#METABOLOMICS WORKBENCH allisonjlevy_20180725_085830 DATATRACK_ID:1464 STUDY_ID:ST001027 ANALYSIS_ID:AN001685 PROJECT_ID:PR000685
VERSION             	1
CREATED_ON             	July 30, 2018, 2:58 pm
#PROJECT
PR:PROJECT_TITLE                 	Influence of Data-Processing Strategies on Normalized Lipid Levels using an
PR:PROJECT_TITLE                 	Open-Source LC-HRMS/MS Lipidomics Workflow
PR:PROJECT_TYPE                  	MS Data Processing
PR:PROJECT_SUMMARY               	Lipidomics is an emerging field with significant potential for improving
PR:PROJECT_SUMMARY               	clinical diagnosis and our understanding of health and disease. While the
PR:PROJECT_SUMMARY               	diverse biological roles of lipids contribute to their clinical utility, the
PR:PROJECT_SUMMARY               	unavailability of lipid internal standards representing each species, make lipid
PR:PROJECT_SUMMARY               	quantitation analytically challenging. The common approach is to employ one or
PR:PROJECT_SUMMARY               	more internal standards for each lipid class examined and use a single point
PR:PROJECT_SUMMARY               	calibration for normalization (relative quantitation). To aid in standardizing
PR:PROJECT_SUMMARY               	and automating this relative quantitation process, we developed LipidMatch
PR:PROJECT_SUMMARY               	Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most
PR:PROJECT_SUMMARY               	open source lipidomics workflows. While the effect of lipid structure on
PR:PROJECT_SUMMARY               	relative quantitation has been investigated, applying LMN we show that
PR:PROJECT_SUMMARY               	data-processing can significantly affect lipid semi-quantitative amounts.
PR:PROJECT_SUMMARY               	Polarity and adduct choice had the greatest effect on normalized levels; when
PR:PROJECT_SUMMARY               	calculated using positive versus negative ion mode data, one fourth of lipids
PR:PROJECT_SUMMARY               	had greater than 50 % difference in normalized levels. Based on our study,
PR:PROJECT_SUMMARY               	sodium adducts should not be used for statistics when sodium is not added
PR:PROJECT_SUMMARY               	intentionally to the system, as lipid levels calculated using sodium adducts did
PR:PROJECT_SUMMARY               	not correlate with lipid levels calculated using any other adduct. Relative
PR:PROJECT_SUMMARY               	quantification using smoothing versus not smoothing, and peak area versus peak
PR:PROJECT_SUMMARY               	height, showed minimal differences, except when using peak area for overlapping
PR:PROJECT_SUMMARY               	isomers which were difficult to deconvolute. By characterizing sources or
PR:PROJECT_SUMMARY               	variation introduced during data-processing and introducing automated tools,
PR:PROJECT_SUMMARY               	this work helps increase through-put and improve data-quality for determining
PR:PROJECT_SUMMARY               	relative changes across groups.
PR:INSTITUTE                     	University of Florida
PR:DEPARTMENT                    	Chemistry
PR:LABORATORY                    	Richard Yost Laboratory
PR:LAST_NAME                     	Levy
PR:FIRST_NAME                    	Allison
PR:ADDRESS                       	214 Leigh Hall, PO Box 117200, Gainesville, Florida, 32611, USA
PR:EMAIL                         	allisonjlevy@ufl.edu
PR:PHONE                         	352-392-0515
#STUDY
ST:STUDY_TITLE                   	Influence of Data-Processing Strategies on Normalized Lipid Levels using an
ST:STUDY_TITLE                   	Open-Source LC-HRMS/MS Lipidomics Workflow
ST:STUDY_SUMMARY                 	Lipidomics is an emerging field with significant potential for improving
ST:STUDY_SUMMARY                 	clinical diagnosis and our understanding of health and disease. While the
ST:STUDY_SUMMARY                 	diverse biological roles of lipids contribute to their clinical utility, the
ST:STUDY_SUMMARY                 	unavailability of lipid internal standards representing each species, make lipid
ST:STUDY_SUMMARY                 	quantitation analytically challenging. The common approach is to employ one or
ST:STUDY_SUMMARY                 	more internal standards for each lipid class examined and use a single point
ST:STUDY_SUMMARY                 	calibration for normalization (relative quantitation). To aid in standardizing
ST:STUDY_SUMMARY                 	and automating this relative quantitation process, we developed LipidMatch
ST:STUDY_SUMMARY                 	Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most
ST:STUDY_SUMMARY                 	open source lipidomics workflows. While the effect of lipid structure on
ST:STUDY_SUMMARY                 	relative quantitation has been investigated, applying LMN we show that
ST:STUDY_SUMMARY                 	data-processing can significantly affect lipid semi-quantitative amounts.
