logo

OR SEARCH CITATIONS

My Activity
Publications
CONTENT TYPES

Figure 1Loading Img
Download Hi-Res ImageDownload to MS-PowerPointCite This:Anal. Chem. 2009, 81, 19, 7974-7980
RETURN TO ISSUEPREVArticleNEXT

Compensation for Systematic Cross-Contribution Improves Normalization of Mass Spectrometry Based Metabolomics Data

View Author Information
RIKEN Plant Science Center, Tsurumi-ku, Suehiro-cho, 1-7-22 Yokohama, Kanagawa, 230-0045, Japan, and Umeå Plant Science Center, Umeå University, 901 87 Umeå, Sweden
* To whom correspondence should be addressed. E-mail: [email protected]
†RIKEN Plant Science Center.
‡Umeå Plant Science Center (UPSC).
Cite this: Anal. Chem. 2009, 81, 19, 7974–7980
Publication Date (Web):September 10, 2009
https://doi.org/10.1021/ac901143w
Copyright © 2009 American Chemical Society
RIGHTS & PERMISSIONS
Article Views
1886
Altmetric
-
Citations
101
LEARN ABOUT THESE METRICS

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

Read OnlinePDF (1016 KB)
Get e-Alerts
Supporting Info (1)»
SUBJECTS:

Abstract

Most mass spectrometry based metabolomics studies are semiquantitative and depend on efficient normalization techniques to suppress systematic error. A common approach is to include isotope-labeled internal standards (ISs) and then express the estimated metabolite abundances relative to the IS. Because of problems such as insufficient chromatographic resolution, however, the analytes may directly influence estimates of the IS, a phenomenon known as cross-contribution (CC). Normalization using ISs that suffer from CC effects will cause significant loss of information if the interfering analytes are associated with the studied factors. We present a novel normalization algorithm, which compensates for systematic CC effects that can be traced back to a linear association with the experimental design. The proposed method was found to be superior at purifying the signal of interest compared to current normalization methods when applied to two biological data sets and a multicomponent dilution mixture. Our method is applicable to data from randomized and designed experiments that use ISs to monitor the systematic error.

Supporting Information

ARTICLE SECTIONS
Jump To

Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

Terms & Conditions

Electronic Supporting Information files are available without a subscription to ACS Web Editions. The American Chemical Society holds a copyright ownership interest in any copyrightable Supporting Information. Files available from the ACS website may be downloaded for personal use only. Users are not otherwise permitted to reproduce, republish, redistribute, or sell any Supporting Information from the ACS website, either in whole or in part, in either machine-readable form or any other form without permission from the American Chemical Society. For permission to reproduce, republish and redistribute this material, requesters must process their own requests via the RightsLink permission system. Information about how to use the RightsLink permission system can be found at http://pubs.acs.org/page/copyright/permissions.html.

Cited By

This article is cited by 101 publications.

