Animal brain metabolite identification method based on mass spectrometry imaging technology and application

Abstract

The application provides an animal brain metabolite identification method based on mass spectrometry imaging technology and application, and establishes a mouse brain tissue slice endogenous metabolite DESI-MSI analysis method based on mass spectrometry imaging technology, which comprises the following steps: after preparing metabolite control sample imaging samples and brain tissue slice imaging samples, multiple animal brain tissues are attached to the same glass slide, mass spectrometry imaging condition optimization is carried out by using the control sample imaging samples, and mass spectrometry imaging analysis is carried out by using the brain tissue slice imaging samples; the image of the biological sample is divided into multiple structure regions in combination with tissue morphological characteristics; for the structure regions, multivariate statistical analysis is carried out to obtain different metabolites in different structure regions; metabolite identification is carried out based on parent ions and characteristic daughter ion profiles; and the method is used for disease mechanism research and drug intervention effect evaluation on diseases.

Description

Technical Field

[0001] This invention belongs to the field of biological detection technology and relates to the identification of animal brain metabolites, specifically to a method for identifying animal brain metabolites based on mass spectrometry imaging technology and its application in identifying the effects of different drugs. Background Technology

[0002] A striking feature of the brain is the unique morphological structure of its anatomical regions, each containing a large number of cell types involved in highly integrated molecular programs during brain development and functional maintenance. Brain metabolic homeostasis is crucial for brain function, and detailed brain metabolome mapping helps to understand the network architecture and function connecting different brain regions. Recent studies have found significant regional differences in the brain metabolome and proteome, indicating that different brain regions have unique characteristics in metabolism and protein expression. The mammalian brain relies on various small molecule metabolites (such as neurotransmitters, lipids, and amino acids) to achieve its complex and delicate functions; these small molecule metabolites play important roles in brain signal transduction, energy metabolism, and structural maintenance.

[0003] Multiple studies have demonstrated that the pathogenesis of Alzheimer's disease (AD) is spatiotemporally specific. At different stages of disease progression, different functional areas of the brain exhibit varying pathological and metabolic characteristics. Early and progressive metabolic disorders precede brain atrophy and neuronal dysfunction, which is a key feature of AD. Therefore, a comprehensive analysis of the metabolic characteristics of different brain regions in AD, and an analysis of metabolic reprogramming, may lead to effective treatment strategies, contributing to the prevention of brain diseases and the extension of healthy lifespan.

[0004] Metabolomics analysis based on chromatography-mass spectrometry (GC-MS) has become a powerful tool for brain metabolism research. However, the tissue homogenization process loses the spatial distribution information of metabolic changes in heterogeneous tissues, only reflecting the average metabolic level of the entire tissue. Mass spectrometry-mass spectrometry (MSI) is a molecular imaging technique that combines mass spectrometry analysis and image visualization, enabling multi-point detection, multi-dimensional data acquisition, and imaging of biological tissue samples without the need for specific labeling. Spatial metabolomics based on MSI performs spatial-dimensional metabolomics analysis of micro-regions of tissue, obtaining the spatial metabolic characteristics of metabolite types and amounts in different regions.

[0005] Previous studies have shown that AD cell models and AD animal models with different mechanisms all exhibit systemic metabolic disorders. After administration of Buwangsan compound, the distribution characteristics of each active ingredient in different brain regions are not entirely the same. Regulating the metabolic activity of AD mice from the perspective of the peripheral system can effectively intervene in their metabolic processes. However, research on the metabolic characteristics of different brain regions in AD is still insufficient, and there is currently no literature reporting the regulatory effect of Buwangsan on metabolites in different brain regions. Summary of the Invention

[0006] This invention addresses the aforementioned problems by establishing a DESI-MSI analysis method for endogenous metabolites in mouse brain tissue sections based on mass spectrometry imaging technology. Combined with bioinformatics analysis methods, it aims to study the metabolic remodeling of six brain regions under AD pathological conditions and analyze the regulatory mechanism of Buwangsan on metabolic disorders in AD from the perspective of brain metabolic regulation.

[0007] Based on this, the technical solution of the present invention is as follows:

[0008] In a first aspect, the present invention provides a method for identifying animal brain metabolites based on mass spectrometry imaging technology, comprising the following steps:

[0009] (1) Sample preparation: Multiple animal brain tissues were frozen and sectioned and mounted on the same glass slide;

[0010] (2) Mass spectrometry imaging: DESI-MSI mass spectrometry imaging of the frozen brain slices in step (1) was performed according to the detection conditions optimized by metabolite reference standards in advance;

[0011] (3) Data analysis: The data analysis system was used to preprocess the MSI data of the brain tissue slices. The highest number of compound information in the data file was selected. The total ion count normalization method was used to process the imaging of the tissue slices. After compensating for the matrix effect, the MSI data was analyzed and a specific ion distribution image was generated. The biological specimen image was segmented into multiple structural regions, including the olfactory bulb, piriform cortex, brainstem, midbrain, cortex, hippocampus, striatum, amygdala, thalamus, and cerebellum, in combination with the tissue morphology characteristics. Multivariate statistical analysis was performed on the above structural regions to obtain the differential metabolites in different structural regions. Metabolite identification was further performed based on the parent ion and characteristic daughter ion profiles. The parent ion and characteristic daughter ion were selected according to the parent ion and characteristic daughter ion of the database and metabolite reference standards.

[0012] Preferably, in step (1), the animal brain tissue slices attached to the same glass slide include control brain tissue, diseased brain tissue, and several brain tissues after drug administration intervention.

[0013] Furthermore, the brain tissue after drug administration intervention refers to brain tissue after intervention using a traditional Chinese medicine system, which is made of calamus, poria cocos, poria cocos sclerotium, ginseng, and polygala tenuifolia in a mass ratio of 2:5:5:5:7.

[0014] Preferably, in step (2), the metabolite reference standards include, but are not limited to, uridine, citric acid, adenine, L-carnitine, phosphoserine, creatine, 8-aminooctanoic acid, adenosine, L-phenylalanine, and histidine.

[0015] Mass spectrometry was performed using analytical electrospray ionization quadrupole time-of-flight mass spectrometry; the mass spectrometry imaging was based on the optimization of conditions using metabolite reference standards, including optimization of spray solvent, spray solvent flow rate, and capillary voltage.

