Sensor chip and method thereof

Abstract

The present disclosure generally relates to sensor chips and methods for detecting analytes. In particular, the present disclosure relates to a sensor chip for detecting an analyte in a subject having a neurodegenerative disease. The sensor chip includes a conductive layer on a membrane support layer with a plurality of apertures extending through the conductive layer and the membrane support layer and arranged such that illumination of the conductive layer and / or the membrane support layer produces surface plasmon resonance.

Description

[0001] This application is a divisional application of the patent application with the application date of January 30, 2020, application number 202080012067.0, and invention title "Sensor Chip and Its Method".

[0002] Cross - Reference to Related Applications

[0003] This application claims priority to Singapore Provisional Application No. 10201900937Q, titled SENSOR CHIP AND METHODS THEREOF, filed on January 31, 2019. Technical Field

[0004] The present disclosure generally relates to sensor chips and methods for detecting analytes. Specifically, the present disclosure relates to sensor chips for detecting analytes in subjects suffering from neurodegenerative diseases and other diseases such as amyloidosis. Background Art

[0005] Surface plasmon resonance (SPR) sensing is a widely used technique in laboratories for characterizing biomolecular interactions, such as antibody-antigen interactions. This technique generally involves immobilizing a ligand capture molecule on a metal surface and measuring the change in refractive index when the ligand binds to the capture molecule. This technique is a label-free technique that does not require the use of specialized labels or dyes for sensitive measurement of intermolecular interactions. It is currently being developed for laboratory diagnosis of patients suffering from different diseases such as Alzheimer's disease, hepatitis, diabetes, and cancer.

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

[0007] However, current AD diagnosis and disease monitoring are subjective and late-stage. It is achieved by using published criteria for clinical and neuropsychological assessments. These methods lack sensitivity and specificity, especially in the early stages where symptoms are mild and significantly overlap with various other diseases. New molecular diagnostic assays are being developed, including cerebrospinal fluid measurements and brain amyloid plaque imaging by positron emission tomography (PET); however, these tests face limitations because they require invasive lumbar punctures or are too expensive for broader clinical use. As a result, there is a high level of interest in finding serum biomarkers for AD that can assist in early diagnosis and disease monitoring.

[0008] Accordingly, there is a need to overcome or at least mitigate one or more of the above problems. Summary of the Invention

[0009] A sensor chip and method for detecting an analyte by surface plasmon resonance are disclosed herein.

[0010] In one aspect, a sensor chip is provided that includes a conductive layer on a membrane support layer, wherein a plurality of holes extend through the conductive layer and the membrane support layer and are arranged such that illumination of the conductive layer and / or the membrane support layer generates surface plasmon resonance.

[0011] In one aspect, an imaging system is provided that includes a light source, a detector, and a sensor chip as defined by the present invention, wherein the light source is arranged to illuminate the sensor chip and the detector is positioned to detect light transmitted through the sensor chip.

[0012] In one aspect, a method of manufacturing a sensor chip is provided, the method comprising the steps of:

[0013] a) providing a top membrane support layer;

[0014] b) depositing a conductive layer on the top membrane support layer;

[0015] c) forming a plurality of holes extending through the membrane support layer, the holes also extending through the conductive layer and being arranged such that illumination of the conductive layer and / or the top membrane support layer generates surface plasmon resonance.

[0016] In one aspect, a method of detecting an analyte in a sample is provided, the method comprising:

[0017] a) capturing the analyte on the surface of a sensor chip as defined by the present invention;

[0018] b) Detecting the binding of a second recognition molecule to an analyte captured on the surface of the sensor chip, wherein the second recognition molecule is specific for the analyte, and an increase in the binding of the second recognition molecule as compared to a control sample indicates the presence of the analyte in the sample.

[0019] In one aspect, a kit is provided that includes a sensor chip as defined in the present invention.

[0020] In one aspect, a method for detecting a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising:

[0021] a) Contacting a sample obtained from the subject with the surface of a sensor chip as defined in the present invention;

[0022] b) Detecting the binding of a second recognition molecule to an analyte captured on the surface of the sensor chip;

[0023] wherein the second recognition molecule is specific for the analyte, and an increase in the binding of the second recognition molecule as compared to a sample obtained from a control subject indicates that the subject has a neurodegenerative disease.

[0024] Also provided is the use of the sensor chip as described in the present invention in a method for detecting a neurodegenerative disease or amyloidosis in a subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Some embodiments of the present invention will now be described by way of non-limiting example only with reference to the accompanying drawings, wherein:

[0026] Figure 1: The APEX platform for analyzing Aβ bound to circulating exosomes.

[0027] (a) Exosomes associate with 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 vesicles actively secreted by mammalian cells. Through their surface glycoproteins and glycolipids, exosomes can associate with released Aβ protein. (b) Transmission electron micrograph of exosome-bound Aβ. Exosomes (SH-SY5Y) derived from neural cells were treated with Aβ42 aggregates and labeled with gold nanoparticles (10nm) through Aβ42-specific antibodies. Nanoparticles appear as blocky dots (indicated by red arrows). (c) Schematic diagram of APEX assay. In order to enable sensitive mapping at the nanoscale, exosomes were first immunocaptured onto plasmonic nanosensors (before amplification). By in situ enzymatic amplification, insoluble optical deposits were locally formed on sensor-bound exosomes (after amplification). The deposits were spatially defined for molecular colocalization analysis and changed the refractive index for SPR signal enhancement. Note that to compensate for enzymatic amplification, the nanosensor was back-illuminated (away from enzyme activity) for analytical stability. The deposition resulted in a red-shift in the transmitted light through the nanosensor. (d) Representative schematic of the APEX amplification transmission spectrum shift. Specific exosome binding (before) and subsequent amplification mapping (after) were monitored as a transmission spectrum shift (Δλ) using the APEX platform. au, arbitrary units. (e) Exosome-bound Aβ was measured in blood samples from patients with Alzheimer's disease (AD), mild cognitive impairment (MCI), and controls without cognitive impairment (NCI) using the APEX platform. Blood measurements were correlated with corresponding PET imaging of brain amyloid plaque deposition. (f) Photo of the APEX microarray. Each sensor chip contains 6x 10 sensing elements composed of a uniformly fabricated plasmonic lattice for multiplexed measurements. For sensor fabrication, characterization, and design optimization, see respectively. Figures 7 - 10 .

[0028] Figure 2 : APEX signal amplification and multiplexing profiling.

[0029] (a) Stepwise APEX transmission spectral changes. A series of operations were performed, namely antibody conjugation (anti-CD63) on the sensor, exosome binding, enzyme labeling, and enzyme deposition, and the resulting spectral shifts were monitored. Although enzyme labeling did not cause any significant changes, the formation of deposits led to a signal enhancement of ~400% (****(P < 0.0001, n.s. not significant, Student's t-test). (b) Comparison of APEX signal amplification and optical deposition area coverage. The increase in area coverage was determined by scanning electron microscopy (SEM) analysis (****P < 0.0001, Student's t-test). All data were normalized to the data before signal amplification. The inset (right) shows SEM images of exosomes bound to the sensor before and after APEX amplification. (c) Finite-difference time-domain simulation using backlighting. The APEX sensor design, rather than the gold-on-glass design, enables the generation of an enhanced electromagnetic field by backlighting. Backlighting minimizes the effect of directly incident light on enzyme activity (occurring at the top of the sensor). The arrow indicates the direction of incident illumination. (d) Real-time sensing plot of APEX amplification kinetics. Different concentrations of the optical substrate (3,3'-diaminobenzidine tetrahydrochloride; high: 1 mg / ml, low: 0.01 mg / ml) were used to monitor the amplification efficiency. All data were normalized to the negative control, implemented with an IgG isotype control antibody. (e) Comparison of the detection sensitivities of APEX, ELISA, and Western blot. The detection limits (dashed lines) of APEX before and after amplification were determined by titrating known amounts of exosomes and measuring their CD63 signals. (f) Specificity of the APEX assay for measuring target proteins. Assays were developed for amyloid-β (Aβ42), amyloid precursor protein (APP), α-synuclein (α-syn), close homolog of L1 (CHL1), insulin receptor substrate 1 (IRS-1), neural cell adhesion molecule (NCAM), and tau protein. All assays demonstrated specific detection. The heatmap signals were normalized to the assays (rows). All measurements were performed in triplicate, and the data are shown as mean ± s.d. in a, b, d, and e.

[0030] Figure 3: Preferential association between Aβ aggregates and exosomes.

[0031] (a) Schematic diagram of Aβ protein aggregation. We changed the degree of aggregation and used filtration methods to prepare small Aβ42 aggregates and large Aβ42 aggregates separately. (b) Characterization of Aβ protein aggregates. (Left) Transmission electron micrograph showing the spherical morphology of the prepared Aβ42 aggregates. (Right) Dynamic light scattering analysis confirmed the unimodal size distribution of the differently sized preparations. (c) Schematic diagram of exosome-Aβ association analysis. Aβ42 aggregates (small aggregates vs. large aggregates) were immobilized on the APEX sensor and treated with equimolar concentrations of exosomes from neuronal cells (SH-SY5Y) to determine the association kinetics. All exosome binding data were normalized to the corresponding Aβ42 aggregation surface area immobilized on the sensor (see Methods for details). (d) Real-time sensing plot of exosome binding kinetics. Exosomes associated more firmly with Aβ42 aggregates compared to similarly sized bovine serum albumin (BSA) control aggregates (see Figure 13 ). Importantly, exosomes showed a stronger affinity for larger Aβ42 aggregates (right) compared to their binding affinity for smaller Aβ42 aggregates (left). Note the difference in the scale on the y-axis. All binding affinities (KD) were determined from normalized exosome binding data relative to the BSA control. KD (small) / KD (large) = 5.27. (e) Differential association of multiple extracellular vesicles with Aβ42 aggregates. Vesicles were from different cell sources, namely neurons, glial cells, endothelial cells, monocytes, erythrocytes, platelets, and epithelial cells, and were used at equimolar concentrations for binding analysis. Using the APEX platform, we first measured the direct binding of vesicles to the Aβ42-functionalized sensor (direct). Subsequently, for each cell source, we labeled the bound vesicles with a source-specific marker (cell-source-specific marker) or a pan-exosome marker (i.e., CD63, pan-exosome marker) and measured the associated APEX signal amplification. All measurements were performed relative to an IgG isotype control antibody in triplicate. Data are shown as mean ± s.d. in e.

[0032] Figure 4 : Clinical relevance of circulating exosome-bound Aβ to brain imaging.

[0033] (a) Representative reconstructed PET brain images from clinical subjects showing increased brain amyloid plaque load. Standardized uptake value ratios (SUVRs) of specific brain regions were normalized to mean cerebellar gray matter intensity to determine brain amyloid plaque load. (b) Correlation of different circulating Aβ42 populations with overall average PET brain imaging (n=72). Using the APEX assay, we measured signals from exosome-bound Aβ42 (left), unbound Aβ42 (middle), 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). When correlated with overall imaging data of brain amyloid plaques, the unbound Aβ42 population (middle, R 2 =0.0193) or total Aβ42 (right, R 2 =0.1471), the best correlation was shown for exosome-bound Aβ42 measurements (left, R 2 =0.9002). (c) Analysis of different circulating Aβ42 populations in distinguishing different clinical groups (n=84). Only APEX measurements of circulating exosome-bound Aβ42 (left) can distinguish AD clinical groups (AD and MCI) as well as other normal (NCI) and clinical controls (VaD, VMCI group and acute stroke). Neither unbound Aβ42 measurements (middle) nor total Aβ42 measurements (right) showed any statistical significance between different clinical groups (**P<0.01, **P<0.0001, ns, not significant, Student's t-test). All measurements were performed in triplicate relative to IgG isotype control antibodies. Data are shown as mean ± sd in bc.

[0034] Figure 5 : Characterization of extracellular vesicles shed by neuronal cells.

[0035] (a) Scanning electron micrograph of neuronal cells (SH-SY5Y) showing massive release of nanoscale extracellular vesicles from the cells. (b) High-magnification image of the released vesicles. (c) Unimodal size distribution of extracellular vesicles determined by nanoparticle tracking analysis, showing an average diameter of ~150 nm. (d) Western blot analysis of vesicle lysates. Vesicles were lysed and immunoblotted against exosome markers (LAMP 1, ALIX, HSP90, HSP70, CD63, Flotillin1, TSG101), neuronal marker (NCAM), and negative markers including lipoprotein (APOE) and other membrane compartment markers (Calnexin, GRP94). (e) Transmission electron micrographs of dual immunolabeling with gold nanoparticles of different sizes (CD63, 20 nm; Aβ42, 5 nm) confirmed the co-localization of the two markers on the same vesicles, indicating the presence of exosome-bound Aβ.

[0036] Figure 6 : APEX amplification products.

[0037] Scanning electron micrographs of APEX sensors (a) before amplification, with exosomes captured onto the sensor via anti-CD63 antibody, and (b) after amplification, showing the local growth of insoluble optical deposits from a soluble substrate (3,3'-diaminobenzidine tetrahydrochloride). The resulting APEX signal amplification was closely correlated with the increase in the area coverage of the local deposits.

[0038] Figure 7 : Large-scale fabrication of APEX microarray sensors.

[0039] All APEX sensors were fabricated on 8-inch silicon (Si) wafers. The fabrication steps included: (1) Preparation of a 10-nm silicon dioxide (SiO 2 ) layer by thermal oxidation and deposition of 145-nm silicon nitride (Si 3 N 4 ) on the wafer by low-pressure chemical vapor deposition (LPCVD). (2) After coating with photoresist, deep ultraviolet (DUV) lithography was performed to define a nanopore array pattern in the resist. The pattern was transferred to the Si 3 N 4 film by reactive ion etching (RIE). (3) After removal of the photoresist, a thin protective layer (100 nm) of SiO 2 was deposited on the front side of the wafer using plasma-enhanced chemical vapor deposition (PECVD). To enable light transmission, the back side of the wafer was spin-coated with photoresist; lithography was used to define the sensing area. (4) The Si 3 N 4 and SiO2 , and then silicon is etched with potassium hydroxide (KOH) and tetramethylammonium hydroxide (TMAH). (5) After etching, dilute hydrofluoric acid (DHF) (1:100) is used to remove the SiO 2 protective layer. (6) Ti / Au (10 nm / 100 nm) is deposited on the Si 3 N 4 film.

[0040] Figure 8 : Characterization of the APEX microarray sensor.

