Universal serum sers detection method based on silver nanoparticle concentration regulation

Abstract

This invention discloses a universal serum SERS detection method based on silver nanoparticle concentration regulation. The specific steps are as follows: The original serum sample to be tested is taken and mixed with silver nanoparticle solutions of different concentration gradients to obtain a series of gradient mixtures; Raman spectroscopy is performed on each mixture to collect corresponding serum SERS spectral data; the signal quality of multiple spectra is compared to screen the optimal working concentration of silver nanoparticles suitable for the current serum system, and serum metabolite information analysis is completed based on the high-quality SERS spectrum at this concentration. This invention, by preparing highly stable silver nanocolloids and implementing gradient concentration regulation, allows silver nanoparticles to form a suitable dispersion and localized enhancement state in the complex serum environment, regulating the competitive adsorption behavior of proteins and metabolites, increasing the probability of low-abundance metabolites entering SERS hotspots, and achieving rapid, trace, label-free detection of original serum samples without complex pretreatment.

Description

Technical Field

[0001] This invention belongs to the field of biosensing and liquid biopsy detection technology, specifically to a general serum SERS detection method based on the concentration regulation of silver nanoparticles. Background Technology

[0002] Surface-enhanced Raman scattering (SERS) is a molecular fingerprint detection technology with high sensitivity, fast response, and label-free recognition characteristics. It has shown outstanding application potential in the fields of biomedicine and disease screening. It can perform rapid spectral analysis of the body's metabolic state based on serum samples, providing a new technical path for non-invasive clinical testing.

[0003] Serum matrix components are complex, and high-abundance proteins tend to form dense adsorption layers on the surface of noble metal nanostructures, occupying a large number of SERS activity hotspots and directly masking the characteristic signals of low-abundance disease-related metabolites. Ultimately, this leads to insufficient target detection signal intensity, and the system's detection stability and repeatability cannot meet the requirements of clinical applications.

[0004] To address the issue of high-abundance proteins interfering with the detection of low-abundance metabolites in serum, existing technologies primarily employ sample pretreatment methods such as ultrafiltration, specialized protein removal, centrifugation, or sample dilution. These methods aim to reduce the protein content in serum, weaken competitive protein adsorption and the protein crown effect, and decrease the occupancy of protein on SERS active sites, thereby attempting to improve the detection efficiency of low-abundance metabolites. However, in conventional SERS serum detection systems, the concentration of metal nanoparticles is often set as a fixed experimental parameter or only undergoes routine, simple optimization, without targeted regulation of the core mechanisms of action, such as interfacial adsorption and hotspot distribution, within the complex serum system.

[0005] Existing sample pretreatment methods are cumbersome and have long testing cycles. The pretreatment process is prone to loss of small molecule metabolites, compromising the integrity and fidelity of the original serum metabolic information. Furthermore, because nanoparticle concentration is not used as a core regulatory variable, the interface state between serum and nanoparticles, the protein crown formation process, and the distribution of nanohotspots cannot be precisely controlled. This results in low hotspot utilization efficiency, poor spectral signal stability, and poor detection repeatability, making it difficult to rapidly obtain accurate metabolite spectral data from trace amounts of raw serum samples. This significantly limits the application of this technology in rapid clinical screening and large-scale sample testing scenarios.

[0006] Therefore, developing a SERS detection system that requires no complex pretreatment and can be directly adapted to raw serum samples, and achieving efficient signal capture of low-abundance metabolites by precisely controlling the interface state of nanoparticles, has become a key technical problem that urgently needs to be solved in this field. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a universal serum SERS detection method based on silver nanoparticle concentration regulation. By preparing stable silver nanocolloids and gradient regulating their particle concentration, the silver nanoparticles form a suitable dispersion and local enhancement state in the complex serum environment, thereby regulating the competitive adsorption behavior of proteins and metabolites, increasing the probability of low-abundance metabolites entering the SERS hotspot region, and realizing rapid, trace, label-free SERS detection of raw serum samples.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a universal serum SERS detection method based on silver nanoparticle concentration regulation, comprising the following steps: The original serum samples to be tested were mixed with silver nanoparticle solutions of different concentration gradients to obtain silver nanoparticle-serum mixtures under different silver nanoparticle concentration conditions. Surface-enhanced Raman scattering (SERS) spectra of silver nanoparticle-serum mixtures under different silver nanoparticle concentrations were collected, and the corresponding serum SERS spectral data were acquired. Based on serum SERS spectral data obtained under different silver nanoparticle concentrations, the working concentration of silver nanoparticles was screened to determine the optimal working concentration of silver nanoparticles suitable for serum liquid biopsy. Based on the SERS spectrum obtained at the optimal working concentration of silver nanoparticles, the molecular fingerprint information of metabolites in serum was detected and analyzed.

