Study on the Anti-Inflammatory Mechanism of Coumarins in Peucedanum decursivum Based on Spatial Metabolomics Combined with Network Pharmacology

Peucedanum decursivum (Miq.) Maxim (P. decursivum) is a traditional Chinese medicinal plant with pharmacological effects such as anti-inflammatory and anti-tumor effects, the root of which is widely used as medicine. Determining the spatial distribution and pharmacological mechanisms of metabolites is necessary when studying the effective substances of medicinal plants. As a means of obtaining spatial distribution information of metabolites, mass spectrometry imaging has high sensitivity and allows for molecule visualization. In this study, matrix-assisted laser desorption mass spectrometry (MALDI-TOF-MSI) and network pharmacology were used for the first time to visually study the spatial distribution and anti-inflammatory mechanism of coumarins, which are metabolites of P. decursivum, to determine their tissue localization and mechanism of action. A total of 27 coumarins were identified by MALDI-TOF-MSI, which mainly concentrated in the cortex, periderm, and phloem of the root of P. decursivum. Network pharmacology studies have identified key targets for the anti-inflammatory effect of P. decursivum, such as TNF, PTGS2, and PRAKA. GO enrichment and KEGG pathway analyses indicated that coumarins in P. decursivum mainly participated in biological processes such as inflammatory response, positive regulation of protein kinase B signaling, chemical carcinogenesis receptor activation, pathways in cancer, and other biological pathways. The molecular docking results indicated that there was good binding between components and targets. This study provides a basis for understanding the spatial distribution and anti-inflammatory mechanism of coumarins in P. decursivum.

Mass spectrometry imaging (MSI) can map the spatial distribution characteristics of the components of a sample.It combines mass spectrometry ion-scanning technology with professional image processing software to directly analyze biological tissue sections and generate two-dimensional ion density maps of compounds with arbitrary mass-tocharge ratios (m/z), thereby enabling a rapid and comprehensive analysis and research on the composition, relative abundance, and spatial distribution of substances in cells or tissues [13].MSI has the advantages of high sensitivity, high spatial resolution, high degree of visualization, and low risk of contamination and degradation [14].MALDI-TOF-MSI is the most widely used MSI method [15] that can visualize and analyze the chemical composition and spatial distribution characteristics of samples [16][17][18].Kuo and his colleagues [19] first proposed a method based on MALDI-MSI and molecular network analysis to study plant metabolites.They used agarwood stems as slice materials and LC-MS and MALDI-MSI to analyze and track agarwood metabolites and found that agarwood secondary metabolites were concentrated in the resin.MSI can be used to study the spatial distribution characteristics of secondary metabolites in medicinal plants [20], their synthesis and transport pathways [21,22], accumulation patterns [23,24], and involvement in plant stress defense [25].It provides new ideas for the specific research on medicinal plant components and the development of quality and safety testing.
Network pharmacology is based on the theory of systems biology.It uses the construction of biological networks as a method, high-throughput omics data, various databases, and the literature as a basis, and computer science and technology as the main scientific and technological means to conduct comprehensive and holistic analyses of the efficacy, toxicity, bioavailability, and mechanism of action of new drugs [26].Network pharmacology reveals and analyzes the multi-level and multi-angle biological network relationships between "drugs, genes, targets, and diseases" to predict the possible mechanism of action of drugs and provide important reference for discovering their pharmacological efficacy [27].Jiang [28] combined MSI with network toxicology to explore the potential targets and metabolic mechanisms of hepatotoxicity of Polygonum multiflorum Thunb component D, and the binding activity of toxic components and core targets were matched by molecular docking.
Therefore, the powerful combination of MSI and network pharmacology provides detailed information about the metabolites of medicinal plants and can constitute a platform for the determination of the pharmacological mechanisms of the bioactive metabolites of medicinal plants.In this research, MSI was used to determine the distribution of coumarins in the roots of P. decursivum, and network pharmacology was used to explore the antiinflammatory mechanism of P. decursivum.A comprehensive analysis of P. decursivum was conducted to provide a reference for the quality evaluation and pharmacological application of P. decursivum.

