Sensor chip and method thereof

Abstract

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 with neurodegenerative diseases. The sensor chip includes 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 so that illumination of the conductive layer and / or the membrane support layer produces surface plasmon resonance.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Singapore Provisional Application No. 10201900937Q filed on January 31, 2019, entitled SENSOR CHIP AND METHODS THEREOF. Technical Field

[0003] The present disclosure generally relates to sensor chips and methods for detecting analytes. In particular, the present disclosure relates to sensor chips for detecting analytes in subjects with neurodegenerative diseases and other diseases, such as amyloidosis. Background Art

[0004] Surface plasmon resonance (SPR) sensing is a widely used technique in laboratories to characterize biomolecular interactions, such as antibody-antigen interactions. The technique is usually based on immobilizing ligand capture molecules on a metal surface and measuring the change in refractive index when the ligand binds to the capture molecule. The technique is a label-free technique that does not require the use of specialized markers or dyes for sensitive measurements of molecular interactions. It is currently being developed for laboratory diagnosis of patients with different diseases such as Alzheimer's, hepatitis, diabetes and cancer.

[0005] 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 progressive loss of memory and cognitive function. Affected individuals show significant limitations in self-care, social and occupational functioning. However, AD molecular features may already be present and develop long before these full-blown clinical symptoms appear. They include extracellular amyloid β (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.

[0006] However, current AD diagnosis and disease monitoring are subjective and late. It is achieved by clinical and neuropsychological assessment using published criteria. These methods lack sensitivity and specificity, especially in the early stages when symptoms are milder 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 wider clinical applications. As a result, there is a high interest in finding serological biomarkers for AD that assist in early diagnosis and disease monitoring.

[0007] Therefore, there is a need to overcome or at least mitigate one or more of the above problems. Summary of the invention

[0008] Disclosed herein are sensor chips and methods for detecting analytes by surface plasmon resonance.

[0009] In one aspect, a sensor chip is provided, comprising a conductive layer on a membrane support layer, wherein a plurality of holes extend through the conductive layer and the membrane support layer and are arranged such that illumination of the conductive layer and / or the membrane support layer generates surface plasmon resonance.

[0010] In one aspect, an imaging system is provided, comprising a light source, a detector and a sensor chip as defined in 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.

[0011] In one aspect, a method for manufacturing a sensor chip is provided, the method comprising the following steps:

[0012] a) providing a top membrane support layer;

[0013] b) depositing a conductive layer on the top membrane support layer;

[0014] c) forming a plurality of holes extending through the membrane support layer, the holes also extending through the conductive layer and arranged such that illumination of the conductive layer and / or the top membrane support layer produces surface plasmon resonance.

[0015] In one aspect, a method for detecting an analyte in a sample is provided, the method comprising:

[0016] a) capturing the analyte onto the surface of the sensor chip defined in the present invention;

[0017] b) detecting 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, wherein increased binding of the second recognition molecule compared to a control sample indicates the presence of the analyte in the sample.

[0018] In one aspect, a kit is provided, which comprises the sensor chip defined in the present invention.

[0019] In one aspect, a method of detecting a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising:

[0020] a) contacting a sample obtained from a subject with the surface of a sensor chip defined in the present invention;

[0021] b) detecting the binding of the second recognition molecule to the analyte captured on the surface of the sensor chip;

[0022] Wherein the second recognition molecule is specific for the analyte, increased binding of the second recognition molecule compared to a sample obtained from a control subject indicates that the subject suffers from a neurodegenerative disease.

[0023] Also provided is use of the sensor chip of the present invention in a method for detecting a neurodegenerative disease or amyloidosis in a subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Some embodiments of the present invention will now be described, by way of non-limiting examples only, with reference to the accompanying drawings, in which:

[0025] Figure 1: APEX platform for analysis of circulating exosome-bound Aβ.

[0026] (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. Figure 7-10 .

[0027] Figure 2 : APEX signal amplification and multiplexing profiling.

[0028] (a) Stepwise APEX transmission spectral changes. We performed a series of operations, namely antibody conjugation (anti-CD63), exosome binding, enzyme labeling, and enzyme deposition on the sensor, and monitored the resulting spectral shifts. Although enzyme labeling did not induce any significant changes, deposit formation resulted in a ∼400% signal enhancement (****(P < 0.0001, ns 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 data before signal amplification. Inset (right) shows SEM images of sensor-bound exosomes before and after APEX amplification. (c) Finite-difference time-domain simulation using backside illumination. The APEX sensor design, rather than the gold-on-glass design, enables the generation of enhanced electromagnetic fields through backside illumination. Backside illumination minimizes the effects of directly incident light on enzyme activity (occurring on the top of the sensor). Arrows indicate the direction of incident illumination. (d) Real-time sensorgrams of APEX amplification kinetics. Different concentrations of optical substrate (3,3'-diaminobenzidine tetrahydrochloride; high: 1 mg / ml, low: 0.01 mg / ml) was used to monitor amplification efficiency. All data were normalized to negative controls, performed with IgG isotope control antibodies. (e) Comparison of APEX, ELISA, and Western blot detection sensitivity. The APEX detection limit (dashed line) before and after amplification was determined by titrating known amounts of exosomes and measuring their CD63 signal. (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. Heat map signals were normalized by assay (row). All measurements were performed in triplicate, and data are shown as mean ± sd in a, b, d, and e.

[0029] Figure 3: Preferential association between Aβ aggregates and exosomes.

[0030] (a) Schematic diagram of Aβ protein aggregation. We varied the degree of clustering and used a filtration method to prepare small and large Aβ42 aggregates, respectively. (b) Characterization of Aβ protein aggregates. (Left) Transmission electron micrographs show the spherical morphology of the prepared Aβ42 aggregates. (Right) Dynamic light scattering analysis confirmed the unimodal size distribution of preparations of different sizes. (c) Schematic diagram of exosome-Aβ association analysis. Aβ42 aggregates (small aggregates vs. large aggregates) were immobilized on an APEX sensor and treated with equal concentrations of exosomes from neural cells (SH-SY5Y) to determine the association kinetics. All exosome binding data were normalized to the corresponding Aβ42 aggregate surface area immobilized on the sensor (see Methods for details). (d) Real-time sensorgram of exosome binding kinetics. Compared with bovine serum albumin (BSA) control aggregates of similar size (see Fig.13 ), exosomes more strongly associate with Aβ42 aggregates. Importantly, exosomes show a stronger affinity for larger Aβ42 aggregates (right) than for smaller Aβ42 aggregates (left). Note the proportional difference on the y-axis. All binding affinities (KD) are determined from normalized exosome binding data and relative to BSA control. KD(small) / KD(large) = 5.27. (e) Differential association of multiple extracellular vesicles with Aβ42 aggregates. The vesicles were derived from different cell sources, namely neurons, glial cells, endothelial cells, monocytes, erythrocytes, platelets, and epithelial cells, and were used for binding analysis at equal concentrations. Using the APEX platform, we first measured the direct binding of vesicles to the Aβ42 functionalized sensor (direct). Next, for each cell source, we labeled the bound vesicles with a source-specific marker (cell source-specific marker) or a pan-exosomal marker (i.e., CD63, a pan-exosomal marker) and measured the associated APEX signal amplification. All measurements were performed in triplicate relative to an IgG isotope control antibody. Data are shown in e as mean ± sd.

[0031] Figure 4 : Clinical relevance of circulating exosome-bound Aβ and brain imaging.

[0032] (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 exosome-bound Aβ42 measurement showed the best correlation (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.

[0033] Figure 5 : Characterization of extracellular vesicles shed by neuronal cells.

[0034] (a) Scanning electron micrograph of a neuronal cell (SH-SY5Y) showing the massive release of nanosized extracellular vesicles from the cell. (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 for exosome markers (LAMP 1, ALIX, HSP90, HSP70, CD63, Flotillin 1, TSG101), neuronal markers (NCAM), and negative markers including lipoproteins (APOE) and other membrane compartment markers (Calnexin, GRP94). (e) Transmission electron micrographs of double immunolabeling with gold nanoparticles of different sizes (CD63, 20 nm; Aβ42, 5 nm) confirm the colocalization of both markers on the same vesicle, indicating the presence of exosome-bound Aβ.

[0035] Figure 6 : APEX amplification products.

[0036] Scanning electron micrographs of an APEX sensor (a) before amplification, after exosomes were captured onto the sensor by anti-CD63 antibodies, and (b) after amplification, showing local growth of insoluble optical deposits from a soluble substrate (3,3'-diaminobenzidine tetrahydrochloride). The resulting APEX signal amplification correlated well with the increase in area coverage of the local deposits.

[0037] Figure 7 : Large-scale fabrication of APEX microarray sensors.

[0038] All APEX sensors are fabricated on 8-inch silicon (Si) wafers. The fabrication steps include: (1) a 10 nm silicon dioxide (SiO2) layer is prepared by thermal oxidation, and 145 nm silicon nitride (Si3N4) is deposited on the wafer by low-pressure chemical vapor deposition (LPCVD). (2) After coating with photoresist, deep ultraviolet (DUV) lithography is performed to define the nanohole array pattern in the resist. The pattern is transferred to the Si3N4 film by reactive ion etching (RIE). (3) After removing the photoresist, a thin protective layer (100 nm) of SiO2 is deposited on the front side of the wafer using plasma enhanced chemical vapor deposition (PECVD). To achieve light transmission, the back side of the wafer is spin-coated with photoresist; photolithography is used to define the sensing area. (4) Si3N4 and SiO2 are etched by RIE, and then silicon is etched by potassium hydroxide (KOH) and tetramethylammonium hydroxide (TMAH). (5) After etching, the SiO2 protective layer is removed using dilute hydrofluoric acid (DHF) (1:100). (6) Ti / Au (10nm / 100nm) is deposited on the Si3N4 film.

[0039] Figure 8 : Characterization of the APEX microarray sensor.

[0040] (a) Photograph of an 8-inch wafer showing the mass production of APEX microarray sensor chips. Each wafer consists of >2000 photosensitive elements. (b) Scanning electron micrograph of the highly uniform nanopores in an APEX sensor. The inset shows a magnified view of the nanopore lattice.

[0041] Fig. 9 : Gradual spectral changes.

[0042] APEX sensors were conjugated with (a) anti-CD63 antibodies for exosome capture or (b) isotope control antibodies. All sensors were treated with equal concentrations of exosomes from a neural cell line (SH-SY5Y) prior to APEX amplification. While the sensors showed similar degrees of surface functionalization with antibodies (antibody conjugation), only the anti-CD63 functionalized sensors showed significant spectral shifts associated with exosome binding and APEX amplification, respectively. Note that in the control sensors, the spectral changes caused by APEX amplification were negligible in the absence of exosome binding. au, arbitrary units.

[0043] Fig.10 : Optimization of APEX sensor performance.

