Exosome lateral flow quantitative detection method, biosensor and kit

Abstract

The present invention relates to the field of biomedicine, and in particular to a method, biosensor, and kit for quantitative detection of exosomes by lateral flow chromatography. The method quantitatively analyzes serum exosomes by combining a paper lateral flow strip (LFS) biosensor with multivariate spectral unmixing using surface-enhanced Raman spectroscopy (SERS). Cancer exosomes in human serum samples are quantified, and with the aid of a multivariate curve resolution-partial least squares fitting method, accurate non-invasive breast cancer typing and surgical efficacy assessment are performed. Clinical trials have shown that surgical treatment of breast cancer patients is accompanied by a significant reduction in SKBR3 or MCF exosomes in the serum, indicating that the combination of the SERS-LFS biosensor and multivariate spectral unmixing technology, leveraging its powerful ability to quantify exosomes in clinical samples, has great potential for breast cancer subtyping and treatment monitoring.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and in particular to a quantitative detection method for exosomes by lateral flow chromatography, a biosensor and a kit. Background Art

[0002] The quantitative analysis of circulating biomarkers present in biological fluids, commonly known as liquid biopsy, has the potential to be used in the clinic for minimally or non-invasive diagnosis and management of cancer, which is mainly driven by the rising incidence and prevalence of cancer. A large number of biological materials have been used for liquid biopsy, including circulating tumor cells, circulating proteins, cell-free nucleic acids, metabolites and tumor extracellular vesicles. Among them, exosomes are nanoscale (30-150 nm) extracellular entities that are produced by almost all types of cells and contain multiple key components inherited from their parent cells, including proteins, nucleic acids, metabolites, lipids, etc. More importantly, exosomes are characterized by high abundance (>10 9 particles / ml) and excellent stability in all body fluids. These outstanding features together make exosomes an ideal choice for non-invasive diagnosis and prognosis of cancer, far superior to the traditional circulating biomarkers mentioned above. Targeting the surface membrane proteins of exosomes is the main strategy for exosome-based liquid biopsy, which is widely adopted in Western blot analysis, enzyme-linked immunosorbent assay, flow cytometry, and emerging optical and electrochemical methods. The detection principles of these methods mostly involve the specific interaction of a single surface exosome biomarker or multiple exosome biomarkers with biological recognition elements (such as antibodies, inducers). However, the detection performance of these methods is affected by the co-expression of protein biomarkers in exosomes from different sources and their subtle differences. Therefore, there is an urgent need for alternative quantitative tools for exosomes directly in complex biological samples, which can be used for minimally invasive or non-invasive cancer diagnosis.

[0003] The ideal strategy for exosome-based cancer diagnostics is to quantify cancer-specific exosomes, rather than surface exosomal biomarkers. Because exosomes contain multiple surface membrane proteins, individual exosomes of a specific origin can be defined by a linear combination of their exosomal surface proteins. Existing technologies have developed optical and electrochemical biosensing platforms for the simultaneous quantification of multiple cancer-specific biomarkers in clinical samples, with high sensitivity and excellent specificity. However, few studies have focused on the simultaneous quantification of specific biomarkers expressed on the same exosome. The difficulty in quantifying exosomes in clinical samples lies in the precise definition of individual exosomes using their molecular characteristics.

[0004] Raman spectroscopy is a powerful analytical tool for the compositional analysis of biological samples, such as exosomes. It enables omniscient molecular profiling of exosomes with molecular specificity, non-destructiveness, water insensitivity, excellent multiplexing capabilities, and minimal sample preparation. Multivariate spectral unmixing analysis of Raman spectroscopy can identify and quantify pure components in complex biological samples. Indeed, multivariate spectral unmixing analysis has been successfully used to determine the molecular composition and spatial distribution in various hyperspectral images. Summary of the Invention

[0005] Based on the above-mentioned defects of the prior art, the present invention provides a surface-enhanced Raman spectroscopy (SERS) for quantifying serum exosomes by combining a paper lateral flow strip (LFS) biosensor with multivariate spectral unmixing analysis, rather than quantifying exosomal proteins. First, SERS spectra of two different exosomes, SKBR and MCF exosomes, derived from SKBR-3 and MCF-7 breast cancer cells, were constructed, and then used as basic spectra for spectral unmixing-assisted quantitative analysis of exosomes. The SERS-LFS biosensor of the present invention is capable of absolute quantification of serum exosomes, with detection limits of both exosomes as low as ~10 6 The present invention further illustrates the application of this strategy in the quantitative dual detection of serum exosomes directly from breast cancer patients. The results showed that patients with HER2+ and Luminal A breast cancer who did not undergo surgery were enriched in SKBR and MCF exosomes in their serum, respectively. Surgical treatment of these breast cancer patients was accompanied by a significant decrease in SKBR or MCF exosomes in their serum. These results demonstrate that the combination of SERS-LFS biosensors and multivariate spectral unmixing technology, leveraging the powerful quantitative capabilities of exosomes in clinical samples, has great potential for breast cancer subtyping and treatment monitoring.

[0006] The present invention provides a quantitative surface-enhanced Raman spectroscopy-lateral flow strip (SERS-LFS) biosensor that quantifies cancer exosomes in human serum samples and, with the assistance of multivariate spectral unmixing, enables accurate non-invasive breast cancer typing and surgical outcome assessment. The design of the present invention is mainly based on the basic SERS spectra of single exosomes, and is used to directly quantify exosomes in serum samples. These spectra are constructed by weighted combinations of SERS spectra associated with their surface exosomal proteins. The unique advantage of the method of the present invention is that it quantifies exosomes that are characteristic of cancer molecular subtypes, rather than the quantification of surface exosomal biomarkers commonly implemented in previously reported methods. As a proof of concept, two different breast cancer cell lines, SKBR-3 and MCF cells, were selected, and cellular exosomes of SKBR-3 and MCF-7 cells (denoted as SKBR exosomes and MCF exosomes) were obtained, respectively. SKBR-3 and MCF cells display different expression levels of the surface exosomal proteins HER2 and MUC1, which have been widely used to model HER2+ and Luminal A breast cancer subtypes. Two spectrally distinct SERS probes—HER2 and MUC1 probes—with distinct spectral signatures were designed to specifically identify the exosomal proteins HER2 and MUC1. The SERS spectrum of a single exosome (SKBR or MCF exosome) was defined as the weighted sum of the SERS spectra of the HER2 and MUC1 probes. Leveraging the significant advantages of SERS, LFA, exosomes as cancer biomarkers, and multivariate spectral unmixing, the method of the present invention can quantitatively detect multiple exosomes in clinical samples with high sensitivity and selectivity, enabling accurate cancer diagnosis.

[0007] The first aspect of the present invention provides a method for quantitative detection of exosomes by lateral flow chromatography, the detection method comprising the following steps:

[0008] S1. Constructing surface-enhanced Raman spectra of single exosomes: SKBR-3 exosomes and MCF-7 exosomes were incubated in equimolar solutions of HER2 probe and MUC1 probe, respectively. SERS datasets from the spectra of SKBR-3 exosomes and MCF-7 exosomes were collected to obtain the base spectra of the two exosomes.

[0009] S2. Construct standard curves for individual exosomes as a function of concentration: Measure the Raman spectra of individual exosomes at different known concentrations, perform least squares fitting with the exosome base spectrum, and calculate the exosome loading, which is related to the exosome concentration. Construct standard concentration curves based on the loading.