[0009] Furthermore, the silver nanoparticle solutions with different concentration gradients are prepared as follows: monodisperse silver nanocolloids are prepared at room temperature using an alkaline reduction system, with hydroxylamine hydrochloride as a reducing agent and sodium hydroxide as an alkaline auxiliary agent, to reduce silver nitrate aqueous solution to obtain silver nanocolloids, and the obtained silver nanocolloids are used as the base stock solution for preparing silver nanoparticle solutions with different concentration gradients.

[0010] Furthermore, the concentration gradient of the silver nanoparticle colloidal substrate stock solution was controlled: the silver nanoparticle colloidal stock solution was concentrated by centrifugation, different volumes of supernatant were removed, and the solution was homogenized and resuspended to prepare silver nanoparticle solutions with low, medium, and high concentrations, wherein the low concentration range was 1.31 × 10⁻⁶. 12 ~2.62×10 12 particles / mL, medium concentration range 5.24×10 12 ~2.62×10 13 particles / mL, with a high concentration range of 5.24 × 10⁻⁶. 13 ~1.048×10 14 particles / mL.

[0011] Furthermore, the optimal concentration of the silver nanoparticle solution is 2.62 × 10⁻⁶. 13 particles / mL.

[0012] Furthermore, during the detection process, the untreated raw serum sample was directly mixed with silver nanoparticle solutions of different concentration gradients to construct the detection system. The volume of the raw serum sample to be tested was 2-4 μL, and the corresponding volume of the working solution of silver nanoparticles was 4-6 μL.

[0013] Furthermore, the volume of the original serum sample to be tested was 3 μL, and the corresponding volume of the working solution of silver nanoparticles was 5 μL.

[0014] Furthermore, a confocal Raman spectrometer with a 532nm excitation source was used for Raman spectroscopy detection. The laser power was 50% and the exposure time was 3s. SERS spectra of silver nanoparticle-serum mixtures corresponding to each concentration gradient were collected under the same conditions, and the total detection time for a single test did not exceed 2 minutes.

[0015] Furthermore, before screening for the optimal silver nanoparticle solution concentration, the following steps are also included: Microscopic morphology-assisted verification steps: Scanning electron microscopy was used to observe the microscopic morphology of silver nanoparticle-serum mixtures at different concentration gradients. Concentration ranges in which silver nanoparticles were uniformly distributed, without aggregation or stacking, were selected as the effective concentration screening ranges. The steps for verifying the dispersion stability of the mixture are as follows: the silver nanoparticle-serum mixtures at various concentration gradients are characterized by ultraviolet-visible spectroscopy. The dispersion stability of silver nanoparticles in serum is determined by detecting the peak shape, offset, and full width at half maximum (FWHM) parameters of the surface plasmon resonance peaks, and invalid concentration groups with aggregation distortion are eliminated.

[0016] Furthermore, the screening criteria for the optimal silver nanoparticle solution concentration are as follows: the optimal spectral signal-to-noise ratio, the highest richness of characteristic peaks of serum small molecule metabolites, and the best spectral signal repeatability and stability are used as the core screening criteria to screen out the optimal working concentration of silver nanoparticle solution suitable for the detection of raw serum.

[0017] Furthermore, after selecting the optimal silver nanoparticle solution concentration, a spectral standardization processing step is also included: the serum SERS spectrum obtained at the optimal working concentration of silver nanoparticles is subjected to background subtraction, smoothing and noise reduction and data normalization processing in sequence to obtain a standardized serum molecular fingerprint spectrum, and serum metabolite information analysis and liquid biopsy sample feature characterization are performed based on the standardized serum molecular fingerprint spectrum.

[0018] Compared with the prior art, the present invention has at least the following beneficial effects: This invention provides a universal serum SERS detection method based on silver nanoparticle concentration regulation. After obtaining the serum sample, no further protein removal, ultrafiltration, or other complex separation and purification processes are required; the sample can be directly mixed with a silver nanoparticle working solution for SERS detection. This simplifies the serum molecular fingerprint detection process, reduces sample processing steps and detection time, and lowers the risk of loss of small molecule metabolites or compositional changes during complex pretreatment, thus facilitating the preservation of more complete original serum molecular information. The method requires only a microliter of serum for detection, and the SERS spectral acquisition process for a single sample can be completed in a short time, making it suitable for rapid molecular information acquisition from liquid biopsy samples. Furthermore, this invention differs from conventional detection methods that use a single fixed-concentration SERS substrate. It uses the working concentration of silver nanoparticles as a controllable key interface parameter. By setting a silver nanoparticle gradient covering low, medium, and high concentration ranges, the interfacial interactions between silver nanoparticles and serum proteins and small molecule metabolites, as well as the state of local SERS activity hotspots, can be adjusted. By using concentration gradient screening to obtain a working window suitable for complex serum systems, it is beneficial to reduce the excessive occupation of active sites on the surface of silver nanoparticles by high-abundance proteins, improve the identifiability of serum small molecule metabolite characteristic signals, and thus improve the information richness, repeatability and stability of serum SERS molecular fingerprint.