[0044] (a) Comparison of sensor performance using backside 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 backside to compensate for APEX enzymatic amplification. The optimized APEX design has a nanopore diameter of 230 nm, patterned with a regular period of 450 nm in a 100 nm thick gold layer suspended on a silicon nitride membrane. This double-layer plasmonic structure supports SPR excitation by backside illumination. (b) Transmission spectra of the optimized sensors change with increasing refractive index. The increase in refractive index causes a shift in the transmission spectrum and shifts the resonance peak to longer wavelengths. (c) The spectral shift is linearly related to the increase in refractive index. (d) APEX reproducibility and repeatability. APEX enzymatic amplification was performed on the same sample and measurements were performed between different users, sensor chips, and measurement times. The measurements showed the following analytical coefficients of variation: between groups = 2.76%, within groups = 4.14%, and total = 4.59%. All measurements were performed in triplicate or more, and data are shown in (a) as mean ± SD. au, arbitrary units, ns, not significant, Student's t-test.

[0045] Fig.11: APEX workflow and amplification efficiency.

[0046] (a) APEX workflow for detecting proteins (extravesicular and intravesicular) and miRNAs. (b) APEX amplification efficiency for different molecular targets. APEX signals were obtained for the following targets: extravesicular proteins, Aβ42 proteins, intravesicular proteins, heat shock protein 90; miRNA, miRNA-9. All signals were normalized to the signal before addition of the optical substrate to determine the amplification ratio. Measurements were performed in triplicate and data are shown as mean ± sd in (b).

[0047] Fig.12 : A fibril structure assembled from large Aβ aggregates.

[0048] Amyloid fibrils were observed after 2 hours of incubation of the prepared large Aβ42 aggregates. The formed structures were immunolabeled with gold nanoparticles (15 nm) by anti-Aβ42 antibodies and characterized by transmission electron microscopy.

[0049] Fig.13 : Preparation of BSA control aggregates.

[0050] (a) Schematic diagram of bovine serum albumin aggregates. We varied the heating time to prepare small 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.

[0051] FIG. 14 : Extracellular vesicles isolated from different cell sources.

[0052] Extracellular vesicles were derived from (a) neurons (SH-SY5Y), (b) glial cells (GLI36), (c) endothelial cells (HUVEC), (d) monocytes (THP-1), (e) erythrocytes, (f) platelets, (g) prostate-derived epithelial cells (PC-3), and (h) ovarian-derived epithelial cells (SK-OV-3). All vesicles were characterized by nanoparticle tracking analysis.

[0053] Fig.15 : APEX measurements of different circulating Aβ populations.

[0054] (a) APEX assay configuration for characterization of 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 filtrate. (b) Incubation of fibronectin with exosome-bound Aβ42 resulted in negligible APEX signal change. (c) Negative controls (APOE lipoprotein containing Aβ42 protein, human serum albumin / HSA) showed negligible signal, indicating the specificity of the APEX assay for exosome-bound Aβ42. All measurements were performed in triplicate and data are shown as mean ± sd.

[0055] Fig.16 : Characterization of Aβ populations in clinical samples.

[0056] (a) Exosome-bound Aβ42 population. We enriched Aβ42 directly from native plasma samples and measured the relative levels of co-localized signals for exosome markers (CD63, CD81, and CD9) and neuronal markers (NCAM, L1CAM, and CHL-1) in the captured Aβ42. All markers were detectable, with CD63 being 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 cutoff = 50 nm, Nuclepore, Whatman). The filtrate showed negligible vesicle counts as determined by nanoparticle tracking analysis. (c) The plasma filtrate also showed negligible signals for exosome markers (CD63) and neuronal markers (NCAM), indicating that exosomes were effectively removed by filtration. Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy controls without cognitive impairment (NCI). All measurements were performed in triplicate and data are shown as mean ± sd.

[0057] Fig.17 : Correlation of exosome-bound Aβ42 with regional brain amyloid load.

[0058] We identified imaging SUVRs in specific brain regions, namely the cingulate region affected by early AD and the occipital region affected by late AD. 2 =0.6863), APEX measurements of exosome-bound Aβ42 were consistent with imaging data of areas 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 ± sd.

[0059] Fig.18 : Comparison of PET imaging in clinical subjects with different diagnoses.

[0060] PET imaging of brain amyloid plaque burden was performed in patients (n=72) with different clinical diagnoses: AD (n=17), MCI (n=18), NCI (n=16), VaD (n=9), and VMCI (n=12). The standardized uptake value ratio (SUVR) of the overall mean plaque deposition could distinguish between AD clinical groups (AD and MCI) and other healthy subjects (NCI) and clinical controls (VaD and VMCI) (**P<0.01, ****P<0.0001, Student's t-test).

[0061] FIG. 19 : Extracellular vesicles in clinical samples.

[0062] (a) Representative analysis of extracellular vesicles (measured by nanoparticle tracking analysis) from blood samples of subjects with different clinical diagnoses (AD=17, MCI=18, NCI=16, VaD=9, VMCI=12, and acute stroke=12). Comparison of (b) vesicle size and (c) vesicle concentration from clinical blood samples (n=84). Notably, no significant differences in vesicle size and concentration were found among samples with different clinical diagnoses (ns, not significant, Student's t-test).

[0063] Fig. 20 : Comparison of APEX assay techniques, sensor design and fabrication.

[0064] Fig.21 Shown (a) Inhibition of amyloid protein aggregation. (b) Dynamic light scattering analysis confirms the unimodal size distribution and size differences when incubating amyloid proteins with and without inhibitors. (c) Real-time sensorgram of exosome binding kinetics. Exosomes show a stronger affinity for larger Aβ42 aggregates (untreated) than for smaller Aβ42 aggregates (treated with inhibitors).

[0065] Fig. 22Real-time sensorgrams of exosome binding kinetics are shown. Exosomes more strongly associate with aggregates of amyloid-forming proteins (e.g., Aβ, APP, α-Syn, IRS-1, Tau, APOE, SOD1, TDP-43, bassoon, fibronectin) than with similar-sized bovine serum albumin (BSA) control aggregates.

[0066] Fig.23 The specificity of the APEX assay for measuring target miRNA molecules is shown. 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 heat map signal was normalized by assay (row). DETAILED DESCRIPTION

[0067] Disclosed herein is a sensor chip and method for detecting analytes by surface plasmon resonance.

[0068] In one aspect, a sensor chip is provided, comprising a conductive layer on a membrane support layer, wherein a plurality of holes extend through the conductive layer and the membrane support layer and are arranged such that illumination of the conductive layer and / or the membrane support layer generates surface plasmon resonance.

[0069] In one embodiment, the design of multilayer structured materials (conductive metal and supporting substrate) enables plasmon coupling. This design can support bidirectional excitation of surface plasmon resonance, where the SPR performance from bidirectional illumination (from top or from bottom) is comparable.

[0070] The term "conductive layer" as used herein can be a conductive material that generates surface plasmon resonance when excited by electromagnetic energy (e.g., light waves). The conductive material can refer to, for example, a metallic conductive material. Such metallic conductive materials can be any metal, including precious 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 additional layers of protective and reinforcing materials.

[0071] A conductive material may be "optically visible" when it exhibits significant scattering intensity in the optical region (ultraviolet-visible-infrared spectrum, including wavelengths of about 100 nanometers (nm) to 3000 nm). A conductive material may be "visually visible" when it exhibits significant scattering intensity in the wavelength band of about 380 nm to 750 nm that can be detected by the human eye (i.e., the visible spectrum).

[0072] In one embodiment, the membrane support layer is a structured membrane support layer.

[0073] 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 multilayer plasma structures.

[0074] The diameter and period of the plurality of holes extending through the conductive layer and the membrane support layer may be varied to achieve different resonant wavelengths and penetration of the evanescent wave.

[0075] The plurality of holes include symmetrical circular holes, spatially anisotropic shapes such as ellipses, slits, and also include any holes that are triangular, square, rectangular or polygonal. Combinations of holes of different shapes can also be used. The holes may have a 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 220 nm or less, about 230 nm or less, about 240 nm or less, about 250 nm or less, about 260 nm or less, about 270 nm or less, about 280 nm or less, about 290 nm or less, about 300 nm or less, about 310 nm or less, about 320 nm or less, about 330 nm or less, about 340 nm or less, about 360 nm or less, about 370 nm or less, about 380 nm or less, about 390 nm or less, about 400 nm or less, about 410 nm or less, about 420 nm or less, about 430 nm or less, about 440 nm or less, about 450 nm or less, about 460 nm or less, about 470 nm or less, about 480 nm or less, about 490 nm or less, about 500 nm or less, about 510 nm or less or less, about 100 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 a size or diameter of about 10 nm or less.

[0076] In one embodiment, the hole may have a size or diameter of about 150nm to about 450nm. In one embodiment, the hole has a size or diameter selected from 150nm, 160nm, 170nm, 180nm, 190nm, 200nm, 210nm, 220nm, 230nm, 240nm, 250nm, 260nm, 270nm, 280nm, 290nm, 300nm, 310nm, 320nm, 330nm, 340nm, 350nm, 360nm, 370nm, 380nm, 390nm, 400nm, 410nm, 420nm, 430nm, 440nm, 450nm or anywhere in between. In one embodiment, the hole is a hole and has a diameter of 230nm.

[0077] The term "periodic" refers to the recurrence or repetition of the holes occurring at regular intervals by their positioning on the sensor chip. Thus, the term "periodic" refers to a regular predefined pattern of the holes relative to each other.

[0078] A surface plasmon resonance sensor chip may include a periodic array of holes. The regular periodicity may allow tight control of the resonance wavelength and penetration of the evanescent wave. In one embodiment, the holes have a periodicity of about 250 nm to about 650 nm. In one embodiment, the pores have a periodicity selected from 250nm, 260nm, 270nm, 280nm, 290nm, 300nm, 310nm, 320nm, 330nm, 340nm, 350nm, 360nm, 370nm, 380nm, 390nm, 400nm, 410nm, 420nm, 430nm, 440nm, 450nm, 460nm, 470nm, 480nm, 490nm, 500nm, 510nm, 520nm, 530nm, 540nm, 550nm, 560nm, 570nm, 580nm, 590nm, 600nm, 610nm, 620nm, 630nm, 640nm, and 650nm, or anywhere in between. In one embodiment, the pores have a periodicity of 450nm.

[0079] In one embodiment, the aperture is arranged so 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.

[0080] In one embodiment, the conductive layer and membrane support layer are disposed on a substrate having voids formed therein in regions adjacent to the plurality of holes such that illumination of the conductive layer and / or membrane support layer in any direction produces surface plasmon resonance.

[0081] In one embodiment, the sensor chip includes a first recognition molecule fixed on the surface of the conductive layer. The first recognition molecule can be fixed on the surface using techniques known in the art. For example, the first recognition molecule can be adsorbed on the surface. Alternatively, before fixing the first recognition molecule, the surface can be coated with streptavidin or an avidin layer. The first recognition molecule can be biotinylated and fixed on the surface by 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 cross-linking 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. The surface can then be activated by carbodiamine cross-linking in a mixture of excess NHS / EDC dissolved in MES buffer and conjugated with the first recognition molecule.

[0082] The term "recognition molecule" may refer to a molecule that is capable of specifically binding to an analyte."Recognition molecule" may be an antibody, nucleic acid, peptide, aptamer, small molecule or other synthetic reagent.

