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Response to Comment on "METLIN: A Technology Platform for Identifying Knowns and Unknowns" Carlos Guijas, and Gary Siuzdak Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b04081 • Publication Date (Web): 09 Oct 2018 Downloaded from http://pubs.acs.org on October 11, 2018
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Analytical Chemistry
Response to Comment on "METLIN: A Technology Platform for Identifying Knowns and Unknowns"
METLIN Reinvented Carlos Guijas and Gary Siuzdak Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, California, United States. Response: We agree with this commentary, additional data acquisition parameters would be beneficial for the community as a whole. However, we are currently in the process of a major METLIN data production effort. This effort has allowed us to expand METLIN1 from 14,000 (in 2017) to now over 150,000 molecular standards each containing MS/MS data at multiple collision energies in both positive and negative ionization modes. We have explored the idea of broadening the acquisition parameters, including adding more collision energies. Unfortunately, these additional parameters did not have a significant positive effect on the data. Empirical data quality and maintaining high throughput have been of utmost importance in our decision process, a goal we try to sustain while maintaining a successful acquisition rate as determined by manual curation. Therefore, we have made the strategic decision to acquire data at parameters that will provide the most useful data without sacrificing quality or throughput. Future: Given the success and throughput of our platform, we welcome the donation of verified standards, especially from the plant community, and we encourage feedback on MS/MS spectral data quality. We understand this data is crucial for both the identification of known molecules via comparison with experimental data1-2, characterizing active metabolites3, as well as for the identification of unknowns1, 4 via development of more effective bioinformatic tools based on that newly added data. In fact, we could potentially create a specialized parameter set to analyze selected classes of compounds at higher collision energies to facilitate the identification of molecules that do not readily dissociate at the standard collision energies, thus aiding the generation of pseudo-MS3 spectra for structural elucidation and isomer characterization. A particularly interesting example of the utility of such an approach was the case of 9-PAHSA, a class of lipids with antidiabetic and anti-inflammatory properties, where it was possible to assign the positional isomers fragmenting at high collision energies5. References: 1. Guijas, C.; Montenegro-Burke, J. R.; Domingo-Almenara, X.; Palermo, A.; Warth, B.; Hermann, G.; Koellensperger, G.; Huan, T.; Uritboonthai, W.; Aisporna, A. E.; Wolan, D. W.; Spilker, M. E.; Benton, H. P.; Siuzdak, G., METLIN: A Technology Platform for Identifying Knowns and Unknowns. Analytical chemistry 2018, 90 (5), 3156-3164. 2. Domingo-Almenara, X.; Montenegro-Burke, J. R.; Ivanisevic, J.; Thomas, A.; Sidibe, J.; Teav, T.; Guijas, C.; Aisporna, A. E.; Rinehart, D.; Hoang, L.; Nordstrom, A.; Gomez-Romero, M.; Whiley, L.; Lewis, M. R.; Nicholson, J. K.; Benton, H. P.; Siuzdak, G., XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules. Nature methods 2018, 15 (9), 681-684. 3. Guijas, C.; Montenegro-Burke, J. R.; Warth, B.; Spilker, M. E.; Siuzdak, G., Metabolomics activity screening for identifying metabolites that modulate phenotype. Nature biotechnology 2018, 36 (4), 316-320. 4. Benton, H. P.; Wong, D. M.; Trauger, S. A.; Siuzdak, G., XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization. Analytical chemistry 2008, 80 (16), 6382-9.
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5. Yore, M. M.; Syed, I.; Moraes-Vieira, P. M.; Zhang, T.; Herman, M. A.; Homan, E. A.; Patel, R. T.; Lee, J.; Chen, S.; Peroni, O. D.; Dhaneshwar, A. S.; Hammarstedt, A.; Smith, U.; McGraw, T. E.; Saghatelian, A.; Kahn, B. B., Discovery of a class of endogenous mammalian lipids with anti-diabetic and anti-inflammatory effects. Cell 2014, 159 (2), 318-32.
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