Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in frozen human plasma

John A. Bowden; Alan Heckert; Candice Z. Ulmer; Christina M. Jones; Jeremy P. Koelmel; Laila Abdullah; Linda Ahonen; Yazen Alnouti; Aaron M. Armando; John M. Asara; Takeshi Bamba; John R. Barr; Jonas Bergquist; Christoph H. Borchers; Joost Brandsma; Susanne B. Breitkopf; Tomas Cajka; Amaury Cazenave-Gassiot; Antonio Checa; Michelle A. Cinel; Romain A. Colas; Serge Cremers; Edward A. Dennis; James E. Evans; Alexander Fauland; Oliver Fiehn; Michael S. Gardner; Timothy J. Garrett; Katherine H. Gotlinger; Jun Han; Yingying Huang; Aveline Huipeng Neo; Tuulia Hyötyläinen; Yoshihiro Izumi; Hongfeng Jiang; Houli Jiang; Jiang Jiang; Maureen Kachman; Reiko Kiyonami; Kristaps Klavins; Christian Klose; Harald C. Köfeler; Johan Kolmert; Therese Koal; Grielof Koster; Zsuzsanna Kuklenyik; Irwin J. Kurland; Michael Leadley; Karen Lin; Krishna Rao Maddipati; Danielle McDougall; Peter J. Meikle; Natalie A. Mellett; Cian Monnin; M. Arthur Moseley; Renu Nandakumar; Matej Oresic; Rainey Patterson; David Peake; Jason S. Pierce; Martin Post; Anthony D. Postle; Rebecca Pugh; Yunping Qiu; Oswald Quehenberger; Parsram Ramrup; Jon Rees; Barbara Rembiesa; Denis Reynaud; Mary R. Roth; Susanne Sales; Kai Schuhmann; Michal Laniado Schwartzman; Charles N. Serhan; Andrej Shevchenko; Stephen E. Somerville; Lisa St John-Williams; Michal A. Surma; Hiroaki Takeda; Rhishikesh Thakare; J. Will Thompson; Federico Torta; Alexander Triebl; Martin Trötzmüller; S. J.Kumari Ubhayasekera; Dajana Vuckovic; Jacquelyn M. Weir; Ruth Welti; Markus R. Wenk; Craig E. Wheelock; Libin Yao; Min Yuan; Xueqing Heather Zhao; Senlin Zhou

doi:10.1194/jlr.M079012

Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in frozen human plasma

John A. Bowden, Alan Heckert, Candice Z. Ulmer, Christina M. Jones, Jeremy P. Koelmel, Laila Abdullah, Linda Ahonen, Yazen Alnouti, Aaron M. Armando, John M. Asara, Takeshi Bamba, John R. Barr, Jonas Bergquist, Christoph H. Borchers, Joost Brandsma, Susanne B. Breitkopf, Tomas Cajka, Amaury Cazenave-Gassiot, Antonio Checa, Michelle A. CinelRomain A. Colas, Serge Cremers, Edward A. Dennis, James E. Evans, Alexander Fauland, Oliver Fiehn, Michael S. Gardner, Timothy J. Garrett, Katherine H. Gotlinger, Jun Han, Yingying Huang, Aveline Huipeng Neo, Tuulia Hyötyläinen, Yoshihiro Izumi, Hongfeng Jiang, Houli Jiang, Jiang Jiang, Maureen Kachman, Reiko Kiyonami, Kristaps Klavins, Christian Klose, Harald C. Köfeler, Johan Kolmert, Therese Koal, Grielof Koster, Zsuzsanna Kuklenyik, Irwin J. Kurland, Michael Leadley, Karen Lin, Krishna Rao Maddipati, Danielle McDougall, Peter J. Meikle, Natalie A. Mellett, Cian Monnin, M. Arthur Moseley, Renu Nandakumar, Matej Oresic, Rainey Patterson, David Peake, Jason S. Pierce, Martin Post, Anthony D. Postle, Rebecca Pugh, Yunping Qiu, Oswald Quehenberger, Parsram Ramrup, Jon Rees, Barbara Rembiesa, Denis Reynaud, Mary R. Roth, Susanne Sales, Kai Schuhmann, Michal Laniado Schwartzman, Charles N. Serhan, Andrej Shevchenko, Stephen E. Somerville, Lisa St John-Williams, Michal A. Surma, Hiroaki Takeda, Rhishikesh Thakare, J. Will Thompson, Federico Torta, Alexander Triebl, Martin Trötzmüller, S. J.Kumari Ubhayasekera, Dajana Vuckovic, Jacquelyn M. Weir, Ruth Welti, Markus R. Wenk, Craig E. Wheelock, Libin Yao, Min Yuan, Xueqing Heather Zhao, Senlin Zhou

Research output: Contribution to journal › Article › peer-review

245 Citations (Scopus)

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 laboratory using a different lipidomics workflow. A total of 1,527 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 interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. 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.