[0083] The term "analyte" refers to a substance present in a sample to be detected or measured on a sensor chip. An "analyte" may include cells, viruses, nucleic acids, lipids, proteins, peptides, glycopeptides, nanovesicles, microvesicles, exosomes, extracellular vesicles, sugars, metabolites, or a combination thereof or tissue state. An "analyte" may be, for example, a peptide or nucleic acid (e.g., miRNA) biomarker that is bound to or associated with an exosome. An "analyte" may also be, for example, a complex between a cell and a protein, or a complex between a protein and a nucleic acid.

[0084] In one embodiment, the first recognition molecule is an antibody or a fragment thereof. For example, the antibody may be an antibody that recognizes a pan-exosomal marker or a marker associated with or bound to exosomes. For example, the antibody may be an antibody that is specific to CD63, CD9, or CD81 that is abundant and characteristic in exosomes. The antibody may also be specific to markers of cell-specific origin (e.g., CHL1, L1CAM, or NCAM). The antibody may also recognize biomarkers associated with or bound to exosomes. For example, the antibody may be an anti-Aβ antibody that recognizes Aβ, or an antibody that recognizes APP, α-syn, or Tau that is bound or associated with exosomes.

[0085] 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, and epitope-binding fragments of any of the above. The antibodies provided by the present invention can be monospecific, bispecific, trispecific or more multispecific. Multispecific antibodies can be specific for different epitopes of a polypeptide or can be specific for a polypeptide and a heterologous epitope, such as a heterologous polypeptide or a solid support material.

[0086] The terms "protein" and "polypeptide" are used interchangeably and refer to any polymer of amino acids (dipeptides or larger) linked by peptide bonds or modified peptide bonds. Polypeptides with less than about 10-20 amino acid residues are generally referred to as "peptides". The polypeptides of the present invention may contain non-peptide components, such as carbohydrate groups. Carbohydrate and other non-peptide substituents may be added to the polypeptide by the cell 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 may still be present.

[0087] As used herein, "nucleic acid" may be RNA or DNA, may be single-stranded or double-stranded, and may be, for example, a nucleic acid encoding a protein of interest, a polynucleotide, an oligonucleotide, a nucleic acid analog, such as a peptide-nucleic acid (PNA), a pseudo-complementary PNA (pc-PNA), a locked nucleic acid (LNA), etc. Such nucleic acid sequences include, for example, but not limited to, nucleic acid sequences encoding proteins, such as those acting as transcriptional repressors, antisense molecules, ribozymes, small inhibitory nucleic acid sequences, such as, but not limited to, RNAi, shRNAi, siRNA, micro RNAi (mRNAi), antisense oligonucleotides, etc.

[0088] As used herein, "nanovesicle" may refer to a naturally occurring or synthetic vesicle that includes a cavity inside. The nanovesicle may include a lipid bilayer membrane that encapsulates the contents of the internal cavity. The nanovesicle may include a liposome, an exosome, an extracellular vesicle, a microvesicle, an apoptotic vesicle (or apoptotic body), a vacuole, a lysosome, a transport vesicle, a secretory vesicle, a gas vesicle, a matrix vesicle, or a multivesicular body. The nanovesicle may have a size of about 1000nm or less, about 900nm or less, about 800nm ​​or less, about 700nm or less, about 600nm or less, about 500nm or less, about 450nm or less, about 400nm or less, about 350nm or less, about 300nm or less, about 250nm or less, about 240nm or less, about 230nm or less, about 220nm or less, about 210nm or less, about 200nm or less, about 190nm or less. The size of the nanostructured carbon foam may be about 100 nm or less, about 150 nm or less, about 160 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.

[0089] Exosomes are a type of nanovesicle, also known in the art as extracellular vesicles, microvesicles or microparticles. These vesicles are shed by eukaryotic cells or shed from the plasma membrane to the outside of the cell. These membrane vesicles vary in size, with a diameter of about 10 to about 5000 nm. Small vesicles (about 10-1000 nm in diameter, preferably 30-100 nm) released by intracellular multivesicular body exocytosis are referred to in the art as "exosomes". The methods and compositions described in the present invention are also applicable to other vesicles of all sizes.

[0090] The term "sample" refers to any sample containing an analyte or testing the presence of an analyte. The sample includes a cell or organism (bacteria, virus), a cell or organism cracked, a cell extract, a nuclear extract, a cell or biological component, an extracellular fluid, a culture medium for in vitro cultured cells or organisms, blood, plasma, serum, gastrointestinal secretions, urine, ascites, a homogenate of a tissue or tumor, synovial fluid, feces, saliva, sputum, cystic fluid, amniotic fluid, cerebrospinal fluid, peritoneal fluid, lung lavage fluid, semen, lymph, tears, pleural effusion, nipple aspirate, breast milk, skin, respiratory tract, intestinal and urogenital tract external sections and prostatic fluid. The sample can be a virus or bacterial sample, a sample obtained from an environmental source (e.g., a polluted water body, an air sample or a soil sample) and a food industry sample. The sample can be a biological sample, which refers to or is obtained from a living organism. The organism can be in vivo (e.g., a complete organism), or can be in vitro (e.g., a cell or organ grown in culture). "Biological sample" also refers to cells or cell groups, or a certain amount of tissue or liquid from a subject. The most common situation is to take out a sample from a subject, but the term "biological sample" can also refer to cells or tissues analyzed in vivo, i.e., not taken out from a subject. "Biological sample" usually includes cells from a subject, but the term can also refer to non-cellular biological materials, such as non-cellular parts of blood, saliva or urine. Biological samples can come from the resection of primary, secondary or metastatic tumors, bronchoscopic biopsy or hollow needle biopsy, or cell blocks from pleural effusions. In addition, fine needle aspiration of biological samples is also very useful. In one embodiment, the biological sample is a primary ascites cell. Biological samples also include explants and primary and / or transformed cell cultures from patient tissues. Biological samples can be provided by taking out a cell sample from a subject, but can also be achieved by using previously separated cells or cell extracts (e.g., separated by another person at another time, and / or for another purpose). Archival tissues can also be used, such as tissues with treatment or outcome history. Biological samples include, but are not limited to, tissue biopsies, abrasions (e.g., oral abrasions), whole blood, plasma, serum, urine, saliva, cell culture, or cerebrospinal fluid. The samples analyzed by the compositions and methods of the present invention may have been processed to purify or enrich for exosomes contained therein. In one embodiment, the sample is blood.

[0091] In one aspect, an imaging system is provided, comprising a light source, a detector and a sensor chip as defined in the present invention, wherein the detector is positioned to detect light generated by the light source and transmitted through the sensor chip.

[0092] In one aspect, a kit is also provided, which comprises the sensor chip defined in the present invention.

[0093] The kit may further include a second recognition molecule, which is specific for the analyte captured on the sensor chip surface or the analyte associated with the captured analyte. The kit may include one or more second recognition molecules, each of which is specific for one or more analytes, so that one or more analytes can be detected.

[0094] The second recognition molecule can allow for 1) signal amplification, 2) co-localization analysis (e.g., detection of different targets found simultaneously in the same vesicle), and 3) differentiation of analyte subpopulations based on molecular and tissue differences.

[0095] The second recognition molecule can be coupled to a signal amplification portion, wherein the signal amplification portion can induce the formation of insoluble aggregates with increased optical density relative to the captured analyte, and increase the measured surface plasmon resonance signal. For example, the kit may include a second recognition molecule, which is an antibody (e.g., an antibody specific for Aβ42). The second recognition molecule can be conjugated with horseradish peroxidase. The kit may further include an enzyme substrate. Therefore, horseradish peroxidase can induce the formation of insoluble aggregates with increased optical density relative to the captured analyte on the sensor chip surface.

[0096] The term "signal amplification molecule" refers to a molecule that can induce 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 transmission wavelength (spectral shift) or transmission intensity, thereby helping to increase 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 an aggregate. The secondary antibody can be further coupled to an enzyme, gold particles or macromolecules, thereby helping to form larger aggregates to increase the optical density.

[0097] In one embodiment, the present invention relates to a highly sensitive analytical platform, amplified plasma exosomes (APEX), for detecting exosome-bound amyloid β (Aβ) directly from blood samples of Alzheimer's disease (AD) patients. The analytical method can use transmission surface plasmon resonance (SPR) and in situ enzymatic conversion of optical products for multiplexed population analysis. The APEX technology can achieve 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.

[0098] In one aspect, a method for manufacturing a sensor chip is provided, the method comprising the following steps:

[0099] a) providing a top membrane support layer;

[0100] b) depositing a conductive layer on the top membrane support layer;

[0101] c) forming a plurality of holes extending through the membrane support layer, the holes also extending through the conductive layer, and arranged such that illumination of the conductive layer and / or the top membrane support layer produces surface plasmon resonance.

[0102] The method may include:

[0103] coating a top membrane support layer and a bottom membrane support layer on the top and bottom surfaces of the silicon substrate;

[0104] providing a photoresist layer on the top film support layer, and

[0105] A plurality of holes are defined in the photoresist by deep ultraviolet lithography (DUV), and the pattern of the plurality of holes is transferred to the top membrane support layer by reactive ion etching (RIE).

[0106] The method may further comprise the following steps:

[0107] removing the photoresist on the top film support layer and coating a silicon dioxide protective layer on the surface of the top film support layer;

[0108] coating a photoresist layer on the bottom film support layer;

[0109] defining a sensing area in the photoresist by photolithography; and transferring the pattern of the sensing area to a bottom membrane support layer by reactive ion etching (RIE);

[0110] transferring the pattern of the sensing area to a silicon substrate;

[0111] removing the protective layer on the surface of the top membrane support layer using dilute hydrofluoric acid;

[0112] and depositing a conductive layer on the top membrane support layer.

[0113] The term "sensing region" as used herein refers to a region of a sensor chip comprising a plurality of holes arranged such that illumination of the plasma layer and / or the membrane support layer generates surface plasmon resonance.

[0114] As used herein, the term "resist" refers to a thin layer used to transfer an image or pattern to a substrate on which it is deposited. The resist can be patterned by photolithography to form a temporary mask at the (sub) micrometer level that protects selected areas of the underlying substrate during subsequent processing steps (usually etching). The materials (usually viscous solutions) used to prepare the thin layer are also covered by the term resist. Resists are typically a mixture of a polymer or its precursor with other small molecules (e.g., a photoacid generator) that is specifically configured for a given lithographic technique. For example, resists used in photolithography are called "photoresists." Resists used in electron beam lithography processes are called "electron beam resists."

[0115] In one aspect, a method for detecting an analyte in a sample is provided, the method comprising:

[0116] capturing the analyte onto the surface of the sensor chip defined in the present invention; and

[0117] Binding of a second recognition molecule to the analyte captured on the sensor chip surface is detected, wherein the second recognition molecule is specific for the analyte, wherein increased binding of the second recognition molecule compared to a control sample indicates the presence of the analyte in the sample.

[0118] Described method can comprise the combination of one or more second recognition molecules (sequentially or simultaneously) that detect one or more analytes have specificity.This allows to detect, quantify and analyze the tissue state (for example, co-location) of multiple analytes or biomarkers in sample.Every analyte can be identified by the set of different first and second recognition molecules.The first and second recognition molecules can identify the same analyte or different analytes respectively.The different combinations of the first and second recognition molecules can allow the co-location of detection analytes.This can allow to detect multiple analytes simultaneously, and can allow to detect the co-location of these molecules.