[0010] S3. Obtain a Raman spectral dataset of the mixed sample to be tested and perform multivariate analysis and spectral unmixing on the data: Spectral unmixing is performed using a multivariate curve resolution-partial least squares fitting algorithm to obtain the loading of each exosome;

[0011] S4. Substitute the obtained loading value into the standard concentration curve to obtain the actual concentration of each exosome.

[0012] In the present invention, two breast cancer cell lines, SKBR-3 and MCF-7 cells, were selected to isolate cellular exosomes because they have different molecular characteristics and have been widely used to simulate HER2+ and Luminal A breast cancer subtypes.

[0013] Multivariate analysis and spectral unmixing of SERS data were performed in MATLAB R2022a (MathWorks). Spectral preprocessing was performed on the raw SERS data in the 600-1650 cm range to remove cosmic peaks and perform background subtraction. Spectral unmixing was first performed using the Multivariate Curve Resolving-Alternative Least Squares (MCR-ALS) algorithm to decompose the complex spectrum into pure component spectra with quantitative contributions, which can be used to reconstruct the SERS spectrum of the exosomes of interest. In the MCR-ALS model, the SERS spectrum of the mixture (Specmix) can be described as follows.

[0014]

[0015] Where Speci is the SERS spectrum of the ith component, with its contribution Ai (i = 1, 2, ..., n), and E represents spectral noise (experimental error). The number of components (n) present in the complex spectrum is determined by principal component analysis (PCA). In the MCR-ALS algorithm, convergence is achieved when the relative standard deviation of the residuals between the experimental and calculated values ​​for two consecutive iterations is <0.1%. The present invention utilizes MCR-ALS-based spectral unmixing to obtain component spectra and then reconstructs the SERS spectra of individual exosomes. Using the SERS spectra of all individual exosomes obtained above as the base spectrum, the present invention quantitatively determines the concentration of multiple exosomes in the complex matrix using standard least squares methods.

[0016] Statistical analysis was performed in MATLAB R2022a. All experiments were performed in triplicate unless otherwise stated. Results are presented as mean ± standard deviation (SD), and the statistical significance of differences was determined using the Wilcoxon rank-sum test.

[0017] As a preferred embodiment, the method for constructing the HER2 probe and the MUC1 probe specifically includes:

[0018] Gold nanostars were used as the proton substrate and silicon as the protective layer. They were encoded with two different Raman probe molecules, 5,5'-dithiobis(2-nitrobenzoic acid) and 4-mercaptobenzoic acid. The encoded products were further conjugated with HER2 detection aptamers and MUC1 detection aptamers to obtain HER2 and MUC1 probes.

[0019] Furthermore, the SERS probe preparation method includes the following steps: To produce spectrally distinct SERS probes, 10 μM of two different Raman molecules, DTNB and 4-MBA, were added to 15 mL of deionized water containing 0.77 μg / mL AuSts. After mixing at 25°C for 30 minutes under magnetic stirring, the reaction solution was centrifuged at 7000 rpm to remove unbound Raman molecules. AuSts modified with DNTP and 4-MBA were redispersed in 15 mL of deionized water, and 150 μL of ammonium hydroxide and 30 μL of MPTMS were added dropwise under magnetic stirring. The reaction was carried out at 30°C for 8 hours. The reaction solution was centrifuged and washed at least three times with deionized water to obtain two SERS nanoparticles, AuSt@DTNB@SiO2 NPs and AuSt@MBA@SiO2 NPs, encoded with DTNB and 4-MBA, respectively.

[0020] The SERS NPs obtained above were dispersed in 1.0 mL of ethanol containing 16 μL of TEPSA and 7.0 mg of mPEG-silane. After 4 hours of magnetic stirring, TEPSA / MPEG co-modified SERS NPs were obtained and washed with ethanol and then deionized water. The NPs were then dispersed in phosphate-buffered saline (PBS, pH 7.4) with 2.0 mg / mL of EDC and sulfo-NHS. The mixture was vortexed and maintained at 25°C under magnetic stirring for 30 minutes. The mixture was centrifuged at 4000 rpm, and the supernatant was discarded. The resulting particles were dispersed in 1.0 mL of PBS, pre-added with 10 μL of 100 mM K₂CO₃ and 7.0 μL of 0.1 nM aptamer (HER2 detection aptamer for DTNB-encoded SERS NPs and MUC1 detection aptamer for MBA-encoded SERS NPs). After incubation overnight at 4°C, 50 μL of 2.0 mg / mL BSA was added to prevent nonspecific protein adsorption. Finally, the reaction mixture was centrifuged at 4000 rpm and washed three times with PBS. The resulting particles were DTNB-encoded SERS NPs bound to the HER2 detection aptamer and MBA-encoded SERS NPs bound to the MUC1 detection aptamer, hereinafter referred to as the HER2 probe and MUC1 probe, respectively. The HER2 and MUC1 probes were stored at 4°C in 0.25 mL of elution buffer, which consisted of 0.5% BSA, 10% sucrose, 0.1% PVP, and 1.0% Tween-20 in PBS, to a final concentration of 260 μg / mL.

[0021] As a preferred embodiment, the DNA sequence of the HER2 detection aptamer is 5'-NH2-(CH2)6-TTT GGGCCG TCG AAC ACG AGC ATG GTG CGT GGA CCT AGG ATG ACC TGA GTA CTG TCC-3'.

[0022] As a preferred embodiment, the DNA sequence of the MUC1 detection aptamer is 5'-NH2-(CH2)6-TTT TTGCAG TTGATC CTT TGGATACCC TGG-3'.

[0023] As a preferred embodiment, the method for constructing gold nanostars specifically includes:

[0024] Step 1. Preparation of gold nanoseeds: A diluted aqueous solution of chloroauric acid, an aqueous solution of sodium citrate, and a sodium borohydride solution were stirred and reacted, and PVP was added and continued to stir to obtain a gold nanoseed solution;

[0025] Step 2. Preparation of gold nanostars: Mix HAuCl4 aqueous solution with PVP and DMF solutions, add gold nanoseed solution at appropriate times, and obtain gold nanostars after purification.

[0026] Furthermore, gold nanoseeds were prepared by diluting 0.5 mL of a 50 mM aqueous solution of chloroauric acid to 90 mL with water. Then, 2 mL of a 38.8 mM aqueous solution of sodium citrate was quickly added, followed by the slow addition of 1 mL of a freshly prepared sodium borohydride solution (sodium borohydride dissolved in a 38.8 mM aqueous solution of sodium citrate at a concentration of 0.075 wt%). The reaction solution was stirred at room temperature overnight. Subsequently, 5.0 g of PVP was added to 50 mL of the gold nanoseed solution, and the mixture was stirred at room temperature for 24 hours to obtain the desired gold nanoseed solution.

[0027] Next, gold nanostars were prepared: First, gold nanostars were synthesized using a seed-mediated growth method. In a typical synthesis, 82 μL of a 50 mM aqueous HAuCl₄ solution was added to 15 mL of DMF containing 1.5 g of PVP. Once the solution turned from light yellow to colorless, the PVP-capped gold seeds were immediately introduced to a final concentration of 57 nM. The reaction continued at room temperature for 3 hours, producing a dark blue solution indicating the formation of gold nanostars. The gold nanostars were purified by sequential centrifugation and washing with deionized water and ethanol at least twice, resulting in the gold nanostars being stored in deionized water at a concentration of 0.77 μg / mL.

[0028] A second aspect of the present invention provides a biosensor.