  1. Nicolas Di Giovanni, Marie-Alice Meuwis, Edouard Louis, Jean-François Focant. Untargeted Serum Metabolic Profiling by Comprehensive Two-Dimensional Gas Chromatography–High-Resolution Time-of-Flight Mass Spectrometry. Journal of Proteome Research 2020, 19 (3) , 1013-1028. https://doi.org/10.1021/acs.jproteome.9b00535
  2. Sakda Khoomrung, Intawat Nookaew, Partho Sen, Thorunn A. Olafsdottir, Josefine Persson, Thomas Moritz, Peter Andersen, Ali M. Harandi, Jens Nielsen. Metabolic Profiling and Compound-Class Identification Reveal Alterations in Serum Triglyceride Levels in Mice Immunized with Human Vaccine Adjuvant Alum. Journal of Proteome Research 2020, 19 (1) , 269-278. https://doi.org/10.1021/acs.jproteome.9b00517
  3. Bernhard Drotleff, Michael Lämmerhofer. Guidelines for Selection of Internal Standard-Based Normalization Strategies in Untargeted Lipidomic Profiling by LC-HR-MS/MS. Analytical Chemistry 2019, 91 (15) , 9836-9843. https://doi.org/10.1021/acs.analchem.9b01505
  4. Sili Fan, Tobias Kind, Tomas Cajka, Stanley L. Hazen, W. H. Wilson Tang, Rima Kaddurah-Daouk, Marguerite R. Irvin, Donna K. Arnett, Dinesh K. Barupal, Oliver Fiehn. Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data. Analytical Chemistry 2019, 91 (5) , 3590-3596. https://doi.org/10.1021/acs.analchem.8b05592
  5. Jiaqing Chen, Pei Zhang, Mengying Lv, Huimin Guo, Yin Huang, Zunjian Zhang, and Fengguo Xu . Influences of Normalization Method on Biomarker Discovery in Gas Chromatography–Mass Spectrometry-Based Untargeted Metabolomics: What Should Be Considered?. Analytical Chemistry 2017, 89 (10) , 5342-5348. https://doi.org/10.1021/acs.analchem.6b05152
  6. Alysha M. De Livera, Marko Sysi-Aho, Laurent Jacob, Johann A. Gagnon-Bartsch, Sandra Castillo, Julie A. Simpson, and Terence P. Speed . Statistical Methods for Handling Unwanted Variation in Metabolomics Data. Analytical Chemistry 2015, 87 (7) , 3606-3615. https://doi.org/10.1021/ac502439y
  7. San-Yuan Wang, Ching-Hua Kuo, and Yufeng J. Tseng . Batch Normalizer: A Fast Total Abundance Regression Calibration Method to Simultaneously Adjust Batch and Injection Order Effects in Liquid Chromatography/Time-of-Flight Mass Spectrometry-Based Metabolomics Data and Comparison with Current Calibration Methods. Analytical Chemistry 2013, 85 (2) , 1037-1046. https://doi.org/10.1021/ac302877x
  8. Alysha M. De Livera, Daniel A. Dias, David De Souza, Thusitha Rupasinghe, James Pyke, Dedreia Tull, Ute Roessner, Malcolm McConville, and Terence P. Speed . Normalizing and Integrating Metabolomics Data. Analytical Chemistry 2012, 84 (24) , 10768-10776. https://doi.org/10.1021/ac302748b
  9. Henning Redestig, Makoto Kobayashi, Kazuki Saito, and Miyako Kusano . Exploring Matrix Effects and Quantification Performance in Metabolomics Experiments Using Artificial Biological Gradients. Analytical Chemistry 2011, 83 (14) , 5645-5651. https://doi.org/10.1021/ac200786y
  10. Rency S. Varghese, Amrita Cheema, Prabhdeep Cheema, Marc Bourbeau, Leepika Tuli, Bin Zhou, Mira Jung, Anatoly Dritschilo and Habtom W. Ressom . Analysis of LC−MS Data for Characterizing the Metabolic Changes in Response to Radiation. Journal of Proteome Research 2010, 9 (5) , 2786-2793. https://doi.org/10.1021/pr100185b
  11. Natasa P. Kalogiouri, Reza Aalizadeh, Marilena E. Dasenaki, Nikolaos S. Thomaidis. Application of High Resolution Mass Spectrometric methods coupled with chemometric techniques in olive oil authenticity studies - A review. Analytica Chimica Acta 2020, 1134 , 150-173. https://doi.org/10.1016/j.aca.2020.07.029
  12. Yoshinori Utsumi, Maho Tanaka, Chikako Utsumi, Satoshi Takahashi, Akihiro Matsui, Atsushi Fukushima, Makoto Kobayashi, Ryosuke Sasaki, Akira Oikawa, Miyako Kusano, Kazuki Saito, Mikiko Kojima, Hitoshi Sakakibara, Punchapat Sojikul, Jarunya Narangajavana, Motoaki Seki. Integrative omics approaches revealed a crosstalk among phytohormones during tuberous root development in cassava. Plant Molecular Biology 2020, 36 https://doi.org/10.1007/s11103-020-01033-8
  13. Kelly E. Mercer, Gabriella A. M. Ten Have, Lindsay Pack, Renny Lan, Nicolaas E. P. Deutz, Sean H. Adams, Brian D. Piccolo. Net release and uptake of xenometabolites across intestinal, hepatic, muscle, and renal tissue beds in healthy conscious pigs. American Journal of Physiology-Gastrointestinal and Liver Physiology 2020, 319 (2) , G133-G141. https://doi.org/10.1152/ajpgi.00153.2020
  14. Kyonoshin Maruyama, Kaoru Urano, Miyako Kusano, Tetsuya Sakurai, Hironori Takasaki, Miho Kishimoto, Kyouko Yoshiwara, Makoto Kobayashi, Mikiko Kojima, Hitoshi Sakakibara, Kazuki Saito, Kazuo Shinozaki. Metabolite/phytohormone–gene regulatory networks in soybean organs under dehydration conditions revealed by integration analysis. The Plant Journal 2020, 103 (1) , 197-211. https://doi.org/10.1111/tpj.14719