[0119] In one embodiment, the method comprises detecting two or more analytes that are co-localized in a sample. The two or more analytes may, for example, be present in or on a cell. Alternatively, the two or more analytes may, for example, be bound to the same exosome.

[0120] In one embodiment, the first recognition molecule is an antibody that recognizes Aβ42 and the second recognition molecule is another antibody that recognizes Aβ42.

[0121] 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.

[0122] The detection of the "binding" of the second recognition molecule to the analyte captured on the sensor chip surface can be performed by spectral shift (change in transmission wavelength) or change in transmission intensity at a fixed wavelength. For example, the analyte captured on the sensor chip surface will have an initial reference wavelength. After binding to the second recognition molecule, the transmission wavelength may shift to a longer wavelength.

[0123] The change in transmission resonance wavelength (or spectral shift expressed as (Δλ)) or change in transmission intensity at a fixed wavelength in the sample can be compared to 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 is increased.

[0124] "Increased binding of the second recognition molecule" in the sample compared to the control sample can be determined by comparing the change in spectral shift after binding of the second recognition molecule or the change in transmission intensity at a fixed wavelength between the sample and the control sample. An increase in spectral shift or a change in transmission intensity may indicate an increase in binding of the second recognition molecule to the analyte.

[0125] In one embodiment, the increase in spectral shift or transmission intensity may refer to an increase of 1.2 times or greater between a subject and a control subject. The term may also refer to an increase selected from 1.1 times, 1.3 times, 1.4 times, 1.5 times, 1.6 times, 1.7 times, 1.8 times, 1.9 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times, 11 times, 12 times, 13 times, 14 times, 15 times, 16 times, 17 times, 18 times, 19 times, 20 times, 21 times, 22 times, 23 times, 24 times, 25 times, 26 times, 27 times, 28 times, 29 times, 30 times, 31 times, 32 times, 33 times, 34 times, 35 times, 36 times, 37 times, 38 times, 39 times, 40 times, 41 times, 42 times, 43 times, 44 times, 45 times, 46 times, , 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 increase.

[0126] The second recognition molecule can be a molecule specific to the analyte. The second recognition molecule can be coupled to the signal amplification portion, and wherein the signal amplification portion can induce the formation of an insoluble aggregate with an increased optical density relative to the captured analyte. For example, the analyte captured on the sensor chip surface will have an initial reference wavelength. After combining the second recognition molecule, the transmission wavelength may be shifted to a longer wavelength. When the second recognition molecule is coupled to the signal amplification portion, due to the increase in optical density, the transmission wavelength may be shifted to a longer wavelength.

[0127] 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.

[0128] The signal amplification portion can be an enzyme. In one embodiment, the signal amplification portion is an enzyme. The enzyme can be horseradish peroxidase (HRP), alkaline phosphatase, glucose oxidase, beta-lactamase or beta-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 portion. For example, the first biorecognition molecule can be an antibody covalently fused to horseradish peroxidase, and the enzyme is covalently linked to the antibody using techniques known in the art.

[0129] The method may further include contacting the enzyme with an enzyme substrate. The enzyme substrate may be an enzyme that can form an insoluble product in the presence of an enzyme 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 can be used. After the enzymatic reaction of HRP, these substrates can be converted into insoluble products. In one embodiment, the enzyme substrate is 3,3'-diaminobenzidine tetrahydrochloride.

[0130] 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.

[0131] 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 aggregation biomarker. The first recognition molecule may be specific for the analyte.

[0132] In one embodiment, the first recognition molecule is an antibody. For example, the antibody can be an antibody that recognizes a pan-exosomal marker or a marker associated with or bound to exosomes. For example, the antibody can be an antibody that is specific to CD63, CD9, or CD81 that is abundant and characteristic in exosomes. The antibody can also be specific to cell-specific source markers (e.g., CHL1, L1CAM, or NCAM). The antibody can also recognize biomarkers 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 that is bound to or associated with exosomes.

[0133] 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.

[0134] The term "biomarker" as used in this application is understood to be an agent or entity whose presence or level is associated with an event of interest. A biomarker can be a cell, a protein, a nucleic acid, a peptide, a glycopeptide, an exosome, or a combination thereof. For example, the biomarker is Aβ42 or Tau peptide, the presence or level of which indicates whether the subject suffers from 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, the presence or level of which indicates whether the subject suffers from a neurodegenerative disease or amyloidosis, or is at risk of developing it.

[0135] In one embodiment, there is provided use of a sensor chip as defined in the present invention for detecting an analyte.

[0136] In one aspect, a method of detecting a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising:

[0137] a) contacting a sample with the surface of a sensor chip defined in the present invention; and

[0138] b) Detecting the binding of the second recognition molecule to the analyte captured on the sensor chip surface.

[0139] Wherein the second recognition molecule is specific for the analyte, increased binding of the second recognition molecule compared to a control subject indicates that the subject suffers from a neurodegenerative disease or amyloidosis.

[0140] The term "subject" refers to any animal, including any vertebrate or mammal, particularly a human, and may also refer to, for example, a human or patient.

[0141] The term "control subject" refers to a subject known not to suffer from a neurodegenerative disease or amyloidosis, or a subject who is not at risk of suffering from 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 a control subject.

[0142] In one embodiment, the biomarker is an exosome-bound or exosome-associated biomarker. In one embodiment, the biomarker is an exosome-bound aggregation biomarker. The biomarker may be selected from, 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 a combination thereof and a related complex.

[0143] The neurodegenerative disease may be selected from Alzheimer's disease and mild cognitive impairment.

[0144] The method may further comprise treating a subject found to have a neurodegenerative disease or amyloidosis.

[0145] The term "treat" as used herein may 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 regression of the 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.

[0146] In one embodiment, the term "treating" refers to administering a drug to slow the progression of a neurodegenerative disease or amyloidosis.

[0147] 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 increased level of the exosome-bound biomarker compared to a reference indicates that the subject suffers from a neurodegenerative disease or amyloidosis.

[0148] In one aspect, a method of detecting a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising detecting the level of an exosome-bound aggregation biomarker in a sample obtained from the subject, wherein an increased level of the exosome-bound aggregation biomarker compared to a reference indicates that the subject suffers from the neurodegenerative disease or amyloidosis.

[0149] The method may include detecting one or more exosome-bound biomarkers in a sample. This allows for the detection of co-localization or presence of multiple biomarkers present on the same exosome.

[0150] The biomarker may be selected from, but not limited to, Aβ, APP, α-Syn or Tau.

[0151] The method may include detecting the level of a molecular subtype of an exosome-bound biomarker.

[0152] In one embodiment, the Aβ is Aβ42 or Aβ40.

[0153] In one embodiment, the Aβ is a prefibrillar aggregate. Prefibrillar Aβ aggregates have been found to preferentially bind to exosomes. Enhancing the binding between Aβ In one embodiment, the method defined in the present invention comprises detecting exosomes bound to prefibrillar Aβ aggregates.

[0154] In one embodiment, the aggregation biomarker is a pre-fibrillar aggregate. The aggregation biomarker may be a pre-fibrillar aggregate of Aβ. Alternatively, the aggregation biomarker may be a pre-fibrillar aggregate of APP, α-Syn, or Tau.

[0155] In one embodiment, reference is made to a control subject.

[0156] In one embodiment, a method of measuring exosome association as a surrogate to determine the pre-fibrillar tissue state of protein aggregates is provided, wherein an increase in the level of protein aggregates in the pre-fibrotic tissue state compared to a control indicates that the subject suffers from a neurodegenerative disease or amyloidosis.

[0157] In one embodiment, the method further comprises detecting an exosomal biomarker selected from CD63, CD9 and CD81, wherein the exosomal biomarker is co-localized with the exosomal binding biomarker. In some embodiments, the exosomal 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 thereof and related complexes.

[0158] In one embodiment, the method further comprises detecting neural biomarkers comprising NCAM, L1CAM, and CHL-1, wherein the neural biomarkers co-localize with the exosome-bound biomarkers.

[0159] In one embodiment, a method for measuring different tissues and molecular subpopulations of biomarkers is provided. For example, the method may include measuring exosome-bound biomarkers, free (unbound) biomarkers, and total (exosome-bound and unbound) biomarkers. The method may also include measuring the relative concentrations of different biomarkers to better predict disease.

[0160] In one embodiment, the neurodegenerative disease is selected from Alzheimer's disease, mild cognitive impairment, vascular dementia and vascular mild cognitive impairment.

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

[0162] The method may further comprise treating a subject suffering from a neurodegenerative disease or amyloidosis.

[0163] The method can be further associated with brain imaging studies (e.g., PET imaging). This includes correlation with 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 is provided, and the circulating biomarkers are associated with imaging of specific brain regions (PET).

[0164] The present invention is based on the following findings: 1) biomarkers (including different markers that co-localize) can be used to measure and characterize different molecular, biophysical and tissue subpopulations of circulating Aβ, 2) circulating exosome-bound Aβ in the blood can be closely correlated with PET imaging of Aβ deposition (overall average) across different patient populations; 3) circulating exosome-bound Aβ in the blood can be closely correlated with PET imaging of Aβ (early AD area, cingulate gyrus area) 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).

[0165] The method may include administering a therapeutically effective amount of a drug to a subject in need of treatment. For example, the drug may be a cholinesterase inhibitor, such as donepezil, rivastigmine, or galantamine. The drug may also be an NMDA receptor antagonist, such as memantine. The drug may be a combination of a cholinesterase inhibitor and an NMDA receptor antagonist, such as a combination of donepezil and memantine. The drug may be a BACE1 inhibitor, such as AZD3293; or an antibody, such as an anti-amyloid antibody, such as Aducanumab. The drug may also be an anti-tau drug, such as TRx0237 (LMTX). In some embodiments, a therapeutically effective amount of one or more drugs described herein, or a combination of two or more drugs described herein, may be administered to a subject in need of treatment.

[0166] In some embodiments, molecules that can effectively reduce the number of starch-forming protein aggregates may be potential candidate molecules for treatment. Therefore, 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, leucomethylthioninium 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)phenylvinylbenzene (BSB), Chicago sky blue 6B, cyclodextrin, daunomycin hydrochloride, dimethyl yellow, direct red 80, 2,2-dihydroxydiphenylformamide Ketone, hexadecyltrimethylammonium bromide (C16), hemin chloride, heme, indomethacin, juglone, resorcinol 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, rolytetracycline, quinacrine dihydrochloride, thioflavin S, ThT and trimethyl (tetradecyl) ammonium bromide (C17), diallyl tartaric acid, eosin Y, fenofibrate, cuproline, nystatin, octadecyl sulfate and rhodamine B.