[0041] (a) Photograph of an 8-inch wafer showing the large-scale fabrication of the APEX microarray sensor chip. Each wafer consists of >2000 photosensitive elements. (b) Scanning electron micrograph of highly uniform nanopores in the APEX sensor. The inset shows an enlarged view of the nanopore lattice.

[0042] Figure 9 : Stepwise spectral changes.

[0043] The APEX sensors are conjugated with (a) anti-CD63 antibody for exosome capture or (b) isotope control antibody. Before APEX amplification, all sensors are treated with exosomes from a neuronal cell line (SH-SY5Y) at equal concentration. Although the sensors show a similar degree of surface functionalization using antibodies (antibody conjugation), only the anti-CD63-functionalized sensors show significant spectral shifts associated with exosome binding and APEX amplification, respectively. It should be noted that in the control sensors, the spectral changes caused by APEX amplification are negligible in the absence of exosome binding. a.u., arbitrary unit.

[0044] Figure 10 : Optimization of the APEX sensor performance.

[0045] (a) Comparison of sensor performance using back-illumination. We compared the SPR transmission intensity, full width at half maximum (FWHM) of the spectral peak, and detection sensitivity of different APEX sensors with varying nanopore diameters. All sensors were illuminated from the back to compensate for APEX enzymatic amplification. The optimized APEX design had a nanopore diameter of 230 nm and was patterned in a 100-nm-thick gold layer suspended on a silicon nitride membrane with a regular period of 450 nm. This bilayer plasmonic structure supported SPR excitation through back-illumination. (b) The transmission spectrum of the optimized sensor changed with increasing refractive index. The increase in refractive index caused a change in the transmission spectrum and shifted the resonance peak to a longer wavelength. (c) The spectral shift was linearly correlated with the increase in refractive index. (d) APEX reproducibility and repeatability. APEX enzymatic amplification was performed on the same sample and measurements were made between different users, sensor chips, and measurement times. The measurement results showed the following coefficients of variation for analysis: between groups = 2.76%, within groups = 4.14%, total = 4.59%. All measurements were performed in triplicate or more, and the data in (a) are shown as mean ± s.d. a.u., arbitrary unit, n.s., not significant, Student's t-test.

[0046] Figure 11 : APEX workflow and amplification efficiency.

[0047] (a) APEX workflow for detecting proteins (extracellular and intracellular) and miRNAs. (b) APEX amplification efficiency for different molecular targets. APEX signals were obtained for the following targets: extracellular protein, Aβ42 protein, intracellular protein, heat shock protein 90; miRNA, miRNA-9. All signals were normalized to the signal before adding the optical substrate to determine the amplification ratio. Measurements were performed in triplicate, and the data in (b) are shown as mean ± s.d.

[0048] Figure 12 : Protofibril structures assembled from large Aβ aggregates.

[0049] Amyloid protofibrils were observed after 2 hours of incubation of the prepared large Aβ42 aggregates. The formed structures were immunolabeled with gold nanoparticles (15 nm) using an anti-Aβ42 antibody and characterized by transmission electron microscopy.

[0050] Figure 13 : Preparation of BSA control aggregates.

[0051] (a) Schematic of bovine serum albumin aggregates. We varied the heating time to prepare small BSA control aggregates and large BSA control aggregates, respectively. (b) Characterization of bovine serum albumin aggregates. The hydrodynamic diameter of BSA aggregates was determined by dynamic light scattering analysis. Both aggregates showed a unimodal size distribution. The diameter of the small aggregates was ~15 nm, and the diameter of the large aggregates was ~100 nm.

[0052] Figure 14: Extracellular vesicles isolated from different cell sources.

[0053] Extracellular vesicles were derived from (a) neurons (SH-SY5Y), (b) glial cells (GLI36), (c) endothelial cells (HUVEC), (d) monocytes (THP-1), (e) red blood cells, (f) platelets, (g) prostate-derived epithelial cells (PC-3), and (h) ovarian-derived epithelial cells (SK-OV-3), respectively. All vesicles were characterized by nanoparticle tracking analysis.

[0054] Figure 15 : APEX measurements of different circulating Aβ populations.

[0055] (a) APEX assay configuration for characterizing different circulating Aβ populations in clinical plasma samples. Exosome-bound Aβ42 and total Aβ42 populations were measured from native plasma, while unbound Aβ42 populations were detected from plasma filtrates. (b) Incubation of fibronectin with exosome-bound Aβ42 resulted in negligible APEX signal changes. (c) Negative controls (APOE lipoprotein containing Aβ42 protein, human serum albumin / HSA) showed negligible signals, indicating the specificity of the APEX assay for exosome-bound Aβ42. All measurements were performed in triplicate, and the data are shown as mean ± s.d.

[0056] Figure 16 : Characterization of Aβ populations in clinical samples.

[0057] (a) Exosome-bound Aβ42 population. We directly enriched Aβ42 from native plasma samples and measured the relative levels of co-localization signals for exosome markers (CD63, CD81, and CD9) and neuronal markers (NCAM, L1CAM, and CHL-1) in the captured Aβ42. All markers were detectable, and CD63 was the most highly expressed marker in the clinical samples tested. (b) Plasma filtrate used to characterize the unbound Aβ42 population. To evaluate the unbound Aβ42 population, we prepared vesicle-free plasma filtrate using membrane filtration (size cut-off = 50 nm, Nuclepore, Whatman). As determined by nanoparticle tracking analysis, the filtrate showed negligible vesicle counts. (c) The plasma filtrate also showed negligible exosome marker (CD63) and neuronal marker (NCAM) signals, indicating effective removal of exosomes by filtration. Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy controls without cognitive impairment (NCI). All measurements were performed in triplicate, and the data are shown as mean ± s.d.

[0058] Figure 17 : Correlation of exosome-bound Aβ42 with regional cerebral amyloid burden.

[0059] We determined the imaging SUVR of specific brain regions, namely the cingulate gyrus region affected by early AD and the occipital lobe region affected by late AD. Compared with the imaging data of the region affected by late AD (b, R 2 = 0.6863), the APEX measurement results of exosome-bound Aβ42 were more consistent with the imaging data of the region affected by early AD (a, R 2 = 0.8808). Alzheimer's disease (AD, n = 17), mild cognitive impairment (MCI, n = 18), vascular dementia (VaD, n = 9), vascular mild cognitive impairment (VMCI, n = 12), healthy controls without cognitive impairment (NCI, n = 16). All measurements were performed in triplicate, and the data are shown as mean ± s.d.

[0060] Figure 18 : PET imaging comparison in clinical subjects with different diagnoses.

[0061] PET imaging of cerebral amyloid plaque load was performed in patients with different clinical diagnoses (n = 72): AD (n = 17), MCI (n = 18), NCI (n = 16), VaD (n = 9), and VMCI (n = 12). The standardized uptake value ratio (SUVR) of overall mean plaque deposition could distinguish the AD clinical groups (AD and MCI) from other healthy subjects (NCI) and clinical controls (VaD and VMCI) (**P < 0.01, ****P < 0.0001, Student's t-test).

[0062] Figure 19: Extracellular vesicles in clinical samples.

[0063] (a) Representative analysis of extracellular vesicles (measured by nanoparticle tracking analysis) was performed on blood samples from subjects with different clinical diagnoses (AD = 17, MCI = 18, NCI = 16, VaD = 9, VMCI = 12, and acute stroke = 12). (b) Vesicle size and (c) vesicle concentration were compared from clinical blood samples (n = 84). Notably, no significant differences in vesicle size and concentration were found among samples with different clinical diagnoses (n.s., not significant, Student's t-test).

[0064] Figure 20 : Comparison of APEX assay technology, sensor design, and fabrication.

[0065] Figure 21 Showed (a) inhibition of amyloid-forming protein aggregation. (b) Dynamic light scattering analysis confirmed the unimodal size distribution and size differences upon incubation of amyloid-forming proteins with and without inhibitor. (c) Real-time sensing plots of exosome binding kinetics. Exosomes showed stronger affinity for larger Aβ42 aggregates (untreated) compared to smaller Aβ42 aggregates (treated with inhibitor).

[0066] Figure 22 Real-time sensing plots of exosome binding kinetics were shown. Exosomes associated more firmly with amyloid-forming protein (such as Aβ, APP, α-Syn, IRS-1, Tau, APOE, SOD1, TDP-43, bassoon, fibronectin) aggregates compared to bovine serum albumin (BSA) control aggregates of similar size.

[0067] Figure 23 Showed the specificity of the APEX assay for measuring target miRNA molecules. Assays were developed for miR-9, miR-15b, miR-29b, miR-29c, miR-107, miR-146a, and miR-181c. All assays showed specific detection. The heatmap signals were normalized by the assay (rows). Detailed implementation

[0068] A sensor chip and method for detecting an analyte by surface plasmon resonance are disclosed herein.

[0069] In one aspect, a sensor chip is provided that includes a conductive layer on a membrane support layer, wherein a plurality of holes extend through the conductive layer and the membrane support layer and are arranged such that illumination of the conductive layer and / or the membrane support layer generates surface plasmon resonance.

[0070] In one embodiment, the design of the multi-layer structured material (conductive metal and support substrate) enables plasmon coupling. This design can support bidirectional excitation of surface plasmon resonance, where the SPR performance from bidirectional illumination (from the top or from the bottom) is comparable.

[0071] The term "conductive layer" as used herein can be a conductive material that generates surface plasmon resonance when excited by electromagnetic energy (such as light waves). The conductive material can refer to, for example, a metallic conductive material. Such metallic conductive materials can be any metal, including noble metals, alkali metals, transition metals, and alloys. Examples of conductive materials include, but are not limited to, gold, rhodium, palladium, silver, platinum, osmium, iridium, titanium, aluminum, copper, lithium, sodium, potassium, nickel, metal alloys, indium tin oxide, aluminum zinc oxide, gallium zinc oxide, titanium nitride, and graphene. In one embodiment, the conductive material is gold, silver, aluminum, sodium, indium, or titanium. The metal can be in its bare form or coated with an additional layer of protective and enhancing material.

[0072] When the conductive material exhibits significant scattering intensity in the optical region (ultraviolet-visible-infrared spectrum, including wavelengths from about 100 nanometers (nm) to 3000 nm), it can be "optically visible". When the conductive material exhibits significant scattering intensity in the wavelength band from about 380 nm to 750 nm (i.e., the visible spectrum) that can be detected by the human eye, it can be "visually visible".

[0073] In one embodiment, the membrane support layer is a structured membrane support layer.

[0074] 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.

[0075] The diameters and periods of the plurality of holes extending through the conductive layer and the membrane support layer can vary to achieve different resonance wavelengths and penetration of the evanescent wave.

[0076] The plurality of holes includes symmetric circular holes, spatially anisotropic shapes such as ellipses, slits, and also includes any holes that are triangular, square, rectangular, or polygonal. Combinations of holes of different shapes can also be used. The holes can have a size or diameter of about 1500 nm or less, about 1400 nm or less, about 1300 nm or less, about 1200 nm or less, about 1100 nm or less, about 1000 nm or less, about 900 nm or less, about 800 nm or less, about 700 nm or less, about 600 nm or less, about 500 nm or less, about 450 nm or less, about 400 nm or less, about 350 nm or less, about 300 nm or less, about 250 nm or less, about 240 nm or less, about 230 nm or less, about 220 nm or less, about 210 nm or less, about 200 nm or less, about 190 nm or less, about 180 nm or less, about 170 nm or less, about 160 nm or less, about 150 nm or less, about 140 nm or less, about 130 nm or less, about 120 nm or less, about 110 nm or less, about 100 nm or less, about 90 nm or less, about 80 nm or less, about 70 nm or less, about 60 nm or less, about 50 nm or less, about 40 nm or less, about 30 nm or less, about 20 nm or less, or about 10 nm or less.

[0077] In one embodiment, the holes can have a size or diameter of about 150 nm to about 450 nm. In one embodiment, the holes have a size or diameter selected from 150 nm, 160 nm, 170 nm, 180 nm, 190 nm, 200 nm, 210 nm, 220 nm, 230 nm, 240 nm, 250 nm, 260 nm, 270 nm, 280 nm, 290 nm, 300 nm, 310 nm, 320 nm, 330 nm, 340 nm, 350 nm, 360 nm, 370 nm, 380 nm, 390 nm, 400 nm, 410 nm, 420 nm, 430 nm, 440 nm, 450 nm, or anywhere in between. In one embodiment, the holes are holes and have a diameter of 230 nm.

[0078] The term "periodic" refers to the recurrence or repetition of holes that occur at regular intervals by the positioning of the holes on the sensor chip. Thus, the term "periodic" refers to a regular predefined pattern of holes relative to each other.

[0079] The surface plasmon resonance sensor chip may include a periodic array of holes. The regular periodicity may allow for strict control of the resonance wavelength and the penetration of the evanescent wave. In one embodiment, the holes have a periodicity of from about 250 nm to about 650 nm. In one embodiment, the holes have a periodicity selected from 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 in between. In one embodiment, the holes have a periodicity of 450 nm.

[0080] In one embodiment, the holes are arranged such that the decay length of the surface plasmon resonance generated upon illumination is approximately equal to the diameter of the target of the first recognition molecule.

[0081] In one embodiment, the conductive layer and the film support layer are disposed on a substrate that has voids formed therein in a region adjacent to the plurality of holes such that illumination of the conductive layer and / or the film support layer in any direction generates surface plasmon resonance.

[0082] In one embodiment, the sensor chip includes a first recognition molecule immobilized on the surface of a conductive layer. The first recognition molecule can be immobilized on the surface using techniques well known in the art. For example, the first recognition molecule can be adsorbed on the surface. Alternatively, prior to immobilizing the first recognition molecule, the surface can be coated with a streptavidin or avidin layer. The first recognition molecule can be biotinylated and immobilized on the surface via streptavidin-biotin conjugation. In one embodiment, the surface can be incubated with polyethylene glycol (PEG) molecules. The surface can be incubated with active (carboxylated) thiol-PEG. Then, the surface can be activated by carbodiimide crosslinking in a mixture of excess NHS / EDC dissolved in MES buffer and conjugated with the first recognition molecule. In an alternative embodiment, the surface can be incubated with a mixture of polyethylene glycol (PEG) containing long active (carboxylated) thiol-PEG and short inactive methylated thiol-PEG. The ratio of long active (carboxylated) thiol-PEG to short inactive methylated thiol-PEG can be optimized to achieve maximum functional binding. Then, the surface can be activated by carbodiimide crosslinking in a mixture of excess NHS / EDC dissolved in MES buffer and conjugated with the first recognition molecule.

