For metabolomic analysis, the raw mass spectra were assigned and analyzed using TraceFinder software (Version 3.2; Thermo Fisher Scientific), with reference to an in-house database containing MS/MS spectra of over 1500 metabolites.
For Lipidomics analysis, the raw mass spectra were then processed and analyzed using Lipidsearch software (Version 4.2; Thermo Fisher Scientific).
"],"disease":[""],"extraction_protocol":["For the extraction of polar metabolites, 90 μL of plasma was processed using a solvent mixture composed of 360 μL of pre-chilled methanol (MS grade) and internal standards (13C6 L-Lysine and 13C615N4 L-Arginine, Silante). After thorough vortexing and mixing, plasma samples were placed at -80 °C for 1 h to induce protein precipitation. Subsequently, centrifugation at 14,000 x g for 10 min at 4 °C was performed, and equal volumes of supernatant were carefully collected. The collected supernatant was transferred to a fresh tube and evaporated using a vacuum centrifuge. To ensure consistency, a quality control (QC) sample was generated by pooling 10 μL of supernatant from each individual sample. The dried extracts were redissolved in a 100 μL solution containing internal standards (for positive mode, Gln-N15, inosine-4N15; Trp-D5; and for negative mode, cholic acid-D4, inosine-4N15, stearic acid-D35, succinate-D4 and Trp-D5) prior to undergoing MS analysis.
For lipid extraction, 90 μL of plasma was treated with a solvent mixture of pre-chilled dichloromethane/methanol (2:1, v:v, MS grade), along with an internal standard (PE (16:0-d31-18:1)). The mixture was vigorously vortexed, and the lower dichloromethane layer was collected after centrifugation at 300 rpm for 15 min at 4 °C. The collected layer was transferred to a new tube. To this, pre-chilled water (MS grade, 1/5 volume of dichloromethane) was added, thoroughly mixed, and subjected to centrifugation at 10,000 rpm for 20 min at 4 °C. The lower dichloromethane layer was collected and dried using a vacuum centrifuge. To maintain consistency, a quality control (QC) sample was created by combining 10 μL of the dichloromethane layer from each sample. The dried extracts were reconstituted with 250 μL of a solvent mixture containing internal standards (D31-Cer(d18:1_16:0), DG(14:0_14:0), PC(14:0_14:0), PE(14:0_14:0), D31-PE(16:0_18:1), and PS(14:0_14:0)) before undergoing MS analysis. To ensure the uniformity of retention time and signal intensity in the mass spectrum, the QC sample was injected between every 15 to 20 sample injections.
"],"organism":["Homo sapiens"],"data_transformation_protocol":["Metabolites identified in over 90% of individuals were selected, and subsequent total intensity normalization was performed for each sample. More than 96.8% of metabolites exhibited a coefficient of variation (CV) of less than 30% in the quality control (QC) samples. Missing values were imputed based on the K nearest neighbors (KNN) algorithm (R package: NormalizeMets)3. Regarding the lipidome analysis, the total number of carbon atoms and double bonds within the fatty acid side chain of each lipid were summed separately, and then the abundance of identical lipid species with matching numbers of carbon atoms and double bonds in their fatty acid side chain were combined for each sample. Subsequently, the total intensity of each sample was normalized. To obtain high quality data, metabolites and lipids with CV values less than 0.25 were applied for further analysis.
"],"study_factor":["Gender","Age"],"metabolights_link":["https://www.ebi.ac.uk/metabolights/MTBLS12015"],"submitter_email":["xina0xiao@163.com"],"sample_collection_protocol":["This study encompassed 503 participants sourced from cohort1 of the West China Health and Aging Trend (WCHAT) study. All participants met the following criteria: 1) ethnicity: Han Chinese; 2) absence of cancers and other serious illnesses; 3) life expectancy of more than 6 months; and 4) successful completion of both the questionnaire and the physical examination.
"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"omics_type":["Metabolomics"],"instrument_platform":["Liquid Chromatography MS - negative - reverse phase","Liquid Chromatography MS - positive - hilic","Liquid Chromatography MS - - reverse phase"],"study_design":["Sarcopenia","Sarcosine","untargeted metabolites","aging"],"chromatography_protocol":["Polar metabolites were analyzed by reversed-phase-high performance liquid chromatography tandem Q Exactive mass spectrometry (Thermo Fisher Scientific) in both positive and negative mode. In positive ionization mode, the separation of metabolites was carried out using a BEH Amide column (2.1 x 100 mm, 1.7 μm, Waters) at a flow rate of 300 μL/min. Mobile phase A consisted of a mixture of 5% water, 95% acetonitrile and 1% formic acid, while mobile phase B comprised 50% water, 50% acetonitrile, 10 mM ammonium formate, and 1% formic acid. A non-linear gradient elution over 20 min was programmed as follows: 1% B (0.0 min), 1% B (2.0 min), 5% B (3.0 min), 60% B (11.0 min), 60% B (14.0 min), 99% B (15.0 min), 99% B (17.0 min), 1% B (17.1 min) and 1% B (20.0 min). In negative ionization mode, metabolites were separated using a BEH C18 column (2.1 x 100 mm, 1.7 μm, Waters). The gradient procedure was initiated at 1% mobile phase B with a flow rate of 200 μL/min and progressed as follows: 1% B (0.0 min), 1% B (3.0 min), 99% B (12.0 min), 99% B (17.0 min), 1% B (17.1 min) and 1% B (20.0 min).
