[1] OLIVER S G, WINSON M K, KELL D B, et al. Systematic functional analysis of the yeast genome [J]. Trends in Biotechnology, 1998, 16(9): 373-378. doi: 10.1016/S0167-7799(98)01214-1
[2] NICHOLSON J K, WILSON I D. Understanding ‘global’ systems biology: Metabonomics and the continuum of metabolism [J]. Nature Reviews Drug Discovery, 2003, 2(8): 668-676. doi: 10.1038/nrd1157
[3] DUNN W B, BAILEY N J C, JOHNSON H E. Measuring the metabolome: Current analytical technologies [J]. The Analyst, 2005, 130(5): 606-625. doi: 10.1039/b418288j
[4] FELL D A. Beyond genomics [J]. Trends in Genetics:TIG, 2001, 17(12): 680-682. doi: 10.1016/S0168-9525(01)02521-5
[5] FIEHN O. Metabolomics: the link between genotypes and phenotypes [J]. Plant Molecular Biology, 2002, 48(1/2): 155-171. doi: 10.1023/A:1013713905833
[6] WINDER C L, DUNN W B, SCHULER S, et al. Global metabolic profiling of Escherichia coli cultures: An evaluation of methods for quenching and extraction of intracellular metabolites [J]. Analytical Chemistry, 2008, 80(8): 2939-2948. doi: 10.1021/ac7023409
[7] BRUCE S J, TAVAZZI I, PARISOD V, et al. Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry [J]. Analytical Chemistry, 2009, 81(9): 3285-3296. doi: 10.1021/ac8024569
[8] RICO E, GONZÁLEZ O, BLANCO M E, et al. Evaluation of human plasma sample preparation protocols for untargeted metabolic profiles analyzed by UHPLC-ESI-TOF-MS [J]. Analytical and Bioanalytical Chemistry, 2014, 406(29): 7641-7652. doi: 10.1007/s00216-014-8212-y
[9] DETTMER K, NÜRNBERGER N, KASPAR H, et al. Metabolite extraction from adherently growing mammalian cells for metabolomics studies: Optimization of harvesting and extraction protocols [J]. Analytical and Bioanalytical Chemistry, 2011, 399(3): 1127-1139. doi: 10.1007/s00216-010-4425-x
[10] KIM S, LEE D Y, WOHLGEMUTH G, et al. Evaluation and optimization of metabolome sample preparation methods for Saccharomyces cerevisiae [J]. Analytical Chemistry, 2013, 85(4): 2169-2176. doi: 10.1021/ac302881e
[11] JOHNSON C H, IVANISEVIC J, SIUZDAK G. Metabolomics: beyond biomarkers and towards mechanisms [J]. Nature Reviews Molecular Cell Biology, 2016, 17(7): 451-459. doi: 10.1038/nrm.2016.25
[12] WANT E J, NORDSTRÖM A, MORITA H, et al. From exogenous to endogenous: The inevitable imprint of mass spectrometry in metabolomics [J]. Journal of Proteome Research, 2007, 6(2): 459-468. doi: 10.1021/pr060505+
[13] MARX V. Boost that metabolomic confidence [J]. Nature Methods, 2020, 17(1): 33-36. doi: 10.1038/s41592-019-0694-2
[14] WISHART D S, FEUNANG Y D, MARCU A, et al. HMDB 4.0: The human metabolome database for 2018 [J]. Nucleic Acids Research, 2017, 46(D1): D608-D617.
[15] ORTMAYR K, CAUSON T J, HANN S, et al. Increasing selectivity and coverage in LC-MS based metabolome analysis [J]. TrAC Trends in Analytical Chemistry, 2016, 82: 358-366. doi: 10.1016/j.trac.2016.06.011
[16] GIKA H G, WILSON I D, THEODORIDIS G A. LC-MS-based holistic metabolic profiling. Problems, limitations, advantages, and future perspectives [J]. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 2014, 966: 1-6. doi: 10.1016/j.jchromb.2014.01.054
[17] KITTERINGHAM N R, JENKINS R E, LANE C S, et al. Multiple reaction monitoring for quantitative biomarker analysis in proteomics and metabolomics [J]. Journal of Chromatography B, 2009, 877(13): 1229-1239. doi: 10.1016/j.jchromb.2008.11.013
[18] CICCIMARO E, BLAIR I A. Stable-isotope dilution LC-MS for quantitative biomarker analysis [J]. Bioanalysis, 2010, 2(2): 311-341. doi: 10.4155/bio.09.185
[19] KOK M G M, NIX C, NYS G, et al. Targeted metabolomics of whole blood using volumetric absorptive microsampling [J]. Talanta, 2019, 197: 49-58. doi: 10.1016/j.talanta.2019.01.014
[20] BREIER M, WAHL S, PREHN C, et al. Targeted metabolomics identifies reliable and stable metabolites in human serum and plasma samples [J]. PLoS One, 2014, 9(2): e89728. doi: 10.1371/journal.pone.0089728
[21] KUHRING M, EISENBERGER A, SCHMIDT V, et al. Concepts and software package for efficient quality control in targeted metabolomics studies: MeTaQuaC [J]. Analytical Chemistry, 2020, 92(15): 10241-10245. doi: 10.1021/acs.analchem.0c00136
[22] MAZZINI F N, COOK F, GOUNARIDES J, et al. Plasma and stool metabolomic biomarkers of non-alcoholic fatty liver disease in Argentina [J]. MedRxiv, 2020, 8: 20165308.
