{
"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST001027","ANALYSIS_ID":"AN001684","VERSION":"1","CREATED_ON":"July 30, 2018, 2:58 pm"},

"PROJECT":{"PROJECT_TITLE":"Influence of Data-Processing Strategies on Normalized Lipid Levels using an Open-Source LC-HRMS/MS Lipidomics Workflow","PROJECT_TYPE":"MS Data Processing","PROJECT_SUMMARY":"Lipidomics is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While the diverse biological roles of lipids contribute to their clinical utility, the unavailability of lipid internal standards representing each species, make lipid quantitation analytically challenging. The common approach is to employ one or more internal standards for each lipid class examined and use a single point calibration for normalization (relative quantitation). To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows. While the effect of lipid structure on relative quantitation has been investigated, applying LMN we show that data-processing can significantly affect lipid semi-quantitative amounts. Polarity and adduct choice had the greatest effect on normalized levels; when calculated using positive versus negative ion mode data, one fourth of lipids had greater than 50 % difference in normalized levels. Based on our study, sodium adducts should not be used for statistics when sodium is not added intentionally to the system, as lipid levels calculated using sodium adducts did not correlate with lipid levels calculated using any other adduct. Relative quantification using smoothing versus not smoothing, and peak area versus peak height, showed minimal differences, except when using peak area for overlapping isomers which were difficult to deconvolute. By characterizing sources or variation introduced during data-processing and introducing automated tools, this work helps increase through-put and improve data-quality for determining relative changes across groups.","INSTITUTE":"University of Florida","DEPARTMENT":"Chemistry","LABORATORY":"Richard Yost Laboratory","LAST_NAME":"Levy","FIRST_NAME":"Allison","ADDRESS":"214 Leigh Hall, PO Box 117200, Gainesville, Florida, 32611, USA","EMAIL":"allisonjlevy@ufl.edu","PHONE":"352-392-0515"},

"STUDY":{"STUDY_TITLE":"Influence of Data-Processing Strategies on Normalized Lipid Levels using an Open-Source LC-HRMS/MS Lipidomics Workflow","STUDY_SUMMARY":"Lipidomics is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While the diverse biological roles of lipids contribute to their clinical utility, the unavailability of lipid internal standards representing each species, make lipid quantitation analytically challenging. The common approach is to employ one or more internal standards for each lipid class examined and use a single point calibration for normalization (relative quantitation). To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows. While the effect of lipid structure on relative quantitation has been investigated, applying LMN we show that data-processing can significantly affect lipid semi-quantitative amounts. Polarity and adduct choice had the greatest effect on normalized levels; when calculated using positive versus negative ion mode data, one fourth of lipids had greater than 50 % difference in normalized levels. Based on our study, sodium adducts should not be used for statistics when sodium is not added intentionally to the system, as lipid levels calculated using sodium adducts did not correlate with lipid levels calculated using any other adduct. Relative quantification using smoothing versus not smoothing, and peak area versus peak height, showed minimal differences, except when using peak area for overlapping isomers which were difficult to deconvolute. By characterizing sources or variation introduced during data-processing and introducing automated tools, this work helps increase through-put and improve data-quality for determining relative changes across groups.","INSTITUTE":"University of Florida","DEPARTMENT":"Chemistry","LABORATORY":"Richard Yost Laboratory","LAST_NAME":"Levy","FIRST_NAME":"Allison","ADDRESS":"214 Leigh Hall, PO Box 117200, Gainesville, Florida, 32611, USA","EMAIL":"allisonjlevy@ufl.edu","PHONE":"3523920515"},

"SUBJECT":{"SUBJECT_TYPE":"Human","SUBJECT_SPECIES":"Homo sapiens","TAXONOMY_ID":"9606"},
"SUBJECT_SAMPLE_FACTORS":[
{
"Subject ID":"-",
"Sample ID":"QC2_33_ddtargetedneg",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC2_47_ddtargetedneg",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC2_48_ddtargetedneg",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC3_34_ddtargetedneg",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC3_49_ddtargetedneg",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC1_26_ddtargetedpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC1_28_ddtargetedpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC1_51_ddtargetedpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC1_53_ddtargetedpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC2_27_ddtargetedpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC2_29_ddtargetedpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC2_54_ddtargetedpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC3_30_ddtargetedpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC3_52_ddtargetedpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC3_55_ddtargetedpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC1_32_ddtargetedneg",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC1_14_fullAIFneg",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC1_37_fullAIFneg",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC1_02_fullAIFpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC2_12_fullAIFpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC3_01_fullAIFpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"QC3_01b_fullAIFpos",
"Factors":{"type":"QC"}
},
{
"Subject ID":"-",
"Sample ID":"blank_13_neg",
"Factors":{"type":"Blank"}
},
{
"Subject ID":"-",
"Sample ID":"blank_31_neg",
"Factors":{"type":"Blank"}
},
{
"Subject ID":"-",
"Sample ID":"blank_01_pos",
"Factors":{"type":"Blank"}
},
{
"Subject ID":"-",
"Sample ID":"blank_01c_pos",
"Factors":{"type":"Blank"}
},
{
"Subject ID":"-",
"Sample ID":"blank_25_pos",
"Factors":{"type":"Blank"}
},
{
"Subject ID":"-",
"Sample ID":"blank_50_pos",
"Factors":{"type":"Blank"}
}
],
"COLLECTION":{"COLLECTION_SUMMARY":"National Institute for Standards and Technology (NIST) standard reference material (SRM 1950) Metabolites in Frozen Human Plasma was purchased for use in this study.","SAMPLE_TYPE":"Blood (plasma)"},

