Metabolomics quantitatively measures metabolites in a given biological system and facilitates the understanding of physiological and pathological activities. With the recent advancement of mass spectrometry (MS) technology, liquid chromatography-mass spectrometry (LC-MS) with data-independent acquisition (DIA) has been emerged as a powerful technology for untargeted metabolomics due to its capability to acquire all MS2 spectra and high quantitative accuracy. In this trend article, we first introduced the basic principles of several common DIA techniques including MSE, all ion fragmentation (AIF), SWATH, and MSX. Then, we summarized and compared the data analysis strategies to process DIA-based untargeted metabolomics data, including metabolite identification and quantification. We think the advantages of the DIA technique will enable its broad application in untargeted metabolomics.
This is a preview of subscription content,to check access.
Access this article
Patti GJ, Yanes O, Siuzdak G. Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012;13(4):263–9.
Newgard CB. Metabolomics and metabolic diseases: where do we stand? Cell Metab. 2017;25(1):43–56.
Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016;17(7):451–9.
Cajka T, Fiehn O. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal Chem. 2016;88(1):524–45.
Want EJ, Wilson ID, Gika H, Theodoridis G, Plumb RS, Shockcor J, et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat Protoc. 2010;5(6):1005–18.
Cai Y, Zhu Z-J. A high-throughput targeted metabolomics workflow for the detection of 200 polar metabolites in central carbon metabolism. Microbial Metabolomics. Berlin: Springer; 2019. p. 263–74.
Cai Y, Weng K, Guo Y, Peng J, Zhu Z-J. An integrated targeted metabolomic platform for high-throughput metabolite profiling and automated data processing. Metabolomics. 2015;11(6):1575–86.
Zhou J, Liu H, Liu Y, Liu J, Zhao X, Yin Y. Development and evaluation of a parallel reaction monitoring strategy for large-scale targeted metabolomics quantification. Anal Chem. 2016;88(8):4478–86.
Schrimpe-Rutledge AC, Codreanu SG, Sherrod SD, McLean JA. Untargeted metabolomics strategies-challenges and emerging directions. J Am Soc Mass Spectrom. 2016;27(12):1897–905.
Zhu Z-J, Schultz AW, Wang J, Johnson CH, Yannone SM, Patti GJ, et al. Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat Protoc. 2013;8(3):451.
Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, et al. METLIN: a metabolite mass spectral database. Ther Drug Monit. 2005;27(6):747–51.
Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K, et al. MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom. 2010;45(7):703–14.
Zha H, Cai Y, Yin Y, Wang Z, Li K, Zhu ZJ. SWATHtoMRM: development of high-coverage targeted metabolomics method using SWATH technology for biomarker discovery. Anal Chem. 2018;90:4062–70.
Venable JD, Dong MQ, Wohlschlegel J, Dillin A, Yates JR. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods. 2004;1:39–45.
Silva JC, Denny R, Dorschel CA, Gorenstein M, Kass IJ, Li GZ, et al. Quantitative proteomic analysis by accurate mass retention time pairs. Anal Chem. 2005;77:2187–200.
Geiger T, Cox J, Mann M. Proteomics on an orbitrap benchtop mass spectrometer using all-ion fragmentation. Mol Cell Proteomics. 2010;9:2252–61.
Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics. 2012;11:O111.016717.
Panchaud A, Scherl A, Shaffer SA, Haller PDV, Kulasekara HD, Miller SI, et al. PAcIFIC: how to dive deeper into the proteomics ocean. Anal Chem. 2011;81:6481–8.
Egertson JD, Kuehn A, Merrihew GE, Bateman NW, MacLean BX, Ting YS, et al. Multiplexed MS/MS for improved data-independent acquisition. Nat Methods. 2013;10:744–6.
Naz S, Gallart-Ayala H, Reinke SN, Mathon C, Blankley R, Chaleckis R, et al. Development of a liquid chromatography-high resolution mass spectrometry metabolomics method with high specificity for metabolite identification using all ion fragmentation acquisition. Anal Chem. 2017;89(15):7933–42.
Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol. 2018;14(8):e8126.
Hopfgartner G, Tonoli D, Varesio E. High-resolution mass spectrometry for integrated qualitative and quantitative analysis of pharmaceuticals in biological matrices. Anal Bioanal Chem. 2012;402(8):2587–96.
Siegel D, Meinema AC, Permentier H, Hopfgartner G, Bischoff R. Integrated quantification and identification of aldehydes and ketones in biological samples. Anal Chem. 2014;86(10):5089–100.
Roemmelt AT, Steuer AE, Poetzsch M, Kraemer T. Liquid chromatography, in combination with a quadrupole time-of-flight instrument (LC QTOF), with sequential window acquisition of all theoretical fragment-ion spectra (SWATH) acquisition: systematic studies on its use for screenings in clinical and forensic toxicology and comparison with information-dependent acquisition (IDA). Anal Chem. 2014;86(23):11742–9.
