Metabonomics analysis method for 3D printed intravascular stent

Abstract

The invention belongs to the field of metabonomics analysis, and particularly relates to a metabonomics analysis method of a 3D printing intravascular stent. According to the invention, nano metabonomics is established through the combination of the detection analysis of a non-targeted water-soluble / lipid metabonomics method, the detection analysis of targeted water-soluble / lipid metabonomics and space metabonomics, and the biological reaction of 3D-PVS is clarified. A nano metabonomics method with multi-dimensional biological information is used for researching the biological effect of 3D-PVS on the metabolic level, and potential biochemical, medical and industrial application and development directions of 3D-PVS are disclosed on the basis of nano metabonomics research results. The method also has the following technical advantages and characteristics: the operation is convenient, the cost is economical and practical, the materials are environment-friendly and environment-friendly, the design is simple, the method is suitable for laboratory research and outdoor test, the non-targeting-targeting combined nano metabonomics research of 3D-PVS biological response characteristics can be conveniently carried out in real time, and the application prospect is wide.

Description

Technical Field

[0001] The present invention belongs to the field of metabolomics analysis, and specifically relates to a metabolomics analysis method for a 3D printed vascular stent. Background Art

[0002] Three-dimensional (3D) printing technology, also known as additive manufacturing, is a method that uses computer-aided manufacturing to stack materials layer by layer and form a complete three-dimensional entity. Its advantages include reducing raw material consumption, saving production energy consumption, and reducing time costs. In addition, this technology is flexible in product production and is not limited by product shape and structure. 3D printing technology has been widely used in many fields around the world, including manufacturing, construction, electronics, biomedicine, aerospace, automobile, textile and food industries. Among them, 3D printed vascular stents (3D-PVS) have become an important direction for the application of 3D printing materials in the biomedical field, providing a new option for the construction of vascular stents.

[0003] At present, cardiovascular disease (CVD) has become the main cause of death or disability in developed countries. Current clinical treatment methods mainly include bypass surgery, stent implantation and drug therapy. Among them, commercial cardiovascular stents are one of the most effective devices for treating cardiovascular stenosis. According to the different basic materials and stent surfaces, vascular stents can be divided into three categories: bare metal stents (BMS), drug-eluting stents (DES) and bioresorbable stents (BRS). However, due to problems such as morphological heterogeneity, unsatisfactory stability and biocompatibility, standard stents manufactured by traditional methods may not be suitable for all blood vessels, leading to problems such as stent thrombosis and myocardial infarction. In contrast, 3D-PVS has the advantages of improved biocompatibility, enhanced mechanical properties and reduced risk of restenosis in stents, especially when it comes to large lesion areas and vascular branches caused by a single stent. In addition, designing a personalized stent structure based on the specific vascular structure of each patient is considered to be a better solution. Therefore, 3D-PVS has excellent application prospects in the biomedical field.

[0004] However, the biocompatibility analysis of 3D-PVS is still at the phenotypic or cellular level, and it is urgent to explore deeper molecular mechanisms to provide theoretical and technical support for the clinical application of 3D-PVS. However, there are few reports on combining the metabolomics analysis platform with the study of 3D-PVS biological characteristics, which brings obstacles to the biocompatibility analysis of 3D-PVS and is not conducive to the practical application of 3D-PVS.

[0005] In metabolomics research, one of the challenges is how to separate various metabolites from biological samples and conduct effective detection. Usually, the chemical composition of the metabolites of organisms is complex, and there are multiple impurities interfering with the various target compounds to be analyzed. Therefore, before detection, specific methods are usually required to process and extract biological samples. In addition, detection conditions such as LC-MS / MS and DESI-MSI should also be designed according to the type and content of the target metabolites to be detected.

[0006] Therefore, it is of great significance to study the effects of 3D-PVS on the metabolites and metabolic pathways of cells and organisms and to develop a deep metabolomics analysis method for 3D-PVS, so as to further promote the clinical application of 3D-PVS. Summary of the invention

[0007] In view of the above problems, the present invention provides an analytical method for the effect of 3D-PVS on cell and animal metabolomics, aiming to evaluate the effect of 3D-PVS on cell and organism metabolism and provide a theoretical basis for the subsequent clinical application of 3D-PVS.

[0008] The present invention provides a metabolomics analysis method for a 3D printed vascular stent, characterized in that the method comprises the following steps:

[0009] Step 1, performing DESI-MSI scanning on a biological sample to obtain a DESI-MSI detection result of a fat-soluble metabolite; the biological sample is a biological sample treated with a 3D printed vascular stent material;

[0010] Wherein, the DESI-MSI detection conditions include: the solvent composition is a methanol aqueous solution with a volume content of 90-98% containing 0.05-0.2% formic acid; and / or, the solvent flow rate is 4-6 μL / min; and / or, the sheath gas of the electrospray atomization is N2, and the pressure is 0.1-0.3 Mbar; and / or, the scanning mode of the biological sample is a charged droplet impact mode; and / or, the spray incidence angle is 70-75°;

[0011] Step 2: performing spatial metabolomics analysis on the DESI-MSI detection results.

[0012] Preferably, in step 1, the 3D printed vascular stent material is prepared by a high-resolution projection micro-stereolithography 3D printer and a photosensitive polymer; a cylindrical vascular stent with a hollow structure.

[0013] Preferably, in step 1, the biological sample is a frozen section of a tissue or organ of an animal model, and the animal of the animal model is a SD rat; the 3D printed vascular stent material is processed as 3D printed vascular stent material intravenously implanted; the frozen section is made at least 48 hours after the animal model is implanted with the 3D printed vascular stent material.

[0014] Preferably, in step 1, the detection conditions of DESI-MSI also include: in positive mode, the voltage is 4-5 kV, and in negative mode, the voltage is -4 to -5 kV; the m / z range is 100 to 1200; and the solvent also contains 0.05-0.2% volume content of leucine enkephalin.

[0015] Preferably, step 2 comprises the following steps:

[0016] Step 2.1, analyzing the DESI-MSI detection results using HDI software, and obtaining a processed lipid metabolite DESI-MSI spectrum through a peak area integration algorithm;

[0017] Step 2.2: Based on the human metabolome database, the R language algorithm was used to perform metabolite identification on the processed lipid metabolite DESI-MSI spectra to obtain spatial metabolomics results and screen out characteristic metabolites.