[23] DOMINGO-ALMENARA X, MONTENEGRO-BURKE J R, IVANISEVIC J, et al. XCMS-MRM and METLIN-MRM: A cloud library and public resource for targeted analysis of small molecules [J]. Nature Methods, 2018, 15(9): 681-684. doi: 10.1038/s41592-018-0110-3
[24] ZHENG F J, ZHAO X J, ZENG Z D, et al. Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography–mass spectrometry [J]. Nature Protocols, 2020, 15(8): 2519-2537. doi: 10.1038/s41596-020-0341-5
[25] ZHOU J T, LIU H Y, LIU Y, et al. Development and evaluation of a parallel reaction monitoring strategy for large-scale targeted metabolomics quantification [J]. Analytical Chemistry, 2016, 88(8): 4478-4486. doi: 10.1021/acs.analchem.6b00355
[26] SCHILLING B, MACLEAN B, HELD J M, et al. Multiplexed, scheduled, high-resolution parallel reaction monitoring on a full scan QqTOF instrument with integrated data-dependent and targeted mass spectrometric workflows [J]. Analytical Chemistry, 2015, 87(20): 10222-10229. doi: 10.1021/acs.analchem.5b02983
[27] TANG H, FANG H S, YIN E, et al. Multiplexed parallel reaction monitoring targeting histone modifications on the QExactive mass spectrometer [J]. Analytical Chemistry, 2014, 86(11): 5526-5534. doi: 10.1021/ac500972x
[28] CHO K, SCHWAIGER-HABER M, NASER F J, et al. Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics [J]. Analytica Chimica Acta, 2021, 1149: 338210. doi: 10.1016/j.aca.2021.338210
[29] CLANCY M V, ZYTYNSKA S E, MORITZ F, et al. Metabotype variation in a field population of tansy plants influences aphid host selection [J]. Plant, Cell & Environment, 2018, 41(12): 2791-2805.
[30] MARR S, HAGEMAN J A, WEHRENS R, et al. LC-MS based plant metabolic profiles of thirteen grassland species grown in diverse neighbourhoods [J]. Scientific Data, 2021, 8: 52. doi: 10.1038/s41597-021-00836-8
[31] SMITH C A, WANT E J, O'MAILLE G, et al. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification [J]. Analytical Chemistry, 2006, 78(3): 779-787. doi: 10.1021/ac051437y
[32] GUO J, HUAN T. Comparison of full-scan, data-dependent, and data-independent acquisition modes in liquid chromatography-mass spectrometry based untargeted metabolomics [J]. Analytical Chemistry, 2020, 92(12): 8072-8080. doi: 10.1021/acs.analchem.9b05135
[33] BENTON H P, IVANISEVIC J, MAHIEU N G, et al. Autonomous metabolomics for rapid metabolite identification in global profiling [J]. Analytical Chemistry, 2015, 87(2): 884-891. doi: 10.1021/ac5025649
[34] FENAILLE F, BARBIER SAINT-HILAIRE P, ROUSSEAU K, et al. Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand? [J]. Journal of Chromatography A, 2017, 1526: 1-12. doi: 10.1016/j.chroma.2017.10.043
[35] DEFOSSEZ E, BOURQUIN J, von REUSS S, et al. Eight key rules for successful data-dependent acquisition in mass spectrometry-based metabolomics [J]. Mass Spectrometry Reviews, 2021, 7: 1-13.