[0016] The spray solvent is selected from a methanol and water system or an acetonitrile and water system, and formic acid or ammonia or ammonium formate is added to the above system.

[0017] Furthermore, the spray solvent is selected from any one of the following: 95%MeOH+0.1%FA, 80%ACN+0.2%FA, 90%MeOH+0.1% ammonia, 90%MeOH, 98%MeOH, 80%MeOH;

[0018] The spray solvent flow rate was selected from 0.5 μL·min. -1 ~ 4μL·min -1 The capillary voltage is selected from 0.4kV to 0.8kV.

[0019] Furthermore, the mass spectrometry conditions were set as follows: nitrogen as the nebulizer gas, pressure of 13 psi, ion source temperature of 120 °C, nebulizer incident angle of 60°, collection angle of 10°, and positive and negative ion mode scan rates of 200 μL·min. -1 The mass spectrometry analysis mode is sensitive mode, with a scanning range of m / z 50~1200, a pixel size of 100×100 μm, and a scanning speed of 200 μm / s.

[0020] Preferably, in step (3), the method for preprocessing the MSI data is as follows: the data file is processed using HDI imaging data processing software as follows: peak detection, calibration, deconvolution, data reduction, and isotope elimination;

[0021] Standard curves for the metabolite reference standards were plotted using MassLynx and the Progenesis Bridge plugin based on the MSI data of the brain region metabolite reference standards in advance. The standard curves were then subjected to least squares linear regression simulation analysis to calculate the correlation coefficient r and the linear equation, thereby obtaining the relative lower limit of quantitation for each compound.

[0022] In a second aspect, the present invention provides the application of the above-described method for identifying animal brain metabolites based on mass spectrometry imaging technology in drug screening for brain injury diseases.

[0023] Preferably, the brain injury disease is Alzheimer's disease.

[0024] The role and effect of invention

[0025] This invention establishes a DESI-MSI analysis method for endogenous metabolites in brain tissue slices. Combined with bioinformatics analysis, it studies the metabolic remodeling of six brain regions under the pathological state of Alzheimer's disease (AD). At the same time, it analyzes the regulatory mechanism of Buwangsan on metabolic disorders in AD from the perspective of brain metabolic regulation, providing new insights for the treatment of brain injury diseases, especially Alzheimer's disease. Attached Figure Description

[0026] Figure 1 The flowchart of the research process of the method of the present invention is shown;

[0027] Figure 2 The development and optimization of the DESI-MSI imaging method (capillary voltage optimization in negative ion mode) are shown, in which (A) uridine; (B) citrate; (C) adenine; (D) L-carnitine; (E) phosphoserine;

[0028] Figure 3 The development and optimization of the DESI-MSI imaging method (negative ion mode spray flow rate optimization) are shown, in which (A) uridine; (B) citrate; (C) adenine; (D) L-carnitine; (E) phosphoserine;

[0029] Figure 4 The development and optimization of the DESI-MSI imaging method (positive ion mode capillary voltage optimization) are shown, in which (A) creatine; (B) 8-aminooctanoic acid; (C) adenosine; (D) L-phenylalanine; (E) histidine;

[0030] Figure 5 The development and optimization of the DESI-MSI imaging method are shown (standard curve), in which (A) uridine (ESI) - (B) Citric acid (ESI) - (C) Adenine (ESI) - (D) L-carnitine (ESI) - (E) Phosphoserine (ESI) - (F) Creatine (ESI) + (G) 8-Aminooctanoic acid (ESI) + ); (H) adenosine (ESI) + (I) L-phenylalanine (ESI) + (J) Histidine (ESI) + );

[0031] Figure 6Typical metabolic mass spectrometry imaging is shown, where (A) γ-aminobutyric acid; (B) choline; (C) taurine; (D) creatine; (E) hypoxanthine; (F) dopamine; (G) acetylcholine; (H) glutamine; (I) glutamate; (J) carnitine; (K) phosphoserine; (L) adenosine; (M) alanine; (N) glutamine; (O) sorbitol lactate; (P) succinate; (Q) taurine; (R) glutathione; (S) aspartic acid; (T) glutamate; (U) heptanulose; (V) ascorbic acid; (W) glucose; (X) adenosine; (Y) phosphoribose; (Z) cytidine; (AA) uridine; where AL is the positive ion mode imaging and M-AA is the negative ion mode imaging.

[0032] Figure 7 The selection of ROI regions in each group of mice in DESI-MSI imaging is shown, including (A) amygdala; (B) cerebellum; (C) cortex; (D) striatum; (E) hippocampus; and (F) thalamus. Each image, from left to right, represents the control group, AD model group, low-dose Buwangsan group, and high-dose Buwangsan group.

[0033] Figure 8 The OPLS-DA score maps of metabolites in different brain regions of the control group and the model group are shown. (A, G) amygdala; (B, H) cerebellum; (C, I) cortex; (D, J) striatum; (E, K) hippocampus; (F, L) thalamus; where AF represents positive ion mode and GL represents negative ion mode.

[0034] Figure 9 The permutation test plots of OPLS-DA analysis of metabolites in different brain regions of the control and model groups are shown, where (A, G) is the amygdala; (B, H) is the cerebellum; (C, I) is the cortex; (D, J) is the striatum; (E, K) is the hippocampus; and (F, L) is the thalamus; where AF represents the positive ion mode and GL represents the negative ion mode.

[0035] Figure 10 PLS-DA score maps of metabolites in different brain regions are shown, where (A, G) is the amygdala; (B, H) is the cerebellum; (C, I) is the cortex; (D, J) is the striatum; (E, K) is the hippocampus; and (F, L) is the thalamus; where AF represents the positive ion mode and GL represents the negative ion mode.

[0036] Figure 11 Volcano plot analysis of metabolites in different brain regions of the control and model groups is shown, where (A, G) is the amygdala; (B, H) is the cerebellum; (C, I) is the cortex; (D, J) is the striatum; (E, K) is the hippocampus; and (F, L) is the thalamus; where AF represents the positive ion pattern and GL represents the negative ion pattern.