  15. Lauren R Brink, Kelly E Mercer, Brian D Piccolo, Sree V Chintapalli, Ahmed Elolimy, Anne K Bowlin, Katelin S Matazel, Lindsay Pack, Sean H Adams, Kartik Shankar, Thomas M Badger, Aline Andres, Laxmi Yeruva. Neonatal diet alters fecal microbiota and metabolome profiles at different ages in infants fed breast milk or formula. The American Journal of Clinical Nutrition 2020, 111 (6) , 1190-1202. https://doi.org/10.1093/ajcn/nqaa076
  16. Zhiqiang Pang, Jasmine Chong, Shuzhao Li, Jianguo Xia. MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics. Metabolites 2020, 10 (5) , 186. https://doi.org/10.3390/metabo10050186
  17. Thomas Züllig, Martin Trötzmüller, Harald C. Köfeler. Lipidomics from sample preparation to data analysis: a primer. Analytical and Bioanalytical Chemistry 2020, 412 (10) , 2191-2209. https://doi.org/10.1007/s00216-019-02241-y
  18. Bernhard Drotleff, Simon R. Roth, Kerstin Henkel, Carlos Calderón, Jörg Schlotterbeck, Merja A. Neukamm, Michael Lämmerhofer. Lipidomic profiling of non-mineralized dental plaque and biofilm by untargeted UHPLC-QTOF-MS/MS and SWATH acquisition. Analytical and Bioanalytical Chemistry 2020, 412 (10) , 2303-2314. https://doi.org/10.1007/s00216-019-02364-2
  19. Vadim D. Nikitushkin, Sandra Trenkamp, Galina R. Demina, Margarita O. Shleeva, Arseny S. Kaprelyants. Metabolic profiling of dormant Mycolicibacterium smegmatis cells’ reactivation reveals a gradual assembly of metabolic processes. Metabolomics 2020, 16 (2) https://doi.org/10.1007/s11306-020-1645-8
  20. Atsushi Fukushima, Takeshi Kuroha, Keisuke Nagai, Yoko Hattori, Makoto Kobayashi, Tomoko Nishizawa, Mikiko Kojima, Yoshinori Utsumi, Akira Oikawa, Motoaki Seki, Hitoshi Sakakibara, Kazuki Saito, Motoyuki Ashikari, Miyako Kusano. Metabolite and Phytohormone Profiling Illustrates Metabolic Reprogramming as an Escape Strategy of Deepwater Rice during Partially Submerged Stress. Metabolites 2020, 10 (2) , 68. https://doi.org/10.3390/metabo10020068
  21. Joseph Diab, Terkel Hansen, Rasmus Goll, Hans Stenlund, Einar Jensen, Thomas Moritz, Jon Florholmen, Guro Forsdahl. Mucosal Metabolomic Profiling and Pathway Analysis Reveal the Metabolic Signature of Ulcerative Colitis. Metabolites 2019, 9 (12) , 291. https://doi.org/10.3390/metabo9120291
  22. Saad Sulieman, Miyako Kusano, Chien Van Ha, Yasuko Watanabe, Muna Ali Abdalla, Mostafa Abdelrahman, Makoto Kobayashi, Kazuki Saito, Karl H. Mühling, Lam-Son Phan Tran. Divergent metabolic adjustments in nodules are indispensable for efficient N2 fixation of soybean under phosphate stress. Plant Science 2019, 289 , 110249. https://doi.org/10.1016/j.plantsci.2019.110249
  23. Qingxia Yang, Jiajun Hong, Yi Li, Weiwei Xue, Song Li, Hui Yang, Feng Zhu. A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies. Briefings in Bioinformatics 2019, 45 https://doi.org/10.1093/bib/bbz137
  24. Qingnan Chu, Zhimin Sha, Hayato Maruyama, Linzhang Yang, Gang Pan, Lihong Xue, Toshihiro Watanabe. Metabolic reprogramming in nodules, roots, and leaves of symbiotic soybean in response to iron deficiency. Plant, Cell & Environment 2019, 42 (11) , 3027-3043. https://doi.org/10.1111/pce.13608
  25. Juan Rodríguez-Coira, María Delgado-Dolset, David Obeso, Mariana Dolores-Hernández, Guillermo Quintás, Santiago Angulo, Domingo Barber, Teresa Carrillo, María Escribese, Alma Villaseñor. Troubleshooting in Large-Scale LC-ToF-MS Metabolomics Analysis: Solving Complex Issues in Big Cohorts. Metabolites 2019, 9 (11) , 247. https://doi.org/10.3390/metabo9110247
  26. Jan Stanstrup, Corey Broeckling, Rick Helmus, Nils Hoffmann, Ewy Mathé, Thomas Naake, Luca Nicolotti, Kristian Peters, Johannes Rainer, Reza Salek, Tobias Schulze, Emma Schymanski, Michael Stravs, Etienne Thévenot, Hendrik Treutler, Ralf Weber, Egon Willighagen, Michael Witting, Steffen Neumann. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019, 9 (10) , 200. https://doi.org/10.3390/metabo9100200
  27. Alehagen, Johansson, Aaseth, Alexander, Surowiec, Lundstedt-Enkel, Lundstedt. Significant Changes in Metabolic Profiles after Intervention with Selenium and Coenzyme Q10 in an Elderly Population. Biomolecules 2019, 9 (10) , 553. https://doi.org/10.3390/biom9100553
  28. Jing Ma, Alla Karnovsky, Farsad Afshinnia, Janis Wigginton, Daniel J Rader, Loki Natarajan, Kumar Sharma, Anna C Porter, Mahboob Rahman, Jiang He, Lee Hamm, Tariq Shafi, Debbie Gipson, Crystal Gadegbeku, Harold Feldman, George Michailidis, Subramaniam Pennathur, . Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease. Bioinformatics 2019, 35 (18) , 3441-3452. https://doi.org/10.1093/bioinformatics/btz114