[0018] Preferably, the analysis method further comprises the following steps:

[0019] Step A, "3D printed vascular scaffold material-cell" co-culture: co-culturing the 3D printed vascular scaffold material with cells;

[0020] Step B, metabolite extraction and non-targeted nano-metabolomics detection: extracting and detecting water-soluble metabolites and / or lipid metabolites from the cells co-cultured in step A;

[0021] Step C, "3D printed vascular scaffold material-animal" co-culture: co-culturing the 3D printed vascular scaffold material with the animal;

[0022] Step D, metabolite extraction and targeted nano-metabolomics detection: extracting and detecting water-soluble metabolites and / or lipid metabolites from the animals after co-culture in step C;

[0023] Wherein, the detection of water-soluble metabolites in step B is carried out by LC-MS detection, and the chromatographic conditions are as follows: the mobile phase is composed of phase A and phase B, phase A is 5-10% acetonitrile aqueous solution, phase B is 90-95% acetonitrile aqueous solution, and the chromatographic column filler is amidoethylene bridge hybrid particles;

[0024] The detection of lipid metabolites in step B is performed by LC-MS detection, and the chromatographic conditions are as follows: the mobile phase consists of phase A and phase B, wherein phase A is a 50-60% acetonitrile aqueous solution, phase B is a 90-95% isopropanol acetonitrile solution, and the chromatographic column filler is C18 silica gel particles;

[0025] The detection of water-soluble metabolites in step D is carried out by LC-MS / MS detection, and the detection mode is a multiple reaction monitoring mode; the LC-MS / MS detection comprises triple quadrupole mass spectrometry detection and liquid chromatography detection;

[0026] The water-soluble metabolites in step D are detected by LC-MS / MS, and the chromatographic conditions are as follows: mobile phase A is a water:methanol:acetonitrile solution with a volume ratio of 1:1:1 or 1:1:1.5, and mobile phase B is an isopropanol solution containing 5-7 mM ammonium acetate.

[0027] Preferably, in step D, the method for extracting water-soluble metabolites is: taking animal tissue, adding steel balls and 80% methanol aqueous solution, homogenizing the tissue to obtain a suspension; cooling the suspension at -80°C, then, ultrasonicating and centrifuging, and taking the supernatant.

[0028] And / or, in the step D, the lipid metabolite extraction method is: taking animal tissue, adding steel balls and methanol, homogenizing the tissue to obtain a suspension; adding methyl tert-butyl ether and water to the suspension, centrifuging, and taking the supernatant to obtain;

[0029] And / or, in step C, the 3D printed vascular stent material is implanted into the animal's vein and allowed to act for at least 48 hours.

[0030] Preferably, the specific method for detecting the water-soluble metabolites in step D includes:

[0031] The chromatographic conditions include a flow rate of 200-300 μL / min, a mobile phase A of a 5-10% acetonitrile aqueous solution containing 5-10 mM ammonium acetate and 0.1-0.2% acetic acid by volume, and a mobile phase B of a 90-95% acetonitrile aqueous solution containing 5-10 mM ammonium acetate and 0.1-0.2% acetic acid by volume; a column temperature of 40-50° C.; an injection volume of 1-2 μL in a positive mode and 6-8 μL in a negative mode;

[0032] and / or, the mass spectrometry conditions include using an electrospray ionization source; in positive mode, the interface capillary is maintained at 450-500° C., the ion source gas flow is 40-50 psi, and the spray voltage is 4.5-5.5 kV; in negative mode, the interface capillary is maintained at 500-550° C., the ion source gas flow is 40-50 psi, and the spray voltage is -4.5 to -5.5 kV;

[0033] And / or, the specific method for detecting lipid metabolites in step D includes:

[0034] The chromatographic conditions included, for the positive ion mode, a flow rate of 250-300 μL / min, mobile phase A was a water:methanol:acetonitrile solution with a volume ratio of 1:1:1 or 1:1:1.5, and mobile phase B was an isopropanol solution containing 5-7 mM ammonium acetate; the chromatographic column was a C18 column; the column temperature was 40-45°C, and the injection volume was 2-3 μL; for the negative ion mode, the flow rate was 200-700 μL / min, the mobile phase A was a dichloromethane:acetonitrile solution with a volume ratio of 7:93, and the mobile phase B was an acetonitrile aqueous solution with a volume ratio of 1:1 or 1:1.2; Phenomenex Luna NH2 column, the column temperature was 30-35°C, and the injection volume was 2-3 μL.

[0035] And / or, the mass spectrometry conditions include, for positive ion mode, using a heated electrospray ionization source, the interface capillary is maintained at 500-550°C, the sheath gas flow is 50-55 psi, the auxiliary gas flow is 50-55 psi, and the spray voltage is 5-5.2 kV. The mass spectrometer also uses a heated electrospray ionization source, the interface capillary is maintained at 500-550°C, the sheath gas flow is 50-55 psi, the auxiliary gas flow is 50-55 psi, and the spray voltage is -4.5 to -5.5 kV; for negative ion mode, the sheath gas flow is 50-55 psi, the auxiliary gas flow is 50-55 psi, and the spray voltage is -4.5 to -5.5 kV.

[0036] Preferably, in step A, the co-cultivation time is 12-48 hours;

[0037] In step B, the method for extracting water-soluble metabolites is:

[0038] Wash the cells with phosphate buffer, add trypsin to digest, obtain a cell suspension, add 75-80% methanol aqueous solution, ultrasonically treat for 20-30 minutes, vortex for 20-30 minutes, centrifuge at 10000-13000 rpm for 5-10 minutes, and the centrifugation temperature is 3-4°C, and take the supernatant to obtain;

[0039] In step B, the extraction method of lipid metabolites is:

[0040] The biological sample is washed with phosphate buffer, and trypsin is added for digestion to obtain a cell suspension, methanol is added, and the suspension is vortexed for 5 to 10 minutes. Methyl tert-butyl ether and water are added in sequence, and the suspension is centrifuged at 10,000 to 13,000 rpm for 5 to 10 minutes at a centrifugal temperature of 3 to 4° C. The supernatant is obtained.