Original language	English
Pages (from-to)	2275-2288
Number of pages	14
Journal	Journal of Lipid Research
Volume	58
Issue number	12
DOIs	https://doi.org/10.1194/jlr.M079012
Publication status	Published - 2017

All Science Journal Classification (ASJC) codes

Biochemistry
Endocrinology
Cell Biology

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10.1194/jlr.M079012

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Bowden, J. A., Heckert, A., Ulmer, C. Z., Jones, C. M., Koelmel, J. P., Abdullah, L., Ahonen, L., Alnouti, Y., Armando, A. M., Asara, J. M., Bamba, T., Barr, J. R., Bergquist, J., Borchers, C. H., Brandsma, J., Breitkopf, S. B., Cajka, T., Cazenave-Gassiot, A., Checa, A., ... Zhou, S. (2017). Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in frozen human plasma. Journal of Lipid Research, 58(12), 2275-2288. https://doi.org/10.1194/jlr.M079012

Bowden, JA, Heckert, A, Ulmer, CZ, Jones, CM, Koelmel, JP, Abdullah, L, Ahonen, L, Alnouti, Y, Armando, AM, Asara, JM, Bamba, T, Barr, JR, Bergquist, J, Borchers, CH, Brandsma, J, Breitkopf, SB, Cajka, T, Cazenave-Gassiot, A, Checa, A, Cinel, MA, Colas, RA, Cremers, S, Dennis, EA, Evans, JE, Fauland, A, Fiehn, O, Gardner, MS, Garrett, TJ, Gotlinger, KH, Han, J, Huang, Y, Neo, AH, Hyötyläinen, T, Izumi, Y, Jiang, H, Jiang, H, Jiang, J, Kachman, M, Kiyonami, R, Klavins, K, Klose, C, Köfeler, HC, Kolmert, J, Koal, T, Koster, G, Kuklenyik, Z, Kurland, IJ, Leadley, M, Lin, K, Maddipati, KR, McDougall, D, Meikle, PJ, Mellett, NA, Monnin, C, Moseley, MA, Nandakumar, R, Oresic, M, Patterson, R, Peake, D, Pierce, JS, Post, M, Postle, AD, Pugh, R, Qiu, Y, Quehenberger, O, Ramrup, P, Rees, J, Rembiesa, B, Reynaud, D, Roth, MR, Sales, S, Schuhmann, K, Schwartzman, ML, Serhan, CN, Shevchenko, A, Somerville, SE, St John-Williams, L, Surma, MA, Takeda, H, Thakare, R, Thompson, JW, Torta, F, Triebl, A, Trötzmüller, M, Ubhayasekera, SJK, Vuckovic, D, Weir, JM, Welti, R, Wenk, MR, Wheelock, CE, Yao, L, Yuan, M, Zhao, XH & Zhou, S 2017, 'Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in frozen human plasma', Journal of Lipid Research, vol. 58, no. 12, pp. 2275-2288. https://doi.org/10.1194/jlr.M079012

@article{8102655e70f24b56913c4937164f056a,

title = "Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in frozen human plasma",

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 laboratory using a different lipidomics workflow. A total of 1,527 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 interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. 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.",