[0083] The term "recognition molecule" can refer to a molecule capable of specifically binding to an analyte. A "recognition molecule" can be an antibody, nucleic acid, peptide, aptamer, small molecule, or other synthetic reagent.

[0084] The term "analyte" refers to a substance present in a sample to be detected or measured on the sensor chip. An "analyte" can include cells, viruses, nucleic acids, lipids, proteins, peptides, glycopeptides, nanovesicles, microvesicles, exosomes, extracellular vesicles, sugars, metabolites, or a combination thereof or tissue states. An "analyte" can be, for example, a peptide or nucleic acid (e.g., miRNA) biomarker associated with or bound to exosomes. An "analyte" can also be, for example, a complex between cells and proteins, or a complex between proteins and nucleic acids.

[0085] In one embodiment, the first recognition molecule is an antibody or a fragment thereof. For example, the antibody can be an antibody that recognizes a pan-exosome marker or a marker associated with or bound to exosomes. For example, the antibody can be specific for CD63, CD9, or CD81, which are abundant and characteristic in exosomes. The antibody can also be specific for cell-specific origin markers (e.g., CHL1, L1CAM, or NCAM). The antibody can also recognize a biomarker associated with 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 associated with or bound to exosomes.

[0086] 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 (scFv), Fab fragments, F(ab') fragments, disulfide-linked Fvs (sdFv) (including bispecific sdFvs), and anti-idiotypic (anti-Id) antibodies, as well as epitope-binding fragments of any of the foregoing. The antibodies provided by the present invention can be monospecific, bispecific, trispecific or greater multispecific. Multispecific antibodies can be specific for different epitopes of a polypeptide or can be specific for a polypeptide as well as a heterologous epitope, such as a heterologous polypeptide or a solid support material.

[0087] The terms "protein" and "polypeptide" are used interchangeably and refer to any polymer of amino acids (dipeptide 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 present invention can contain non-peptide components, such as carbohydrate groups. Carbohydrates and other non-peptide substituents can be added to the polypeptide by the cells in which the polypeptide is produced and vary with the cell type. Polypeptides are defined in the present invention according to their amino acid backbone structure; substituents such as carbohydrate groups are generally not specified but can still be present.

[0088] As used herein, "nucleic acid" can be RNA or DNA, can be single-stranded or double-stranded, and can be, for example, a nucleic acid encoding a protein of interest, polynucleotide, oligonucleotide, nucleic acid analog, such as peptide-nucleic acid (PNA), pseudocomplementary PNA (pc-PNA), locked nucleic acid (LNA), etc. Such nucleic acid sequences include, for example, but are not limited to, nucleic acid sequences encoding proteins, such as those acting as transcriptional repressors, antisense molecules, ribozymes, small inhibitory nucleic acid sequences, such as, but not limited to, RNAi, shRNAi, siRNA, microRNAi (mRNAi), antisense oligonucleotides, etc.

[0089] As used in the present invention, "nanovesicles" may refer to naturally occurring or synthetic vesicles that include an internal cavity. The nanovesicles may include a lipid bilayer membrane that encloses the contents of the internal cavity. The 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. The nanovesicles may have a size of about 1000 nm or less, about 900 nm or less, about 800 nm or less, about 700 nm or less, about 600 nm or less, about 500 nm or less, about 450 nm or less, about 400 nm or less, about 350 nm or less, about 300 nm or less, about 250 nm or less, about 240 nm or less, about 230 nm or less, about 220 nm or less, about 210 nm or less, about 200 nm or less, about 190 nm or less, about 180 nm or less, about 170 nm or less, about 160 nm or less, about 150 nm or less, about 140 nm or less, about 130 nm or less, about 120 nm or less, about 110 nm or less, about 100 nm or less, about 90 nm or less, about 80 nm or less, about 70 nm or less, about 60 nm or less, about 50 nm or less, about 40 nm or less, about 30 nm or less, about 20 nm or less, or about 10 nm or less.

[0090] Exosomes are a type of nanovesicle that are also referred to in the art as extracellular vesicles, microvesicles, or microparticles. These vesicles are shed from eukaryotic cells or are released from the plasma membrane to the exterior of the cell. These membrane vesicles vary in size, with diameters ranging from about 10 to about 5000 nm. Small vesicles (with diameters of about 10 - 1000 nm, preferably 30 - 100 nm) released by exocytosis of intracellular multivesicular bodies are referred to in the art as "exosomes". The methods and compositions described in the present invention are equally applicable to other vesicles of all sizes.

[0091] The term "sample" refers to any sample that contains an analyte or in which the presence of an analyte is being tested. The sample includes samples derived from or containing cells, organisms (bacteria, viruses), lysed cells or organisms, cell extracts, nuclear extracts, components of cells or organisms, extracellular fluid, culture medium of in vitro cultured cells or organisms, blood, plasma, serum, gastrointestinal secretions, urine, ascites, homogenates of tissues or tumors, synovial fluid, feces, saliva, sputum, cyst fluid, amniotic fluid, cerebrospinal fluid, peritoneal fluid, bronchoalveolar lavage fluid, semen, lymph fluid, tears, pleural effusion, nipple aspirate, breast milk, skin, external sections of the respiratory tract, intestine, and urogenital tract, and prostatic fluid. The sample can be a viral or bacterial sample, a sample obtained from an environmental source (e.g., a contaminated water body, an air sample, or a soil sample), and a sample from the food industry. The sample can be a biological sample, which refers to a sample derived from or obtained from a living organism. The organism can be in vivo (e.g., a whole organism), or it can be in vitro (e.g., cells or organs grown in culture). "Biological sample" also refers to a cell or population of cells, or a quantity of tissue or fluid from a subject. Most commonly, the sample is taken from within the subject, but the term "biological sample" can also refer to cells or tissue analyzed in vivo, i.e., not removed from the subject. A "biological sample" typically contains cells from the subject, but the term can also refer to acellular biological material, such as the acellular portion of blood, saliva, or urine. A biological sample can be from the resection of a primary, secondary, or metastatic tumor, a bronchoscopic biopsy, or a core needle biopsy, or from a cell block of a pleural effusion. Additionally, fine needle aspiration of biological samples is also useful. In one embodiment, the biological sample is primary ascites cells. Biological samples also include explants from patient tissue and primary and / or transformed cell cultures. A biological sample can be provided by taking a cell sample from a subject, but can also be achieved by using previously isolated cells or cell extracts (e.g., isolated by another person at another time, and / or for another purpose). Archived tissue, such as tissue with a treatment or outcome history, can also be used. Biological samples include, but are not limited to, tissue biopsies, abrasions (e.g., oral abrasions), whole blood, plasma, serum, urine, saliva, cell cultures, or cerebrospinal fluid. The sample analyzed by the compositions and methods of the present invention may have been processed to purify or enrich the exosomes contained therein. In one embodiment, the sample is blood.

[0092] In one aspect, there is provided an imaging system that includes a light source, a detector, and a sensor chip as defined in the present invention, wherein the detector is positioned to detect light transmitted through the sensor chip generated by the light source.

[0093] In one aspect, there is also provided a kit that includes a sensor chip as defined in the present invention.

[0094] The kit may further comprise a second recognition molecule that is specific for an analyte captured on the surface of the sensor chip or an analyte associated with the captured analyte. The kit may contain one or more second recognition molecules, each of which is specific for one or more analytes, such that one or more analytes can be detected.

[0095] The second recognition molecule may allow for 1) signal amplification, 2) co-localization analysis (e.g., detection of different targets found simultaneously in the same vesicle), and 3) discrimination of analyte subsets based on molecular and tissue differences.

[0096] The second recognition molecule may be conjugated to a signal amplification moiety, where the signal amplification moiety is capable of inducing the formation of insoluble aggregates with increased optical density relative to the captured analyte and increasing the measured surface plasmon resonance signal. For example, the kit may contain a second recognition molecule that is an antibody (e.g., an antibody specific for Aβ42). The second recognition molecule may be conjugated to horseradish peroxidase. The kit may further comprise an enzyme substrate. Thus, horseradish peroxidase is capable of inducing the formation of insoluble aggregates with increased optical density relative to the captured analyte on the surface of the sensor chip.

[0097] The term "signal amplification molecule" refers to a molecule that is capable of inducing the formation of insoluble aggregates on the surface of the sensor chip, thereby increasing the optical density relative to the captured analyte. When the second recognition molecule binds to the analyte on the surface of the sensor chip, this will result in a greater change in the transmitted wavelength (spectral shift) or transmitted intensity, thus contributing to an increase in the sensitivity of the sensor chip. The "signal amplification molecule" can be, for example, an enzyme such as horseradish peroxidase, which reacts with an enzyme substrate to form insoluble aggregates on the surface of the sensor chip. The "signal amplification molecule" can also be a secondary antibody that binds to the second recognition molecule on the surface of the sensor chip and forms aggregates. The secondary antibody can be further conjugated to an enzyme, gold particles, or a macromolecule, thereby facilitating the formation of larger aggregates to increase the optical density.

[0098] In one embodiment, the present invention relates to a highly sensitive analytical platform, namely amplified plasmonic exosomes (APEX), for directly detecting amyloid-β (Aβ) bound to exosomes from blood samples of Alzheimer's disease (AD) patients. The analytical method can utilize transmission surface plasmon resonance (SPR) and in-situ enzymatic conversion of optical products for multiplex population analysis. The APEX technology enables multi-parameter in-situ mapping of exosome contents (e.g., proteins and miRNAs). The APEX platform can be used to measure different populations of circulating Aβ (exosome-bound, unbound, and total) and different tissue states of circulating Aβ, and correlate these blood measurements with PET imaging of brain amyloid plaque load.

[0099] In one aspect, a method of fabricating a sensor chip is provided, the method comprising the steps of:

[0100] a) providing a top membrane support layer;

[0101] b) depositing a conductive layer on the top membrane support layer;

[0102] c) forming a plurality of holes extending through the membrane support layer, the holes also extending through the conductive layer, and arranging them such that illumination of the conductive layer and / or the top membrane support layer generates surface plasmon resonance.

[0103] The method may include:

[0104] coating a top membrane support layer and a bottom membrane support layer on the top and bottom surfaces of a silicon substrate;

[0105] providing a photoresist layer on the top membrane support layer, and

[0106] defining a plurality of holes in the photoresist by deep ultraviolet lithography (DUV), and transferring the pattern of the plurality of holes to the top membrane support layer by reactive ion etching (RIE).

[0107] The method may further include the steps of:

[0108] removing the photoresist on the top membrane support layer, and coating a silica protection layer on the surface of the top membrane support layer;

[0109] coating a photoresist layer on the bottom membrane support layer;

[0110] defining a sensing area in the photoresist by lithography; and transferring the pattern of the sensing area to the bottom membrane support layer by reactive ion etching (RIE);

[0111] transferring the pattern of the sensing area to the silicon substrate;

[0112] Remove the protective layer on the surface of the top membrane support layer with dilute hydrofluoric acid;

[0113] And deposit a conductive layer on the top membrane support layer.

[0114] As used herein, the term "sensing region" refers to a region in the sensor chip that includes a plurality of holes, which are arranged such that illumination of the plasma layer and / or the membrane support layer generates surface plasmon resonance.

[0115] As used in the present invention, the term "resist" refers to a thin layer used to transfer an image or pattern to the substrate on which it is deposited. The resist can be patterned by lithography to form a (sub)micron-scale temporary mask that protects selected regions of the underlying substrate in subsequent processing steps (usually etching). The materials used to prepare the thin layer (usually a viscous solution) are also covered by the term resist. Resists are typically mixtures of polymers or their precursors with other small molecules (such as photoacid generators), which are specifically configured for a given lithography technique. For example, a resist used in lithography is called a "photoresist". A resist used in an electron beam lithography process is called an "electron beam resist".

[0116] In one aspect, a method for detecting an analyte in a sample is provided, the method comprising:

[0117] Capturing the analyte on the surface of a sensor chip as defined in the present invention; and

[0118] Detecting the binding of a second recognition molecule to the analyte captured on the surface of the sensor chip, wherein the second recognition molecule is specific for the analyte, and wherein an increase in the binding of the second recognition molecule compared to a control sample indicates the presence of the analyte in the sample.

[0119] The method may include detecting the binding of one or more second recognition molecules (sequentially or simultaneously) that are 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 can be recognized by a different set of first and second recognition molecules. The first and second recognition molecules can recognize the same analyte or different analytes, respectively. Different combinations of the first and second recognition molecules can allow for the detection of the co-localization of analytes. This can allow for the simultaneous detection of multiple analytes and can allow for the detection of the co-localization of these molecules.

[0120] In one embodiment, the method includes detecting two or more analytes co-localized in a sample. Two or more analytes can be present, for example, inside or on a cell. Alternatively, two or more analytes can be bound, for example, to the same exosome.

[0121] In one embodiment, the first recognition molecule is an antibody that recognizes Aβ42, and the second recognition molecule is another antibody that recognizes Aβ42.

[0122] In one embodiment, the first recognition molecule is an antibody that recognizes Aβ42, and the second recognition molecule is an antibody that recognizes CD63, which is used to detect the co-localization of Aβ42 and CD63.

[0123] The detection of the "binding" of the second recognition molecule to the analyte captured on the surface of the sensor chip can be carried out by spectral shift (change in transmission wavelength) or change in transmission intensity at a fixed wavelength. For example, the analyte captured on the surface of the sensor chip will have an initial reference wavelength. After binding the second recognition molecule, the transmission wavelength may shift to a longer wavelength.

[0124] The change in the transmission resonance wavelength (or spectral shift represented by (△λ)) in the sample or the change in transmission intensity at a fixed wavelength can be compared with the change observed in the control sample. For example, this can be used to determine whether the binding of the second recognition molecule to the captured analyte has increased.

[0125] An "increase in the binding of the second recognition molecule" in the sample compared to the control sample can be determined by comparing the change in spectral shift or the change in transmission intensity at a fixed wavelength after binding the second recognition molecule between the sample and the control sample. An increase in spectral shift or a change in transmission intensity may indicate an increase in the binding of the second recognition molecule to the analyte.