[0041] Preferably, the detection results of water-soluble metabolites and / or lipid metabolites in step B and step D are used for bioinformatics analysis, and the bioinformatics analysis comprises the following steps:

[0042] Step E1, using Progenesis QI to pre-process the detection results of water-soluble metabolites obtained in step B, and / or using MS-DIAL to pre-process the detection results of lipid metabolites obtained in step B;

[0043] Step E2, using Analyst to preprocess the detection results of water-soluble metabolites obtained in step D, and / or using MultiQuant to preprocess the detection results of lipid metabolites obtained in step D;

[0044] Step E3, the data after preprocessing in steps E1 and E2 are processed by R language algorithm, wherein the k-nearest neighbor algorithm is used to interpolate missing values, and the robust local estimation based on quality control and scattered point smoothing signal correction are applied for normalization;

[0045] Step E4, using principal component analysis, partial least squares discriminant analysis and orthogonal partial least squares discriminant analysis to identify differences between groups.

[0046] The non-targeted-targeted nano-metabolomics analysis method of 3D-PVS biological response characteristics provided by the present invention has achieved the following beneficial effects:

[0047] 1. The biological property analysis method of 3D-PVS proposed in the present invention not only realizes the omics test of metabolites through non-targeted-targeted nano-metabolomics technology, but also effectively evaluates the effect of 3D-PVS on cell metabolism. This method ensures that 3D-PVS fully contacts and interacts with cells or rats, and by eliminating interfering factors, using LC-MS / MS and bioinformatics methods, effectively studies the effect of 3D printed vascular stents on cell metabolism.

[0048] 2. The present invention constructs a reliable evaluation system, which allows cells in a culture dish or in vivo to be in the atmosphere of a "3D printed vascular stent bath", so that the two can fully contact and ensure full interaction.

[0049] 3. The present invention constructs an efficient testing system. By combining LC-MS / MS and bioinformatics analysis software, the entire process is completed under unified testing conditions, which greatly improves the testing efficiency while ensuring the reliability and scientificity of the test results.

[0050] 5. The non-targeted-targeted combined nano-metabolomics analysis method of the 3D-PVS biological response characteristics of the present invention also has the advantages of easy operation, economical cost, environmentally friendly materials, and simple design.

[0051] 6. The present invention is suitable for non-targeted-targeted combined nano-metabolomics testing in the laboratory or outdoors, which is convenient for real-time metabolomics research on the biological characteristics of 3D-PVS and can effectively evaluate the biological characteristics of the stent. In addition, through subsequent condition optimization and technical improvement, the test and analysis effects will be further improved.

[0052] 7. The non-targeted-targeted nano-metabolomics analysis method provided by the present invention was used to examine the interaction between 3D-PVS and cells and rats, detect and analyze the metabolites of cells and rats, examine the types and contents of metabolites and metabolic pathways of cells and rats under the action of 3D-PVS, and clarify the mechanism of the influence of 3D-PVS on metabolomics. This method provides a scientific basis for evaluating the biological response characteristics of 3D-PVS and has broad application prospects.

[0053] Obviously, according to the above contents of the present invention, in accordance with common technical knowledge and customary means in the art, without departing from the above basic technical ideas of the present invention, other various forms of modification, replacement or change may be made.

[0054] The above contents of the present invention are further described in detail below through specific implementation methods in the form of embodiments. However, this should not be understood as the scope of the above subject matter of the present invention being limited to the following examples. All technologies realized based on the above contents of the present invention belong to the scope of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A logical diagram of the application of 3D-PVS to the analytical methods of cell and animal metabolomics;

[0056] Figure 2 is the scanning electron microscope image of 3D-PVS;

[0057] Figure 3 The total reactive oxygen fluorescence image of cells, the mitochondrial reactive oxygen fluorescence image and the quantitative analysis result image of mitochondrial length;

[0058] Figure 4 The results of the non-targeted nano-metabolomics method to measure the water-soluble metabolites and lipid metabolites of cells;

[0059] Figure 5 The implantation process of 3D-PVS and the changes in biomarker content in the adjacent jugular vein tissue;

[0060] Figure 6Results of water-soluble metabolites and lipid metabolites of animals measured by targeted nano-metabolomics approach;

[0061] Figure 7 This is a diagram of the spatial metabolomics detection results. DETAILED DESCRIPTION

[0062] In the following examples and experimental examples, reagents and materials not particularly described are all commercially available.

[0063] Example 1 Non-targeted-targeted nano-metabolomics analysis method for 3D-PVS biological response characteristics

[0064] Non-targeted-targeted nano-metabolomics analysis of 3D-PVS bio-response characteristics Figure 1 As shown, the specific steps are as follows:

[0065] 1. 3D-PVS material

[0066] The 3D-PVS material comes from the Department of Mechanical Engineering of City University of Hong Kong (Hong Kong, China). It is prepared by a high-resolution projection micro-stereolithography 3D printer Nano Arch S140 (BMF Material Technology Co., Ltd.) and a commercial biocompatible photosensitive polymer (BMF Material Technology Co., Ltd.). It has a viscosity (25°C) of 300, a tensile strength of 56.0 MPa, an elongation at break of 6.2%, an elastic modulus of 1614 MPa, a flexural strength of 106.6 MPa, a flexural modulus of 3.5 GPa, a thermal expansion coefficient of 170.3 μm / m / C at 50°C-100°C, a thermal expansion coefficient of 178.7 μm / m / C at 100°C-150°C, a heat deformation temperature (@0.45 MPa) of 85.7°C, and a hardness (SHORED) of 84.0. The preparation method adopts the method reported in the literature "Direct 3D printing of thin-walled cardiovascular stents with negative Poisson's ratio (NPR) structure and functional metallic coating, CompositeStructures 306 (2023) 116572". The microscopic morphology of 3D-PVS is shown in the figure. Figure 2 shown.

[0067] 2. Construction of the “3D-PVS-cell / rat” model

[0068] "3D-PVS-cell" model construction: Human umbilical vein endothelial cells (HUVECs) were cultured in Dulbecco Modified Eagle Medium (DMEM) containing 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin solution and incubated in a humidified incubator at 5% CO2 and 37°C. In order to maintain cell activity, the culture medium was replaced every two days, and other conditions remained unchanged.

