Space metabonomics analysis method and application thereof

Abstract

The invention discloses a space metabonomics analysis method and application thereof, relates to the technical field of metabonomics analysis, and solves the problem that an analysis result of an existing conventional space metabonomics analysis method is not accurate enough. To-be-analyzed biological tissue samples are taken and divided into groups; preparing all groups of samples into slices, adsorbing the slices on the same glass slide, carrying out mass spectrum imaging detection, and synchronously collecting metabolite spatial distribution signals and mass spectrum intensity data of all the samples; the method comprises the following steps: preprocessing collected original data, identifying the distribution condition of known exogenous components in a medicine in a tissue slice, and delimiting a region with high signal intensity as a region of interest; randomly selecting a plurality of pixel points in the region of interest of each sample; and integrating the mass spectrum data corresponding to the pixel points selected from all the parallel samples in the same group, and sequentially carrying out PCA, OPLS-DA, differential metabolite screening and KEGG pathway enrichment analysis. The method can be applied to research on the hepatotoxicity of the polygonum multiflorum and the prepared polygonum multiflorum.

Description

Technical Field

[0001] This invention relates to the field of metabolomics analysis technology, specifically to a spatial metabolomics analysis method and its application. Background Technology

[0002] Spatial metabolomics is a cutting-edge interdisciplinary field combining metabolomics and spatial imaging technology. Its core objective is to achieve qualitative and quantitative analysis and spatial distribution visualization of metabolites in tissue sections while preserving the original spatial location information of biological tissues. Compared to traditional metabolomics, which uses tissue homogenization for sample pretreatment, spatial metabolomics does not destroy the spatial structure of the sample. It can rely on in-situ analysis to preserve spatial information and select specific regions for analysis based on the spatial distribution characteristics of substances in tissues, thereby obtaining more accurate results. This characteristic has led to its widespread application in many fields such as tumor research, neuroscience, drug development, and plant and microbiology.

[0003] The realization of spatial metabolomics mainly relies on mass spectrometry imaging technology. Currently, commonly used techniques include matrix-assisted laser desorption / ionization mass spectrometry (MALDI-MSI), desorption electrospray ionization mass spectrometry (DESI-MSI), and secondary ion mass spectrometry (SIMS-MSI). Their core workflow encompasses sample preparation, data acquisition, and data analysis. Among these, aerodynamically assisted desorption / ionization mass spectrometry (AFADESI-MSI), as a sensitive open-type molecular imaging technique, overcomes the limitations of liquid chromatography-mass spectrometry (LC-MS / MS) metabolomics methods that suffer from spatial information loss due to sample homogenization, thanks to its advantages of being label-free, highly specific, and highly sensitive. It can simultaneously acquire the structure, content, and spatial distribution information of both targeted and non-targeted metabolites, providing a new visual perspective and multidimensional information depth for fields such as drug metabolism, tumor metabolism, central nervous system diseases, environmental toxicology, and medicinal plant research.

[0004] However, current conventional spatial metabolomics analysis still has significant operational limitations. In the sample preparation stage, typically one sample from each of the control and experimental groups is prepared on the same slide for MSI testing, repeated 3-4 times. During data processing, PCA and OPLS-DA analyses are performed after each test, and the most representative results are selected for presentation. Differential metabolite screening involves exporting the data from each test and then integrating the data from 3-4 samples from each group for analysis. The core problem with this approach is that slight fluctuations in instrument status during each test can lead to differences in the measured mass spectrometry signal intensity. If the instrument stability is insufficient, significant batch-to-batch variability can occur. This instrument-induced variability, rather than individual biological variability, amplifies intra-group differences, masking the true differences between groups and ultimately resulting in inaccurate analytical results. On the other hand, one of the core advantages of spatial metabolomics is the ability to select specific tissue regions for analysis. However, conventional analysis usually involves comparing adjacent slices of MSI samples with optical imaging or H&E stained slices, and selecting regions of interest based on different structural partitions of the tissue. For organs such as the liver that do not have clear structural partitions, only pixels can be randomly selected, which completely wastes the spatial information advantage of spatial metabolomics. Summary of the Invention

[0005] To address the issue of insufficient accuracy in existing conventional spatial metabolomics analysis methods, this invention proposes a spatial metabolomics analysis method and its application.

