameter of 230 nm and a regular periodic pattern of 450 nm in a 100 nm thick gold layer resting on a silicon nitride membrane. This bilayer plasmonic structure supports SPR excitation by backside illumination. (b) Change in transmission spectrum of the optimized sensor with increasing refractive index. Increasing the refractive index induced a change in the transmission spectrum, shifting the resonance peak to longer wavelengths. (c) The spectral shift showed a linear correlation with increasing refractive index. (d) Reproducibility and repeatability of APEX. APEX enzymatic amplification was performed on the same sample and measured with different users, sensor chips, and measurement times. The measurements showed the following analytical coefficients of variation: between groups = 2.76%, within groups = 4.14%, overall = 4.59%. All measurements were performed in triplicate or more and in (a) data are presented as mean ± standard deviation. au: arbitrary units, ns: not significant, Student's t-test. [Figure 11]APEX workflow and amplification efficiency. (a) APEX workflow for detecting proteins (extravesicular and intravesicular) and miRNA. (b) APEX amplification efficiency for different molecular targets. APEX signals were acquired for the following targets: extravesicular protein, Aβ42 protein; intravesicular protein, heat shock protein 90; miRNA, miRNA-9. All signals were normalized to the signal before addition of the photosubstrate to determine the amplification rate. Measurements were performed in triplicate and data in (b) are presented as mean ± standard deviation. [Figure 12] Fibrillar structures formed by the assembly of large Aβ aggregates. Amyloid fibrils were observed after 2 h incubation of the prepared large Aβ42 aggregates. The formed structures were immunolabeled with gold nanoparticles (15 nm) via anti-Aβ42 antibodies and characterized by transmission electron microscopy. [Figure 13] Preparation of BSA control aggregates. (a) Schematic of BSA protein aggregation. Small and large BSA control aggregates were prepared by varying the duration of heating. (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 have a diameter of approximately 15 nm and the large aggregates have a diameter of approximately 100 nm. [Figure 14-1] Figure 14: Extracellular vesicles isolated from various cell origins. Extracellular vesicles were obtained from (a) neuronal cells (SH-SY5Y), (b) glial cells (GLI36), (c) endothelial cells (HUVEC), (d) monocytes (THP-1), (e) red blood cells, (f) platelets, (g) epithelial cells of prostate origin (PC-3), and (h) epithelial cells of ovarian origin (SK-OV-3). All vesicles were characterized by nanoparticle tracking analysis. [Figure 14-2] See description of Figure 14-1. [Figure 15]APEX measurements of different circulating Aβ populations. (a) APEX assay setup for characterization of different circulating Aβ populations in clinical plasma samples. Exosome-bound and total Aβ42 populations were measured from native plasma, while unbound Aβ42 populations were detected from plasma filtrate. (b) Incubation of exosome-bound Aβ42 with fibronectin produced only negligible changes in the APEX signal. (c) Negative controls (APOE lipoprotein and Aβ42 protein, human serum albumin / HSA) showed only negligible signals, demonstrating that the APEX assay is specific for exosome-bound Aβ42. All measurements were performed in triplicate and data are presented as mean ± standard deviation. [Figure 16] Characterization of Aβ populations in clinical samples. (a) Exosome-bound Aβ42 population. Aβ42 was enriched directly from native plasma samples and the relative levels of colocalized signals of exosomal markers (CD63, CD81, and CD9) and neuronal markers (NCAM, L1CAM, and CHL-1) in captured Aβ42 were measured. All markers were detectable, but CD63 was the most highly expressed marker across all clinical samples tested. (b) Plasma filtrate for characterization of unbound Aβ42 population. To assess the unbound Aβ42 population, vesicle-free plasma filtrate was prepared using membrane filtration (size cutoff = 50 nm, Nuclepore, Whatman). The filtrate showed negligible vesicle numbers as determined by nanoparticle tracking analysis. (c) Plasma filtrate also showed negligible signals for exosomal markers (CD63) and neuronal markers (NCAM), demonstrating that filtration efficiently removed exosomes. Alzheimer's disease (AD), mild cognitive impairment (MCI), and non-cognitively impaired (NCI) healthy controls. All measurements were performed in triplicate and data are presented as mean ± standard deviation. [Figure 17]Correlation of exosome-bound Aβ42 with regional brain amyloid burden. Imaging SUVR was determined for specific brain regions, i.e., cingulate regions in early AD and occipital regions in late AD. APEX measurements of exosome-bound Aβ42 were more consistent with imaging data in early AD regions (a, R2 = 0.8808) than with imaging data in late AD regions (b, R2 = 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 non-cognitively impaired healthy controls (NCI, n = 16). All measurements were performed in triplicate and data are presented as mean ± standard deviation. [Figure 18] Comparison of PET imaging in clinical subjects with different diagnoses. Patients with different clinical diagnoses (n = 72) underwent PET imaging of cerebral amyloid plaque burden: AD (n = 17), MCI (n = 18), NCI (n = 16), VaD (n = 9), and VMCI (n = 12). Standardized uptake value ratios (SUVR) of global mean plaque deposits distinguished between AD clinical groups (AD and MCI), as well as between other healthy subjects (NCI) and clinical controls (VaD and VMCI). (**P < 0.01, ****P < 0.0001, Student's t-test). [Figure 19A] Extracellular vesicles in clinical samples. (a) Representative analysis of extracellular vesicles in blood samples from subjects with different clinical diagnoses (AD = 17, MCI = 18, NCI = 16, VaD = 9, VMCI = 12, and acute stroke = 12) measured by nanoparticle tracking analysis. Comparison of (b) vesicle size and (c) vesicle concentration of clinical blood samples (n = 84). Note that neither vesicle size nor concentration is significantly different between samples with different clinical diagnoses (ns: not significant difference, Student's t-test). [Figure 19B] See legend to Figure 19A. [Figure 19C] See legend to Figure 19A. [Figure 20-1] Figure 20: Comparison of APEX assay technology, sensor design, and fabrication. [Figure 20-2]See description of Figure 20-1. [Figure 21] (a) Inhibition of starch forming protein aggregation. (b) Dynamic light scattering analysis confirmed the unimodal size distribution and size differences when starch forming proteins were incubated with or without inhibitors. (c) Real-time sensorgram of exosome binding kinetics. Exosomes demonstrated a much stronger affinity for larger Aβ42 aggregates (untreated) compared to smaller Aβ42 aggregates (treated with inhibitors). [Figure 22] Real-time sensorgrams of exosome binding kinetics show that exosomes bound more avidly to starch forming protein (e.g., Aβ, APP, α-Syn, IRS-1, tau, APOE, SOD1, TDP-43, bassoon, and fibronectin) aggregates compared to bovine serum albumin (BSA) control aggregates of similar size. [Figure 23] Specificity of the APEX assay to measure target miRNA molecules. Assays were developed for miR-9, miR-15b, miR-29b, miR-29c, miR-107, miR-146a, and miR-181c. Specific detection was demonstrated in all assays. Heatmap signals were normalized across assays (rows). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Detailed Description Disclosed herein are methods for detecting neurodegenerative diseases and methods for treating same.

[0015] In one aspect, a sensor chip is provided that includes a conductive layer on a membrane support layer with a plurality of apertures extending through the conductive layer and the membrane support layer, the plurality of apertures being positioned such that illumination of the conductive layer and / or the membrane support layer produces surface plasmon resonance.

[0016] In one embodiment, the design of the multilayered structured materials (conductive metal and supporting substrate) allows for plasmonic coupling, which supports bidirectional excitation of surface plasmon resonance and provides comparable SPR performance with illumination in two directions (from above and below).

[0017] The term "conductive layer" as used herein may be a conductive material that exhibits surface plasmon resonance when excited with electromagnetic energy, such as light waves. The conductive material may refer to, for example, a metallic conductive material. Such metallic conductive materials may be any metal, such as 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 aspect, the conductive material is gold, silver, aluminum, sodium, indium, or titanium. The metal may be in its pure form or may be coated with additional layers of protective and strengthening materials.

[0018] A conductive material may be "optically observable" if it exhibits significant scattering intensity in the optical range (ultraviolet-visible-infrared spectrum) that includes wavelengths from approximately 100 nanometers (nm) to 3000 nm. A conductive material may be "visually observable" if it exhibits significant scattering intensity in the wavelength range detectable by the human eye, i.e., the visible spectrum, approximately 380 nm to 750 nm.

[0019] In one embodiment, the membrane support layer is a structured membrane support layer.

[0020] In one embodiment, the membrane support layer is silicon nitride or sodium dioxide. Other support materials include substrates that can be patterned to form coupled multi-layer plasmonic structures.

[0021] The diameter and periodicity of the multiple apertures through the conductive layer and membrane support layer can be varied to achieve different resonant wavelengths and evanescent wave penetration.

[0022] The multiple apertures include symmetric circular holes, spatially anisotropic shapes such as ellipses, slits, and also any aperture that is triangular, square, rectangular, or polygonal. A combination of holes of different shapes can also be used. The aperture may be less than about 1500 nm, less than about 1400 nm, less than about 1300 nm, less than about 1200 nm, less than about 1100 nm, less than about 1000 nm, less than about 900 nm, less than about 800 nm, less than about 700 nm, less than about 600 nm, less than about 500 nm, less than about 450 nm, less than about 400 nm, less than about 350 nm, less than about 300 nm, less than about 250 nm, less than about 240 nm, less than about 230 nm, less than about 220 nm, less than about 210 nm, less than about 200 nm, less than about 190 nm, less than about 180 nm, less than about 170 nm, less than about 160 nm, less than about 150 nm, less than about 140 nm, less than about 130 nm, less than about 120 nm, less than about 110 nm, less than about 100 nm, less than about 90 nm, less than about 80 nm, less than about 70 The nanoparticles may have a dimension or diameter of less than about 10 nm, less than about 60 nm, less than about 50 nm, less than about 40 nm, less than about 30 nm, less than about 20 nm, or less than about 10 nm.

[0023] In one embodiment, the aperture may have a size or diameter of about 150 nm to about 450 nm. In one embodiment, the aperture has a size or diameter selected from the group consisting of 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, 410 nm, 420 nm, 430 nm, 440 nm, 450 nm, or anywhere in between. In one embodiment, the aperture is a hole and has a diameter of 230 nm.

[0024] The term "periodicity" may refer to the repetition or repetition of apertures at regular intervals due to their positioning on the sensor chip. Thus, the term "periodic" refers to a regular, predetermined pattern of apertures relative to one another.

[0025] A surface plasmon resonance sensor chip can include a periodic array of apertures. This regular periodicity can allow for tight control of the resonant wavelength and evanescent wave penetration. In one embodiment, the apertures have a periodicity of about 250 nm to about 650 nm. In one embodiment, the apertures have a periodicity selected from the group consisting of 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, 410 nm, 420 nm, 430 nm, 440 nm, 450 nm, 460 nm, 470 nm, 480 nm, 490 nm, 500 nm, 510 nm, 520 nm, 530 nm, 540 nm, 550 nm, 560 nm, 570 nm, 580 nm, 590 nm, 600 nm, 610 nm, 620 nm, 630 nm, 640 nm, and 650 nm, or anywhere therebetween. In one embodiment, the apertures have a periodicity of 450 nm.

[0026] In one embodiment, the aperture is positioned such that the surface plasmon resonance resulting from the illumination has an attenuation length approximately equal to the diameter of the target of the first recognition molecule.

[0027] In one embodiment, the conductive layer and membrane support layer are disposed on a substrate having voids fabricated therein in areas adjacent to the plurality of apertures to permit bidirectional illumination of the conductive layer and / or membrane support layer, thereby generating surface plasmon resonance.