[0126] In one embodiment, an increase in spectral shift or transmission intensity may refer to an increase of 1.2-fold or greater between a subject and a control subject. The term may also refer to an increase selected from 1.1-fold, 1.3-fold, 1.4-fold, 1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold, 1.9-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 11-fold, 12-fold, 13-fold, 14-fold, 15-fold, 16-fold, 17-fold, 18-fold, 19-fold, 20-fold, 21-fold, 22-fold, 23-fold, 24-fold, 25-fold, 26-fold, 27-fold, 28-fold, 29-fold, 30-fold, 31-fold, 32-fold, 33-fold, 34-fold, 35-fold, 36-fold, 37-fold, 38-fold, 39-fold, 40-fold, 41-fold, 42-fold, 43-fold, 44-fold, 45-fold, 46-fold, 47-fold, 48-fold, 49-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.

[0127] The second recognition molecule may be a molecule specific for an analyte. The second recognition molecule may be coupled to a signal amplification moiety, and wherein the signal amplification moiety is capable of inducing the formation of insoluble aggregates that increase the optical density relative to the captured analyte. For example, an analyte captured on the surface of a sensor chip will have an initial reference wavelength. After binding the second recognition molecule, the transmission wavelength may shift to a longer wavelength. When the second recognition molecule is coupled to a signal amplification moiety, the transmission wavelength may shift to a longer wavelength due to the increase in optical density.

[0128] The second recognition molecule may be fused to the signal amplification moiety. Alternatively, the second recognition molecule may be conjugated to the signal amplification moiety.

[0129] The signal amplification moiety may be an enzyme. In one embodiment, the signal amplification moiety is an enzyme. The enzyme may be horseradish peroxidase (HRP), alkaline phosphatase, glucose oxidase, β-lactamase, or β-galactosidase or an enzyme fragment 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 may be an antibody covalently fused to horseradish peroxidase, and the enzyme is covalently linked to the antibody using techniques well known in the art.

[0130] The method may further include contacting an enzyme with an enzyme substrate. The enzyme substrate may be an enzyme that is capable of forming an insoluble product in the presence of or under the action of an enzyme. For horseradish peroxidase (HRP), preparations such as 3-amino-9-ethylcarbazole, 3,3',5,5'-tetramethylbenzidine, or chloronaphthol, 4-chloro-1-naphthol, etc. can be used. After the enzymatic reaction of HRP, these substrates are capable of being converted into insoluble products. In one embodiment, the enzyme substrate is 3,3'-diaminobenzidine tetrahydrochloride.

[0131] In an alternative embodiment, the signal amplification moiety may be a secondary antibody capable of binding to the second recognition molecule. The binding of the secondary antibody to the second recognition molecule may induce the formation of insoluble aggregates.

[0132] In one embodiment, the first recognition molecule is immobilized on the surface of a surface plasmon resonance sensor chip, wherein the first recognition molecule is capable of capturing an analyte on the surface of the sensor chip. The analyte may be an exosome-bound or associated biomarker. The analyte may be an exosome-bound or associated aggregated biomarker. The first recognition molecule may be specific for the analyte.

[0133] In one embodiment, the first recognition molecule is an antibody. For example, the antibody may be an antibody that recognizes a pan-exosome marker or a marker associated with or bound to exosomes. For example, the antibody may be specific for CD63, CD9, or CD81 that are abundant and characteristic in exosomes. The antibody may also be specific for cell-specific origin markers (such as CHL1, L1CAM, or NCAM). The antibody may also recognize a biomarker associated with or bound to exosomes. For example, the antibody may be an anti-Aβ antibody that recognizes Aβ, or an antibody that recognizes APP, α-syn, or Tau that is bound to or associated with exosomes.

[0134] The term "control sample" refers to a sample that does not contain an analyte. The "control sample" can be used as a comparison with a sample to determine whether the sample contains an analyte of interest.

[0135] The term "biomarker" as used in this application should be understood as a reagent or entity whose presence or level is related to an event of interest. Biomarkers can be cells, proteins, nucleic acids, peptides, glycopeptides, exosomes, or combinations thereof. For example, the biomarker is Aβ42 or Tau peptide, whose presence or level indicates whether a subject has a neurodegenerative disease or amyloidosis, or is at risk of developing it. In another example, the biomarker is exosome-bound or exosome-associated Aβ42 or Tau peptide, whose presence or level indicates whether a subject has a neurodegenerative disease or amyloidosis, or is at risk of developing it.

[0136] In one embodiment, there is provided the use of the sensor chip defined by the present invention for detecting an analyte.

[0137] In one aspect, there is provided a method for detecting a neurodegenerative disease or amyloidosis in a subject, the method comprising:

[0138] a) contacting a sample with the surface of the sensor chip defined by the present invention; and

[0139] b) detecting the binding of a second recognition molecule to an analyte captured on the surface of the sensor chip.

[0140] Wherein the second recognition molecule is specific for the analyte, and an increase in the binding of the second recognition molecule as compared to a control subject indicates that the subject has a neurodegenerative disease or amyloidosis.

[0141] The term "subject" refers to any animal, including any vertebrate or mammal, particularly a human, and may also refer to, for example, an individual or a patient.

[0142] The term "control subject" refers to a subject known not to have a neurodegenerative disease or amyloidosis, or a subject at no risk of having a neurodegenerative disease or amyloidosis. A "control subject" may also be a healthy subject. A "control subject" may be a subject without cognitive impairment (NCI). The term includes samples obtained from control subjects.

[0143] In one embodiment, the biomarker is an exosome-binding or exosome-related biomarker. In one embodiment, the biomarker is an exosome-binding aggregated biomarker. The biomarker may be selected from, but not limited to, Aβ, APP, α-Syn or Tau. In one embodiment, the Aβ is Aβ42. In another embodiment, the Aβ is Aβ40. In one embodiment, the biomarker is Tau. In some embodiments, the biomarker is selected from Aβ, APP, α-Syn, CD9, CD63, CD81, ALIX, TSG101, Flotilin-1, Flotilin-2, LAMP-1, HSP70, HSP90, CHL1, IRS-1, L1CAM, NCAM, Tau, APOE, SOD1, TDP-43, bassoon, fibronectin, DNA and RNA or combinations and related complexes thereof.

[0144] The neurodegenerative disease may be selected from Alzheimer's disease and mild cognitive impairment.

[0145] The method may further comprise treating a subject found to have a neurodegenerative disease or amyloidosis.

[0146] As used herein, the term "treatment" can refer to (1) preventing or delaying the onset 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 a disease, i.e., causing the regression of a disease or at least one or more symptoms of a disease; and / or (4) causing a reduction in the severity of one or more symptoms of a disease.

[0147] In one embodiment, the term "treatment" refers to administering a drug to slow the progression of a neurodegenerative disease or amyloidosis.

[0148] The present invention provides a method for detecting a neurodegenerative disease or amyloidosis in a subject, the method comprising detecting the level of an exosome-bound biomarker in a sample obtained from the subject, wherein an increase in the level of the exosome-bound biomarker as compared to a reference indicates that the subject has a neurodegenerative disease or amyloidosis.

[0149] In one aspect, there is provided a method for detecting a neurodegenerative disease or amyloidosis in a subject, the method comprising detecting the level of an aggregated biomarker bound to exosomes in a sample obtained from the subject, wherein an increase in the level of the exosome-bound aggregated biomarker as compared to a reference indicates that the subject has a neurodegenerative disease or amyloidosis.

[0150] The method may comprise detecting one or more exosome-bound biomarkers in the sample. This allows for the detection of the co-localization or presence of multiple biomarkers present on the same exosome.

[0151] The biomarker may be selected from, but not limited to, Aβ, APP, α-Syn or Tau.

[0152] The method may comprise detecting the level of a molecular subtype of the exosome-bound biomarker.

[0153] In one embodiment, the Aβ is Aβ42 or Aβ40.

[0154] In one embodiment, the Aβ is a pre-fibrillar aggregate. It has been found that pre-fibrillar Aβ aggregates preferentially bind to exosomes. Enhancing the binding between Aβs. In one embodiment, the method defined by the present invention comprises detecting exosomes bound to pre-fibrillar Aβ aggregates.

[0155] In one embodiment, the aggregated biomarker is a pre-fibrillar aggregate. The aggregated biomarker may be a pre-fibrillar aggregate of Aβ. Alternatively, the aggregated biomarker may be a pre-fibrillar aggregate of APP, α-Syn or Tau.

[0156] In one embodiment, reference is made to control subjects.

[0157] In one embodiment, a method is provided for measuring exosome association as an alternative to determining the prefibrillar tissue state of protein aggregates, wherein an increase in the level of protein aggregates in a prefibrillar tissue state compared to a control indicates that the subject has a neurodegenerative disease or amyloidosis.

[0158] In one embodiment, the method further comprises detecting exosome biomarkers selected from CD63, CD9, and CD81, wherein the exosome biomarker co-localizes with an exosome-binding biomarker. In some embodiments, the exosome biomarker is selected from Aβ, APP, α-Syn, CD9, CD63, CD81, ALIX, TSG101, Flotilin-1, Flotilin-2, LAMP-1, HSP70, HSP90, CHL1, IRS-1, L1CAM, NCAM, Tau, APOE, SOD1, TDP-43, bassoon, fibronectin, DNA, and RNA or combinations and associated complexes thereof.

[0159] In one embodiment, the method further comprises detecting neurobiological markers, which include NCAM, L1CAM, and CHL-1, wherein the neurobiological marker co-localizes with an exosome-binding biomarker.

[0160] In one embodiment, a method is provided for measuring different tissue and molecular subsets of biomarkers. For example, the method may include measuring exosome-binding biomarkers, free (unbound) biomarkers, and total (exosome-bound and unbound) biomarkers. The method may also include measuring the relative concentrations of different biomarkers to better predict disease.

[0161] In one embodiment, the neurodegenerative disease is selected from Alzheimer's disease, mild cognitive impairment, vascular dementia, and vascular mild cognitive impairment.

[0162] In one embodiment, the neurodegenerative disease is selected from Alzheimer's disease and mild cognitive impairment.

[0163] The method may further comprise treating a subject having a neurodegenerative disease or amyloidosis.

[0164] The method can be further associated with brain imaging studies (such as PET imaging). This includes the correlation with the imaging of specific brain regions. In one embodiment, a method for identifying and measuring circulating biomarkers associated with brain imaging (PET) is provided. In one embodiment, a method for identifying and measuring circulating biomarkers associated with the imaging of a specific brain region (PET) is provided.

[0165] The present invention is based on the following findings: 1) Biomarkers (including co-localized different markers) can be used to measure and characterize different molecular, biophysical, and tissue subsets of circulating Aβ; 2) Circulating exosome-bound Aβ in blood is closely correlated with PET imaging (overall average) of Aβ deposition across different patient populations; 3) Circulating exosome-bound Aβ in blood is closely correlated with PET imaging of Aβ (early AD region, cingulate gyrus region) across different patient populations; 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).

[0166] The method can include administering a therapeutically effective amount of a drug to a subject in need of treatment. For example, the drug can be a cholinesterase inhibitor, such as donepezil, rivastigmine, or galantamine. The drug can also be an NMDA receptor antagonist, such as memantine. The drug can be a combination of a cholinesterase inhibitor and an NMDA receptor antagonist, such as a combination of donepezil and memantine. The drug can be a BACE1 inhibitor, such as AZD3293; or an antibody, such as an anti-amyloid protein antibody, such as Aducanumab. The drug can 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 in the present invention, or a combination of two or more of the drugs described in the present invention, can be administered to a subject in need of treatment.

[0167] In some embodiments, molecules that can effectively reduce the number of amyloid protein aggregates may be potential candidate molecules for treatment. Thus, in some embodiments, the method may include administering to a subject in need of treatment one or more of the following molecules (drugs), or a combination of two or more of the following molecules (drugs): methylene blue, leucomethylene blue bis(hydrogen methanesulfonate), 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, cetyltrimethylammonium bromide (C16), hemin chloride, heme, indomethacin, juglone, resorcin blue, meclocycline sulfosalicylate, melatonin, myricetin, 1,2-naphthoquinone, nordihydroguaiaretic acid, R()-normorphine hydrobromide, orange G, o-vanillin (2-hydroxy-3-methoxybenzaldehyde), phenazine, phthalocyanine, rifamycin SV, phenol red, rolitetracycline, quinacrine mustard dihydrochloride, thioflavin S, ThT, and trimethyl(tetradecyl)ammonium bromide (C17), diallyltartaric acid, eosin Y, fenofibrate, bathocuproine, nystatin, octadecyl sulfate, and rhodamine B.

[0168] An increased level of exosome-bound biomarker may refer to an increase of 1.2-fold or higher in level between a subject and a control subject. The term "increased level" may also refer to an increase selected from 1.1-fold, 1.3-fold, 1.4-fold, 1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold, 1.9-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 11-fold, 12-fold, 13-fold, 14-fold, 15-fold, 16-fold, 17-fold, 18-fold, 19-fold, 20-fold, 21-fold, 22-fold, 23-fold, 24-fold, 25-fold, 26-fold, 27-fold, 28-fold, 29-fold, 30-fold, 31-fold, 32-fold, 33-fold, 34-fold, 35-fold, 36-fold, 37-fold, 38-fold, 39-fold, 40-fold, 41-fold, 42-fold, 43-fold, 44-fold, 45-fold, 46-fold, 47-fold, 48-fold, 49-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.

[0169] In one aspect, a method for detecting a subject at risk of developing a neurodegenerative disease is provided, the method comprising detecting the level of an exosome-bound biomarker in a sample obtained from the subject, wherein an increase in the level of the exosome-bound biomarker as compared to a control subject indicates that the subject has a neurodegenerative disease.

[0170] In one aspect, a method for detecting and treating a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising:

[0171] a) detecting the level of an exosome-bound aggregated biomarker in a sample obtained from the subject, an increase in the level of the exosome-bound aggregated biomarker as compared to a reference indicating that the subject has a neurodegenerative disease or amyloidosis; and b) treating the subject having a neurodegenerative disease or amyloidosis.

[0172] In one aspect, a method for treating a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising:

[0173] a) detecting the level of an exosome-bound aggregated biomarker in a sample obtained from the subject, an increase in the level of the exosome-bound aggregated biomarker as compared to a reference indicating that the subject has a neurodegenerative disease or amyloidosis; and b) treating the subject having a neurodegenerative disease or amyloidosis.

[0174] In one embodiment, a method for detecting and slowing the progression of a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising:

[0175] a) detecting the level of an exosome-bound aggregated biomarker in a sample obtained from the subject, an increase in the level of the exosome-bound aggregated biomarker as compared to a reference indicating that the subject has a neurodegenerative disease or amyloidosis; and b) treating the subject having a neurodegenerative disease or amyloidosis.

[0176] 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 an exosome-bound biomarker in the sample, an increase in the level of the exosome-bound biomarker as compared to a reference indicating the degree of aggregation of the biomarker.