[0069] Cells were digested and passaged with 0.25% trypsin-EDTA. All experiments used HUVECs in the exponential growth phase. Before exposure to 3D-PVS, HUVECs were first washed with PBS, then 1 mL of 0.25% trypsin-EDTA was added and 5 × 10 5 Then, 2 mL of HUVECs were evenly divided into six equal parts and seeded into each well of a 6-well plate. The attached HUVECs were then exposed to a low concentration (5 mg / mL) or high concentration (15 mg / mL) of 3D-PVS aqueous suspension and gently shaken to obtain adequate dispersion.

[0070] "3D-PVS-rat" model construction: A 3D-PVS-rat interaction model was established by implanting a 3D printed vascular stent in the jugular vein. The rats used were SD rats, and the mice were kept in cages, 6 per cage, with sufficient feed and water, and the bedding was changed every two days. First, take the SD rat, cut the neck skin of the anesthetized rat to locate the jugular vein. Then use a hemostat to block the blood flow at both ends. Cut the jugular vein 2mm and insert a 3D-PVS. Then suture the vein, loosen the hemostat, suture the skin, and continue feeding for 48h with normal diet and boiled water.

[0071] 3. Detection and analysis of non-targeted metabolites

[0072] After the low-concentration and high-concentration "3D-PVS-cell" samples were exposed to 3D-PVS for 48 hours, they were washed 2-3 times with pre-cooled PBS, and then 1 mL of 0.25% trypsin-EDTA was added to obtain a cell suspension, and the following operations were performed:

[0073] (1) Extraction of non-targeted water-soluble metabolites: Take 1 mL of cell suspension (cell concentration is 2,000,000 cells / mL) and add 1 mL of pre-cooled 80% spiked CH3OH (containing 2 μg / mL of 13 C5-Glutamate- 15 N, 2 μg / mL 13C2-succinic acid, 0.5 μg / mL D4-glutaric acid as internal standard) was quenched at -80°C for 30 minutes. Subsequently, ultrasonic treatment was performed in an ice bath for 10 minutes and oscillation was performed at 1500 rpm for 3 minutes. After centrifugation at 13000 rpm for 5 minutes, the supernatant was transferred to a new tube and vacuum dried for 3 hours.

[0074] (2) Detection of non-targeted water-soluble metabolites

[0075] The dried non-targeted water-soluble metabolite samples were redissolved in 200 μL HILIC solution (70% acetonitrile aqueous solution containing 10 mM ammonium formate, of which 2 μg / mL 13 C3-Sodium lactate, 1.34 μg / mL 13 C9-L-Tyrosine- 15 N, 1 μg / mL 13 C4-Aspartic acid- 15 N as internal standard), and oscillate at 1500 rpm for 10 minutes, and then centrifuge at 13000 rpm for 10 minutes. The obtained supernatant is retained for the following quality control (QC) tests:

[0076] The QC sample was prepared by mixing 30 μL of water-soluble metabolite samples obtained from three parallel samples of cells. The high-resolution LC-MS system used a Q Extractive PlusTM mass spectrometer connected to an Ultimate3000TM liquid chromatograph, and non-targeted analysis was performed in data-dependent acquisition (DDA) mode. The HPLC conditions were as follows: the flow rate was 300 μL / min, the mobile phase A was a 10% volume acetonitrile aqueous solution (containing 10 mM ammonium acetate and 0.15% acetic acid), and the mobile phase B was a 90% volume acetonitrile aqueous solution (containing 10 mM ammonium acetate and 0.15% acetic acid). An ACQUITY UPLC BEH Amide column was used, and the column temperature was 35°C. The injection volume was 5 μL. The mass spectrometer consisted of a heated electrospray ionization (H-ESI) source and an Orbitrap mass spectrometer. The interface capillary temperature was maintained at 350°C, and the sheath gas flow rate in the positive and negative modes was 35 (units) and the auxiliary gas flow rate was 10 (units). The spray voltage was 3.5 kV for both positive and negative ion modes. This test was performed in data-dependent MS2 mode, scanning 60-900 m / z (primary scan) and top 10 (secondary scan).

[0077] (3) Extraction of non-targeted lipid metabolites

[0078] Take 1 mL of cell suspension (cell concentration is 2,000,000 cells / mL), centrifuge at 1500 rpm and 4°C for 5 minutes, remove the supernatant, and then add 300 μL CH3OH and 5 μL Mass spectrometry standards (Avanti, USA). After 30 minutes of ultrasonic treatment in an ice bath, shake at 1550 rpm for 5 minutes. The above suspension was mixed with 1 mL of methyl tert-butyl ether (MTBE) and gently shaken (1500 rpm, 10 ° C, 1 hour). Then, 250 μL of H2O was added and continued to shake gently for 1 hour. Subsequently, the supernatant was collected by centrifugation (1500 rpm, 10 minutes, 4 ° C), and a nitrogen stream was used for 30 minutes to concentrate the sample.

[0079] (4) Detection of non-targeted lipid metabolites

[0080] The concentrated non-targeted lipid metabolite sample was dissolved in 300 μL 50% CH2Cl2 / 50% CH3OH, and oscillated (1500 rpm, 10°C, 15 minutes) and centrifuged (14000 rpm, 10 minutes, 4°C) respectively. The supernatant was collected for the following QC tests:

[0081] The QC sample was prepared by mixing 50 μL of lipid metabolite samples obtained from three parallel cells for LC-MS detection. The HPLC conditions were as follows: the flow rate was 300 μL / min, the mobile phase A was a 60% acetonitrile aqueous solution (containing 10 mM ammonium formate and 0.1% formic acid), and the mobile phase B was a 90% volume content of isopropanol / acetonitrile solution (containing 10 mM ammonium formate and 0.1% formic acid). A UPLC CSH C18 column (100×2.1 mm, 1.7 μm, Waters Acquity) was used, and the column temperature was 55°C. The injection volume was 2 μL. The capillary temperature of the mass spectrometer interface was maintained at 320°C, the sheath gas flow rate in the positive and negative modes was 40 (units), and the auxiliary gas flow rate was 15 (units). The spray voltage in the positive and negative ion modes was also 3.5 kV. This test scans 150-1800 m / z (primary scan) and top 10 (secondary scan) in data-dependent MS2 mode.

