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Journal of Lipid Research: Full Article
Harmonizing Lipidomics: NIST Interlaboratory Comparison Exercise for Lipidomics using
Standard Reference Material 1950 – Metabolites in Frozen Human Plasma
John A. Bowden*1, Alan Heckert2, Candice Z. Ulmer1, Christina M. Jones1, Jeremy P. Koelmel3
Laila Abdullah4, Linda Ahonen5, Yazen Alnouti6, Aaron Armando7, John M. Asara8,9, Takeshi Bamba10,
John R. Barr11, Jonas Bergquist12, Christoph H. Borchers13-16, Joost Brandsma17, Susanne B. Breitkopf8,
Tomas Cajka18, Amaury Cazenave-Gassiot19, Antonio Checa20, Michelle A Cinel21, Romain A. Colas22,
Serge Cremers23, Edward A. Dennis7, James E. Evans4, Alexander Fauland20, Oliver Fiehn18,24, Michael S.
Gardner11, Timothy J. Garrett3, Katherine H. Gotlinger25, Jun Han13, Yingying Huang26, Aveline Huipeng
Neo19, Tuulia Hyötyläinen27, Yoshihiro Izumi10, Hongfeng Jiang23, Houli Jiang25, Jiang Jiang7, Maureen
Kachman28, Reiko Kiyonami26, Kristaps Klavins29, Christian Klose30, Harald C. Köfeler31, Johan Kolmert20,
Therese Koal29, Grielof Koster17, Zsuzsanna Kuklenyik11, Irwin J. Kurland32, Michael Leadley33, Karen
Lin13, Krishna Rao Maddipati34, Danielle McDougall3, Peter J. Meikle21, Natalie A Mellett21, Cian
Monnin35, M. Arthur Moseley36, Renu Nandakumar23, Matej Oresic37, Rainey Patterson3, David Peake26,
Jason S. Pierce38, Martin Post33, Anthony D. Postle17, Rebecca Pugh39, Yunping Qiu32, Oswald
Quehenberger40, Parsram Ramrup35, Jon Rees11, Barbara Rembiesa38, Denis Reynaud33, Mary R. Roth41,
Susanne Sales42, Kai Schuhmann42, Michal Laniado Schwartzman25, Charles N. Serhan22, Andrej
Shevchenko42, Stephen E. Somerville43, Lisa St. John-Williams36, Michal A. Surma30, Hiroaki Takeda10,
Rhishikesh Thakare6, J. Will Thompson36, Federico Torta19, Alexander Triebl31, Martin Trötzmüller31, S. J.
Kumari Ubhayasekera12, Dajana Vuckovic35, Jacquelyn M. Weir21, Ruth Welti41, Markus R. Wenk19, Craig
E. Wheelock20, Libin Yao41, Min Yuan8, Xueqing Heather Zhao32, Senlin Zhou34
1 National Institute of Standards and Technology, Marine Biochemical Sciences Group, Chemical
Sciences Division, Hollings Marine Laboratory, 331 Fort Johnson Road, Charleston, SC 29412 USA
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2 National Institute of Standards and Technology, Statistical Engineering Division, 100 Bureau Drive,
Gaithersburg, MD 20899 USA
3 University of Florida, Department of Pathology, Immunology and Laboratory Medicine, Gainesville,
FL 32610 USA
4 The Roskamp Institute, 2040 Whitfield Avenue, Sarasota, FL, 34243 USA
5 Steno Diabetes Center Copenhagen, DK-2820 Gentofte, Denmark
6 Department of Pharmaceutical Sciences, 986025 Nebraska Medical Center, Omaha, NE 68198-6025
USA
7 Department of Chemistry and Biochemistry and Department of Pharmacology, School of Medicine,
University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0601 USA
8 Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, MA USA
9 Department of Medicine, Harvard Medical School, Boston, MA USA
10 Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of
Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582 Japan
11 Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control
and Prevention, 4770 Buford Hwy MS-F50, Atlanta, GA 30341 USA
12 Department of Chemistry-BMC, Analytical Chemistry, Uppsala University, Sweden
13 University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, 3101-4464
Markham Street, Victoria, BC V8Z 7X8 Canada
14 Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, V8P
5C2, Canada
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15 Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, Montreal,
Quebec, H3T 1E2, Canada
16 Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill
University, Montreal, Quebec, H3T 1E2, Canada
17 University of Southampton, Faculty of Medicine, Academic Unit of Clinical & Experimental Sciences,
Southampton General Hospital, Tremona Road, Southampton, SO16 6YD, UK
18 NIH West Coast Metabolomics Center, UC Davis Genome Center, 451 Health Sci Drive, Davis, CA
95616 USA
19 National University of Singapore, Yong Loo Lin School of Medicine, Department of Biochemistry; and
Singapore Lipidomic Incubator (SLING), Life Sciences Institute, 28 Medical Drive 03-03, 117456
Singapore
20 Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics,
Karolinska Institutet, Stockholm, 171 77, Sweden
21 Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne VIC 3004, Australia
22 Center for Experimental Therapeutics and Reperfusion Injury, Department of Anesthesiology,
Perioperative and Pain Medicine, Blg for Transformative Medicine, Brigham and Women’s Hospital
and Harvard Medical School, Boston, Massachusetts 02115, USA
23 Biomarker Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia
University Medical Center, 630 West 168th Street, New York, NY 10032, USA
24 King Abdulaziz University, Biochemistry Department, Jeddah, Saudi-Arabia
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25 Department of Pharmacology, New York Medical College School of Medicine, 15 Dana Road, Valhalla,
NY 10595 USA
26 Thermo Fisher Scientific, 355 River Oaks Parkway, San Jose, CA 95134 USA
27 Department of Chemistry, Örebro University, 702 81 Örebro, Sweden
28 Metabolomics Core, BRCF, University of Michigan, 1000 Wall St, Ann Arbor, MI 48105-5714 USA
29 Biocrates Life Sciences AG, Eduard-Bodem-Gasse 8, 6020 Innsbruck, Austria
30 Lipotype GmbH, Tatzberg 47, 01307 Dresden, Germany
31 Core Facility for Mass Spectrometry, Medical University of Graz, Stiftingtalstrasse 24, 8010 Graz,
Austria
32 Stable Isotope and Metabolomics Core Facility, Diabetes Research Center, Albert Einstein College of
Medicine, 1301 Morris Park Ave, Bronx, NY 10461 USA
33 Analytical Facility of Bioactive Molecules, The Hospital for Sick Children Research Institute, 686 Bay
Street, Toronto, ON, M5G 0A4, Canada
34 Lipidomics Core Facility and Department of Pathology, Wayne State University, 5101 Cass Avenue,
Detroit, MI 48202 USA
35 Department of Chemistry and Biochemistry, Concordia University, 7141 Sherbrooke Street
West, Montréal, QC, H4B 1R6, Canada
36 Proteomics and Metabolomics Shared Resource, Duke University School of Medicine, B02 Levine
Science Research Center, 450 Research Drive, Durham, NC 27710 USA
37 Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku,
Finland
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38 Department of Biochemistry and Molecular Biology, Medical University South Carolina, 173 Ashley
Ave. Charleston, SC 29425 USA
39 National Institute of Standards and Technology, Chemical Sciences Division, Environmental Specimen
Bank Group, Hollings Marine Laboratory, 331 Fort Johnson Road, Charleston, SC 29412 USA
40 Department of Medicine and Department of Pharmacology, School of Medicine, University of
California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0601 USA
41 Kansas Lipidomics Research Center, Division of Biology, Kansas State University, Ackert Hall, 1717
Claflin Rd., Manhattan, KS 66506 USA
42 Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, 01307 Dresden,
Germany
43 Medical University of South Carolina, Hollings Marine Laboratory, 311 Fort Johnson Road, Charleston,
SC 29412 USA
* Corresponding author: John A. Bowden, Ph.D.