[0037] Figure 12Schematic diagrams of metabolite identification based on MS / MS mass spectrometry and mass spectrometry imaging profiles are shown, where (A) creatine reference standard; (B) brain region distribution of creatine; (C) 8-aminooctanoic acid reference standard; (D) brain region distribution of 8-aminooctanoic acid; (E) adenosine reference standard; (F) brain region distribution of adenosine; (G) citrate reference standard; (H) brain region distribution of citrate; (I) uridine reference standard; (J) brain region distribution of uridine; (K) adenine reference standard; (L) brain region distribution of adenine.

[0038] Figure 13 This study presents an overview of differentially metabolites in the brain regions of AD mice, including (A) classification of differentially metabolites in each brain region; (B) analysis of the characteristics of changes in the content of differentially metabolites in each brain region; (C) Venn diagram analysis of differentially metabolites in each brain region; (D) quantity of differentially metabolites in each brain region; (E) pathway enrichment analysis of differentially metabolites; (F) correlation analysis of differentially metabolites in the control group; and (G) correlation analysis of differentially metabolites in the AD model group.

[0039] Figure 14 This study presents an overview of the regulatory effects of Buwangsan on differentially metabolites in the brain regions of AD mice, including (A) the number of differentially metabolites brought back by Buwangsan; (B) pathway enrichment analysis of differentially metabolites brought back by Buwangsan; and (C) correlation analysis of differentially metabolites in the high-dose group of Buwangsan.

[0040] Figure 15 An integrated analysis of the metabolic pathways in different brain regions of AD mice regulated by Buwangsan was presented.

[0041] Figure 16 This study demonstrated the regulatory effect of Buwangsan on the purine metabolism pathway in the brain, including (A) DESI-MSI imaging of purine metabolism in the brains of mice in each group; and (B) the relative response of inosine in different brain regions of mice in each group. Note: Compared with the control group, * P < 0.05, ** P < 0.01, *** P < 0.001; compared with the AD model group, # P < 0.05, ## P < 0.01, ### P < 0.001. Detailed Implementation

[0042] The following embodiments and experimental examples further illustrate the present invention and should not be construed as limiting the invention. The embodiments do not include a detailed description of conventional methods, which are well known to those skilled in the art and described in numerous publications.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as are familiar to those skilled in the art. Furthermore, any methods and materials similar to or equivalent to those described herein may be applied to this invention, and the preferred embodiments and materials described in the specific implementation are for illustrative purposes only.

[0044] The analysis process of the identification method of this invention is described in [reference needed]. Figure 1 The details are as follows:

[0045] I. Experimental Materials

[0046] (a) Reagents and Materials

[0047] Uric acid, citric acid, adenine, L-carnitine, phosphoserine, creatine, 8-aminooctanoic acid, adenosine, L-phenylalanine, histidine, and other reference standards were purchased from Shanghai Yifei Biotechnology Co., Ltd. and Shanghai Shidander Standards Technical Service Co., Ltd., with each reference standard having a concentration ≥98%. Leucine-enkephalin was purchased from Sigma-Aldrich (USA). Mass spectrometry-grade acetonitrile and formic acid were purchased from Fisher Chemical Company (USA). Analytical grade anhydrous ethanol was purchased from Shanghai Titan Technology Co., Ltd.; purified water was purchased from Shanghai Watsons Daily Necessities Co., Ltd.; Sanfu red ink pens were purchased from Neway Daily Necessities (Shanghai) Co., Ltd.; Sanfu black ink pens were purchased from Neway Daily Necessities (Shanghai) Co., Ltd.; other reagents were all analytical grade. Polygala tenuifolia, ginseng, Poria cocos, Poria cocos sclerotium, Acorus tatarinowii, and other Chinese medicinal herbs were purchased from Shanghai Bailutang Pharmacy.

[0048] (II) Experimental Instruments

[0049] DESI XS / Xevo G3 QTOF: Waters Corporation, USA; Microtome: Leica CM1950, Germany; Standard slides: SLIDE.26MMX76MM.44 WELL.MICRO-44; HDI: Waters Corporation, USA; MassLynx acquisition software: Waters Corporation, USA; Micropipette: Eppendorf, Germany; Milli-Q™ pure water system: Millipore, USA; Gavage syringe: Luoyang Shangchun Biotechnology Co., Ltd.; Electronic balance: METTLER TOLEDO, USA; Centrifuge: ThermoFisher Scientific, USA; Freeze dryer: Ningbo Xinzhi Biotechnology Co., Ltd.; Vortex mixer: ThermoFisher Scientific, USA; -80℃ freezer: ThermoFisher Scientific, USA.

[0050] (III) Laboratory Animals

[0051] SPF-level APPswe / PS1dE9 mice and C57BL / 6 male mice of the same age were used as research subjects, with a total of 32 mice. Among them, there were 24 APPswe / PS1dE9 mice (body weight 26 g ± 4 g), and 8 C57BL / 6 male mice (body weight 26 g ± 2 g) as control mice. The mice were all purchased from the Experimental Animal Center of Zhejiang Province (Experimental Animal Center of Hangzhou Medical College), with the animal production license number: SCXK(Zhe) 2019-0002. They were housed in the Experimental Animal Center of the Department of Pharmacy, Naval Medical University. The cage conditions were as follows: the temperature was kept constant at 22 ± 2°C, the humidity range was 40%-60%, and the day-night cycle time was 12 h. The animal experiment protocol of this study was reviewed and approved by the Experimental Animal Ethics Committee of Naval Medical University. The mice started the experiment after 7 days of adapting to the feeding environment. The APPswe / PS1dE9 mice were randomly assigned to the AD model group, the low-dose Buwang San group, and the high-dose Buwang San group.

[0052] II. Experimental Methods

[0053] (I) Preparation of reference substance solutions and imaging samples

[0054] Weigh accurately 5 mg of reference substances of uridine, citric acid, adenine, L-carnitine, phosphoserine, creatine, 8-aminooctanoic acid, adenosine, L-phenylalanine, and histidine respectively and place them in a 5 mL volumetric flask. Add methanol to dissolve and prepare a reference substance stock solution with a concentration of 1 mg·mL -1 of the reference substance stock solution.