[0082] 4. Detection and analysis of targeted metabolites

[0083] After the rats were exposed to 3D-PVS for 48 hours, they were generally anesthetized and the jugular vein samples were taken. The collected jugular vein samples were not washed with PBS.

[0084] (1) Extraction of targeted water-soluble metabolites

[0085] 5 mg of jugular vein tissue was taken from each rat (n=6), and 6 steel balls and 200 μL of pre-cooled 80% CH3OH solution (containing 2 μg / mL of two internal standards) were added. 13 C2-succinic acid and 13 C5- 15NL-glutamate), and the tissue was homogenized three times with a cycle of 30 seconds. After homogenization, the suspension was vortexed at 1500rpm for 30 seconds and cooled at -80°C for 30 minutes. Subsequently, the mixture was ultrasonically treated in an ice bath for 10 minutes and centrifuged at 13000rpm at 4°C for 10 minutes. 400 μL of the supernatant was collected and dried at 30°C for 3 hours.

[0086] (2) Detection of targeted water-soluble metabolites

[0087] Take the dried water-soluble metabolite sample and use 500 μL HILIC solution (70% acetonitrile aqueous solution containing 10 mM ammonium acetate and 0.2% acetic acid, of which 1.34 μg / mL 13 C2-L-tyrosine and 2 μg / m 13 C1-L-lactic acid as internal standard) was re-dissolved, sonicated for 5 minutes, vortexed at 1500 rpm for 30 minutes, sonicated again for 5 minutes, and centrifuged at 13300 rpm at 4°C for 10 minutes. The obtained 100 μL supernatant was subjected to the following QC tests:

[0088] QC samples were prepared by mixing 20 μL of water-soluble metabolite samples from each rat (n = 6) and subjected to LC-MS / MS analysis using an AB Sciex triple quadrupole 6500 mass spectrometer and a Nexera LC-30A UPLC system (AB Sciex, Framingham, MA) in multiple reaction monitoring (MRM) mode. The 228 metabolites selected (100 in positive mode and 128 in negative mode) represented the main metabolic pathways. The UPLC conditions were as follows: flow rate of 300 μL / min, mobile phase (A) of 10% acetonitrile in water (containing 10 mM ammonium acetate and 0.2% acetic acid), and mobile phase (B) of 90% acetonitrile in water (containing 10 mM ammonium acetate and 0.2% acetic acid). An ACQUITY UPLC BEH Amide column (2.1 mm × 100 mm, 1.7 μm, Waters, UK) was used and heated to 40 °C. The injection volume was 1 μL in positive mode and 8 μL in negative mode. The mass spectrometry conditions were as follows: an electrospray ionization (ESI) source was used. In positive mode, the interface capillary was maintained at 500°C, the ion source gas flow (Gas1 and Gas2) was 50 psi, and the spray voltage was 5.5 kV. In negative mode, the interface capillary was maintained at 550°C, the ion source gas flow (Gas1 and Gas2) was 50 psi, and the spray voltage was -4.5 kV. All samples were randomly injected.

[0089] (3) Extraction of targeted lipid metabolites

[0090] 5 mg of jugular vein tissue was taken from each rat (n=6), and 6 steel balls and 240 μL of pre-cooled CH3OH were added: Mass spectrometry standard (Avanti, USA) = 225:3 was homogenized (30 seconds each time, three times in total). Subsequently, 800 μL MTBE was added to the suspension and mixed for 30 minutes, followed by gentle addition of 200 μL H2O and gentle shaking for 10 minutes. By centrifugation at 13300 rpm at 4°C for 10 minutes, 600 μL supernatant was collected and dried at 30°C for 2 hours.

[0091] (4) Detection of targeted lipid metabolites

[0092] The dried lipid metabolite samples were redissolved in 150 μL 50% CH2Cl2 / 50% CH3OH solution (containing 10 mM ammonium acetate), vortexed (1500 rpm, 10°C, 30 seconds) and centrifuged again at 13300 rpm at 4°C for 10 minutes. 50 μL of the supernatant was taken for the following QC tests:

[0093] The QC samples were prepared by mixing 40 μL of lipid metabolite samples obtained from each rat (n = 6). Lipidomics studies were performed using an LC-MS / MS system, which was combined with a Qtrap 5500 mass spectrometer and a Shimadzu LC-30A liquid chromatograph (AB SCIEX, USA) in multiple reaction monitoring mode. For the positive ion mode, the HPLC conditions were as follows: the flow rate was 300 μL / min, the mobile phase (A) was a 1:1:1 volume ratio of water:methanol:acetonitrile solution (containing 7 mm ammonium acetate), and the mobile phase (B) was an isopropanol solution containing 7 mm ammonium acetate. A Phenomenex Kinetex C18 column (2.1 mm × 100 mm, 2.6 μm, Phenomenex, US) was run at 45 °C, and the injection volume was 3 μL. The mass spectrometer conditions were as follows: a heated electrospray ionization (H-ESI) source was used. The interface capillary was maintained at 550°C, the sheath gas flow was 55 psi, the auxiliary gas flow was 55 psi, and the spray voltage was 5.2 kV. For the negative ion mode, the HPLC conditions included a flow rate of 200-700 μL / min, a mobile phase (A) of 7:93 volume ratio of dichloromethane:acetonitrile solution (containing 5 mm ammonium acetate), and a mobile phase (B) of 1:1 volume ratio of water:acetonitrile solution (containing 2 mm ammonium acetate, pH = 8.2). A Phenomenex Luna NH2 column (2.0 mm × 100 mm, 3 μm, Phenomenex, US) was operated at 35°C, and the injection volume was 3 μL. The mass spectrometer also used a heated electrospray ionization (H-ESI) source. The interface capillary was maintained at 550°C, the sheath gas flow was 50 psi, the auxiliary gas flow was 50 psi, and the spray voltage was -4.5 kV.