National Institute of Standards and Technology (NIST)
Hollings Marine Laboratory
331 Fort Johnson Road
Charleston, SC 29412 USA
E-mail address: john.bowden@nist.gov
Telephone: 843-725-4820, Fax: 843-762-8742
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Running Title: Interlaboratory comparison exercise for lipidomics using SRM 1950
Abbreviations: bile acids (BA), ceramides (CER), certified reference material (CRM), cholesteryl esters
(CE), certificate of analysis (COA), coefficient of dispersion (COD), diacylglycerols (DAG), fatty acyls
(FA), free fatty acids (FFA), free cholesterol (FC), glycerolipids (GL), glycerophospholipids (GP),
hexosylceramide (HexCer), hydroxyeicosatetraenoic acid (HETE), LIPID Metabolites and Pathways
Strategy (LIPID MAPS), lysophosphatidylcholines (LPC), lysophosphatidylethanolamines (LPE), mass
spectrometry (MS), median of means (MEDM), National Institute of Standards and Technology (NIST),
phosphatidylcholines (PC), phosphatidylethanolamines (PE), phosphatidylglycerols (PG),
phosphatidylinositols (PI), phosphatidylserines (PS), sphingolipids (SP), sphingomyelin (SM), Standard
Reference Material (SRM), sterols (ST), triacylglycerols (TAG)
Supplementary key words (5): fatty acyls, glycerolipids, interlaboratory comparison exercise, lipids,
lipidomics, phospholipids, quality control, quantitation, sphingolipids, Standard Reference Material 1950,
sterols
Abstract
As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we
performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM)
1950 – Metabolites in Frozen Human Plasma, a commercially available reference material. The
interlaboratory study comprised 31 diverse laboratories, with each lab using a different lipidomics
workflow. A total of 1527 unique lipids were measured across all laboratories, and consensus location
estimates and associated uncertainties were determined for 339 of these lipids measured at the sum
composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950
serve as community-wide benchmarks for intra- and inter-laboratory quality control and method validation.
These analyses were performed using non-standardized laboratory-independent workflows. The consensus
locations were also compared to a previous examination of SRM 1950 by the LIPID MAPS consortium.
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While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid
mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion
regarding areas in need of improvement.
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INTRODUCTION
The relationship between lipids and human health has been explored as early as the 1900s, where
lipids were noted as important nutritional factors (1, 2) and were frequently found to be altered from
homeostatic concentrations in pathophysiological conditions (3-5). Throughout the century, lipids have
been increasingly used to evaluate human health. However, it was not until the early 2000s, with the advent
of mass spectrometric (MS) approaches (6, 7), that the potential of lipid research could be realized. With
the increased capacity to interrogate the lipidome, the number and types of human health applications
employing lipid analysis has steadily risen (8-11). Over this period of rapid advancement, the lipidomics
community, with leading endeavors from LIPID Metabolites and Pathways Strategy (LIPID MAPS), has
pursued efforts to characterize several lipidomes, improve quantitative measurements, and delineate the
complicated milieu of lipid interactions and pathways (12, 13). In 2010, LIPID MAPS formed a consortium
to define the constituents of the mammalian lipidome using the National Institute of Standards and
Technology (NIST) Standard Reference Material (SRM) 1950 – Metabolites in Frozen Human Plasma (14).
The resulting lipidome was earmarked at 588 lipid species above error thresholds. This concerted effort
was achieved piecemeal by separate core laboratories via contributions predominantly employing triple
quadrupole MS technology for targeted lipid class measurements.
Within the past five years, advances in chromatography and the advent of high-resolution mass
spectrometry (HRMS) have resulted in the measurement of a greater spectrum of lipids within the lipidome
using a single platform (15-17). With this enhanced coverage of the lipidome, there is an increased
probability of characterizing lipid pathways perturbed by disease. This is supported by the dramatic increase
in potential biomarker discovery applications in lipidomics using untargeted platforms (18, 19). However,
as the lipidomics field expands from targeted assays, using predominantly triple quadrupole technology to
untargeted and perhaps back to targeted assays using state of the art technology across a diverse range of
workflows and platforms, it is important for the lipidomics community to monitor and improve
measurement activities.
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The same inherent qualities that lend themselves to the maturation of lipidomics and its widespread
use as an approach to examine human health – namely the vast complexity in lipid structure, function, and
abundance and their ubiquitous existence at membrane, cellular, tissue, and systemic levels (20, 21) – also
imbue a variety of measurement challenges. Despite these challenges, lipidomic studies continue to emerge
at an increased rate and with a push toward precision medicine (22-26). However, a substantial roadblock
in the progression of translating lipidomics from the bench to routine clinical settings is the lack of
standardization or harmonization within the lipidomics community (27). Without standardization, the
assessment of data quality independent of time, place, and procedure is difficult (28, 29). As the field of
lipidomics continues to progress, it will be critical to be able to control, minimize, or at the very least,
understand intra- and inter-laboratory variability to ensure confidence in the discovery of real biological
differences (30, 31). Several excellent lipidomics reviews (15, 32-34) conclude that the differences in
methodology within the lipidomics community are extensive. This variation in lipidomics methodology has
a direct impact on the resultant lipid profiles observed, affecting the number, type, and quantity of lipids
observed (30, 31, 35). To date, the exact impact of this methodological diversity on community-wide lipid
measurement and agreement is unknown.
Interlaboratory studies, where participants are instructed to perform a specific analysis on a
homogenous and stable reference material followed by an evaluation and comparison of data at both an
intra- and inter-laboratory level, are exercises well suited to critically evaluate the agreement of
measurement within the lipidomics community and highlight areas of concern. NIST and others have
coordinated interlaboratory studies across disciplines for a wide variety of analytes, including omics-based
profiles (36-43). For the latter, specifically for proteomics and metabolomics, interlaboratory studies have
been presented with the theme of addressing the lack of agreement within the community by highlighting
the need to develop standards, guidelines, and protocols, and to identify ways to evaluate lab performance,
quality control, and dissemination (43-46). The paucity of commercially available reference materials for
lipidomics, as well as the lack of a reason to extend quality control practices beyond the intra-laboratory
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level have limited the ability to benchmark data within the lipidomics community. The use of SRM 1950
as a control material for small molecule-based omics studies has been supported by a recent white paper on
metabolomics-enabled precision medicine (47), where it is recommended that this certified reference
material (CRM) be used as a material to aid in standardization and quality assessment across time and
laboratories, at least until new reference materials are created. NIST produced this commercially available
homogeneous material to aid in standardizing clinical measurements; other reports have noted its potential
as a metabolomics reference material (14, 48-52). We propose that SRM 1950 has equal value as a quality
control sample for lipidomics and thus would be a suitable material for an interlaboratory comparison
exercise.
Since 2014, NIST has been conducting an interlaboratory comparison exercise for lipidomics using
SRM 1950. To provide a true cross-section of the lipidomics community, 31 national and international
laboratories, composed of both global and targeted lipidomic methodologies spanning across academia,
industry, and core facilities have participated. The interlaboratory study was designed to highlight 1) the
extent of agreement present in current lipidomic measurement within the community, 2) determine
consensus locations with associated uncertainties for lipids present in SRM 1950, and 3) highlight the
challenges present in current lipid measurements in regards to lipid methodology employed. In this paper,
we address the first two goals above, while a follow-up paper will address methodologies used and the
effect on quantitation. Reference results have been established for 339 lipids present in SRM 1950 that can
be used by laboratories to assess whether their data agree with the lipidomics community. These consensus
locations are compared to the concentration values noted from the LIPID MAPS consortium (14).