[0167] An increased level of an exosome-bound biomarker may refer to an increase of 1.2-fold or more between the level of a subject and a control subject, and 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 times, 42 times, 43 times, 44 times, 45 times, 46 times, 47 times, 48 ​​times, 49 times, 50 times, 51 times, 52 times, 53 times, 54 times, 55 times, 56 times, 57 times, 58 times, 59 times, 60 times, 61 times, 62 times, 63 times, 64 times, 65 times, 66 times, 67 times, 68 times, 69 times, 70 times, 71 times, 72 times, 73 times, 74 times, 75 times, 76 times, 77 times, 78 times, 79 times, 80 times, 81 times, 82 times, 83 times, 84 times, 85 times, 86 times, 87 times, 88 times, 89 times, 90 times, 91 times, 92 times, 93 times, 94 times, 95 times, 96 times, 97 times, 98 times, 99 times and 100 times increase.

[0168] In one aspect, a method of detecting a subject at risk for 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 compared to a control subject indicates that the subject has a neurodegenerative disease.

[0169] In one aspect, a method of detecting and treating a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising:

[0170] a) detecting the level of an exosome-bound aggregation biomarker in a sample obtained from the subject, an increased level of the exosome-bound aggregation biomarker compared to a reference indicating that the subject suffers from a neurodegenerative disease or amyloidosis; and b) treating the subject suffering from a neurodegenerative disease or amyloidosis.

[0171] In one aspect, a method of treating a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising:

[0172] a) detecting the level of an exosome-bound aggregation biomarker in a sample obtained from the subject, an increased level of the exosome-bound aggregation biomarker compared to a reference indicating that the subject suffers from a neurodegenerative disease or amyloidosis; and b) treating the subject suffering from a neurodegenerative disease or amyloidosis.

[0173] In one embodiment, a method of detecting and slowing progression of a neurodegenerative disease or amyloidosis in a subject is provided, the method comprising:

[0174] a) detecting the level of an exosome-bound aggregation biomarker in a sample obtained from the subject, an increased level of the exosome-bound aggregation biomarker compared to a reference indicating that the subject suffers from a neurodegenerative disease or amyloidosis; and b) treating the subject suffering from a neurodegenerative disease or amyloidosis.

[0175] In one aspect, a method for determining the aggregation state of a biomarker in a sample is provided, the method comprising detecting the level of exosome-bound biomarker in the sample, wherein an increase in the level of the exosome-bound biomarker compared to a reference indicates the degree of aggregation of the biomarker.

[0176] The method may include the step of contacting the sample with a population of exosomes prior to detecting the level of the exosome-bound biomarker.

[0177] In one embodiment, the aggregated biomarkers in the sample preferentially bind to exosomes compared to non-aggregated biomarkers.

[0178] The sample may be obtained from a subject.

[0179] In one embodiment, an increased level of aggregation of the biomarker compared to a reference indicates the presence of a neurodegenerative disease or amyloidosis in the subject.

[0180] The method may further comprise treating a subject suffering from a neurodegenerative disease or amyloidosis.

[0181] 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 an exosome-bound biomarker in the sample, wherein an increase in the level of the exosome-bound biomarker compared to a reference indicates the degree of aggregation of the biomarker.

[0182] Those skilled in the art will appreciate that, in addition to these specific descriptions, the invention described herein is susceptible to changes and modifications. It should be understood that the present invention includes all such changes and modifications that fall within the spirit and scope. The present invention also includes all steps, features, compositions and compounds mentioned or indicated in this specification, either individually or in combination, and any and all combinations of any two or more of the steps or features.

[0183] In this specification and the following claims, unless the context requires otherwise, the word "comprise" and variations such as "comprising" and "containing", will be understood to imply the inclusion of stated integers or steps or groups of integers or steps but not the exclusion of any other integers or steps or groups of integers or steps.

[0184] Reference in this specification to any prior publication (or information derived therefrom) or any known matter is not, and should not be taken as, an admission or recognition or any form of implication that the prior publication (or information derived therefrom) or known matter constitutes part of the common general knowledge in the field to which this specification relates.

[0185] 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.

[0186] Example

[0187] Reference in this specification to any prior publication (or information derived therefrom) or any known matter is not, and should not be taken as, an admission or recognition or any form of implication 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 relates.

[0188] Materials and methods

[0189] Cell culture. Human cell lines SH-SY5Y (neural cells), 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 essential medium (DMEM, Gibco). SH-SY5Y was cultured in Dulbecco's modified Eagle's medium: nutrient mixture F-12 medium (DMEM / F12 Gibco). PC-3, THP-1, and HUVEC were grown in F-12K, RPMI-1640, and EGM-2 medium, respectively. Except for EGM-2 supplemented with 5% fetal bovine serum (FBS), all other media were supplemented with 10% FBS and penicillin-streptomycin.

[0190] Exosome isolation and quantification. Cells from passages 1-15 were cultured in vesicle-depleted medium (containing 5% depleted FBS) for 48 h before collecting vesicles. All media containing exosomes were filtered through a 0.2 μm membrane filter (Millipore), separated by differential centrifugation (first 10,000g, then 100,000g), and used for exosome analysis using the APEX platform. For exosomes isolated from blood cells and platelets, blood cells were from blood fractionation and platelets were from platelet-enriched plasma. These components were washed in HEPES-buffered saline and incubated at 37°C with 2 mM calcium chloride and 2 μM calcium ionophore (A23187) to stimulate the production of exosomes. All vesicles were then collected as described above. To independently quantify exosome concentrations, a nanoparticle tracking analysis (NTA) system (NS300, Nanosight) was used. Exosome concentration was adjusted to obtain ∼50 vesicles in the field of view for optimal counting. The same system settings were used for all NTA measurements to ensure consistency.

[0191] APEX sensor fabrication. The APEX sensor is fabricated on an 8-inch silicon (Si) wafer. In brief, a 10 nm silicon dioxide (SiO2) layer is prepared by thermal oxidation, and 145 nm silicon nitride (Si3N4) is deposited on the wafer by low-pressure chemical vapor deposition (LPCVD). After coating with photoresist, deep ultraviolet (DUV) lithography is performed to define the nanohole array pattern in the resist. The pattern is transferred to the Si3N4 membrane by reactive ion etching (RIE). After removing the photoresist, a thin SiO2 protective layer (100 nm) is deposited on the front side of the wafer using plasma enhanced chemical vapor deposition (PECVD). To achieve light transmission, photoresist is spin-coated on the back side of the wafer; photolithography is used to define the sensing area. Si3N4 and SiO2 are etched by RIE, and then Si is etched by potassium hydroxide (KOH) and tetramethylammonium hydroxide (TMAH). After etching, the SiO2 protective layer was removed using dilute hydrofluoric acid (DHF) (1:100). Finally, Ti / Au (10nm / 100nm) was deposited on the Si3N4 membrane. All nanopore sizes and sensor uniformity were characterized by scanning electron microscopy (JEOL 6701).

[0192] Channel assembly. Standard soft lithography was used to fabricate the multichannel flow cell. The mold was prepared using SU-8 negative resist (SU8-2025, Microchem). The photoresist was 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 was baked again with stirring before development. The developed mold was chemically treated with trichlorosilane vapor in a desiccator for 15 min before subsequent use. Polydimethylsiloxane polymer (PDMS) and cross-linker were mixed in a ratio of 10:1 and cast onto the SU-8 mold. After curing at 65°C for 4 h, the PDMS layer was cut from the mold and assembled onto the APEX sensor. All inlets and outlets were sampled using a 1.1 mm biopsy punch.

[0193] Optical setup and spectral analysis. A tungsten halogen lamp (StockerYale Inc.) was used to illuminate the APEX sensor through a 10X microscope objective. The transmitted light was collected by an optical fiber and input into a spectrometer (Ocean Optics). All measurements were performed in a closed box at room temperature to eliminate ambient light interference. The transmitted light intensity was digitally recorded in counts versus wavelength (330nm-1600nm). For spectral analysis, the spectral peaks were determined by fitting the transmission peaks using a local regression method, and a customized R program was used. All fits were done locally. That is, for the fit at point x, points near point x were used for fitting and weighted by their distance from x. Compared to multi-order polynomial curve fitting, the method can eliminate the result variations caused by the number of data points analyzed and the data range. When determining the optimal sensor geometry ( Fig.10 ), using spectral changes to quantify peak transmission intensity, peak shape (full width at half maximum, FWHM), and detection sensitivity in response to changes in refractive index, respectively. The measured transmission spectra showed consistency between different sensors, with an SD of 0.03nm for the baseline spectral peak position. All spectral shifts (Δλ) were determined as changes in the transmission spectrum peak and calculated relative to appropriate control experiments (see below for details).

[0194] Sensor surface functionalization. To confer molecular specificity to the APEX sensor, 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) at room temperature for 2 h. After washing, the surface was activated by carbodiimide cross-linking 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 probe was removed by PBS washing. The conjugated sensors were stored in PBS at 4°C for subsequent use. All sensor surface modifications were monitored spectroscopically to ensure uniform functionalization.

[0195] Table 1. List of markers and their probes used for profiling.

[0196]

[0197]

[0198]

[0199]

[0200] APEX signal amplification. To establish APEX amplification, enzymatic growth of insoluble optical products for signal enhancement was incorporated, and the optical substrate concentration and reaction duration for establishing the platform were optimized. In brief, exosomes were incubated with CD63-functionalized APEX sensors (BD Biosciences) for 10 min. The bound vesicles were then labeled with biotinylated anti-CD63 antibodies (Ancell, 10 min). As a control experiment, an equal amount of biotinylated IgG isotope control antibody (Biolegend) was used on the bound vesicles to determine the amplification efficiency. After washing the unbound antibody, high-sensitivity 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 an 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. The localized deposition of insoluble optical products was confirmed by scanning electron microscopy. Fig.11 Shown in a.

[0201] Using this set of conditions, known amounts of exosomes were further titrated and their associated APEX signals were measured. The APEX detection limit was determined as the lowest target concentration that could generate a detectable signal = 3x (sd of control background signal).

[0202] APEX protein detection. All sensor surfaces were blocked with 2% w / v bovine serum albumin (BSA) to reduce nonspecific protein binding. Exosomes were introduced into the functionalized sensors, incubated for 10 min at room temperature to capture the exosomes, and washed with PBS to remove unbound exosomes. As described above, for extravesicular protein targets, exosomes were directly labeled with detection antibodies for APEX amplification. For intravesicular protein targets, exosomes were additionally fixed and permeabilized (eBioscience) before labeling with detection antibodies. Spectral measurements were performed before and after APEX amplification and analyzed by a customized 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, exosome lysates were incubated with biotinylated RNA probes (350 nM) for 15 min to hybridize with the target miRNA strand. The mixture was introduced onto the functionalized sensor in binding buffer (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. Highly sensitive 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 on an ELISA plate (ThermoScientific) 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 at room temperature for 2 h. After incubation with a secondary antibody conjugated to horseradish peroxidase (Thermo Scientific) and a chemiluminescent substrate (ThermoScientific), 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 (ThermoScientific) and quantified using bicinchoninic acid assay (BCA assay, Thermo Scientific). Protein lysates were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), transferred to polyvinylidene difluoride membrane (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 neuronal marker NCAM (R&D Systems). After incubation with horseradish peroxide-conjugated secondary antibodies (Cell Signaling), enhanced chemiluminescence was used for immunodetection (Thermo Scientific).