[0028] In one embodiment, the sensor chip includes a first recognition molecule immobilized on the surface of the conductive layer. The first recognition molecule can be immobilized on the surface using techniques well known in the art. For example, the first recognition molecule can be adsorbed to the surface. Alternatively, the surface can be coated with a layer of streptavidin or avidin prior to immobilization of the first recognition molecule. The first recognition molecule can be biotinylated and immobilized on the surface by streptavidin-biotin binding. In one embodiment, the surface can be incubated with polyethylene glycol (PEG) molecules. The surface can be incubated with activated (carboxylated) thiol-PEG. The surface can then be activated by carbodiimide crosslinking in a MES buffer mixture with excess NHS / EDC dissolved therein, and can bind to the first recognition molecule. In an alternative embodiment, the surface can be incubated with a polyethylene glycol (PEG) mixture containing long activated (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 obtain maximum functional binding. The surface can then be activated by carbodiimide crosslinking in an MES buffer mixture with excess NHS / EDC and coupled to the first recognition molecule.

[0029] The term "recognition molecule" may refer to a molecule that can specifically bind to an analyte. A "recognition molecule" may be an antibody, a nucleic acid, a peptide, an aptamer, a small molecule, or other synthetic agent.

[0030] The term "analyte" refers to a substance present in a sample that is to be detected or measured on a sensor chip. An "analyte" may include a cell, a virus, a nucleic acid, a lipid, a protein, a peptide, a glycopeptide, a nanovesicle, a microvesicle, an exosome, an extracellular vesicle, a sugar, a metabolite, or a combination thereof, or a tissue state. An "analyte" may be, for example, a peptide or a nucleic acid biomarker (such as miRNA) bound or associated with an exosome. An "analyte" may also be, for example, a cell-protein or protein-nucleic acid complex.

[0031] In one embodiment, the first recognition molecule is an antibody or a fragment thereof. The antibody can be, for example, an antibody that recognizes a pan-exosome marker or a marker associated or bound to exosomes. For example, the antibody can be an antibody specific to CD63, CD9, or CD81, which are abundant and characteristic of exosomes. The antibody can also be specific to a cell origin specific marker, such as CHL1, L1CAM, or NCAM. The antibody can also recognize a biomarker associated or bound to exosomes. For example, the antibody can be an anti-Aβ antibody that recognizes Aβ, or an antibody that recognizes APP, α-syn, or tau that are bound or bound to exosomes.

[0032] As used herein, the term "antibody" includes, but is not limited to, synthetic antibodies, monoclonal antibodies, recombinantly produced antibodies, multispecific antibodies (including bispecific antibodies), human antibodies, humanized antibodies, chimeric antibodies, single chain Fvs (scFvs), Fab fragments, F(ab') fragments, disulfide-linked Fvs (sdFvs) (including bispecific sdFvs), and anti-idiotypic (anti-Id) antibodies, as well as epitope-binding fragments of any of these. The antibodies provided herein may be monospecific, bispecific, trispecific, or even multispecific. Multispecific antibodies may be specific for different epitopes of a single polypeptide, or may be specific for both a single polypeptide and a heterologous epitope, such as a heterologous polypeptide or solid support.

[0033] The terms "protein" and "polypeptide" are used interchangeably and refer to any polymer of amino acids (dipeptides or greater) linked by peptide bonds or modified peptide bonds. Polypeptides of less than about 10-20 amino acid residues are commonly referred to as "peptides." The polypeptides of the invention may contain non-peptide components, such as carbohydrate groups. Carbohydrates and other non-peptide substituents may be added to the polypeptide depending on the cell producing the polypeptide, and will vary from cell type to cell type. Polypeptides are defined herein in terms of their amino acid backbone structures, and substituents such as carbohydrate groups are not specifically specified, but may be present nonetheless.

[0034] As used herein, a "nucleic acid" may be RNA or DNA, may be single-stranded or double-stranded, and may be, for example, a nucleic acid encoding a protein of interest, a polynucleotide, an oligonucleotide, a nucleic acid analog, such as peptide-nucleic acid (PNA), pseudo-complementary PNA (pc-PNA), locked nucleic acid (LNA), etc. Such nucleic acid sequences include, for example, but are not limited to, proteins that act as transcriptional repressors, antisense molecules, nucleic acid sequences encoding ribosomes, small inhibitory nucleic acid sequences, such as, but not limited to, RNAi, shRNAi, siRNA, microRNAi (mRNAi), antisense oligonucleotides, etc.

[0035] As used herein, "nanovesicles" may refer to naturally occurring or synthetic vesicles that contain an internal cavity. Nanovesicles may include a lipid bilayer membrane that surrounds the components of the internal cavity. Nanovesicles may include liposomes, exosomes, extracellular vesicles, microvesicles, apoptotic vesicles (or apoptotic bodies), vacuoles, lysosomes, transport vesicles, secretory vesicles, gas vesicles, matrix vesicles, or multivesicular bodies. Nanovesicles may be less than about 1000 nm, less than about 900 nm, less than about 800 nm, less than about 700 nm, less than about 600 nm, less than about 500 nm, less than about 450 nm, less than about 400 nm, less than about 350 nm, less than about 300 nm, less than about 250 nm, less than about 240 nm, less than about 230 nm, less than about 220 nm, less than about 210 nm, less than about 200 nm, less than about 190 nm, less than about 180 nm, less than about 170 nm, less than about 160 nm, less than about 150 nm, less than about 140 nm, less than about 130 nm, less than about 120 nm, less than about 110 nm, less than about 100 nm, less than about 90 nm, less than about 80 nm, less than about 70 nm, less than about 60 nm, less than about 50 nm, less than about 40 nm, less than about 30 nm, less than about 20 nm, or less than about 10 It may have dimensions of less than nm.

[0036] Exosomes are a type of nanovesicle, also referred to in the art as extracellular vesicles, microvesicles, or microparticles. These vesicles are released from eukaryotic cells to the outside of the cell or detach from the cell membrane to the outside of the cell. These membrane vesicles are heterogeneous in size, with diameters ranging from about 10 nm to about 5000 nm. These small vesicles (approximately 10-1000 nm in diameter, preferably 30-100 nm) released by 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 any size.

[0037] The term "sample" refers to any sample that contains an analyte or is tested for the presence of an analyte. Such samples include samples derived from or containing cells, organisms (bacteria, viruses), lysed cells or organisms, cell extracts, nuclear extracts, components of cells or organisms, extracellular fluids, in vitro culture media of cells or organisms, blood, plasma, serum, gastrointestinal secretions, urine, peritoneal fluid, tissue or tumor homogenates, synovial fluid, stool, saliva, sputum, cyst fluid, amniotic fluid, cerebrospinal fluid, peritoneal fluid, alveolar lavage fluid, semen, lymph, tears, pleural fluid, nipple aspirate, breast milk, external sections of the skin and respiratory tract, intestinal tract, and urogenital tract, and prostatic fluid. Samples can be viral or bacterial samples, samples taken from environmental sources such as polluted water bodies, air samples, or soil samples, as well as samples from the food industry. Samples can also be biological samples, which means that the sample is derived from or taken from a living organism. The organism may be in vivo (e.g., a whole organism) or in vitro (e.g., a cell or organ grown in culture). A "biological sample" may also refer to a cell or cell population, or a volume of tissue or fluid, from a subject. While a sample is most often removed from a subject, the term "biological sample" may also refer to cells or tissues that are analyzed in vivo, i.e., without removal from a subject. Although a "biological sample" often contains cells from a subject, the term may also refer to non-cellular biological material, such as the non-cellular portion of blood, saliva, or urine. Biological samples may be derived from resections of primary, secondary, or metastatic tumors, bronchoscopic or core needle biopsies, or cell bodies from pleural effusions. In addition, fine needle aspirate biological samples are also useful. In one embodiment, the biological sample is a primary ascites cell. Biological samples also include explants from patient tissues and primary and / or transformed cell cultures. A biological sample can be prepared by removing a cell sample from a subject, but can also be done using previously isolated cells or cell extracts (e.g., isolated by another person, at another time, and / or for another purpose). Archived tissue, such as that with a treatment history or prognosis history, can also be used.Biological samples include, but are not limited to, tissue biopsy, scraping (e.g., oral scraping), whole blood, plasma, serum, urine, saliva, cell culture, or cerebrospinal fluid. The sample analyzed by the compositions and methods described herein may be processed to purify or enrich the exosomes contained therein. In one embodiment, the sample is blood.

[0038] In one aspect, an imaging system is provided that includes a light source, a detector, and a sensor chip as defined herein, the detector positioned to detect light generated at the light source and transmitted through the sensor chip.

[0039] In one aspect, a kit is also provided that includes a sensor chip as defined herein.

[0040] The kit may further comprise a second recognition molecule specific for the analyte captured on the surface of the sensor chip or bound to the captured analyte. The kit may comprise one or more second recognition molecules each specific for one or more analytes such that the one or more analytes can be detected.

[0041] The second recognition molecule may enable (1) signal amplification, (2) colocalization analysis (e.g., detection of different targets simultaneously present in the same vesicle), and (3) differentiation of analyte subpopulations based on molecular and organizational differences.

[0042] The second recognition molecule may be bound to the signal amplification portion, which is capable of inducing the formation of insoluble aggregates with increased optical density compared to the captured analyte and increasing the measured surface plasmon resonance signal. For example, the kit may include a second recognition molecule that is an antibody (such as an antibody specific for Aβ42). The second recognition molecule may be bound to a horseradish peroxidase enzyme. The kit may further include an enzyme substrate. The horseradish peroxidase enzyme can thus induce the formation of insoluble aggregates with increased optical density compared to the captured analyte on the surface of the sensor chip.

[0043] The term "signal amplification molecule" may refer to a molecule that can induce the formation of insoluble aggregates on the surface of the sensor chip, thereby increasing the optical density compared to the captured analyte. This helps to increase the sensitivity of the sensor chip by causing a larger change in transmission wavelength (spectral shift) or a change in transmission intensity when the second recognition molecule binds to the analyte on the surface of the sensor chip. The "signal amplification molecule" may be, for example, an enzyme such as horseradish peroxidase, which reacts with the enzyme substrate to form insoluble aggregates on the surface of the sensor chip. The "signal amplification molecule" may be a secondary antibody that binds to the second recognition molecule on the surface of the sensor chip and causes aggregates to form. The secondary antibody may be further bound to an enzyme, gold particle, or large molecule that helps to form larger aggregates to increase the optical density.

[0044] In one aspect, the present invention relates to a sensitive analytical platform, so-called Amplified Plasmonic Exosomes (APEX), for detecting exosome-bound amyloid-β (Aβ) directly from blood samples of Alzheimer's disease (AD) patients. This analytical method may allow for multiplex population analysis using transmission surface plasmon resonance (SPR) and in situ enzymatic conversion of light products. The APEX technology may allow for multiparametric in situ profiling of exosomal components (e.g. proteins and miRNAs). The APEX platform is used to measure different circulating Aβ populations (exosome-bound, unbound, and total) as well as different circulating Aβ tissue states, and correlate these blood measurements with PET imaging of cerebral amyloid plaque burden.

[0045] In one aspect, a method of manufacturing a sensor chip is provided, the method comprising: (a) providing an upper membrane support layer; (b) depositing a conductive layer on the upper membrane support layer; (c) forming a plurality of apertures through the membrane support layer. the plurality of apertures also extend through the conductive layer and are positioned such that illumination of the conductive layer and / or the upper membrane support layer produces surface plasmon resonance.

[0046] In some embodiments, the method comprises: coating an upper membrane support layer on an upper surface of a silicon substrate and a lower membrane support layer on a lower surface of the silicon substrate; providing a layer of photoresist on the upper membrane support layer; and Defining a plurality of apertures in the photoresist by deep ultraviolet lithography (DUV) and transferring a pattern of the plurality of apertures into the top membrane support layer by reactive ion etching (RIE). Further includes:

[0047] In some embodiments, the method comprises: removing the photoresist of the upper membrane support layer and coating a silicon dioxide protective layer on the surface of the upper membrane support layer; coating the underlying membrane support layer with a layer of photoresist; defining sensing areas in the photoresist by photolithography; transferring the pattern of the sensing area into the lower membrane support layer by reactive ion etching (RIE); transferring the pattern of the sensing area onto the silicon substrate; removing the protective layer on the surface of the upper membrane support layer with diluted hydrogen fluoride; and depositing the conductive layer on the upper membrane support layer. Further includes:

[0048] The term "sensing area," as used herein, refers to the area of ​​the sensor chip that includes a plurality of holes that are arranged such that illumination of the plasmonic layer and / or membrane support layer produces surface plasmon resonance.