[0006] The technical solution of the present invention is as follows: A spatial metabolomics analysis method includes the following steps: S1. Take biological tissue samples to be analyzed and divide them into groups according to the experimental purpose; S2. Prepare all biological tissue samples from all groups into sections, adsorb them onto the same glass slide, and perform mass spectrometry imaging detection to simultaneously acquire the spatial distribution signals of metabolites and mass spectrometry intensity data of all samples. S3. After preprocessing the collected raw data, identify the distribution of known exogenous components in the drug in the tissue sections, and delineate the regions with high signal intensity as regions of interest; randomly select multiple pixels within the region of interest for each sample. S4. Integrate the mass spectrometry data corresponding to the selected pixels in all parallel samples of the same group, and perform principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), differential metabolite screening, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis in sequence.

[0007] Preferably, the division into groups includes a control group and one or more drug treatment groups, with each group including 3 to 4 parallel samples.

[0008] Preferably, the preprocessing includes: converting the RAW format raw data to CDF format using Xcalibur software, importing it into the Advanced Mass Spectrometry Imaging System Workstation v1.0, and removing background signal interference.

[0009] Preferably, the number of pixels is 50 to 100.

[0010] Preferably, the screening threshold for differential metabolites is p < 0.05 and |log2FoldChange| < 0.58.

[0011] This invention also provides an application of the above-mentioned spatial metabolomics analysis method in the study of hepatotoxicity of Polygonum multiflorum and processed Polygonum multiflorum.

[0012] Compared with existing technologies, this invention addresses the core pain points of current spatial metabolomics analysis, such as batch instrument errors and blind selection of organs without clear regional divisions. Through the synergistic innovative design of simultaneous analysis of the same slice and selection of drug core regions, it brings several outstanding technical advantages. The specific beneficial effects of this invention are as follows: This invention breaks with the conventional practice of testing in batches, and tests all parallel samples from all groups simultaneously on the same slide. This avoids signal differences caused by fluctuations in instrument status between different test batches, reduces interference from non-biological factors from the source of testing, and makes the differences within groups more consistent with the differences of real biological individuals, providing a more stable raw data foundation for subsequent analysis.

[0013] For organs such as the liver that lack clear structural divisions, this invention abandons the logic of traditional morphological division or random point selection. Based on the spatial distribution of exogenous drug components, it accurately identifies the core areas where drugs are enriched and have the most direct effects for point selection analysis. This not only retains the spatial information advantages of spatial metabolomics, but also allows data collection to focus on key sites of action, significantly reducing the interference of metabolic signals from irrelevant areas.

[0014] By using the same sample and testing method to reduce systematic errors, and by focusing on effective signals through point selection in the core drug region, dual optimization makes the metabolic differences between the control and experimental groups more easily apparent in multivariate statistical analysis, resulting in a significantly enhanced separation trend between groups and more accurate screening of differential metabolites. Meanwhile, clearly defined parameters such as sample quantity and pixel selection range ensure that the experimental operation is repeatable and easily promoted, improving the efficiency of implementing the technical solution.

[0015] The analytical method of this invention is not only applicable to general spatial metabolomics research, but can also be specifically applied to particular scenarios such as drug action mechanisms and toxicity evaluation, including research on the hepatotoxicity of traditional Chinese medicine. By combining it with pathological detection and biochemical index analysis from multiple dimensions, it can reveal in depth the regulatory pathways of drugs on tissue metabolism and key molecules, providing more comprehensive technical support for drug development, disease mechanism research, and other fields. Its applicability covers multiple cutting-edge directions such as tumor research, neuroscience, and medicinal plant research. Attached Figure Description

[0016] Figure 1 This is a density distribution map of OPLS-DA prediction scores in Example 1; Figure 2 This is a density distribution map of OPLS-DA prediction scores in Example 2; Figure 3 Why is the distribution of emodin in the liver of Polygonum multiflorum and processed Polygonum multiflorum shown by mass spectrometry? Figure 4 Why is pathological examination of liver tissue of Polygonum multiflorum and processed Polygonum multiflorum necessary? Figure 5 Why are serum liver and kidney function indicators of Polygonum multiflorum and processed Polygonum multiflorum tested? Figure 6 Why does Polygonum multiflorum and processed Polygonum multiflorum contain PCA and OPLS-DA in the liver? Figure 7 Why is there a volcano diagram of liver metabolites of Polygonum multiflorum and processed Polygonum multiflorum? Figure 8 Why did the liver of Polygonum multiflorum and processed Polygonum multiflorum show KEGG enrichment? Detailed Implementation

[0017] To make the technical solutions of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that the following embodiments are only used to better understand the technical solutions of the present invention and should not be construed as limiting the present invention.