[0019] Furthermore, this invention employs a room-temperature preparation process involving the synergistic reduction of silver nitrate by hydroxylamine hydrochloride and sodium hydroxide to independently prepare monodisperse silver nanoparticle colloids in an alkaline reduction system. This yields a silver nanoparticle substrate stock solution with an average particle size of approximately 91.9 ± 2.0 nm, uniform particle distribution, and stable structure. This solution can be pre-produced in batches and stored at 4°C for extended periods, providing a stable and reliable substrate material for subsequent gradient concentration control and standardized detection, ensuring consistency in detection across multiple batches of experiments.

[0020] Furthermore, this invention utilizes gradient centrifugation to concentrate the silver nanoparticle stock solution, dividing it into three controllable concentration ranges. This allows for targeted adaptation to the complex interfacial reaction characteristics of the serum matrix, achieving precise control over the serum adsorption environment and hotspot distribution, and accurately matching the signal enhancement requirements of different metabolites. The optimal ratio of 3 μL serum to 5 μL silver nanoparticle solution is preferred, significantly reducing the consumption of clinical serum samples while ensuring rich spectral information and signal stability.

[0021] Furthermore, the method of this invention fixes and unifies the Raman spectroscopy detection parameters, establishes a multi-dimensional comprehensive screening standard, accurately screens the optimal working concentration, performs standardized correction processing on the acquired raw SERS spectra, and combines ultraviolet-visible spectroscopy characterization and scanning electron microscopy microstructure to assist in verification. Finally, through spectral comparison and similarity quantification analysis with multi-gradient molecular weight cutoff ultrafiltration pretreated samples, it is verified that the method of this invention can completely retain the spectral information of serum small molecule metabolites, with high spectral peak consistency and extremely low distortion, which is significantly better than traditional pretreatment methods. Attached Figure Description

[0022] Figure 1 This is a flowchart of the serum label-free SERS detection process based on silver nanoparticle concentration regulation.

[0023] Figure 2 This is a diagram showing the particle size and concentration distribution of silver nanoparticles.

[0024] Figure 3 Serum SERS spectra under different serum / silver nanocolloid volume ratios.

[0025] Figure 4 The SERS spectra of the mixture of silver nanoparticle colloid and serum under different silver nanoparticle concentrations are shown.

[0026] Figure 5 The images show the UV-Vis absorption spectra of silver nanoparticles of different concentrations mixed with serum.

[0027] Figure 6 Comparison of the full width at half maximum (FWHM) of the UV absorption peaks of silver nanoparticles and silver nanoparticle-serum mixture.

[0028] Figure 7 SEM images of silver nanoparticles of different concentrations mixed with serum.

[0029] Figure 8 Comparison of SERS spectra of raw serum and serum samples after ultrafiltration with different molecular weight cutoffs.

[0030] Figure 9 This is a similarity analysis diagram of the spectra obtained by the method of the present invention and different ultrafiltration treatment methods. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0032] like Figure 1 As shown, this invention provides a universal serum SERS detection method based on silver nanoparticle concentration regulation, the specific steps of which are as follows: S1. Preparation of silver nanocolloids Silver nanoparticles were prepared by reducing silver nitrate with hydroxylamine hydrochloride. Specifically, hydroxylamine hydrochloride solution and sodium hydroxide solution were thoroughly mixed to obtain a mixed solution, which was an alkaline reducing system. Subsequently, under continuous stirring, the mixed solution was rapidly added to an aqueous solution of silver nitrate, allowing silver ions to be rapidly reduced to silver nanoparticles at room temperature. After a certain reaction time, stable silver nanoparticles were obtained and stored at 4°C for later use.