  29. Izabella Surowiec, Tomas Skotare, Rickard Sjögren, Sandra Gouveia-Figueira, Judy Orikiiriza, Sven Bergström, Johan Normark, Johan Trygg. Joint and unique multiblock analysis of biological data – multiomics malaria study. Faraday Discussions 2019, 218 , 268-283. https://doi.org/10.1039/C8FD00243F
  30. Kui Deng, Fan Zhang, Qilong Tan, Yue Huang, Wei Song, Zhiwei Rong, Zheng-Jiang Zhu, Kang Li, Zhenzi Li. WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis. Analytica Chimica Acta 2019, 1061 , 60-69. https://doi.org/10.1016/j.aca.2019.02.010
  31. Izabella Surowiec, Raymond Noordam, Kate Bennett, Marian Beekman, P. Eline Slagboom, Torbjörn Lundstedt, Diana van Heemst. Metabolomic and lipidomic assessment of the metabolic syndrome in Dutch middle-aged individuals reveals novel biological signatures separating health and disease. Metabolomics 2019, 15 (2) https://doi.org/10.1007/s11306-019-1484-7
  32. Christophe Junot, François Fenaille. Metabolomics using Fourier transform mass spectrometry. 2019,,, 325-356. https://doi.org/10.1016/B978-0-12-814013-0.00011-9
  33. Dominik Reinhold, Harrison Pielke-Lombardo, Sean Jacobson, Debashis Ghosh, Katerina Kechris. Pre-analytic Considerations for Mass Spectrometry-Based Untargeted Metabolomics Data. 2019,,, 323-340. https://doi.org/10.1007/978-1-4939-9236-2_20
  34. Yang Gao, Yanhua Chen, Xiaofei Yue, Jiuming He, Ruiping Zhang, Jing Xu, Zhi Zhou, Zhonghua Wang, Rui Zhang, Zeper Abliz. Development of simultaneous targeted metabolite quantification and untargeted metabolomics strategy using dual-column liquid chromatography coupled with tandem mass spectrometry. Analytica Chimica Acta 2018, 1037 , 369-379. https://doi.org/10.1016/j.aca.2018.08.042
  35. Eirini Baira, Ioanna Dagla, Eleni Siapi, Panagiotis Zoumpoulakis, Panagiotis Simitzis, Michael Goliomytis, Stelios G. Deligeorgis, Alexios-Leandros Skaltsounis, Evagelos Gikas. UHPLC–HRMS-based tissue untargeted metabolomics study of naringin and hesperidin after dietary supplementation in chickens. Food Chemistry 2018, 269 , 276-285. https://doi.org/10.1016/j.foodchem.2018.06.146
  36. Charmion I. Cruickshank-Quinn, Sean Jacobson, Grant Hughes, Roger L. Powell, Irina Petrache, Katerina Kechris, Russell Bowler, Nichole Reisdorph. Metabolomics and transcriptomics pathway approach reveals outcome-specific perturbations in COPD. Scientific Reports 2018, 8 (1) https://doi.org/10.1038/s41598-018-35372-w
  37. Atsushi Fukushima, Shoko Hikosaka, Makoto Kobayashi, Tomoko Nishizawa, Kazuki Saito, Eiji Goto, Miyako Kusano. A Systems Analysis With “Simplified Source-Sink Model” Reveals Metabolic Reprogramming in a Pair of Source-to-Sink Organs During Early Fruit Development in Tomato by LED Light Treatments. Frontiers in Plant Science 2018, 9 https://doi.org/10.3389/fpls.2018.01439
  38. Akira Iwase, Kento Mita, David S. Favero, Nobutaka Mitsuda, Ryosuke Sasaki, Makoto Kobayshi, Yumiko Takebayashi, Mikiko Kojima, Miyako Kusano, Akira Oikawa, Hitoshi Sakakibara, Kazuki Saito, Jun Imamura, Keiko Sugimoto. WIND1 induces dynamic metabolomic reprogramming during regeneration in Brassica napus. Developmental Biology 2018, 442 (1) , 40-52. https://doi.org/10.1016/j.ydbio.2018.07.006
  39. Izabella Surowiec, Erik Johansson, Hans Stenlund, Solbritt Rantapää-Dahlqvist, Sven Bergström, Johan Normark, Johan Trygg. Quantification of run order effect on chromatography - mass spectrometry profiling data. Journal of Chromatography A 2018, 1568 , 229-234. https://doi.org/10.1016/j.chroma.2018.07.019
  40. Yoshio Tamura, Tetsuya Mori, Ryo Nakabayashi, Makoto Kobayashi, Kazuki Saito, Seiichi Okazaki, Ning Wang, Miyako Kusano. Metabolomic Evaluation of the Quality of Leaf Lettuce Grown in Practical Plant Factory to Capture Metabolite Signature. Frontiers in Plant Science 2018, 9 https://doi.org/10.3389/fpls.2018.00665
  41. Daniel Stoessel, Jan-Patrick Stellmann, Anne Willing, Birte Behrens, Sina C. Rosenkranz, Sibylle C. Hodecker, Klarissa H. Stürner, Stefanie Reinhardt, Sabine Fleischer, Christian Deuschle, Walter Maetzler, Daniela Berg, Christoph Heesen, Dirk Walther, Nicolas Schauer, Manuel A. Friese, Ole Pless. Metabolomic Profiles for Primary Progressive Multiple Sclerosis Stratification and Disease Course Monitoring. Frontiers in Human Neuroscience 2018, 12 https://doi.org/10.3389/fnhum.2018.00226
  42. Alysha M. De Livera, Gavriel Olshansky, Julie A. Simpson, Darren J. Creek. NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data. Metabolomics 2018, 14 (5) https://doi.org/10.1007/s11306-018-1347-7
  43. Humberto Mitio Horikoshi, Yoshihiko Sekozawa, Makoto Kobayashi, Kazuki Saito, Miyako Kusano, Sumiko Sugaya. Metabolomics analysis of 'Housui' Japanese pear flower buds during endodormancy reveals metabolic suppression by thermal fluctuation. Plant Physiology and Biochemistry 2018, 126 , 134-141. https://doi.org/10.1016/j.plaphy.2018.02.028