[0018] The Polygonum multiflorum and processed Polygonum multiflorum used in the following examples were purchased from Anguo Anxing Traditional Chinese Medicine Pieces Co., Ltd., and their shapes were compared with those listed in the 2020 edition of the Chinese Pharmacopoeia, Volume I, on medicinal materials and processed medicinal pieces. The following equipment was used: fully automated biochemical analyzer (Shenzhen Leidu Life Science & Technology); tissue fixative (Saiwell); panoramic slide scanner, CaseViewer 2.4 (3DHISTECH); chromatographic grade acetonitrile and formic acid (Thermo Fisher Scientific); cryostat (Leica Biosystems); Orbitrap Exploris 240 mass spectrometer (Thermo Fisher Scientific); AFADESI-MSI and advanced mass spectrometry imaging system workstation v1.0 (Vicot (Beijing) Technology Co., Ltd.).

[0019] Example 1. (1) Preparation of Chinese herbal extracts: Based on the reported main components of Polygonum multiflorum, the commonly used extraction method for effective components of traditional Chinese medicine, the heating reflux method, was employed for extraction, with 70% ethanol selected as the solvent. 500g of Polygonum multiflorum was added, and 8 times its volume (approximately 4L) of 70% ethanol was added. After soaking, the mixture was heated under reflux at 95℃ for 2 hours, filtered, and the residue was returned to the flask. Another 8 times its volume of 70% ethanol was added, and the mixture was heated under reflux for 2 hours, for a total of 3 extractions. The filtrates were combined, evaporated under reduced pressure to a concentration of 2g / mL, and stored at -20℃.

[0020] (2) Animal experiments: Male SD rats, weighing 200±20g, were purchased from Liaoning Changsheng Biotechnology Co., Ltd. (Experimental Animal License No. SCXK(Liaoning)2020-0001). The ambient temperature was 25±2℃, and the humidity was 50±5%, with free access to food and water. After one week of acclimatization, the rats were randomly divided into two groups: a control group and a Polygonum multiflorum group, with four rats in each group. The extract was preheated to 37℃ and administered by gavage at a dose of 10mL / kg of rat body weight, once daily for 8 weeks. Thirty minutes after the last administration, the rats were anesthetized by intraperitoneal injection of 20% urethane solution (5 mL / kg). Blood was collected via the abdominal aorta, and the liver was subsequently harvested. The liver was immediately flash-frozen in liquid nitrogen and stored at -80℃.

[0021] (3) MSI test: Four samples were taken from each of the control group and the Polygonum multiflorum group. The bottom of the sample was fixed to the sample head with embedding agent, and the sample was cut into thin slices with a thickness of 12 μm using a cryostat at about -18℃. All sample slices were attached to the same glass slide, with an appropriate distance between each sample.

[0022] MSI measurements were performed using an AFADESI-MSI instrument paired with an Orbitrap Exploris 240 mass spectrometer. Sample solvent preparation: acetonitrile and pure water in a 4:1 ratio (…). v / v Mix in a ratio of 4:1 (positive spectrum); acetonitrile and pure water are mixed in a ratio of 4:1 (positive spectrum). v / v Mix the components in the specified proportions, then add 0.1% formic acid (negative spectrum). MSI settings: solvent flow rate 6 μL / min, spray gas nitrogen, spray voltage ±5500V; spray probe speed in the x-axis direction 160 μm / s, y-axis interval 200 μm per row. Mass spectrometer settings: full scan range, scan range m / z 100-1000; ion transmission tube temperature 350℃; sheath gas flow rate 4.28 L / min, auxiliary gas flow rate 2.46 L / min.

[0023] (4) Data processing: The raw data was converted from RAW to CDF format using Xcalibur software and imported into the Advanced Mass Spectrometry Imaging System Workstation v1.0 for data processing. After removing the background signal, the m / z values ​​of the drug-derived components were input to observe their distribution. Regions with high signal intensity were selected as regions of interest, and 50 pixels were randomly selected within these regions. The mass spectrometry data of each group of four samples were merged (200 pixels) for classification and prediction.

[0024] Example 2. The operation is the same as in Example 1, except that in this example, there are 3 samples in each of the control group and the Polygonum multiflorum group, and 100 pixels (300 pixels) are randomly selected in the region of interest for classification prediction.

[0025] The OPLS-DA prediction score density distribution maps of Examples 1 and 2 are shown below. Figure 1 and Figure 2 As shown. Comparison Figure 1 and Figure 2 It can be seen that the differences between the groups in the two embodiments are very significant. In Embodiment 1, the two groups of samples show a stronger separation trend in the OPLS-DA model, which makes it easier to reflect the differences between the two groups.

[0026] Therefore, in Example 3, the spatial metabolomics of Polygonum multiflorum and processed Polygonum multiflorum proposed in this invention was investigated using the analytical method based on Example 1.