[0029] As a preferred embodiment, the biosensor includes at least a sample pad, a conjugate pad, a T-line, a C-line absorption pad and a bottom plate, wherein the T-line is loaded with a CD63 capture aptamer, the C-line is loaded with a HER2 capture aptamer and a MUC1 capture aptamer, and the conjugate pad is made of glass fiber material and is loaded with an equimolar ratio solution of a HER2 probe and a MUC1 probe bound to a detection aptamer.

[0030] Furthermore, in the SERS-LFS strip, the sample and conjugate pads, the NC membranes for T- and C-lines, and the absorption pads were assembled in an orderly manner on a plastic adhesive PVC substrate, with an overlap of 2.0 mm between each two adjacent pads. The distance between the T- and C-lines on the NC membrane was 6 mm.

[0031] Loaded CD63 (0.2 μg / mm ) capture aptamer on T line;

[0032] Line C was loaded with 0.2 μg / mm of HER2 and MUC1 capture aptamers at a fixed ratio of 1:1;

[0033] Conjugate pad: 250 μL of an equimolar mixture of HER2 and MUC1 probes at a total concentration of 50 μL / cm was evenly dispersed on the conjugate pad.

[0034] NC membrane pretreatment: T-line and C-line were pre-modified with 100 μg / mL streptavidin.

[0035] As a preferred embodiment, the DNA sequence of the CD63 capture aptamer is: 5'-biotin-TT CAC CCCACC TCG CTC CCG TGACAC TAATGC TA-3'.

[0036] As a preferred embodiment, the DNA sequence of the MUC1 capture aptamer is 5'-biotin-TT TTC CAGGGTATC CAAAGGATCAAC TGC-3'.

[0037] As a preferred embodiment, the DNA sequence of the HER2 capture aptamer is 5'-biotin-TT TTG CAGTTGATC CTT TGGATACCC TGG-3'.

[0038] As a preferred embodiment, the NC membrane is of type MDI CNPC-SS12, WHATMAN FF120HP or PALLVIVID 90.

[0039] As a preferred embodiment, the biosensor further comprises a buffer system for dissolving the sample.

[0040] The buffer system may include Tween-20. In the buffer system, the mass percentage of Tween-20 may be 0.25-2.0%, such as 0.25%, 0.5%, 1.0%, 1.5% and 2.0%, preferably 1.0%.

[0041] As a preferred embodiment, in the T-line, the loading amount of the CD63 capture aptamer can be 0.1-0.4 μg / mm, such as 0.1 μg / mm, 0.2 μg / mm, 0.3 μg / mm and 0.4 μg / mm.

[0042] As a preferred embodiment, in the conjugate pad, in the equimolar solution of the HER2 probe and the MUC1 probe, the total concentration of the SERS probe may be 38-390 μg / mL, such as 38, 75, 130, 260 and 390 μg / mL.

[0043] The SERS-LFS biosensor was tested for cellular exosomes and human serum exosomes. Following optimization experiments, the analytical performance of the SERS-LFS biosensor was evaluated for the detection of cellular exosomes and serum exosomes, respectively. 250 μL of an equimolar mixture of HER2 and MUC1 probes at an optimal total concentration was uniformly dispersed onto the conjugate pad at a rate of 50 μL / cm. T- and C-lines were premodified with 100 μg / mL streptavidin and then coated with a CD63 capture aptamer and an equimolar mixture of HER2 and MUC1 capture aptamers, respectively. The conjugate pad and treated NC membrane were dried at 37°C for 1 hour. The assembled pad was cut into 3.0 mm wide strips and stored in a desiccator at 25°C until use.

[0044] Cell exosome samples were prepared at varying concentrations by adding SKBR3 or MCF-7 exosomes to PBS or serum prepared with 50% FBS in PBS. For the detection of clinical serum exosomes, serum exosomes isolated directly from human serum samples were used. One hundred microliters of cell or serum exosomes were dropped onto the sample pad. After a 15-minute incubation, the test strip was subjected to SERS measurements. Each experiment was performed in triplicate, and the final result is the average of the three measurements.

[0045] The third aspect of the present invention provides a kit for quantitative detection of exosomes by lateral flow chromatography.

[0046] The fourth aspect of the present invention provides uses of the detection method, biosensor and kit in detecting target analytes.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. The present invention constructs two different exosome SERS spectra from SKBR-3 and MCF-7 breast cancer cells, respectively, and then uses them as the basic spectra for the quantitative analysis of exosomes without spectral mixing;

[0049] 2. The SERS-LFS biosensor prepared by the present invention can be used for absolute quantification of serum exosomes, and the detection limit of both exosomes is as low as 10 6 In clinical trials, surgical treatment of breast cancer patients was accompanied by a significant decrease in SKBR3 or MCF-7 exosomes in serum, indicating that the SERS-LFS biosensor, combined with its multivariate spectral unmixing technology and powerful quantitative ability for exosomes in clinical samples, has great potential for breast cancer subtype analysis and treatment monitoring.

[0050] 3. The detection method of the present invention can not only analyze two types of exosomes, but can also be used to quantitatively analyze multiple types of exosomes. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1-7 To construct the SERS spectrum of a single exosome. Figure 1 (a) (i) Western blot analysis of CD63, HER2, MUC1, and 3-phosphoglycerate dehydrogenase (GAPDH) in SKBR and MCF exosomes, and (ii) their expression levels relative to GAPDH. Figure 2 (b) Optical extinction spectra of synthesized AuSts, SERS NPs, and HER2 probes. Figure 3 (c) TEM image of SERS NPs composed of DTNB-encoded AuSts and a silicon protective layer. A schematic diagram of the 3D structure is also shown. Figure 4 (d) Zeta potential of synthesized AuSts, SERS NPs, and HER2 probes. Data are presented as mean ± sd (n = 3 independent experiments). Figure 5 Figure (e) shows (i) the structures of HER2 and MUC1 probes, (ii) their targeted binding to proteins HER2 and MUC1, and (iii) MCR-ALS spectroscopy to demix the complex SERS spectra of SKBR3 and MCF-7 exosomes into two component spectra (CHER2 and CMUC1), which are highly similar to the SERS spectra of HER2 and MUC1 probes. Figure 6 (f) SERS relative intensity (α / β) from MCR-ALS spectrum unmixing, Figure 7 Middle (g) Composite SERS spectra of individual SKBR and MCF exosomes (Specexosome), constructed by the weighted sum of the SERS spectra of the two bound probes. Specexosome = α-CHER2 + β-CMUC1, where α and β are the weights (abundances) of the HER2 and MUC1 protein biomarkers in the corresponding exosomes.

[0052] Figure 8-10 Optimize the test parameters of SERS-LFS biosensor. Figure 8 (a) (i) Schematic diagram of the SERS-LFS biosensor for quantitative detection of two different exosomes on one T-line, and (ii) the structures of the complex formed on the T-line when both SKBR3 and MCF-7 exosomes are present and (iii) the complex formed on the C-line. Figure 9 (b) Schematic diagram of spectral unmixing-assisted bicomplex detection for cancer diagnosis. Figure 10(c) Effects of the following factors on SERS-LFS performance: (i) mass percentage of Tween-20 in the running buffer; (ii) NC membrane type—SS12, FF120HP, and P90; (iii) loading of CD63 capture aptamer in the T-line (0.1, 0.2, 0.3, and 0.4 μg / mm); and (iv) total concentration of HER2 and MUC1 probes, fixed at a 1:1 ratio. Photographs of various test conditions are also shown in the corresponding panels. Data are presented as mean ± SD (n = 3 independent experiments), with individual circles representing experimental data for each measurement.