[0206] Protein aggregation. The freeze-dried NH4OH-treated Aβ42 protein (rPeptide) was resuspended in NaOH (60mM, 4°C), sonicated in PBS34 and the pH was adjusted to pH 7.4. The protein was immediately filtered through a 0.2μm membrane filter (Millipore), and the filtrate was used as a smaller Aβ42 aggregate. In order to prepare larger Aβ42 aggregates, the protein was treated as described above and incubated for 1h under stirring to induce further aggregation before filtering through a 0.2μm membrane filter (Millipore). The filtrate was used as a large Aβ aggregate. To prepare BSA aggregates of similar size as a control, 2% w / v BSA was dissolved in PBS and heated at 80°C for 1h and 2h to induce aggregation of large control aggregates and small control aggregates, respectively.

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

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

[0209] Taking into account the size differences of protein aggregates and the exponential decay of sensitivity associated with SPR as the distance from the sensing surface increases, the following formula was used:

[0210]

[0211] The total surface area of ​​the conjugated aggregates interacting with the exosomes was calculated as: where S is the signal, z is the distance from the sensor surface, E is the electric field at z = 0 and is a constant in this case, and 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 approximately spherical, supported by transmission electron micrographs (Fig. 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 area to estimate the number of available binding sites for interaction with exosomes. All exosome binding data (Δλ) were normalized to their respective protein binding sites. Normalized Aβ42 binding data were made relative to a BSA control of similar size and fitted to determine the binding affinity constant, KD.

[0213] Scanning electron microscopy. All samples were fixed with half-volume 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 then sputter-coated with gold (Leica) before imaging in 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 to 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 NUH and NUS Institutional Review Boards (2015 / 00441, 2015 / 00406 and 2016 / 01201). All subjects were recruited according to the protocol approved by the Institutional Review Board, and informed consent was obtained. All recruited subjects received multiple neuropsychological assessments at the National University Hospital of Singapore (NUH), including the Mini Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCa) and the Vascular Dementia Battery (VDB) for cognitive assessment. The clinical diagnosis of AD, MCI or NCI was performed by neuropsychological assessment and the evaluation of clinical features and blood investigations. The clinical diagnosis of VaD and VMCI was performed 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 standards 46-48 and were independent of APEX measurements. Acute stroke plasma samples were collected within 24h after patients diagnosed with stroke were hospitalized. Longitudinal plasma samples were collected from patients during the one-year follow-up, but PET brain imaging was not performed. For plasma collection, venous blood (5 ml) was drawn from subjects into EDTA tubes before injection of PET radiotracer (if applicable) and processed immediately. Briefly, all blood samples were centrifuged at 400g (4°C) for 10 min. Plasma was transferred without disturbing the buffy coat and centrifuged again at 1100g (4°C) for 10 min. All plasma samples were de-identified and stored at -80°C prior to measurement using the APEX platform. All APEX measurements were performed without knowing the PET imaging results and clinical diagnosis.

[0216] Clinical APEX measurements. All plasma samples were supplemented according to Fig.15The measurements were performed using the assay configuration outlined in a. In short, to measure the exosome-bound Aβ42 population, we used native plasma samples directly, captured by Aβ42 and detected by CD63 (Aβ42+CD63+), without any vesicle purification or separation. To illustrate the presence of unbound Aβ42 populations in plasma samples, we used size exclusion filtration (size cutoff = 50 nm, Whatman) to remove large-size retentates. This is necessary because the assay configuration based on Aβ42 capture and Aβ42 detection cannot distinguish between unbound and total Aβ42. To measure unbound Aβ, we evaluated the plasma filtrate by Aβ42 capture and Aβ42 detection (Aβ42+Aβ42+). It is worth noting that this filtration was only implemented to prove the presence of an unbound Aβ42 population; it is unnecessary in a clinical setting where only more reflective exosome-bound Aβ42 is directly measured from a native plasma sample. To measure total Aβ42, we directly evaluated 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 a sample-matched negative control in which we incubated the same sample on a control sensor functionalized with an IgG isotype control antibody. All measurements were performed relative to the IgG control to account for nonspecific binding of the sample match.

[0217] Positron emission tomography (PET) imaging. After blood draw, subjects were scanned using a Siemens 3T Biograph mMR system (Siemens Healthineers) to simultaneously acquire PET and MR images. PET data were acquired 40-70 min after intravenous injection of 370 MBq of 11C-Pittsburgh compound B (PiB). MR data were acquired using a 12-channel head receiving coil 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 parcellations 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 parcellations. The mean SUVR for a specific region was calculated, and the overall mean SUVR for each patient was calculated by averaging the SUVRs for all brain regions.

[0219] Statistical analysis. All measurements were performed in triplicate and data are presented as mean ± SD. Significance testing was performed by two-sided Student's t-test. 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. Adjusted P < 0.05 values ​​were determined as significant. One-way paired ANOVA tests were used to determine analytical and biological coefficients of variation (i.e., within-group, between-group, and total). For clinical studies, correlation analysis was performed by linear regression to determine 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 plasma analysis of exosome-bound Aβ

[0222] One of the earliest pathological features of AD is the brain deposition of Aβ. These plaques are formed by clusters of abnormal amyloid protein fragments, mainly the hydrophobic splice variant Aβ42. Aβ protein is released into the extracellular space and can circulate in the blood. Also found in the extracellular space, exosomes are nanoscale membrane vesicles secreted by mammalian cells through the fusion of multivesicular endosomes with the plasma membrane. During the biogenesis of this exosome, glycoproteins and glycolipids are incorporated into the invaginated plasma membrane and are sorted into newly formed exosomes10,11. Through these surface markers, exosomes can associate and bind to extracellular Aβ protein (Figure 1a). Multimodal characterization of extracellular vesicles derived from neuronal sources (SH-SY5Y cells) confirmed their exosomal 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 (Figures 1b and Figure 5 ).

[0223] To evaluate exosome-Aβ association, an APEX platform for amplified, multi-parameter profiling of exosome molecular colocalization was developed. The system measures transmission SPR through a periodic array of plasmonic nanoholes patterned in a double-layer photonic structure and uses in situ enzymatic conversion to rapidly grow insoluble optical products on bound exosomes (Figure 1c). To compensate for APEX enzymatic deposition (occurring on top of the sensor), size-matched plasmonic nanoholes are patterned in the coupled double-layer photonic system to illuminate the exosomes via backside illumination (away from enzyme activity, Fig. 20 ) enhanced SPR measurements. The resulting enzymatic deposition not only stably altered the refractive index for SPR signal amplification, as indicated by a red shift in the transmission spectrum (spectral shift Δλ, Figure 1d), but was also spatially confined for molecular colocalization analysis. Scanning electron micrographs of sensor-bound exosomes before and after APEX amplification confirmed the localized growth of optical deposits 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 the measurements were 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 ) Sensor microarrays are fabricated 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 profiling

[0226] Enzymatic APEX amplification was developed for the first time. A series of sensor functionalizations, namely antibody conjugation, exosome binding, enzyme labeling, and optical product amplification, were performed, and the stepwise total spectral shift (accumulated 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 neural cells (SHSY5Y). To promote local deposition of the insoluble optical product, horseradish peroxidase was introduced as a cascade enzyme and was used to catalyze the conversion of its soluble substrate (3,3'-diaminobenzidine tetrahydrochloride). The sensor-bound vesicles were enzymatically labeled using another anti-CD63 antibody. Although the enzymatic labeling did not induce any significant spectral changes, the formation of the optical product resulted in a signal enhancement of ∼400%. In contrast, control experiments performed with an IgG isotope control antibody showed minimal background changes ( Fig. 9 Importantly, this SPR signal amplification is strongly associated with an increase in the areal coverage of highly localized optical deposits, as confirmed by scanning electron microscopy ( Figure 2 b).

[0227] To compensate for enzymatic amplification (which occurs on the top of the sensor), the APEX sensor design was optimized to improve its analytical performance and stability. Fig. 20 ), the double-layer plasmon structure of APEX enables SPR excitation via backside illumination ( Figure 2 c). The new optimized design not only exhibits strong transmission SPR through back illumination ( Fig.10 ac), and also showed analytical stability ( Fig.10 d), which may be due to reduced direct incident illumination on enzyme activity (i.e., temperature fluctuations). The APEX assay was further established by optimizing enzyme substrate concentration and reaction duration ( Figure 2 d). With continuous backside illumination, real-time spectral changes associated with different substrate concentrations were monitored and significant signal amplification was achieved in <10 min, allowing the entire APEX workflow to be completed in <1 h.

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

[0229] Using the APEX platform, we have further developed methods to analyze multiple markers associated with neurodegenerative diseases. Figure 2 f) Specific assays were established for: amyloid beta (Aβ42), amyloid precursor protein (APP), alpha-synuclein (α-syn), close homolog L1 (CHL1), insulin receptor substrate 1 (IRS-1), neural cell adhesion molecule (NCAM), and tau protein. Importantly, by further developing the APEX assay workflow ( Fig.11 a), the platform showed signal amplification capability to detect extra- and intra-vesicular proteins as well as exosomal miRNAs ( Fig.11 b) All detection probes used for assay development are listed 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. In order to simulate the different stages of amyloid seeding and fibrillation, Aβ42 aggregates of different sizes were prepared, which are the main component of amyloid plaques. The degree of clustering was varied to form Aβ42 aggregates of different sizes (Figure 3a, see Methods for experimental details), and their spherical morphology and unimodal size distribution were confirmed by transmission electron microscopy and dynamic light scattering analysis (Figure 3b). It is also important to note that larger Aβ42 aggregates showed a strong tendency to form fibril structures ( Fig.12 ).

[0232] To determine the kinetics of exosome-Aβ association, prepared Aβ42 aggregates were immobilized on the APEX platform and the sensor was incubated with equal concentrations of neuronal exosomes (Figure 3c). In contrast, in a control experiment, bovine serum albumin (BSA) aggregates of similar size were prepared and characterized ( Fig.13 ). As demonstrated by measuring real-time exosome binding, exosomes associate more strongly with Aβ42 aggregates, regardless of exosome size, compared to similarly sized BSA controls (Fig. 3d). More importantly, vesicles exhibited significantly higher binding affinity (>5-fold) for larger Aβ42 aggregates (Fig. 3d, right) compared to their binding affinity for smaller Aβ42 aggregates (Fig. 3d, left). All affinities were normalized to Aβ42 aggregate surface area and performed relative to the corresponding BSA controls (see Methods for details).

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

[0234] Brain plaque burden revealed by blood exosome-bound Aβ

[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 burden. To test this hypothesis, different APEX assays were developed using different antibodies to evaluate different populations of circulating Aβ42 from clinical blood samples ( Fig.15 a). Specifically, to characterize the Aβ42 population associated with exosomes, the 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 ( Fig.15 bc), also reflects functional relevance: as the association between pre-fibrillar Aβ aggregates and exosomes is enhanced, the associated CD63 signal can be considered as a surrogate measure of the relative amount of Aβ42 in pre-fibrillar versus total circulating Aβ42. To account for the presence of unbound Aβ42 populations, large retentates (e.g., exosomes) were removed from plasma using size exclusion filtration prior to measuring Aβ42 in plasma filtrate. Finally, to measure total circulating Aβ42, native plasma was evaluated by direct Aβ42 enrichment and Aβ42 detection.