[0177] The method may comprise the step of contacting the sample with a population of exosomes prior to detecting the level of the exosome-bound biomarker.

[0178] In one embodiment, the aggregated biomarker in the sample preferentially binds to exosomes as compared to a non-aggregated biomarker.

[0179] The sample can be obtained from a subject.

[0180] In one embodiment, an increase in the degree of aggregation of a biomarker compared to a reference indicates that the subject has a neurodegenerative disease or amyloidosis.

[0181] The method can further include treating a subject having a neurodegenerative disease or amyloidosis.

[0182] In one embodiment, a method for determining the aggregation state of a biomarker in a sample is provided, the method comprising contacting the sample with a population of exosomes and detecting the level of exosome-bound biomarker in the sample, an increase in the level of exosome-bound biomarker relative to a reference indicating the degree of aggregation of the biomarker.

[0183] Those skilled in the art will appreciate that, apart from those specifically described, the inventive concepts described herein are susceptible to variation and modification. It should be understood that the invention includes all such variations and modifications that fall within the spirit and scope. The invention also includes all steps, features, compositions, and compounds referred to or indicated in this specification, singly or jointly, and any and all combinations of any two or more of said steps or features.

[0184] In this specification and the appended claims, unless the context otherwise requires, the word "comprising" and variations such as "comprises" and "comprising" are to be understood to include the stated integer or step or group of integers or steps but not to exclude any other integer or step or group of integers or steps.

[0185] Any reference in this specification to any prior publication (or information derived therefrom) or to any matter known is not, and should not be taken as, an admission or acknowledgment or any form of suggestion that the prior publication (or information derived therefrom) or known matter forms part of the common general knowledge in the field to which this specification pertains.

[0186] Certain embodiments of the invention will now be described with reference to the following examples, which are for illustrative purposes only and are not intended to limit the general scope described above.

[0187] Examples

[0188] Any reference in this specification to any prior publication (or information derived therefrom) or to any matter known is not, and should not be taken as, an admission or acknowledgment or any form of suggestion that the prior publication (or information derived therefrom) or known matter forms part of the common general knowledge in the field to which this specification pertains.

[0189] Materials and Methods

[0190] Cell culture. Human cell lines SH-SY5Y (neurons), HUVEC (umbilical vein endothelial cells), THP-1 (monocytes), PC-3 (prostate epithelial cells), and SK-OV-3 (ovarian epithelial cells) were obtained from the American Type Culture Collection. GLI36 (glia) and SK-OV-3 were grown in Dulbecco's Modified Eagle Medium (DMEM, Gibco). SH-SY5Y was cultured in Dulbecco's Modified Eagle Medium: Nutrient Mixture F-12 Medium (DMEM / F12 Gibco). PC-3, THP-1, and HUVEC were grown in F-12K, RPMI-1640, and EGM-2 medium, respectively. All other media were supplemented with 10% fetal bovine serum (FBS) and penicillin-streptomycin, except for EGM-2, which was supplemented with 5% FBS.

[0191] Exosome isolation and quantification. Cells at passages 1-15 were cultured for 48 h in vesicle-free medium (containing 5% depleted FBS) prior to collecting the vesicles. All media containing exosomes were filtered through a 0.2-μm membrane filter (Millipore), isolated by differential centrifugation (first at 10,000 g and subsequently at 100,000 g), and used for exosome analysis using the APEX platform. For exosomes isolated from blood cells and platelets, the blood cells were from blood fractionation and the platelets were from platelet-rich plasma. These fractions were washed in HEPES-buffered saline and incubated at 37 °C with 2 mM calcium chloride and 2 μM calcium ionophore (A23187) to stimulate exosome production. All vesicles were then collected as described above. To independently quantify exosome concentration, a nanoparticle tracking analysis (NTA) system (NS300, Nanosight) was used. The exosome concentration was adjusted to obtain ~50 vesicles in the field of view for optimal counting. All NTA measurements were made with the same system settings to ensure consistency.

[0192] APEX sensor fabrication. APEX sensors were fabricated on 8-inch silicon (Si) wafers. Briefly, a 10-nm silicon dioxide (SiO 2 ) layer was prepared by thermal oxidation, and a 145-nm silicon nitride (Si 3 N 4 ) was deposited on the wafer by low-pressure chemical vapor deposition (LPCVD). After coating with photoresist, deep ultraviolet (DUV) lithography was performed to define a nanopore array pattern in the resist. The pattern was transferred to Si 3 N 4membrane. After removing the photoresist, a thin layer of SiO is deposited on the front side of the wafer using plasma-enhanced chemical vapor deposition (PECVD). 2 Protective layer (100 nm). To achieve light transmission, photoresist is spin-coated on the back side of the wafer; photolithography is used to define the sensing area. Si 3 N 4 and SiO 2 are etched by RIE, and then Si is etched by potassium hydroxide (KOH) and tetramethylammonium hydroxide (TMAH). After etching, diluted hydrofluoric acid (DHF) (1:100) is used to remove the SiO 2 protective layer. Finally, Ti / Au (10 nm / 100 nm) is deposited on the Si 3 N 4 membrane. All nanopore sizes and sensor uniformity are characterized by scanning electron microscopy (JEOL 6701).

[0193] Channel assembly. Standard soft lithography is used to fabricate a multi-channel flow cell. A mold is prepared using SU-8 negative photoresist (SU8-2025, Microchem). The photoresist is spin-coated on a silicon wafer at 2000 rpm for 30 s and baked at 65 °C and 95 °C for 2 min and 5 min, respectively. After UV light exposure, the resist is baked again under stirring before development. The developed mold is chemically treated with trichlorosilane vapor in a dryer for 15 min before subsequent use. Polydimethylsiloxane polymer (PDMS) and crosslinker are mixed in a ratio of 10:1 and poured onto the SU-8 mold. After curing at 65 °C for 4 h, the PDMS layer is cut from the mold and assembled onto the APEX sensor. All inlets and outlets are processed for samples using a 1.1 mm biopsy puncher.

[0194] Optical setup and spectral analysis. A tungsten halogen lamp (StockerYale Inc.) is used to illuminate the APEX sensor through a 10X microscope objective. The transmitted light is collected by an optical fiber and input into a spectrometer (Ocean Optics). All measurements are carried out at room temperature in a closed box to eliminate ambient light interference. The transmitted light intensity is digitally recorded as counts versus wavelength (330 nm - 1600 nm). For spectral analysis, the spectral peak is determined using a custom R program by fitting the transmission peak using a local regression method. All fittings are done locally. That is, for the fitting at point x, the points near point x are used for fitting and weighted by their distance from x. Compared with the multi-order polynomial curve fitting, this method can eliminate the result variation caused by the number of data points and data range being analyzed. When determining the optimal sensor geometry ( Figure 10) Spectral changes were used to quantify the peak transmission intensity, peak shape (full width at half maximum, FWHM), and detection sensitivity in response to refractive index changes, respectively. The measured transmission spectra showed consistency among different sensors, with an s.d. of 0.03 nm for the peak position of the baseline spectra. All spectral shifts (Δλ) were determined as the change in the peak of the transmission spectra and were calculated relative to an appropriate control experiment (see details below).

[0195] Sensor surface functionalization. To confer molecular specificity to the APEX sensors, the prepared Au surface was first incubated with a mixture of polyethylene glycol (PEG) containing long active (carboxylated) thiol-PEG and short inactive methylated thiol-PEG (Thermo Scientific) (1:3 active:inactive, 10 mM in PBS) for 2 h at room temperature. After washing, the surface was activated by carbodiimide crosslinking in an excess NHS / EDC mixture dissolved in MES buffer and conjugated with specific probes and ligands (e.g., antibodies and Aβ42 aggregates). All probe information is shown in Table 1. Excess unbound probes were removed by washing with PBS. The conjugated sensors were stored in PBS at 4 °C for subsequent use. Spectral monitoring was performed for all sensor surface modifications to ensure uniform functionalization.

[0196] Table 1. List of markers and their probes used for mapping.

[0197]

[0198]

[0199]

[0200] APEX signal amplification. To establish APEX amplification, the enzymatic growth of insoluble optical products for signal enhancement was incorporated, and the optical substrate concentration and reaction duration for establishing the platform were optimized. Briefly, exosomes were incubated with CD63-functionalized APEX sensors (BD Biosciences) for 10 min. The bound vesicles were then labeled with biotinylated anti-CD63 antibody (Ancell, 10 min). As a control experiment, an equal amount of biotinylated IgG isotype control antibody (Biolegend) was used on the bound vesicles to determine the amplification efficiency. After washing away the unbound antibodies, highly sensitive horseradish peroxidase conjugated to neutravidin (Thermo Scientific) was reacted with the bound vesicles before introducing different concentrations of 3,3'-diaminobenzidine tetrahydrochloride (Life Technologies) as the optical substrate. Real-time spectral changes were monitored to determine the optimal substrate concentration and reaction duration. The optimized conditions were 1 mg / mL, 3 min. All flow rates for incubation and washing were maintained at 3 μl / min and 10 μl / min, respectively. Local deposition of the insoluble optical product was confirmed by scanning electron microscopy. This optimized workflow is shown in Figure 11 panel a.

[0201] Using this set of conditions, known amounts of exosomes were further titrated, and their associated APEX signals were measured. The APEX limit of detection was determined to be the lowest target concentration capable of generating a detection signal = 3 x (s.d. of the control background signal).

[0202] APEX protein detection. All sensor surfaces were blocked with 2% w / v bovine serum albumin (BSA) to reduce non-specific protein binding. Exosomes were introduced onto the functionalized sensors, incubated at room temperature for 10 min to capture exosomes, and washed with PBS to remove unbound exosomes. As described above, for extracellular protein targets, exosomes were directly labeled with detection antibodies for APEX amplification. For intracellular protein targets, exosomes were subjected to additional fixation and permeabilization (eBioscience) before labeling with detection antibodies. Spectral measurements were performed before and after APEX amplification and analyzed by a custom R program.

[0203] APEX miRNA detection. The APEX sensor was functionalized with p19 protein (New England Biolabs) via its chitin-binding domain and blocked with 2% w / v BSA. To detect miRNA, the exosome lysate was incubated with biotinylated RNA probes (350 nM) for 15 min to hybridize with the target miRNA strand. The mixture was introduced onto the functionalized sensor in binding buffer (1× p19 binding buffer, pH 7.0, 40 U RNase inhibitor, 0.1 mg / mL BSA) to enable p19 to capture the hybridized miRNA target / RNA probe duplex. High-sensitivity horseradish peroxidase conjugated to neutravidin (Thermo Scientific) was introduced into the bound biotinylated duplex for APEX amplification. Spectral measurements were performed and analyzed by a customized R program.

[0204] Enzyme-linked immunosorbent assay (ELISA). The capture antibody (5 μg / ml) was adsorbed onto an ELISA plate (Thermo Scientific) and blocked with Superblock (Thermo Scientific) before incubation with the sample. After washing with PBST (PBS containing 0.05% Tween 20), the detection antibody (2 μg / ml) was added and incubated for 2 h at room temperature. After incubation with a secondary antibody conjugated to horseradish peroxidase (Thermo Scientific) and a chemiluminescent substrate (Thermo Scientific), the chemiluminescent intensity was measured for protein detection (Tecan).

[0205] Western blotting. Exosomes isolated by ultracentrifugation were lysed in radioimmunoprecipitation assay (RIPA) buffer containing protease inhibitors (Thermo Scientific) and quantified using the bicinchoninic acid assay (BCA assay, Thermo Scientific). Protein lysates were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), transferred onto polyvinylidene difluoride membranes (PVDF, Invitrogen), and immunoblotted with antibodies against protein markers: HSP90 (Cell Signaling), HSP70 (BioLegend), Flotilin 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), enhanced chemiluminescence was used for immunodetection (Thermo Scientific).

[0206] Protein aggregation. Lyophilized NH 4 OH-treated Aβ42 protein (rPeptide) was resuspended in NaOH (60 mM, 4 °C), sonicated in PBS34, and the pH was adjusted to pH 7.4. The protein was immediately filtered through a 0.2-μm membrane filter (Millipore), and the filtrate was used as smaller Aβ42 aggregates. To prepare larger Aβ42 aggregates, the protein was treated as described above and incubated for 1 h with stirring to induce further aggregation before filtering through a 0.2-μm membrane filter (Millipore). The filtrate was used as large Aβ aggregates. To prepare BSA aggregates of similar size as a control, 2% w / v BSA was dissolved in PBS and heated at 80 °C for 1 h and 2 h to induce aggregation of large control aggregates and small control aggregates, respectively.

[0207] Determination of aggregate size. The hydrodynamic diameters of Aβ42 and BSA aggregates were determined by dynamic light scattering analysis (Zetasizer Nano ZSP, Malvern). Measurements were performed 3 × 14 times at 4 °C. The Z-average diameter and polydispersity were analyzed. For each measurement, the autocorrelation function and polydispersity index were monitored to ensure the sample quality of size determination.

[0208] Characterization of exosome-Aβ association. The prepared protein aggregates (Aβ42 and BSA control) were used for surface functionalization on the APEX sensor by the aforementioned EDC / NHS coupling. Unbound protein aggregates were washed away with PBS. The amount of conjugated protein was measured by the resulting shift in the transmission spectrum. This information was used to determine the number of conjugated protein aggregates and their associated total protein surface area for exosome binding (detailed below), thereby normalizing the binding affinity. After surface functionalization with protein aggregates, exosomes (10 10 / ml) were introduced onto the sensor. Spectral changes were measured every 3 s over a total duration of 480 s to construct a real-time kinetic sensing map. The exosome association kinetics and binding affinity of protein aggregates of different sizes were determined.

[0209] Considering the differences in protein aggregate size and the exponential decay of sensitivity associated with SPR (as the distance from the sensing surface increases), the following formula

[0210]

[0211] was used to calculate the total surface area of the conjugated aggregates interacting with exosomes: where S is the signal, z is the distance from the sensor surface, E is the electric field at z = 0 and is constant in this case, l d is the decay length and is set to 200 nm in the current sensor design, and r is the radius of the conjugated protein aggregate.

[0212] All protein aggregates were approximated as spherical, supported by transmission electron micrographs (Figure 3b, left), and their r was determined by dynamic light scattering analysis. The above formula was used to determine the number of protein aggregates conjugated to the sensor and their respective total surface areas to estimate the number of available binding sites interacting with exosomes. All exosome binding data (△λ) were normalized to their respective protein binding sites. The normalized Aβ42 binding data were made relative to a BSA control of similar size and were fitted to determine the binding affinity constant KD.