MATERIALS AND METHODS
Standard Reference Material (SRM) 1950
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A vial of SRM 1950 – Metabolites in Frozen Human Plasma was shipped on dry ice to participating
laboratories. In collaboration with the National Institute of Diabetes and Digestive and Kidney Diseases
(NIDDK), NIST developed SRM 1950 in 2006 as a “normal” human plasma reference material. A full
description of this material is provided in its Certificate of Analysis (COA, www.nist.gov/srm). In brief,
this plasma material was constructed from 100 fasted individuals in the age range of (40 to 50) years who
represented the average composition of the US population, as defined by race, sex, and health (extreme
health cohorts were excluded) (53). Due to these factors and its commercial availability, this material was
selected for use in the interlaboratory lipidomics comparison exercise.
Overview of Exercise
Participants in the exercise were provided a data submission template that contained several tabs
focused on obtaining basic laboratory and method information: sample preparation, sample introduction
and chromatography, mass spectrometric approach, and data processing. Unless the participant declined to
disclose details, information was obtained on sample chain of custody, extraction methodology, internal
standard selection, chromatographic methods, mass spectrometer type, scanning approach employed
(global and/or targeted), and the data handling/software utilized. For the analysis of SRM 1950, each
laboratory was asked to employ the analytical procedures traditionally used in their laboratories and to
report lipids identified and quantified (in triplicate) at nmol/mL plasma concentration levels. Laboratories
were informed that all information, which could be used to link laboratories to their submitted data, would
be excluded in resulting publications.
The template, which also listed potential target lipid species, is reproduced in NIST Internal Report (IR)
8185 (54).
Organization of Submitted Data
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Each participating laboratory submitted an Excel workbook that contained lipid identifications and
the respective triplicate concentration measurements (nmol/mL). Upon receipt of data, the mean and
standard deviation were calculated for lipids with three replicates and non-zero concentration. Submitted
data entries (lipid species name, m/z reported, and the adduct utilized) were compared to LipidPioneer (55)
for accuracy and consistency. Specifically, LipidPioneer was used to calculate the m/z of various adducts
observed given the lipid name. Features were flagged and researchers contacted if discrepancies were
observed between the lipid name and the m/z reported. Submission errors found in lipid species assignment,
mass assignment, and/or adduct reported were edited and subsequently verified by the laboratory.
Laboratories reported lipids by fatty acyl constituents and/or by the sum composition (total carbons : total
double bonds, C:DB) according to the shorthand nomenclature proposed by the International Lipid
Classification and Nomenclature Committee (56). All entries were converted to sum composition for
comparison across all laboratories. To accomplish this, concentrations for isomer lipid species per replicate
were summed and the three replicate sums were used to calculate the mean and standard deviation. As an
example, each replicate concentration of PC(16:1_18:1) and PC(16:0_18:2) were summed and reported as
PC(34:2). Lipid isomers were included in the summation if they were reported by at least two laboratories.
Calculation of Final Consensus Locations and Uncertainties
The concept of calculating a consensus value and its associated uncertainty for measurements from
multiple laboratories has been well studied and there are many approaches available to address this
challenge (57). We considered several methods for estimating the consensus location and associated
uncertainty for each submitted lipid species. The consensus approach employed for this exercise was the
median of means (MEDM) method (58). The MEDM consensus value (“location”) is simply the median of
laboratory means. An associated standard uncertainty for the MEDM consensus value, u, is
√(π/2m)×1.483×MAD, where m and MAD denote the number of laboratories and the median absolute
deviation of the laboratory means, respectively (58). Analogous to the sample coefficient of variation, the
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sample coefficient of dispersion (COD) (59), expressed as a percentage, was calculated as 100*u/MEDM
for each lipid species. These COD values were used to facilitate evaluation of the quality or "usefulness"
of the consensus estimates. For evaluation purposes, the MEDM were deemed acceptable for quality control
activities if they had a COD value less than 40 %.
The data in this study contained several extreme outliers (laboratory mean lipid concentrations).
These outliers violated the normality assumptions made by more statistically efficient consensus estimation
methods, such as Vangel-Rukhin (VR, (60, 61)) and DerSimonian-Laird (DSL, (62)). The presence of these
outliers resulted in unrepresentative consensus values for these two methods. However, the MEDM method
generated reasonable and representative consensus locations without requiring the omission of outlier
laboratories from the analysis.
MEDM location estimates (nmol/mL) are only reported for lipids that were measured by at least
five laboratories. NIST IR 8185 (54) details the consensus estimates and uncertainties in both tabular and
graphical formats.
Final Consensus Location Comparison
The final consensus location estimates and the associated uncertainties determined in this study
were compared to the lipid concentrations noted previously in the analysis of SRM 1950 conducted by the
LIPID MAPS consortium (14) using predominantly triple quadrupole technology for targeted lipid class
measurements. A percent change was calculated for lipids in SRM 1950, comparing the MEDM calculated
in this study to the previously published values of the LIPID MAPS consortium. The values obtained from
the LIPID MAPS consortium were set as the reference values in the percent change calculation. The final
MEDM lipid species were summed by class to reflect those lipids that were common to the LIPID MAPS
consortium.
RESULTS AND DISCUSSION
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Construction of the Interlaboratory Comparison Exercise
Lipid measurements were obtained from a diverse collection of laboratories that represent the
current cross-section of lipid measurement within the community. Invitations were sent to 100 potential
participants, spanning laboratories with differing levels of experience, publication history, and lipid
methodology. Of these, 31 laboratories submitted lipidomic data with one laboratory submitting two
lipidomic data sets from different MS platforms. The participants consisted of 55 % U.S. / 45 %
international-based, 52 % global / 48 % targeted profiling, and 78 % academic / 22 % commercial
laboratories (representing industry and government entities). Global profiling laboratories are here defined
as those laboratories reporting at least three lipid categories within a data submission. Targeted profiling
laboratories are defined here as those laboratories reporting values for lipids in less than three lipid
categories. Lipid categories are classified as fatty acyls (FA), glycerolipids (GL), glycerophospholipids
(GP), polyketides, prenol lipids, saccharolipids, sphingolipids (SP), and sterols (ST) (63, 64).
Interlaboratory Breakdown of the SRM 1950 Plasma Lipidome
Since the inception of lipidomics, there have been numerous reports aimed at ascertaining the
composition of the human plasma lipidome. Based on the degree of lipid identification (sum composition
vs individual isomers), it has been reported that anywhere between 150 and 700 lipids could be present
within the human plasma lipidome (14, 65-72). As lipidomic techniques advance, it is possible that many
more lipids will be identified. The LIPID MAPS report on SRM 1950 in 2011, for example, employing
targeted class-specific analyses, noted 588 lipid species. At the sum composition level, 1527 unique lipid
identifications were reported in the current study. This value should be viewed conservatively as it includes
the sum of several isomeric lipid species. A breakdown of the lipid species reported, by lipid class, sub-
class, and number of laboratories reporting, can be found in NIST IR 8185 (54). The 1527 lipid species
represent five lipid categories: FA (n = 177), GL (n = 317), GP (n = 679), SP (n = 236), and ST (n = 118).