[0094] 5. Spatial metabolomics detection and analysis

[0095] Spatial metabolomics and lipidome analysis were performed by the spatial metabolomics method of DESI-MSI. Jugular vein sections (thickness of 8 μm) were mounted on glass microscope slides and stored at -80°C. Subsequently, molecular images of frozen sections of jugular vein were examined using DESI-MSI. Jugular vein imaging was performed using a DESI (2D, Prosolia, USA) source connected to a mass spectrometer (Synapt G2-Si, Waters, USA). The DESI-MSI detection conditions are as follows: DESI-MSI was performed in both positive and negative modes. In the positive mode, the voltage was 4.5 kV, the m / z range was 100 to 1200, and the spatial resolution of the jugular vein sample was 80 μm (spray point diameter). A solvent system compatible with histology (a 95% CH3OH aqueous solution containing 0.1% formic acid and 0.1% leucine enkephalin) was used with a flow rate of 6 μL / min. In negative mode, the voltage was -5 kV, the m / z range was 100 to 1200, and the spatial resolution of the jugular vein sample was also 80 μm (spray point diameter). A solvent system compatible with histology was used (95% CH3OH aqueous solution containing 0.1% formic acid and 0.1% leucine enkephalin) with a flow rate of 6 μL / min. N2 gas flow (0.2 Mbar) was used as the sheath gas for electrospray atomization. The jugular vein sections were scanned on a two-dimensional moving platform by charged droplet impact. The above experiments were carried out under optimized conditions, in which the distance from the inlet to the surface was set to 2 mm, the spray incidence angle was set to 72°, and the distance from the spray to the inlet was set to 5 mm.

[0096] Afterwards, the DESI-MSI detection results were imported into the HDI software, and the processed lipid metabolite DESI-MSI spectrum was obtained through the peak area integration algorithm. Then, according to the human metabolome database, the R language algorithm was used to perform metabolite identification on the processed lipid metabolite DESI-MSI spectrum to obtain the spatial metabolomics results and screen out characteristic metabolites.

[0097] 6. Bioinformatics statistical analysis

[0098] The raw data of non-targeted water-soluble metabolome and non-targeted lipid metabolome were preprocessed using ProgenesisQI (v2.4) and MS-DIAL (v3.98), respectively, and the raw data of targeted water-soluble metabolome and targeted lipid metabolome were preprocessed using Analyst (v1.7.2) and MultiQuant (v3.0.3), respectively. The preprocessed data, including sample information, metabolite identity, and peak intensity, were further processed in R (v3.5.1). During this process, the k-nearest neighbor (KNN) algorithm was used to interpolate missing values, and the quality control-based robust local estimated scatter smoothing (LOESS) signal correction (QC-RLSC) was applied for normalization. Subsequently, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were applied to identify differences between groups. The t-test significance level of all metabolites was set at p < 0.05, and the projected variable importance value (VIP) in the OPLS-DA model was required to be greater than 1. The metabolic network and pathway analysis of the lipidome was performed based on Cytoscape (v3.8.2).

[0099] The technical solution of the present invention is further described below through experiments. The extraction and detection methods of metabolite samples in the following experimental examples are all based on the method of Example 1.

[0100] Experimental Example 1 Effect of 3D-PVS on Cell Biological Effects

[0101] 1. Experimental Methods

[0102] 1. Determination of the effect of 3D-PVS on superoxide and mitochondrial size in cells

[0103] To measure mitochondrial superoxide production, HUVECs were incubated with MitoSOX Red (2.5 μM) at 37°C for 30 min, rinsed with PBS, and resuspended in 200 μL PBS for imaging. To assess intracellular superoxide production, HUVECs were incubated with DCFH-DA (10 μM) at 37°C for 30 min, followed by fluorescence imaging.

[0104] Mitochondrial depolarization was measured using the Mitochondrial Membrane Potential Assay Kit (JC-1). Fluorescence imaging was performed using a spinning disk confocal super-resolution microscope. Mitochondrial morphology was characterized using the MitoTracker red CMXRos kit.

[0105] 2. Determination of the effect of 3D-PVS on non-targeted metabolites

[0106] According to the method of Example 1, a "3D-PVS-cell" model was constructed, and non-targeted metabolites were extracted and detected.

[0107] 2. Experimental Results

[0108] 1. Effects of 3D-PVS on mitochondria

[0109] The effects on mitochondria are as follows Figure 3 As shown: Co-culture of 3D-PVS and HUVEC cells induces the accumulation of reactive oxygen species (ROS) in the cytoplasmic matrix and mitochondria, and causes significant shrinkage of mitochondria.

[0110] 2. Effect of 3D-PVS on non-targeted metabolites

[0111] Results from Figure 4 As shown: 3D-PVS intervention in HUVEC cells showed obvious reprogramming results in both water-soluble metabolome and lipid metabolome. Through non-targeted water-soluble nano-metabolomics, it can be obtained that amino acids, organic acids, fatty acids and esters show obvious metabolic changes. And through non-targeted lipid metabolomics, it can be seen that compared with the control group, under the influence of 3D-PVS, the contents of phospholipid molecules such as phosphatidylcholine (PC), phosphatidylserine (PS), phosphatidylethanolamine (PE), and phosphatidylinositol (PI) have changed significantly.

[0112] The above results show that 3D-PVS can induce the accumulation of reactive oxygen species and the shrinkage of mitochondria in cells. Through the non-targeted nano-metabolomics analysis method provided by the present invention, it was found that 3D-PVS can significantly change the water-soluble metabolites of cells and the changes in the components of lipid metabolism. Within a concentration range of less than 15 mg / mL, 3D-PVS has a certain effect on amino acid or organic acid-related metabolism and phospholipid, sphingolipid or glycerolipid-related metabolism.

[0113] Experimental Example 2 Effects of 3D-PVS on Biological Effects of Rats

[0114] 1. Experimental Methods

[0115] 1. Determination of the effect of 3D-PVS on the oxidation level in rat jugular vein tissue

[0116] The changes in the biomarker content in the adjacent jugular vein tissue were measured as follows:

[0117] Weigh 50 mg of rat jugular vein tissue sample, add four steel balls and 100 μL pre-cooled PBS solution, grind, centrifuge at 4 °C and 13000 rpm for 10 min, take the supernatant, and use commercially available kits to measure the activities of ROS, superoxide dismutase (SOD), malondialdehyde (MDA), glutathione peroxidase (GPx) and catalase (CAT) in the supernatant.

[0118] 2. Determination of the effect of 3D-PVS on targeted metabolites

[0119] According to the method of Example 1, a "3D-PVS-rat" model was constructed, targeted metabolites were extracted and detected, and spatial metabolomics detection and analysis were performed.