author = "Bowden, {John A.} and Alan Heckert and Ulmer, {Candice Z.} and Jones, {Christina M.} and Koelmel, {Jeremy P.} and Laila Abdullah and Linda Ahonen and Yazen Alnouti and Armando, {Aaron M.} and Asara, {John M.} and Takeshi Bamba and Barr, {John R.} and Jonas Bergquist and Borchers, {Christoph H.} and Joost Brandsma and Breitkopf, {Susanne B.} and Tomas Cajka and Amaury Cazenave-Gassiot and Antonio Checa and Cinel, {Michelle A.} and Colas, {Romain A.} and Serge Cremers and Dennis, {Edward A.} and Evans, {James E.} and Alexander Fauland and Oliver Fiehn and Gardner, {Michael S.} and Garrett, {Timothy J.} and Gotlinger, {Katherine H.} and Jun Han and Yingying Huang and Neo, {Aveline Huipeng} and Tuulia Hy{\"o}tyl{\"a}inen and Yoshihiro Izumi and Hongfeng Jiang and Houli Jiang and Jiang Jiang and Maureen Kachman and Reiko Kiyonami and Kristaps Klavins and Christian Klose and K{\"o}feler, {Harald C.} and Johan Kolmert and Therese Koal and Grielof Koster and Zsuzsanna Kuklenyik and Kurland, {Irwin J.} and Michael Leadley and Karen Lin and Maddipati, {Krishna Rao} and Danielle McDougall and Meikle, {Peter J.} and Mellett, {Natalie A.} and Cian Monnin and Moseley, {M. Arthur} and Renu Nandakumar and Matej Oresic and Rainey Patterson and David Peake and Pierce, {Jason S.} and Martin Post and Postle, {Anthony D.} and Rebecca Pugh and Yunping Qiu and Oswald Quehenberger and Parsram Ramrup and Jon Rees and Barbara Rembiesa and Denis Reynaud and Roth, {Mary R.} and Susanne Sales and Kai Schuhmann and Schwartzman, {Michal Laniado} and Serhan, {Charles N.} and Andrej Shevchenko and Somerville, {Stephen E.} and {St John-Williams}, Lisa and Surma, {Michal A.} and Hiroaki Takeda and Rhishikesh Thakare and Thompson, {J. Will} and Federico Torta and Alexander Triebl and Martin Tr{\"o}tzm{\"u}ller and Ubhayasekera, {S. J.Kumari} and Dajana Vuckovic and Weir, {Jacquelyn M.} and Ruth Welti and Wenk, {Markus R.} and Wheelock, {Craig E.} and Libin Yao and Min Yuan and Zhao, {Xueqing Heather} and Senlin Zhou",

note = "Funding Information: 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 [including Lipid Metabolites and Pathways Strategy (LIPID MAPS)] U54 GM069338, R01 GM20501-41, and P30 DK064391 (E.A.D., O.Q.); the Research Center for Transomics Medicine, Kyushu University, supported by grants from ALCA, AMED-CREST, and Japan Science and Technology Agency (JPMJCR1395) (T.B., Y.I., H.T.); Natural Sciences and Engineering Research Council of Canada (C.M., P.R., D.V.); Swedish Research Council Grant 2015-4870 (J.Be.); National Center for Advancing Translational Sciences Grant UL1 TR000040 (Hon.J., R.N., S.C.); Foundation for the National Institutes of Health Grants 5P01CA120964 and 5P30CA006516 (J.M.A., S.B.B., M.Y.); National Institute of General Medical Sciences Grant PO1 GM095467 (R.A.C., C.N.S.); National Center for Research Resources Grant S10RR027926 (K.R.M., S.Z.); National Institutes of Health Grants P20 HL113452 and U24 DK097154 (T.C., O.F.); Southeast Center for Integrated Metabolomics (SECIM) via National Institutes of Health Grant U24 DK097209 (R.Pa., D.M., T.J.G., J.P.K.); Austrian Science Fund (FWF) Grant P26148-N19 (H.C.K., M.T., A.T.); the Southampton Centre for Biomedical Research (SCBR)/NIHR Southampton Respiratory Biomedical Research Unit (G.K., A.D.P., J.Br.); 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) (J.H., K.L., C.H.B.); Foundation for the National Institutes of Health Grant P01 HL034300 (Mass Spectrometry Core) (M.L.S., K.H.G., Hou.J.); Canadian Institutes of Health Research Grant FDN143309 and Canadian Foundation for Innovation Grant CFI 12156 (M.P., M.L., D.R.); the Kansas Lipidomics Research Center (KLRC), supported by National Science Foundation Grants (MCB 1413036, MCB 0920663, DBI 0521587, and DBI 1228622), Kansas INBRE (Foundation for the National Institutes of Health 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 (R.W., M.R.R., L.Y.); Swedish Heart-Lung Foundation Grants HLF 20140469 and HLF 20150640 (C.E.W., J.K., A.F., A.C.); the Stable Isotope and Metabolomics Core Facility of the Diabetes Research and Training Center (DRTC) of the Albert Einstein College of Medicine, supported by National Institutes of Health/NCI Grant P60DK020541 (X.H.Z., Y.Q., and I.J.K.); Biomarkers Core Laboratory, Irving Institute for Clinical and Translational Research, and Columbia University Medical Center acknowledge support from the National Center for Advancing Translational Sciences, National Institutes of Health Grant UL1 TL001873); the National University of Singapore through Life Sciences Institute and the Yong Loo Lin School of Medicine Department of Biochemistry (A.C-G., F.T., M.R.W.) and the Department of Biological Sciences (A.H.N.); and Singapore National Research Foundation Grant NRFI2015-05 (M.R.W.). Additional support was provided by the Leading Edge Endowment Fund (University of Victoria), the Segal McGill Chair in Molecular Oncology at McGill University (Montreal, Quebec, Canada), the Warren Y. Soper Charitable Trust, and the Alvin Segal Family Foundation to the Jewish General Hospital (Montreal, Quebec, Canada) (C.H.B). Certain commercial equipment, instruments, or materials are identified in this paper to adequately specify 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 and does not necessarily represent the official views of the National Institute of Standards and Technology, the National Institutes of Health, or any of the participating organizations. Manuscript received 8 July 2017 and in revised form 2 October 2017. Published, JLR Papers in Press, October 6, 2017 DOI https://doi.org/10.1194/jlr.M079012 Publisher Copyright: Copyright {\textcopyright} 2017 by the American Society for Biochemistry and Molecular Biology, Inc.",