[36] BENDALL S C, HUGHES C, CAMPBELL J L, et al. An enhanced mass spectrometry approach reveals human embryonic stem cell growth factors in culture [J]. Molecular & Cellular Proteomics, 2009, 8(3): 421-432.
[37] EDMANDS W M, FERRARI P, ROTHWELL J A, et al. Polyphenol metabolome in human urine and its association with intake of polyphenol-rich foods across European countries [J]. The American Journal of Clinical Nutrition, 2015, 102(4): 905-913. doi: 10.3945/ajcn.114.101881
[38] KOELMEL J P, KROEGER N M, GILL E L, et al. Expanding lipidome coverage using LC-MS/MS data-dependent acquisition with automated exclusion list generation [J]. Journal of the American Society for Mass Spectrometry, 2017, 28(5): 908-917. doi: 10.1007/s13361-017-1608-0
[39] ZHANG Z Q. Automated precursor ion exclusion during LC-MS/MS data acquisition for optimal ion identification [J]. Journal of the American Society for Mass Spectrometry, 2012, 23(8): 1400-1407. doi: 10.1007/s13361-012-0401-3
[40] HOOPMANN M R, MERRIHEW G E, von HALLER P D, et al. Post analysis data acquisition for the iterative MS/MS sampling of proteomics mixtures [J]. Journal of Proteome Research, 2009, 8(4): 1870-1875. doi: 10.1021/pr800828p
[41] GINÉ R, CAPELLADES J, BADIA J M, et al. HERMES: a molecular-formula-oriented method to target the metabolome [J]. Nature Methods, 2021, 18(11): 1370-1376. doi: 10.1038/s41592-021-01307-z
[42] AcquireX Intelligent Data Acquisition Workflow[C].[2022-02-08].https://www.thermofisher.cn/cn/zh/home/industrial/mass-spectrometry/liquid-chromatography-mass-spectrometry-lc-ms/lc-ms-software/lc-ms-data-acquisition-software/acquirex-intelligent-data-acquisition-workflow.html.C].[2022--02-08].https://www.thermofisher.cn/cn/zh/home/industrial/mass-spectrometry/liquid-chromatography-mass-spectrometry-lc-ms/lc-ms-software/lc-ms-data-acquisition-software/acquirex-intelligent-data-acquisition-workflow.html.
[43] BROECKLING C D, HOYES E, RICHARDSON K, et al. Comprehensive tandem-mass-spectrometry coverage of complex samples enabled by data-set-dependent acquisition [J]. Analytical Chemistry, 2018, 90(13): 8020-8027. doi: 10.1021/acs.analchem.8b00929
[44] BILLS B, BARSHOP W D, SHARMA S, et al. Novel real-time library search driven data acquisition strategy for identification and characterization of metabolites [J]. Analytical Chemistry, 2022, 94(9): 3749-3755. doi: 10.1021/acs.analchem.1c04336
[45] WANDY J, DAVIES V, van der HOOFT J J, et al. In silico optimization of mass spectrometry fragmentation strategies in metabolomics [J]. Metabolites, 2019, 9(10): 219. doi: 10.3390/metabo9100219
[46] DAVIES V, WANDY J, WEIDT S, et al. Rapid development of improved data-dependent acquisition strategies [J]. Analytical Chemistry, 2021, 93(14): 5676-5683. doi: 10.1021/acs.analchem.0c03895
[47] CHEN L Y, ZHOU L, CHAN E C Y, et al. Characterization of the human tear metabolome by LC-MS/MS [J]. Journal of Proteome Research, 2011, 10(10): 4876-4882. doi: 10.1021/pr2004874
[48] SMITH C A, O'MAILLE G, WANT E J, et al. METLIN: a metabolite mass spectral database [J]. Therapeutic Drug Monitoring, 2005, 27(6): 747-751. doi: 10.1097/01.ftd.0000179845.53213.39
[49] HORAI H, ARITA M, KANAYA S, et al. MassBank: A public repository for sharing mass spectral data for life sciences [J]. Journal of Mass Spectrometry, 2010, 45(7): 703-714. doi: 10.1002/jms.1777
[50] SIEGEL D, MEINEMA A C, PERMENTIER H, et al. Integrated quantification and identification of aldehydes and ketones in biological samples [J]. Analytical Chemistry, 2014, 86(10): 5089-5100. doi: 10.1021/ac500810r
[51] WRONA M, MAURIALA T, BATEMAN K P, et al. ‘All-in-One’ analysis for metabolite identification using liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry with collision energy switching [J]. Rapid Communications in Mass Spectrometry, 2005, 19(18): 2597-2602. doi: 10.1002/rcm.2101
[52] PLUMB R S, JOHNSON K A, RAINVILLE P, et al. UPLC/MS(E);A new approach for generating molecular fragment information for biomarker structure elucidation [J]. Rapid Communications in Mass Spectrometry, 2006, 20(13): 1989-1994. doi: 10.1002/rcm.2550