[0027] Example 3. Spatial metabolomics analysis of hepatotoxicity of Polygonum multiflorum and processed Polygonum multiflorum: (1) Preparation of Chinese herbal extracts: Based on the reported main component characteristics of Polygonum multiflorum and processed Polygonum multiflorum, the commonly used extraction method for effective components of traditional Chinese medicine, the heating reflux method, was employed for extraction, with 70% ethanol selected as the solvent. 500g of Polygonum multiflorum or processed Polygonum multiflorum was added, and 8 times its volume (approximately 4L) of 70% ethanol was added. After soaking, the mixture was heated under reflux at 95℃ for 2 hours, filtered, and the residue was returned to the flask. Another 8 times its volume of 70% ethanol was added, and the mixture was heated under reflux for 2 hours, for a total of 3 extractions. The filtrates were combined, evaporated under reduced pressure to a concentration of 2 g / mL, and stored at -20℃.

[0028] (2) Animal experiments: Male SD rats, weighing 200±20g, were purchased from Liaoning Changsheng Biotechnology Co., Ltd. (Experimental Animal License No. SCXK(Liaoning)2020-0001). The ambient temperature was 25±2℃, and the humidity was 50±5%, with free access to food and water. After one week of acclimatization, the rats were randomly divided into three groups: a control group, a Polygonum multiflorum group, and a processed Polygonum multiflorum group, with 12 rats in each group. The extract was preheated to 37℃ and administered by gavage at a dose of 10 mL / kg of rat body weight, once daily for 8 weeks. Thirty minutes after the last administration, four rats from each group were anesthetized by intraperitoneal injection of 20% urethane solution (5 mL / kg). Blood was collected via the abdominal aorta, and the liver was subsequently harvested. The liver was immediately divided into two parts: one part was immediately fixed in a tissue fixative for histopathological examination; the other part was immediately flash-frozen in liquid nitrogen and stored at -80℃ for MSI analysis. Eight rats remaining in each group were sacrificed in the same manner 12 hours after the last administration. Blood was collected via the abdominal aorta. The whole blood was allowed to stand at room temperature for 1 hour, then centrifuged at 3000 r / min for 10 min. The supernatant serum was collected for liver function index detection.

[0029] (3) Histopathological examination: The fixed tissues were dehydrated using a routine gradient of ethanol, cleared with xylene, embedded in paraffin, and cut into 5 μm thick sections, which were then stained with hematoxylin and eosin (H&E). The tissue sections were examined under microscopes at different magnifications (200× and 400×).

[0030] (4) Liver function test: Serum liver function indicators were analyzed using a fully automated biochemical analyzer: alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB), alkaline phosphatase (ALP), total bilirubin (TBIL), direct bilirubin (DBIL), and total bile acids (TBA).

[0031] (5) MSI test: Four samples were taken from each of the control group, the Polygonum multiflorum group, and the processed Polygonum multiflorum group. The bottom of the sample was fixed to the sample head with embedding agent, and the sample was cut into thin slices with a thickness of 12 μm using a cryostat at about -18℃. All sample slices were adsorbed onto the same glass slide, with an appropriate distance between each sample.

[0032] MSI measurements were performed using an AFADESI-MSI instrument paired with an Orbitrap Exploris 240 mass spectrometer. Sample solvent preparation: acetonitrile and pure water in a 4:1 ratio (…). v / v Mix in a ratio of 4:1 (positive spectrum); acetonitrile and pure water are mixed in a ratio of 4:1 (positive spectrum). v / vMix the components in the specified proportions, then add 0.1% formic acid (negative spectrum). MSI settings: solvent flow rate 6 μL / min, spray gas nitrogen, spray voltage ±5500V; spray probe speed in the x-axis direction 160 μm / s, y-axis interval 200 μm per row. Mass spectrometer settings: full scan range, scan range m / z 100-1000; ion transmission tube temperature 350℃; sheath gas flow rate 4.28 L / min, auxiliary gas flow rate 2.46 L / min.

[0033] (6) Data processing: The raw data was converted from RAW to CDF format using Xcalibur software and imported into the Advanced Mass Spectrometry Imaging System Workstation v1.0 for data processing. After removing background signals, the m / z values ​​of the drug-derived components were input to observe their distribution. Based on previous studies, the m / z values ​​of drug components were searched in liver MSI images. The results showed that emodin was the main drug component, indicating that emodin is a major hepatotoxic component. Figure 3 Based on the distribution shown in the MSI images, 50 pixels were randomly selected from the high-concentration areas of emodin for spatial metabolomics analysis. The pixel mass spectrometry data from each group of 4 samples (200 pixels) were merged for PCA and OPLS-DA. The mass spectrometry data were then exported... p Differential metabolites were screened using a threshold of <0.05 and |log2FoldChange| <0.58. MetaboAnalyst was then used to perform KEGG pathway enrichment analysis on the identified differential metabolites.