Selection of the Optimal Thickness of Frozen Sections
The thickness of a tissue slice could affect its integrity.If the slice is broken, it will be impossible to obtain a complete image [29].Complete and clear tissue sections are the key to obtaining high-quality images [30].The root of P. decursivum was cut into slices with thicknesses of 20 µm, 25 µm, 30 µm, 35 µm, and 40 µm, whose integrity and expansion were observed in bright field using an upright and inverted fluorescence microscope.The results showed that when the slice thickness was less than 30 µm, the tissue was severely fragmented, and slicing was more difficult.When the slice thickness exceeded 30 µm, the slice was too thick, and the field of view was dark, reducing the clarity of the observation, although the slice integrity and stretchability were high.Therefore, 30 µm was selected as the slice thickness for the root of P. decursivum (Figure 1).

Fluorescence Imaging of the Distribution of Coumarins in the Root of P. decursivum
The frozen sections of P. decursivum were observed using the DAPI channel of an upright and inverted fluorescence microscope.The results showed that the cork layer, the inner cork secretory cavity, the phloem secretory duct, and the xylem emitted blue fluorescence, but it was difficult to determine the location of coumarins in the sections of P. decursivum.Therefore, taking advantage of the fact that coumarins are easily soluble in organic solvents, an ethanol treatment was applied.After the slices of P. decursivum were treated with ethanol, the blue fluorescence of the secretory cavity in the inner layer of the cork and the secretory duct of the phloem disappeared, but the blue fluorescence of the cork layer and wood was still present (Figure 2A1,A2,C1,C2).This indicated that the fluorescence in the secretory cavity of the inner layer of the plug and the secretory duct of the phloem was produced by coumarin.Hence, in P. decursivum, coumarin is mainly distributed in the secretory tissues, such as the secretory cavity and secretory duct (Figure 2B1,B2).

Fluorescence Imaging of the Distribution of Coumarins in the Root of P. decursivum
The frozen sections of P. decursivum were observed using the DAPI channel of an upright and inverted fluorescence microscope.The results showed that the cork layer, the inner cork secretory cavity, the phloem secretory duct, and the xylem emitted blue fluorescence, but it was difficult to determine the location of coumarins in the sections of P. decursivum.Therefore, taking advantage of the fact that coumarins are easily soluble in organic solvents, an ethanol treatment was applied.After the slices of P. decursivum were treated with ethanol, the blue fluorescence of the secretory cavity in the inner layer of the cork and the secretory duct of the phloem disappeared, but the blue fluorescence of the cork layer and wood was still present (Figure 2(A1,A2,C1,C2)).This indicated that the fluorescence in the secretory cavity of the inner layer of the plug and the secretory duct of the phloem was produced by coumarin.Hence, in P. decursivum, coumarin is mainly distributed in the secretory tissues, such as the secretory cavity and secretory duct (Figure 2(B1,B2)).

Fluorescence Imaging of the Distribution of Coumarins in the Root of P. decursivum
The frozen sections of P. decursivum were observed using the DAPI channel of an upright and inverted fluorescence microscope.The results showed that the cork layer, the inner cork secretory cavity, the phloem secretory duct, and the xylem emitted blue fluorescence, but it was difficult to determine the location of coumarins in the sections of P. decursivum.Therefore, taking advantage of the fact that coumarins are easily soluble in organic solvents, an ethanol treatment was applied.After the slices of P. decursivum were treated with ethanol, the blue fluorescence of the secretory cavity in the inner layer of the cork and the secretory duct of the phloem disappeared, but the blue fluorescence of the cork layer and wood was still present (Figure 2A1,A2,C1,C2).This indicated that the fluorescence in the secretory cavity of the inner layer of the plug and the secretory duct of the phloem was produced by coumarin.Hence, in P. decursivum, coumarin is mainly distributed in the secretory tissues, such as the secretory cavity and secretory duct (Figure 2B1,B2).