[0055] Accurately pipette appropriate amounts of each reference substance stock solution into a 10 mL volumetric flask, mix well, add methanol to dilute and make up the volume to prepare a mixed reference substance solution with a final concentration of 10 μg·mL -1 The above reference substance solutions were all stored at 4°C. Before imaging, pipette 1 μL of the mixed reference substance solution, spot it at the center position of the reference substance glass plate, and dry it at room temperature.

[0056] (II) Administration of drugs to animals and sample preparation

[0057] Weigh accurately the Chinese herbal pieces according to the mass ratio of polygala root, ginseng, poria, Poria cum Radix Pini, and acorus tatarinowii of 7:5:5:5:2, with a total of 200 g. After weighing, crush the Chinese herbal pieces and place them in a round-bottom flask. Add 10 times the amount of 60% ethanol solution, soak for 2 h, then heat under reflux for 2 h, and then filter to collect the filtrate. This process was repeated twice, and the samples were reserved separately. Then, add 10 times the amount of pure water, continue to reflux for 2 h, filter and reserve the sample. Finally, mix the filtrates extracted three times and perform freeze-drying treatment to obtain a powdery product, which was stored in a desiccator.

[0058] In this experiment, mice in each group were administered the drug via gavage. First, the lyophilized powder of Buwangsan extract was dissolved in pure water to prepare a homogeneous suspension of the appropriate concentration. The dosage for the low-dose Buwangsan group was 1.6 g / kg. -1 ·d -1 The dosage of Buwangsan in the high-dose group was 2.24 g·kg. -1 ·d -1 The drug concentration was calculated based on the mass of the raw drug, and the administration volume was 100 μL, with continuous administration for 135 days. Mice in the control group and AD model group were administered 100 μL of pure water by gavage.

[0059] After enucleating the eyeballs of mice to collect blood, the mice's hearts were perfused with pre-cooled physiological saline. After perfusion, the intact brain tissue was quickly separated, and the surface moisture was blotted dry with filter paper. The processed brain tissue was then placed flat in the caps of 50 mL centrifuge tubes, and the tubes were tightened. The centrifuge tubes were then flash-frozen in liquid nitrogen. Finally, the flash-frozen brain tissue was transferred to an ultra-low temperature freezer at -80°C for long-term storage.

[0060] Mouse brain tissue samples were removed from an ultra-low temperature freezer at -80°C and placed in a freezer at -20°C overnight. After being cut along the midline of the sagittal plane, the samples were fixed with an appropriate amount of embedding gel on a base. The cryostat temperature was set to -15°C, and the section thickness was 10 μm. Sections from the control group and the drug-treated group were sequentially mounted on the same glass slide and stored at -80°C.

[0061] (III) DESI-MSI Analysis

[0062] The mass spectrometry scanning modes include positive and negative ion modes. The signal sensitivity of different types of traditional Chinese medicine components was improved by optimizing the spray and mass spectrometry conditions. First, to obtain the optimal physical parameters of the DESI ion source, Sanfu red and black ink were applied to glass slides. The signal response values ​​of the red ink (m / z 666.0600) and the black ink (m / z 443.2335) were used as indicators for the positive and negative ion modes, respectively. The angle and distance between the spray nozzle and the slide were adjusted while monitoring signal changes to obtain the optimal physical parameters of the DESI ion source. In addition, the nitrogen flow rate was also optimized until the signals in both the positive and negative ion modes (m / z 666.0600) and the negative ion mode (m / z 443.2335) were highest and stable at 5 × 10⁻⁶. 6 The above are fixed physical parameters.

[0063] The DESI-MSI analytical method was optimized based on the limits of quantitation under different modes and acquisition conditions. Optimization included adjusting the spray solvent flow rate (0.5 μL·min⁻¹). -1 1.5 μL·min -13 μL·min -1 The capillary voltages were (0.45 kV, 0.55 kV, 0.65 kV, 0.75 kV). Other acquisition conditions were: electrospray solvent (80% ACN), nebulizer gas was nitrogen, pressure was 13 psi (approximately 0.09 MPa), ion source temperature was 120℃, nebulizer incident angle was 60°, collection angle was 10°, and positive and negative ion mode scan rate was 200 μL·min. -1 The mass spectrometry analysis mode was sensitive, with a scan range of m / z 50–1,200, a pixel size of 100 × 100 μm, and a scan speed of 200 μm·s. -1 At 100 ng·mL -1 Leucine-enkephalin (ESI) + :m / z 556.2771, ESI - The solution (m / z 555.2615) is used as a calibration standard solution to calibrate the instrument.

[0064] (iv) Data processing and statistical analysis

[0065] MSI data of brain region metabolite controls and mouse brain slices were acquired using MassLynx version 4.2, and HDI imaging data processing software was used to perform a series of processing on the data files, including peak detection, calibration, defolding, data reduction, and isotope elimination.

[0066] MSI data of brain region metabolite reference standards were used to construct standard curves for the metabolite reference standards using MassLynx and the Progenesis Bridge plugin. Least squares linear regression simulation analysis was performed on the standard curves to calculate the correlation coefficient r and the linear equation, and the relative lower limit of quantitation for each compound was obtained.

[0067] In the analysis of MSI in mouse brain slices, information on the 2,000 compounds with the highest response from the data file was selected. The total ion count normalization method was used to process the tissue slice images, and after compensating for matrix effects, the MSI data was analyzed to generate specific ion distribution images. Brain regions were identified using H&E staining and Nissl staining images, and key regions of interest (ROIs) were delineated based on these regions. ROIs in the amygdala, cerebellum, cortex, striatum, hippocampus, and thalamus were selected for analysis in the control group, drug-treated group, and model drug-treated group. The area per unit area (mm²) of each ROI was calculated. 2The mass spectrometry response values of ( ) were subjected to Log transformation and Pareto scaling of the two-dimensional matrix data using SIMCA-P14.1 (Sartorius Stedim, Germany), followed by multivariate statistical analysis. Through OPLS-DA analysis, VIP values and OPLS-DA / S-plot were obtained.

[0068] The obtained m / z values were imported into metabolite databases HMDB (https: / / hmdb.ca / ) and Metlin (https: / / metlin.scripps.edu / ) for metabolite annotation and identified based on the exact mass number (mass-to-charge ratio tolerance < 10 ppm). Pathway analysis was performed in MetaboAnalyst 5.0 (https: / / www.metaboanalyst.ca / ), and the enrichment method used was the hypergeometric test, with the topological analysis index selected as relative betweenness centrality.