year = "2017",

doi = "10.1194/jlr.M079012",

language = "English",

volume = "58",

pages = "2275--2288",

journal = "Journal of Lipid Research",

issn = "0022-2275",

publisher = "American Society for Biochemistry and Molecular Biology Inc.",

number = "12",

}

TY - JOUR

T1 - Harmonizing lipidomics

T2 - NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in frozen human plasma

AU - Bowden, John A.

AU - Heckert, Alan

AU - Ulmer, Candice Z.

AU - Jones, Christina M.

AU - Koelmel, Jeremy P.

AU - Abdullah, Laila

AU - Ahonen, Linda

AU - Alnouti, Yazen

AU - Armando, Aaron M.

AU - Asara, John M.

AU - Bamba, Takeshi

AU - Barr, John R.

AU - Bergquist, Jonas

AU - Borchers, Christoph H.

AU - Brandsma, Joost

AU - Breitkopf, Susanne B.

AU - Cajka, Tomas

AU - Cazenave-Gassiot, Amaury

AU - Checa, Antonio

AU - Cinel, Michelle A.

AU - Colas, Romain A.

AU - Cremers, Serge

AU - Dennis, Edward A.

AU - Evans, James E.

AU - Fauland, Alexander

AU - Fiehn, Oliver

AU - Gardner, Michael S.

AU - Garrett, Timothy J.

AU - Gotlinger, Katherine H.

AU - Han, Jun

AU - Huang, Yingying

AU - Neo, Aveline Huipeng

AU - Hyötyläinen, Tuulia

AU - Izumi, Yoshihiro

AU - Jiang, Hongfeng

AU - Jiang, Houli

AU - Jiang, Jiang

AU - Kachman, Maureen

AU - Kiyonami, Reiko

AU - Klavins, Kristaps

AU - Klose, Christian

AU - Köfeler, Harald C.

AU - Kolmert, Johan

AU - Koal, Therese

AU - Koster, Grielof

AU - Kuklenyik, Zsuzsanna

AU - Kurland, Irwin J.

AU - Leadley, Michael

AU - Lin, Karen

AU - Maddipati, Krishna Rao

AU - McDougall, Danielle

AU - Meikle, Peter J.

AU - Mellett, Natalie A.

AU - Monnin, Cian

AU - Moseley, M. Arthur

AU - Nandakumar, Renu

AU - Oresic, Matej

AU - Patterson, Rainey

AU - Peake, David

AU - Pierce, Jason S.

AU - Post, Martin

AU - Postle, Anthony D.

AU - Pugh, Rebecca

AU - Qiu, Yunping

AU - Quehenberger, Oswald

AU - Ramrup, Parsram

AU - Rees, Jon

AU - Rembiesa, Barbara

AU - Reynaud, Denis

AU - Roth, Mary R.

AU - Sales, Susanne

AU - Schuhmann, Kai

AU - Schwartzman, Michal Laniado

AU - Serhan, Charles N.

AU - Shevchenko, Andrej

AU - Somerville, Stephen E.

AU - St John-Williams, Lisa

AU - Surma, Michal A.

AU - Takeda, Hiroaki

AU - Thakare, Rhishikesh

AU - Thompson, J. Will

AU - Torta, Federico

AU - Triebl, Alexander

AU - Trötzmüller, Martin

AU - Ubhayasekera, S. J.Kumari

AU - Vuckovic, Dajana

AU - Weir, Jacquelyn M.

AU - Welti, Ruth

AU - Wenk, Markus R.

AU - Wheelock, Craig E.