[0120] 2. Experimental Results

[0121] 1. Effect of 3D-PVS on oxidation level in rat jugular vein tissue

[0122] The results are as follows Figure 5 As shown: the implantation of 3D-PVS induces the accumulation of ROS in tissues; the significant increase in MDA indicates that the degree of lipid oxidation is aggravated; the activity of antioxidant enzymes such as SOD, GPx, and CAT is inhibited, indicating that 3D-PVS weakens the activity of related antioxidant enzymes, resulting in the inhibition of ROS degradation, causing ROS accumulation in rat jugular vein tissue and lipid peroxidation.

[0123] 2. Effects of 3D-PVS on targeted metabolites in rat jugular vein tissue

[0124] The results are as follows Figure 6 As shown: 3D-PVS implantation significantly affects the levels of a large number of water-soluble metabolites and lipid metabolites. DESI-MSI results are as follows Figure 7 As shown: the detection method of the present invention can be used to well obtain the types and distribution of lipid metabolites in rat jugular vein tissue. The types of lipid substances include sphingomyelin (SM) (34:1), SM (36:1), SM (38:6), PC (16:0), PE (18:0), diacylglycerol (DAG) (12:0), oleic acid, and linoleic acid. After 3D-PVS implantation, their spatial distribution shows significant differences.

[0125] The above experimental results show that the implantation of 3D-PVS induces the accumulation of ROS in rat tissues. Through the targeted nano-metabolomics analysis method provided by the present invention, it is found that 3D-PVS significantly changes the component changes and spatial distribution of water-soluble metabolites and lipid metabolism in animal tissues. 3D-PVS induces glycolysis and amino acid metabolism in vivo, including tryptophan metabolism, cysteine ​​and methionine metabolism, glutamate metabolism and lysine metabolism; 3D-PVS also improves the homeostasis of some lipid categories (including PC, PE, PS, monoacylglyceride MAG, DAG, ceramide Cer, SM, cholesterol ester CE, acylcarnitine Car and free fatty acid FFA).

[0126] The above experimental results show that the present invention firstly performs non-targeted nano-metabolomics detection on the cell model, preliminarily analyzing that 3D-PVS induces metabolic disorders in the cells. Then, targeted nano-metabolomics detection is performed in the rat model to deeply analyze the metabolic reprogramming of animals induced by 3D-PVS.

[0127] Through the above embodiments and experimental examples, it can be seen that the present invention establishes nano-metabolomics and clarifies the biological response of 3D-PVS through the detection and analysis of non-targeted water-soluble / lipid metabolomics, the detection and analysis of targeted water-soluble / lipid metabolomics and the combination of spatial metabolomics. The present invention uses the nano-metabolomics method with multidimensional biological information for the first time to study the biological effects of 3D-PVS at the metabolic level, which is an innovative exploration of 3D-PVS at the metabolic level, and reveals its potential biochemical, medical and industrial applications and development directions based on the results of nano-metabolomics research. The present invention also has the following technical advantages and characteristics: easy operation, economical cost, environmentally friendly materials, simple design, suitable for laboratory research and outdoor testing, and convenient for real-time non-targeted-targeted combined nano-metabolomics research on the biological response characteristics of 3D-PVS. In addition, nano-metabolomics based on 3D-PVS can be regarded as a method innovation of classical metabolomics, which further promotes the technical and methodological progress of nano-metabolomics in clarifying biomedical application intervention measures based on 3D printed nanomaterials.

Claims

1. A metabolomics analysis method for 3D printed vascular stents, characterized in that: The method comprises the following steps: Step 1, performing DESI-MSI scanning on a biological sample to obtain a DESI-MSI detection result of a fat-soluble metabolite; the biological sample is a biological sample treated with a 3D printed vascular stent material; Wherein, the DESI-MSI detection conditions include: the solvent composition is a methanol aqueous solution with a volume content of 90-98% containing 0.05-0.2% formic acid; and / or, the solvent flow rate is 4-6 μL / min; and / or, the sheath gas of the electrospray atomization is N2, and the pressure is 0.1-0.3 Mbar; and / or, the scanning mode of the biological sample is a charged droplet impact mode; and / or, the spray incidence angle is 70-75°; Step 2: performing spatial metabolomics analysis on the DESI-MSI detection results.

2. The analytical method according to claim 1, characterized in that: In step 1, the 3D printed vascular stent material is prepared by a high-resolution projection micro-stereolithography 3D printer and a photosensitive polymer; a cylindrical vascular stent with a hollow structure.

3. The analysis method according to claim 1, characterized in that: In step 1, the biological sample is a frozen section of a tissue or organ of an animal model, and the animal of the animal model is a SD rat; the 3D printed vascular stent material is processed as 3D printed vascular stent material intravenously implanted; the frozen section is made at least 48 hours after the animal model is implanted with the 3D printed vascular stent material.

4. The analysis method according to claim 1, characterized in that In step 1, the detection conditions of DESI-MSI also include: in positive mode, the voltage is 4-5 kV, and in negative mode, the voltage is -4 to -5 kV; the m / z range is 100 to 1200; and the solvent also contains 0.05-0.2% volume content of leucine enkephalin.

5. The analysis method according to claim 1, characterized in that: Step 2 includes the following steps: Step 2.1, analyzing the DESI-MSI detection results using HDI software, and obtaining a processed lipid metabolite DESI-MSI spectrum through a peak area integration algorithm; Step 2.2: Based on the human metabolome database, the R language algorithm was used to perform metabolite identification on the processed lipid metabolite DESI-MSI spectra to obtain spatial metabolomics results and screen out characteristic metabolites.