[0033] S2. Constructing a control system with different concentrations of silver nanoparticles The silver nanoparticles obtained in step S1 were centrifuged and concentrated. After removing part of the supernatant, the mixture was resuspended to obtain silver nanoparticle systems with different concentration gradients. By adjusting the concentration of silver nanoparticles, the number of particles, the interfacial coverage, the distribution of local hot spots, and the protein / metabolite competitive adsorption state in the serum system were changed.

[0034] S3. Constructing a serum label-free SERS detection system The serum sample to be tested is thoroughly mixed with the silver nanoparticle colloid obtained in step S2 at a certain volume, allowing the proteins, lipids, and small molecule metabolites in the serum to interact with the surface of the silver nanoparticles. By regulating the concentration of silver nanoparticles, the excessive occupation of SERS active sites by high-abundance proteins is reduced, while low-abundance small molecule metabolites are promoted to enter the local enhancement region formed by the silver nanoparticles.

[0035] In a preferred embodiment, in step S3, after thawing the cryopreserved serum sample, 3 μL of the serum sample is thoroughly mixed with 5 μL of silver nanoparticle colloid to obtain a silver nanoparticle-serum mixture. This volume ratio can obtain a more stable serum metabolite-related SERS spectrum while reducing the amount of serum sample used. The silver nanoparticle-serum mixture is then added to a capillary tube, wherein the capillary tube has an inner diameter of 1 mm, an outer diameter of 2 mm, and a length of 20 mm.

[0036] S4. Collect SERS spectra of serum samples The silver nanoparticle-serum mixture obtained in step S3 was placed in a confocal Raman spectrometer (532 nm excitation source) and detected at 50% laser power to acquire label-free SERS spectral information of the serum. No ultrafiltration, protein removal, centrifugation, or complex labeling treatment of the serum sample is required during the detection process. Since the silver nanoparticle colloid can be prepared in advance, the serum sample can be mixed with the silver nanoparticle colloid for detection, and the total detection time for a single test can be controlled within 1-2 minutes. The obtained SERS spectra can be further processed by background subtraction, smoothing and noise reduction, and normalization for obtaining serum metabolite molecular fingerprint information and analyzing differences between samples.

[0037] S5. Comparison with ultrafiltration pretreatment detection methods To verify the ability of the method of this invention to acquire spectral information related to low-abundance metabolites in serum, the serum SERS spectra obtained by the method of this invention were compared with the SERS spectra of serum components after treatment with 3 kDa, 10 kDa, 30 kDa, 50 kDa, and 100 kDa ultrafiltration tubes. By comparing the peak position similarity between the direct detection spectra of the original serum and the spectra of the ultrafiltration small molecule components, the ability of the method of this invention to acquire serum small molecule metabolite information without complex pretreatment conditions was evaluated.

[0038] The present invention will be described in more detail below through specific embodiments to facilitate understanding by those skilled in the art. Unless otherwise specified, the reagents used in the present invention are all conventional reagents, and the instruments and methods employed are also conventional instruments and methods in the art.

[0039] Example 1: Preparation and characterization of silver nanocolloids (Ag NPs) 1. Take 10 mL of 15 mM hydroxylamine hydrochloride solution and 10 mL of 30 mM NaOH solution and mix thoroughly to obtain a mixed solution, which is an alkaline reducing system. Then, under continuous stirring, the above mixture is quickly added to 180 mL of 1.11 mM AgNO3 solution and reacted at room temperature for about 20 min to obtain stable silver nanoparticles, which are then stored at 4 °C for later use.

[0040] 2. The obtained silver nanoparticles were characterized using nanoparticle tracking analysis (NTA) to characterize their particle size and concentration. For example... Figure 2 As shown, the silver nanoparticles exhibit a unimodal distribution with an average particle size of approximately 91.9 ± 2.0 nm and a mode particle size of approximately 73.3 nm. After 100-fold dilution, the particle concentration was measured to be 2.62 × 10⁻⁶. 10 Based on the particles / mL, the calculated concentration of the original solution is approximately 2.62 × 10⁻⁶. 12 particles / mL.

[0041] Example 2: Optimization of the mixing volume of serum and silver nanocolloids Human serum samples stored at -80℃ were removed and slowly thawed under low temperature conditions. Different volumes of serum samples were mixed with silver nanoparticles to obtain silver nanoparticle-serum mixtures (MACE-Ag NPs), and the effect of serum volume on SERS spectral signal was investigated. Figure 3As shown, when the volume of silver nanoparticles is 5 μL and the volume of serum sample is 3 μL, a relatively stable and informative serum SERS spectrum can be obtained with a lower serum consumption. Therefore, a detection system consisting of 5 μL of silver nanoparticles and 3 μL of serum is preferred. The Raman signal was measured using a laser confocal Raman spectrometer (Renishaw, Invia) with a laser wavelength of 532 nm and an exposure time of 3 s.