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Due to a high incidence of over-reporting observed within the study, lipid species were included in
the final MEDM analysis only if reported by at least five laboratories (e.g., 745 lipids identified at the sum
composition level were reported by only one laboratory). In total, there were 339 lipids that were reported
by ≥ 5 laboratories: FA (n = 14), GL (n = 83), GP (n = 150), SP (n = 58), and ST (n = 34). A dissection of
the number of lipids by class for those lipids with MEDM values is shown in Fig. 1A. The final calculated
MEDM with CODs ≤ 40 % (n = 254), represent the most probable interval for which the true concentration
value resides in SRM 1950, especially after factoring in the diverse methodologies employed by
participating laboratories. It should be noted that the participating laboratories applied independent
protocols in this exercise and henceforth did not align their acquisition parameters, extraction protocols, or
workflows in assessing the sample. While all laboratories employed different workflows, trends between
MEDM location and COD, and the number of laboratories reporting and COD, were observed. The top-50
most concentrated lipids with MEDM locations had an average COD of (26 ± 11)% and were measured by
an average of (15 ± 4)laboratories. Conversely, the bottom-50 least concentrated lipids with MEDM
locations had an average COD of (35 ± 19)% and were measured by an average of (7 ± 2)laboratories. The
COD values for the top-50 lipids, by concentration, were significantly lower (p-value of two-sided t-test <
0.005) than the bottom-50 lipids. In addition, the number of labs reporting for a given lipid species was
inversely proportional to the COD, as expected (see (54) for additional details).
Breakdowns of the consensus estimates organized by lipid category are presented for FA (Table 1),
GL (Table 2), GP (Table 3), SP (Table 4), and ST (Table 5). The top five lipid classes using COD ≤ 40 %
criterion are: TAG (n = 42), PC (n = 53), SM (n = 30), PE (n = 29), and LPC (n = 25). All major lipid
classes are represented (Fig. 2.) We endorse these consensus locations for use in quality control.
There were 97 lipids with COD ≤ 20 %, representing several lipid classes including: BA (n = 6),
CE (n = 2), CER (n = 6), DAG (n = 1), eicosanoids (n = 1), free cholesterol, FFA (n = 2), LPC (n = 13), PC
(n = 30), PE (n = 12), PI (n = 12), SM (n = 6), and TAG (n = 5). This data suggests that the community
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measures phospholipids more consistently (specifically LPC, PC, PE and PI species) relative to other lipid
classes. Approximately, 52 %, 48 %, 34 % and 80 % of the LPC, PC, PE, and PI species, respectively, were
measured with a COD ≤ 20 %. However, for several of the lipids in the LPC class, even though the 40 %
COD criterion is satisfied, a significant number of laboratory means fall outside ± 2 times the standard error
of the consensus location estimate. Although this can be explained by noting that the uncertainty for the
MEDM method is controlled by the 25 % of the laboratory means both above and below the final MEDM
estimate, some caution is warranted in using lipids from this class for quality control purposes.
There were 85 lipids with MEDM estimates associated with COD > 40 % (Supplemental Tables
S1 to S5 for lipid categories FA, GL, GP, SP, and ST, respectively) in 13 lipid classes: CE (n = 4), CER
(n = 7), FFA (n = 6), DAG (n = 19), HexCer (n = 1), LPE (n = 2), PC (n = 10), PE (n = 6), PG (n = 2), PI
(n = 2), PS (n = 1), SM (n = 8), and TAG (n = 17). The classes with the greatest percentage of lipids with
COD > 40 % were CER (40 %), DAG (79 %), FFA (54 %), and TAG (28 %). These findings lend greater
insight into the lipids and lipid classes most affected by measurement diversity and emphasize a need to
improve measurement uniformity. The lipids with COD > 40 % should not be used for quality control;
rather, we suggest that these lipids and lipid classes represent challenges requiring improvement in lipid
measurement.
By lipid class, the largest overall lipid concentration using the lipids having MEDM values was
attributed to CE (47 %), PC (18 %), cholesterol (12 %), TAG (9 %), and SM (5 %), as shown in Fig. 1B.
The lipid category with the fewest MEDM values was the fatty acyls, which comprised FFA (n = 11) and
eicosanoids (n = 3), as shown in Table 1. As part of this exercise, SRM 1950 was sent to nine targeted
laboratories for eicosanoid measurement. Eicosanoids are defined here as lipid mediator analogs produced
from polyunsaturated fatty acids. Only six laboratories provided eicosanoid concentrations (two
laboratories were not able to measure any eicosanoids in SRM 1950, one laboratory failed to respond). In
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total, 143 eicosanoids were measured by at least one laboratory; however, only three (5-HETE, 12-HETE,
and 15-HETE) were measured by at least five laboratories.
Table 2 lists the MEDM estimates for two lipid classes of the GL category: DAG (n = 24) and TAG
(n = 59). Table 3 lists the estimates for the numerous lipids of several classes in the GP category, including
LPC (n = 25), LPE (n = 8), PC (n = 63), PE (n = 35), PG (n = 3), PI (n = 15), and PS (n = 1). Table 4 lists
the estimates for three classes in the SP category, including ceramides (n = 15), hexosyl ceramides (n = 5),
and sphingomyelins (n = 38). Table 5 lists the estimates for the ST category, including cholesteryl esters
(n = 19), bile acids (n = 14), and free cholesterol. These ST lipids represent about 59 % of the total lipid
concentration of SRM 1950 (See Fig. 1B.).
Additional consensus location values for those lipids with only three to four laboratories reporting
(n = 192) are listed in Supplemental Table S6 to expand the lipidome coverage for SRM 1950. These
“tentative” values are calculated using the DSL estimator, which is more reliable than the MEDM with
small numbers of normally distributed data (62). For inclusion as a “tentative” location, we set the criteria
at having a DSL-based COD ≤ 40 % and the percent difference between the DSL and MEDM estimates
≤ 20 %. There were 62 lipids that fit this criterion (Supplemental Table S6), largely represented by
eicosanoids (n = 20) and TAG (n = 7). One lipid with a “tentative” value was total cholesterol, which has a
NIST certified concentration of (3917 ± 85)nmol/mL reported on the SRM 1950 COA. The DSL estimate
for total cholesterol, as calculated using the interlaboratory submissions, was (3980 ± 24)nmol/mL, which
was within the uncertainty of the certified reference value note on the COA.
Usefulness of Final Consensus Values
Certified reference materials are widely employed to assess measurement methodologies. For
example, a laboratory can have confidence that the process or method employed provided a quality
measurement if their measured value agrees with the certified value within the combined uncertainties of
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the measured and certified values. Moreover, CRMs can also be used to evaluate different sources of
variability (e.g., sample preparation, instrumental data acquisition, and analysis), determine the long-term
robustness of measurement processes, and validate methods (73). SRM 1950 is a CRM produced by NIST
with certified reference values for amino acids, cholesterol, vitamins, total fatty acids, and other clinical
markers. While the consensus values generated for SRM 1950 in this interlaboratory study are not certified,
the values are a cross-section of measurements obtained within the lipidomics community using a CRM
with which researchers can assess measurement methodology (e.g., quantitation performance). The
calculated consensus locations provide the lipidomics community the opportunity to extend quality control
activities beyond the typical practices performed internally using in-house materials. On a wider scale,
SRM 1950 has 339 robustly measured lipids (by sum composition), which can help benchmark lipid
measurement within the community. A new automated lipid validation tool, LipidQC, has been introduced
(74), which allows users to rapidly compare their experimental SRM 1950 lipid concentrations to the
consensus estimates generated from this interlaboratory exercise. Use of SRM 1950 for quality control can
now be a first step toward community-wide harmonization, which is a vital component in uncovering the
full potential of lipidomics in clinical science.
Comparison of Consensus Locations to LIPID MAPS Consortium Concentrations
The calculated consensus values were compared to the lipid concentrations noted in a report by
Quehenberger et al. where lipids were investigated in SRM 1950 by several members of the LIPID MAPS
consortium using targeted (class-specific) methods (14). It is important to note that this interlaboratory
study was unique in that the LIPID MAPS study only employed a single expert laboratory for each lipid
class using predominantly triple quadrupole technology. Therefore, the LIPID MAPS study did not provide
information on the state of lipid measurements across the community at large, nor include methods using
both targeted and untargeted workflows with the latest instrumentation. In total, the LIPID MAPS study
reported 588 lipids in SRM 1950 from several lipid classes, while the interlaboratory exercise reported
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1527 individual lipid species. A comparison of the reported LIPID MAPS species to those reported in
the interlaboratory exercise (by five or more laboratories) resulted in 226 overlapping lipid species.