[0236] Next, we conducted a feasibility clinical study to address the following key questions: (1) Can APEX measure circulating Aβ42 directly from blood samples? (2) How do different blood-derived Aβ42 populations correlate with brain plaque burden? (3) Can specific populations of circulating Aβ42 distinguish different clinical groups?

[0237] To achieve these objectives, 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 of vascular dementia (VaD, n=9) and neurovascular damage (i.e., vascular mild cognitive impairment, VMCI, n=12; and acute stroke, n=12). All clinical information is shown in Table 2. All recruited subjects underwent blood sampling and APEX analysis. With the exception of patients with acute stroke, all subjects consented to simultaneous PET imaging of brain amyloid plaques. Plasma samples were collected immediately prior to PET imaging with the Pittsburgh compound B (PiB) radiotracer. PET imaging showed widespread brain plaque burden ( Figure 4 a), and demonstrated regional changes in the brain ( Table 2 ), consistent with other published clinical studies.

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

[0239]

[0240]

[0241] AD: Alzheimer's disease, MCI: mild cognitive impairment, NCI: no cognitive impairment, VaD: vascular dementia, VMCI: vascular mild cognitive impairment.

[0242] Using the developed APEX assay ( Fig.11 a), we evaluated the 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 exosomal markers (i.e., CD63, CD9, and CD81) and neuronal markers (i.e., NCAM, L1CAM, and CHL-1), suggesting that neuronal exosomes may constitute a substantial portion of the population ( Fig.16 a). Since unbound Aβ42 measurements were performed from plasma filtrates, we further characterized these filtrates and confirmed negligible vesicle numbers and minimal co-localization signals with exosome and neuronal markers ( Fig.16 bc). When correlated with whole body PET amyloid imaging, compared with unbound Aβ42 ( Figure 4 b, middle, R 2 =0.0193) or total Aβ42 ( Figure 4 b, right, R 2 =0.1471), the exosome-bound Aβ42 measurement showed the best correlation ( Figure 4 b, left, R 2 =0.9002). Interestingly, unlike the poor and negative correlation shown by total Aβ42 measurements (as shown in this study and other published reports), the associated CD63 measurements from the exosome-bound Aβ42 population showed a high correlation and positive association with PET imaging of brain amyloid plaques. We attribute this finding to similar binding preferences of exosomes and PET tracers for Aβ42: (1) exosomes showed enhanced association with prefibrillar Aβ42 aggregates, particularly larger aggregates that are prone to fibril formation (Figure 3d); and (2) PET tracers bound tightly to larger amyloid fibrils but little to smaller aggregates. Notably, this superior correlation also demonstrated brain region specificity; exosome-bound Aβ42 measurements showed increased expression of CD63 in the cingulate gyrus (an area affected by early AD, Fig.17 a) Stronger correlation with brain plaque load than in the occipital lobe (the area affected in late AD, Fig.17 b).

[0243] APEX analysis of only exosome-bound Aβ42, but not of the unbound or total Aβ42 population, showed good specificity in distinguishing clinical diagnoses. Figure 4 d). In particular, exosome-bound Aβ42 measurement could distinguish not only AD clinical groups (i.e., AD and MCI, P < 0.01) but also other healthy and clinical controls (P < 0.0001, Student's t-test). This demonstrated specificity is comparable to that of PET brain amyloid imaging in distinguishing different clinical groups ( Fig.18 On the other hand, nanoparticle tracking analysis of plasma extracellular vesicles did not reveal any significant differences in vesicle size or concentration across all clinical groups ( FIG. 19 ).

[0244] Example 2

[0245] AD is the most common severe dementia. Due to its complex and progressive neuropathology, early detection and timely intervention are critical for the success of disease-modifying therapies. Despite the strong interest in finding serological biomarkers for AD, their development has been plagued by several challenges. First, unlike their counterparts in cerebrospinal fluid, circulating pathological AD molecules are present at much lower concentrations. Plasma Aβ levels are often close to the detection limit of conventional ELISA; this limitation may have led to multiple conflicting findings in published reports. Second, there is little correlation between plasma Aβ analysis and brain plaque deposition, the earliest pathological hallmark of AD. One possible reason for this may stem from the different measurement methods. The PET imaging probes commonly used to determine brain amyloid burden preferentially measure insoluble fibril deposition, whereas conventional ELISA measures soluble Aβ in plasma. In addition, the potential relevance of blood-based measurements to brain pathology may have been obscured by previous whole blood measurements. However, this discrepancy warrants a more fundamental question—whether there is a subpopulation of circulating Aβ protein that better reflects fibrillary pathology in the brain.

[0246] A dedicated analytical platform (APEX) was developed for multiparametric analysis of exosome-bound Aβ, unbound Aβ, and total Aβ directly from plasma to distinguish different populations of circulating Aβ. Specifically, it exploits new advances in sensor design, device fabrication, and assay development to achieve enhanced optical performance and detection capabilities ( Fig. 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, a state-of-the-art manufacturing process for large-scale precise nanopatterning. These advances have driven the APEX technology to achieve 1) improved optical performance (i.e., enhanced transmission intensity to enable SPR detection via bidirectional light 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 protein and RNA targets), but also facilitates co-localization analysis of exosomes for multi-parameter population studies, as insoluble deposits are only formed locally when multiple targets are found simultaneously in exosomes. With these combined advances, 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 protofibrillar Aβ, a key building block of protofibrillar amyloid plaques. We further identified and quantified a subpopulation of exosome-bound amyloid (CD63+Aβ42+) in clinical plasma samples from different clinical populations (i.e., AD, MCI, cognitively normal controls, and clinical controls of other neurodegenerative and neurovascular diseases) and found that the subpopulation was highly correlated with brain amyloid plaque load.

[0247] Evaluation of different populations of circulating Aβ may lead to a paradigm shift in AD research and clinical care. Increasing evidence suggests that prefibrillar Aβ aggregates may be toxic drivers of AD neurodegeneration. Their preferential association with exosomes and recent findings on the enrichment of exosomal markers in human amyloid plaques not only elucidate possible novel mechanisms of plaque seeding but also suggest the importance of exosome-bound Aβ as a circulating biomarker more reflective of complex AD pathology. Therefore, it is foreseeable that this study could complement other preclinical and clinical studies, including those on technology development and biomarker refinement. In terms of technology development, for example, while IP-mass spectrometry enables unbiased molecular screening and is very valuable for biomarker discovery, especially in detecting different molecular isoforms and variants (e.g., (APP)669–711 and Aβ1–40), APEX technology provides rapid and sensitive readouts from native plasma samples without the extensive sample processing typically required for mass spectrometry measurements, and is therefore suitable for targeted clinical measurements. In terms of biomarker improvements, as demonstrated in the current study, analysis of distinct circulating Aβ populations could reveal novel correlates previously masked by whole blood measurements and advance future blood-based clinical management of AD. Importantly, with more than 400 clinical trials currently ongoing for AD, further envisioned approaches that could enhance the developed approach to redefine current standards of patient care. With additional technical innovations, such as on-chip exosome processing, combinatorial analysis of other AD markers, and validation in longitudinal clinical cohorts, the developed technology could provide integrated capabilities to facilitate minimally invasive early detection, molecular stratification, and serial monitoring, all of which are critical for objective evaluation of disease-modifying therapies at different stages of clinical trials.

[0248] Example 3

[0249] This example demonstrates that incubation of amyloid aggregates with an inhibitor reduces spontaneous protein aggregation.

[0250] method

[0251] Aggregate size determination. The kinetic diameter of amylin and BSA aggregates was determined by dynamic light scattering analysis (Zetasizer Nano ZSP, Malvern). 3×14 measurements were performed 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 quality of the sample for size determination.

[0252] Protein aggregation.Freeze-dried starch-forming protein is resuspended in NaOH (60mM, 4°C), ultrasonically treated, and the pH value is adjusted to pH 7.4 in PBS. Protein is filtered immediately through a 0.2 μm membrane filter (Millipore), and the filtrate is used as a small initial aggregate. In order to prepare larger aggregates, protein is treated as described above, and incubated for 1 h under stirring to induce further aggregation. For the treatment with inhibitors, 10 μM of inhibitors (e.g., methylene blue) are added to the protein before incubation. Aggregate size determination is performed at the end of incubation.

[0253] Optical analysis. For experimental analysis, a tungsten-halogen lamp (StockerYale Inc.) was used to illuminate the back of the APEX sensor 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 in a closed box at room temperature to eliminate ambient light interference. The transmitted light intensity was recorded in counts versus wavelength. For spectral analysis, the spectral peaks were determined using a customized R program by fitting the transmission peak using a local regression method. All fits are done locally. That is, for the fit at point x, points near point x are used for fitting and weighted by their distance from x. Compared with fitting by multi-order polynomial curves, this method can eliminate the result changes caused by the number of data points analyzed and the data range. All spectral shifts (△λ) are determined as changes in the peak of the transmission spectrum and are calculated relative to appropriate control experiments.

[0254] Characterization of exosome-protein association. The prepared protein aggregates (Aβ42 and BSA control) were used for surface functionalization of APEX sensors by EDC / NHS coupling as described above. Unbound protein aggregates were washed away with PBS. The amount of conjugated protein was measured from the resulting transmission spectrum shift. We used this information to determine the number of conjugated protein aggregates and their associated total protein surface area for exosome binding (see below for details) to normalize the binding affinity. After surface functionalization with protein aggregates, exosomes were introduced into the sensor. Spectral changes were measured every 3 s over a total duration of 480 s to construct a real-time kinetic sensorgram. Exosome association kinetics and binding affinity of protein aggregates of different sizes were determined.

[0255] Taking into account the size differences of protein aggregates and the exponential decay of sensitivity associated with SPR with increasing distance from the sensor surface, the total surface area of ​​the conjugated aggregates interacting with the exosomes was calculated using the following formula:

[0256]

[0257] Where S is the signal, z is the distance from the sensor surface, E is the electric field when z = 0 and is a 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.

[0258] All protein aggregates were approximately spherical, with r determined by dynamic light scattering analysis. We used the above formula to determine the number of protein aggregates conjugated to the sensor and their respective total surface area to estimate the number of available binding sites for interaction with exosomes. All exosome binding data (Δλ) were normalized to their respective protein binding sites. Normalized Aβ42 binding data were performed relative to a similarly sized BSA control and fitted to determine the binding affinity constant K D .

[0259] miRNA profiling. Exosome lysates were incubated with biotinylated RNA probes, followed by RNA dual capture and APEX signal amplification on p19-functionalized APEX sensors.

[0260] result

[0261] The pathology of several neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis) involves the aggregation of misfolded amyloid proteins. Therapies targeting protein aggregation involve various strategies to clear aggregated amyloid proteins, including dissociating amyloid aggregates or inhibiting amyloid aggregation. Potential candidate molecules that have been shown to be effective in reducing the number of amyloid aggregates and therefore useful for disease-modifying therapies include methylene blue, leucomethylthioninium 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)phenylvinylbenzene (BSB), Chicago Sky Blue 6B, cyclodextrin, daunomycin hydrochloride, dimethyl yellow, direct red 80, 2,2-dihydroxybenzophenone, hexadecyltrimethyl bromide ammonium (C16), hemin chloride, heme, indomethacin, juglone, resorcinol 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, rolytetracycline, quinacrine dihydrochloride, thioflavin S, ThT and trimethyl (tetradecyl) ammonium bromide (C17), diallyl tartaric acid, eosin Y, fenofibrate, cuproline, nystatin, octadecyl sulfate and rhodamine B.