[0213] Scanning electron microscopy. All samples were fixed with half-strength Karnovsky fixative and washed twice with PBS. After dehydration in a series of increasing ethanol concentrations, the samples were transferred to critical drying (Leica) and subsequently sputter-coated with gold (Leica) before imaging with a scanning electron microscope (JEOL 6701).

[0214] Transmission electron microscopy. Exosomes were immunolabeled with gold nanoparticles (15 nm, Ted Pella), fixed with 2% paraformaldehyde and transferred onto copper grids (Ted Pella). Bound vesicles were washed and counterstained with a mixture of uranyl oxalate and methylcellulose. Dried samples were imaged with a transmission electron microscope (JEOL 2200FS).

[0215] Clinical sample collection. The study was approved by the Institutional Review Boards of NUH and NUS (2015 / 00441, 2015 / 00406, and 2016 / 01201). All subjects were recruited according to a protocol approved by the Institutional Review Board and provided informed consent. All recruited subjects underwent a battery of neuropsychological assessments at the National University of Singapore Hospital (NUH), including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCa), and Vascular Dementia Battery (VDB) for cognitive assessment. Clinical diagnoses of AD, MCI, or NCI were made by neuropsychological assessment and evaluation of clinical features and blood investigations. Clinical diagnoses of VaD and VMCI were made by a combination of neuropsychological assessment, clinical history of stroke, and the degree of cerebrovascular disease observed by magnetic resonance imaging (MRI). All clinical assessments and classifications were performed according to published criteria46 - 48 and were independent of APEX measurements. Acute stroke plasma samples were collected within 24 h of hospitalization of patients diagnosed with stroke. During the one-year follow-up period, longitudinal plasma samples were collected from the patients, but PET brain imaging was not performed. For plasma collection, venous blood (5 ml) was drawn from subjects into EDTA tubes before injection of the PET radiotracer (if applicable) and processed immediately. Briefly, all blood samples were centrifuged at 400 g (4 °C) for 10 min. Plasma was transferred without disturbing the buffy coat and centrifuged again at 1100 g (4 °C) for 10 min. All plasma samples were de-identified and stored at -80 °C before measurement using the APEX platform. All APEX measurements were performed with the PET imaging results and clinical diagnoses unknown.

[0216] Clinical APEX measurement. All plasma samples were according to the supplement Figure 15Measurements were performed using the assay configuration outlined in a. Briefly, to measure the exosome-bound Aβ42 population, we directly used native plasma samples and detected Aβ42 capture and CD63 (Aβ42+CD63+) without any vesicle purification or isolation. To demonstrate the presence of an unbound Aβ42 population in the plasma sample, we used size exclusion filtration (size cut-off = 50 nm, Whatman) to remove large-sized retentates. This was necessary because the assay configuration based on Aβ42 capture and Aβ42 detection could not distinguish between unbound and total Aβ42. To measure unbound Aβ, we evaluated the plasma filtrate by Aβ42 capture and Aβ42 detection (Aβ42+Aβ42+). It should be noted that this filtration was only performed to demonstrate the presence of an unbound Aβ42 population; it is unnecessary in a clinical setting where only the more reflective exosome-bound Aβ42 would be directly measured from native plasma samples. To measure total Aβ42, we directly evaluated the native plasma samples by Aβ42 capture and Aβ42 detection (Aβ42+Aβ42+). For all measurements, we used 5% BSA as a blocking agent for the APEX sensor. We also included sample-matched negative controls in which we incubated the same samples on control sensors functionalized with IgG isotype control antibodies. All measurements were made relative to the IgG control to account for sample-matched non-specific binding.

[0217] Positron emission tomography (PET) imaging. After blood sampling, the subjects were scanned using a Siemens 3T Biograph mMR system (Siemens Healthineers) to acquire PET and MR images 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 receive coil and consisted of ultrashort echo time (UTE) images for PET attenuation correction and T1-weighted magnetization-prepared gradient echo (MPRAGE) images (1 mm isotropic resolution, TI / TE / TR = 900 / 3.05 / 1950 ms).

[0218] PET data analysis. T1-weighted MPRAGE images were processed using Freesurfer (5.3.0) to generate cortical segmentation for PET data analysis. PET images were reconstructed using the ordinary Poisson ordered subset expectation maximization (OP-OSEM) algorithm and smoothed using a 4-mm Gaussian filter. The data were attenuated using μ-map-based UTE. Subsequently, the resulting attenuation-corrected standardized uptake value (SUV) images were co-registered with the MPRAGE images using the Advanced Normalization Tools (ANTs), and the standardized uptake value ratio (SUVR) relative to the mean cerebellar gray matter intensity was calculated using subject-specific Freesurfer segmentation. The mean SUVR of specific regions was calculated, and the overall mean SUVR for each patient was calculated by averaging the SUVR of all brain regions.

[0219] Statistical analysis. All measurements were performed in triplicate, and the data are shown as mean ± s.d. Significance tests were performed by two-tailed Student's t-tests. For inter-sample comparisons, multiple groups of samples were tested, and the resulting P-values were adjusted for multiple hypothesis testing using the Bonferroni correction. Values of P < 0.05 after adjustment were determined to be significant. One-way paired ANOVA tests were used to determine the coefficients of analytical and biological variation (i.e., within-group, between-group, and total). For clinical studies, correlation analysis was performed by linear regression to determine the goodness of fit (R 2 ). All statistical analyses were performed using the R package (version 3.4.2) and Graphpad Prism 7.

[0220] Example 1

[0221] Amplified plasmonic assay of exosome-bound Aβ

[0222] One of the earliest pathological features of AD is the cerebral deposition of Aβ. These plaques are formed by the clustering of abnormal amyloid fragments, mainly the hydrophobic spliced variant Aβ42. Aβ proteins are released into the extracellular space and can circulate in the blood. In the extracellular space, exosomes are also found, which are nanoscale membrane vesicles secreted by mammalian cells through the fusion of multivesicular endosomes with the plasma membrane. During this exosome biogenesis, glycoproteins and glycolipids are incorporated into the invaginated plasma membrane and sorted into the newly formed exosomes 10, 11. Through these surface markers, exosomes can associate and bind with extracellular Aβ proteins ( Figure 1a ). Multimodal characterization of extracellular vesicles derived from neuron-derived (SH-SY5Y cells) confirmed their exosome morphology, size distribution, and molecular composition ( Figure 5 ). Transmission electron microscopy analysis of the vesicles further revealed their ability to bind to Aβ42 protein aggregates ( Figure 1b and Figure 5 ).

[0223] To evaluate exosome-Aβ association, an APEX platform for amplified, multi-parameter mapping of exosome molecular co-localization was developed. The system measures transmission SPR through a periodic array of plasmonic nanopores patterned in a bilayer photonic structure and uses in situ enzymatic conversion to rapidly grow an insoluble optical product on bound exosomes (Figure 1c). To compensate for APEX enzymatic deposition (which occurs on top of the sensor), size-matched plasmonic nanopores are patterned in a coupled bilayer photonic system to enhance SPR measurements by back-illumination (away from the enzyme activity, Figure 20 ). The resulting enzymatic deposition not only stably alters the refractive index for SPR signal amplification, as shown by a red shift (spectral shift Δλ, Figure 1d) in the transmission spectrum, but is spatially confined for molecular co-localization analysis. Scanning electron micrographs of sensor-bound exosomes before and after APEX amplification confirm the local growth of the optical deposit after enzymatic conversion ( Figure 6 ).

[0224] Thus, using the developed APEX platform, the association of Aβ protein with exosomes was measured directly from clinical blood samples of AD patients and control subjects and correlated with PET imaging of global and regional brain plaque deposition (Figure 1e). For high-throughput, multiplexed clinical analysis, advanced fabrication methods (i.e., deep ultraviolet lithography, Figure 7 ) were used to fabricate sensor microarrays on 8-inch wafers; each wafer can accommodate more than 40 microarray chips with >2000 photosensitive elements ( Figure 8 a). Figure 1f shows a photograph of the developed APEX microarray chip used for parallel measurements in this study. Scanning electron micrographs of the developed sensors show highly uniform fabrication ( Figure 8 b).

[0225] Optimized signal amplification for multiplexed mapping

[0226] Enzymatic APEX amplification was developed for the first time. A series of sensor functionalizations were performed, namely antibody conjugation, exosome binding, enzyme labeling, and optical product amplification, and the stepwise total spectral shift (cumulative Δλ, Figure 2a). The sensor was functionalized with an antibody against CD63 (a type III lysosomal membrane protein that is enriched and characteristic in exosomes) to capture vesicles from neuronal cells (SHSY5Y). To promote the local deposition of insoluble optical products, horseradish peroxidase was introduced as a cascade enzyme and used to catalyze the conversion of its soluble substrate (3,3'-diaminobenzidine tetrahydrochloride). The sensor-bound vesicles were enzymatically labeled with another anti-CD63 antibody. Although the enzymatic labeling did not cause any significant spectral changes, the formation of the optical products led to a signal enhancement of ~400%. In contrast, control experiments with IgG isotype control antibodies showed minimal background changes ( Figure 9 ). Importantly, as confirmed by scanning electron microscopy, this SPR signal amplification was closely related to an increase in the area coverage of highly localized optical deposits ( Figure 2 b).

[0227] To compensate for the enzymatic amplification (occurring on top of the sensor), the APEX sensor design was optimized to improve its analytical performance and stability. Compared with the established glass-gold design that only supports front illumination ( Figure 20 ), the bilayer plasmonic structure of APEX enabled SPR excitation to be achieved by back illumination ( Figure 2 c). The new optimized design not only exhibited strong transmission SPR by back illumination ( Figure 10 a-c), but also showed analytical stability ( Figure 10 d), which might be due to the reduced direct incident illumination on enzyme activity (i.e., temperature fluctuations). The APEX assay was further established by optimizing the enzyme substrate concentration and reaction duration ( Figure 2 d). By continuous back illumination, the real-time spectral changes associated with different substrate concentrations were monitored, and it was found that significant signal amplification could be achieved within <10 min, enabling the entire APEX workflow to be completed within <1 h.

[0228] Under these optimized conditions, the next step was to measure the detection sensitivity of APEX for exosome quantification. Neuronal-derived vesicles (SH-SY5Y) were quantified by standard nanoparticle tracking analysis. Using anti-CD63 antibodies, we conducted titration experiments ( Figure 2 e). It was determined that the optimized APEX amplification could increase the detection sensitivity by 10-fold, with an established limit of detection (LOD) of ~200 exosomes. This observed sensitivity is the best LOD reported so far for the measurement of a large number of exosomes, and is 10 5 times and 10 3 times better than Western blotting and chemiluminescent ELISA, respectively.

[0229] Using the microarray APEX platform, methods for analyzing multiple markers associated with neurodegenerative diseases were further developed. Specific assays were established for the following protein markers ( Figure 2 f): amyloid-β (Aβ42), amyloid precursor protein (APP), α-synuclein (α-syn), L1 close homolog (CHL1), insulin receptor substrate 1 (IRS-1), neural cell adhesion molecule (NCAM), and tau protein. Importantly, by further developing the APEX assay workflow ( Figure 11 a), the platform demonstrated signal amplification capabilities for detecting extracellular and intracellular proteins as well as exosomal miRNAs ( Figure 11 b). All detection probes used for assay development are shown in Table 1.

[0230] Enhanced binding between Aβ aggregates and exosomes

[0231] Using the developed APEX platform, the association of exosomes with pathological Aβ proteins in different structural forms was then evaluated. To mimic different stages of amyloid seeding and protofibril formation, Aβ42 aggregates of different sizes were prepared, which are the main components of amyloid plaques. The degree of clustering was altered to form Aβ42 aggregates of different sizes (Figure 3a, experimental details are provided in the Methods), and their spherical morphology and unimodal size distribution were confirmed by transmission electron microscopy and dynamic light scattering analysis (Figure 3b). It should also be noted that larger Aβ42 aggregates showed a strong tendency to form fibrillar structures ( Figure 12 ).

[0232] To determine the kinetics of exosome-Aβ association, the prepared Aβ42 aggregates were immobilized on the APEX platform, and the sensor was incubated with an equal concentration of exosomes from neurons (Figure 3c). In contrast, in the control experiment, BSA aggregates of similar size were prepared and characterized ( Figure 13 ). By measuring real-time exosome binding, it was demonstrated that exosomes associated more strongly with Aβ42 aggregates compared to similar-sized BSA controls, regardless of the size of the exosomes (Figure 3d). More importantly, compared to their binding affinity for smaller Aβ42 aggregates (Figure 3d, left), vesicles showed significantly higher binding affinity (>5-fold) for larger Aβ42 aggregates (Figure 3d, right). All affinities were normalized to the surface area of the Aβ42 aggregates and were relative to the corresponding BSA controls (see Methods for details).

[0233] Next, using extracellular vesicles from different cell sources, their respective associations with larger Aβ42 aggregates were measured using the APEX platform ( Figure 3e)。Equal concentrations of vesicles from different cellular sources, determined by nanoparticle tracking analysis (Figure 14), were incubated with Aβ42-functionalized sensors. Notably, among all the tested cellular sources, vesicles from neurons, erythrocytes, platelets, and epithelial cells showed stronger association with Aβ42 aggregates, while vesicles from glial and endothelial cells showed negligible binding. Subsequently, a panel of specific markers for these respective cellular sources, as well as the pan-exosome marker (i.e., CD63), were used for APEX signal amplification of the bound vesicles. CD63 showed consistent performance in enhancing the signals of all the tested vesicles. Through this marker identification, CD63 was thus used to develop an APEX assay to identify and measure Aβ bound to exosomes (defined as Aβ42+CD63+; Figure 15 )。

[0234] Brain plaque load shown by Aβ bound to blood exosomes

[0235] Given the enhanced binding between exosomes and prefibrillar Aβ aggregates (the building blocks of amyloid plaques), it was hypothesized that exosome-bound Aβ could serve as a circulating biomarker reflecting brain plaque load. To test this hypothesis, different APEX assays were developed using different antibodies to evaluate different populations of circulating Aβ42 from clinical blood samples ( Figure 15 a). Specifically, to characterize the population of Aβ42 bound to exosomes, an APEX assay was designed to enrich Aβ42 directly from native plasma and measure the relative amount of CD63 associated with the captured Aβ42. This assay configuration not only showed specific detection of the Aβ42+CD63+ population ( Figure 15 b-c), but also reflected functional relevance: as the binding between prefibrillar Aβ aggregates and exosomes increased, the associated CD63 signal could be regarded as a surrogate measure of the relative amount of Aβ42 in prefibrils among total circulating Aβ42. To account for the presence of the unbound Aβ42 population, size exclusion filtration was used to remove large-sized retentates (such as exosomes) from plasma before measuring Aβ42 in the plasma filtrate. Finally, to measure total circulating Aβ42, native plasma was evaluated by direct Aβ42 enrichment and Aβ42 detection.