Bruderer T, Varesio E, Hopfgartner G. The use of LC predicted retention times to extend metabolites identification with SWATH data acquisition. J Chromatogr B. 2017;1071:3–10.
Zhu X, Chen Y, Subramanian R. Comparison of information-dependent acquisition, SWATH, and MS(All) techniques in metabolite identification study employing ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Anal Chem. 2014;86(2):1202–9.
Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015;12:523–6.
Li H, Cai Y, Guo Y, Chen F, Zhu ZJ. MetDIA: targeted metabolite extraction of multiplexed MS/MS spectra generated by data-independent acquisition. Anal Chem. 2016;88:8757–64.
Chen G, Walmsley S, Cheung GCM, Chen L, Cheng CY, Beuerman RW, et al. Customized consensus spectral library building for untargeted quantitative metabolomics analysis with data independent acquisition mass spectrometry and MetaboDIA workflow. Anal Chem. 2017;89:4897–906.
Bonner R, Hopfgartner G. SWATH data independent acquisition mass spectrometry for metabolomics. Trends Anal Chem. 2018. https://doi.org/10.1016/j.trac.2018.10.014.
Zhang Y, Bilbao A, Bruderer T, Luban J, Strambio-De-Castillia C, Lisacek F, et al. The use of variable Q1 isolation windows improves selectivity in LC-SWATH-MS acquisition. J Proteome Res. 2015;14:4359–71.
Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006;78(3):779–87.
Pluskal T, Castillo S, Villar-Briones A, Orešič M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformat. 2010;11(1):395.
Rost HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods. 2016;13(9):741–8.
Ni Y, Su M, Qiu Y, Jia W, Du X. ADAP-GC 3.0: improved peak detection and deconvolution of co-eluting metabolites from GC/TOF-MS data for metabolomics studies. Anal Chem. 2016;88(17):8802–11.
Bruderer T, Varesio E, Hidasi AO, Duchoslav E, Burton L, Bonner R, et al. Metabolomic spectral libraries for data-independent SWATH liquid chromatography mass spectrometry acquisition. Anal Bioanal Chem. 2018;410(7):1873–84.
Tsou CC, Avtonomov D, Larsen B, Tucholska M, Choi H, Gingras AC, et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods. 2015;12(3):258–64 7 p following 64.
Rost HL, Rosenberger G, Navarro P, Gillet L, Miladinovic SM, Schubert OT, et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol. 2014;32(3):219–23.
MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. BMC Bioinformat. 2010;26(7):966–8.
Li Z, Li Y, Chen W, Cao Q, Guo Y, Wan N, et al. Integrating MS1 and MS2 scans in high-resolution parallel reaction monitoring assays for targeted metabolite quantification and dynamic 13C-labeling metabolism analysis. Anal Chem. 2017;89:877–85.
Broeckling CD, Heuberger AL, Prince JA, Ingelsson E, Prenni JE. Assigning precursor–product ion relationships in indiscriminant MS/MS data from non-targeted metabolite profiling studies. Metabolomics. 2013;9(1):33–43.
Broeckling CD, Afsar FA, Neumann S, Ben-Hur A, Prenni JE. RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal Chem. 2014;86(14):6812–7.
Wang L, Su B, Zeng Z, Li C, Zhao X, Lv W, et al. Ion-pair selection method for pseudotargeted metabolomics based on SWATH MS acquisition and its application in differential metabolite discovery of type 2 diabetes. Anal Chem. 2018:11401–08.
Zheng X, Wojcik R, Zhang X, Ibrahim YM, Burnum-Johnson KE, Orton DJ, et al. Coupling front-end separations, ion mobility spectrometry, and mass spectrometry for enhanced multidimensional biological and environmental analyses. Annu Rev Anal Chem. 2017;10(1):71–92.
Zhou Z, Tu J, Zhu ZJ. Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era. Curr Opin Chem Biol. 2018;42:34–41.
Hong P, Bernstein W, Wei J, Lin C, Kernel-based component decomposition for glycan mixture separation using ion mobility spectrometry-MS/MS, 66th annual ASMS conference on mass spectrometry and allied topics, San Diego, CA, June 3-7; 2018.
The work has been supported by the National Natural Science Foundation of China (Grants 21575151).
The authors declare that they have no conflict of interest.
Published in the topical collection Young Investigators in (Bio-)Analytical Chemistry with guest editors Erin Baker, Kerstin Leopold, Francesco Ricci, and Wei Wang.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
About this article
Cite this article
Wang, R., Yin, Y. & Zhu, ZJ. Advancing untargeted metabolomics using data-independent acquisition mass spectrometry technology. Anal Bioanal Chem 411, 4349–4357 (2019). https://doi.org/10.1007/s00216-019-01709-1