6. The analysis method according to claim 1, characterized in that: The analysis method further comprises the following steps: Step A, "3D printed vascular scaffold material-cell" co-culture: co-culturing the 3D printed vascular scaffold material with cells; Step B, metabolite extraction and non-targeted nano-metabolomics detection: extracting and detecting water-soluble metabolites and / or lipid metabolites from the cells co-cultured in step A; Step C, "3D printed vascular scaffold material-animal" co-culture: co-culturing the 3D printed vascular scaffold material with the animal; Step D, metabolite extraction and targeted nano-metabolomics detection: extracting and detecting water-soluble metabolites and / or lipid metabolites from the animals after co-culture in step C; Wherein, the detection of water-soluble metabolites in step B is carried out by LC-MS detection, and the chromatographic conditions are as follows: the mobile phase is composed of phase A and phase B, phase A is 5-10% acetonitrile aqueous solution, phase B is 90-95% acetonitrile aqueous solution, and the chromatographic column filler is amidoethylene bridge hybrid particles; The detection of lipid metabolites in step B is performed by LC-MS detection, and the chromatographic conditions are as follows: the mobile phase consists of phase A and phase B, wherein phase A is a 50-60% acetonitrile aqueous solution, phase B is a 90-95% isopropanol acetonitrile solution, and the chromatographic column filler is C18 silica gel particles; The detection of water-soluble metabolites in step D is carried out by LC-MS / MS detection, and the detection mode is a multiple reaction monitoring mode; the LC-MS / MS detection comprises triple quadrupole mass spectrometry detection and liquid chromatography detection; The water-soluble metabolites in step D are detected by LC-MS / MS, and the chromatographic conditions are as follows: mobile phase A is a water:methanol:acetonitrile solution with a volume ratio of 1:1:1 or 1:1:1.5, and mobile phase B is an isopropanol solution containing 5-7 mM ammonium acetate.

7. The analysis method according to claim 6, characterized in that: In the step D, the method for extracting water-soluble metabolites is as follows: taking animal tissue, adding steel balls and 80% methanol aqueous solution, homogenizing the tissue to obtain a suspension; cooling the suspension at -80°C, then, ultrasonicating and centrifuging, and taking the supernatant. And / or, in the step D, the lipid metabolite extraction method is: taking animal tissue, adding steel balls and methanol, homogenizing the tissue to obtain a suspension; adding methyl tert-butyl ether and water to the suspension, centrifuging, and taking the supernatant to obtain; And / or, in step C, the 3D printed vascular stent material is implanted into the animal's vein and allowed to act for at least 48 hours.

8. The analysis method according to claim 6, characterized in that The specific method for detecting the water-soluble metabolites in step D includes: The chromatographic conditions include a flow rate of 200-300 μL / min, a mobile phase A of a 5-10% acetonitrile aqueous solution containing 5-10 mM ammonium acetate and 0.1-0.2% acetic acid by volume, and a mobile phase B of a 90-95% acetonitrile aqueous solution containing 5-10 mM ammonium acetate and 0.1-0.2% acetic acid by volume; a column temperature of 40-50° C.; an injection volume of 1-2 μL in a positive mode and 6-8 μL in a negative mode; and / or, the mass spectrometry conditions include using an electrospray ionization source; in positive mode, the interface capillary is maintained at 450-500° C., the ion source gas flow is 40-50 psi, and the spray voltage is 4.5-5.5 kV; in negative mode, the interface capillary is maintained at 500-550° C., the ion source gas flow is 40-50 psi, and the spray voltage is -4.5 to -5.5 kV; And / or, the specific method for detecting lipid metabolites in step D includes: The chromatographic conditions included, for the positive ion mode, a flow rate of 250-300 μL / min, mobile phase A was a water:methanol:acetonitrile solution with a volume ratio of 1:1:1 or 1:1:1.5, and mobile phase B was an isopropanol solution containing 5-7 mM ammonium acetate; the chromatographic column was a C18 column; the column temperature was 40-45°C, and the injection volume was 2-3 μL; for the negative ion mode, the flow rate was 200-700 μL / min, the mobile phase A was a dichloromethane:acetonitrile solution with a volume ratio of 7:93, and the mobile phase B was an acetonitrile aqueous solution with a volume ratio of 1:1 or 1:1.2; Phenomenex Luna NH2 column, the column temperature was 30-35°C, and the injection volume was 2-3 μL. And / or, the mass spectrometry conditions include, for positive ion mode, using a heated electrospray ionization source, the interface capillary is maintained at 500-550°C, the sheath gas flow is 50-55 psi, the auxiliary gas flow is 50-55 psi, and the spray voltage is 5-5.2 kV. The mass spectrometer also uses a heated electrospray ionization source, the interface capillary is maintained at 500-550°C, the sheath gas flow is 50-55 psi, the auxiliary gas flow is 50-55 psi, and the spray voltage is -4.5 to -5.5 kV; for negative ion mode, the sheath gas flow is 50-55 psi, the auxiliary gas flow is 50-55 psi, and the spray voltage is -4.5 to -5.5 kV.

9. The analysis method according to claim 6, characterized in that: In step A, the co-cultivation time is 12-48 hours; In step B, the method for extracting water-soluble metabolites is: Wash the cells with phosphate buffer, add trypsin to digest, obtain a cell suspension, add 75-80% methanol aqueous solution, ultrasonically treat for 20-30 minutes, vortex for 20-30 minutes, centrifuge at 10000-13000 rpm for 5-10 minutes, and the centrifugation temperature is 3-4°C, and take the supernatant to obtain; In step B, the extraction method of lipid metabolites is: The biological sample is washed with phosphate buffer, and trypsin is added for digestion to obtain a cell suspension, methanol is added, and the suspension is vortexed for 5 to 10 minutes. Methyl tert-butyl ether and water are added in sequence, and the suspension is centrifuged at 10,000 to 13,000 rpm for 5 to 10 minutes at a centrifugal temperature of 3 to 4° C. The supernatant is obtained.

10. The analysis method according to claim 6, characterized in that: The detection results of the water-soluble metabolites and / or lipid metabolites in step B and step D are used for bioinformatics analysis, and the bioinformatics analysis comprises the following steps: Step E1, using Progenesis QI to pre-process the detection results of water-soluble metabolites obtained in step B, and / or using MS-DIAL to pre-process the detection results of lipid metabolites obtained in step B; Step E2, using Analyst to preprocess the detection results of water-soluble metabolites obtained in step D, and / or using MultiQuant to preprocess the detection results of lipid metabolites obtained in step D; Step E3, the data after preprocessing in steps E1 and E2 are processed by R language algorithm, wherein the k-nearest neighbor algorithm is used to interpolate missing values, and the robust local estimation based on quality control and scattered point smoothing signal correction are applied for normalization; Step E4, using principal component analysis, partial least squares discriminant analysis and orthogonal partial least squares discriminant analysis to identify differences between groups.

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