  44. Henning Redestig, Jedrzej Szymanski, Masami Y. Hirai, Joachim Selbig, Lothar Willmitzer, Zoran Nikoloski, Kazuki Saito. Data Integration, Metabolic Networks and Systems Biology. 2018,,, 261-316. https://doi.org/10.1002/9781119312994.apr0469
  45. Yasunori Ichihashi, Miyako Kusano, Makoto Kobayashi, Kenji Suetsugu, Satoko Yoshida, Takanori Wakatake, Kie Kumaishi, Arisa Shibata, Kazuki Saito, Ken Shirasu. Transcriptomic and Metabolomic Reprogramming from Roots to Haustoria in the Parasitic Plant, Thesium chinense. Plant and Cell Physiology 2018, 59 (4) , 729-738. https://doi.org/10.1093/pcp/pcx200
  46. Daniel Stoessel, Claudia Schulte, Marcia C. Teixeira dos Santos, Dieter Scheller, Irene Rebollo-Mesa, Christian Deuschle, Dirk Walther, Nicolas Schauer, Daniela Berg, Andre Nogueira da Costa, Walter Maetzler. Promising Metabolite Profiles in the Plasma and CSF of Early Clinical Parkinson's Disease. Frontiers in Aging Neuroscience 2018, 10 https://doi.org/10.3389/fnagi.2018.00051
  47. Ioanna Dagla, Dimitra Benaki, Eirini Baira, Nikolaos Lemonakis, Hemant Poudyal, Lindsay Brown, Anthony Tsarbopoulos, Alexios-Leandros Skaltsounis, Emmanouel Mikros, Evagelos Gikas. Alteration in the liver metabolome of rats with metabolic syndrome after treatment with Hydroxytyrosol. A Mass Spectrometry And Nuclear Magnetic Resonance - based metabolomics study. Talanta 2018, 178 , 246-257. https://doi.org/10.1016/j.talanta.2017.09.029
  48. Kelli M. Sas, Jiahe Lin, Thekkelnaycke M. Rajendiran, Tanu Soni, Viji Nair, Lucy M. Hinder, Hosagrahar V. Jagadish, Thomas W. Gardner, Steven F. Abcouwer, Frank C. Brosius, Eva L. Feldman, Matthias Kretzler, George Michailidis, Subramaniam Pennathur. Shared and distinct lipid-lipid interactions in plasma and affected tissues in a diabetic mouse model. Journal of Lipid Research 2018, 59 (2) , 173-183. https://doi.org/10.1194/jlr.M077222
  49. Ibrahim Karaman, Rui Climaco Pinto, Gonçalo Graça. Metabolomics Data Preprocessing: From Raw Data to Features for Statistical Analysis. 2018,,, 197-225. https://doi.org/10.1016/bs.coac.2018.08.003
  50. Tomokazu Ushijima, Kousuke Hanada, Eiji Gotoh, Wataru Yamori, Yutaka Kodama, Hiroyuki Tanaka, Miyako Kusano, Atsushi Fukushima, Mutsutomo Tokizawa, Yoshiharu Y. Yamamoto, Yasuomi Tada, Yutaka Suzuki, Tomonao Matsushita. Light Controls Protein Localization through Phytochrome-Mediated Alternative Promoter Selection. Cell 2017, 171 (6) , 1316-1325.e12. https://doi.org/10.1016/j.cell.2017.10.018
  51. R. E. Patterson, A. S. Kirpich, J. P. Koelmel, S. Kalavalapalli, A. M. Morse, K. Cusi, N. E. Sunny, L. M. McIntyre, T. J. Garrett, R. A. Yost. Improved experimental data processing for UHPLC–HRMS/MS lipidomics applied to nonalcoholic fatty liver disease. Metabolomics 2017, 13 (11) https://doi.org/10.1007/s11306-017-1280-1
  52. Izabella Surowiec, Erik Johansson, Frida Torell, Helena Idborg, Iva Gunnarsson, Elisabet Svenungsson, Per-Johan Jakobsson, Johan Trygg. Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics. Metabolomics 2017, 13 (10) https://doi.org/10.1007/s11306-017-1248-1
  53. Rachel Spicer, Reza M. Salek, Pablo Moreno, Daniel Cañueto, Christoph Steinbeck. Navigating freely-available software tools for metabolomics analysis. Metabolomics 2017, 13 (9) https://doi.org/10.1007/s11306-017-1242-7
  54. Sakda Khoomrung, Kwanjeera Wanichthanarak, Intawat Nookaew, Onusa Thamsermsang, Patcharamon Seubnooch, Tawee Laohapand, Pravit Akarasereenont. Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine. Frontiers in Pharmacology 2017, 8 https://doi.org/10.3389/fphar.2017.00474
  55. Bo Li, Jing Tang, Qingxia Yang, Shuang Li, Xuejiao Cui, Yinghong Li, Yuzong Chen, Weiwei Xue, Xiaofeng Li, Feng Zhu. NOREVA: normalization and evaluation of MS-based metabolomics data. Nucleic Acids Research 2017, 45 (W1) , W162-W170. https://doi.org/10.1093/nar/gkx449
  56. Jong-Myong Kim, Taiko Kim To, Akihiro Matsui, Keitaro Tanoi, Natsuko I. Kobayashi, Fumio Matsuda, Yoshiki Habu, Daisuke Ogawa, Takuya Sakamoto, Sachihiro Matsunaga, Khurram Bashir, Sultana Rasheed, Marina Ando, Hiroko Takeda, Kanako Kawaura, Miyako Kusano, Atsushi Fukushima, Takaho A. Endo, Takashi Kuromori, Junko Ishida, Taeko Morosawa, Maho Tanaka, Chieko Torii, Yumiko Takebayashi, Hitoshi Sakakibara, Yasunari Ogihara, Kazuki Saito, Kazuo Shinozaki, Alessandra Devoto, Motoaki Seki. Acetate-mediated novel survival strategy against drought in plants. Nature Plants 2017, 3 (7) https://doi.org/10.1038/nplants.2017.97