[0042] Example 3: Optimization of Silver Nanoparticle Concentration 1) Concentration gradient SERS spectroscopy of silver nanoparticles and screening of optimal concentration Silver nanoparticle colloids were centrifuged at 10,000 rpm for 5 min, and after removing different volumes of supernatant, the colloids were resuspended to obtain silver nanoparticle systems with different concentration gradients, namely 2.62 × 10⁻⁶. 12 5.24×10 12 1.31×10 12 2.62×10 13 5.24×10 13 1.048×10 14 particles / mL, corresponding to the attached [data]. Figure 4 The c~h experimental groups. All experiments used the optimized 3μL serum sample volume and mixing volume ratio obtained in Example 2. Different concentrations of silver nanoparticles were thoroughly mixed with the serum samples. SERS spectra were acquired using uniform capillary injection and uniform Raman parameters, ultimately obtaining serum SERS spectral results at different concentration gradients, such as... Figure 4 As shown, with changes in silver nanoparticle concentration, the protein-related and metabolite-related characteristic peaks in the serum SERS spectrum exhibited different trends. As the silver nanoparticle concentration gradually increased from group c to group h, the spectral signals showed a regular change: the signal intensity of the characteristic peaks corresponding to serum proteins gradually decreased, while the SERS signal generated by small molecule metabolites entering the hotspot region of the silver nanoparticles continuously increased. Based on a comprehensive comparison of the signal-to-noise ratio, metabolite characteristic peak richness, and signal stability of each group's spectra, group f (silver nanoparticle concentration 2.62 × 10⁻⁶) was ultimately determined. 13 (particles / mL) is the optimal working concentration for label-free SERS detection in serum.

[0043] 2) Ultraviolet-visible absorption spectroscopy characterizes the dispersion stability of silver nanoparticles To verify the dispersion state of silver nanoparticles at different concentrations in a serum system and to eliminate the interference of particle aggregation on the detection results, a UV-Vis absorption spectroscopy characterization experiment was conducted. Pure silver nanoparticles (…) were tested separately. Figure 5 (middle a spectral line), pure serum sample ( Figure 5The UV-Vis absorption spectra of the middle b line were used as blank controls; then the UV-Vis absorption spectra of MACE-Ag NPs after mixing 3 μL of serum with six different concentrations of silver nanoparticles (c~h) at the optimal volume ratio were measured. Figure 5 The spectral comparison results (C~H lines) showed that after mixing silver nanoparticles of all concentrations with serum, the surface plasmon resonance absorption peaks of the silver nanoparticles only showed a slight red shift, without obvious shift distortion, and the full width at half maximum (FWHM) of the absorption peaks was basically close to that of the original pure silver nanocolloids. No peak broadening, splitting, or other aggregation characteristics were observed. This preliminarily proves that silver nanoparticles of all concentrations did not undergo large-scale aggregation in the complex serum matrix and could be stably dispersed in the serum system.

[0044] To further quantify the dispersion stability, a UV-Vis spectrophotometer (Evolution 201, 200, 260 Bio) was used to detect pure silver nanocolloids and silver nanoparticle-serum mixtures. The full width at half maximum (FWHM) of the surface plasmon resonance peaks was extracted and compared. Figure 6 The half-width at half-maximum (WHM) of the absorption peaks of pure silver nanocolloids and silver nanoparticle-serum mixtures are compared. The data results show that the WHM values ​​of the absorption peaks of pure silver nanocolloids and silver nanoparticle-serum mixtures are very similar, with no significant difference. This further confirms from a quantitative data perspective that the silver nanoparticles, after concentration regulation according to this invention, have excellent dispersion stability in the serum system and good system homogeneity.

[0045] 3) SEM microscopic morphology observation of particle dispersion state To visually verify the dispersion effect of silver nanoparticles from a microscopic morphology perspective, cold field emission scanning electron microscopy (Hitachi High-Tech Corporation, SU8600) was used to characterize the silver nanoparticle-serum mixtures at six concentrations (c~h). Figure 7 The images show the microstructures of six groups, a to f, where a to f correspond to a concentration of 2.62 × 10⁻⁶. 12 5.24×10 12 1.31×10 12 2.62×10 13 5.24×10 13 1.048×10 14The silver nanoparticle-serum mixture was prepared using particles / mL. Microscopic SEM observation clearly showed that, under all tested concentration conditions, the silver nanoparticles were uniformly dispersed on the surface and inside the serum matrix. The particles were evenly distributed, without stacking or agglomeration, and no obvious particle aggregation was observed. The microscopic morphology results directly corroborated the conclusions of the UV spectroscopy, demonstrating that the concentration control method of this invention can maintain stable dispersion of silver nanoparticles in complex serum biological systems, providing a reliable substrate state guarantee for accurate and stable serum SERS detection.