Selection of the Matrix
In MALDI-TOF-MSI, there are significant differences in the resolution efficiency of different matrices, and the properties of the matrices themselves also affect the effectiveness of sample detection.Therefore, the choice of the matrix plays an important role in the analysis of compounds in a sample [31].In the study, nodakenin, imperatorin, and oxypeucedanin were selected as representative coumarins for MALDI-TOF-MSI matrix selection; nodakenin is an indicator of the quality of P. decursivum [3].Three commonly used matrices (DHB, CHCA, and 9-AA) were compared in both positive and negative ion detection modes [32,33].The results showed that the [M] + , [M+H] + , [M+Na] + , and [M+K] + signals of the three standards were detected in positive mode with CHCA as the matrix with high intensity (Figure 3 and Table 1).The structures of the identified coumarins are provided in the Supporting Materials (Figure S1).

Selection of the Matrix
In MALDI-TOF-MSI, there are significant differences in the resolution efficiency of different matrices, and the properties of the matrices themselves also affect the effectiveness of sample detection.Therefore, the choice of the matrix plays an important role in the analysis of compounds in a sample [31].In the study, nodakenin, imperatorin, and oxypeucedanin were selected as representative coumarins for MALDI-TOF-MSI matrix selection; nodakenin is an indicator of the quality of P. decursivum [3].Three commonly used matrices (DHB, CHCA, and 9-AA) were compared in both positive and negative ion detection modes [32,33].The results showed that the [M] + , [M+H] + , [M+Na] + , and [M+K] + signals of the three standards were detected in positive mode with CHCA as the matrix with high intensity (Figure 3 and Table 1).The structures of the identified coumarins are provided in the Supporting Materials (Figure S1).

Distribution Characteristics of Coumarins in the Root of P. decursivum
Based on the above experiments, frozen sections of P. decursivum were analyzed by MALDI-TOF-MSI.According to the detected MS peak (Figure 4), the standard mass-tocharge ratios of coumarins in P. decursivum roots reported in the literature were used for comparison, and the signal peaks were assigned (Table 2).MALDI-TOF-MSI was used to detect 27 coumarins in the root of P. decursivum.

Distribution Characteristics of Coumarins in the Root of P. decursivum
Based on the above experiments, frozen sections of P. decursivum were analyzed MALDI-TOF-MSI.According to the detected MS peak (Figure 4), the standard mass charge ratios of coumarins in P. decursivum roots reported in the literature were used comparison, and the signal peaks were assigned (Table 2).MALDI-TOF-MSI was use detect 27 coumarins in the root of P. decursivum.In cross sections of the root of P. decursivum, the pith, xylem, phloem, cortex, and periderm are visible from the inside to the outside [55].As shown in Figure 5, coumarins in P. decursivum were mainly distributed in the periderm, cortex, and phloem, which is consistent with the results of previous studies [56][57][58] In cross sections of the root of P. decursivum, the pith, xylem, phloem, cortex, and periderm are visible from the inside to the outside [55].As shown in Figure 5, coumarins in P. decursivum were mainly distributed in the periderm, cortex, and phloem, which is consistent with the results of previous studies [56][57][58] Coumarin is a secondary metabolite derived from the phenylpropanoid pathway.Since the biosynthesis pathway of plant secondary metabolites is affected by the environment, growth period, and genes, the accumulation and distribution of secondary metabolites are affected by these factors as well [59,60].Therefore, the distribution of coumarins in different tissues is different.Coumarin is a secondary metabolite derived from the phenylpropanoid pathway.Since the biosynthesis pathway of plant secondary metabolites is affected by the environment, growth period, and genes, the accumulation and distribution of secondary metabolites are affected by these factors as well [59,60].Therefore, the distribution of coumarins in different tissues is different.