[0034] The experimental results are as follows: Histopathological examination results as follows Figure 4 As shown, no obvious abnormalities were observed in the livers of rats in the control group, while rats in the drug-treated groups showed varying degrees of liver damage. Under a microscope, the Polygonum multiflorum group showed obvious inflammatory cell infiltration (red arrow), hepatic sinusoidal dilation (green arrow), congestion (yellow arrow), and hepatocyte hydrops (blue arrow), while the processed Polygonum multiflorum group showed mild inflammatory cell infiltration (red arrow) and moderate hydrops (blue arrow). The damage in the Polygonum multiflorum group was more severe than that in the processed Polygonum multiflorum group.

[0035] Liver function test results, such as Figure 5 As shown, significant abnormalities were observed in TBIL, DBIL, and TBA levels in the treated rats, with the TBIL and DBIL levels being more severe in the Polygonum multiflorum group. In conclusion, after 8 weeks of continuous administration of Polygonum multiflorum or its processed extract, rats developed significant liver damage, and Polygonum multiflorum showed greater toxicity than its processed form.

[0036] Spatial metabolomics analysis of the liver yielded results for PCA and OPLS-DA levels in the liver of Polygonum multiflorum and processed Polygonum multiflorum. Figure 6 As shown, both PCA and OPLS-DA revealed significant separation between groups, indicating substantial differences in the liver metabolome among them. The volcano diagram of liver metabolites of Polygonum multiflorum and processed Polygonum multiflorum is shown below. Figure 7 Table 1 shows the differentially metabolites identified in the liver of Polygonum multiflorum, and Table 2 shows the differentially metabolites identified in the liver of processed Polygonum multiflorum. KEGG pathway enrichment analysis was performed on the differentially metabolites, and the results are shown in Table 1. Figure 8 The results showed that both Polygonum multiflorum and processed Polygonum multiflorum mainly regulate the purine metabolism pathway.

[0037] As can be seen from the above, applying the analytical method provided by this invention to specific research scenarios on the hepatotoxicity of Polygonum multiflorum and processed Polygonum multiflorum, and using AFADESI-MSI technology to achieve in-situ detection and spatial distribution analysis of metabolites, combined with pathological examination and biochemical index verification, not only successfully distinguished the different effects of Polygonum multiflorum and processed Polygonum multiflorum on liver metabolism, but also accurately identified key toxicity-related components such as emodin and core regulatory pathways such as purine metabolism pathways. This verifies the practicality and reliability of the method of this invention in actual scientific research scenarios, and demonstrates its application potential in drug toxicity evaluation, mechanism of action analysis, and other fields, further corroborating the innovation and industrial value of the technical solution of this invention.

[0038] Table 1

[0039] Table 2

[0040] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A spatial metabolomics analysis method, characterized in that, Includes the following steps: S1. Take biological tissue samples to be analyzed and divide them into groups according to the experimental purpose; S2. Prepare all biological tissue samples from all groups into sections, adsorb them onto the same glass slide, and perform mass spectrometry imaging detection to simultaneously acquire the spatial distribution signals of metabolites and mass spectrometry intensity data of all samples. S3. After preprocessing the collected raw data, identify the distribution of known exogenous components in the drug in the tissue sections, and delineate the regions with high signal intensity as regions of interest; randomly select multiple pixels within the region of interest for each sample. S4. Integrate the mass spectrometry data corresponding to the selected pixels in all parallel samples of the same group, and perform PCA, OPLS-DA, differential metabolite screening and KEGG pathway enrichment analysis in sequence.

2. The spatial metabolomics analysis method according to claim 1, characterized in that, The groups are divided into control groups and one or more drug treatment groups, with each group including 3 to 4 parallel samples.

3. The spatial metabolomics analysis method according to claim 1, characterized in that, The preprocessing includes: converting the RAW format raw data to CDF format using Xcalibur software, importing it into the Advanced Mass Spectrometry Imaging System Workstation v1.0, and removing background signal interference.

4. The spatial metabolomics analysis method according to claim 1, characterized in that, The number of pixels is 50 to 100.

5. The spatial metabolomics analysis method according to claim 1, characterized in that, The screening threshold for differential metabolites was p < 0.05 and |log2FoldChange| < 0.

58.

6. The application of a spatial metabolomics analysis method as described in any one of claims 1 to 5 in the study of hepatotoxicity of Polygonum multiflorum and processed Polygonum multiflorum.

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