ST:STUDY_SUMMARY                 	Polarity and adduct choice had the greatest effect on normalized levels; when
ST:STUDY_SUMMARY                 	calculated using positive versus negative ion mode data, one fourth of lipids
ST:STUDY_SUMMARY                 	had greater than 50 % difference in normalized levels. Based on our study,
ST:STUDY_SUMMARY                 	sodium adducts should not be used for statistics when sodium is not added
ST:STUDY_SUMMARY                 	intentionally to the system, as lipid levels calculated using sodium adducts did
ST:STUDY_SUMMARY                 	not correlate with lipid levels calculated using any other adduct. Relative
ST:STUDY_SUMMARY                 	quantification using smoothing versus not smoothing, and peak area versus peak
ST:STUDY_SUMMARY                 	height, showed minimal differences, except when using peak area for overlapping
ST:STUDY_SUMMARY                 	isomers which were difficult to deconvolute. By characterizing sources or
ST:STUDY_SUMMARY                 	variation introduced during data-processing and introducing automated tools,
ST:STUDY_SUMMARY                 	this work helps increase through-put and improve data-quality for determining
ST:STUDY_SUMMARY                 	relative changes across groups.
ST:INSTITUTE                     	University of Florida
ST:DEPARTMENT                    	Chemistry
ST:LABORATORY                    	Richard Yost Laboratory
ST:LAST_NAME                     	Levy
ST:FIRST_NAME                    	Allison
ST:ADDRESS                       	214 Leigh Hall, PO Box 117200, Gainesville, Florida, 32611, USA
ST:EMAIL                         	allisonjlevy@ufl.edu
ST:PHONE                         	3523920515
#SUBJECT
SU:SUBJECT_TYPE                  	Human
SU:SUBJECT_SPECIES               	Homo sapiens
SU:TAXONOMY_ID                   	9606
#SUBJECT_SAMPLE_FACTORS:         	SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data
SUBJECT_SAMPLE_FACTORS           	-	QC2_33_ddtargetedneg	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC2_47_ddtargetedneg	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC2_48_ddtargetedneg	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC3_34_ddtargetedneg	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC3_49_ddtargetedneg	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC1_26_ddtargetedpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC1_28_ddtargetedpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC1_51_ddtargetedpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC1_53_ddtargetedpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC2_27_ddtargetedpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC2_29_ddtargetedpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC2_54_ddtargetedpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC3_30_ddtargetedpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC3_52_ddtargetedpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC3_55_ddtargetedpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC1_32_ddtargetedneg	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC1_14_fullAIFneg	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC1_37_fullAIFneg	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC1_02_fullAIFpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC2_12_fullAIFpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC3_01_fullAIFpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	QC3_01b_fullAIFpos	type:QC	
SUBJECT_SAMPLE_FACTORS           	-	blank_13_neg	type:Blank	
SUBJECT_SAMPLE_FACTORS           	-	blank_31_neg	type:Blank	
SUBJECT_SAMPLE_FACTORS           	-	blank_01_pos	type:Blank	
SUBJECT_SAMPLE_FACTORS           	-	blank_01c_pos	type:Blank	
SUBJECT_SAMPLE_FACTORS           	-	blank_25_pos	type:Blank	
SUBJECT_SAMPLE_FACTORS           	-	blank_50_pos	type:Blank	
#COLLECTION
CO:COLLECTION_SUMMARY            	National Institute for Standards and Technology (NIST) standard reference
CO:COLLECTION_SUMMARY            	material (SRM 1950) Metabolites in Frozen Human Plasma was purchased for use in
CO:COLLECTION_SUMMARY            	this study.
CO:SAMPLE_TYPE                   	Blood (plasma)
#TREATMENT
TR:TREATMENT_SUMMARY             	No treatments were applied to the NIST SRM 1950 materials.