Active Ingredient and Disease Target Prediction
After searching the TCMSP and Swiss Target Prediction databases, 11 ingredients were selected (Table 3) for target prediction, and a total of 44 active ingredient targets were obtained (Figure 6a).The disease targets were searched with "inflammation" as the keyword, and the targets obtained from the databases were merged and deduplicated, obtaining a total of 2203 targets.The active ingredient targets and disease targets were analyzed and represented in a Venn diagram in Venny2.1.0;a total of 27 intersection targets were obtained (Figure 6b), which are potential targets for the anti-inflammatory effect of P. decursivum.analyzed and represented in a Venn diagram in Venny2.1.0;a total of 27 intersecti targets were obtained (Figure 6b), which are potential targets for the anti-inflammato effect of P. decursivum.

Protein-Protein Interaction (PPI) Network Analysis
The 27 intersection targets obtained as described in "Section 2.5.1" were import into the STRING database to analyze protein interactions.After the data were export Cytoscape was used to obtain a PPI network diagram, where the higher the interacti score, the darker the color, and the closer the interaction relationship between t examined proteins.As can be seen from Figure 7a, TNF has the darkest color, indicati that TNF is the key target responsible for the plant anti-inflammatory effects.Af screening the hub targets using MCC, the top 10 targets were obtained, and the P network of the core targets was established (Figure 7b).

Protein-Protein Interaction (PPI) Network Analysis
The 27 intersection targets obtained as described in "Section 2.5.1" were imported into the STRING database to analyze protein interactions.After the data were exported, Cytoscape was used to obtain a PPI network diagram, where the higher the interaction score, the darker the color, and the closer the interaction relationship between the examined proteins.As can be seen from Figure 7a, TNF has the darkest color, indicating that TNF is the key target responsible for the plant anti-inflammatory effects.After screening the hub targets using MCC, the top 10 targets were obtained, and the PPI network of the core targets was established (Figure 7b).By performing a GO enrichment analysis of the intersecting targets related to P. decursivum active ingredient targets and anti-inflammatory disease targets, a total of 162 GO entries were obtained (p < 0.01).A total of 117 entries were related to biological

Gene Ontology (GO) Enrichment Analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis
By performing a GO enrichment analysis of the intersecting targets related to P. decursivum active ingredient targets and anti-inflammatory disease targets, a total of 162 GO entries were obtained (p < 0.01).A total of 117 entries were related to biological processes (BPs), mainly involved in inflammatory response, positive regulation of protein kinase B signaling, and positive regulation of peptidyl serine physiology.Twenty-two entries were related to cellular components (CCs), mainly, the plasma membrane, neuron projections, and the cell surface.Twenty-three entries were related to molecular functions (MFs), mainly, protein homodimerization activity, identical protein binding and acetylcholine binding.According to the p-value, the top 10 GO enrichment analysis results are shown in Figure S3 (Supporting Materials).Based on the number of enriched genes after deleting duplicates, BPs, CCs, and MFs showed a total of 21 intersecting genes (Figure 8a).A total of 59 related pathways were identified through KEGG pathway enrichment analysis, including chemical cancer receptor activation, pathways in cancer, IL-17 signaling pathway.The top 15 pathways were selected from small to large according to the p-value, and a bar chart was drawn (Figure 8b).

Gene Ontology (GO) Enrichment Analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis
By performing a GO enrichment analysis of the intersecting targets related to P. decursivum active ingredient targets and anti-inflammatory disease targets, a total of 162 GO entries were obtained (p < 0.01).A total of 117 entries were related to biological processes (BPs), mainly involved in inflammatory response, positive regulation of protein kinase B signaling, and positive regulation of peptidyl serine physiology.Twenty-two entries were related to cellular components (CCs), mainly, the plasma membrane, neuron projections, and the cell surface.Twenty-three entries were related to molecular functions (MFs), mainly, protein homodimerization activity, identical protein binding and acetylcholine binding.According to the p-value, the top 10 GO enrichment analysis results are shown in Figure S3 (Supporting Materials).Based on the number of enriched genes after deleting duplicates, BPs, CCs, and MFs showed a total of 21 intersecting genes (Figure 8a).A total of 59 related pathways were identified through KEGG pathway enrichment analysis, including chemical cancer receptor activation, pathways in cancer, IL-17 signaling pathway.The top 15 pathways were selected from small to large according to the p-value, and a bar chart was drawn (Figure 8b).