[0046] Example 4: Comparison of SERS spectra of ultrafiltration pretreated serum and the direct detection method of the present invention This embodiment compares the SERS spectra of raw serum and serum samples pretreated with ultrafiltration at different molecular weight cutoffs to verify the impact of traditional ultrafiltration pretreatment on serum molecular fingerprint information, highlighting the technical advantages of this invention: no pretreatment and direct detection. Figure 8 .in, Figure 8 The image shows a comparison of the SERS spectra of the original serum solution and serum samples treated with ultrafiltration at different molecular weight cutoffs. From top to bottom, the image shows the original serum solution (blue spectral lines) and the SERS spectra of the serum after treatment with ultrafiltration tubes with molecular weight cutoffs of 3 kDa, 10 kDa, 30 kDa, 50 kDa, and 100 kDa (purple spectral lines).

[0047] The experiment used homologous serum samples from the same batch to ensure the consistency of the sample substrate. The samples were evenly divided into multiple groups: one group did not undergo any pretreatment and served as the serum stock solution control group; the serum samples of the other groups were subjected to standardized ultrafiltration pretreatment using ultrafiltration tubes with molecular weight cutoffs of 3 kDa, 10 kDa, 30 kDa, 50 kDa, and 100 kDa, respectively, to remove large molecular weight proteins and impurities in the serum, resulting in multiple groups of differentiated ultrafiltration treated serum samples.

[0048] Serum stock solution and serum samples after each gradient ultrafiltration treatment were mixed with the optimal concentration of silver nanoparticles selected in Example 3, according to the optimal volume ratio optimized in Example 2. 3 μL of each serum component was used for sample preparation. All samples underwent SERS spectroscopy testing using the same capillary injection method and consistent Raman spectroscopy acquisition parameters. The final results were obtained... Figure 8 The spectral comparison results are shown.

[0049] Depend on Figure 8The spectral comparison results clearly show that, compared to the complete SERS spectrum of the original serum sample, the serum spectra after ultrafiltration pretreatment with different molecular weight cutoffs exhibit significant changes in the position, relative intensity, and number of characteristic peaks. Ultrafiltration selectively removes large molecular proteins and some related components from serum, resulting in the loss of inherent biological components and directly destroying the original SERS molecular fingerprint information of serum, failing to accurately reflect the complete component state of serum. In contrast, this invention eliminates the need for any sample pretreatment steps such as ultrafiltration, centrifugation, or purification. It achieves direct serum detection solely through the adjustment of silver nanoparticle concentration, maximizing the preservation of the original molecular information of the serum sample, and resulting in detection results that more closely reflect the true physiological state of serum.

[0050] Example 5: Quantitative Analysis of Spectral Similarity (Pearson Correlation Coefficient PCC) This embodiment uses the Pearson correlation coefficient (PCC) to quantitatively analyze the serum SERS spectral distortion caused by ultrafiltration pretreatment, and quantitatively evaluates the accuracy and reliability of the pretreatment-free direct detection method of this invention. Figure 9 .in, Figure 9 This is a similarity analysis diagram of the spectra obtained by the method of the present invention and different ultrafiltration treatment methods, which intuitively presents the similarity differences between the spectra under different ultrafiltration conditions and the original spectra.

[0051] In this experiment, the original serum SERS spectrum obtained by direct detection of silver nanoparticle concentration regulation in Example 4 was used as the sole reference standard. This reference spectrum completely preserves the fingerprint information of all biological components in the serum and is authentic and reliable. Based on this, the Pearson correlation coefficient (PCC) between this reference spectrum and the serum SERS spectra after treatment with 3 kDa, 10 kDa, 30 kDa, 50 kDa, and 100 kDa molecular weight cutoff ultrafiltration tubes was calculated, and a spectral similarity analysis chart was plotted based on the calculation results. Figure 9 .

[0052] The closer the Pearson correlation coefficient is to 1, the higher the similarity between the two sets of spectra and the lower the degree of spectral distortion; the lower the value, the more severe the damage to serum components caused by ultrafiltration pretreatment, and the more prominent the spectral distortion and aberration problems. Figure 9 Data analysis revealed that the Pearson correlation coefficients of all ultrafiltration-treated serum samples decreased to varying degrees compared to the original direct detection benchmark spectra, confirming that ultrafiltration pretreatment caused distortion in serum SERS spectra. Specifically, the smaller the molecular weight cutoff of the ultrafiltration tube, the more significant the removal effect on large molecular components in serum; the greater the loss of original serum components, the lower the corresponding spectral similarity, and the more severe the distortion of the detection results.

