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MB Sample ID: SA130686

Local Sample ID:M_control_1
Subject ID:SU001624
Subject Type:Cultured cells
Subject Species:Homo sapiens
Taxonomy ID:9606

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Combined analysis:

Analysis ID AN002578 AN002579
Analysis type MS MS
Chromatography type HILIC HILIC
Chromatography system Thermo Dionex Ultimate 3000 Thermo Dionex Ultimate 3000
Column ZIC-pHILIC (150 x 4.6mm,5um) ZIC-pHILIC (150 x 4.6mm,5um)
MS Type ESI ESI
MS instrument type Orbitrap Orbitrap
MS instrument name Thermo Q Exactive Orbitrap Thermo Exactive Plus Orbitrap
Ion Mode POSITIVE NEGATIVE
Units Intensity Intensity

MS:

MS ID:MS002390
Analysis ID:AN002578
Instrument Name:Thermo Q Exactive Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Mass spectrometry was performed in polarity switching mode, with the following settings: resolution 35 000, AGC 1x106, m/z range 85-1275, sheath gas 50, auxiliary gas 20, sweep gas 2, probe temperature 150 °C, and capillary temperature 300 °C. For positive ionisation mode the source voltage was set at +4 kV and the S-lens voltage at +50 V. For negative ionisation mode the source voltage was set at -3.5 kV and the S-lens voltage at -50 V. Mass calibration was performed for each polarity before running a metabolomics batch to ensure mass accuracy of < 2 ppm. Approximately 300 authentic metabolite standards were analysed at the start of each batch to provide accurate retention times to facilitate metabolite identification. Metabolomics samples were analysed in random order with periodic injections of the pooled QC, and blank samples, to assess analytical quality and aid downstream metabolite identification procedures. Metabolomics LC-MS Data Processing Raw metabolite data was processed using XCMS (Centwave) software for peak picking and mzMatch.R software for alignment and annotation of related metabolite peaks. Metabolites were then identified using the Excel-based IDEOM software by matching the mass of each peak and its retention time with a database, using a mass accuracy window of 2 ppm and a retention time window of 5% for metabolites matching authentic standards, and 35% for other putative metabolites based on a retention time prediction model (15). Noise and mass spectrometry artefacts were filtered using previously described algorithms (15, 16) to minimise false identifications. Detection of stable isotope labelled metabolite peaks were performed using mzMatch-ISO (17). Initial statistical analysis was performed with IDEOM using peak intensities (height) for all detected putative metabolites. Untargeted multivariate analysis was performed using MetaboAnalyst 4.0 (18), where the complete data sets were also analysed using enrichment analysis and pathway analysis functions (19, 20). Further analysis was undertaken using TraceFinderTM (Thermo) to obtain manually curated accurate peak areas for targeted univariate analyses for metabolites in key pathways. Metabolomics data are presented as fold-change values from 2 individual experiments, each with 4 replicates. Labelled metabolomics data is from a single metabolomics experiment and presented as a mean ± SD. Differences were determined using Student’s t-test where significant interactions were observed. Significance was determined at p values < 0.05.
Ion Mode:POSITIVE
  
MS ID:MS002391
Analysis ID:AN002579
Instrument Name:Thermo Exactive Plus Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Mass spectrometry was performed in polarity switching mode, with the following settings: resolution 35 000, AGC 1x106, m/z range 85-1275, sheath gas 50, auxiliary gas 20, sweep gas 2, probe temperature 150 °C, and capillary temperature 300 °C. For positive ionisation mode the source voltage was set at +4 kV and the S-lens voltage at +50 V. For negative ionisation mode the source voltage was set at -3.5 kV and the S-lens voltage at -50 V. Mass calibration was performed for each polarity before running a metabolomics batch to ensure mass accuracy of < 2 ppm. Approximately 300 authentic metabolite standards were analysed at the start of each batch to provide accurate retention times to facilitate metabolite identification. Metabolomics samples were analysed in random order with periodic injections of the pooled QC, and blank samples, to assess analytical quality and aid downstream metabolite identification procedures. Metabolomics LC-MS Data Processing Raw metabolite data was processed using XCMS (Centwave) software for peak picking and mzMatch.R software for alignment and annotation of related metabolite peaks. Metabolites were then identified using the Excel-based IDEOM software by matching the mass of each peak and its retention time with a database, using a mass accuracy window of 2 ppm and a retention time window of 5% for metabolites matching authentic standards, and 35% for other putative metabolites based on a retention time prediction model (15). Noise and mass spectrometry artefacts were filtered using previously described algorithms (15, 16) to minimise false identifications. Detection of stable isotope labelled metabolite peaks were performed using mzMatch-ISO (17). Initial statistical analysis was performed with IDEOM using peak intensities (height) for all detected putative metabolites. Untargeted multivariate analysis was performed using MetaboAnalyst 4.0 (18), where the complete data sets were also analysed using enrichment analysis and pathway analysis functions (19, 20). Further analysis was undertaken using TraceFinderTM (Thermo) to obtain manually curated accurate peak areas for targeted univariate analyses for metabolites in key pathways. Metabolomics data are presented as fold-change values from 2 individual experiments, each with 4 replicates. Labelled metabolomics data is from a single metabolomics experiment and presented as a mean ± SD. Differences were determined using Student’s t-test where significant interactions were observed. Significance was determined at p values < 0.05.
Ion Mode:NEGATIVE
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