[0069] Statistical analysis and graph plotting were performed using SPSS Statistics 23 (IBM, USA) and GraphPad Prism 8 (Graphpad Software, USA). The t-test (Student’s t test) was used for comparison between two groups, and one-way ANOVA was used for comparison among multiple groups. A P value < 0.05 was considered to indicate a significant difference.

[0070] III. Experimental Results

[0071] Aiming at the spatio-temporal specificity of brain metabolism in AD, based on mass spectrometry imaging combined with spatial metabolomics technology, the metabolic regulation mechanism of Buwangsan on different brain regions of AD mice was systematically analyzed. By optimizing parameters such as the spray solvent and its flow rate, capillary voltage, etc., spatial metabolome analysis was performed on 6 brain regions including the amygdala, cerebellum, and cortex. A total of 226 differential metabolites in the brain regions were identified, involving 15 categories of chemical substances and 83 metabolic pathways, among which 63 metabolites (27.9%) showed significant regional specificity. After the intervention of Buwangsan, 123 metabolites showed a callback trend, and its mechanism of action was closely related to the regulation of key pathways such as arachidonic acid metabolism and glycerophospholipid metabolism.

[0072] (I) Optimization of the DESI-MSI imaging method

[0073] There are many types of brain metabolites. In this non-targeted spatial metabolomics based on MSI, different categories of metabolites were selected to optimize the imaging method, and different spray solvent flow rates (0.5 μL·min -1 , 1.5 μL·min-1 3 μL·min -1 The effects of capillary voltage (0.45 kV, 0.55 kV, 0.65 kV, 0.75 kV) on the MSI signal of metabolites were investigated. The relative responses of different concentrations of metabolites under different conditions and the lower limits of response for different metabolites were used as references to determine the optimal MSI conditions. The collection conditions for metabolites uridine, citrate, adenine, L-carnitine, and phosphoserine were optimized under negative ion mode. Figure 2 As shown, uridine and adenine showed the highest response at 0.55 kV, while the other compounds showed little difference in response at the four voltages; therefore, 0.55 kV was ultimately determined as the final voltage for the negative ion mode. Figure 3 As shown, uridine showed the best response at a spray solvent flow rate of 1.5 μL·min⁻¹, while the other compounds showed little difference in response at the three spray solvent flow rates. Considering the overall effect of spray solvent flow rate on resolution, a spray solvent flow rate of 1.5 μL·min⁻¹ was finally determined as the final flow rate for the negative ion mode.

[0074] In positive ion mode, the collection conditions for metabolites creatine, 8-aminooctanoic acid, adenosine, L-phenylalanine, and histidine were optimized, such as... Figure 4 As shown, all the above compounds showed the best response at a voltage of 0.55 kV. Therefore, 0.55 kV was finally determined as the final voltage. Combining the optimization of the spray solvent flow rate in the positive ion mode and the influence of the spray solvent flow rate on the resolution, the final spray solvent flow rate of 1.5 μL·min⁻¹ was finally determined as the final flow rate in the positive ion mode.

[0075] Using optimized acquisition conditions, the relative response values ​​of each metabolite were extracted. A standard curve for the control was plotted using MassLynx and the Progenesis Bridge plugin. Least squares linear regression simulation analysis was performed on the standard curve to calculate the correlation coefficient r and the linear equation, as follows: Figure 5 As shown, after optimization of the collection conditions, the responses of different types of compounds in positive and negative ion modes showed a linear relationship with concentration within a certain range. Finally, the responses, relative limits of quantification, and r of each metabolite reference standard in negative ion mode were... 2 They are: uridine, y = 2.083x + 4637.64, 10 ppb, r 2 =0.71; Citric acid, y = 3.02685x + 2578.69, 10 ppb, r 2 = 0.99; adenine, y = 0.00142518x + 159.299, 10 ppb, r 2= 0.33; L-carnitine, y = 0.0065976x + 6.4193, 156 ppb, r 2 =0.86; Phosphosserine, y = 0.132332x + 548.056, 78 ppb, r 2 = 0.93; Response, relative lower limit of quantification, and r of each metabolite reference standard under positive ion mode. 2 The values ​​are: creatine, y = 0.927905x + 16.7605, 78 ppb, r 2 = 0.98; 8-Aminooctanoic acid, y = 0.009624x + 470.874, 78 ppb, r 2 = 0.92; adenosine, y = 0.59459x + 561.883, 78 ppb, r 2 = 0.97; L-phenylalanine, y = 0.0962991 x + 2.85305, 156 ppb, r 2 =0.99; histidine, y = 0.994556x + 120.587, 78 ppb, r 2 = 0.99.

[0076] (II) DESI-MSI imaging of mouse brain and distribution characteristics of typical brain metabolites

[0077] After acquiring raw data from mouse brain slices using MassLynx, HDI imaging data processing software was used to process the data files, extracting information from the 2,000 compounds with the highest response. Total ion count normalization was used for preliminary processing of the tissue slice images. Mass spectrometry images were extracted to observe typical metabolites. Brain imaging in positive ion mode for γ-aminobutyric acid (GABA), choline, taurine, creatine, hypoxanthine, dopamine, acetylcholine, glutamine, glutamate, carnitine, 6-hydrazinenicotinic acid, phosphoserine, glycerophosphatecholine, adenosine, and inosine is shown below. Figure 6 A- Figure 6As shown in Figure L, creatine, glutamate, and carnitine are distributed in almost all areas of the brain; γ-aminobutyric acid (GABA) is distributed in the piriform cortex, amygdala, and other areas; choline and acetylcholine are distributed in all areas, but with relatively low responses; taurine is distributed in the striatum, olfactory bulb, cortex, and hippocampus; hypoxanthine is distributed in all areas, with higher concentrations in the piriform cortex, amygdala, striatum, thalamus, and midbrain; dopamine is mainly distributed in the striatum and piriform cortex; glutamine is distributed in all areas except the brainstem and midbrain; carnitine is mainly distributed in the cerebellum; phosphatidylserine is distributed in the cortex, hippocampus, and thalamus; and adenosine is distributed in the striatum, olfactory bulb, piriform cortex, and other parts.