[0053] Figure 11-12 This is the quantitative SERS detection of exosomes in PBS. Figure 11 (a, b) (i) Photograph (left) and SERS image (right), (ii) different exosome concentrations (0, 1.0, 5.0, 10, 50, 100, 500, 1000 and 10000×10 7 The average SERS spectrum on the T line under the condition of (particles / ml), Figure 12 (iii) Corresponding calibration curves from the unmixed spectra of (a) SKBR3 exosomes and (b) MCF-7 exosomes in PBS. The SERS images in panel (i) were created using the unmixed S (red) or M (green) values. Data are presented as mean ± SD (n = 3 independent experiments).

[0054] Figure 13 Quantitative SERS detection of exosomes in serum. (a, b) (i) Different exosome concentrations (0, 5.0, 10, 50, 100 and 500×10 7 (ii) The corresponding calibration curves of the unmixed spectra of (a) SKBR3 exosomes and (b) MCF-7 exosomes in serum. Data are presented as mean ± sd (n = 3 independent experiments).

[0055] Figure 14-17 It is a quantitative double detection of serum exosomes. Figure 14 (a) Loading curves of two MCR components (MC1 and MC2); Figure 15 (b) Corresponding two-dimensional plot of S values ​​versus M values ​​for the SERS dataset of spectrally unmixed SKBR and MCF exosomes in serum at different ratios ([SKBR exosomes]:[MCF exosomes]: 0:6, 1:5, 2:4, 3:3, 4:2, 5:1, and 6:0) using MCR-ALS. Constructed SERS spectra of individual MCF exo (olive dashed curve) and SKBR exo (red dashed curve) are also shown along with MC1 and MC2 in panel a. Figure 16(c) The total exosomes were collected from different concentrations of exosomes (1.0, 5.0, 10, 50, 100 and 500×10 7 The ratio of S to M values ​​obtained by mixing exosomes in serum with 500 μg / mL (particles / mL). Figure 17 (d) Three-dimensional plot of S and M values ​​after spectral unmixing of SERS data of mixed exosomes with different total concentrations and different exosome ratios in serum.

[0056] Figure 18 Simultaneous quantification of serum SKBR3 and MCF-7 exosomes in clinical samples for breast cancer subtype and surgical outcome assessment. (a) Distribution of SKBR3 and MCF-7 exosome concentrations in various clinical samples from healthy individuals (n=15), patients with HER2+ breast cancer who underwent surgery (n=8) or underwent surgery (n=7), and patients with Luminal A breast cancer who underwent surgery (n=10) or underwent surgery (n=14). (b) Concentrations of SKBR3 and MCF-7 exosomes in clinical serum. Within the same group, the left side represents SKBR3 exosomes, and the right side represents MCF-7 exosomes. The first and second dashed lines represent the mean concentrations of SKBR exosomes and MCF exosomes, respectively, in serum from healthy individuals. Data are presented as mean ± SD, calculated from all clinical samples in each group. Individual circles represent experimental data for each clinical sample. P values ​​were calculated using the Wilcoxon rank sum test.

[0057] Figure 19 Isolation of cellular exosomes and serum exosomes. (a) SKBR and MCF exosomes were isolated from the cell culture media of SKBR-3 and MCF-7 cells, respectively, by standard size exclusion chromatography (SEC).

[0058] Figure 20 Characterization of exosomes from SKBR-3 and MCF-7 cells. (a, b) (i) TEM image and (ii) size distribution and particle image of (a) SKBR exosomes and (b) MCF exosomes measured by NTA.

[0059] Figure 21 Schematic diagram of the preparation of HER2 and MUC1 probes, which were individually encoded with DTNB and 4-MBA, respectively, using AuSts as the proton core and a silica protective layer as the outer layer.

[0060] Figure 22 SERS spectra of HER2 probe and MUC1 probe.

[0061] Figure 23Unmixing of the MCR-ALS spectra of SKBR and MCF efflux. (a) Unmixing of the MCR-ALS spectra of the composite SERS spectrum of SKBR and MCF into (a) a two-component (MC1 and MC2) spectrum, (b) a three-component (MC1, MC2, and MC3) spectrum, (c) a four-component (MC1, MC2, MC3, and MC4) spectrum, and (d) a five-component (MC1, MC2, MC3, MC4, and MC5) spectrum.

[0062] Figure 24 SEM images of the SERS-LFS biosensor T line (a) without MCF exos (negative control) and (b) with 5.0×10 9 Particles / mL in case of MCF exos (positive test).

[0063] The experimental materials of the present invention come from the following sources:

[0064] Chloroauric acid (HAuCl4-4H2O, 99.0% trace metal base) was purchased from Shanghai Siwei Chemical Technology Co., Ltd.

[0065] Polyvinylpyrrolidone (PVP, average MW ≈ 10 kg / mol), 1-(3-(dimethylamino)propyl)-3-ethylcarbodiimide hydrochloride (EDC), sulfo-N-hydroxysuccinimide (sulfo-NHS), 5,5′-dithiobis(2-nitrobenzoic acid) (DTNB, 98.0%), 4-mercaptobenzoic acid (4-MBA, 90.0%), Tween-20, sucrose, streptavidin, and casein sodium salt were purchased from Sigma-Aldrich.

[0066] (3-Mercaptopropyl)trimethoxysilane (MPTMS, 97.0%) was purchased from Aladdin Reagent Co., Ltd.

[0067] Dimethylformamide (DMF, anhydrous 99.8%) and sodium borohydride (NaBH4, >98.0%) were from Sinopharm Chemical Reagent Co., Ltd.

[0068] 3-(Triethoxysilyl)propylsuccinic anhydride (TEPSA) is provided by TCI.

[0069] Ammonium hydroxide (25-28%, v / v) was purchased from Shanghai MacLean Biochemical Co., Ltd.

[0070] Methoxypolyethylene glycol silane (mPEG-silane, average MW≈2 kg / mol) was purchased from Hunan Huateng Pharmaceutical Co., Ltd.

[0071] Bovine serum albumin (BSA) was provided by YankeGene.

[0072] Goat anti-mouse antibody was purchased from Hunan Maizhixin Biotechnology Co., Ltd.

[0073] All types of nitrocellulose membranes (NCs) (SS12, FF120HP, and P90, see Table 3 in the Supporting Information for details), sample pads, binder pads, absorbent pads, and polyvinyl chloride (PVC) adhesive-backed cards were provided by Shanghai Jiening Biotechnology Co., Ltd.

[0074] The aptamers used in the present invention were ordered from Shanghai Sangon Biotech Co., Ltd. Their sequences are shown below: CD63 capture aptamer (T line), 5'-biotin-TTT CAC CCC ACC TCG CTC CCG TGACAC TAA TGC TA-3'; MUC1 detection aptamer, 5'-NH2-(CH2)6-TTT TTG CAG TTGATC CTT TGGATA CCC TGG-3'; MUC1 capture aptamer (C line), 5'-biotin-TT TTC CAG GGTATC CAAAGG ATCAAC TGC-3'. HER2 detection aptamer, 5'-NH2-(CH2)6-TT GGG CCG TCGAACACGAGC ATG GTG CGT GGACCTAGGATGACC TGAGTACTGTCC-3', HER2 capture aptamer (line C), 5'-biotin-TTT TTG CAG TTGATC CTT TGGATACCC TGG-3'.