  57. Judy Orikiiriza, Izabella Surowiec, Elisabeth Lindquist, Mari Bonde, Jimmy Magambo, Charles Muhinda, Sven Bergström, Johan Trygg, Johan Normark. Lipid response patterns in acute phase paediatric Plasmodium falciparum malaria. Metabolomics 2017, 13 (4) https://doi.org/10.1007/s11306-017-1174-2
  58. Eri Adams, Takae Miyazaki, Aya Hayaishi-Satoh, Minwoo Han, Miyako Kusano, Himanshu Khandelia, Kazuki Saito, Ryoung Shin. A novel role for methyl cysteinate, a cysteine derivative, in cesium accumulation in Arabidopsis thaliana. Scientific Reports 2017, 7 (1) https://doi.org/10.1038/srep43170
  59. Miyako Kusano, Makoto Kobayashi, Yumiko Iizuka, Atsushi Fukushima, Kazuki Saito. Unbiased profiling of volatile organic compounds in the headspace of Allium plants using an in-tube extraction device. BMC Research Notes 2016, 9 (1) https://doi.org/10.1186/s13104-016-1942-5
  60. Farsad Afshinnia, Thekkelnaycke M. Rajendiran, Alla Karnovsky, Tanu Soni, Xue Wang, Dawei Xie, Wei Yang, Tariq Shafi, Matthew R. Weir, Jiang He, Carolyn S. Brecklin, Eugene P. Rhee, Jeffrey R. Schelling, Akinlolu Ojo, Harold Feldman, George Michailidis, Subramaniam Pennathur, Lawrence J. Appel, Alan S. Go, John W. Kusek, James P. Lash, Raymond R. Townsend. Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort. Kidney International Reports 2016, 1 (4) , 256-268. https://doi.org/10.1016/j.ekir.2016.08.007
  61. Maryam Nasr Esfahani, Miyako Kusano, Kien Huu Nguyen, Yasuko Watanabe, Chien Van Ha, Kazuki Saito, Saad Sulieman, Luis Herrera-Estrella, Lam-Son Phan Tran. Adaptation of the symbiotic Mesorhizobium –chickpea relationship to phosphate deficiency relies on reprogramming of whole-plant metabolism. Proceedings of the National Academy of Sciences 2016, 113 (32) , E4610-E4619. https://doi.org/10.1073/pnas.1609440113
  62. Anders A. Bengtsson, Johan Trygg, Dirk M. Wuttge, Gunnar Sturfelt, Elke Theander, Magdalena Donten, Thomas Moritz, Carl-Johan Sennbro, Frida Torell, Christian Lood, Izabella Surowiec, Stefan Rännar, Torbjörn Lundstedt, . Metabolic Profiling of Systemic Lupus Erythematosus and Comparison with Primary Sjögren’s Syndrome and Systemic Sclerosis. PLOS ONE 2016, 11 (7) , e0159384. https://doi.org/10.1371/journal.pone.0159384
  63. Elin Chorell, Mats Ryberg, Christel Larsson, Susanne Sandberg, Caroline Mellberg, Bernt Lindahl, Henrik Antti, Tommy Olsson. Plasma metabolomic response to postmenopausal weight loss induced by different diets. Metabolomics 2016, 12 (5) https://doi.org/10.1007/s11306-016-1013-x
  64. Xiaotao Shen, Xiaoyun Gong, Yuping Cai, Yuan Guo, Jia Tu, Hao Li, Tao Zhang, Jialin Wang, Fuzhong Xue, Zheng-Jiang Zhu. Normalization and integration of large-scale metabolomics data using support vector regression. Metabolomics 2016, 12 (5) https://doi.org/10.1007/s11306-016-1026-5
  65. Pernilla Lindén, Olivier Keech, Hans Stenlund, Per Gardeström, Thomas Moritz. Reduced mitochondrial malate dehydrogenase activity has a strong effect on photorespiratory metabolism as revealed by 13 C labelling. Journal of Experimental Botany 2016, 67 (10) , 3123-3135. https://doi.org/10.1093/jxb/erw030
  66. Lunzhao Yi, Naiping Dong, Yonghuan Yun, Baichuan Deng, Dabing Ren, Shao Liu, Yizeng Liang. Chemometric methods in data processing of mass spectrometry-based metabolomics: A review. Analytica Chimica Acta 2016, 914 , 17-34. https://doi.org/10.1016/j.aca.2016.02.001
  67. Lara R. Dugas, Elin Chorell, Jacob Plange-Rhule, Estelle V. Lambert, Guichan Cao, Richard S. Cooper, Brian T. Layden, Denise Scholten, Tommy Olsson, Amy Luke, Julia H. Goedecke. Obesity-related metabolite profiles of black women spanning the epidemiologic transition. Metabolomics 2016, 12 (3) https://doi.org/10.1007/s11306-016-0960-6
  68. Aili Bao, Zhijun Liang, Zhuqing Zhao, Hongmei Cai. Overexpressing of OsAMT1-3, a High Affinity Ammonium Transporter Gene, Modifies Rice Growth and Carbon-Nitrogen Metabolic Status. International Journal of Molecular Sciences 2015, 16 (12) , 9037-9063. https://doi.org/10.3390/ijms16059037
  69. Aili Bao, Zhuqing Zhao, Guangda Ding, Lei Shi, Fangsen Xu, Hongmei Cai. The Stable Level of Glutamine synthetase 2 Plays an Important Role in Rice Growth and in Carbon-Nitrogen Metabolic Balance. International Journal of Molecular Sciences 2015, 16 (12) , 12713-12736. https://doi.org/10.3390/ijms160612713
  70. Eszter Tóth, Helga Hevér, Olivér Ozohanics, András Telekes, Károly Vékey, László Drahos. Simple correction improving long-term reproducibility of HPLC-MS. Journal of Mass Spectrometry 2015, 50 (10) , 1130-1135. https://doi.org/10.1002/jms.3629
  71. Miyako Kusano, Ivan Baxter, Atsushi Fukushima, Akira Oikawa, Yozo Okazaki, Ryo Nakabayashi, Denise J. Bouvrette, Frederic Achard, Andrew R. Jakubowski, Joan M. Ballam, Jonathan R. Phillips, Angela H. Culler, Kazuki Saito, George G. Harrigan. Assessing metabolomic and chemical diversity of a soybean lineage representing 35 years of breeding. Metabolomics 2015, 11 (2) , 261-270. https://doi.org/10.1007/s11306-014-0702-6