For lipids, LC separation was accomplished using a Cortecs C18 column (2.1 x 100 mm, 2.7 μm, Waters). Mobile phase A consisted of a mixture of 40% water, 60% acetonitrile and 10 mM ammonium acetate, while mobile phase B comprised 10% acetonitrile and 90% isopropanol. A 35-min gradient elution procedure was adopted, featuring a non-linear increase in mobile phase B: 30% B (0.0 min), 30% B (3.0 min), 33% B (4.5 min), 45% B (7.0 min), 52% B (8.0 min), 58% B (11.0 min), 66% B (14.0 min), 70% B (17.0 min), 75% B (21.0 min), 98% B (23.0 min), 98% B (30.0 min), 30% B (30.5 min) and 30% B (35.0 min). The flow rate was set at 250 μL/min. This chromatographic setup ensured efficient separation and subsequent analysis of the polar metabolites and lipids present in the samples.
"],"publication":["Sarcosine decreases in sarcopenia and enhances muscle regeneration and adipose thermogenesis by activating ant-inflammatory macrophages."],"curator_keywords":["Sarcopenia","Sarcosine","untargeted metabolites","aging"],"submitter_affiliation":["Sichuan University"],"submitter_name":["Xina Xiao"],"mass_spectrometry_protocol":["For metabolomic analysis, a comprehensive full MS scan was performed using RP-HPLC tandem MS (Q Exactive Orbitrap) in positive mode across the range of 70-1050 m/z and in negative mode from 80-1200 m/z, with a resolution set at 70,000 and 60,000, respectively. MS/MS spectra were acquired using a stepped energy approach (15, 30 and 45 NCE) for both modes to fragment the top 10 most abundant parent ions. Fragment ions were acquired at resolutions of 17,500 and 15,000 for positive and negative modes, respectively. The raw mass spectra were assigned and analyzed using TraceFinder software (Version 3.2; Thermo Fisher Scientific), with reference to an in-house database containing MS/MS spectra of over 1500 metabolites.
Lipidomics analysis was performed exclusively in positive mode using RP-HPLC tandem MS (Q Exactive HF orbitrap; Thermo Fisher Scientific). Full-scan MS data acquisition covered the range of 240-2000 m/z, operating at a resolution of 60,000. The top 10 most abundant parent ions were subjected to fragmentation using stepped energies (15, 30 and 45 NCE) to generate MS/MS spectra, which were analyzed at a resolution of 15,000. The raw mass spectra were then processed and analyzed using Lipidsearch software (Version 4.2; Thermo Fisher Scientific).
"],"additional_accession":[]},"is_claimable":false,"name":"Sarcosine decreases in sarcopenia and enhances muscle regeneration and adipose thermogenesis by activating ant-inflammatory macrophages (cohort1)","description":"Age-related changes in circulating metabolites significantly influence systemic physiology and may contribute to diseases like sarcopenia. Although metabolic dysregulation is closely linked to sarcopenia, the roles of specific metabolites remain unclear. Here we performed comprehensive plasma metabolomic and lipidomic analyses across two cohorts comprising 1,013 individuals. Our findings revealed the metabolic characteristics of sarcopenia, including a notable decline in plasma sarcosine levels in both aging and sarcopenic individuals. Functional studies showed that sarcosine helps maintain muscle mass homeostasis during aging, promotes adipose thermogenesis and enhances muscle regeneration. These effects may increase energy expenditure and restore metabolic balance to reduce chronic inflammation and improve insulin sensitivity, which are crucial for managing sarcopenia. We demonstrate that sarcosine activated the GCN2 signaling pathway to enhance anti-inflammatory macrophage polarization, promoting adipose thermogenesis and muscle regeneration. This study underscores the potential of sarcosine supplementation as an adjunctive strategy for preventing sarcopenia in older adults.
cohort1 is reported in the current study MTBLS12015.
cohort2 is reported in MTBLS12036.
","dates":{"publication":"2025-05-06","submission":"2025-01-06"},"accession":"MTBLS12015","cross_references":{}}