[0075] All chemicals and solvents were used as received without further purification.

[0076] Deionized (DI) water (Milli-Q grade, 18.2 MΩ-cm, 25° C.) was used in all experiments.

[0077] The characterization and instrumentation of the present invention are as follows:

[0078] Optical extinction spectra were recorded on an Agilent Cary 5000 UV-Vis-NIR spectrophotometer.

[0079] The hydrodynamic size and zeta potential were measured using a Malvern ZetasizerNano ZSE ZEN3700 instrument.

[0080] Transmission electron microscopy (TEM) was performed using a FEI Tecnai G2 F20 S-TWIN TMP transmission electron microscope with an accelerating voltage of 200 kV.

[0081] Scanning electron microscopy (SEM) was performed on a Hitachi Regulus 8230 field emission scanning electron microscope.

[0082] The quantitative analysis of gold was performed using an Agilent 5100 inductively coupled plasma-optical emission spectrometer (ICP-OES).

[0083] Nanoparticle tracking analysis (NTA) was performed on a Particle Metrix ZetaView system.

[0084] All SERS measurements were performed on a Renishaw inVia Qontor confocal Raman microscope equipped with a Peltier-cooled air-cooled charge-coupled device detector, using a 785 nm diode laser (maximum output power 300 mW), a 1200 lines / mm holographic grating and a 50 L× objective lens (NA = 0.75).

[0085] All SERS spectra were acquired at a laser power of 4.9 mW on the sample with an integration time of 0.5 s. For SERS mapping, the test strip was placed on an automated motorized stage and scanned over a 1 mm × 9 mm area with a step size of 100 μm. All SERS data were spectrally processed to remove cosmic spikes and background baseline using the integrated WiRE 5.3 software. DETAILED DESCRIPTION

[0086] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0087] Example

[0088] Quantitative evaluation of serum exosomes in clinical patients.

[0089] Isolation of cellular exosomes. Two breast cancer cell lines, SKBR-3 and MCF-7, were purchased from the Cell Bank of the Chinese Academy of Sciences and used in this work to extract cancer cell exosomes. MCF-7 cells were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin / streptomycin in a humidified incubator at 37°C and 5% CO2, while SKBR-3 cells were cultured in DMEM supplemented with high glucose and pyruvate, with other conditions being the same. The cell culture medium was harvested, and cellular exosomes were separated using size exclusion chromatography (SEC) to obtain SKBR-3 and MCF-7 cell-derived exosomes (denoted as SKBR exosomes and MCF exosomes, respectively). The hydrodynamic size of the eluate was measured by dynamic light scattering analysis, and vesicles with a typical exosome size of 30-150 nm were pooled and considered cellular exosomes. The collected exosomes were stored in PBS at -80°C, and their concentration was determined by NTA.

[0090] Isolation of Human Serum Exosomes. This clinical experiment was approved by the Ethics Committee of the Second Hospital of Shandong University (approval number: KYLL-2022P138) and conducted in accordance with ethical standards. With informed consent, human blood samples were collected from healthy individuals (n = 15) and breast cancer patients (n = 39). Histological characteristics of these individuals were verified by immunohistochemical staining and are summarized in Table 1. Human serum exosomes were isolated using the commercial ExoQuick Exosome Isolation Kit according to the manufacturer's instructions. Human blood samples were first pre-clotted and then centrifuged at 3000 g for 15 minutes to remove debris and cells. The supernatant of each human blood sample was gently aspirated and transferred to a new microcentrifuge tube, followed by the addition of the isolation reagent at a 1:4 ratio. The samples were then incubated at 4°C for 1 hour and centrifuged at 1500 g for 30 minutes to obtain the exosome pellet. Finally, the exosome pellet was resuspended in PBS at a 1:100 ratio and stored at −80°C.

[0091] The present invention explored the applicability of the strategy developed in the present invention in the clinical diagnosis of breast cancer by testing human serum exosomes from healthy people (n=15) and patients with two different molecular subtypes of breast cancer, including HER2+ breast cancer patients (n=8 unoperated, n=7 operated) and Luminal A breast cancer patients (n=10 unoperated, n=14 operated). Their pathological information was verified by immunohistochemical staining test and summarized in Table 1. Serum exosomes were obtained from human serum using the commercial ExoQuick Exosome Isolation Kit according to the manufacturer's instructions. After spectral unmixing, the present invention was based on Figure 13 The corresponding concentrations of SKBR and MCF exosomes were determined using the established calibration curves shown. Figure 18 As shown in Figure a, the quantitative results show that these clinical samples were divided into five groups, verifying the unique ability of the current method of the present invention in distinguishing these clinical samples. The average serum exosome concentration of HER2+ breast cancer patients who did not undergo surgery was determined to be: SKBR exosomes were approximately 25.3×10 7 particles / ml, MCF exosomes are approximately 3.13×10 7 Particles / mL ( Figure 18 The average serum exosome concentration of SKBR exosomes in patients with luminal A breast cancer who did not undergo surgery was approximately 0.87×10 7 particles / ml, MCF exosomes were 69.1×10 7 Particles / mL. It is clear that SKBR and MCF exosomes are enriched in serum samples of HER2+ and luminal A breast cancer patients, respectively. These results are consistent with the results of Western blot analysis, as high expression of HER2 and MUC1 is usually found in SKBR and MCF exosomes, respectively. In addition, the concentration of SKBR exosomes in the serum of HER2+ breast cancer patients was significantly higher than that in the serum of healthy subjects (P value = 1.23×10 -4 ), while the concentration of serum MCF exosomes in patients with Luminal A breast cancer was significantly higher than that in healthy controls (P value = 3.59 × 10 -4 Further analysis revealed that surgical treatment of breast tumors significantly reduced the concentration of serum SKBR exosomes in patients with HER2+ breast cancer, as well as the concentration of serum MCF exosomes in patients with Luminal A breast cancer ( Figure 18 b). Therefore, the present invention demonstrates that the current SERS-LFS biosensor combined with spectral unmixing can sensitively and specifically quantify two different exosomes in clinical serum for non-invasive breast cancer subtype and surgical outcome assessment.

[0092] Table 1. Pathological information of breast cancer patients identified by immunohistochemical staining for isolation of serum exosomes for clinical measurement.

[0093]

[0094]

[0095] See also Figure 1-7, construction of SERS spectra of exosomes. The present invention selects two breast cancer cell lines, SKBR-3 and MCF-7 cells, to isolate cellular exosomes because they have different molecular characteristics and have been widely used to simulate HER2+ and Luminal A breast cancer subtypes. The present invention uses standard size exclusion chromatography (SEC) technology to separate and purify cellular exosomes of SKBR-3 and MCF-7 cells - SKBR exosomes and MCF exosomes, respectively ( Figure 19 ). Specifically, the cell culture medium of SKBR-3 and MCF-7 cells was collected and centrifuged to remove free cells, debris, and large vesicles. The supernatant was filtered with a 100 kDa Millipore filter and purified with a sepharose CL-2B column. Vesicles with a size of 30-150 nm were collected as cell exosomes and characterized by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and Western blot analysis. TEM images showed that the exosomes of SKBR and MCF had a round morphology with a diameter of approximately 100 nm ( Figure 20 )( Figure 20 The exosomes in the figure refer to the exosomes from “MCF”. NTA measurements further confirmed that the size of the collected exosomes fell within the typical size range of exosomes ( Figure 20 Western blot analysis also confirmed the high expression of characteristic exosomal CD63 on SKBR and MCF exosomes ( Figure 1 (a, quantification is shown in Table 2). Notably, Western blot analysis further revealed differential expression levels of surface exosomal HER2 and MUC1 in the two cell types, with HER2 and MUC1 levels in SKBR exosomes 6.2-fold higher and 3.9-fold lower, respectively, than in MCF exosomes. These results clearly demonstrate the successful isolation of high-quality exosomes from the cell culture media of SKBR-3 and MCF-7 cells.

