Summary of Study ST001027
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000685. The data can be accessed directly via it's Project DOI: 10.21228/M84H56 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST001027 |
| 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 |
| allisonjlevy@ufl.edu | |
| Phone | 3523920515 |
| Submit Date | 2018-07-25 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2018-08-27 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| Project ID: | PR000685 |
| Project DOI: | doi: 10.21228/M84H56 |
| 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 |
Subject:
| Subject ID: | SU001066 |
| Subject Type: | Human |
| Subject Species: | Homo sapiens |
| Taxonomy ID: | 9606 |
Factors:
Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)
| mb_sample_id | local_sample_id | type |
|---|---|---|
| SA064506 | blank_01_pos | Blank |
| SA064507 | blank_13_neg | Blank |
| SA064508 | blank_01c_pos | Blank |
| SA064509 | blank_31_neg | Blank |
| SA064510 | blank_25_pos | Blank |
| SA064511 | blank_50_pos | Blank |
| SA064512 | QC1_14_fullAIFneg | QC |
| SA064513 | QC1_37_fullAIFneg | QC |
| SA064514 | QC1_02_fullAIFpos | QC |
| SA064515 | QC3_01_fullAIFpos | QC |
| SA064516 | QC1_32_ddtargetedneg | QC |
| SA064517 | QC3_01b_fullAIFpos | QC |
| SA064518 | QC2_12_fullAIFpos | QC |
| SA064519 | QC3_55_ddtargetedpos | QC |
| SA064520 | QC1_26_ddtargetedpos | QC |
| SA064521 | QC1_28_ddtargetedpos | QC |
| SA064522 | QC3_49_ddtargetedneg | QC |
| SA064523 | QC3_34_ddtargetedneg | QC |
| SA064524 | QC2_47_ddtargetedneg | QC |
| SA064525 | QC2_48_ddtargetedneg | QC |
| SA064526 | QC1_51_ddtargetedpos | QC |
| SA064527 | QC1_53_ddtargetedpos | QC |
| SA064528 | QC3_30_ddtargetedpos | QC |
| SA064529 | QC3_52_ddtargetedpos | QC |
| SA064530 | QC2_54_ddtargetedpos | QC |
| SA064531 | QC2_29_ddtargetedpos | QC |
| SA064532 | QC2_27_ddtargetedpos | QC |
| SA064533 | QC2_33_ddtargetedneg | QC |
| Showing results 1 to 28 of 28 |
Collection:
| Collection ID: | CO001060 |
| 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 ID: | TR001080 |
| Treatment Summary: | No treatments were applied to the NIST SRM 1950 materials. |
Sample Preparation:
| Sampleprep ID: | SP001073 |
| 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 |
Combined analysis:
| Analysis ID | AN001684 | AN001685 |
|---|---|---|
| Analysis type | MS | MS |
| Chromatography type | Reversed phase | Reversed phase |
| Chromatography system | Thermo Dionex Ultimate 3000 RS | Thermo Dionex Ultimate 3000 RS |
| Column | Waters Acquity BEH C18 (150 x 2.1mm,1.7um) | Waters Acquity BEH C18 (150 x 2.1mm,1.7um) |
| MS Type | ESI | ESI |
| MS instrument type | Orbitrap | Orbitrap |
| MS instrument name | Thermo Q Exactive Orbitrap | Thermo Q Exactive Orbitrap |
| Ion Mode | POSITIVE | NEGATIVE |
| Units | peak area | peak area |
Chromatography:
| Chromatography ID: | CH001185 |
| 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 Table 1: 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. |
| Instrument Name: | Thermo Dionex Ultimate 3000 RS |
| Column Name: | Waters Acquity BEH C18 (150 x 2.1mm,1.7um) |
| Column Temperature: | 50 |
| Flow Gradient: | 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 |
| Flow Rate: | 0.5 mL/min |
| Solvent A: | 60% acetonitrile/40% water; 0.1% formic acid; 10 mM ammonium formate |
| Solvent B: | 90% isopropanol/8% acetonitrile/2% water; 0.1% formic acid; 10 mM ammonium formate |
| Chromatography Type: | Reversed phase |
MS:
| MS ID: | MS001559 |
| Analysis ID: | AN001684 |
| Instrument Name: | Thermo Q Exactive Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| Ion Mode: | POSITIVE |
| MS ID: | MS001560 |
| Analysis ID: | AN001685 |
| Instrument Name: | Thermo Q Exactive Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| Ion Mode: | NEGATIVE |
