MetaboLightsapplication/xmlftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14481/m_MTBLS14481_LC-MS_alternating_normal-phase_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14481/a_MTBLS14481_LC-MS_alternating_normal-phase.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14481/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14481/s_MTBLS14481.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14481<p>The acquired MS data pretreatments including peak picking, peak grouping, retention time correction, second peak grouping, and annotation of isotopes and adducts was performed using XCMS software. LC−MS raw data files were converted into mzXML format and then processed by the XCMS, CAMERA and metaX toolbox implemented with the R software. Each ion was identified by combining retention time (RT) and m/z data. Intensities of each peaks were recorded and a three dimensional matrix containing arbitrarily assigned peak indices (retention time-m/z pairs), sample names (observations) and ion intensity information (variables) was generated.</p><p>The online KEGG, HMDB database was used to annotate the metabolites by matching the exact molecular mass data (m/z) of samples with those from database. If a mass difference between observed and the database value was less than 10 ppm, the metabolite would be annotated and the molecular formula of metabolites would further be identified and validated by the isotopic distribution measurements. We also used a in-house fragment spectrum library of metabolites to validate the metabolite identidification.</p><p>Statistical analysis was primarily conducted using R (v4.0). Metabolite data underwent three key processing steps: first, data filtering to remove samples with over 80% missing values or quality control (QC) samples with over 50% missing data; second, data imputation using the K-nearest neighbor (KNN) method; and third, data standardization via Probabilistic quotient normalization (PQN).Cluster heatmaps were generated with the R package pheatmap. Principal component analysis (PCA) and significant differential metabolite analysis were performed using the R package metaX. Partial least squares discriminant analysis (PLSDA) was carried out with the R package ropls, and variable importance in projection (VIP) values for each variable were calculated. Correlation analysis was conducted using Pearson's correlation coefficient from the R package cor. The final significant differential metabolites were identified based on three criteria: P-value &lt;0.05 from t-test, fold change &gt;1.2, and VIP ≥1 from PLSDA analysis.Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using hypergeometric tests, with P-value &lt;0.05 indicating significant enrichment. Metabolite set enrichment analysis was conducted using GSEA (v4.1.0), and KEGG pathways with |NES| &gt;1, a nominal P-value &lt;0.05 and FDR&lt;0.25 were considered significantly different between the two groups. Network diagrams were constructed based on the pathways of the metabolites to illustrate their interactions.</p>MetaboLightsPublicLiquid Chromatography MS - alternating - normal-phase<p>Chromatography: An ACQUITY UPLC HSS T3 column (100 mm×2.1 mm, 1.8 µm, Waters) was used for separation. The mobile phase consists of phase A (5 mmol/L ammonium acetate + 5 mmol/L acetic acid + water) and phase B (acetonitrile). Gradient elution conditions were set as follows: 0~0.8 min , 2% ~ 70%B; 0.8~2.8 min , 70% ~ 90% B; 2.8~5.3 min, 90% ~ 99% B; 5.3~5.9 min, 99% B; 5.9~7.5 min , 99% ~ 2% B;7.5~7.6 min , 2% B;7.6~10.0 min, 2% B; The flow rate is 0.35 mL/min. The injection volume for each sample was 4 µL. The column oven was maintained at 40℃.</p>Metabolomics analysis revealed the effects of different treatments on the metabolic profile of HaCaT cells.Pengxiu DaiLiaocheng UniversityCell Linemass spectrometry assay<p>Weigh 50 mg (±5 mg) of the sample and add 500 μL of 80 % icy methanol solution, sonicated and vortexed it. Incubated for 30 min at -20℃ to precipitate proteins, and centrifuged at 20000 g for 10 min at 4℃, supernatant centrifuged for 5 min again. The supernatant was transferred to a fresh vial for UPLC-HRMS analysis. The quality control (QC) sample was prepared by mixing an equal aliquot of the supernatant of samples.</p>Homo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS14481<p>The acquired MS data pretreatments including peak picking, peak grouping, retention time correction, second peak grouping, and annotation of isotopes and adducts was performed using XCMS software. LC−MS raw data files were converted into mzXML format and then processed by the XCMS, CAMERA and metaX toolbox implemented with the R software. Each ion was identified by combining retention time (RT) and m/z data. Intensities of each peaks were recorded and a three dimensional matrix containing arbitrarily assigned peak indices (retention time-m/z pairs), sample names (observations) and ion intensity information (variables) was generated.