#SAMPLEPREP
SP:SAMPLEPREP_SUMMARY            	Lipids were isolated from 20 µL of National Institute for Standards and
SP:SAMPLEPREP_SUMMARY            	Technology (NIST) standard reference material (SRM 1950) Metabolites in Frozen
SP:SAMPLEPREP_SUMMARY            	Human Plasma. Lipid internal standards purchased from Avanti Lipids (Alabaster,
SP:SAMPLEPREP_SUMMARY            	AL), which included lysophosphatidylcholine (LPC(17:0)), phosphatidylcholine
SP:SAMPLEPREP_SUMMARY            	(PC(17:0/17:0)), phosphatidylglycerol (PG(17:0/17:0)), phosphatidylethanolamine
SP:SAMPLEPREP_SUMMARY            	(PE(17:0/17:0)), phosphatidylserine (PS(17:0/17:0)), triglyceride
SP:SAMPLEPREP_SUMMARY            	(TG(15:0/15:0/15:0)), ceramide (Cer(d18:1/17:0)), and sphingomyelin
SP:SAMPLEPREP_SUMMARY            	(SM(d18:1/17:0)), were spiked into the plasma at 1.4 nmol, 0.92 nmol, 0.93 nmol,
SP:SAMPLEPREP_SUMMARY            	0.97 nmol, 0.92 nmol, 0.26 nmol, 1.3 nmol, and 0.98 nmol, respectively.
SP:SAMPLEPREP_SUMMARY            	13C2-cholesterol was purchased from Cambridge Isotope Laboratories (Tewksbury,
SP:SAMPLEPREP_SUMMARY            	MA), and spiked in at 1.8 nmol. The extraction was performed using the Matyash
SP:SAMPLEPREP_SUMMARY            	method [1] and samples were reconstituted in 200 µL of isopropanol. [1]
SP:SAMPLEPREP_SUMMARY            	Matyash, V., Liebisch, G., Kurzchalia, T.V., Shevchenko, A., Schwudke, D.: Lipid
SP:SAMPLEPREP_SUMMARY            	extraction by methyl-tert-butyl ether for high-throughput lipidomics. J. Lipid
SP:SAMPLEPREP_SUMMARY            	Res. 49, 1137–1146 (2008). doi:10.1194/jlr.D700041-JLR200
#CHROMATOGRAPHY
CH:CHROMATOGRAPHY_SUMMARY        	Liquid Chromatography Protocol Samples were injected onto a Waters (Milford, MA)
CH:CHROMATOGRAPHY_SUMMARY        	BEH C18 UHPLC column (50 x 2.1 mm, 1.7 µm) held at 50 °C with mobile phase A
CH:CHROMATOGRAPHY_SUMMARY        	consisting of acetonitrile:water (60:40, v/v) with 10 mM ammonium formate and
CH:CHROMATOGRAPHY_SUMMARY        	0.1% formic acid and mobile phase B consisting of isopropanol:acetonitrile:water
CH:CHROMATOGRAPHY_SUMMARY        	(90:8:2) with 10 mM ammonium formate and 0.1% formic acid at a flow rate of 0.5
CH:CHROMATOGRAPHY_SUMMARY        	mL/min. A Dionex Ultimate 3000 RS UHLPC system (Thermo Scientific, San Jose, CA)
CH:CHROMATOGRAPHY_SUMMARY        	coupled to a Thermo Q-Exactive mass spectrometer (San Jose, CA) was employed for
CH:CHROMATOGRAPHY_SUMMARY        	data acquisition. The UHPLC gradient use in this experiment is shown in Table 1.
CH:CHROMATOGRAPHY_SUMMARY        	Time (min) C (%) D (%) 0,,80,,20 1,,80,,20 3,,70,,30 4,,55,,45 6,,40, 60
CH:CHROMATOGRAPHY_SUMMARY        	8,,35,,65 10 ,35,,65 15 ,,10,,90 17,,2,,98 18,,2,,98 19,,80,,20 23,,80, 20
CH:CHROMATOGRAPHY_SUMMARY        	Gradient for reverse phase liquid chromatography of lipids. Mobile phase C
CH:CHROMATOGRAPHY_SUMMARY        	consisted of 60:40 acetonitrile:water and mobile phase D consisted of 90:8:2
CH:CHROMATOGRAPHY_SUMMARY        	isopropanol:acetonitrile:water, with both containing 0.1% formic acid 10 mM
CH:CHROMATOGRAPHY_SUMMARY        	ammonium formate. The flow rate was 500 µL/min.
CH:CHROMATOGRAPHY_TYPE           	Reversed phase
MS:INSTRUMENT_NAME               	Thermo Q Exactive Orbitrap
CH:COLUMN_NAME                   	Waters Acquity BEH C18 (150 x 2.1mm, 1.7um)
#ANALYSIS
AN:ANALYSIS_TYPE                 	MS
#MS
MS:MS_COMMENTS                   	-
MS:INSTRUMENT_NAME               	Thermo Q Exactive Orbitrap
MS:INSTRUMENT_TYPE               	Orbitrap
MS:MS_TYPE                       	ESI
MS:ION_MODE                      	NEGATIVE
MS:MS_RESULTS_FILE               	ST001027_AN001685_Results.txt	UNITS:peak area
#END