[0049] As used herein, the term "resist" refers to a thin layer used to transfer an image or pattern to a substrate on which it is deposited. Resist can be patterned lithographically to form a (sub)micrometer-scale temporary mask that protects selected areas of the underlying substrate during a subsequent processing step, typically etching. The material (typically a viscous solution) used to create this thin layer is also encompassed by the term resist. Resists are generally mixtures of polymers or their precursors and other small molecules (e.g. photoacid generators) specially formulated for a given lithography technique. Resists used in photolithography are called, for example, "photoresists". Resists used in electron beam lithography are called "e-beam resists".

[0050] In one aspect, a method for detecting an analyte in a sample is provided, the method comprising: (a) capturing an analyte on the surface of a sensor chip as defined herein; and (b) detecting binding between a second recognition molecule and the analyte captured on the surface of the sensor chip; wherein the second recognition molecule is specific for the analyte, and increased binding of the second recognition molecule compared to a control sample indicates the presence of the analyte in the sample.

[0051] The method may include detecting (sequentially or simultaneously) the binding of one or more second recognition molecules specific for 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 may be recognized by a different set of first and second recognition molecules. The first and second recognition molecules may each recognize the same analyte or different analytes. Various combinations of first and second recognition molecules may allow for the detection of co-localization of analytes. This may allow for the simultaneous detection of multiple analytes and may allow for the detection of co-localization of these molecules.

[0052] In one embodiment, the first recognition molecule is an antibody that recognizes Aβ42, and the second recognition molecule is another antibody that recognizes Aβ42.

[0053] In one embodiment, the first recognition molecule is an antibody that recognizes Aβ42, the second recognition molecule is an antibody that recognizes CD63, and co-localization of Aβ42 and CD63 is detected.

[0054] The detection of "binding" between the second recognition molecule and the analyte captured on the surface of the sensor chip can be by a spectral term shift (change in transmission wavelength) or a change in transmission intensity at a fixed wavelength. For example, the analyte captured on the surface of the sensor chip has an initial reference wavelength. After binding of the second recognition molecule, the transmission wavelength can shift to a longer wavelength.

[0055] The change in transmission resonant wavelength (or spectral term shift (Δλ)) or change in transmission intensity at a fixed wavelength of the sample can be compared to that observed in a control sample, thereby determining, for example, whether there is increased binding of the second recognition molecule to the captured analyte.

[0056] "Increased binding of the second recognition molecule" in a sample compared to a control sample can be determined by comparing the change in the spectral shift or the change in transmission intensity at a fixed wavelength after binding of the second recognition molecule between the sample and the control sample. An increased change in the spectral shift or the change in transmission intensity can indicate increased binding of the second recognition molecule to the analyte.

[0057] In one embodiment, a spectral shift or increased change in transmission intensity can refer to a 1.2-fold or greater increase between a subject and a control subject. This term is used for: 1.1x, 1.3x, 1.4x, 1.5x, 1.6x, 1.7x, 1.8x, 1.9x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x, 11x, 12x, 13x, 14x, 15x, 16x, 17x, 18x, 19x, 20x, 21x, 22x, 23x, 24x, 25x, 26x, 27x, 28x, 29x, 30x, 31x, 32x, 33x, 34x, 35x, 36x, 37x, 38x, 39x, 40x, 41x, 42x, 43x, 44x, 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, fold, 50-fold, 51-fold, 52-fold, 53-fold, 54-fold, 55-fold, 56-fold, 57-fold, 58-fold, 59-fold, 60-fold, 61-fold, 62-fold, 63-fold, 64-fold, 65-fold, 66-fold, 67-fold, 68-fold, 69-fold, 70-fold, 71-fold, 72-fold, 73-fold, 74-fold, 75-fold, 76-fold, 77-fold, 78-fold, 79-fold, 80-fold, 81-fold, 82-fold, 83-fold, 84-fold, 85-fold, 86-fold, 87-fold, 88-fold, 89-fold, 90-fold, 91-fold, 92-fold, 93-fold, 94-fold, 95-fold, 96-fold, 97-fold, 98-fold, 99-fold, and 100-fold.

[0058] The second recognition molecule can be a recognition molecule specific to the analyte. The second recognition molecule can be coupled to a signal amplification portion, which is capable of inducing the formation of insoluble aggregates with increased optical density compared to the captured analyte. For example, the analyte captured on the surface of the sensor chip has an initial reference wavelength. After binding of the second recognition molecule, the transmission wavelength can shift to a longer wavelength. When the second recognition molecule binds to the signal amplification portion, the transmission wavelength can also shift to a longer wavelength due to the increase in optical density.

[0059] The second recognition molecule may be fused to the signal amplification moiety or alternatively, the second recognition molecule may be conjugated to the signal amplification moiety.

[0060] The signal amplification moiety can be an enzyme. In one embodiment, the signal amplification moiety is an enzyme. The enzyme can be horseradish peroxidase (HRP), alkaline phosphatase, glucose oxidase, β-lactamase, or β-galactoxidase, or enzymatic fragments thereof. In one embodiment, the enzyme is horseradish peroxidase. In one embodiment, the first biorecognition molecule is fused to the signal amplification moiety. For example, the first biorecognition molecule can be an antibody covalently fused to a horseradish peroxidase enzyme, and the enzyme is covalently linked to the antibody by techniques well known in the art.

[0061] The method may further comprise contacting the enzyme with an enzyme substrate. The enzyme substrate may be capable of forming an insoluble product in the presence of the enzyme or by the action of the enzyme. For horseradish peroxidase (HRP), preparations such as 3-amino-9-ethylcarbazole, 3,3',5,5'-tetramethylbenzidine, or chloronaphthol, 4-chloro-1-naphthol may be used. These substrates may become insoluble products after the enzymatic reaction of HRP.

[0062] In one embodiment, the enzyme substrate is 3,3'-diaminobenzidine tetrahydrochloride.

[0063] In an alternative embodiment, the signal amplification moiety may be a secondary antibody capable of binding to a second recognition molecule, the binding of which may induce the formation of insoluble aggregates.

[0064] In one embodiment, a first recognition molecule is immobilized on the surface of a surface plasmon resonance sensor chip, and the first recognition molecule is capable of capturing an analyte on the surface of the sensor chip. The analyte can be an exosome-bound or exosome-associated biomarker. The analyte can be an exosome-bound aggregation biomarker. The first recognition molecule can be specific for the analyte.

[0065] In one embodiment, the first recognition molecule is an antibody. The antibody can be, for example, an antibody that recognizes a pan-exosome marker, or a marker associated or bound to exosomes. For example, the antibody can be specific for CD63, CD9, or CD81, which are abundant and characteristic of exosomes. The antibody can also be specific for cell origin specific markers such as CHL1, L1CAM, or NCAM. The antibody can also recognize a biomarker associated or bound to exosomes. For example, the antibody can be an anti-Aβ antibody that recognizes Aβ, or an antibody that recognizes APP, α-syn, or tau that are bound or bound to exosomes.

[0066] The term "control sample" refers to a sample that does not contain an analyte. A "control sample" can be used as a comparison to a sample to determine whether the sample contains the analyte of interest.

[0067] The term "biomarker" as used herein is understood to be an agent or element whose presence or level correlates with an event of interest. A biomarker can be a cell, a protein, a nucleic acid, a peptide, a glycopeptide, an exosome, or a combination thereof. For example, a biomarker is Aβ42 or tau peptide, whose presence or level indicates that a subject suffers from or is at risk of developing a neurodegenerative disease or amyloidosis. In another example, a biomarker is an exosome-bound Aβ42 or tau peptide, whose presence or level indicates that a subject suffers from or is at risk of developing a neurodegenerative disease or amyloidosis. In some embodiments, a biomarker is an exosome-associated biomarker.

[0068] In one aspect there is provided a use of a sensor chip as defined herein for detecting an analyte.

[0069] In one aspect, a method of detecting a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising: (a) contacting the sample with the surface of a sensor chip as defined herein; and (b) detecting binding between a second recognition molecule and the analyte captured on the surface of the sensor chip; wherein the second recognition molecule is specific for the analyte, and increased binding of the second recognition molecule compared to a control subject indicates that the subject is afflicted with a neurodegenerative disease or amyloidosis.

[0070] The term "subject" refers to any animal, such as any vertebrate or mammal, particularly a human, and may also be referred to as, for example, an individual or a patient.

[0071] The term "control subject" refers to a subject known not to have a neurodegenerative disease or amyloidosis, or a subject not at risk of having a neurodegenerative disease or amyloidosis. A "control subject" may be a healthy subject. A "control subject" may be a person who is not cognitively impaired (NCI). The term includes a sample taken from a control subject.

[0072] In one embodiment, the biomarker is an exosome-bound or exosome-associated biomarker. In one embodiment, the biomarker is an exosome-bound aggregation biomarker. The biomarker may be selected from the group consisting of, but not limited to, Aβ, APP, α-Syn, tau, APOE, SOD1, TDP-43, bassoon, and fibronectin. In one embodiment, the Aβ is Aβ42. In another embodiment, the 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 the group consisting of CD63, CD9, CD81, ALIX, TSG101, flotillin-1, flotillin-2, LAMP-1, HSP70, HSP90, RNA, and DNA.

[0073] The neurodegenerative disease may be selected from the group consisting of 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 a tauopathy.

[0074] The method may further include treating a subject found to be suffering from a neurodegenerative disease or amyloidosis.

[0075] The term "treating," as used herein, may refer to (1) preventing or delaying the appearance of one or more symptoms of a disease; (2) inhibiting the development of a disease or one or more symptoms of a disease; (3) alleviating the disease, i.e., causing the regression of a disease or at least one or more symptoms of a disease; and / or (4) causing a decrease in the severity of one or more symptoms of a disease.

[0076] In one aspect, the term "treating" refers to administering a drug to slow the progression of a neurodegenerative disease or amyloidosis.

[0077] Provided herein is a method for detecting a neurodegenerative disease or amyloidosis in a subject, the method comprising detecting a level of an exosome-bound biomarker in a sample taken from the subject, wherein an increased level of the exosome-bound biomarker compared to a reference indicates that the subject is affected by a neurodegenerative disease or amyloidosis.

[0078] In one aspect, a method is provided for detecting a neurodegenerative disease or amyloidosis in a subject, the method comprising detecting a level of an exosome-bound aggregation biomarker in a sample obtained from the subject, wherein an increased level of the exosome-bound aggregation biomarker compared to a reference indicates that the subject is affected by a neurodegenerative disease or amyloidosis.

[0079] The method may include detection of one or more exosome-bound biomarkers in a sample, which allows for detection of co-localization or the presence of multiple biomarkers present on the same exosome.

[0080] The biomarkers may be selected from the group consisting of, but not limited to, Aβ, APP, α-Syn, Tau, APOE, SOD1, TDP-43, bassoon, and / or fibronectin.

[0081] The method may include detecting a level of a molecular subtype of an exosome-bound biomarker.

[0082] In one embodiment, the Aβ is Aβ42 or Aβ40. In some embodiments, the Aβ is Aβ42, Aβ40, Aβ39, or Aβ38.

[0083] In one embodiment, the Aβ is a prefibrillar aggregate. Prefibrillar Aβ aggregates have been found to preferentially associate with exosomes. In one embodiment, the method defined herein comprises detecting exosomes bound to prefibrillar Aβ aggregates.