[0078] In negative ion mode, alanine, glucose, cytidine, uridine, and phosphoribose are distributed in almost all areas of the brain; glutamine is distributed in all areas except the brainstem and midbrain, consistent with the distribution characteristics in positive ion mode; sorbitol lactate is distributed in almost all areas of the brain, with a higher concentration in the brainstem; succinic acid is distributed in the cortex, striatum, and hippocampus; glutathione is mainly distributed in the hippocampus, cortex, and striatum; aspartic acid is distributed in the cortex, cerebellum, midbrain, and brainstem; glutamate, heptanulose, and ascorbic acid show similar distribution patterns, found in the cortex, striatum, hippocampus, thalamus, and cerebellum; adenosine monophosphate is mainly distributed in the cortex and cerebellum. Figure 6 As shown in (M-AA).

[0079] (III) Spatial metabolomics analysis of mouse brain

[0080] To depict the metabolomic profiles of different brain regions in mice and the impact of Alzheimer's disease (AD) on overall metabolic remodeling, and to investigate the effects of Buwangsan on metabolism in different brain regions, this chapter systematically analyzes six brain regions using mass spectrometry imaging. Brain tissues from the control group, model group, and model drug-treated groups with different dosages were selected. Regions of interest (ROIs) were designated as the amygdala, cerebellum, cortex, striatum, hippocampus, and thalamus, respectively. ROIs are shown below. Figure 7 As shown.

[0081] Obtain VIP values ​​and OPLS-DA graphs through OPLS-DA. Figure 8 ), using R 2 X, R 2 Y and Q 2 The models were evaluated (Table 1). OPLS-DA results under both positive and negative ion modes showed that all 12 OPLS-DA models under both modes could capture information from the independent variables well, accurately predict the dependent variable based on the independent variables, and exhibited good predictive ability. The control group and AD model group samples for each brain region were divided into two discrete clusters, with significant differences between the groups. Furthermore, the results of 200 permutation model tests (…) Figure 9 The display shows all Qs on the left. 2 The values ​​are all less than the original values ​​on the right, and Q 2 The regression line of the point intersects the negative Y-axis, indicating that the original model is effective and the next step of differential component analysis can be carried out.

[0082] Table 1. Model quality of OPLS-DA analysis of metabolites in different brain regions in the control and model groups.

[0083]

[0084] To systematically investigate the effect of Buwangsan's efficacy in treating Alzheimer's disease (AD) on peripheral circulation, partial least squares discriminant analysis (PLS-DA) was used to analyze the control group, AD model group, low-dose Buwangsan group, and high-dose Buwangsan group in this study. (PLS-DA score chart) Figure 10 The results showed that the metabolic profiles of the control group and the AD group were significantly separated, while the metabolic trajectory of mice after gavage administration of Buwangsan approached that of the control group. These results indicate that Buwangsan can regulate the pathological process of AD at the peripheral global metabolite level and exert a therapeutic effect on AD.

[0085] (iv) Identification of differentially metabolites in different brain regions of mice

[0086] 1. Screening and identification of differential metabolites

[0087] The ROI response values ​​between the control group and the treatment group were analyzed using a t-test. Combined with multivariate statistical analysis and differential metabolites obtained from positive and negative ion modes based on FC values, differential metabolites between the CN group and the AD group under positive and negative ion collection modes were screened using VIP > 1, P < 0.05, and FC < 0.83 or > 1.20 as screening criteria. Specific details are as follows: Figure 11 As shown in the figure. The size of the dots represents the VIP value; orange indicates a significant upregulation compared to the CN group, and blue indicates a significant downregulation. Based on the screened m / z values ​​and mass spectrometry information, identification was performed using the HMDB database. Metabolites were identified multiple times under different mass spectrometry acquisition modes or in different additive forms. After verification, the mass spectrometry response with the highest relative response was used as the statistical basis.

[0088] Simultaneously, some metabolites were identified based on the secondary fragmentation characteristics of the reference standard; secondary mass spectrometry data from mouse brain slices were collected for further structural identification of the metabolites, such as... Figure 12As shown, the secondary mass spectrometry of creatine extracted a precursor ion of m / z 132.0817 and a characteristic daughter ion fragment of m / z 90.0577. The secondary fragment characteristics of the metabolite identified from mouse brain slices were identical to those of the control. Furthermore, in the secondary mass spectrometry images acquired from mouse slices, the imaging contours of the precursor ion of m / z 132.0817 and the daughter ion of m / z 90.0577 were clearly visible. Figure 1 In summary, the structure of brain metabolites was identified based on two aspects: secondary structure characteristics and imaging profiles. Through database comparison and secondary structure identification, a total of 226 potential biomarkers for Alzheimer's disease were preliminarily identified, including 38 identified in positive ion mode and 188 identified in negative ion mode. Among these, *Buwangsan* showed a significant retrograde effect on 123 differentially expressed metabolites.

[0089] 2. Profiles of differentially expressed metabolites in different brain regions of mice

[0090] In the analysis of differentially metabolites, based on the HMDB classification annotation, these metabolites were mainly distributed across 15 chemical categories. The number of metabolites in each chemical category is as follows: Figure 13 As shown in A, the most common categories of metabolites are amino acids, peptides and analogues, fatty acids and conjugates, carbohydrates and carbohydrate conjugates, glycerophosphates, glyceroglycolipids, glycerophosphocholine, bile acids, alcohols and their derivatives, quaternary ammonium salts, purines and purine derivatives, linolenic acid and its derivatives, glycerophosphoethanolamine, tricarboxylic acids and their derivatives, purine ribonucleotides, fatty acylthioesters, pyrimidines and pyrimidine derivatives, totaling 15 categories.

[0091] Different brain regions have different potential biomarkers, such as Figure 13 As shown in C and 13D, there are 148 metabolites in the AN region, 154 in the CB region, 148 in the Cortex region, 92 in the Cpu region, 157 in the Hippo region, and 123 in the TM region. Compared with CN in the six brain regions, 63 metabolites (27.9% of all differentially expressed metabolites) showed different trends in the six brain regions; 163 metabolites showed the same trend, of which 131 (57.9%) showed the same trend in 2 to 5 brain regions; and 32 metabolites shared a common trend across the six brain regions. (Details are as follows...) Figure 13 As shown in B and 13C.