A comparison of these overlapping species, organized by lipid class, is shown in Supplemental
Tables S7 to S16. The individual MEDM and LIPID MAPS study values were also summed by lipid class
and the results (derived values in Supplemental Table S17) were compared in Figs. 3A (high concentration
lipids) and 3B (low concentration lipids). The sum of the 226 lipids in common from the LIPID MAPS
study (8438 ± 106, nmol/mL) was significantly higher than that of the same lipid species determined in this
exercise (6218 ± 475, nmol/mL). As shown in Figs. 3A and 3B, this difference was driven mostly by PC,
PE, and TAG species. The main contributors to the difference between the two studies were phospholipids
and to a lesser extent non-polar lipids. This coincided with a large percent change in the interlaboratory
consensus estimates relative to the LIPID MAPS measurements, with percent changes: LPC (+48 %), LPE
(-80 %), PC (-56 %), PE (-83 %), PI (+58 %), and TAG (-54 %). In addition to methodological differences,
reporting at the sum composition level might contribute to some of these differences, as the isomer lipids
contributing to the sums may not be the same. Overall, the total lipid content for common lipids showed
that the LIPID MAPS sum was 30 % larger than the summed composition of common lipids that were
determined in this exercise, signifying a difference in measurement effects between studies, an aspect that
will be addressed with future efforts.
Future of Lipidomic Quantitation
To date, no clear community-wide consensus exists for the best approach to quantify lipids.
Quantitation in lipidomics is a polarizing subject within the community, with both methodological and
philosophical differences to consider. The community has limited agreement on the definition of current
quantitation approaches (absolute, semi-, and relative) and determination of the essential guidelines to
perform each approach. Furthermore, the discussion of quantitation becomes more convoluted when
assessing strategies for both targeted and global profiling approaches because neither has been explicitly
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studied. There is a quantitation tradeoff between these two approaches. Generally, targeted approaches
employ calibration curves and appropriate standards, which improve quantitation, while global approaches
typically provide more lipid identifications in a single analysis. Even in targeted studies for lipidomics,
appropriate standards are often not available and single point calibration is commonly used. The lipidomics
community is implementing relative quantitation experiments to increase accuracy in untargeted studies,
with a focus on monitoring lipid species changes between sample groups rather than determining the exact
concentration of lipids (75-77). Laboratories generally employ semi-quantitative approaches to provide
concentrations for lipid species; however, several assumptions are generally made using this approach (32,
77-79).
One major impediment to uniform quantitation within the community is the lack of suitable internal
standards. To date, several different types of internal standards have been employed (odd-chained,
deuterated, or 13C-labeled), but each has limitations. Ideally, multiple internal standards should be employed
for all types and classes of lipids to improve quantitation. However, the availability of lipids that can serve
as internal standards is limited. In this study, the specific internal standards utilized largely influenced the
reported final lipid concentration. For example, if a laboratory quantified a lipid class with an internal
standard from a different class, often the concentration values were quite different from those obtained from
laboratories using standards from the appropriate lipid class. We found that several odd-chain lipids, often
used by laboratories as exogenous internal standards, were reported as endogenous lipids by participating
laboratories in this exercise (e.g., CE 17:0, n = 6; LPC 17:0, n = 6; SM d35:1, n = 9; and TAG 51:3 n = 5;
n indicates number of incidences).
Comparing the consensus values from this exercise (using a variety of quantitation mass
spectrometry platforms: triple quadrupole, quadrupole time-of-flight, and orbitrap) to the concentration
values obtained using the targeted triple quadrupole platforms, we found that the targeted approaches
generally had significantly higher calculated concentration values. Future studies will further explore the
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contribution of analytical platforms and lipidomics workflows to the final concentration calculated using
the interlaboratory data. As the community begins to develop and establish guidelines for quality assurance
and quality control, discussions need to include acceptable practices for quantitation across the varying
platforms present within the lipidomics community.
CONCLUSION
The purpose of this lipidomics interlaboratory comparison exercise was to identify the metrological
questions and/or gaps that exist in current lipidomics measurement. To determine the principal areas of
need, the interlaboratory exercise was initiated using a commercially available CRM, SRM 1950. This
interlaboratory study provides an initial outlook into the variance associated with current lipid
methodologies. The robustly measured SRM 1950 consensus estimates can be used for community-wide
quality control and quality assessment. These values were compared to those previously reported by LIPID
MAPS, with significant discrepancies for specific lipid classes between both studies, and thus requires
further attention to understand the reasons behind this difference. From a community perspective, the
exercise also provided valuable insight into the potential strengths and weaknesses of current lipidomic
measurement. Future efforts resulting from this interlaboratory study will focus on making the data
available to the community and examining the influence that the laboratory-provided methodology had on
the resultant trends in the collective data. We currently intend to provide a supplemental survey to direct
future measurement efforts regarding lipidomics measurement.
DISCLAIMER
Certain commercial equipment, instruments, or materials are identified in this paper to specify adequately
the experimental procedures. Such identification does not imply recommendation or endorsement by the
National Institute of Standards and Technology; nor does it imply that the materials or equipment identified
are necessarily the best for the purpose. Furthermore, the content is solely the responsibility of the authors
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and does not necessarily represent the official views of the National Institute of Standards and Technology,
the U.S. National Institutes of Health, or of any of the participating organizations.
ACKNOWLEDGEMENTS/GRANT SUPPORT:
The work performed in this study would not be possible without the funding support (either partial or full)
for each contributing laboratory. At the request of these funded laboratories, we would like to acknowledge
the following grant support statements: Grants from the National Institutes of Health (NIH) including Lipid
Metabolites and Pathways Strategy (LIPID MAPS) U54 GM069338, R01 GM20501-41, and P30
DK064391 (EAD, OQ); The Research Center for Transomics Medicine, Kyushu University, supported by
grants from JST, ALCA, and AMED-CREST (JPMJCR1395) (TB, YI, HT); Natural Sciences and
Engineering Research Council of Canada (CM, PR, DV); SRC Grant 2015-4870 (JB); UL1 TR000040 (HJ,
RN, SC); NIH 5P01CA120964 and NIH 5P30CA006516 (JMA, SBB, MY); NIH/NIGMS PO1 GM095467
(RAC/CNS); National Center for Research Resources, National Institutes of Health Grant S10RR027926
(KRM, SZ); the US National Institutes of Health (NIH) Grants P20 HL113452 and U24 DK097154 (TC,
OF); Southeast Center for Integrated Metabolomics (SECIM) via NIH U24 DK097209 (RP, DM, TG, JPK);
the Austrian Science Fund (FWF) (P26148-N19) (HCK, MT, AT); The Southampton Centre for Biomedical
Research (SCBR) / NIHR Southampton Respiratory Biomedical Research Unit (GK, ADP, JB); The
Metabolomics Innovation Centre (TMIC) through the Genome Innovations Network (GIN) funding from
Genome Canada, Genome Alberta and Genome British Columbia for operations (205MET and 7203) and
technology development (215MET and MC3T) (JH, KL, CHB); NIH P01 HL034300 (Mass Spectrometry
Core) (MLS, KHG, HJ); CIHR, FDN143309, and Canadian Foundation of Innovation (CFI 12156) Grants
(MP, ML, DR); The Kansas Lipidomics Research Center (KLRC), supported by NSF grants MCB 1413036,
MCB 0920663, DBI 0521587, DBI 1228622, Kansas INBRE (NIH Grant P20 RR16475 from the INBRE
Program of the National Center for Research Resources), NSF EPSCoR grant EPS-0236913, Kansas
Technology Enterprise Corporation, and Kansas State University (RW, MRR, LY); the Swedish Heart-
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23
Lung Foundation (HLF 20140469 and HLF 20150640) (CEW, JK, AF, AC); the Stable Isotope and
Metabolomics Core Facility of the Diabetes Research and Training Center (DRTC) of the Albert Einstein
College of Medicine, supported by NIH/NCI Grant P60DK020541 (XHZ, YQ, and IJK); Biomarkers core
laboratory, Irving Institute for Clinical and Translational Research, Columbia University Medical Center
acknowledge support from National Center for Advancing Translational Sciences, NIH (CTSA, UL1
TL001873); and the National University of Singapore through Life Sciences Institute and the Yong Loo
Lin School of Medicine Department of Biochemistry (ACG, FT, and MRW) and the Department of
Biological Sciences (AHN); the Singapore National Research Foundation (NRFI2015-05, MRRW).