[0053] In summary, this invention, by precisely controlling the concentration of silver nanoparticles, eliminates the need for ultrafiltration pretreatment in traditional detection processes, enabling direct label-free SERS detection of raw serum samples. This completely avoids problems such as serum component loss, spectral distortion, and inaccurate detection results caused by ultrafiltration pretreatment. It can completely and accurately restore all molecular fingerprint information of serum samples, possessing significant advantages such as strong versatility, low sample consumption, high detection accuracy, and no sample loss. It is suitable for rapid and accurate SERS detection of various serum samples.

[0054] Furthermore, this invention uses silver nanoparticles as a SERS-enhancing substrate and employs a precise particle concentration control strategy to achieve label-free, highly sensitive detection of complex serum systems. Building upon this foundation, further in-depth research and technological expansion can be conducted in this field, focusing on multiple dimensions including material systems, control parameters, detection samples, and detection devices.

[0055] Further research into enhancing substrate materials could explore the application effects of other metallic nanomaterials with surface plasmon resonance enhancement properties in serum SERS detection. In addition to the silver nanoparticles used in this invention, the enhancing properties of gold nanoparticles, gold-silver alloy nanoparticles, copper nanostructures, and their multi-component composite systems could be systematically investigated. By optimizing the concentration, size, and surface modification state of these nanomaterials, the local electromagnetic field distribution and SERS hotspot characteristics within complex serum systems could be further controlled, potentially further optimizing the label-free detection efficiency of body fluid metabolites and forming a multi-material-adaptive SERS enhanced detection system.

[0056] At the level of in-depth research on core regulatory parameters, this invention utilizes the concentration of silver nanoparticles to achieve precise control over interfacial competitive adsorption and hotspot distribution. Further development can expand to include diverse physical control methods to achieve equivalent or even superior interfacial control effects. On one hand, by precisely controlling the nanoparticle size distribution, the optical enhancement properties and interfacial adsorption behavior of nanoparticles can be optimized based on the size effect. On the other hand, the microstructure of nanoparticles can be controlled, systematically exploring the influence of different structural morphologies such as spherical, dendritic, and star-shaped particles on the SERS hotspot generation density, distribution location, and signal enhancement efficiency. Through the refined control of these parameters, signal optimization methods under complex serum matrices can be further enriched, and the regulatory strategy of the SERS detection system can be improved.

[0057] In terms of expanding the sample system, this invention uses untreated raw serum as the core sample. It can be subsequently expanded to include serum, tears, and urine for various clinical testing scenarios, creating a diversified testing system. Liquid biopsy can analyze proteins, metabolites, and related molecular information in various body fluids such as serum, tears, and urine to achieve disease screening and physiological status assessment. The three types of body fluids differ in matrix characteristics, sample content, and interfering components, offering differentiated clinical application value. Future research can focus on the compatibility of different serum pretreatment and dilution systems, while exploring stable detection schemes for trace amounts of tears and optimizing strategies for identifying low-abundance metabolites in the urine matrix. This expansion into multiple body fluid systems effectively overcomes the limitations of single-serum testing, significantly improving the versatility and clinical applicability of this SERS detection technology.

[0058] Regarding the optimization of the detection carrier, this invention employs a capillary tube to perform serum SERS detection in a liquid phase system. Subsequent studies can be conducted on the compatibility of different detection carriers. The detection stability, optical path compatibility, and sample utilization of different detection devices, such as cuvettes and irregularly shaped quartz tubes, can be investigated. By optimizing the carrier structure and detection method, the versatility and operability of the detection system can be further improved, providing technical support for the subsequent development of large-scale, standardized clinical testing equipment.

Claims

1. A universal serum SERS detection method based on silver nanoparticle concentration regulation, characterized in that, Includes the following steps: The original serum samples to be tested were mixed with silver nanoparticle solutions of different concentration gradients to obtain silver nanoparticle-serum mixtures under different silver nanoparticle concentration conditions. Surface-enhanced Raman scattering (SERS) spectra of silver nanoparticle-serum mixtures under different silver nanoparticle concentrations were collected, and the corresponding serum SERS spectral data were acquired. Based on serum SERS spectral data obtained under different silver nanoparticle concentrations, the working concentration of silver nanoparticles was screened to determine the optimal working concentration of silver nanoparticles suitable for serum liquid biopsy. Based on the SERS spectrum obtained at the optimal working concentration of silver nanoparticles, the molecular fingerprint information of metabolites in serum was detected and analyzed.