[0092] Based on mass spectrometry imaging results, correlation analysis was performed on the relative responses of 163 differentially expressed metabolites in brain regions showing the same trend. The analysis revealed varying degrees of correlation among the 163 differentially expressed metabolites in both the CN and AD groups. Figure 13As shown in F, metabolites in group CN exhibit varying degrees of correlation. The 25 metabolites with the highest number of correlated metabolites were selected and displayed. For example, N-palmitoyl aspartate showed significant correlations with all 24 other metabolites, and was significantly positively correlated with physalin E acetate and L-enkephalin; it was significantly negatively correlated with 11,12-epoxyeicosatotrienoic acid, D-glucuronic acid, kynurenic acid, and isocitrate, indicating a mutually regulatory and influencing relationship among these differentially metabolized substances. Figure 13 As shown in G, there are varying degrees of correlation among metabolites in the AD group, but the correlation between metabolites changes compared to the CN group, indicating that the metabolic pathways and mutual regulation of metabolites in AD mice differ from those in normal mice.

[0093] After significant metabolite enrichment, 28 metabolic pathways were identified, of which 15 were relatively enriched. These mainly included glycerophospholipid metabolism, linoleic acid and citric acid cycles, arachidonic acid metabolism, phenylalanine metabolism, alanine, aspartic acid and glutamate metabolism, butyrate metabolism, glycerol ester metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, glyoxylate and dicarboxylate metabolism, arginine and proline metabolism, histidine metabolism, porphyrin metabolism, pyruvate metabolism, and glycine, serine and threonine metabolism.

[0094] The successfully annotated metabolites were subjected to pathway enrichment analysis, and the metabolites were enriched into 83 metabolic pathways. Among them, the 25 pathways with the most significant enrichment were as follows: Figure 13 As shown in E, the metabolic pathways include glutamate metabolism, the Warburg effect, purine metabolism, α-linolenic acid and linoleic acid metabolism, the ammonia cycle, the citric acid cycle (tricarboxylic acid cycle), alanine metabolism, gluconeogenesis, the urea cycle, glutathione metabolism, the glucose-alanine cycle, acetyl transfer to mitochondria, amino sugar metabolism, phenylacetic acid metabolism, lactose degradation, cysteine ​​metabolism, malate-aspartate shuttle, sphingolipid metabolism, glycolysis, aspartate metabolism, pyruvate metabolism, glycine and serine metabolism, branched-chain fatty acid oxidation, and phenylalanine and tyrosine metabolism. In summary, metabolomics studies of different brain regions in AD mice based on mass spectrometry imaging can provide insights into the metabolic characteristics of the brain from multiple perspectives, including the types of metabolites, the diversity of metabolic pathways, and the heterogeneity of metabolic regions.

[0095] (v) The metabolic regulation effect of Buwangsan on differential metabolites in different brain regions

[0096] 1. Effects of Buwangsan on differential metabolites in different brain regions of AD mice

[0097] Of the 226 AD-related differentially expressed metabolites in different brain regions, the high-dose group of Buwangsan showed a significant retrograde effect on 123 of them, including 70 in the AN region, 79 in the CB region, 64 in the Crotex region, 40 in the Cpu region, 63 in the Hippo region, and 64 in the TM region. (Details are as follows...) Figure 14 As shown in A, among the 123 differentially expressed metabolites with significant retrograde effects, 91 metabolites showed a consistent trend across different brain regions. Correlation analysis revealed that, for example... Figure 14 As shown in C, these differentially metabolized substances exhibit mutual regulation and influence, and their correlation with the AD group changes. Metabolic pathway analysis was performed on 123 differentially metabolites, and after enrichment of significantly regressed metabolites, 28 metabolic pathways were obtained. Among these, 15 pathways were relatively enriched, mainly including glycerol phospholipid metabolism, linoleic acid and citric acid cycles, arachidonic acid metabolism, phenylalanine metabolism, alanine, aspartic acid, and glutamate metabolism, butyrate metabolism, glycerol ester metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, glyoxylate and dicarboxylate metabolism, arginine and proline metabolism, histidine metabolism, porphyrin metabolism, pyruvate metabolism, and glycine, serine, and threonine metabolism, etc. Figure 14 As shown in B.

[0098] 2. Effects of Buwangsan on metabolic pathways in different brain regions of AD mice

[0099] Metabolic pathway analysis was performed on differentially expressed metabolites in different brain regions of AD mice after intervention with Buwangsan, such as... Figure 15 As shown, the intervention of Buwangsan (a traditional Chinese medicine) in different brain regions of mice involved metabolic reprogramming of 27 metabolic pathways, including unsaturated fatty acid biosynthesis, glycerophospholipid metabolism, linoleic acid, citric acid cycle, and arachidonic acid metabolism. Among these, 16 metabolic pathways showed significant changes in six brain regions. In the AN region, the main changes were in metabolic pathways such as unsaturated fatty acid biosynthesis, arachidonic acid metabolism, glycerophospholipid metabolism, linoleic acid, phenylalanine metabolism, glycerol ester metabolism, arginine, and proline metabolism. In the cortical region, the main changes were in arachidonic acid metabolism, glycerophospholipid metabolism, citric acid cycle, purine metabolism, unsaturated fatty acid biosynthesis, phenylalanine metabolism, glycerol ester metabolism, alanine, aspartate, and glutamate metabolism. The occurrence of Alzheimer's disease (AD) leads to the disruption of multiple metabolic pathways in different brain regions of mice, and the intervention of Buwangsan produced varying degrees of reversal on the effects of these pathways in different brain regions of AD mice.

[0100] 4. The metabolic regulatory effect of Buwangsan on the purine metabolism pathway.

[0101] In the brains of AD mice, purine metabolism pathways are altered compared to normal mice. A metabolite profile of the purine metabolism pathway is shown in the image below. Figure 16As shown, purine metabolism undergoes significant changes in different brain regions. The content of its core metabolite, inosine, is significantly downregulated in five brain regions (CB, Cortex, Cpu, Hippo, and TM) in AD mice. After administration of Buwangsan, the high-dose group showed a significant recovery trend in inosine content in four brain regions (CB, Cortex, Cpu, and Hippo) compared to the control group.