We would also like to thank Kayla Carter (US Center for Disease Control and Prevention), Debra Ellisor
(NIST), John Kucklick (NIST), Amanda Moors (NIST), Katrice Lippa (NIST), Dave Duewer (NIST), and
William Blaner (Columbia University II CTR Core Laboratory) for their contributions. CHB is grateful for
support from the Leading Edge Endowment Fund (University of Victoria) and for support from the Segal
McGill Chair in Molecular Oncology at McGill University (Montreal, Quebec, Canada). CHB is also
grateful for support from the Warren Y. Soper Charitable Trust and the Alvin Segal Family Foundation to
the Jewish General Hospital (Montreal, Quebec, Canada)
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Table 1. Final consensus location estimates for fatty acyl (FA) lipids measured in SRM 1950
Lipid
Number
of Labs
Units
Consensus
Location
Standard
Uncertainty
COD (%)
FFA 16:0
5
nmol/mL
43
13
31
FFA 18:3
6
nmol/mL
2.9
0.62
21
FFA 20:4
7
nmol/mL
4.7
1.5
31
FFA 20:5
7
nmol/mL
0.42
0.056
13
FFA 22:6
8
nmol/mL
1.5
0.17
11
5-HETE
5
pmol/mL
10
1.3
13
12-HETE
5
pmol/mL
6.8
1.5
23
15-HETE
5
pmol/mL
2.4
0.64
27
MEDM consensus estimates shown were calculated for those lipids measured by at least five laboratories
and had COD values ≤ 40 %. The abbreviations identify free fatty acids (FFA) and hydroxyeicosatetraenoic
acids (HETEs).
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Table 2. Final consensus location estimates for glycerolipids (GL) measured in SRM 1950
Lipid
Number
of Labs
Units
Consensus
Location
Standard
Uncertainty
COD (%)
DAG 30:0
7
nmol/mL
0.83
0.17
20
DAG 34:1
16
nmol/mL
6.1
2.4
40
DAG 36:2
16
nmol/mL
6.2
2.2
36
DAG 36:3
15
nmol/mL
8.4
3.3
39
DAG 36:4
12
nmol/mL
2.8
1.0
38
TAG 46:2
8
nmol/mL
3.6
1.3
37
TAG 48:0
10
nmol/mL
4.5
1.2
26
TAG 48:1
16
nmol/mL
13
3.2
24
TAG 48:2
15
nmol/mL
16
2.8
18
TAG 48:4
5
nmol/mL
1.3
0.23
18
TAG 49:1
9
nmol/mL
2.0
0.42
21
TAG 49:2
6
nmol/mL
1.8
0.56
31
TAG 50:0
11
nmol/mL
3.8
0.83
22
TAG 50:1
14
nmol/mL
38
10
26
TAG 50:2
15
nmol/mL
47
12
26
TAG 50:3
16
nmol/mL
23
6.6
29
TAG 50:4
15
nmol/mL
8.7
2.9
34
TAG 50:5
7
nmol/mL
1.6
0.64
40
TAG 51:1
7
nmol/mL
1.8
0.48
27
TAG 51:2
8
nmol/mL
4.8
1.1
22
TAG 51:3
5
nmol/mL
4.8
1.9
39
TAG 52:1
11
nmol/mL
14
2.9
20
TAG 52:2
16
nmol/mL
44
14
33
TAG 52:3
16
nmol/mL
100
29
28
TAG 52:4
15
nmol/mL
48
17
35
TAG 52:5
13
nmol/mL
15
5.7
39
TAG 52:6
8
nmol/mL
4.0
1.4
35
TAG 52:7
5
nmol/mL
0.39
0.13
33
TAG 53:2
9
nmol/mL
1.9
0.41
21
TAG 53:3
6
nmol/mL
3.7
1.1
29
TAG 53:4
6
nmol/mL
2.4
0.76
32
TAG 54:1
10
nmol/mL
3.2
0.91
29
TAG 54:2
13
nmol/mL
8.2
2.6
31
TAG 54:3
15
nmol/mL
26
9.8
37
TAG 54:4
15
nmol/mL
36
13
35
TAG 54:5
15
nmol/mL
27
11
38
TAG 54:6
16
nmol/mL
14
5.1
37
TAG 54:7
7
nmol/mL
5.6
1.5
26
TAG 56:2
5
nmol/mL
0.69
0.23
33
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Lipid
Number
of Labs
Units
Consensus
Location
Standard
Uncertainty
COD (%)
TAG 56:3
6
nmol/mL
1.4
0.14
10
TAG 56:4
10
nmol/mL
2.0
0.56
28
TAG 56:5
12
nmol/mL
4.1
1.4
33
TAG 56:7
8
nmol/mL
13
2.7
20
TAG 56:9
5
nmol/mL
0.71
0.27
38
TAG 58:7
5
nmol/mL
2.0
0.64
32
TAG 58:8
9
nmol/mL
0.68
0.21
31
TAG 58:9
6
nmol/mL
1.2
0.27
22
MEDM consensus estimates shown were calculated for those lipids measured by at least five laboratories
and had COD values ≤ 40 %. The abbreviations identify diacylglycerols (DAG) and triacylglycerols (TAG).