</p><p>The online KEGG, HMDB database was used to annotate the metabolites by matching the exact molecular mass data (m/z) of samples with those from database. If a mass difference between observed and the database value was less than 10 ppm, the metabolite would be annotated and the molecular formula of metabolites would further be identified and validated by the isotopic distribution measurements. We also used a in-house fragment spectrum library of metabolites to validate the metabolite identidification.&nbsp;</p><p>Statistical analysis was primarily conducted using R (v4.0). Metabolite data underwent three key processing steps: first, data filtering to remove samples with over 80% missing values or quality control (QC) samples with over 50% missing data; second, data imputation using the K-nearest neighbor (KNN) method; and third, data standardization via Probabilistic quotient normalization (PQN).Cluster heatmaps were generated with the R package pheatmap. Principal component analysis (PCA) and significant differential metabolite analysis were performed using the R package metaX. Partial least squares discriminant analysis (PLSDA) was carried out with the R package ropls, and variable importance in projection (VIP) values for each variable were calculated. Correlation analysis was conducted using Pearson's correlation coefficient from the R package cor. The final significant differential metabolites were identified based on three criteria: P-value &lt;0.05 from t-test, fold change &gt;1.2, and VIP ≥1 from PLSDA analysis.Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using hypergeometric tests, with P-value &lt;0.05 indicating significant enrichment. Metabolite set enrichment analysis was conducted using GSEA (v4.1.0), and KEGG pathways with |NES| &gt;1, a nominal P-value &lt;0.05 and FDR&lt;0.25 were considered significantly different between the two groups. Network diagrams were constructed based on the pathways of the metabolites to illustrate their interactions.</p>ECM@SF?AGS?CS?LA@ExosomeECM@SF?AGS?CS?LAdaipengxiu@lcu.edu.cn<p>Weigh 50 mg (±5 mg) of the sample and add 500 μL of 80 % icy methanol solution, sonicated and vortexed it. Incubated for 30 min at -20℃ to precipitate proteins, and centrifuged at 20000 g for 10 min at 4℃, supernatant centrifuged for 5 min again. The supernatant was transferred to a fresh vial for UPLC-HRMS analysis. The quality control (QC) sample was prepared by mixing an equal aliquot of the supernatant of samples.</p>MetabolomicsCell Lineprofile spectrumMetabolomicsnormalPositive Controluntargeted analysisThermo Scientific Vanquish Flex UHPLC SystemHomo sapiensNegative Controldata-independent acquisitionblank samplecollision-induced dissociationThermo Fisher Scientific (China)OpenMSion mobility separationQ Exactive PlusHaCaTQuality ControlCell Lineprofile spectrumMetabolomicsnormalPositive Controluntargeted analysisThermo Scientific Vanquish Flex UHPLC SystemHomo sapiensNegative Controldata-independent acquisitionblank samplecollision-induced dissociationThermo Fisher Scientific (China)OpenMSQ Exactive Plusion mobility separationHaCaTQuality Control<p>A high-resolution tandem mass spectrometer Q-Exactive Plus (Thermo Fisher Scientific) was used to detect metabolites eluted form the column. Each sample was operated in both positive and negative electrospray ionization mode. ESI temperature is 350℃. The voltage is +3800 volts in positive ion mode and -3400 volts in negative ion mode. The sweep gas pressure of the ion source is 0 Arb, Gas 1 (Auxiliary gas) pressures set to 15 Arb, Gas 2 (Sheath gas) pressures set to 50 Arb. The mass spectrometric data were obtained with full scan and data-dependent acquisition (DDA) modes. In one acquisition cycle, the full scan acquisition range is 70-1050 Da. Then, the top 5 signal ions with a signal accumulation intensity of more than 100000 were selected from the full scan for DDA scanning, the DDA resolution is 17.5k, Maximum IT is set to 50 ms. Dynamic exclusion is set to 6 s.</p>falseMetabolomics analysis revealed the effects of different treatments on the metabolic profile of HaCaT cellsTo investigate the regulatory effects of the ECM@SF-AGS-CS-LA hydrogel (HC), exosomes (HB), and their combination (HA) on the metabolic profiles of wound tissues in diabetic mice, HaCaT keratinocytes were employed as an in vitro cellular model. Untargeted metabolomics profiling was performed on HaCaT cells subjected to the respective treatments. Five pairwise comparisons were conducted: HC versus HD (diabetic control), HB versus HD, HA versus HD, HA versus HC, and HA versus HB. KEGG pathway enrichment analysis was applied to identify biologically relevant pathways significantly associated with differentially abundant metabolites (P < 0.05). This analysis revealed 8, 28, 30, 30, and 97 significantly enriched pathways in the HC vs HD, HB vs HD, HA vs HD, HA vs HC, and HA vs HB comparisons, respectively.2026-05-142026-05-14MTBLS14481