AU - Yao, Libin

AU - Yuan, Min

AU - Zhao, Xueqing Heather

AU - Zhou, Senlin

N1 - Funding Information: 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 [including Lipid Metabolites and Pathways Strategy (LIPID MAPS)] U54 GM069338, R01 GM20501-41, and P30 DK064391 (E.A.D., O.Q.); the Research Center for Transomics Medicine, Kyushu University, supported by grants from ALCA, AMED-CREST, and Japan Science and Technology Agency (JPMJCR1395) (T.B., Y.I., H.T.); Natural Sciences and Engineering Research Council of Canada (C.M., P.R., D.V.); Swedish Research Council Grant 2015-4870 (J.Be.); National Center for Advancing Translational Sciences Grant UL1 TR000040 (Hon.J., R.N., S.C.); Foundation for the National Institutes of Health Grants 5P01CA120964 and 5P30CA006516 (J.M.A., S.B.B., M.Y.); National Institute of General Medical Sciences Grant PO1 GM095467 (R.A.C., C.N.S.); National Center for Research Resources Grant S10RR027926 (K.R.M., S.Z.); National Institutes of Health Grants P20 HL113452 and U24 DK097154 (T.C., O.F.); Southeast Center for Integrated Metabolomics (SECIM) via National Institutes of Health Grant U24 DK097209 (R.Pa., D.M., T.J.G., J.P.K.); Austrian Science Fund (FWF) Grant P26148-N19 (H.C.K., M.T., A.T.); the Southampton Centre for Biomedical Research (SCBR)/NIHR Southampton Respiratory Biomedical Research Unit (G.K., A.D.P., J.Br.); 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) (J.H., K.L., C.H.B.); Foundation for the National Institutes of Health Grant P01 HL034300 (Mass Spectrometry Core) (M.L.S., K.H.G., Hou.J.); Canadian Institutes of Health Research Grant FDN143309 and Canadian Foundation for Innovation Grant CFI 12156 (M.P., M.L., D.R.); the Kansas Lipidomics Research Center (KLRC), supported by National Science Foundation Grants (MCB 1413036, MCB 0920663, DBI 0521587, and DBI 1228622), Kansas INBRE (Foundation for the National Institutes of Health 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 (R.W., M.R.R., L.Y.); Swedish Heart-Lung Foundation Grants HLF 20140469 and HLF 20150640 (C.E.W., J.K., A.F., A.C.); the Stable Isotope and Metabolomics Core Facility of the Diabetes Research and Training Center (DRTC) of the Albert Einstein College of Medicine, supported by National Institutes of Health/NCI Grant P60DK020541 (X.H.Z., Y.Q., and I.J.K.); Biomarkers Core Laboratory, Irving Institute for Clinical and Translational Research, and Columbia University Medical Center acknowledge support from the National Center for Advancing Translational Sciences, National Institutes of Health Grant UL1 TL001873); the National University of Singapore through Life Sciences Institute and the Yong Loo Lin School of Medicine Department of Biochemistry (A.C-G., F.T., M.R.W.) and the Department of Biological Sciences (A.H.N.); and Singapore National Research Foundation Grant NRFI2015-05 (M.R.W.). Additional support was provided by the Leading Edge Endowment Fund (University of Victoria), the Segal McGill Chair in Molecular Oncology at McGill University (Montreal, Quebec, Canada), the Warren Y. Soper Charitable Trust, and the Alvin Segal Family Foundation to the Jewish General Hospital (Montreal, Quebec, Canada) (C.H.B). Certain commercial equipment, instruments, or materials are identified in this paper to adequately specify 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 and does not necessarily represent the official views of the National Institute of Standards and Technology, the National Institutes of Health, or any of the participating organizations. Manuscript received 8 July 2017 and in revised form 2 October 2017. Published, JLR Papers in Press, October 6, 2017 DOI https://doi.org/10.1194/jlr.M079012 Publisher Copyright: Copyright © 2017 by the American Society for Biochemistry and Molecular Biology, Inc.

PY - 2017

Y1 - 2017

N2 - 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 laboratory using a different lipidomics workflow. A total of 1,527 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 interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. 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.

AB - 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 laboratory using a different lipidomics workflow. A total of 1,527 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 interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. 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.

UR - http://www.scopus.com/inward/record.url?scp=85037028318&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85037028318&partnerID=8YFLogxK

U2 - 10.1194/jlr.M079012

DO - 10.1194/jlr.M079012

M3 - Article

C2 - 28986437

AN - SCOPUS:85037028318

SN - 0022-2275

VL - 58

SP - 2275

EP - 2288

JO - Journal of Lipid Research

JF - Journal of Lipid Research

IS - 12

ER -

Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in frozen human plasma

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