[0084] In one embodiment, the aggregation biomarker is a prefibrillar aggregate. The aggregation biomarker can be a prefibrillar aggregate of Aβ. Alternatively, the aggregation biomarker can be a prefibrillar aggregate of APP, α-Syn, or tau.

[0085] In one embodiment, the reference is a control subject.

[0086] In one aspect, a method is provided in which exosome binding is measured as a surrogate for determining the prefibrillar organization state of protein aggregates, where increased levels of protein aggregates in the prefibrillar organization state compared to a control indicates that the subject is afflicted with a neurodegenerative disease or amyloidosis.

[0087] In one embodiment, the method further comprises detecting an exosome biomarker selected from the group consisting of CD63, CD9, CD81, ALIX, TSG101, flotillin-1, flotillin-2, LAMP-1, HSP70, HSP90, DNA, and RNA, wherein the exosome biomarker co-localizes with the exosome-bound biomarker.

[0088] In one embodiment, the method further comprises detecting a neuronal biomarker selected from the group consisting of NCAM, L1CAM, CHL-1, and IRS-1, wherein the neuronal biomarker co-localizes with the exosome-bound biomarker.

[0089] In one embodiment, a method is provided for measuring the tissue and molecular subpopulations of different biomarkers.For example, the method can include measuring the exosome-bound biomarker, the free (unbound) biomarker, and the total (exosome-bound and unbound) biomarker.The method can also include measuring the relative concentration of different biomarkers to better predict disease.

[0090] In one embodiment, the neurodegenerative disease is selected from the group consisting of 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 a tauopathy.

[0091] In one embodiment, the neurodegenerative disease is selected from the group consisting of Alzheimer's disease and mild cognitive impairment.

[0092] The method may further include treating the subject suffering from a neurodegenerative disease or amyloidosis.

[0093] The method may further be correlated with brain imaging studies such as PET imaging. This includes correlation with imaging of specific brain regions. In one aspect, a method is provided for identifying and measuring circulating biomarkers that correlate with brain imaging (PET). In one aspect, a method is provided for identifying and measuring circulating biomarkers that correlate with imaging (PET) of specific brain regions.

[0094] The present invention is based on the findings that (1) biomarkers (including co-localized individual markers) can be used to measure and characterize different molecular, biophysical, and tissue subpopulations of circulating Aβ; (2) circulating exosome-bound Aβ in blood can be strongly correlated with PET imaging of Aβ deposits (global average) across different patient populations; (3) circulating exosome-bound Aβ in blood can be strongly correlated with PET imaging of Aβ (early AD areas, swaths) across different patient populations; and (4) circulating exosome-bound Aβ can distinguish clinical subgroups (such as Alzheimer's disease, mild cognitive impairment, no cognitive impairment, vascular dementia, vascular mild cognitive impairment, and acute stroke).

[0095] The method may include administering a therapeutically effective amount of a drug to a subject in need of treatment. The drug may be a cholinesterase inhibitor, such as donepezil, rivastigmate, 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, may be administered to a subject in need of treatment.

[0096] In some aspects, molecules that are effective in reducing the amount of starch forming protein aggregates may be potential candidates for therapeutic agents. Thus, in some embodiments, the method comprises administering to a subject in need of treatment one or more of the following molecules (drugs): methylthioninium chloride, leuco-methylthioninium bis(hydromethanesulfonate), curcumin, acid fuchsin, epigallocatechin gallate, safranal, Congo Red, apigenin, azure C, basic blue 41, (trans,trans)-1-bromo-2,5-bis-(3-hydroxycarbonyl-4-hydroxy)styrylbenzene (BSB), Chicago Sky Blue 6B, -cyclodextrin, daunomycin hydrochloride, dimethyl yellow, direct red 80, 2,2-dihydroxybenzophenone, hexadecyltrimethylammonium bromide (C16), hemin chloride, hematin, indomethacin, juglone, lacmoid, meclocycline sulfosalicylate, melatonin, myricetin, 1,2-naphthoquinone, nordihydroguaiaretic acid, R( In some embodiments, the method may include administering one or more of, or a combination of two or more of, benzodiazepine, benzoyl perfluorooctanoate ...

[0097] An increased level of an exosome-bound biomarker can refer to a 1.2-fold or greater increase between a subject and a control subject. The term "increased level" refers to any of the following: 1.1x, 1.3x, 1.4x, 1.5x, 1.6x, 1.7x, 1.8x, 1.9x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x, 11x, 12x, 13x, 14x, 15x, 16x, 17x, 18x, 19x, 20x, 21x, 22x, 23x, 24x, 25x, 26x, 27x, 28x, 29x, 30x, 31x, 32x, 33x, 34x, 35x, 36x, 37x, 38x, 39x, 40x, 41x, 42x, 43x, 44x, 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, It may also refer to an increase selected from the group consisting of 8x, 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, and 100x.

[0098] In one aspect, a method is provided for detecting a subject at risk of developing a neurodegenerative disease, the method comprising detecting a level of an exosome-bound aggregation biomarker in a sample obtained from the subject, wherein an increased level of the exosome-bound aggregation biomarker compared to a reference indicates that the subject is affected by the neurodegenerative disease.

[0099] In one aspect, a method of detecting and treating a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising: (a) detecting a level of an exosome-bound aggregation biomarker in a sample obtained from a subject, wherein an increased level of the exosome-bound aggregation biomarker compared to a reference indicates that the subject is afflicted with a neurodegenerative disease or amyloidosis; and (b) treating a subject suffering from a neurodegenerative disease or amyloidosis. Includes.

[0100] In one aspect, a method of treating a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising: (a) detecting a level of an exosome-bound aggregation biomarker in a sample obtained from a subject, wherein an increased level of the exosome-bound aggregation biomarker compared to a reference indicates that the subject is afflicted with a neurodegenerative disease or amyloidosis; and (b) treating a subject suffering from a neurodegenerative disease or amyloidosis. Includes.

[0101] In one aspect, a method is provided for detecting and slowing the progression of a neurodegenerative disease or amyloidosis in a subject, the method comprising: (a) detecting a level of an exosome-bound aggregation biomarker in a sample obtained from a subject, wherein an increased level of the exosome-bound aggregation biomarker compared to a reference indicates that the subject is afflicted with a neurodegenerative disease or amyloidosis; and (b) treating a subject suffering from a neurodegenerative disease or amyloidosis. Includes.

[0102] In one aspect, a method for determining the aggregation state of a biomarker in a sample is provided, the method comprising detecting a level of an exosome-bound biomarker in the sample, where an increased level of the exosome-bound biomarker compared to a reference is indicative of the degree of aggregation of the biomarker.

[0103] The method may include contacting the sample with an exosome population prior to detecting the level of the exosome-bound biomarker.

[0104] In one aspect, aggregated biomarkers in a sample preferentially bind to exosomes compared to non-aggregated biomarkers.

[0105] The sample may be taken from a subject.

[0106] In one aspect, increased aggregation of the biomarker compared to the reference is indicative of a neurodegenerative disease or amyloidosis in the subject.

[0107] The method may further include treating the subject suffering from a neurodegenerative disease or amyloidosis.

[0108] In one aspect, a method of determining the aggregation state of a biomarker in a sample is provided, the method comprising contacting the sample with a population of exosomes and detecting a level of an exosome-bound biomarker in the sample, where an increased level of the exosome-bound biomarker compared to a reference is indicative of the degree of aggregation of the biomarker.

[0109] Those skilled in the art will understand that the invention described herein is capable of variations and modifications other than those specifically described. It is to be understood that the invention includes all such variations and modifications that are within its spirit and scope. The invention also includes all steps, features, compositions, and compounds referred to or shown in this specification, individually or collectively, and any and all combinations of any two or more of such steps or features.

[0110] Throughout this specification and the claims that follow, unless the context otherwise requires, the word "comprise" and variations thereof, such as "comprises" and "comprising," are to be understood to imply the inclusion of a stated integer or step, or group of integers or steps, but not the exclusion of any other integers or steps, or group of integers or steps.

[0111] The reference herein to any prior publication (or information derived therefrom) or to any known matter is not, and should not be construed as, an acknowledgement or admission or in any way an indication that such prior publication (or information derived therefrom) or known matter forms part of the common general knowledge in the field to which this specification pertains.

[0112] Specific aspects of the present invention will now be described with reference to the following examples, which are for illustrative purposes only and are not intended to limit the scope of the generality described above. EXAMPLES

[0113] The reference herein to any prior publication (or information derived therefrom) or to any known matter is not, and should not be construed as, an acknowledgement or admission or in any way an indication that such prior publication (or information derived therefrom) or known matter forms part of the general knowledge within the scope of the endeavor to which this specification pertains.

[0114] material and method Cell culture. Human cell lines SH-SY5Y (neuronal cells), HUVEC (umbilical vein endothelium), THP-1 (monocytes), PC-3 (prostate epithelium), and SK-OV-3 (ovarian epithelium) were obtained from the American Type Culture Collection. GLI36 (glia) and SK-OV-3 were grown in Dulbecco's Modified Essential Medium (DMEM, Gibco). SH-SY5Y was cultured in Dulbecco's Modified Eagle's 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 were supplemented with 10% fetal bovine serum (FBS) and penicillin-streptomycin, except EGM-2, which was supplemented with 5% FBS.

[0115] Exosome isolation and quantification. Cells from passages 1–15 were cultured in vesicle-free medium (5% FBS stripped) for 48 h before vesicles were harvested. All exosome-containing medium was filtered through a 0.2 μm membrane filter (Millipore), isolated by differential centrifugation (first at 10,000 g and then at 100,000 g) and used for exosome analysis using the APEX platform. To isolate exosomes from blood cells and platelets, blood cells were obtained from blood fractions and platelets from platelet-rich plasma. These components were washed with HEPES-buffered saline and incubated with 2 mM calcium chloride and 2 μM calcium ionophore (A23187) at 37 °C to stimulate exosome production. All vesicles were then harvested as previously described. For independent quantification of exosome concentration, a nanoparticle tracking analysis (NTA) system (NS300, Nanosight) was used. For optimal measurements, the exosome concentration was adjusted to ensure that approximately 50 vesicles were in the field of view, and all NTA measurements were performed with the same system settings for consistency.

[0116] Fabrication of APEX sensors. APEX sensors were fabricated on 8-inch silicon (Si) wafers. Briefly, 10 nm of silicon dioxide (SiO 2 ) layer and a 145 nm thick silicon nitride (Si) layer was then grown on the silicon substrate by low pressure chemical vapor deposition (LPCVD). 3 N 4 ) was deposited on the wafer. After coating with photoresist, deep ultraviolet (DUV) lithography was performed to define a nanohole array pattern in the resist. This pattern was then removed by reactive ion etching (RIE) on the Si 3 N 4 After removing the photoresist, a SiO 2 A thin protective layer (100 nm) of Si was deposited on the front side of the wafer. Photoresist was spin-coated on the back side of the wafer to allow light transmission; lithographic methods were used to define the sensing area. Si 3 N 4 and SiO2 The SiO was then etched with potassium hydroxide (KOH) and tetramethylammonium hydroxide (TMAH). After etching, the protective SiO was removed using dilute hydrogen fluoride (DHF) (1:100). 2 Finally, Ti / Au (10 nm / 100 nm) was applied to the Si 3 N 4 All nanohole dimensions and uniformity of the sensors were characterized by scanning electron microscopy (JEOL 6701).

[0117] Channel assembly. Multichannel flow cells were fabricated by standard soft lithography. A mold was fabricated using SU-8 negative resist (SU8-2025, Microchem). A Si wafer was spin-coated with photoresist for 30 s at 2000 rpm and baked at 65°C and 95°C for 2 and 5 min, respectively. After exposure to UV light, the resist was baked again and then developed under agitation. The developed mold was chemically treated with trichlorosilane vapor in a desiccator for 15 min before further use. Polydimethylsiloxane polymer (PDMS) and crosslinker were mixed in a 10:1 ratio and poured into the SU-8 mold. After curing at 65°C for 4 h, the PDMS layer was cut out of the mold and assembled into the APEX sensor. For sample processing, all inlets and outlets were created with a 1.1 mm biopsy punch.