[53] NAVARRO-REIG M, JAUMOT J, PIÑA B, et al. Metabolomic analysis of the effects of cadmium and copper treatment in Oryza sativa L. using untargeted liquid chromatography coupled to high resolution mass spectrometry and all-ion fragmentation [J]. Metallomics, 2017, 9(6): 660-675. doi: 10.1039/C6MT00279J
[54] PERRY S J, NÁSZ S, SAEED M. A high-resolution accurate mass (HR/AM) approach to identification, profiling and characterization of in vitro nefazodone metabolites using a hybrid quadrupole Orbitrap (Q-Exactive) [J]. Rapid Communications in Mass Spectrometry, 2015, 29(17): 1545-1555. doi: 10.1002/rcm.7250
[55] SENTANDREU E, PERIS-DÍAZ M D, SWEENEY S R, et al. A survey of orbitrap all ion fragmentation analysis assessed by an R MetaboList package to study small-molecule metabolites [J]. Chromatographia, 2018, 81(7): 981-994. doi: 10.1007/s10337-018-3536-y
[56] VENTURA G, BIANCO M, CALVANO C D, et al. HILIC-ESI-FTMS with all ion fragmentation (AIF) scans as a tool for fast lipidome investigations [J]. Molecules (Basel, Switzerland), 2020, 25(10): 2310. doi: 10.3390/molecules25102310
[57] van der LAAN T, BOOM I, MALIEPAARD J, et al. Data-independent acquisition for the quantification and identification of metabolites in plasma [J]. Metabolites, 2020, 10(12): 514. doi: 10.3390/metabo10120514
[58] PAGLIA G, ASTARITA G. Metabolomics and lipidomics using traveling-wave ion mobility mass spectrometry [J]. Nature Protocols, 2017, 12(4): 797-813. doi: 10.1038/nprot.2017.013
[59] BLAŽENOVIĆ I, KIND T, JI J, et al. Software tools and approaches for compound identification of LC-MS/MS data in metabolomics [J]. Metabolites, 2018, 8(2): 31. doi: 10.3390/metabo8020031
[60] GILLET L C, NAVARRO P, TATE S, et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: A new concept for consistent and accurate proteome analysis [J]. Molecular & Cellular Proteomics, 2012, 11(6): O111.016717.
[61] ZHANG Y, BILBAO A, BRUDERER T, et al. The use of variable Q1 isolation windows improves selectivity in LC-SWATH-MS acquisition [J]. Journal of Proteome Research, 2015, 14(10): 4359-4371. doi: 10.1021/acs.jproteome.5b00543
[62] AKBAL L, HOPFGARTNER G. Supercritical fluid chromatography-mass spectrometry using data independent acquisition for the analysis of polar metabolites in human urine [J]. Journal of Chromatography A, 2020, 1609: 460449. doi: 10.1016/j.chroma.2019.460449
[63] BONNER R, HOPFGARTNER G. SWATH acquisition mode for drug metabolism and metabolomics investigations [J]. Bioanalysis, 2016, 8(16): 1735-1750. doi: 10.4155/bio-2016-0141
[64] BONNER R, HOPFGARTNER G. SWATH data independent acquisition mass spectrometry for metabolomics [J]. TrAC Trends in Analytical Chemistry, 2019, 120: 115278. doi: 10.1016/j.trac.2018.10.014
[65] WANG R H, YIN Y D, ZHU Z J. Advancing untargeted metabolomics using data-independent acquisition mass spectrometry technology [J]. Analytical and Bioanalytical Chemistry, 2019, 411(19): 4349-4357. doi: 10.1007/s00216-019-01709-1
[66] XIONG Y T, SHI C, ZHONG F, et al. LC-MS/MS and SWATH based serum metabolomics enables biomarker discovery in pancreatic cancer [J]. Clinica Chimica Acta, 2020, 506: 214-221. doi: 10.1016/j.cca.2020.03.043
[67] XU L, XU Z Z, STRASHNOV I, et al. Use of information dependent acquisition mass spectra and sequential window acquisition of all theoretical fragment-ion mass spectra for fruit juices metabolomics and authentication [J]. Metabolomics, 2020, 16(7): 81. doi: 10.1007/s11306-020-01701-2
[68] MOSELEY M A, HUGHES C J, JUVVADI P R, et al. Scanning quadrupole data-independent acquisition, part A: Qualitative and quantitative characterization [J]. Journal of Proteome Research, 2018, 17(2): 770-779. doi: 10.1021/acs.jproteome.7b00464
[69] MESSNER C, DEMICHEV V, BLOOMFIELD N, et al. ScanningSWATH enables ultra-fast proteomics using high-flow chromatography and minute-scale gradients[J]. BioRxiv, 2019,DOI:10.1101/656793.