[0236] Subsequently, we conducted a feasibility clinical study aiming to address the following key questions: (1) Can APEX measure circulating Aβ42 directly from blood samples? (2) What is the correlation between different blood-derived Aβ42 populations and brain plaque load? (3) Can specific populations of circulating Aβ42 distinguish different clinical groups?

[0237] To achieve these goals, age-matched subjects (n = 84) were recruited, who were diagnosed with AD (n = 17), 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 impairment (i.e., vascular mild cognitive impairment, VMCI, n = 12); and acute stroke, n = 12). All clinical information is shown in Table 2. Blood sampling and APEX analysis were performed on all recruited subjects. Except for acute stroke patients, all subjects consented to simultaneous PET imaging of cerebral amyloid plaques. Plasma samples were collected immediately before the injection of the Pittsburgh compound B (PiB) radiotracer for PET imaging. Between and within the clinical groups imaged, PET imaging showed extensive cerebral plaque load ( Figure 4 a), and demonstrated regional cerebral changes (Table 2), consistent with other published clinical studies.

[0238] Table 2 Clinical information and standardized uptake value ratio (SUVR) of PET imaging.

[0239]

[0240] AD: Alzheimer's disease, MCI: mild cognitive impairment, NCI: no cognitive impairment, VaD: vascular dementia, VMCI: vascular mild cognitive impairment.

[0241] Using the developed APEX assay ( Figure 11 a), we evaluated different populations of circulating Aβ42 in these clinical plasma samples, namely exosome-bound Aβ42, unbound populations, and total circulating Aβ42 ( Figure 4 b). The exosome-bound Aβ42 population showed strong co-localization signals with exosome markers (i.e., CD63, CD9, and CD81) and neuronal markers (i.e., NCAM, L1CAM, and CHL-1), indicating that neuronal exosomes may constitute a substantial part of the population ( Figure 16 a). Since unbound Aβ42 measurements were performed on plasma filtrates, we further characterized these filtrates and confirmed their negligible vesicle numbers and minimal co-localization signals with exosome and neuronal markers ( Figure 16 b-c). When correlated with overall PET amyloid imaging, exosome-bound Aβ42 measurements showed the best correlation compared to unbound Aβ42 ( Figure 4 b, middle, R 2 = 0.0193) or total Aβ42 ( Figure 4 b, right, R 2 = 0.1471) ( Figure 4 b, left, R 2= 0.9002). Interestingly, different from the poor and negative associations shown by total Aβ42 measurements (as shown in this study and other published reports), the relevant CD63 measurements from the exosome-bound Aβ42 population showed a highly relevant and positive association with the PET imaging of cerebral amyloid plaques. We attribute this finding to the similar binding preferences of exosomes and PET tracers for Aβ42: (1) exosomes showed enhanced association with prefibrillar Aβ42 aggregates, especially larger aggregates prone to fibril formation (Figure 3d); and (2) PET tracers bind tightly to larger amyloid fibrils but bind little to smaller aggregates. Notably, this superior correlation also demonstrated brain region specificity; exosome-bound Aβ42 measurements showed stronger correlation with brain plaque load in the cingulate gyrus (an area affected early in AD, Figure 17 a) than in the occipital lobe (an area affected late in AD, Figure 17 b).

[0242] When differentiating clinical diagnoses, only the APEX assay of exosome-bound Aβ42 showed good specificity, rather than the APEX assays of the unbound Aβ42 population or the total Aβ42 population ( Figure 4 d). In particular, exosome-bound Aβ42 measurements could not only distinguish the AD clinical groups (i.e., AD and MCI, P < 0.01), but also distinguish other healthy and clinical controls (P < 0.0001, Student's t-test). When differentiating different clinical groups, this proven specificity was comparable to that of PET cerebral amyloid imaging ( Figure 18 ). On the other hand, nanoparticle tracking analysis of plasma extracellular vesicles did not show any significant differences in vesicle size or concentration among all clinical groups (Figure 19).

[0243] Example 2

[0244] AD is the most common form of severe dementia. Due to its complex and progressive neuropathology, early detection and timely intervention are crucial for the success of disease-modifying therapies. Despite intense interest in finding serum biomarkers for AD, its development has been plagued by multiple challenges. First, unlike their cerebrospinal fluid counterparts, circulating pathological AD molecules are present at lower concentrations. Plasma Aβ levels often approach the lower limit of detection of conventional ELISA; this limitation may have contributed to the multiple conflicting findings in the published reports. Second, there is little correlation between plasma Aβ analysis and brain plaque deposition, the earliest pathological hallmark of AD. One possible reason may stem from different measurement methods. PET imaging probes, commonly used to determine brain amyloid burden, preferentially measure insoluble fibril deposits, while conventional ELISA measures soluble Aβ in plasma. Additionally, the potential correlation between blood-based measurements and brain pathology may have been masked by previous whole-blood measurements. However, this discrepancy raises a more fundamental question - whether there are circulating Aβ protein subsets that better reflect the fibrillar pathology in the brain.

[0245] A dedicated analytical platform (APEX) was developed for the multiparametric analysis of exosome-bound Aβ, unbound Aβ, and total Aβ directly from plasma to distinguish different populations of circulating Aβ. Specifically, it leverages recent advances in sensor design, device fabrication, and assay development to achieve enhanced optical performance and detection capabilities ( Figure 20)。In terms of sensor design and fabrication, the APEX platform consists of a periodic array of gold nanopores suspended on a patterned silicon nitride membrane and fabricated by deep ultraviolet lithography, the state-of-the-art manufacturing process for large-scale precise nanopatterning. These advancements have enabled the APEX technology to achieve 1) improved optical performance (i.e., enhanced transmission intensity for SPR detection via bidirectional optical illumination) and 2) reliable large-scale production. In terms of assay technology, the APEX platform utilizes rapid in situ enzymatic conversion to achieve highly localized, amplified signals. This development not only enables sensitive detection of different targets (e.g., intravesicular proteins and RNA targets) but also facilitates exosome co-localization analysis for multi-parameter population studies, as insoluble deposits form locally only when multiple targets are simultaneously found within exosomes. Through these combined advancements, the observed APEX sensitivity is the best reported to date for exosome analysis and exceeds standard ELISA measurements by several orders of magnitude. Using the developed APEX platform, we demonstrated enhanced binding between exosomes and larger precursor fibrillar Aβ, a key building block of fibrillar amyloid plaques. Subpopulations of amyloid proteins (CD63+Aβ42+) bound to exosomes were further identified and quantified in clinical plasma samples from different clinical populations (i.e., AD, MCI, cognitively normal controls, and clinical controls for other neurodegenerative and neurovascular diseases), and were found to be highly correlated with brain amyloid plaque load.

[0246] Evaluating distinct populations of circulating Aβ may lead to a paradigm shift in AD research and clinical care. Growing evidence suggests that prefibrillar Aβ aggregates may be the toxic drivers of AD neurodegeneration. Their preferential association with exosomes, and recent findings of exosome marker enrichment in human amyloid plaques, not only elucidate a possible new mechanism of plaque seeding but also suggest the importance of exosome-bound Aβ as a more reflective circulating biomarker of complex AD pathology. Thus, it is foreseeable that this study can complement other preclinical and clinical studies, including those on the development of technologies and the refinement of biomarkers. In terms of technology development, for example, while IP-MS enables unbiased molecular screening and is highly valuable for biomarker discovery, especially in detecting different molecular isoforms and variants (such as (APP)669–711 and Aβ1–40), APEX technology provides a rapid and sensitive way to read data from native plasma samples without the extensive sample processing typically required for mass spectrometry measurements, and thus is suitable for targeted clinical measurements. In terms of biomarker refinement, as demonstrated by the current study, the analysis of distinct circulating Aβ populations can uncover new correlations previously masked by whole blood measurements and advance future blood-based clinical management of AD. Importantly, there are currently over 400 AD clinical trials, and it is further envisioned that the developed methods can be strengthened to redefine the current patient care standards. Through additional technological innovations, such as exosome handling on a chip, combined analysis of other AD markers, and longitudinal clinical cohort validation, the developed technology can provide comprehensive capabilities to facilitate minimally invasive early detection, molecular stratification, and serial monitoring, all of which are crucial for objectively evaluating disease-modifying therapies at different stages of clinical trials.

[0247] Example 3

[0248] This example shows that incubating amyloid aggregates with an inhibitor reduces spontaneous protein aggregation.

[0249] Method

[0250] Determination of aggregate size. The kinetic diameters of amyloid-forming proteins and BSA aggregates were determined by dynamic light scattering analysis (Zetasizer Nano ZSP, Malvern). Measurements were performed 3×14 times at 4 °C, and the Z-average diameter and polydispersity were analyzed. For each measurement, the autocorrelation function and polydispersity index were monitored to ensure the sample quality for size determination.

[0251] Protein aggregation. The lyophilized amylogenic protein was resuspended in NaOH (60 mM, 4 °C), sonicated, and the pH was adjusted to pH 7.4 in PBS. The protein was immediately filtered through a 0.2-μm membrane filter (Millipore), and the filtrate was used as small initial aggregates. To prepare larger aggregates, the protein was treated as described above and incubated for 1 h with stirring to induce further aggregation. For treatment with an inhibitor, 10 μM of the inhibitor (e.g., methylene blue) was added to the protein before incubation. Aggregate size determination was performed at the end of the incubation.

[0252] Optical analysis. For experimental analysis, a tungsten halogen lamp (Stocker Yale Inc.) was used, and the back of the APEX sensor was illuminated through a ×10 microscope objective. The transmitted light was collected by an optical fiber and input into 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 recorded as counts versus wavelength. For spectral analysis, the spectral peak was determined using a custom R program by fitting the transmission peak using a local regression method. All fittings were done locally. That is, for the fitting at point x, the points near point x were used for fitting and weighted by their distance from x. Compared with fitting by a multi-order polynomial curve, this method can eliminate the result variation caused by the number of data points analyzed and the data range. All spectral shifts (Δλ) were determined as the change in the transmission spectral peak and calculated relative to an appropriate control experiment.

[0253] Characterization of exosome-protein association. The prepared protein aggregates (Aβ42 and BSA control) were used for surface functionalization of the APEX sensor by the aforementioned EDC / NHS coupling. Unbound protein aggregates were washed away with PBS. The amount of conjugated protein was measured from the resulting transmission spectral shift. We used this information to determine the number of conjugated protein aggregates and their associated total protein surface area for exosome binding (see details below) to normalize the binding affinity. After surface functionalization with protein aggregates, exosomes were introduced into the sensor. The spectral changes were measured every 3 s for a total duration of 480 s to construct a real-time kinetic sensing map. The exosome association kinetics and binding affinity of protein aggregates of different sizes were determined.

[0254] Considering the protein aggregate size difference and the sensitivity exponential decay related to SPR (with increasing distance from the sensor surface), the total surface area of the conjugated aggregates interacting with exosomes was calculated using the following formula:

[0255]

[0256] Where S is the signal, z is the distance from the sensor surface, E is the electric field at z = 0 and is constant in this case, l d is the attenuation length and is set to 200 nm in the current sensor design, and r is the radius of the conjugated protein aggregate.

[0257] All protein aggregates are approximated as spherical, and their r is determined by dynamic light scattering analysis. We use the above formula to determine the number of protein aggregates conjugated to the sensor and their respective total surface areas to estimate the number of available binding sites for interaction with exosomes. All exosome binding data (△λ) were normalized according to their respective protein binding sites. The normalized Aβ42 binding data were relative to a BSA control of similar size, and the fit was used to determine the binding affinity constant K D .

[0258] miRNA analysis. Exosome lysates were incubated with biotinylated RNA probes and then subjected to RNA dual capture and APEX signal amplification on p19-functionalized APEX sensors.

[0259] Results

[0260] The pathologies of multiple neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis) involve the aggregation of misfolded amyloid proteins. Treatments targeting protein aggregation involve various strategies for clearing aggregated amyloid proteins, including breaking down amyloid aggregates or inhibiting amyloid aggregation. Potential candidate molecules that have been shown to be effective in reducing the number of amyloid aggregates and can thus be used in disease-modifying therapies include methylene blue, leucomethylene blue bis(hydrogensulfonate), 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, cetyltrimethylammonium bromide (C16), hemin chloride, hemin, indomethacin, juglone, resorcinol blue, meclocycline sulfosalicylate, melatonin, myricetin, 1,2-naphthoquinone, nordihydroguaiaretic acid, R()-norhydrocodone hydrobromide, orange G, o-vanillin (2-hydroxy-3-methoxybenzaldehyde), phenazine, phthalocyanine, rifamycin SV, phenol red, rolitetracycline, quinacrine mustard dihydrochloride, thioflavin S, ThT, and trimethyl(tetradecyl)ammonium bromide (C17), diallyltartaric acid, eosin Y, fenofibrate, bathocuproine, nystatin, octadecyl sulfate, and rhodamine B.

[0261] Since no animal model has been proven to exhibit accurate pathology reflecting AD pathology in the human brain, we used in vitro experiments to model the effect of disease-modifying treatments on inhibiting amyloid-forming protein aggregation. The initial size of the amyloid-forming protein was confirmed by dynamic light scattering analysis before incubating aliquots of the protein with or without an inhibitor. In the absence of an inhibitor, the size of the protein aggregates grew with increasing incubation time due to the spontaneous aggregation of the amyloid-forming protein. However, in the presence of an inhibitor, the increase in size due to spontaneous protein aggregation was minimal, resulting in the formation of smaller protein aggregates.

[0262] After incubation, the amyloid-forming protein was functionalized on the surface of the sensor chip before incubation with neuronal exosomes. Preferential association was observed between neuronal exosomes and larger protein aggregates, as indicated by differences in binding affinity, and reduced binding to smaller protein aggregates treated with an inhibitor was demonstrated. Since the association of exosomes with proteins can serve as a surrogate indicator of protein biophysical and / or biochemical properties (properties affected by disease-modifying therapies), the APEX platform is capable of evaluating the efficacy of disease-modifying treatments.