2. The universal serum SERS detection method based on silver nanoparticle concentration regulation according to claim 1, characterized in that, The silver nanoparticle solutions with different concentration gradients were prepared as follows: monodisperse silver nanocolloids were prepared at room temperature using an alkaline reduction system. Hydroxylamine hydrochloride was used as a reducing agent and sodium hydroxide as an alkaline auxiliary agent to reduce silver nitrate aqueous solution to obtain silver nanocolloids. The obtained silver nanocolloids were used as the base stock solution for preparing silver nanoparticle solutions with different concentration gradients.

3. The universal serum SERS detection method based on silver nanoparticle concentration regulation according to claim 2, characterized in that, Concentration gradient control was performed on the silver nanoparticle colloidal substrate stock solution: the silver nanoparticle colloidal stock solution was concentrated by centrifugation, different volumes of supernatant were removed, and the solution was homogenized and resuspended to prepare silver nanoparticle solutions with low, medium, and high concentrations, where the low concentration range was 1.31 × 10⁻⁶. 12 ~2.62×10 12 particles / mL, medium concentration range 5.24×10 12 ~2.62×10 13 particles / mL, with a high concentration range of 5.24 × 10⁻⁶. 13 ~1.048×10 14 particles / mL.

4. The universal serum SERS detection method based on silver nanoparticle concentration regulation according to claim 3, characterized in that, The optimal concentration of the silver nanoparticle solution is 2.62 × 10⁻⁶. 13 particles / mL.

5. The universal serum SERS detection method based on silver nanoparticle concentration regulation according to claim 1, characterized in that, During the test, the raw serum sample without any pretreatment was directly mixed with silver nanoparticle solutions of different concentration gradients to construct the detection system. The volume of the raw serum sample to be tested was 2-4 μL, and the corresponding volume of the working solution of silver nanoparticles was 4-6 μL.

6. The universal serum SERS detection method based on silver nanoparticle concentration regulation according to claim 5, characterized in that, The volume of the original serum sample to be tested was 3 μL, and the corresponding volume of the working solution of silver nanoparticles was 5 μL.

7. The universal serum SERS detection method based on silver nanoparticle concentration regulation according to claim 1, characterized in that, Raman spectroscopy was performed using a confocal Raman spectrometer with a 532nm excitation source, a laser power of 50%, and an exposure time of 3 seconds. SERS spectra of silver nanoparticle-serum mixtures at each concentration gradient were collected under uniform conditions, and the total duration of a single detection did not exceed 2 minutes.

8. The universal serum SERS detection method based on silver nanoparticle concentration regulation according to claim 1, characterized in that, Before screening for the optimal concentration of silver nanoparticle solution, the following steps are also included: Microscopic morphology-assisted verification steps: Scanning electron microscopy was used to observe the microscopic morphology of silver nanoparticle-serum mixtures at different concentration gradients. Concentration ranges in which silver nanoparticles were uniformly distributed, without aggregation or stacking, were selected as the effective concentration screening ranges. The steps for verifying the dispersion stability of the mixture are as follows: the silver nanoparticle-serum mixtures at various concentration gradients are characterized by ultraviolet-visible spectroscopy. The dispersion stability of silver nanoparticles in serum is determined by detecting the peak shape, offset, and full width at half maximum (FWHM) parameters of the surface plasmon resonance peaks, and invalid concentration groups with aggregation distortion are eliminated.

9. The universal serum SERS detection method based on silver nanoparticle concentration regulation according to claim 1, characterized in that, The selection criteria for the optimal silver nanoparticle solution concentration were as follows: the optimal spectral signal-to-noise ratio, the highest abundance of characteristic peaks of serum small molecule metabolites, and the best spectral signal repeatability and stability were used as the core selection criteria to select the optimal working concentration of silver nanoparticle solution suitable for the detection of raw serum.

10. A universal serum SERS detection method based on silver nanoparticle concentration regulation according to claim 1, characterized in that, After selecting the optimal silver nanoparticle solution concentration, the process also includes a spectral standardization step: the serum SERS spectrum obtained at the optimal working concentration of silver nanoparticles is subjected to background subtraction, smoothing and noise reduction and data normalization in sequence to obtain a standardized serum molecular fingerprint spectrum, and serum metabolite information analysis and liquid biopsy sample feature characterization are performed based on the standardized serum molecular fingerprint spectrum.

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