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Table 3. Final consensus location estimates for glycerophospholipids (GP) measured in SRM 1950
Lipid
Number
of Labs
Units
Consensus
Location
Standard
Uncertainty
COD (%)
LPC 14:0
16
nmol/mL
1.0
0.20
19
LPC 15:0
9
nmol/mL
0.52
0.11
22
LPC 16:0
20
nmol/mL
73
11
15
LPC O-16:0
10
nmol/mL
0.55
0.16
29
LPC P-16:0
8
nmol/mL
0.46
0.13
27
LPC 16:1
19
nmol/mL
2.4
0.35
15
LPC 17:0
6
nmol/mL
1.4
0.24
18
LPC 17:1
6
nmol/mL
0.25
0.071
29
LPC 18:0
20
nmol/mL
27
3.3
12
LPC O-18:0
6
nmol/mL
0.16
0.058
36
LPC 18:1
19
nmol/mL
18
2.3
13
LPC 18:2
19
nmol/mL
22
2.9
13
LPC 18:3
18
nmol/mL
0.44
0.13
30
LPC 20:0
7
nmol/mL
0.10
0.034
34
LPC 20:1
13
nmol/mL
0.19
0.024
12
LPC 20:2
9
nmol/mL
0.23
0.044
19
LPC 20:3
18
nmol/mL
1.8
0.26
15
LPC 20:4
20
nmol/mL
6.0
0.60
10
LPC 20:5
15
nmol/mL
0.33
0.092
28
LPC 22:0
5
nmol/mL
0.025
0.0017
7
LPC 22:1
5
nmol/mL
0.013
0.0046
36
LPC 22:4
8
nmol/mL
0.12
0.041
33
LPC 22:5
12
nmol/mL
0.43
0.13
30
LPC 22:6
17
nmol/mL
0.77
0.14
18
LPC 24:0
5
nmol/mL
0.046
0.015
33
LPE 16:0
14
nmol/mL
0.91
0.27
29
LPE 18:0
15
nmol/mL
1.6
0.55
34
LPE 18:1
14
nmol/mL
1.4
0.47
35
LPE 18:2
16
nmol/mL
1.9
0.56
30
LPE 20:4
14
nmol/mL
1.1
0.41
37
LPE 22:6
12
nmol/mL
0.52
0.18
34
PC 30:0
11
nmol/mL
1.6
0.32
20
PC O-30:0/29:0
7
nmol/mL
0.072
0.026
36
PC O-30:1/P-30:0
7
nmol/mL
0.047
0.0096
20
PC 32:0
18
nmol/mL
7.2
1.0
14
PC O-32:0/31:0
11
nmol/mL
1.5
0.41
28
PC 32:1
18
nmol/mL
13
1.9
15
PC O-32:1/P-32:0/31:1
11
nmol/mL
1.6
0.24
14
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Lipid
Number
of Labs
Units
Consensus
Location
Standard
Uncertainty
COD (%)
PC O-32:2/P-32:1/31:2
8
nmol/mL
0.34
0.093
28
PC 32:3
8
nmol/mL
0.42
0.14
34
PC P-33:1/32:2
16
nmol/mL
2.6
0.37
14
PC 34:0
12
nmol/mL
2.1
0.37
18
PC O-34:0/33:0
10
nmol/mL
0.76
0.17
22
PC 34:1
19
nmol/mL
120
21
17
PC O-34:1/P-34:0/33:1
17
nmol/mL
4.9
0.86
17
PC O-34:2/P-34:1/33:2
17
nmol/mL
5.2
1.3
25
PC O-34:3/P-34:2/33:3
12
nmol/mL
4.7
0.88
19
PC P-35:1/34:2
18
nmol/mL
240
47
19
PC P-35:2/34:3
18
nmol/mL
12
1.7
14
PC O-35:4/34:4
9
nmol/mL
1.0
0.25
24
PC 34:5
5
nmol/mL
0.034
0.0045
13
PC 36:1
17
nmol/mL
26
4.6
17
PC O-36:1/P-36:0/35:1
16
nmol/mL
3.5
0.99
28
PC 36:2
18
nmol/mL
140
25
17
PC O-36:2/P-36:1/35:2
17
nmol/mL
7.4
1.7
22
PC 36:3
17
nmol/mL
100
14
14
PC O-36:3/P-36:2/35:3
12
nmol/mL
3.7
0.82
22
PC 36:4
19
nmol/mL
150
28
19
PC O-36:4/P-36:3/35:4
17
nmol/mL
12
1.4
12
PC 36:5
16
nmol/mL
11
1.8
17
PC O-36:5/P-36:4/35:5
11
nmol/mL
6.9
1.6
23
PC P-36:5/35:6
5
nmol/mL
0.30
0.094
31
PC 36:6
8
nmol/mL
0.28
0.088
32
PC 38:2
15
nmol/mL
2.3
0.20
9
PC O-38:2/37:2
6
nmol/mL
0.98
0.32
32
PC 38:3
14
nmol/mL
26
5.2
20
PC O-38:3/P-38:2/37:3
14
nmol/mL
1.5
0.51
34
PC 38:4
18
nmol/mL
84
14
17
PC O-38:4/P-38:3/37:4
12
nmol/mL
7.4
2.0
27
PC 38:5
18
nmol/mL
42
7.9
19
PC O-38:5/P-38:4/37:5
16
nmol/mL
11
1.6
14
PC 38:6
18
nmol/mL
41
4.4
11
PC O-38:6/P-38:5/37:6
12
nmol/mL
3.6
1.0
29
PC P-38:6/36:0
10
nmol/mL
1.2
0.39
33
PC 40:4
18
nmol/mL
2.9
0.37
13
PC O-40:2/P-40:1
5
nmol/mL
0.069
0.021
30
PC O-40:4/P-40:3/39:4
8
nmol/mL
0.95
0.38
40
PC 40:5
18
nmol/mL
6.7
1.1
16
PC O-40:5/P-40:4/39:5
12
nmol/mL
1.7
0.45
27
PC 40:6
17
nmol/mL
14
2.6
19
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Lipid
Number
of Labs
Units
Consensus
Location
Standard
Uncertainty
COD (%)
PC 40:7
16
nmol/mL
3.5
0.76
21
PC O-40:7/P-40:6/39:7
9
nmol/mL
1.1
0.23
20
PC 40:8
14
nmol/mL
0.73
0.20
28
PC O-42:5/P-42:4
7
nmol/mL
0.79
0.12
15
PE 32:1
6
nmol/mL
0.34
0.12
36
PE 34:1
14
nmol/mL
1.2
0.17
14
PE 34:2
16
nmol/mL
2.2
0.26
12
PE O-34:2/P-34:1
11
nmol/mL
0.78
0.17
22
PE O-34:3/P-34:2
11
nmol/mL
1.5
0.41
27
PE 36:0
11
nmol/mL
0.28
0.10
36
PE 36:1
14
nmol/mL
1.3
0.26
20
PE 36:2
16
nmol/mL
6.7
0.79
12
PE O-36:2/P-36:1/35:2
12
nmol/mL
0.93
0.22
23
PE 36:3
16
nmol/mL
2.4
0.38
16
PE O-36:3/P-36:2/35:3
15
nmol/mL
3.2
0.76
24
PE 36:4
16
nmol/mL
3.1
0.39
13
PE O-36:4/P-36:3
14
nmol/mL
1.6
0.29
18
PE O-36:5/P-36:4
15
nmol/mL
4.9
1.9
38
PE 38:3
14
nmol/mL
0.95
0.20
21
PE 38:4
16
nmol/mL
8.1
1.2
15
PE O-38:4/P-38:3/37:4
9
nmol/mL
0.94
0.18
19
PE 38:5
12
nmol/mL
2.7
0.47
17
PE O-38:5/P-38:4
17
nmol/mL
5.8
1.9
33
PE 38:6
15
nmol/mL
3.2
0.59
19
PE O-38:6/P-38:5
16
nmol/mL
4.9
1.2
25
PE O-38:7/P-38:6
8
nmol/mL
3.5
0.98
28
PE 40:4
10
nmol/mL
0.26
0.082
31
PE 40:5
12
nmol/mL
0.73
0.23
31
PE O-40:5/P-40:4/39:5
12
nmol/mL
0.73
0.13
17
PE 40:6
14
nmol/mL
1.8
0.36
20
PE O-40:6/P-40:5/39:6
14
nmol/mL
1.3
0.31
23
PE 40:7
11
nmol/mL
0.77
0.26
33
PE O-40:7/P-40:6/39:7
14
nmol/mL
2.5
0.72
29
PI 32:1
10
nmol/mL
0.56
0.11
19
PI 34:1
14
nmol/mL
2.4
0.42
17
PI 34:2
14
nmol/mL
2.8
0.38
14
PI 36:1
13
nmol/mL
2.1
0.59
28
PI 36:2
15
nmol/mL
7.7
0.93
12
PI 36:3
14
nmol/mL
2.2
0.29
14
PI 36:4
14
nmol/mL
3.0
0.48
16
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Lipid
Number
of Labs
Units
Consensus
Location
Standard
Uncertainty
COD (%)
PI 38:3
14
nmol/mL
3.4
0.54
16
PI 38:4
17
nmol/mL
19
2.2
11
PI 38:5
15
nmol/mL
2.5
0.44
18
PI 38:6
10
nmol/mL
0.32
0.031
10
PI 40:4
7
nmol/mL
0.30
0.042
14
PI 40:6
12
nmol/mL
0.84
0.16
19
PG 36:2
6
nmol/mL
0.67
0.24
36
MEDM consensus estimates shown were calculated for those lipids measured by at least five laboratories
and had COD values ≤ 40 %. The abbreviations identify lysophosphatidylcholines (LPC),
lysophosphatidylethanolamines (LPE), phosphatidylcholines (PC), phosphatidylethanolamines (PE),
phosphatidylglycerols (PG), and phosphatidylinositols (PI). For PC and PE lipid classes, the isobaric
species (ether-linked) were summed and the possibilities observed by the participants are separated by a
“/”.