[0262] Since no animal model has been shown to exhibit accurate pathology that reflects AD pathology in the human brain, we used in vitro experiments to model the effects of disease-modifying treatments in inhibiting amyloid protein aggregation. The initial size of the amyloid protein was confirmed by dynamic light scattering analysis before incubating aliquots of the protein with or without inhibitors. In the absence of inhibitors, the size of the protein aggregates grew with increasing incubation time due to spontaneous aggregation of the amyloid protein. However, in the presence of inhibitors, the increase in size due to spontaneous protein aggregation was minimal, resulting in the formation of smaller protein aggregates.

[0263] After incubation, amyloid-forming proteins were functionalized on the sensor chip surface prior to incubation with neuronal exosomes. Preferential association of neuronal exosomes with larger protein aggregates was observed, as indicated by differences in binding affinity, and demonstrated reduced binding to smaller protein aggregates treated with inhibitors. Because exosome association with proteins can serve as a surrogate for biophysical and / or biochemical properties of proteins (properties that are affected by disease-modifying therapies), the APEX platform enables efficacy assessment of disease-modifying therapies.

[0264] Having described several embodiment configurations, 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 present invention. In addition, several steps may be taken before, during, or after considering the above factors.

[0265] All publications, serial numbers, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.

[0266] Exemplary embodiments of the present disclosure

[0267] In some aspects and embodiments, the present invention describes:

[0268] A sensor chip comprises 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.

[0269] In some embodiments, the conductive layer is gold, silver, aluminum, sodium, indium, or titanium.

[0270] In some embodiments, the membrane support layer is silicon nitride or silicon dioxide.

[0271] In some embodiments, the pores have a diameter of about 150 nm to 450 nm.

[0272] In some embodiments, the holes are arranged periodically.

[0273] In some embodiments, the pores have a periodicity of about 250 nm to about 650 nm.

[0274] In some embodiments, the first recognition molecule is immobilized on the surface of the conductive layer.

[0275] In some embodiments, the aperture is 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.

[0276] In some embodiments, the conductive layer and membrane support layer are disposed on a substrate having voids formed therein in regions adjacent to the plurality of holes such that illumination of the conductive layer and / or membrane support layer in any direction produces surface plasmon resonance.

[0277] In another aspect, the present invention describes an imaging system comprising a light source, a detector, and a sensor chip as described in any one of 1-9, wherein the light source is configured to illuminate the sensor chip, and the detector is positioned to detect light transmitted through the sensor chip.

[0278] In another aspect, the present invention describes a method for 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 arranged so that illumination of the conductive layer and / or the top membrane support layer produces surface plasmon resonance.

[0279] In some embodiments, the method includes: 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).

[0280] In some embodiments, the method further includes 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 photolithography; 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 a 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 membrane support layer.

[0281] 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 a sensor chip described in 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 to the analyte, 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.

[0282] In some embodiments, the second recognition molecule is specific for the analyte, the second recognition molecule is coupled to a signal amplification moiety, and the signal amplification moiety is capable of inducing the formation of insoluble aggregates having increased optical density relative to the captured analyte.

[0283] In some embodiments, the second recognition molecule is fused to the signal amplification moiety.

[0284] In some embodiments, the signal amplification moiety is an enzyme.

[0285] In some embodiments, the enzyme is horseradish peroxidase.

[0286] In some embodiments, the method further comprises contacting the enzyme with an enzyme substrate.

[0287] In some embodiments, the signal amplification moiety is a secondary antibody capable of binding a second recognition molecule.

[0288] In some embodiments, a first recognition molecule is immobilized on the surface of a surface plasmon resonance sensor chip, wherein the first recognition molecule is capable of capturing an analyte on the sensor chip surface.

[0289] In some embodiments, the analyte is an exosome-bound biomarker.

[0290] In some embodiments, the method includes detecting two or more analytes that co-localize in a sample.

[0291] In another aspect, the present invention describes a kit comprising the sensor chip of the present invention.

[0292] In some embodiments, the kit includes a second recognition molecule for detecting the analyte captured on the sensor chip surface.

[0293] In some embodiments, the second recognition molecule is coupled to a signal amplification moiety, and the signal amplification moiety is capable of inducing the formation of insoluble aggregates having increased optical density relative to the captured analyte.

[0294] 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 a 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, wherein the second recognition molecule is specific to the analyte, and increased binding of the second recognition molecule compared to a control sample or a control subject indicates that the subject suffers from a neurodegenerative disease.

[0295] In some embodiments of the method, the second recognition molecule is coupled to a signal amplification moiety, and the signal amplification moiety is capable of inducing the formation of insoluble aggregates having increased optical density relative to the captured analyte.

[0296] In some embodiments, the analyte is an exosome-associated biomarker.

[0297] In some embodiments, the analyte is an exosome-associated biomarker or a biomarker contained in exosomes.

[0298] 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 thereof and related complexes.

[0299] In some embodiments, the Aβ is Aβ42, Aβ40, Aβ39, or Aβ38.

[0300] 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.

[0301] In some embodiments, the method further comprises treating a subject found to have a neurodegenerative disease.

[0302] In some embodiments, the treatment comprises administering to the subject a therapeutically effective amount of one or more drugs or a combination thereof.

[0303] In some embodiments, the drug is selected from a cholinesterase inhibitor, such as donepezil, rivastigmine, or galantamine; an NMDA receptor antagonist, such as memantine; a combination of a cholinesterase inhibitor and an NMDA receptor antagonist, such as a combination of donepezil and memantine; a BACE1 inhibitor, such as AZD3293; an antibody, such as aducanumab; or an anti-tau drug, such as TRx0237 (LMTX).

[0304] In some embodiments, the drug is selected from methylene blue, leucomethylthioninium 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)phenylvinylbenzene (BSB), Chicago sky blue 6B, cyclodextrin, daunomycin hydrochloride, methyl yellow, direct red 80, 2,2-dihydroxybenzophenone, hexadecyltrimethylammonium bromide (C16), hemin, Heme, indomethacin, juglone, resorcinol 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, rolytetracycline, quinacrine dihydrochloride, thioflavin S, ThT and trimethyl (tetradecyl) ammonium bromide (C17), diallyl tartaric acid, eosin Y, fenofibrate, cuproline, nystatin, octadecyl sulfate and rhodamine B.

Claims

1. A kit comprising: A sensor chip, comprising: a conductive layer disposed on the membrane support layer; a plurality of holes extending through the conductive layer and the membrane support layer, the plurality of holes being arranged so that illumination of the back side of the membrane support layer produces surface plasmon resonance; as well as a first recognition molecule that specifically binds to a first analyte, wherein the first recognition molecule is immobilized on the surface of the conductive layer; and A second recognition molecule that specifically binds to the first analyte or to a second analyte associated with the first analyte, wherein the second recognition molecule is coupled to a signal amplification portion, and wherein the signal amplification portion is capable of inducing the formation of insoluble aggregates with increased optical density relative to the first analyte when the first analyte is captured by the first recognition molecule, and wherein the signal amplification portion is an enzyme.

2. The kit according to claim 1, wherein the first analyte comprises a protein, a nucleic acid, or a combined association thereof or an organized subunit thereof.

3. The kit of claim 1, wherein the first analyte is amyloid, and wherein the second recognition molecule binds to an exosome-associated biomarker.

4. The kit according to claim 3, wherein the amyloid protein is amyloid β, amyloid precursor protein (APP), α-synuclein (α-Syn), insulin receptor substrate 1 (IRS-1), NCAM, tau protein, apolipoprotein (APOE), SOD1, TDP-43, bassoon or fibronectin. The kit according to claim 4 , wherein the amyloid protein is Aβ42, Aβ40, Aβ39 or Aβ38.

6. The kit according to claim 3, wherein the exosome-associated biomarker is CD9, CD63, CD81, ALIX, TSG101, Flotilin-1, LAMP-1, HSP70 or HSP90.

7. The kit according to claim 5, wherein the exosome-associated biomarker is CD9, CD63 or CD81.

8. The kit of claim 1, wherein the conductive layer is gold, silver, aluminum, sodium, indium or titanium.

9. The kit of claim 1, wherein the membrane support layer is silicon nitride.

10. The kit of claim 1, wherein the pores have a diameter of about 150 nm to about 450 nm. The kit according to claim 1 , wherein the holes are arranged periodically.

12. The kit of claim 11, wherein the pores have a periodicity of about 250 nm to about 650 nm.

13. The kit according to claim 1, wherein The aperture is arranged such that the decay length of the surface plasmon resonance generated upon illumination is approximately equal to the diameter of the first analyte.

14. The kit according to claim 1, wherein The conductive layer and the membrane support layer are disposed on a substrate that enables irradiation of the membrane support layer to generate surface plasmon resonance.

15. The kit according to claim 1, wherein the second recognition molecule is fused to the signal amplification portion.

16. The kit of claim 1, wherein the enzyme is horseradish peroxidase.

17. The kit according to claim 1, wherein the kit further comprises a substrate for the enzyme.

18. The kit according to claim 1, wherein the enzyme is coupled to a secondary antibody capable of binding to the second recognition molecule.

19. An imaging system comprising a light source, a detector, and the sensor chip according to claim 1 and a second recognition molecule, wherein: The light source is arranged to illuminate the film support layer of the sensor chip, and the detector is positioned to detect light that transmits the sensor chip.

20. A method for detecting a first analyte in a sample, the method comprising: Capturing the first analyte onto the conductive layer surface of the sensor chip according to any one of claims 1 to 18, wherein the capturing comprises binding the first analyte to the first recognition molecule according to any one of claims 1 to 18; and Detecting binding of the second recognition molecule of any one of claims 1-18 to the first analyte or the second analyte associated with the first analyte, wherein increased binding of the second recognition molecule compared to a control sample indicates the presence of the first analyte in the sample.

21. The method of claim 20, wherein the method further comprises contacting the enzyme with a substrate for the enzyme.

22. The method of claim 20, wherein detecting binding of the second recognition molecule comprises analyzing local measurements of one or both of the first analyte and the second analyte.

23. The method of claim 20, wherein the method comprises detecting two or more analytes that co-localize in a sample.

24. Use of the sensor chip according to any one of claims 1 to 18 in preparing a kit for detecting a neurodegenerative disease or amyloidosis in a subject.

25. The method of claim 24, wherein the neurodegenerative disease is Alzheimer's disease, mild cognitive impairment, Parkinson's disease, amyotrophic lateral sclerosis, multiple sclerosis, progressive supranuclear palsy, or tauopathy.

26. The method of claim 24, wherein the neurodegenerative disease is dementia or vascular mild cognitive impairment.

27. The method of claim 24, wherein the neurodegenerative disease is vascular dementia.

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