[70] MUN D G, RENUSE S, SARASWAT M, et al. PASS-DIA: A data-independent acquisition approach for discovery studies [J]. Analytical Chemistry, 2020, 92(21): 14466-14475. doi: 10.1021/acs.analchem.0c02513
[71] GUO J, SHEN S, XING S P, et al. DaDIA: hybridizing data-dependent and data-independent acquisition modes for generating high-quality metabolomic data [J]. Analytical Chemistry, 2021, 93(4): 2669-2677. doi: 10.1021/acs.analchem.0c05022
[72] CHAPMAN J D, GOODLETT D R, MASSELON C D. Multiplexed and data-independent tandem mass spectrometry for global proteome profiling [J]. Mass Spectrometry Reviews, 2014, 33(6): 452-470. doi: 10.1002/mas.21400
[73] TADA I, CHALECKIS R, TSUGAWA H, et al. Correlation-based deconvolution (CorrDec) to generate high-quality MS2 spectra from data-independent acquisition in multisample studies [J]. Analytical Chemistry, 2020, 92(16): 11310-11317. doi: 10.1021/acs.analchem.0c01980
[74] TSUGAWA H, CAJKA T, KIND T, et al. MS-DIAL: Data-independent MS/MS deconvolution for comprehensive metabolome analysis [J]. Nature Methods, 2015, 12(6): 523-526. doi: 10.1038/nmeth.3393
[75] YIN Y D, WANG R H, CAI Y P, et al. DecoMetDIA: deconvolution of multiplexed MS/MS spectra for metabolite identification in SWATH-MS-based untargeted metabolomics [J]. Analytical Chemistry, 2019, 91(18): 11897-11904. doi: 10.1021/acs.analchem.9b02655
[76] LI H, CAI Y P, GUO Y, et al. MetDIA: targeted metabolite extraction of multiplexed MS/MS spectra generated by data-independent acquisition [J]. Analytical Chemistry, 2016, 88(17): 8757-8764. doi: 10.1021/acs.analchem.6b02122
[77] XUE J C, GUIJAS C, BENTON H P, et al. METLIN MS2 molecular standards database: A broad chemical and biological resource [J]. Nature Methods, 2020, 17(10): 953-954. doi: 10.1038/s41592-020-0942-5
[78] WANG M X, CARVER J J, PHELAN V V, et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking [J]. Nature Biotechnology, 2016, 34(8): 828-837. doi: 10.1038/nbt.3597
[79] CHEN S L, KONG H W, LU X, et al. Pseudotargeted metabolomics method and its application in serum biomarker discovery for hepatocellular carcinoma based on ultra high-performance liquid chromatography/triple quadrupole mass spectrometry [J]. Analytical Chemistry, 2013, 85(17): 8326-8333. doi: 10.1021/ac4016787
[80] CHEN Y H, XU J, ZHANG R P, et al. Assessment of data pre-processing methods for LC-MS/MS-based metabolomics of uterine cervix cancer [J]. The Analyst, 2013, 138(9): 2669-2677. doi: 10.1039/c3an36818a
[81] CHEN L, ZHONG F Y, ZHU J J. Bridging targeted and untargeted mass spectrometry-based metabolomics via hybrid approaches [J]. Metabolites, 2020, 10(9): 348. doi: 10.3390/metabo10090348
[82] WANG Y, LIU F, LI P, et al. An improved pseudotargeted metabolomics approach using multiple ion monitoring with time-staggered ion lists based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry [J]. Analytica Chimica Acta, 2016, 927: 82-88. doi: 10.1016/j.aca.2016.05.008
[83] GU H W, ZHANG P, ZHU J J, et al. Globally optimized targeted mass spectrometry: Reliable metabolomics analysis with broad coverage [J]. Analytical Chemistry, 2015, 87(24): 12355-12362. doi: 10.1021/acs.analchem.5b03812
[84] SHI X J, WANG S, JASBI P, et al. Database-assisted globally optimized targeted mass spectrometry (dGOT-MS): Broad and reliable metabolomics analysis with enhanced identification [J]. Analytical Chemistry, 2019, 91(21): 13737-13745. doi: 10.1021/acs.analchem.9b03107