[0096] Table 2

[0097]

[0098] High-sensitivity SERS analysis requires high-brightness SERS probes. Here, the present invention selects gold nanostars (AuSts) as the plasmonic substrate for creating SERS probes because they have excellent plasmonic properties and a high density of built-in "hot spots" that can achieve large-area near-field enhancement. Therefore, the present invention uses AuSts as a plasmonic substrate with a silicon protective layer to produce two spectrally different SERS nanoparticles (NPs), which are encoded with 5,5'-dithiobis(2-nitrobenzoic acid) (DTNB) and 4-mercaptobenzoic acid (4MBA), respectively ( Figure 21 ). DTNB and 4-MBA encoded SERS NPs were further conjugated with HER2 and MUC1 detection aptamers to obtain HER2 and MUC1 probes, respectively. TEM images clearly show the successful encapsulation of the AuSt core in the silica shell ( Figure 3 ). While the AuSt core in the SERS NPs amplifies the Raman scattering of the embedded Raman molecules (DTNB or 4-MBA), the silica layer prevents these Raman molecules from desorbing from the AuSt surface in a strong clinical environment, greatly improving the stability of these SERS NPs. Compared with the synthesized AuSts, the proton resonance wavelength of the SERS NPs is red-shifted by about 20 nm, and the proton absorption of the HER2 probe is concentrated at 779 nm, which is because the HER2 detection aptamer is attached to the SERS NPs. The surface zeta potential of the SERS NPs increases from that of the synthesized AuSts (-20.0 (±1.9) mV) to -6.7 (±0.3) mV, and then the zeta potential of the HER2 probe decreases slightly to -15.1 (±0.4) mV ( Figure 4 Both the HER2 and MUC1 probes exhibited unique and intense SERS spectral patterns, which are characteristic of their respective Raman reporters, DTNB and 4-MBA ( Figure 22 ).

[0099] In order to construct the SERS spectra of exosomes, the present invention incubated SKBR and MCF exosomes in equimolar solutions of high concentrations of HER2 and MUC1 probes (incubated separately) to ensure that all exosomes of HER2 and MUC1 were bound to their respective probes ( Figure 5 e(i,ii)). After removing the unbound HER2 and MUC1 probes, a SERS dataset of 50 spectra from SKBR exosomes and 50 spectra from MCF exosomes was collected, and then spectral unmixing was performed using the multivariate curve resolution-alternative least squares (MCR-ALS) algorithm. MCR-ALS can decompose complex experimental spectra into pure component spectra (endmembers) and add weights (α for SKBR exos and β for MCF exos) for quantitative estimation of component abundance. Figure 5 As shown in Figure e(iii), the SERS data set is subjected to MCR-ALS spectral unmixing to obtain two component spectra (CHER2 and CMUC1), which show 92% and 88% similarity with the SERS spectra of HER2 and MUC1 probes, respectively. The present invention also attempts to decompose the SERS spectrum into more component spectra (n=3, 4 or 5), but it produces component redundancy ( Figure 23). The present invention demonstrates that the SERS spectrum of a single SKBR or MCF exosome (Specexosome) can be defined by the weighted sum of the SERS spectra of the HER2 and MUC1 probes, as described below. Specexosome = α-CHER2 + β-CMUC1, where α and β are spectral weights associated with HER2 and MUC1 expression, respectively. The average values ​​of the SERS relative intensity (α / β) are: 2.71 (±0.61) for SKBR exosomes and 0.36 (±0.20) for MCF exosomes ( Figure 6 ), which is slightly different from the results of Western blot analysis (HER2 / MUC1 expression ratio: 3.33 for SKBR exosomes and 0.14 for MCF exosomes). This may be due to the different binding affinities of exosomal HER2 and MUC1 to the corresponding inducers. Based on the average value of α / β, the (α, β) values ​​of SKBR exosomes were estimated to be (70.05%, 26.95%) and MCF exosomes were (26.47%, 73.53%). Therefore, the SERS spectrum of a single SKBR or MCF exosome can be realized by the weighted sum of the SERS spectra of HER2 and MUC1 probes, as shown in Figure 2. Figure 7 shown.

[0100] SERS-LFS biosensor design and test parameter optimization. Paper-based lateral flow strip (LFS) biosensors are currently the most widely used form of medical point-of-care diagnostics. Typical examples of LFS biosensors in clinical use include pregnancy tests, COVID-19 tests, and diagnosis of influenza and bacterial infections. Here, the present invention established a SERS-LFS biosensor comprising a test line (T) and a control line (C) to simultaneously quantify the extravasation of SKBR and MCF with the help of multivariate spectral unmixing, as Figure 8As shown. The CD63 capture aptamer is immobilized on the T line and captures exosomes (such as SKBR and MCF exosomes) by specifically binding to the exosomal CD63 protein biomarker. The HER2 and MUC1 capture aptamers are immobilized on the C line and are used to capture the HER2 and MUC1 probes, respectively. The conjugate pad is pre-loaded with HER2 and MUC1 probes. When the exosome sample falls on the sample pad, the exosomes move forward through the nitrocellulose (NC) membrane under the action of capillary force and form complexes with the HER2 and MUC1 probes pre-loaded on the conjugate pad. These complexes continue to flow forward and are subsequently captured by the CD63 capture aptamer in the T line, forming a sandwich structure of CD63 capture aptamer / exosome / SERS probe. At the same time, excess HER2 and MUC1 probes will further react with the HER2 and MUC1 capture aptamers and be captured on the C line. The present invention can measure the SERS spectrum of the complex composed of HER2 and MUC1 probes on the T line and C line, respectively. Since the basic spectra of SKBR and MCF (denoted as SpecSKBR exo and SpecMCF exo) have been Figure 7 It is well established in

[15] that the present invention can unmix the composite SERS spectrum of the T line (denoted as Specmix) with the weights of the two basic spectra to quantify its concentration ( Figure 9 ), which is expressed as follows: Specmix = S-SpecSKBR exo + M-SpecMCF exo, where S and M are the weights (abundances) of the two exosomes in the mixture.

[0101] A series of experiments were conducted to optimize the test parameters of the SERS-LFS biosensor for exosome detection, including the mass percentage of Tween-20 in the running buffer made of 0.5% BSA, 10% sucrose, and 0.1% PVP in PBS (pH 7.4), the NC membrane type, the CD63 capture inducer loading on the T-line, and the total concentration of HER2 and MUC1 probes at a fixed ratio of 1:1. Specifically, 1) for the effect of Tween-20 mass percentage, different amounts of Tween-20 (0.25%, 0.5%, 1.0%, 1.5% and 2.0%) were added to the running buffer, and other parameters were as follows: NC SS12, CD63 capture inducer loading 0.2 μg / mm, total probe concentration 260 μg / mL; 2) for the effect of NC membrane type, three different NC membranes (SS12, FF120HP, P90) were used to investigate the effects of NC membrane with 1.0% Tween-20, CD63 capture inducer loading 2 μg / mm, and total probe concentration 260 μg / mL; 3) for the effect of CD63 capture inducer, different loading amounts of CD63 capture inducer (0.1, 0.2, 0.3 and 0.4 μg / mm) were used on the T line to determine the optimal capture inducer loading amount, NC SS12, total probe concentration 260 μg / mL, and 1.0% Tween-20; 4) Regarding the effect of SERS detection probe, SERS detection probe solutions with different total concentrations (38, 75, 130, 260 and 390 μg / mL) were dispersed on the conjugated pad at a ratio of 1:1 between HER2 probe and MUC1 probe to determine the optimal concentration of the total SERS probe.