[0118] Optical setup and spectral analysis. A tungsten halogen lamp (Stocker Yale Inc.) illuminated the APEX sensor through a 10X microscope objective. A fiber optic collected the transmitted light and sent it to a spectrometer (Ocean Optics). All measurements were performed at room temperature in a closed box to eliminate ambient light interference. The intensity of the transmitted light was digitally recorded as counts versus wavelength (330 nm–1600 nm). For spectral analysis, the spectral peaks were determined by fitting the transmission peaks with a local regression method using a custom R program. All fits were performed locally; that is, for fitting a point x, the fit was performed using the points near x weighted by their distance from x. Compared to fitting with a multi-order polynomial curve, this method could eliminate the variability in the results caused by the number of data points and the data range analyzed. The optimal sensor geometry was determined (Figure 10) using spectral changes quantifying the peak transmission intensity, peak shape (full width at half maximum, FWHM), and detection sensitivity in response to changes in refractive index. Measured transmission spectra demonstrated uniformity across different sensors, with a standard deviation of baseline spectral peak positions of 0.03 nm. All spectral shifts (Δλ) were determined as the change in the transmission spectral peak and calculated relative to the appropriate control experiment (see below for details).

[0119] Sensor surface functionalization. To impart molecular specificity to the APEX sensor, the fabricated Au surface was first incubated with a polyethylene glycol (PEG) mixture containing long active (carboxylated) thiol-PEG and short inactive methylated thiol-PEG (Thermo Scientific) (active:inactive 1:3, 10 mM in PBS) for 2 h at room temperature. After washing, the surface was activated by carbodiimide crosslinking in a MES buffer mixture with excess NHS / EDC dissolved, and conjugated with specific probes and ligands (e.g., antibodies and Aβ42 aggregates). All probe information is listed in Table 1. Excess unbound probe was removed by PBS washing. The conjugated sensor was stored at 4 °C in PBS for later use. All sensor surface modifications were observed spectrally to confirm uniform functionalization.

[0120] Table 1. List of markers and their probes used in profiling TIFF2025029000000001.tif197166TIFF2025029000000002.tif152164

[0121] APEX signal amplification. To establish APEX amplification, we incorporated enzymatic growth of insoluble photoproducts for signal enhancement and optimized the photosubstrate concentration and reaction duration to construct the platform. Briefly, exosomes were incubated with CD63-functionalized APEX sensor (BD Biosciences) for 10 min. The bound vesicles were then labeled with biotinylated anti-CD63 antibody (Ancell, 10 min). As a control experiment, the same amount of biotinylated IgG isotope control antibody (Biolegend) was used for bound vesicles to determine the amplification efficiency. After washing off the unbound antibody, high-sensitivity horseradish peroxidase conjugated with neutravidin (Thermo Scientific) was reacted with the bound vesicles, and then different concentrations of 3,3'-diaminobenzidine tetrahydrochloride (Life Technologies) were added as the photosubstrate. The optimal substrate concentration and reaction duration were determined by observing the real-time spectral changes. The optimized conditions were determined as 1 mg / mL for 3 min. All incubation and washing flow rates were maintained at 3 μl / min and 10 μl / min, respectively. Localized deposition of insoluble photoproducts was confirmed by scanning electron microscopy. The optimized workflow is shown in Figure 11a.

[0122] Using this set of conditions, we further titrated known amounts of exosomes and measured their associated APEX signal. We determined the APEX detection limit = 3 x (standard deviation of background signal from control) as the lowest target concentration that could generate a detectable signal.

[0123] APEX protein detection. All sensor surfaces were blocked with 2% w / v bovine serum albumin (BSA) to reduce nonspecific protein binding. Exosomes were loaded onto the functionalized sensors, incubated at room temperature for 10 min to capture exosomes, and washed with PBS to remove unbound exosomes. For extravesicular protein targets, exosomes were directly labeled with detection antibodies for APEX amplification, as described above. For intravesicular protein targets, exosomes were subjected to additional fixation and permeabilization (eBioscience) before being labeled with detection antibodies. Spectral measurements were performed before and after APEX amplification and analyzed with a custom-designed R program.

[0124] APEX miRNA detection. APEX sensors were functionalized with p19 protein (New England Biolabs) through 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. This mixture was loaded onto the functionalized sensor in binding buffer (1x p19 Binding Buffer, pH 7.0, 40 U RNase inhibitor, 0.1 mg / mL BSA) to allow p19 capture of the hybridized miRNA target / RNA probe duplexes. For APEX amplification, the bound biotinylated duplexes were loaded with high sensitivity horseradish peroxidase conjugated with neutravidin (Thermo Scientific). Spectral measurements were performed and analyzed with a custom-designed R program.

[0125] Enzyme-linked immunosorbent assay (ELISA). ELISA plates (Thermo Scientific) were adsorbed with capture antibody (5 μg / ml), blocked with Superblock (Thermo Scientific), and incubated with samples. After washing with PBST (PBS containing 0.05% Tween 20), detection antibody (2 μg / ml) was added and incubated for 2 h at room temperature. After incubation with horseradish peroxidase-conjugated secondary antibody (Thermo Scientific) and chemiluminescent substrate (Thermo Scientific), proteins were detected by measuring chemiluminescence intensity (Tecan).

[0126] Western blotting. Exosomes isolated by ultracentrifugation were dissolved in radioimmunoprecipitation assay (RIPA) buffer containing protease inhibitors (Thermo Scientific) and quantified by bicinchoninic acid assay (BCA assay, Thermo Scientific). Protein lysates were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), transferred to polyvinylidene difluoride membranes (PVDF, Invitrogen), and immunoblotted with antibodies against protein markers: HSP90 (Cell Signaling), HSP70 (BioLegend), flotillin 1 (BD Biosciences), CD63 (Santa Cruz), ALIX (Cell Signaling), TSG101 (BD Biosciences), LAMP-1 (R&D Systems), and the neuronal marker NCAM (R&D Systems). After incubation with horseradish peroxidase-conjugated secondary antibodies (Cell Signaling), immunodetection was performed by enhanced chemiluminescence (Thermo Scientific).

[0127] Protein aggregation. Lyophilized NH 4OH-treated Aβ42 protein (rPeptide) was resuspended in NaOH (60 mM, 4°C), sonicated, and the pH was adjusted to pH 7.4 in PBS34. The protein was immediately filtered through a 0.2 μm membrane filter (Millipore), and the filtrate was used as the smaller Aβ42 aggregates. For the preparation of larger Aβ42 aggregates, the protein was treated as above, incubated with stirring for 1 h to induce further aggregation, and then filtered through a 0.2 μm membrane filter (Millipore). The filtrate was used as the larger Aβ aggregates. To prepare similarly sized BSA aggregates as a control, 2% w / v BSA was dissolved in PBS and heated at 80°C for 1 and 2 h to induce clustering of the small and large control aggregates, respectively.

[0128] Aggregate sizing. The hydrodynamic diameter of Aβ42 and BSA aggregates was determined by dynamic light scattering analysis (Zetasizer Nano ZSP, Malvern). 3 x 14 measurements were performed at 4°C. Z-average diameter and polydispersity were analyzed. Autocorrelation function and polydispersity were observed for each measurement to ensure the quality of the sizing samples.

[0129] Characterization of exosome-Aβ binding. The prepared protein aggregates (Aβ42 and BSA control) were used for surface functionalization of APEX sensors via EDC / NHS conjugation as described above. Unbound protein aggregates were washed away with PBS. The amount of protein conjugate was measured from the resulting transmission spectral shift. This information was used to determine the number of bound protein aggregates and their total bound protein surface area for exosome binding (see below for more information) in order to normalize the binding affinity. Following surface functionalization with protein aggregates, the sensors were loaded with exosomes (10 10 The spectral changes were measured every 3 seconds for a total of 480 seconds to construct a real-time kinetic sensorgram.

[0130] The exosome binding kinetics and binding affinity of protein aggregates of different sizes were determined. To account for the difference in protein aggregate size, as well as the exponential decay of SPR-related sensitivity (with increasing distance from the sensing surface), the following equation was used: The total surface area of ​​bound aggregates interacting with exosomes was calculated using TIFF2025029000000003.tif13128, where S is the signal, z is the distance from the sensor surface, E is the electric field at z = 0, the constant ld in this case is the decay length, which is set to 200 nm in the current sensor design, and r is the radius of the bound protein aggregate.

[0131] All protein aggregates were close to spheres as seen in transmission electron micrographs (Fig. 3b, left) and their r was determined by dynamic light scattering analysis. The above equation was used to determine the number of protein aggregates bound to the sensor, as well as their respective total surface areas, thereby estimating the number of binding sites available for exosome interaction. All exosome binding data (Δλ) were normalized to the respective protein binding sites. The normalized Aβ42 binding data were relative to a similarly sized BSA control, and the binding affinity constant KD was determined by fitting.

[0132] Scanning electron microscopy. All specimens were fixed in half-strength Karnovsky's fixative and washed twice in PBS. After dehydration through an ascending series of ethanol, specimens were transferred for critical drying (Leica) and then sputter-coated with gold (Leica) before being imaged under a scanning electron microscope (JEOL 6701).

[0133] Transmission electron microscopy. Exosomes were immunolabeled with gold nanoparticles (15 nm, Ted Pella), fixed in 2% paraformaldehyde, and transferred to copper grids (Ted Pella). Bound vesicles were washed and counterstained with a mixture of uranyl oxalate and methylcellulose. Dried samples were imaged under a transmission electron microscope (JEOL 2200FS).

[0134] Clinical sample collection. The study was approved by the NUH and NUS Institutional Review Boards (2015 / 00441, 2015 / 00406, and 2016 / 01201). All subjects were recruited according to an Institutional Review Board-approved protocol and provided informed consent. All recruited subjects underwent several neuropsychological assessments at the National University Hospital (NUH, Singapore), including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCa), and Vascular Dementia Battery (VDB) to assess cognitive abilities. A clinical diagnosis of AD, MCI, or NCI was made by neuropsychological assessment, as well as evaluation of clinical features and blood investigations. A clinical diagnosis of VaD and VMCI was made from 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 criteria46–48 and independent of APEX measurements. Acute stroke plasma samples were collected from patients within 24 hours of hospitalization with a stroke diagnosis. Longitudinal plasma samples were collected from patients at 1-year follow-up outpatient visits without PET brain imaging. For plasma collection, subjects' venous blood (5 ml) was collected into EDTA tubes and processed immediately prior to injection of PET radiotracer (if applicable). Briefly, all blood samples were centrifuged for 10 min at 400 g (4°C). Plasma was transferred without disrupting the buffy coat and centrifuged again for 10 min at 1100 g (4°C). All plasma samples were de-identified and stored at -80°C until measurement on the APEX platform. All APEX measurements were performed blinded to PET imaging results and clinical diagnosis.

[0135] Clinical APEX measurements. All plasma samples were measured according to the assay configuration outlined in Supplementary Fig. 15a. Briefly, to measure the exosome-bound Aβ42 population, no purification or isolation of vesicles was required, but native plasma samples were used directly by Aβ42 capture and CD63 detection (Aβ42+ CD63+). To demonstrate the presence of unbound Aβ42 population in plasma samples, the larger sized filtrate was removed by size-exclusion filtration (cutoff size = 50 nm, Whatman). This was necessary because this assay configuration based on Aβ42 capture and Aβ42 detection could not distinguish between unbound and total Aβ42. To measure unbound Aβ, plasma filtrate was assessed by Aβ42 capture and Aβ42 detection (Aβ42+ Aβ42+). It should be noted that this filtration was only performed to demonstrate the presence of unbound Aβ42 population and is not necessary in the clinical context to directly measure only the more reflective exosome-bound Aβ42 from native plasma samples. To measure total Aβ42, native plasma samples were directly assessed by Aβ42 capture and Aβ42 detection (Aβ42+ Aβ42+). All measurements used 5% BSA as a blocking agent for the APEX sensor. We also included a sample-matched negative control in which the same sample was incubated on a control sensor functionalized with an IgG isotype control antibody. All measurements were relativized to this IgG control to account for sample-matched nonspecific binding.