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Table 4. Final consensus location estimates for sphingolipids (SP) measured in SRM 1950
Lipid
Number
of Labs
Units
Consensus
Location
Standard
Uncertainty
COD (%)
HexCer d34:1
6
nmol/mL
0.86
0.21
25
HexCer d36:1
5
nmol/mL
0.13
0.043
34
HexCer d40:1
5
nmol/mL
2.4
0.68
28
HexCer d42:1
6
nmol/mL
2.7
0.73
27
CER d34:1
17
nmol/mL
0.28
0.044
16
CER d36:1
14
nmol/mL
0.12
0.021
17
CER d38:1
16
nmol/mL
0.11
0.021
20
CER d40:1
18
nmol/mL
0.65
0.12
18
CER d40:2
6
nmol/mL
0.15
0.021
14
CER d41:1
7
nmol/mL
0.67
0.27
40
CER d42:1
19
nmol/mL
1.9
0.47
24
CER d42:2
19
nmol/mL
0.82
0.10
12
SM d31:1
5
nmol/mL
0.19
0.049
25
SM d32:1
14
nmol/mL
8.4
1.4
17
SM d32:2
10
nmol/mL
0.66
0.24
36
SM d33:1
14
nmol/mL
4.7
0.64
14
SM d34:0
14
nmol/mL
5.8
1.3
22
SM d34:1
21
nmol/mL
100
15
15
SM d34:2
17
nmol/mL
16
2.2
14
SM d35:1
9
nmol/mL
2.5
0.58
23
SM d35:2
6
nmol/mL
0.52
0.21
39
SM d36:0
11
nmol/mL
2.0
0.49
24
SM d36:1
22
nmol/mL
20
3.7
18
SM d36:2
22
nmol/mL
9.6
1.5
16
SM d36:3
13
nmol/mL
1.3
0.41
31
SM d37:1
11
nmol/mL
1.0
0.23
23
SM d38:1
17
nmol/mL
11
3.1
27
SM d38:2
17
nmol/mL
5.2
1.3
25
SM d38:3
8
nmol/mL
0.61
0.24
39
SM d39:1
14
nmol/mL
3.6
1.0
29
SM d39:2
9
nmol/mL
0.61
0.16
26
SM d40:1
17
nmol/mL
20
5.1
25
SM d40:2
15
nmol/mL
12
2.8
24
SM d40:3
8
nmol/mL
2.2
0.79
37
SM d41:1
14
nmol/mL
7.7
2.1
27
SM d41:2
14
nmol/mL
5.8
1.4
24
SM d41:3
7
nmol/mL
0.77
0.30
39
SM d42:1
21
nmol/mL
20
5.4
28
SM d42:2
18
nmol/mL
44
11
25
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Lipid
Number
of Labs
Units
Consensus
Location
Standard
Uncertainty
COD (%)
SM d42:3
12
nmol/mL
17
4.7
27
SM d43:2
10
nmol/mL
1.0
0.29
29
SM d44:2
9
nmol/mL
0.40
0.13
32
MEDM consensus estimates shown were calculated for those lipids measured by at least five laboratories
and had COD values ≤ 40 %. The abbreviations identify hexosylceramides (HexCer), ceramides (CER),
and sphingomyelins (SM).
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Table 5. Final consensus location estimates for sterol (ST) lipids measured in SRM 1950
Lipid
# of Labs
Units
Consensus
Location
Standard
Uncertainty
COD (%)
CE 14:0
7
nmol/mL
16
6.0
37
CE 15:0
6
nmol/mL
5.3
1.8
34
CE 16:0
13
nmol/mL
210
58
28
CE 16:1
11
nmol/mL
100
27
27
CE 16:2
5
nmol/mL
1.9
0.46
25
CE 17:1
9
nmol/mL
8.2
1.0
13
CE 18:0
7
nmol/mL
15
3.7
25
CE 18:1
14
nmol/mL
450
110
25
CE 18:2
14
nmol/mL
1,700
430
26
CE 18:3
13
nmol/mL
84
24
28
CE 20:3
13
nmol/mL
35
12
35
CE 20:4
14
nmol/mL
350
58
17
CE 20:5
12
nmol/mL
38
8.6
23
CE 22:5
6
nmol/mL
4.1
1.6
39
CE 22:6
11
nmol/mL
37
9.5
26
Cholesterol
8
nmol/mL
770
110
14
CDCA
7
nmol/mL
0.30
0.11
38
CA
9
nmol/mL
0.12
0.034
28
DCA
9
nmol/mL
0.35
0.083
24
GCDCA
8
nmol/mL
1.1
0.18
17
GDCA
7
nmol/mL
0.43
0.069
16
GLCA
6
nmol/mL
0.025
0.0018
7
GUDCA
6
nmol/mL
0.15
0.024
16
GCA
6
nmol/mL
0.24
0.069
29
LCA
8
nmol/mL
0.014
0.0036
26
TCDCA
9
nmol/mL
0.084
0.0050
6
TCA
9
nmol/mL
0.026
0.0056
22
TDCA
8
nmol/mL
0.040
0.0064
16
TLCA
5
nmol/mL
0.0027
0.00069
26
UDCA
8
nmol/mL
0.11
0.024
22
MEDM consensus estimates shown were calculated for those lipids measured by at least five laboratories
and had COD values ≤ 40 %. The abbreviations identify chenodeoxycholic acid (CDCA), cholic acid (CA),
cholesteryl ester (CE), deoxycholic acid (DCA), glycochenodeoxycholic acid (GCDCA), glycodeoxycholic
acid (GDCA), glycolithocholic acid (GLCA), glycoursodeoxycholic acid (GUDCA), glyocholic acid
(GCA), lithocholic acid (LCA), taurochenodeoxycholic acid (TCDCA), taurocholic acid (TCA),
taurodeoxycholic acid (TDCA), taurolithocholic acid (TLCA), ursodeoxycholic acid (UDCA).
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Fig. 2. Coefficient of dispersion (COD, in %) for the MEDM lipids (n ≥ 5 laboratories reporting) organized
by lipid class. Each point on the figure represents a single sum lipid composition. The COD was calculated
by dividing the standard uncertainty by the final MEDM. CODs not shown in the figure are free cholesterol,
eicosanoids, phosphatidylglycerols, and phosphatidylserines.
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Fig. 3 Sum of MEDM values for the A) most (in µmol/mL plasma) and B) least (in nmol/mL plasma)
concentrated lipid classes (EICO* in pmol/mL plasma) compared to the sum of concentrations provided by
the LIPID MAPS consortium. The comparisons entail summing only the lipids measured in common
between the compared data sets, with the total number of lipids fitting this criterion (per class and total)
provided above each bar graph. Other PL represents the sum of PG and PS species. The error bars associated
with the values standard uncertainties on the location estimates. Further information on this comparison,
including total lipid concentrations, is included in Supplementary Material (Supplementary Table S17).
A
B
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