[0102] IV. Discussion

[0103] Spatial metabolomics involves analyzing the metabolomics of micro-regions within tissues to obtain the spatial distribution characteristics of metabolite types and amounts in different regions. Brain metabolites, downstream of brain gene and protein expression, represent the final outcome of brain function and are important targets for neurological drugs. Existing research has shown that metabolic regulation can treat early synaptic changes and behavioral deficits in Alzheimer's disease (AD). In addition to neurotransmitters such as acetylcholine, dopamine, γ-aminobutyric acid (GABA), and glutamate, brain metabolites such as oxidized cholesterol and L-serine have also been shown to have metabolic regulatory functions in AD and may become new targets for AD treatment. This suggests that studying the spatial metabolic characteristics of the AD brain and its metabolites may improve the clinical efficacy of AD from the perspective of brain metabolic regulation and provide new directions for drug development. Applying spatial metabolomics to brain metabolism research in AD can provide a complete, accurate, and dynamic metabolic network map of AD pathogenesis, revealing the dynamic changes of spatial metabolic markers in the body during AD development, and contributing to a comprehensive explanation of the pathogenesis mechanism.

[0104] The undescribed parts of this invention are the same as or implemented using existing technology. The applicant declares that this invention is illustrated through the above embodiments, but the invention is not limited to the above detailed methods, i.e., it does not mean that the invention must rely on the above detailed methods to be implemented. Those skilled in the art should understand that any improvements to this invention, equivalent substitutions of raw materials for the product of this invention, additions of auxiliary components, and selection of specific methods all fall within the protection and disclosure scope of this invention.

Claims

1. A method for identifying animal brain metabolites based on mass spectrometry imaging technology, characterized in that, Includes the following steps: (1) Sample preparation: Multiple animal brain tissues were frozen and sectioned and mounted on the same glass slide; (2) Mass spectrometry imaging: DESI-MSI mass spectrometry imaging of the frozen brain slices in step (1) was performed according to the detection conditions optimized by metabolite reference standards in advance; (3) Data analysis: The data analysis system was used to preprocess the MSI data of the brain tissue slices. The highest number of compound information in the data file was selected. The total ion count normalization method was used to process the imaging of the tissue slices. After compensating for the matrix effect, the MSI data was analyzed and a specific ion distribution image was generated. The biological specimen image was segmented into multiple structural regions, including the olfactory bulb, piriform cortex, brainstem, midbrain, cortex, hippocampus, striatum, amygdala, thalamus, and cerebellum, in combination with the tissue morphology characteristics. Multivariate statistical analysis was performed on the above structural regions to obtain the differential metabolites in different structural regions. Metabolite identification was further performed based on the parent ion and characteristic daughter ion profiles. The parent ion and characteristic daughter ion were selected according to the parent ion and characteristic daughter ion of the database and metabolite reference standards.

2. The method for identifying animal brain metabolites based on mass spectrometry imaging technology according to claim 1, characterized in that, In step (1), the animal brain tissue sections attached to the same glass slide include control brain tissue, diseased brain tissue, and several brain tissues after drug intervention. In step (2), the mass spectrometry used is analytical electrospray ionization quadrupole time-of-flight mass spectrometry; the metabolite reference standards include, but are not limited to, uridine, citric acid, adenine, L-carnitine, phosphoserine, creatine, 8-aminooctanoic acid, adenosine, L-phenylalanine, and histidine; The content of the mass spectrometry imaging condition optimization analysis based on metabolite reference standards is selected from the optimization of spray solvent, spray solvent flow rate optimization, and capillary voltage optimization.

3. The method for identifying animal brain metabolites based on mass spectrometry imaging technology according to claim 2, characterized in that, In step (1), the brain tissue after drug administration intervention refers to the brain tissue after intervention using a traditional Chinese medicine system, which is made of calamus, poria cocos, poria cocos sclerotium, ginseng and polygala tenuifolia in a mass ratio of 2:5:5:5:

7. In step (2), the spray solvent is selected from a methanol and water system or an acetonitrile and water system, and formic acid or ammonia or ammonium formate is added to the above system.

4. The method for identifying animal brain metabolites based on mass spectrometry imaging technology according to claim 3, characterized in that, In step (2), the spray solvent is selected from any one of the following: 95%MeOH+0.1%FA, 80%ACN+0.2%FA, 90%MeOH+0.1%ammonia, 90%MeOH, 98%MeOH, 80%MeOH; The spray solvent flow rate was selected from 0.5 μL·min. -1 ~ 4μL·min -1 The capillary voltage is selected from 0.4kV to 0.8kV.

5. The method for identifying animal brain metabolites based on mass spectrometry imaging technology according to claim 4, characterized in that, The mass spectrometry conditions were also set as follows: nitrogen as the nebulizer gas, pressure of 13 psi, ion source temperature of 120 °C, nebulizer incident angle of 60°, collection angle of 10°, and scan rate of 200 μL·min for both positive and negative ion modes. -1 The mass spectrometry analysis mode is sensitive mode, with a scanning range of m / z 50~1200, a pixel size of 100×100 μm, and a scanning speed of 200 μm / s.

6. The method for identifying animal brain metabolites based on mass spectrometry imaging technology according to claim 1, characterized in that, In step (3), the method for preprocessing the MSI data is as follows: the data file is processed using HDI imaging data processing software as follows: peak detection, calibration, deconvolution, data reduction, and isotope elimination; Standard curves for the metabolite reference standards were plotted using MassLynx and the Progenesis Bridge plugin based on the MSI data of the brain region metabolite reference standards in advance. The standard curves were then subjected to least squares linear regression simulation analysis to calculate the correlation coefficient r and the linear equation, thereby obtaining the relative lower limit of quantitation for each compound.

7. The application of the method for identifying animal brain metabolites based on mass spectrometry imaging technology as described in any one of claims 1 to 6 in drug screening for brain injury diseases.

8. The application according to claim 7, characterized in that, The brain injury disease mentioned is Alzheimer's disease.

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