[0136] Positron Emission Tomography (PET) Imaging. After blood sampling, subjects were scanned using a Siemens 3T Biograph mMR system (Siemens Healthineers) and PET and MR images were acquired simultaneously. PET data were acquired 40–70 min after intravenous injection of 370 MBq of 11C-Pittsburgh Compound B (PiB). MR data were acquired using a 12-channel head-receiving coil for acquisition and consisted of Ultrashort Echo Time (UTE) images and T1-weighted magnetization-prepared gradient echo (MPRAGE) images for PET attenuation correction (1 mm isotropic resolution, TI / TE / TR = 900 / 3.05 / 1950 ms).

[0137] Analysis of PET data. T1-weighted MPRAGE images were processed with Freesurfer (5.3.0) to obtain cortical segmentations for PET data analysis. PET images were restored using the Ordinary Poisson Ordered Subset Expectation Maximization (OP-OSEM) algorithm and smoothed using a 4 mm Gaussian filter. Data were attenuated using a μ-map based on UTE. The resulting attenuation-corrected standardized uptake value (SUV) images were then co-registered with the MPRAGE images using Advanced Normalization Tools (ANTs), and standardized uptake value ratios (SUVRs) were calculated relative to the mean cerebellar gray matter intensity using subject-specific Freesurfer segmentations. A global mean SUVR was calculated for each patient by calculating the mean SUVR for specific regions and averaging the SUVRs for all brain regions.

[0138] Statistical analysis. All measurements were performed in triplicate and data are presented as mean ± standard deviation. Significance testing was performed by two-tailed Student's t-test. For inter-sample comparisons, multiple pairs of samples were tested respectively and the resulting P values ​​were adjusted for multiple hypothesis testing by Bonferroni correction. Values ​​with adjusted P < 0.05 were determined as significant. Analytical and biological coefficients of variation (i.e., within groups, between groups, and overall) were determined by paired one-way ANOVA tests. In this clinical study, correlations were performed along with linear regression to determine the goodness of fit (R2). All statistical analyses were performed using the R-package (version 3.4.2) and Graphpad Prism 7.

[0139] Example 1 Amplified plasmonic analysis of exosome-bound Aβ One of the earliest pathological features of AD is the deposition of Aβ in the brain. These plaques are formed from the clustering of abnormal amyloid protein fragments, mainly the hydrophobic splice variant Aβ42. Aβ protein can be released into the extracellular space and circulate in the bloodstream. Exosomes, also found in the extracellular space, are nanoscale membrane vesicles secreted from mammalian cells by fusion of multivesicular endosomes with the plasma membrane. During exosome nascent formation, glycoproteins and glycolipids are incorporated into the invaginating plasma membrane, which divides them into nascent exosomes10,11. These surface markers enable exosomes to associate and bind with extracellular Aβ protein (Fig. 1a). Multimodal characterization of extracellular vesicles derived from neuronal origin (SH-SY5Y cells) confirmed their exosome morphology, size distribution, and molecular composition (Fig. 5).

[0140] Transmission electron microscopy analysis of the vesicles further revealed their ability to bind Aβ42 protein aggregates (Fig. 1b and Fig. 5).

[0141] To assess exosome-Aβ binding, we developed the APEX platform for amplified multiparametric profiling of exosome molecular colocalization. The system measures transmission SPR with a periodic array of plasmonic nanoholes patterned as a bilayer photonic structure and also rapidly grows insoluble photoproducts on bound exosomes by in situ enzymatic conversion (Fig. 1c). To complement the APEX enzymatic attachment (occurring on the top surface of the sensor), we patterned plasmonic nanoholes of the same size on the coupled bilayer photonic system and enhanced the SPR measurement by backside illumination (away from the enzymatic activity) (Fig. 20). The resulting enzymatic attachment not only stably changes the refractive index for SPR signal amplification, as demonstrated by a red shift of the transmitted light spectrum (spectral shift Δλ, Fig. 1d), but is also spatially defined for molecular colocalization analysis. Scanning electron microscopy images of sensor-bound exosomes before and after APEX amplification confirmed the localized growth of photoattachments after enzymatic conversion (Fig. 6).

[0142] Thus, the binding of Aβ protein to exosomes was measured directly from clinical blood samples of AD patients and control subjects using the developed APEX platform, and the measurements were correlated with PET imaging of global and local cerebral plaque deposition (Fig. 1e). For high-throughput multiplexed clinical analysis, sensor microarrays were fabricated on 8-inch wafers by advanced fabrication methods (i.e., deep-ultraviolet lithography, Fig. 7), with each wafer accommodating more than 40 microarray chips with >2000 sensing elements (Fig. 8a). Figure 1f shows a photograph of the developed APEX microarray chip used for parallel measurements in this study. Scanning electron microscopy images of the developed sensors revealed that the fabrication was highly uniform (Fig. 8b).

[0143] Optimized signal amplification for multiplex profiling We first developed enzymatic APEX amplification. A series of sensor functionalizations, i.e., antibody binding, exosome binding, enzyme labeling, and photoproduct amplification, were performed to measure the stepwise total spectral shift (cumulative Δλ, Fig. 2a). We functionalized the sensor with an antibody against CD63, a type III lysosomal membrane protein that is abundant and characteristic of exosomes, to capture vesicles from neuronal cells (SHSY5Y). To facilitate the localized attachment of insoluble photoproducts, horseradish peroxidase was incorporated as a cascade enzyme to catalyze the conversion of its soluble substrate (3,3'-diaminobenzidine tetrahydrochloride). The vesicles bound to the sensor were enzymatically labeled with another anti-CD63 antibody. Although the enzyme labeling did not result in significant spectral changes, the formation of the photoproducts led to a signal enhancement of approximately 400%. In comparison, a control experiment using an IgG isotope control antibody demonstrated insignificant background changes (Fig. 9). Importantly, this SPR signal amplification correlated highly with an increased areal extent of highly localized photoattachments as confirmed by scanning electron microscopy (FIG. 2b).

[0144] To complement the enzymatic amplification (occurring on the top surface of the sensor), the APEX sensor design was optimized to improve its analytical performance and stability. Compared to the pre-made gold-on-glass design (Figure 20), which only supports front-side illumination, the APEX bilayer plasmonic structure allows SPR excitation by back-side illumination (Figure 2c). This novel optimized design not only exhibited strong transmission-type SPR by back-side illumination (Figure 10a-c), but also demonstrated analytical stability (Figure 10d), possibly due to the reduction of directly incident illumination (i.e., temperature fluctuations) on the enzyme activity. The APEX assay was further established by optimizing the enzyme substrate concentration as well as the reaction duration (Figure 2d). With constant back-side illumination, real-time spectral changes associated with different substrate concentrations were observed, and it was found that considerable signal amplification was achieved in less than 10 min, thus allowing the entire APEX workflow to be completed in less than 1 h.

[0145] With these optimized conditions in place, we next measured the APEX detection sensitivity for exosome quantification. Neuronal (SH-SY5Y)-derived vesicles were quantified by standard nanoparticle tracking analysis. Titration experiments were performed using anti-CD63 antibodies (Figure 2e). It was determined that the optimized APEX amplification could enhance detection sensitivity by 10-fold, establishing a limit of detection (LOD) of approximately 200 exosomes. This observed sensitivity is the highest LOD reported to date for bulk exosome measurement, and is 10 times higher than Western blotting and chemiluminescence ELISA, respectively. 5 Double and 10 3 It's twice as good.

[0146] Using the microarray APEX platform, we further developed assays to profile diverse markers related to neurodegenerative diseases. We established specific assays for protein markers (Figure 2f), such as amyloid beta (Aβ42), amyloid precursor protein (APP), alpha-synuclein (α-syn), L1 close homolog (CHL1), insulin receptor substrate 1 (IRS-1), neural cell adhesion molecule (NCAM), and tau protein. Importantly, with further development of the APEX assay workflow (Figure 11a), the platform demonstrated signal amplification capabilities for the detection of both extra- and intravesicular proteins as well as exosomal miRNAs (Figure 11b). All detection probes used in the assay development are listed in Table 1.

[0147] Increased binding of Aβ aggregates to exosomes Next, we used the developed APEX platform to evaluate the binding of exosomes with different structural forms of pathological Aβ protein. To mimic various stages of amyloid seeding and fibrillization, we prepared different sized aggregates of Aβ42, the main component of amyloid plaques. We formed Aβ42 aggregates of different sizes with varying degrees of clustering (Fig. 3a, see Methods for experimental details), and their spherical morphology and unimodal size distribution were confirmed by transmission electron microscopy and dynamic light scattering analysis, respectively (Fig. 3b). It was further noted that larger Aβ42 aggregates demonstrated a strong tendency to form fibrillar structures (Fig. S12).

[0148] To determine the kinetics of exosome-Aβ binding, prepared Aβ42 aggregates were immobilized on the APEX platform and the sensor was incubated with equal concentrations of neuronal cell-derived exosomes (Fig. 3c). For comparison, in a control experiment, similarly sized bovine serum albumin (BSA) aggregates (Fig. S13) were prepared and characterized. By measuring real-time exosome binding, it was demonstrated that exosomes could bind Aβ42 aggregates more strongly than similarly sized BSA controls, regardless of the aggregate size (Fig. 3d). More importantly, compared to the binding affinity with smaller Aβ42 aggregates (Fig. 3d, left), vesicles showed significantly higher (>5-fold higher) affinity with larger Aβ42 aggregates (Fig. 3d, right). All affinities were normalized to Aβ42 aggregate surface area and relative to the respective BSA controls (see Methods for details).

[0149] Next, extracellular vesicles from various cell origins were used to measure their binding to larger Aβ42 aggregates using the APEX platform (Fig. 3e). Equal concentrations of vesicles from various cell origins, as determined by nanoparticle tracking analysis (Fig. 14), were incubated with the Aβ42-functionalized sensor. Of all cell origins tested, it was noted that vesicles from neurons, erythrocytes, platelets, and epithelial cells demonstrated stronger binding to Aβ42 aggregates, whereas vesicles from glial and endothelial cells showed only negligible binding. Next, a panel of specific markers for each of these cell origins, as well as a pan-exosome marker (i.e., CD63), were used to amplify the APEX signal of bound vesicles. CD63 performed consistently across all vesicles tested in terms of signal enhancement. With this marker identification, an APEX assay was developed that uses CD63 to identify and measure exosome-bound Aβ (defined as Aβ42+ CD63+; Fig. 15).

[0150] Brain plaque burden revealed by circulating exosome-bound Aβ Given the enhanced binding of exosomes to prefibrillar Aβ aggregates, a component of amyloid plaques, we hypothesized that exosome-bound Aβ could be a circulating biomarker that better reflects cerebral plaque burden. To test this hypothesis, we developed different APEX assays using various antibodies to evaluate different circulating Aβ42 populations from clinical blood samples (Fig. S15a). Specifically, to characterize the exosome-bound Aβ42 population, the APEX assay was designed to directly enrich Aβ42 from native plasma and measure the relative amount of CD63 bound to the captured Aβ42. This assay configuration not only demonstrated specific detection of the Aβ42+ CD63+ population (Fig. S15b-c), but also reflected functional suitability, and the enhanced binding of prefibrillar Aβ aggregates to exosomes allowed the bound CD63 signal to be considered as a surrogate indicator to measure the relative amount of prefibrillar Aβ42 in the total circulating Aβ42. To demonstrate the presence of unbound Aβ42 populations, we removed large sized residues (e.g. exosomes) in plasma by size exclusion filtration, and then measured Aβ42 in the plasma filtrate. Finally, to measure total circulating Aβ42, native plasma was assessed by direct Aβ42 enrichment and Aβ42 detection.