  72. Muhammed Alzweiri, Mohammad Khanfar, Yusuf Al-Hiari. Variations in GC–MS Response Between Analytes and Deuterated Analogs. Chromatographia 2015, 78 (3-4) , 251-258. https://doi.org/10.1007/s10337-014-2842-2
  73. J. Kuligowski, D. Pérez-Guaita, I. Lliso, J. Escobar, Z. León, L. Gombau, R. Solberg, O.D. Saugstad, M. Vento, G. Quintás. Detection of batch effects in liquid chromatography-mass spectrometry metabolomic data using guided principal component analysis. Talanta 2014, 130 , 442-448. https://doi.org/10.1016/j.talanta.2014.07.031
  74. Mingjie Chen, R Shyama Prasad Rao, Yiming Zhang, Cathy Xiaoyan Zhong, Jay J Thelen. A modified data normalization method for GC-MS-based metabolomics to minimize batch variation. SpringerPlus 2014, 3 (1) https://doi.org/10.1186/2193-1801-3-439
  75. Lunzhao Yi, Naiping Dong, Yonghuan Yun, Baichuan Deng, Shao Liu, Yi Zhang, Yizeng Liang. WITHDRAWN: Recent advances in chemometric methods for plant metabolomics: A review. Biotechnology Advances 2014, https://doi.org/10.1016/j.biotechadv.2014.11.008
  76. Alexandra Jauhiainen, Basetti Madhu, Masako Narita, Masashi Narita, John Griffiths, Simon Tavaré. Normalization of metabolomics data with applications to correlation maps. Bioinformatics 2014, 30 (15) , 2155-2161. https://doi.org/10.1093/bioinformatics/btu175
  77. Aili Bao, Zhuqing Zhao, Guangda Ding, Lei Shi, Fangsen Xu, Hongmei Cai, . Accumulated Expression Level of Cytosolic Glutamine Synthetase 1 Gene (OsGS1;1 or OsGS1;2) Alter Plant Development and the Carbon-Nitrogen Metabolic Status in Rice. PLoS ONE 2014, 9 (4) , e95581. https://doi.org/10.1371/journal.pone.0095581
  78. Grant Hughes, Charmion Cruickshank-Quinn, Richard Reisdorph, Sharon Lutz, Irina Petrache, Nichole Reisdorph, Russell Bowler, Katerina Kechris. MSPrep—Summarization, normalization and diagnostics for processing of mass spectrometry–based metabolomic data. Bioinformatics 2014, 30 (1) , 133-134. https://doi.org/10.1093/bioinformatics/btt589
  79. Bedilu Alamirie Ejigu, Dirk Valkenborg, Geert Baggerman, Manu Vanaerschot, Erwin Witters, Jean-Claude Dujardin, Tomasz Burzykowski, Maya Berg. Evaluation of Normalization Methods to Pave the Way Towards Large-Scale LC-MS-Based Metabolomics Profiling Experiments. OMICS: A Journal of Integrative Biology 2013, 17 (9) , 473-485. https://doi.org/10.1089/omi.2013.0010
  80. Jeremie Zander Lindeque, Juan Hidalgo, Roan Louw, Francois Hendrikus van der Westhuizen. Systemic and organ specific metabolic variation in metallothionein knockout mice challenged with swimming exercise. Metabolomics 2013, 9 (2) , 418-432. https://doi.org/10.1007/s11306-012-0459-8
  81. Alysha M. De Livera, Moshe Olshansky, Terence P. Speed. Statistical Analysis of Metabolomics Data. 2013,,, 291-307. https://doi.org/10.1007/978-1-62703-577-4_20
  82. Aurelia Williams, Gerhard Koekemoer, Zander Lindeque, Carolus Reinecke, Debra Meyer. Qualitative serum organic acid profiles of HIV-infected individuals not on antiretroviral treatment. Metabolomics 2012, 8 (5) , 804-818. https://doi.org/10.1007/s11306-011-0376-2
  83. Benoist Lehallier, Jérémy Ratel, Mohamed Hanafi, Erwan Engel. Systematic ratio normalization of gas chromatography signals for biological sample discrimination and biomarker discovery. Analytica Chimica Acta 2012, 733 , 16-22. https://doi.org/10.1016/j.aca.2012.04.019
  84. Rasmus Madsen, Viqar Showkat Banday, Thomas Moritz, Johan Trygg, Kristina Lejon, . Altered Metabolic Signature in Pre-Diabetic NOD Mice. PLoS ONE 2012, 7 (4) , e35445. https://doi.org/10.1371/journal.pone.0035445
  85. Antje Hutschenreuther, Andreas Kiontke, Gerd Birkenmeier, Claudia Birkemeyer. Comparison of extraction conditions and normalization approaches for cellular metabolomics of adherent growing cells with GC-MS. Analytical Methods 2012, 4 (7) , 1953. https://doi.org/10.1039/c2ay25046b
  86. Ali Aboel Dahab, Norman W. Smith. Drug-induced hepatotoxicity: application of mass spectrometry based metabonomics. Analytical Methods 2012, 4 (7) , 1887. https://doi.org/10.1039/c2ay25413a
  87. Joomi LEE, Jeonghyeon PARK, Mi-sun LIM, Sook Jin SEONG, Jeong Ju SEO, Sung Min PARK, Hae Won LEE, Young-Ran YOON. Quantile Normalization Approach for Liquid Chromatography^|^ndash;Mass Spectrometry-based Metabolomic Data from Healthy Human Volunteers. Analytical Sciences 2012, 28 (8) , 801-805. https://doi.org/10.2116/analsci.28.801