Molecular Docking
Th top three active ingredients and core targets with respect to the degree of molecular docking were selected for molecular docking after MCC analysis.In general, if the binding energy between the ligand and the target protein is less than −5 kcal/mol, the binding between the ligand and the receptor protein is stable, and the lower the binding energy, the more stable the binding [61].The molecular docking results showed binding energies of less than −5 kcal/mol, as shown in Table 4. Decursin had the strongest binding affinity with TNF, PTGS2, and PRKACA, leading to the strongest binding stability.The docking results are shown in Figure 9.

Materials and Reagents
The plant materials of P. decursivum used in this experiment were fresh and were acquired from Dashui Village, Luoba Town, Shixing County, Shaoguan City, Guangdong Province (24°43′25.73″N, 114°15′16.14″E) in May.They were identified by Professor Chen Yuan from the Department of Traditional Chinese Medicine Cultivation and Identification

Materials and Reagents
The plant materials of P. decursivum used in this experiment were fresh and were acquired from Dashui Village, Luoba Town, Shixing County, Shaoguan City, Guangdong Province (24 • 43 ′ 25.73 ′′ N, 114 • 15 ′ 16.14 ′′ E) in May.They were identified by Professor Chen Yuan from the Department of Traditional Chinese Medicine Cultivation and Identification at Gansu Agricultural University and confirmed as a plant material of P. decursivum.A voucher specimen (No.GAUAB-PD-20230518) was deposited in the herbarium of the Department of Chinese herbal medicine, Agronomy building of Gansu Agricultural University, Lanzhou, China.

Fluorescence Imaging Technology for Tissue Distribution of Coumarins in the Root of P. decursivum 3.2.1. Selection of Frozen Section Thickness
The slicing method was described in our previous work [62].The fresh root of P. decursivum was washed and cut into small sections of about 0.5 cm, which were placed in 15% glycerol and vacuumed until they sank to the bottom of the bottle.The glycerol solution on the surface was then removed, and the small segments of P. decursivum were precooled in a freezing microtome for 30 min, immersed in OCT embedding medium on a precooled tray, frozen until the OCT medium turned white, and then sliced on a freezing microtome (CM1950, Leica, Germany).The sections were then quickly frozen at −22 • C for 10 min, further cut into tissue sections with thicknesses of 20 µm, 25 µm, 30 µm, 35 µm, and 40 µm, attached to glass slides, sealed with the 15% glycerol solution, and observed under an upright microscope to see if there were bubbles in the tissue.If there were bubbles, a dropper was used to gently expel the bubbles.After the bubbles were completely expelled, a coverslip was placed on the tissue, and its integrity and stretching level were observed in bright field using an upright and inverted fluorescence microscope; pictures were taken.

Localization and Observation of Coumarins
Coumarins usually produce blue fluorescence under ultraviolet light and are easily soluble in methanol, toluene, ether, and other organic solvents [58,63].Hence, the sections were fixed with 95% ethanol for 30 min to remove coumarins and then sealed and observed using the DAPI channel of an upright and inverted fluorescence microscope.The reference standards of nodakenin, imperatorin, and oxypeucedanin were accurately weighed and dissolved in a 70% methanol aqueous solution and then ultrasonically treated for 5 min.Finally, 1 mg/mL standard solutions were prepared.The prepared standard solutions (1 µL) were dripped onto the target plates.After the samples had dried, the matrix solution (1 µL) was dripped to cover the samples.MALDI-TOF-MSI analysis was carried out after drying.