[0151] We then conducted a feasibility clinical trial aimed at addressing the following key questions: (1) can APEX measure circulating Aβ42 directly from blood samples? (2) how well do different blood Aβ42 populations correlate with cerebral plaque burden? and (3) whether specific circulating Aβ42 populations can distinguish different clinical groups.

[0152] To achieve these goals, we recruited age-matched subjects (n = 84) with diagnosed AD (n = 17), diagnosed 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 insufficiency (i.e., vascular mild cognitive impairment, VMCI, n = 12; and acute stroke, n = 12). All clinical information is listed in Table 2. All subjects were recruited for blood sampling and APEX analysis. With the exception of acute stroke patients, all subjects also consented to simultaneous PET imaging of cerebral amyloid plaques. Plasma samples were collected immediately before injection of the Pittsburgh Compound B (PiB) radiotracer for PET imaging. PET imaging revealed a wide range of cerebral plaque burdens between and within the imaged clinical groups (Fig. 4a), demonstrating regional brain heterogeneity (Table 2), consistent with other published clinical studies.

[0153] Table 2. Clinical information and PET standardized uptake value ratio (SUVR) TIFF2025029000000004.tif78160AD: Alzheimer's disease, MCI: Mild cognitive impairment, NCI: No cognitive impairment, VaD: Vascular dementia, VMCI: Vascular mild cognitive impairment

[0154] Using the developed APEX assay (Fig. 11a), we assessed the different circulating Aβ42 populations in these clinical plasma samples, namely exosome-bound Aβ42, unbound, and total circulating Aβ42 (Fig. 4b). The exosome-bound Aβ42 population showed strong colocalization signals with exosome markers (i.e., CD63, CD9, and CD81) and neuronal markers (i.e., NCAM, L1CAM, and CHL-1), suggesting that neuronal exosomes may represent a large proportion in this population (Fig. 16a). As measurements of unbound Aβ42 were performed in plasma filtrates, we further characterized these filtrates, confirming their minute vesicle numbers, as well as minute colocalization of signals with exosome and neuronal markers (Fig. 16b-c). When correlated with global PET amyloid imaging, measurements of exosome-bound Aβ42 were significantly higher than those of unbound Aβ42 (Fig. 4b, center, R 2 = 0.0193) or total Aβ42 (Figure 4b, right, R 2 = 0.1471) (Figure 4b, left, R 2 = 0.9002). Interestingly, unlike the low and negative association demonstrated by total Aβ42 measurements (both in this study and in other published reports), relative CD63 measurements from the exosome-bound Aβ42 population showed a high and positive correlation with PET imaging of brain amyloid plaques. This finding may be due to similar exosome and PET tracer Aβ42 binding preferences, where (1) exosomes showed increased binding to prefibrillar Aβ42 aggregates, especially larger aggregates that can readily form fibrils (Fig. 3d), and (2) PET tracers bind significantly to larger amyloid fibrils but little to smaller aggregates. It should be noted that this high correlation also demonstrated brain region specificity, with exosome-bound Aβ42 measurements showing a stronger correlation with brain plaque burden in the cingulate region (early AD region, Fig. 17a) than in the occipital region (late AD region, Fig. 17b).

[0155] For distinguishing clinical diagnoses, only APEX analysis of exosome-bound Aβ42, but not of unbound or total Aβ42 populations, demonstrated good specificity (Fig. 4d). Specifically, measurements of exosome-bound Aβ42 could distinguish not only between AD clinical groups (i.e., AD vs. MCI, P < 0.01), but also from other healthy and clinical controls (P < 0.0001, Student's t-test). This demonstrated specificity is comparable to PET brain amyloid imaging for distinguishing between different clinical groups (Fig. 18). In contrast, nanoparticle tracking analysis of plasma extracellular vesicles showed no significant differences in vesicle size or concentration across all clinical groups (Fig. 19).

[0156] Example 2 AD is the most common form of severe dementia. Due to its complex and progressive neuropathology, early detection and timely intervention are essential for successful disease-modifying treatment. Although there has been high interest in finding serum biomarkers for AD, their development has been complicated by several challenges. First, unlike their counterparts in cerebrospinal fluid, pathological AD molecules in the bloodstream have been demonstrated to be at much lower concentrations. Plasma Aβ levels tend to be close to the lower detection limit of conventional ELISA assays, and this limitation may have led to some discrepancies in findings in published reports. Second, little correlation has been established between plasma Aβ analysis and brain plaque deposition, the earliest pathological hallmark of AD. One possible reason could be due to different measurement methodologies. PET imaging probes commonly used to determine brain amyloid burden preferentially measure insoluble fibril deposition, whereas conventional ELISA measurements measure soluble Aβ in plasma. In addition, potential correlations between blood-based measurements and brain pathology may have been hidden by the previous uniform blood measurements. However, this difference begs a more fundamental question: whether there are subpopulations of circulating Aβ protein that may better reflect fibrillary pathology in the brain.

[0157] To distinguish between different circulating Aβ populations, we developed a dedicated analytical platform (APEX) for multiparametric analysis of exosome-bound, unbound, and total Aβ directly from plasma. Specifically, we use new technological advances in sensor design, device fabrication, and assay development to achieve increased optical performance and detection capabilities (Figure 20). In terms of sensor design and fabrication, the APEX platform comprises periodic arrays of gold nanoholes on a patterned silicon nitride membrane, fabricated by deep-ultraviolet lithography, a state-of-the-art manufacturing process for large-scale precision nanopatterning. Thanks to these technological advances, the APEX technology achieves (1) improved optical performance (i.e., increased transmission intensity enabling SPR detection with bidirectional light illumination) and (2) reliable large-scale production. In terms of assay technology, the APEX platform leverages rapid in situ enzymatic conversion to achieve a highly localized amplified signal. This development not only allows for sensitive detection of diverse targets (e.g., intravesicular proteins and RNA targets) but also facilitates exosome colocalization analysis for multiparametric population studies, as insoluble deposits are only formed locally when multiple targets are simultaneously present in exosomes. The culmination of these technological advances makes the observed sensitivity of APEX the highest ever reported for exosome profiling, far surpassing standard ELISA measurements. Using the developed APEX platform, we demonstrated increased binding of exosomes to larger prefibrillar Aβ, the main component of fibrillar amyloid plaques. Further identification and quantification of a subpopulation of exosome-bound amyloid (CD63+ Aβ42+) in clinical plasma samples revealed a high correlation with cerebral amyloid plaque burden across a range of clinical populations (i.e., AD, MCI, cognitively normal controls, and clinical controls with other neurodegenerative and neurovascular diseases).

[0158] Assessment of distinct circulating Aβ populations could lead to a paradigm shift in AD research and clinical care. Accumulating evidence supports that prefibrillar Aβ aggregates may function as toxic drivers of AD neurodegeneration. Their preferential association with exosomes, as well as recent findings on the enrichment of exosomal markers in human amyloid plaques, not only shed light on possible new mechanisms of plaque seeding but also suggest the importance of exosome-bound Aβ as a circulating biomarker that better reflects the complex AD pathology. Thus, this study could complement other preclinical and clinical studies, both in terms of technology development and biomarker refinement. In terms of technology development, for example, IP mass spectrometry allows unbiased molecular screening and is beneficial for biomarker discovery, especially for the detection of different molecular isoforms and variants (e.g., (APP)669-711 and Aβ1-40), whereas the present APEX technology provides fast and sensitive readouts from native plasma samples and does not require extensive sample processing typically required for mass spectrometry measurements, making it suitable for targeted clinical measurements. In terms of biomarker refinement, as demonstrated in this study, analysis of different circulating Aβ populations may reveal novel correlations previously hidden by standard blood measurements and advance future blood-based AD clinical management. Importantly, the developed methodology could be augmented in over 400 ongoing AD clinical trials to redefine the current standard of care for patients. With additional technological innovations, such as on-chip exosome processing, combinatorial analysis with other AD markers, and longitudinal clinical cohort validation, the developed technology may provide comprehensive capabilities facilitating minimally invasive early detection, molecular stratification, and serial observation, all of which are crucial for the objective evaluation of disease-modifying treatments in different phases of clinical trials.

[0159] Example 3 This example shows that incubation of starch forming protein aggregates with inhibitors reduced spontaneous protein aggregation.

[0160] method Aggregate sizing. The hydrodynamic diameter of starch forming protein aggregates and BSA aggregates was determined by dynamic light scattering analysis (Zetasizer Nano ZSP, Malvern). 3 x 14 measurements were performed at 4°C. Z-average diameter and polydispersity were analyzed. Autocorrelation function and polydispersity were observed for each measurement to ensure the quality of the sized samples.

[0161] Protein aggregation. Lyophilized starch-forming proteins were resuspended in NaOH (60 mM, 4°C), sonicated and the pH was adjusted to pH 7.4 in PBS. The proteins were immediately filtered through a 0.2 μm membrane filter (Millipore) and the filtrate was used as small initial aggregates. For preparation of larger aggregates, the proteins were treated as above and incubated for 1 h with stirring to induce further aggregation. For treatment with inhibitors, 10 μM of inhibitor (e.g. methylene blue) was added to the protein before incubation. Once the incubation was completed, aggregate sizing was performed.

[0162] Optical analysis. For experimental analysis, the APEX sensor was back-illuminated through a 10X microscope objective with a tungsten halogen lamp (Stocker Yale Inc.). The transmitted light was collected by optical fibers and sent to a spectrometer (Ocean Optics). All measurements were performed at room temperature in a closed box to eliminate ambient light interference. The transmitted light intensity was digitally recorded as counts versus wavelength. For spectral analysis, the spectral peaks were determined by fitting the transmission peaks with a local regression method using a custom R program. All fits were performed locally; that is, for fitting a point x, the fit was performed using points near x weighted by their distance from x. Compared to fitting with multi-order polynomial curves, this method was able to eliminate the variability in the results caused by the number of data points and data range analyzed. All spectral shifts (Δλ) were determined as the change in the transmission spectral peak and calculated relative to the appropriate control experiment.

[0163] Characterization of exosome-protein binding. Prepared protein aggregates (Aβ42 and BSA control) were used for surface functionalization of APEX sensors via EDC / NHS conjugation as described above. 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 total bound protein surface area for exosome binding (see below for more information) to normalize the binding affinity. Following surface functionalization with protein aggregates, exosomes were loaded onto the sensor. Spectral changes were measured every 3 seconds for a total of 480 seconds to construct real-time kinetic sensorgrams. Exosome binding kinetics and binding affinity of protein aggregates of different sizes were determined.

[0164] To account for differences in protein aggregate size, as well as the exponential decay of SPR-related sensitivity (with increasing distance from the sensor surface), the following equation: The total surface area of ​​bound aggregates interacting with exosomes was calculated using TIFF2025029000000005.tif13128, where S is the signal, z is the distance from the sensor surface, E is the electric field at z = 0, the constant ld in this case is the decay length, which is set to 200 nm in the current sensor design, and r is the radius of the bound protein aggregates.

[0165] All protein aggregates were close to spheres, and their r was determined by dynamic light scattering analysis. The above equation was used to determine the number of protein aggregates bound to the sensor, as well as their respective total surface area, thereby estimating the number of binding sites available for exosome interaction. All exosome binding data (Δλ) were normalized to the respective protein binding sites. The normalized Aβ42 binding data were relative to a similarly sized BSA control, and fitting yielded the binding affinity constant K D It was decided.

[0166] miRNA analysis. Exosome lysates were incubated with biotinylated RNA probes, followed by RNA duplex capture on p19-functionalized APEX sensors and APEX signal amplification.