  88. Masaro Akai, Kiyoshi Onai, Miyako Kusano, Mayuko Sato, Henning Redestig, Kiminori Toyooka, Megumi Morishita, Hiroshi Miyake, Akihiro Hazama, Vanessa Checchetto, Ildikò Szabò, Ken Matsuoka, Kazuki Saito, Masato Yasui, Masahiro Ishiura, Nobuyuki Uozumi. Plasma Membrane Aquaporin AqpZ Protein Is Essential for Glucose Metabolism during Photomixotrophic Growth of Synechocystis sp. PCC 6803. Journal of Biological Chemistry 2011, 286 (28) , 25224-25235. https://doi.org/10.1074/jbc.M111.236380
  89. Miyako Kusano, Takayuki Tohge, Atsushi Fukushima, Makoto Kobayashi, Naomi Hayashi, Hitomi Otsuki, Youichi Kondou, Hiroto Goto, Mika Kawashima, Fumio Matsuda, Rie Niida, Minami Matsui, Kazuki Saito, Alisdair R. Fernie. Metabolomics reveals comprehensive reprogramming involving two independent metabolic responses of Arabidopsis to UV-B light. The Plant Journal 2011, 67 (2) , 354-369. https://doi.org/10.1111/j.1365-313X.2011.04599.x
  90. Imhoi Koo, Xiang Zhang, Seongho Kim. Comparison of Spectral Similarity Measures for Compound Identification. 2011,,, 1-4. https://doi.org/10.1109/icbbe.2011.5780011
  91. Miyako Kusano, Mayumi Tabuchi, Atsushi Fukushima, Kazuhiro Funayama, Céline Diaz, Makoto Kobayashi, Naomi Hayashi, Yumiko N. Tsuchiya, Hideki Takahashi, Atsuko Kamata, Tomoyuki Yamaya, Kazuki Saito. Metabolomics data reveal a crucial role of cytosolic glutamine synthetase 1;1 in coordinating metabolic balance in rice. The Plant Journal 2011, 66 (3) , 456-466. https://doi.org/10.1111/j.1365-313X.2011.04506.x
  92. Henning Redestig, Jedrzej Szymanski, Masami Y. Hirai, Joachim Selbig, Lothar Willmitzer, Zoran Nikoloski, Kazuki Saito. Data Integration, Metabolic Networks and Systems Biology. 2011,,, 261-316. https://doi.org/10.1002/9781444339956.ch9
  93. Miyako Kusano, Henning Redestig, Tadayoshi Hirai, Akira Oikawa, Fumio Matsuda, Atsushi Fukushima, Masanori Arita, Shin Watanabe, Megumu Yano, Kyoko Hiwasa-Tanase, Hiroshi Ezura, Kazuki Saito, . Covering Chemical Diversity of Genetically-Modified Tomatoes Using Metabolomics for Objective Substantial Equivalence Assessment. PLoS ONE 2011, 6 (2) , e16989. https://doi.org/10.1371/journal.pone.0016989
  94. Rasmus K Madsen, Torbjörn Lundstedt, Jon Gabrielsson, Carl-Johan Sennbro, Gerd-Marie Alenius, Thomas Moritz, Solbritt Rantapää-Dahlqvist, Johan Trygg. Diagnostic properties of metabolic perturbations in rheumatoid arthritis. Arthritis Research & Therapy 2011, 13 (1) https://doi.org/10.1186/ar3243
  95. Aurélie Roux, Dominique Lison, Christophe Junot, Jean-François Heilier. Applications of liquid chromatography coupled to mass spectrometry-based metabolomics in clinical chemistry and toxicology: A review. Clinical Biochemistry 2011, 44 (1) , 119-135. https://doi.org/10.1016/j.clinbiochem.2010.08.016
  96. Henning Redestig, Miyako Kusano, Kaworu Ebana, Makoto Kobayashi, Akira Oikawa, Yozo Okazaki, Fumio Matsuda, Masanori Arita, Naoko Fujita, Kazuki Saito. Exploring molecular backgrounds of quality traits in rice by predictive models based on high-coverage metabolomics. BMC Systems Biology 2011, 5 (1) , 176. https://doi.org/10.1186/1752-0509-5-176
  97. Celine Diaz, Miyako Kusano, Ronan Sulpice, Mitsutaka Araki, Henning Redestig, Kazuki Saito, Mark Stitt, Ryoung Shin. Determining novel functions of Arabidopsis 14-3-3 proteins in central metabolic processes. BMC Systems Biology 2011, 5 (1) , 192. https://doi.org/10.1186/1752-0509-5-192
  98. Miyako Kusano, Atsushi Fukushima, Henning Redestig, Makoto Kobayashi, Hitomi Otsuki, Hitoshi Onouchi, Satoshi Naito, Masami Yokota Hirai, Kazuki Saito. Comparative metabolomics charts the impact of genotype-dependent methionine accumulation in Arabidopsis thaliana. Amino Acids 2010, 39 (4) , 1013-1021. https://doi.org/10.1007/s00726-010-0562-y
  99. Kazuki Saito, Fumio Matsuda. Metabolomics for Functional Genomics, Systems Biology, and Biotechnology. Annual Review of Plant Biology 2010, 61 (1) , 463-489. https://doi.org/10.1146/annurev.arplant.043008.092035
  100. Mark Stitt, John Lunn, Björn Usadel. Arabidopsis and primary photosynthetic metabolism - more than the icing on the cake. The Plant Journal 2010, 61 (6) , 1067-1091. https://doi.org/10.1111/j.1365-313X.2010.04142.x
  101. . Current literature in mass spectrometry. Journal of Mass Spectrometry 2010,,, 227-234. https://doi.org/10.1002/jms.1645

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

You’ve supercharged your research process with ACS and Mendeley!

STEP 1:
Click to create an ACS ID

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

OOPS

You have to login with your ACS ID befor you can login with your Mendeley account.

MENDELEY PAIRING EXPIRED
Your Mendeley pairing has expired. Please reconnect

This website uses cookies to improve your user experience. By continuing to use the site, you are accepting our use of cookies. Read the ACS privacy policy.

CONTINUE