[0263] Several example configurations have been described and various modifications, alternative structures, and equivalents may be used without departing from the spirit of the present disclosure. For example, the above elements may be components of a larger system where other rules may take precedence over or otherwise modify the application of the invention. Additionally, several steps may be taken before, during, or after considering the above factors.

[0264] All publications, serial numbers, patents, and patent applications cited in this invention are hereby incorporated by reference in their entirety for all purposes.

[0265] Exemplary embodiments of the present disclosure

[0266] In some aspects and embodiments, the present invention describes:

[0267] A sensor chip comprising a conductive layer on a membrane support layer, wherein a plurality of holes extend through the conductive layer and the membrane support layer and are arranged such that illumination of the conductive layer and / or the membrane support layer generates surface plasmon resonance.

[0268] In some embodiments, the conductive layer is gold, silver, aluminum, sodium, indium, or titanium.

[0269] In some embodiments, the membrane support layer is silicon nitride or silica.

[0270] In some embodiments, the holes have a diameter of about 150 nm to 450 nm.

[0271] In some embodiments, the holes are arranged periodically.

[0272] In some embodiments, the holes have a periodicity of from about 250 nm to about 650 nm.

[0273] In some embodiments, a first recognition molecule is immobilized on the surface of the conductive layer.

[0274] In some embodiments, the holes are arranged such that the decay length of surface plasmon resonance generated upon illumination is approximately equal to the diameter of the target of the first recognition molecule.

[0275] In some embodiments, the conductive layer and the membrane support layer are disposed on a substrate, and the substrate has voids formed therein in a region adjacent to the plurality of holes such that illumination of the conductive layer and / or the membrane support layer in any direction generates surface plasmon resonance.

[0276] In another aspect, the present invention describes an imaging system comprising a light source, a detector, and a sensor chip according to any one of 1-9, wherein the light source is arranged to illuminate the sensor chip, and the detector is positioned to detect light transmitted through the sensor chip.

[0277] In another aspect, the present invention describes a method of manufacturing a sensor chip, the method comprising the following main steps: a) providing a top membrane support layer; b) depositing a conductive layer on the top membrane support layer; forming a plurality of holes extending through the membrane support layer, the holes also extending through the conductive layer, and arranging them such that illumination of the conductive layer and / or the top membrane support layer generates surface plasmon resonance.

[0278] In some embodiments, the method comprises: coating a top membrane support layer and a bottom membrane support layer on the top and bottom surfaces of a silicon substrate; providing a photoresist layer on the top membrane support layer, and defining a plurality of holes in the photoresist by deep ultraviolet lithography (DUV), and transferring the pattern of the plurality of holes to the top membrane support layer by reactive ion etching (RIE).

[0279] In some embodiments, the method further comprises the following steps: removing the photoresist on the top membrane support layer, and coating a silica protection layer on the surface of the top membrane support layer; coating a photoresist layer on the bottom membrane support layer; defining a sensing region in the photoresist by lithography; and transferring the pattern of the sensing region to the bottom membrane support layer by reactive ion etching (RIE); transferring the pattern of the sensing region to the silicon substrate; removing the protection layer on the surface of the top membrane support layer with dilute hydrofluoric acid; and depositing a conductive layer on the top membrane support layer.

[0280] In another aspect, the present invention describes a method for detecting an analyte in a sample, the method comprising: a) capturing the analyte onto the surface of the sensor chip according to any one of 1-9; and b) detecting the binding of a second recognition molecule to the analyte captured on the surface of the sensor chip, wherein the second recognition molecule is specific for the analyte, and an increase in the binding of the second recognition molecule as compared to a control sample indicates the presence of the analyte in the sample.

[0281] In some embodiments, the second recognition molecule is specific for the analyte, the second recognition molecule is conjugated to a signal amplification moiety, and the signal amplification moiety is capable of inducing the formation of insoluble aggregates with an increased optical density relative to the captured analyte.

[0282] In some embodiments, the second recognition molecule is fused to the signal amplification moiety.

[0283] In some embodiments, the signal amplification moiety is an enzyme.

[0284] In some embodiments, the enzyme is horseradish peroxidase.

[0285] In some embodiments, the method further comprises contacting the enzyme with an enzyme substrate.

[0286] In some embodiments, the signal amplification moiety is a secondary antibody capable of binding the second recognition molecule.

[0287] In some embodiments, 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 the analyte on the surface of the sensor chip.

[0288] In some embodiments, the analyte is an exosome-bound biomarker.

[0289] In some embodiments, the method comprises detecting two or more analytes co-localized in the sample.

[0290] In yet another aspect, the present invention describes a kit comprising the sensor chip of the present invention.

[0291] In some embodiments, the kit comprises a second recognition molecule for detecting the analyte captured on the surface of the sensor chip.

[0292] In some embodiments, the second recognition molecule is conjugated to a signal amplification moiety, and the signal amplification moiety is capable of inducing the formation of insoluble aggregates with an increased optical density relative to the captured analyte.

[0293] In another aspect, the present invention describes a method for detecting a neurodegenerative disease or amyloidosis in a subject, the method comprising: a) contacting a sample obtained from the subject with the surface of the sensor chip described in any one of 1-9; and b) detecting the binding of a second recognition molecule to an analyte captured on the surface of the sensor chip, the second recognition molecule being specific for the analyte, an increase in the binding of the second recognition molecule as compared to a control sample or a control subject indicating that the subject has a neurodegenerative disease.

[0294] In some embodiments of the method, the second recognition molecule is conjugated to a signal amplification moiety, and the signal amplification moiety is capable of inducing the formation of insoluble aggregates with an increased optical density relative to the captured analyte.

[0295] In some embodiments, the analyte is an exosome-related biomarker.

[0296] In some embodiments, the analyte is an exosome-related biomarker or a biomarker contained in exosomes.

[0297] In some embodiments, the biomarker is selected from Aβ, APP, α-Syn, CD9, CD63, CD81, ALIX, TSG101, Flotilin-1, Flotilin-2, LAMP-1, HSP70, HSP90, CHL1, IRS-1, L1CAM, NCAM, Tau, APOE, SOD1, TDP-43, bassoon, fibronectin, DNA, and RNA, or a combination and related complexes thereof.

[0298] In some embodiments, the Aβ is Aβ42, Aβ40, Aβ39, or Aβ38.

[0299] In some embodiments, the neurodegenerative disease is selected from Alzheimer's disease, mild cognitive impairment, vascular dementia, dementia, Parkinson's disease, amyotrophic lateral sclerosis, multiple sclerosis, progressive supranuclear palsy, tauopathy, and vascular mild cognitive impairment.

[0300] In some embodiments, the method further comprises treating a subject found to have a neurodegenerative disease.

[0301] In some embodiments, the treatment comprises administering to the subject a therapeutically effective amount of one or more drugs or a combination thereof.

[0302] In some embodiments, the drug is selected from cholinesterase inhibitors such as donepezil, rivastigmine or galantamine; NMDA receptor antagonists such as memantine; combinations of cholinesterase inhibitors and NMDA receptor antagonists such as the combination of donepezil and memantine; BACE1 inhibitors such as AZD3293; antibodies such as aducanumab; or anti-tau drugs such as TRx0237 (LMTX).

[0303] In some embodiments, the drug is selected from methylene blue, leucomethylene blue bis(hydrogen methanesulfonate), 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, daunorubicin hydrochloride, methyl yellow, direct red 80, 2,2-dihydroxybenzophenone, cetyltrimethylammonium bromide (C16), hemin chloride, heme, indomethacin, juglone, resorcin blue, meclocycline sulfosalicylate, melatonin, myricetin, 1,2-naphthoquinone, nordihydroguaiaretic acid, R()-normorphine hydrobromide, orange G, o-vanillin (2-hydroxy-3-methoxybenzaldehyde), phenazine, phthalocyanine, rifamycin SV, phenol red, rolitetracycline, quinacrine mustard dihydrochloride, thioflavin S, ThT, and trimethyl(tetradecyl)ammonium bromide (C17), diallyltartaric acid, eosin Y, fenofibrate, bathocuproine, nystatin, octadecyl sulfate, and rhodamine B.

Claims

1. A sensor chip, which comprises a conductive layer located on a membrane support layer, wherein a plurality of holes extend through the conductive layer and the membrane support layer, and are arranged such that illumination of the conductive layer and / or the membrane support layer generates surface plasmon resonance.

2. The sensor chip according to claim 1, wherein the conductive layer is gold, silver, aluminum, sodium, indium or titanium.

3. The sensor chip according to claim 1 or 2, wherein the membrane support layer is silicon nitride or sodium dioxide.

4. The sensor chip according to claim 1, wherein the holes have a diameter of about 150 nm to about 450 nm.

5. The sensor chip according to any one of claims 1-4, wherein the holes are arranged periodically.

6. The sensor chip according to claim 5, wherein the holes have a periodicity of about 250 nm to about 650 nm.

7. The sensor chip according to any one of claims 1-6, which comprises a first recognition molecule fixed on the surface of the conductive layer.

8. The sensor chip according to claim 7, wherein, the holes are arranged such that the decay length of the surface plasmon resonance generated upon illumination is approximately equal to the diameter of the target of the first recognition molecule.

9. The sensor chip according to any one of claims 1-8, wherein, the conductive layer and the membrane support layer are provided on a substrate, and the substrate has voids formed therein in a region adjacent to the plurality of holes, such that illumination of the conductive layer and / or the membrane support layer in any direction can generate surface plasmon resonance.

10. An imaging system, which comprises a light source, a detector and a sensor chip according to any one of claims 1-9, wherein, the light source is arranged to illuminate the sensor chip, and the detector is positioned to detect the light transmitted through the sensor chip.

11. A method of manufacturing a sensor chip, the method comprising the following main steps: a) providing a top membrane support layer; b) depositing a conductive layer on the top membrane support layer; c) forming a plurality of holes extending through the membrane support layer, the holes also extending through the conductive layer, and arranging them such that illumination of the conductive layer and / or the top membrane support layer generates surface plasmon resonance.

12. The method according to claim 11, which comprises: coating a top membrane support layer and a bottom membrane support layer on the top and bottom surfaces of a silicon substrate; providing a photoresist layer on the top membrane support layer; and defining a plurality of holes in the photoresist by deep ultraviolet lithography (DUV), and transferring the pattern of the plurality of holes to the top membrane support layer by reactive ion etching (RIE).

13. The method according to claim 12, which further comprises the following steps: removing the photoresist on the top membrane support layer, and coating a silicon dioxide protective layer on the surface of the top membrane support layer; coating a photoresist layer on the bottom membrane support layer; defining a sensing area in the photoresist by lithography; and transferring the pattern of the sensing area to the bottom membrane support layer by reactive ion etching (RIE); transferring the pattern of the sensing area to the silicon substrate; removing the protective layer on the surface of the top membrane support layer with dilute hydrofluoric acid; and depositing a conductive layer on the top film support layer.

14. A method for detecting an analyte in a sample, the method comprising: a) capturing the analyte on the surface of a sensor chip according to any one of claims 1-9; and b) detecting the binding of a second recognition molecule to the analyte captured on the surface of the sensor chip, wherein the second recognition molecule is specific for the analyte, and an increase in the binding of the second recognition molecule as compared to a control sample indicates the presence of the analyte in the sample.

15. The method according to claim 14, wherein the second recognition molecule is specific for the analyte, the second recognition molecule is conjugated to a signal amplification moiety, and the signal amplification moiety is capable of inducing the formation of insoluble aggregates with an increased optical density relative to the captured analyte.

16. The method according to claim 14 or 15, wherein the second recognition molecule is fused to the signal amplification moiety.

17. The method according to claim 15 or 16, wherein the signal amplification moiety is an enzyme.

18. The method according to claim 17, wherein the enzyme is horseradish peroxidase.

19. The method according to claim 17 or 18, wherein the method further comprises contacting the enzyme with an enzyme substrate.

20. The method according to claim 15 or 16, wherein the signal amplification moiety is a secondary antibody capable of binding the second recognition molecule.

21. The method according to any one of claims 15-20, wherein 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 the analyte on the surface of the sensor chip.

22. The method according to claim 21, wherein the analyte is an exosome-bound biomarker.

23. The method according to claim 14, wherein the method comprises detecting two or more analytes co-localized in the sample.

24. A kit comprising a sensor chip according to any one of claims 1-9.

25. The kit according to claim 24, wherein the kit comprises a second recognition molecule for detecting the analyte captured on the surface of the sensor chip.

26. The kit according to claim 25, wherein the second recognition molecule is conjugated to a signal amplification moiety, and the signal amplification moiety is capable of inducing the formation of insoluble aggregates with an increased optical density relative to the captured analyte.

27. A method for detecting a neurodegenerative disease or amyloidosis in a subject, the method comprising: a) contacting a sample obtained from the subject with the surface of a sensor chip according to any one of claims 1-9; and b) detecting the binding of a second recognition molecule to the analyte captured on the surface of the sensor chip, wherein the second recognition molecule is specific for the analyte, and an increase in the binding of the second recognition molecule as compared to a control sample or a control subject indicates that the subject has a neurodegenerative disease.

28. The method according to claim 27, wherein the second recognition molecule is conjugated to a signal amplification moiety, and the signal amplification moiety is capable of inducing the formation of insoluble aggregates with an increased optical density relative to the captured analyte.

29. The method according to claim 27 or 28, wherein the analyte is an exosome-related biomarker.

30. The method according to claim 27 or 28, wherein the analyte is an exosome-related biomarker or a biomarker contained in exosomes.

31. The method according to any one of claims 27 to 30, wherein the biomarker is selected from Aβ, APP, α-Syn, CD9, CD63, CD81, ALIX, TSG101, Flotilin-1, Flotilin-2, LAMP-1, HSP70, HSP90, CHL1, IRS-1, L1CAM, NCAM, Tau, APOE, SOD1, TDP-43, bassoon, fibronectin, DNA, and RNA, or a combination and related complex thereof.

32. The method according to claim 31, wherein the Aβ is Aβ42, Aβ40, Aβ39, or Aβ38.

33. The method according to any one of claims 27 to 31, wherein the neurodegenerative disease is selected from Alzheimer's disease, mild cognitive impairment, vascular dementia, dementia, Parkinson's disease, amyotrophic lateral sclerosis, multiple sclerosis, progressive supranuclear palsy, tauopathies, and vascular mild cognitive impairment.

34. The method according to claims 28-32, wherein the method further comprises treating a subject found to have a neurodegenerative disease.

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