Britta Spanier1, Anne Laurençon2, Anna Weiser1, Nathalie Pujol3, Shizue Omi3, Aiko Barsch4, Ansgar Korf4, Sven W Meyer4, Jonathan J Ewbank3, Francesca Paladino5, Steve Garvis5, Hugo Aguilaniu2,6, Michael Witting7,8,9. 1. Chair of Metabolic Programming, Technische Universität München, Gregor-Mendel-Straße 2, 85354, Freising, Germany. 2. UMR5242, Ecole Normale Supérieure de Lyon, Centre National de la Recherche Scientifique, Université de Lyon, Lyon, France. 3. Turing Center for Living Systems, Aix Marseille Univ, CNRS, INSERM, CIML, Marseille, France. 4. Bruker Daltonics, Fahrenheitstr. 4, 28359, Bremen, Germany. 5. Laboratoire de Biologie Moléculaire de la Cellule UMR5239 CNRS/ENS Lyon/UCBL/HCL Ecole Normale Supérieure de Lyon 46, allée d'Italie, 69364, Lyon cedex 07, France. 6. Instituto Serrapilheira, Rua Dias Ferreira 78, Leblon, Rio de Janeiro, Brazil. 7. Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany. michael.witting@helmholtz-muenchen.de. 8. Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany. michael.witting@helmholtz-muenchen.de. 9. Chair of Analytical Food Chemistry, Technische Universität München, Alte Akademie 10, 85354, Freising-Weihenstephan, Germany. michael.witting@helmholtz-muenchen.de.
Abstract
INTRODUCTION: Lipidomic profiling allows 100s if not 1000s of lipids in a sample to be detected and quantified. Modern lipidomics techniques are ultra-sensitive assays that enable the discovery of novel biomarkers in a variety of fields and provide new insight in mechanistic investigations. Despite much progress in lipidomics, there remains, as for all high throughput "omics" strategies, the need to develop strategies to standardize and integrate quality control into studies in order to enhance robustness, reproducibility, and usability of studies within specific fields and beyond. OBJECTIVES: We aimed to understand how much results from lipid profiling in the model organism Caenorhabditis elegans are influenced by different culture conditions in different laboratories. METHODS: In this work we have undertaken an inter-laboratory study, comparing the lipid profiles of N2 wild type C. elegans and daf-2(e1370) mutants lacking a functional insulin receptor. Sample were collected from worms grown in four separate laboratories under standardized growth conditions. We used an UPLC-UHR-ToF-MS system allowing chromatographic separation before MS analysis. RESULTS: We found common qualitative changes in several marker lipids in samples from the individual laboratories. On the other hand, even in this controlled experimental system, the exact fold-changes for each marker varied between laboratories. CONCLUSION: Our results thus reveal a serious limitation to the reproducibility of current lipid profiling experiments and reveal challenges to the integration of such data from different laboratories.
INTRODUCTION: Lipidomic profiling allows 100s if not 1000s of lipids in a sample to be detected and quantified. Modern lipidomics techniques are ultra-sensitive assays that enable the discovery of novel biomarkers in a variety of fields and provide new insight in mechanistic investigations. Despite much progress in lipidomics, there remains, as for all high throughput "omics" strategies, the need to develop strategies to standardize and integrate quality control into studies in order to enhance robustness, reproducibility, and usability of studies within specific fields and beyond. OBJECTIVES: We aimed to understand how much results from lipid profiling in the model organism Caenorhabditis elegans are influenced by different culture conditions in different laboratories. METHODS: In this work we have undertaken an inter-laboratory study, comparing the lipid profiles of N2 wild type C. elegans and daf-2(e1370) mutants lacking a functional insulin receptor. Sample were collected from worms grown in four separate laboratories under standardized growth conditions. We used an UPLC-UHR-ToF-MS system allowing chromatographic separation before MS analysis. RESULTS: We found common qualitative changes in several marker lipids in samples from the individual laboratories. On the other hand, even in this controlled experimental system, the exact fold-changes for each marker varied between laboratories. CONCLUSION: Our results thus reveal a serious limitation to the reproducibility of current lipid profiling experiments and reveal challenges to the integration of such data from different laboratories.
Authors: Bo Burla; Makoto Arita; Masanori Arita; Anne K Bendt; Amaury Cazenave-Gassiot; Edward A Dennis; Kim Ekroos; Xianlin Han; Kazutaka Ikeda; Gerhard Liebisch; Michelle K Lin; Tze Ping Loh; Peter J Meikle; Matej Orešič; Oswald Quehenberger; Andrej Shevchenko; Federico Torta; Michael J O Wakelam; Craig E Wheelock; Markus R Wenk Journal: J Lipid Res Date: 2018-08-16 Impact factor: 5.922
Authors: Alexander Triebl; Bo Burla; Jayashree Selvalatchmanan; Jeongah Oh; Sock Hwee Tan; Mark Y Chan; Natalie A Mellet; Peter J Meikle; Federico Torta; Markus R Wenk Journal: J Lipid Res Date: 2019-11-15 Impact factor: 5.922
Authors: 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 Journal: J Lipid Res Date: 2017-10-06 Impact factor: 5.922
Authors: Mai-Britt Mosbech; Rikke Kruse; Eva Bang Harvald; Anne Sofie Braun Olsen; Sandra Fernandez Gallego; Hans Kristian Hannibal-Bach; Christer S Ejsing; Nils J Færgeman Journal: PLoS One Date: 2013-07-19 Impact factor: 3.240