[0102] Specifically: Before evaluating the analytical performance, a series of experiments were performed to optimize the test parameters of the SERS-LFS biosensor, including the mass percentage of Tween-20 in the running buffer, the type of NC membrane, the loading amount of the CD63 capture aptamer in the T-line, and the total concentration of HER2 and MUC1 probes ( Figure 10 MCF explants were used in the following optimization experiments, and the optimal parameters were determined based on the M value of spectral unmixing analysis. Figure 10 As shown in c(i), the M value is the highest at 1.0% Tween-20. The present invention tested the effects of different porous properties of three different NC membranes (SS12, FF120HP and P90) on SERS performance (Table 3). Figure 10 Figure c(ii) shows the best SERS response when using FF120HPNC membrane. As the CD63 capture aptamer loading in the T line increases, the M value first increases and finally reaches saturation above 0.4 μg / mm ( Figure 10Then, the effect of the total concentration of the mixed probe consisting of HER2 and MUC1 probes was investigated in the range of 38-390 μg / mL ( Figure 10 (c(iv)). The results showed that the M value gradually increased with increasing total HER2 and MUC1 probe concentrations, but a strong background SERS signal was observed when the total exosome concentration exceeded 390 μg / mL. Therefore, 1.0% Tween-20 in the running buffer, FF120HPNC membrane, 0.4 μg / mm CD63 capture aptamer loading, and a probe concentration of 390 μg / mL were considered optimal parameters for subsequent experiments.

[0103] Table 3. Porous properties of NC membranes used for optimization experiments in this work.

[0104]

[0105] See also Figure 8-10 The present invention systematically examined the sensitivity of SERS-LFS biosensor for quantitative detection of SKBR and MCF exosomes. Figure 24 The scanning electron microscopy images show that at 5.0×10 9 In a positive test with MCF exosomes at 1000 particles / mL, the MUC1 probe was clearly captured in the T-line, but no significant capture was observed in the absence of exosomes. 7 -10 11 When tested in phosphate buffered saline (PBS, pH 7.4) solutions with various exosome concentrations (particles / ml), the present invention can see the exosome concentration-dependent SERS response of SKBR and MCF exosomes ( Figure 11 (a, b) As the exosome concentration increases, the SERS images created using the S or M values ​​become brighter. The SERS intensity of the T line gradually increases with the increase in exosome concentration. The experimental results show that the S or M value is linearly related to the logarithm of the exosome concentration, ranging from 1.0×10 7 to 1.0×10 11 The fitted linear equations were: y = 419.86 × lg [SKBR exo] + 94.84 (R2 = 0.928) for SKBR exosomes and y = 704.63 × lg [MCF exo] - 246.40 (R2 = 0.976) for MCF exosomes. According to the IUPAC standard method (LOD = 3δ / s, where δ is the standard deviation of the blank measurement (n = 34) and s is the slope of the fitted calibration equation), the detection limit of SKBR exosomes was estimated to be 1.5 × 10 7 particles / ml, MCF exosomes are 1.21×107 particles / ml. When different concentrations of exosomes (0-5.0×10 9 When tested in serum containing 500 particles / mL, the SERS-LFS biosensor showed a surprising increase in sensitivity, with the LOD value of SKBR exosomes being 3.27×10 6 particles / ml, MCF exosomes are 4.80×10 6 Particles / mL ( Figure 13 These results demonstrate that the SERS-LFS biosensor of the present invention, combined with spectral unmixing processing, can achieve exceptional sensitivity in quantifying exosomes in complex biological matrices such as serum.

[0106] See also Figure 13 , dual detection of exosomes in serum. The present invention evaluated the analytical performance of the SERS-LFS biosensor for the simultaneous detection of SKBR and MCF exosomes, supplemented by spectral unmixing. Serum exosome samples were prepared by mixing different total concentrations of SKBR and MCF exosomes (1.0-500×10 7 The SERS dataset consisting of all SERS spectra collected by the T line was spectrally mixed by the MCR-ALS algorithm to obtain two MCR component spectra (MC1 and MC2). The present invention found that the MC1 and MC2 component spectra showed high similarity, with 99% and 98% similarity to the SERS spectra of SKBR exosomes and MCF exosomes, respectively ( Figure 14 ). This shows that with the help of MCR-ALS, the spectra of the SERS data set from the mixed SKBR and MCF exosomes can be unmixed to obtain the spectra of two pure components, namely the SERS spectra of the individual SKBR and MCF exosomes. Subsequently, the spectrum of the exosome mixture was unmixed using the least squares method, since the basic SERS spectra of the individual SKBR and MCF exosomes were completed above. A two-dimensional plot of S value and M value was generated from the spectral unmixing of these SERS data. Figure 15 As shown, the S and M values ​​for each specific concentration ratio of SKBR and MCF exosomes are well-matched, even at different total concentrations; a negative linear relationship between S and M values ​​is observed within the investigated [SKBR exosome]:[MCF exosome] ratio range. As the [SKBR exo]:[MCF exo] ratio increases, the S / M ratio gradually increases across all of the exosome concentrations investigated in this study ( Figure 16). The present invention establishes a quantitative relationship between S and M values ​​and the total exosome concentration and the [SKBRexo]: [MCFexo] ratio, such as Figure 17 Therefore, the present invention is able to determine the absolute concentrations of SKBR and MCF exosomes in serum after unraveling the complex SERS spectra from exosome mixtures with different ratios.

[0107] See also Figure 14-17 , quantitatively evaluate the serum exosomes of clinical patients. The present invention explored the applicability of the strategy developed by the present invention to the clinical diagnosis of breast cancer by testing human serum exosomes from healthy people (n=15) and patients with two different molecular subtypes of breast cancer, including HER2+ breast cancer patients (n=8 unoperated, n=7 operated) and Luminal A breast cancer patients (n=10 unoperated, n=14 operated). Their pathological information was verified by immunohistochemical staining tests and is summarized in Table 1. Serum exosomes were obtained from human serum using the commercial ExoQuick Exosome IsolationKit according to the manufacturer's instructions. After spectral unmixing, the present invention was based on Figure 13 The corresponding concentrations of SKBR and MCF exosomes were determined using the established calibration curves shown. Figure 18 As shown in Figure a, the quantitative results show that these clinical samples were divided into five groups, which verifies the unique ability of the current method of the present invention in distinguishing these clinical samples. The average concentration of serum exosomes in HER2+ breast cancer patients who did not undergo surgery was determined to be: SKBR exosomes were approximately 25.3×10 7 particles / ml, MCF exosomes are 3.13×10 7 Particles / mL ( Figure 18 (b) The average serum exosome concentration of SKBR exosomes in patients with luminal A breast cancer who did not undergo surgery was approximately 0.87×10 7 particles / ml, MCF exosomes were 69.1×10 7 Particles / ml. Clearly, SKBR and MCF exosomes were enriched in serum samples from patients with HER2+ and Luminal A breast cancer, respectively. These results were consistent with those from Western blot analysis, as high expression of HER2 and MUC1 was often found in SKBR and MCF exosomes, respectively. In addition, the concentration of SKBR exosomes in the serum of patients with HER2+ breast cancer was significantly higher than that in the serum of healthy controls (p value = 1.23×10 -4 ), while the concentration of serum MCF exosomes in patients with Luminal A breast cancer was significantly higher than that in healthy controls (p value = 3.59 × 10 -4Further analysis revealed that surgical treatment of breast tumors significantly reduced the concentration of serum SKBR exosomes in patients with HER2+ breast cancer and the concentration of serum MCF exosomes in patients with LuminalA breast cancer ( Figure 18 b). Therefore, the present invention demonstrates that the current SERS-LFS biosensor combined with spectral unmixing can sensitively and specifically quantify two different exosomes in clinical serum for non-invasive breast cancer subtype and surgical outcome assessment.