Spraying of the Substrate
The substrate was sublimated using the substrate sublimation device iMLayer (Shimadzu, Kyoto, Japan) and sprayed under the following conditions: the thickness of DHB (300 mg) at 180 • C was 0.5 µm, the thickness of 9-AA (300 mg) at 220 • C was 0.5 µm, and the thickness of CHCA (300 mg) at 250 • C was 0.5 µm.The tissue was sprayed and sliced.

Sample Preparation
The slicing method is described in "Section 3.2.1".The selected slice thickness was 30 µm.The slices were placed on conductive slides (made of indium tin oxide) in a vacuum dryer for 20 min.Based on the results of the MALDI-TOF-MSI studies, the top 13 components in terms of signal intensity were selected as the main active ingredients for the network pharmacology studies.(Table 5).on the results of the MALDI-TOF-MSI studies, the top 13 components in terms of signal intensity were selected as the main active ingredients for the network pharmacology studies (Table 5).The online tool VENNY2.1 (https://bioinfogp.cnb.csic.es/tools/venny/,accessed on 21 June 2023) was used to intersect the active ingredient targets and anti-inflammatory disease targets and obtain potential anti-inflammatory targets.Then, they were imported into the STRING database (https://www.stringdb.org/,accessed on 21 June 2023), searching the species "Homo sapiens", with the minimum interaction score of "≥0.400".MCC was used to screen out the top 10 core targets based on the relationship between nodes and edges.

GO Enrichment Analysis and KEGG Pathway Analysis
GO enrichment analysis is used to explore potential biomolecular mechanisms, which include BP, CC, and MF.KEGG pathway analysis was also used to identify biological functions and candidate targets.David database (https://david.ncifcrf.gov/home.jsp,accessed on 22 June 2023) was used to GO enrichment analysis and KEGG pathway analysis on intersecting targets.

Molecular Docking
The molecular docking technology combines active ingredients with target proteins to verify the prediction results of network pharmacology in a virtual evaluation manner [64].The target protein crystal structures corresponding to the screened key targets were found in the protein structure database RCSB (https://www.rcsb.org/,accessed on 24 June 2023) in pdb format, and the protein crystals were processed using PyMol 2.3.4 software to separate their respective original ligands.The active ingredient, target protein crystal structure, and original ligand were processed using Autodock Tools 1.5.6 software and saved in pdbqt format.Then, the "grid box" of the target protein and the original ligand was found as the active pocket, and verification of the original ligand docking was performed.Autodock Vina 1.5.6 was used to dock the target protein and the active ingredient, and the appropriate conformation was selected.PyMol 2.3.4 software was used for visualization based on the docking results.