[0167] result The pathology of several neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis, involves the aggregation of misfolded starch-forming proteins. Therapeutics targeting protein aggregation include various strategies to clear aggregated starch-forming proteins, such as disaggregating starch-forming protein aggregates or inhibiting the aggregation of starch-forming proteins. Molecules that have been shown to be effective in reducing the amount of starch-forming protein aggregates and are therefore potential candidates for disease-modifying therapeutics include methylthioninium chloride, leuco-methylthioninium bis(hydromethanesulfonate), curcumin, acid fuchsin, epigallocatechin gallate, safranal, Congo red, apigenin, azure C, basic blue 41, (trans,trans)-1-bromo-2,5-bis-(3-hydroxycarbonyl-4-hydrogen)-1,2-triazuryl-2,5-dihydro ... (roxy)styrylbenzene (BSB), Chicago Sky Blue 6B, -cyclodextrin, daunomycin hydrochloride, dimethyl yellow, direct red 80, 2,2-dihydroxybenzophenone, hexadecyltrimethylammonium bromide (C16), hemin chloride, hematin, indomethacin, juglone, lacmoid, meclocycline sulfosalicylate, melatonin, myricetin, 1,2-naphthoquinone, nordihydroguaiaretic acid, R( )-norapomorphine hydrobromide, orange G, o-vanillin (2-hydroxy-3-methoxybenzaldehyde), ferphenazine, phthalocyanine, rifamycin SV, phenol red, rolitetracycline, quinacrine mustard dihydrochloride, thioflavin S, ThT, and trimethyl(tetradecyl)ammonium bromide (C17), diallyltartaric acid, eosin Y, fenofibrate, neocuproine, nystarin, octadecyl sulfate, and rhodamine B.

[0168] Since no animal models have been demonstrated that display precise pathology that reflects AD pathology in the human brain, in vitro experiments were used to model the effect of disease-modifying treatments inhibiting the aggregation of starch-forming proteins. The initial size of the starch-forming proteins was determined by dynamic light scattering analysis, and then aliquots of the proteins were incubated with or without inhibitors. In the absence of inhibitors, spontaneous aggregation of the starch-forming proteins resulted in increased size of protein aggregates with increasing duration of incubation. However, in the presence of inhibitors, the increase in size as a result of spontaneous protein aggregation was minimal, resulting in the formation of smaller protein aggregates.

[0169] After incubation, starch-forming proteins were functionalized on the sensor chip surface and then incubated with neuronal exosomes. As indicated by the difference in binding affinity, neuronal exosomes were observed to preferentially bind to larger protein aggregates and demonstrated less binding to smaller inhibitor-treated protein aggregates. The APEX platform allows for the effective assessment of disease-modifying therapeutics, since exosome-protein binding can be a surrogate indicator of the biophysical and / or biochemical properties of proteins affected by disease-modifying therapeutics.

[0170] While several example configurations have been described, various modifications, alternative configurations, and equivalents may be used without departing from the spirit of this disclosure. For example, the elements described above may be components of a larger system. Also, some steps may occur before, during, or after the elements described above are discussed.

[0171] All publications, sequence accession numbers, patents, and patent applications cited herein are incorporated by reference in their entirety for all purposes.

[0172] Exemplary Aspects of the Disclosure In several aspects and embodiments, the following are described herein:

[0173] 1. A method for detecting a neurodegenerative disease or amyloidosis in a subject, comprising: detecting a level of an exosome-bound aggregation biomarker in a sample obtained from the subject; wherein an increased level of the exosome-bound aggregation biomarker compared to the reference indicates that the subject is afflicted with a neurodegenerative disease or amyloidosis.

[0174] In some embodiments, the biomarker is selected from the group consisting of Aβ, APP, α-Syn, Tau, APOE, SOD1, TDP-43, bassoon, and / or fibronectin.

[0175] In some aspects, the methods include detecting a level of a molecular subtype of an exosome-bound biomarker.

[0176] In some embodiments, the molecular subtype of Aβ is Aβ42, Aβ40, Aβ39, or Aβ38.

[0177] In some embodiments, the biomarker is a prefibrillar aggregate.

[0178] In some embodiments, the method further comprises detecting an exosome biomarker selected from the group consisting of CD63, CD9, CD81, ALIX, TSG101, flotillin-1, flotillin-2, LAMP-1, HSP70, HSP90, RNA, and DNA, wherein the exosome biomarker co-localizes with the exosome-bound biomarker.

[0179] In some embodiments, the method further comprises detecting a neuronal biomarker selected from the group consisting of NCAM, L1CAM, CHL-1, and IRS-1, wherein the neuronal biomarker co-localizes with the exosome-bound biomarker.

[0180] In some embodiments, the neurodegenerative disease is selected from the group consisting of 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 tauopathy.

[0181] In some embodiments, the neurodegenerative disease is selected from the group consisting of Alzheimer's disease and mild cognitive impairment.

[0182] In some embodiments, the methods include treating a subject suffering from a neurodegenerative disease or amyloidosis.

[0183] In some embodiments, the sample is a tissue biopsy, blood, plasma, serum, or cerebrospinal fluid.

[0184] In some embodiments, the methods are further correlated with brain imaging studies.

[0185] In another aspect, described herein is a method for detecting a subject at risk for developing a neurodegenerative disease or amyloidosis, the method comprising: detecting a level of an exosome-bound aggregation biomarker in a sample obtained from the subject; wherein an increased level of the exosome-bound aggregation biomarker compared to the reference indicates that the subject is afflicted with a neurodegenerative disease or amyloidosis.

[0186] In another aspect, described herein is a method for detecting and treating a neurodegenerative disease or amyloidosis in a subject, the method comprising the steps of: (a) detecting a level of an exosome-bound aggregation biomarker in a sample obtained from the subject, wherein an increased level of the exosome-bound aggregation biomarker compared to a reference indicates that the subject is afflicted with a neurodegenerative disease or amyloidosis; and (b) treating a subject suffering from a neurodegenerative disease or amyloidosis.

[0187] In another aspect, described herein is a method for determining the aggregation state of a biomarker in a sample, the method comprising: Detecting the level of exosome-bound biomarkers in the sample. and an increased level of the exosome-bound biomarker compared to the baseline is indicative of the degree of aggregation of the biomarker.

[0188] In some embodiments, the method comprises contacting the sample with an exosome population prior to detecting the level of the exosome-binding biomarker.

[0189] In some embodiments, aggregated biomarkers in a sample preferentially bind to exosomes compared to non-aggregated biomarkers.

[0190] In some embodiments, the sample is taken from a subject.

[0191] In some embodiments, increased aggregation of the biomarker compared to the reference is indicative of a neurodegenerative disease or amyloidosis in the subject.

[0192] In some embodiments, the methods include treating a subject suffering from a neurodegenerative disease or amyloidosis.

[0193] In some embodiments, treating comprises administering to the subject a therapeutically effective amount of one or more drugs, or a combination thereof.

[0194] In some embodiments, the drug is selected from a cholinesterase inhibitor, an NMDA receptor antagonist, a combination of a cholinesterase inhibitor and an NMDA receptor antagonist, a BACE1 inhibitor, an antibody, a protein aggregation inhibitor, a proteasome inhibitor, a small molecule, a gene therapy, an anti-tau drug, or a combination thereof.

[0195] In some embodiments, the cholinesterase inhibitor is donepezil, Rivastigmate, 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).

[0196] In some embodiments, the drug is methylthioninium chloride, leuco-methylthioninium bis(hydromethanesulfonate), curcumin, acid fuchsin, epigallocatechin gallate, safranal, Congo Red, apigenin, azure C, basic blue 41, (trans,trans)-1-bromo-2,5-bis-(3-hydroxycarbonyl-4-hydroxy)styrylbenzene (BSB), Chicago Sky Blue 6B, -cyclodextrin, daunomycin hydrochloride, dimethyl yellow, direct red 80, 2,2-dihydroxybenzophenone, hexadecyltrimethylammonium bromide (C16), hemin chloride, hematin, indomethacin, juglone, lacmoid, meclocycline sulfosalicylate, melatonin, myricetin, 1,2-naphthoquinone, nordihydroguaiaretic acid, R( )-norapomorphine hydrobromide, orange G, o-vanillin (2-hydroxy-3-methoxybenzaldehyde), ferphenazine, phthalocyanine, rifamycin SV, phenol red, rolitetracycline, quinacrine mustard dihydrochloride, thioflavin S, ThT, and trimethyl(tetradecyl)ammonium bromide (C17), diallyltartaric acid, eosin Y, fenofibrate, neocuproine, nystalin, octadecyl sulfate, and / or rhodamine B.

[0197] Sequence information SEQUENCE LISTING <110> NATIONAL UNIVERSITY OF SINGAPORE NATIONAL UNIVERSITY HOSPITAL (SINGAPORE) PTE LTD <120> METHOD OF DETECTING A NEURODEGENERATIVE DISEASE <150> SG 10201900940Y <151> 2019-01-31 <160> 7 <170> PatentIn version 3.5 <210> 1 <211> 22 <212> RNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: Synthetic probe <400> 1 uuucgguuau cuagcuuuau uu 22 <210> 2 <211> 22 <212> RNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: Synthetic probe <400> 2 uaaaccauga ugugcugcua uu 22 <210> 3 <211> 23 <212> RNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: Synthetic probe <400> 3 cacugauuuc aaauggugcu auu 23 <210> 4 <211> 22 <212> RNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: Synthetic probe <400> 4 acaccaggag aaaucgguca uu 22 <210> 5 <211> 23 <212> RNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: Synthetic probe <400> 5 auagcccugu acaaugcugc uuu 23 <210> 6 <211> 22 <212> RNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: Synthetic probe <400> 6 cccauggaau ucaguucuca uu 22 <210> 7 <211> 22 <212> RNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: Synthetic probe <400> 7 ucaccgacag guugaauguu uu 22

Claims

1. 1. A method for detecting a neurodegenerative disease or amyloidosis in a subject, comprising: detecting a level of an exosome-bound aggregation biomarker in a sample obtained from the subject; Including, The method, wherein an increased level of the exosome-bound aggregation biomarker compared to the reference indicates that the subject is afflicted with a neurodegenerative disease or amyloidosis.

2. 2. The method of 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. Detecting the levels of molecular subtypes of exosome-associated biomarkers 2. The method of claim 1, comprising:

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

5. 2. The method of claim 1, wherein the biomarker is a prefibrillar aggregate.

6. Detecting an exosome biomarker selected from the group consisting of CD63, CD9, CD81, ALIX, TSG101, flotillin-1, flotillin-2, LAMP-1, HSP70, HSP90, RNA, and DNA. Further comprising: The method of any one of claims 1 to 5, wherein said exosome biomarker is co-localized with said exosome-binding biomarker.

7. Detecting a neuronal biomarker selected from the group consisting of NCAM, L1CAM, CHL-1, and IRS-1. Further comprising: The method of any one of claims 1 to 6, wherein said neuronal biomarker is co-localized with said exosome-bound biomarker.

8. 8. The method of any one of claims 1 to 7, wherein the neurodegenerative disease is selected from the group consisting of 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 tauopathy.

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

10. Treating a subject suffering from a neurodegenerative disease or amyloidosis The method of any one of claims 1 to 9, comprising:

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

12. 12. The method of any one of claims 1 to 11, further correlated with brain imaging studies.

13. 1. A method for detecting a subject at risk for developing a neurodegenerative disease or amyloidosis, comprising: detecting a level of an exosome-bound aggregation biomarker in a sample obtained from the subject; Including, The method, wherein an increased level of the exosome-bound aggregation biomarker compared to the reference indicates that the subject is afflicted with a neurodegenerative disease or amyloidosis.

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

15. 1. A method for determining the aggregation state of a biomarker in a sample, comprising: Detecting the level of exosome-bound biomarkers in the sample. Including, The method, wherein an increased level of the exosome-bound biomarker compared to the reference is indicative of the degree of aggregation of the biomarker.

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

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

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

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

20. Treating a subject suffering from a neurodegenerative disease or amyloidosis 20. The method of claim 19, comprising:

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

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

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