"TREATMENT":{"TREATMENT_SUMMARY":"No treatments were applied to the NIST SRM 1950 materials."},

"SAMPLEPREP":{"SAMPLEPREP_SUMMARY":"Lipids were isolated from 20 µL of National Institute for Standards and Technology (NIST) standard reference material (SRM 1950) Metabolites in Frozen Human Plasma. Lipid internal standards purchased from Avanti Lipids (Alabaster, AL), which included lysophosphatidylcholine (LPC(17:0)), phosphatidylcholine (PC(17:0/17:0)), phosphatidylglycerol (PG(17:0/17:0)), phosphatidylethanolamine (PE(17:0/17:0)), phosphatidylserine (PS(17:0/17:0)), triglyceride (TG(15:0/15:0/15:0)), ceramide (Cer(d18:1/17:0)), and sphingomyelin (SM(d18:1/17:0)), were spiked into the plasma at 1.4 nmol, 0.92 nmol, 0.93 nmol, 0.97 nmol, 0.92 nmol, 0.26 nmol, 1.3 nmol, and 0.98 nmol, respectively. 13C2-cholesterol was purchased from Cambridge Isotope Laboratories (Tewksbury, MA), and spiked in at 1.8 nmol. The extraction was performed using the Matyash method [1] and samples were reconstituted in 200 µL of isopropanol. [1] Matyash, V., Liebisch, G., Kurzchalia, T.V., Shevchenko, A., Schwudke, D.: Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J. Lipid Res. 49, 1137–1146 (2008). doi:10.1194/jlr.D700041-JLR200"},

"CHROMATOGRAPHY":{"CHROMATOGRAPHY_SUMMARY":"Liquid Chromatography Protocol Samples were injected onto a Waters (Milford, MA) BEH C18 UHPLC column (50 x 2.1 mm, 1.7 µm) held at 50 °C with mobile phase A consisting of acetonitrile:water (60:40, v/v) with 10 mM ammonium formate and 0.1% formic acid and mobile phase B consisting of isopropanol:acetonitrile:water (90:8:2) with 10 mM ammonium formate and 0.1% formic acid at a flow rate of 0.5 mL/min. A Dionex Ultimate 3000 RS UHLPC system (Thermo Scientific, San Jose, CA) coupled to a Thermo Q-Exactive mass spectrometer (San Jose, CA) was employed for data acquisition. The UHPLC gradient use in this experiment is shown in Table 1. Time (min) C (%) D (%) 0,,80,,20 1,,80,,20 3,,70,,30 4,,55,,45 6,,40, 60 8,,35,,65 10 ,35,,65 15 ,,10,,90 17,,2,,98 18,,2,,98 19,,80,,20 23,,80, 20 Gradient for reverse phase liquid chromatography of lipids. Mobile phase C consisted of 60:40 acetonitrile:water and mobile phase D consisted of 90:8:2 isopropanol:acetonitrile:water, with both containing 0.1% formic acid 10 mM ammonium formate. The flow rate was 500 µL/min.","CHROMATOGRAPHY_TYPE":"Reversed phase","INSTRUMENT_NAME":"Thermo Q Exactive Orbitrap","COLUMN_NAME":"Waters Acquity BEH C18 (150 x 2.1mm, 1.7um)"},

"ANALYSIS":{"ANALYSIS_TYPE":"MS"},

"MS":{"MS_COMMENTS":"-","INSTRUMENT_NAME":"Thermo Q Exactive Orbitrap","INSTRUMENT_TYPE":"Orbitrap","MS_TYPE":"ESI","ION_MODE":"POSITIVE","MS_RESULTS_FILE":"ST001027_AN001684_Results.txt UNITS:peak area"}

}