Conclusions
Coumarin is one of the important secondary metabolites of P. decursivum.Previous studies have shown that coumarin has anti-inflammatory, anti-tumor, and other pharmacological effects.The secondary metabolites of medicinal plants are the basis for their medicinal effects; so, metabolites play a vital role in traditional Chinese medicine.In this study, MALDI-TOF-MSI was used for the first time to elucidate the spatial distribution of coumarins in the root tissue of P. decursivum, and combined network pharmacology and molecular docking were employed to predict the potential targets and pathways at the basis of P. decursivum anti-inflammatory effects.Our research showed that frozen sections of 30 µm thickness were conducive to observing the distribution of coumarins in the root of P. decursivum.After the optimization of matrix and mode, we chose to analyze the samples in the positive mode, with CHCA as the matrix.Through the attribution of signal peaks, a total of 27 coumarins were identified in the roots of P. decursivum, which appeared to be mainly stored in the phloem, cortex, and periderm.This is consistent with previous research that indicated that coumarins were mainly concentrated in the cortex.Based on the results of MALDI-TOF-MSI, the components with the highest signal intensity (including nodakenin, imperatorin, decursin, etc.) were screened in network pharmacology and molecular docking studies.The results showed that a total of 27 targets contributed to the plant's effects.Key targets such as TNF, PTGS2, PRKACA, HSP90AB1, RELA, and NFKBIA appeared to be involved in chemical carcinogenesis-receptor activation, pathways in cancer, IL-17 signaling pathway, cholinergic synapse pathway, and regulation of lipolysis in adipocytes.TNF is mainly secreted by macrophages, can induce cell death in certain tumor cell lines, and is mainly involved in the inflammatory response.It can enhance infection resistance by activating neutrophils and platelets, enhancing the killing ability of macrophages/NK cells, and stimulating the immune system.It can also play a pathological role in various autoimmune diseases and processes, such as graft-versus-host rejection and rheumatoid arthritis [65].PTGS2 is a key molecule with anti-inflammatory and analgesic effects, playing a crucial role in biological processes such as inflammatory response, inflammation-related gene expression, cell apoptosis, and immune response [66].PRKACA is a downstream molecule of the second messenger cAMP.The activated cAMP/PRKACA pathway can inhibit the inflammatory response [67].Molecular docking showed that the binding energies of the core active ingredients and the core target proteins (TNF, PTGS2, and PRKACA) were all < −5.0 kcal/mol, which proved that they had stable binding activities, and decursin exhibited the most stable interaction.
This study integrated multiple disciplines such as histochemistry, computer informatics, and pharmacology to establish a method for the visual characterization of the tissue localization of coumarins and the analysis of their pharmacological action mechanism, which can provide a theoretical basis for the quality evaluation and clinical use of P. decursivum.It can also provide a reference for research on metabolites of other medicinal plants.

Supplementary Materials:
The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/molecules29143346/s1, Figure S1.The structural formula of the compound.Figure S2.MALDI-TOF-MSI of coumarins in the root of P. decursivum.Institutional Review Board Statement: Not applicable.

Figure 4 .
Figure 4. Mass spectrum of P. decursivum using the CHCA substrate.

Figure 4 .
Figure 4. Mass spectrum of P. decursivum using the CHCA substrate.

Figure 5 .
Figure 5. MALDI-TOF-MSI of partially representative coumarins in the root of P. decursivum.

Figure 8 .
Figure 8.(a) The number of intersecting genes for BPs, CCs, and MFs in GO enrichment analysis, (b) KEGG enrichment analysis.

Figure 8 .
Figure 8.(a) The number of intersecting genes for BPs, CCs, and MFs in GO enrichment analysis, (b) KEGG enrichment analysis.

3. 3 .
Study on the Spatial Distribution of Coumarins in the Root of P. decursivum Using MALDI-TOF-MSI 3.3.1.Preparation of Standard Solutions and Screening of Matrices

3. 4 . 3 .
Construction of a PPI Network Diagram and Screening of the Core Targets Figure S3.GO enrichment analysis results.Author Contributions: Conceptualization, Z.L. and Q.L.; data curation, Z.L.; formal analysis, Z.L.; investigation, Z.L. and Q.L.; methodology, Z.L. and Q.L.; project administration, Q.L.; supervision, Q.L.; validation, Z.L.; writing-original draft, Z.L.; writing-review and editing, Q.L.All authors have read and agreed to the published version of the manuscript.Funding: This work was supported by the National Natural Science Foundation of China (32360113 and 31860102), the Youth Tutor Fund project of Gansu Agricultural University (GAU-QDFC-2020-2), the FuXi Young Talents Introduction Projects of Gansu Agricultural University (GSAU-RCZX201704), and the Outstanding Graduate Student Innovation Star Project in Gansu Province (2022-CXZXS-021).

Table 1 .
Signal intensity of representative coumarins in different matrices of P. decursivum.

Table 1 .
Signal intensity of representative coumarins in different matrices of P. decursivum.

Table 2 .
Tentatively identified coumarins from the roots P. decursivum with MALDI-MSI.

Table 2 .
Tentatively identified coumarins from the roots P. decursivum with MALDI-MSI.

Table 3 .
Active ingredient information for target prediction.

Table 3 .
Active ingredient information for target prediction.

Table 5 .
Signal intensity of the active ingredients.