[0108] Due to their rich molecular information, high abundance, and remarkable stability, exosomes represent an emerging class of circulating biomarkers for cancer diagnosis and prognosis. Clinical applications of exosomes require sensitive detection tools capable of rapid, accurate, and specific quantification of exosomes in complex biological environments. Traditionally, exosome-based liquid biopsies have been primarily performed by targeting one or a few exosomal biomarkers. However, their co-expression and subtle differential expression of these exosomal biomarkers in cancer exosomes from diverse sources have significantly hindered their clinical translation. To address these issues, we report a novel, integrated strategy for quantifying serum exosomes through multivariate spectral unmixing of complex SERS spectra collected by a SERS-LFS biosensor. Key to our strategy is the quantitative determination of cancer exosomes in clinical samples, rather than the surface exosomal biomarkers commonly used in previous work. We define the SERS spectra of individual SKBR and MCF exosomes based on the weighted sum of the SERS spectra specific to their respective exosomal protein biomarkers, derived from HER2 and MUC1 probes. The LOD value of the present invention for serum SKBR exosomes was 3.27×10 6 particles / ml, and the LOD value for serum MCF exosomes was 4.80×10 6particles / ml. The present invention further demonstrates that by unmixing the complex SERS spectra of serum exosomes, it is possible to simultaneously quantify serum SKBR and MCF exosomes in clinical samples in a single test. In addition, the clinical applicability of the present invention was further demonstrated by accurately measuring the concentrations of serum SKBR and MCF exosomes in clinical human samples with different pathological conditions. The results showed that HER2+ breast cancer patients who did not undergo surgery showed high concentrations of serum SKBR exosomes, while the serum MCF exosome concentrations of patients with Luminal A breast cancer who did not undergo surgery were very high, significantly higher than those of healthy controls. Surgical treatment of breast tumors significantly reduced the concentrations of SKBR and MCF exosomes in patients with HER2+ and Luminal A breast cancer. The results indicate that the present invention can directly utilize clinical serum samples for breast cancer subtype identification and surgical outcome assessment. The method of the present invention can be expanded to quantitatively detect more breast cancer-related exosomes and can also be applied to exosome-based liquid biopsies for various other diseases (such as lung, liver, COVID-19, Alzheimer's disease), and is expected to be clinically translated in the future.

[0109] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for quantitative detection of exosomes by lateral flow chromatography, characterized in that: The detection method comprises the following steps: S1. Constructing surface-enhanced Raman spectra of single exosomes: SKBR-3 exosomes and MCF-7 exosomes were incubated in equimolar solutions of the HER2 probe and the MUC1 probe, respectively. SERS datasets were collected from the spectra of SKBR-3 exosomes and MCF-7 exosomes to obtain the base spectra of the two exosomes. The method for constructing the HER2 probe and the MUC1 probe specifically includes: Using gold nanostars as a proton substrate and silicon as a protective layer, they were encoded with two different Raman probe molecules, 5,5'-dithiobis(2-nitrobenzoic acid) and 4-mercaptobenzoic acid. The encoded products were further conjugated with HER2 detection aptamers and MUC1 detection aptamers to obtain HER2 and MUC1 probes, respectively. S2. Construct standard curves for individual exosomes as a function of concentration: Measure the Raman spectra of individual exosomes at different known concentrations, perform least squares fitting on the exosome base spectrum, and calculate the exosome loading, which is correlated with the exosome concentration. Construct standard concentration curves based on the loading. S3. Obtaining a Raman spectral dataset of a mixed sample to be tested, wherein the mixed sample to be tested contains SKBR-3 exosomes and MCF-7 exosomes; performing multivariate analysis and spectral unmixing on the data: spectral unmixing is performed using a multivariate curve resolution-partial least squares fitting algorithm to obtain the loading of each exosome; S4. Substitute the obtained loading value into the standard concentration curve to obtain the actual concentration of each exosome.

2. The method for quantitative detection of exosomes by lateral flow chromatography according to claim 1, wherein: The DNA sequence of the HER2 detection aptamer is 5'-NH2-(CH2)6-TTTGGGCCGTCGAACACGAGCATGGTGCGTGGACCTAGGATGACCTGAGTACTGTCC-3'.

3. The method for quantitative detection of exosomes by lateral flow chromatography according to claim 1, characterized in that: The DNA sequence of the MUC1 detection aptamer is 5'-NH2-(CH2)6-TTTTTGCAGTTGATCCTTTGGATACCCTGG-3'.

4. The method for quantitative detection of exosomes by lateral flow chromatography according to claim 1, wherein: The method for constructing the gold nanostar specifically includes: Step 1. Preparation of gold nanoseeds: A diluted aqueous solution of chloroauric acid was stirred with an aqueous solution of sodium citrate and a sodium borohydride solution, and PVP was added and continued to stir to obtain a gold nanoseed solution. Step 2. Preparation of gold nanostars: Mix the HAuCl4 aqueous solution with the PVP and DMF solutions, add the gold nanoseed solution at the appropriate time, and obtain gold nanostars after purification.

5. A biosensor, characterized in that: The biosensor adopts the exosome lateral flow quantitative detection method according to any one of claims 1 to 4. The biosensor comprises at least a sample pad, a conjugate pad, a T-line, a C-line, an absorption pad, and a bottom plate. The T-line is loaded with a CD63 capture aptamer, the C-line is loaded with a HER2 capture aptamer and a MUC1 capture aptamer, and the conjugate pad is made of glass fiber material and is loaded with an equimolar ratio solution of a HER2 probe and a MUC1 probe bound to a detection aptamer.

6. The biosensor according to claim 5, wherein The DNA sequence of the CD63 capture aptamer is: 5'-biotin-TTCACCCCACCTCGCTCCCGTGACACTAATGCTA-3'.

7. The biosensor according to claim 5, characterized in that The DNA sequence of the MUC1 capture aptamer is 5'-biotin-TTTTCCAGGGTATCCAAAGGATCAACTGC-3'.

8. The biosensor according to claim 5, wherein The DNA sequence of the HER2 capture aptamer is 5'-biotin-TTTTGCAGTTGATCCTTTGGATACCCTGG-3'.

9. A detection kit comprising the biosensor according to any one of claims 5 to 8.

10. Use of the detection method according to any one of claims 1 to 4, the biosensor according to any one of claims 5 to 8, and the kit according to claim 9 in detecting a target analyte.

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