[85] GUIJAS C, MONTENEGRO-BURKE J R, DOMINGO-ALMENARA X, et al. METLIN: A technology platform for identifying knowns and unknowns [J]. Analytical Chemistry, 2018, 90(5): 3156-3164. doi: 10.1021/acs.analchem.7b04424
[86] ZHONG F Y, XU M Y, ZHU J J. Development and application of time staggered/mass staggered-globally optimized targeted mass spectrometry [J]. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 2019, 1120: 80-88. doi: 10.1016/j.jchromb.2019.04.051
[87] GAO Y, CHEN Y H, YUE X F, et al. Development of simultaneous targeted metabolite quantification and untargeted metabolomics strategy using dual-column liquid chromatography coupled with tandem mass spectrometry [J]. Analytica Chimica Acta, 2018, 1037: 369-379. doi: 10.1016/j.aca.2018.08.042
[88] ZHANG L, ZHENG W, LI X, et al. A merged method for targeted analysis of amino acids and derivatives using parallel reaction monitoring combined with untargeted profiling by HILIC-Q-Orbitrap HRMS [J]. Journal of Pharmaceutical and Biomedical Analysis, 2021, 203: 114208. doi: 10.1016/j.jpba.2021.114208
[89] LI Y, RUAN Q, LI Y L, et al. A novel approach to transforming a non-targeted metabolic profiling method to a pseudo-targeted method using the retention time locking gas chromatography/mass spectrometry-selected ions monitoring [J]. Journal of Chromatography A, 2012, 1255: 228-236. doi: 10.1016/j.chroma.2012.01.076
[90] LUO P, DAI W D, YIN P Y, et al. Multiple reaction monitoring-ion pair finder: A systematic approach to transform nontargeted mode to pseudotargeted mode for metabolomics study based on liquid chromatography-mass spectrometry [J]. Analytical Chemistry, 2015, 87(10): 5050-5055. doi: 10.1021/acs.analchem.5b00615
[91] ZHA H H, CAI Y P, YIN Y D, et al. SWATHtoMRM: development of high-coverage targeted metabolomics method using SWATH technology for biomarker discovery [J]. Analytical Chemistry, 2018, 90(6): 4062-4070. doi: 10.1021/acs.analchem.7b05318
[92] CHEN Y H, ZHOU Z, YANG W, et al. Development of a data-independent targeted metabolomics method for relative quantification using liquid chromatography coupled with tandem mass spectrometry [J]. Analytical Chemistry, 2017, 89(13): 6954-6962. doi: 10.1021/acs.analchem.6b04727
[93] LUO P, YIN P Y, HUA R, et al. A Large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma [J]. Hepatology (Baltimore, Md. ), 2018, 67(2): 662-675. doi: 10.1002/hep.29561
[94] SHAO Y P, ZHU B, ZHENG R Y, et al. Development of urinary pseudotargeted LC-MS-based metabolomics method and its application in hepatocellular carcinoma biomarker discovery [J]. Journal of Proteome Research, 2015, 14(2): 906-916. doi: 10.1021/pr500973d
[95] WANG L C, SU B Z, ZENG Z D, 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 [J]. Analytical Chemistry, 2018, 90(19): 11401-11408. doi: 10.1021/acs.analchem.8b02377
[96] ZHOU Y, SONG R X, MA C, et al. Discovery and validation of potential urinary biomarkers for bladder cancer diagnosis using a pseudotargeted GC-MS metabolomics method [J]. Oncotarget, 2017, 8(13): 20719-20728. doi: 10.18632/oncotarget.14988
[97] FANG C N, SU B Z, JIANG T Y, et al. Prognosis prediction of hepatocellular carcinoma after surgical resection based on serum metabolic profiling from gas chromatography-mass spectrometry [J]. Analytical and Bioanalytical Chemistry, 2021, 413(12): 3153-3165. doi: 10.1007/s00216-021-03281-z
[98] SUN H Q, CHEN N, WANG X C, et al. The study on the pathogenesis of pediatric lymphoma based on the combination of pseudotargeted and targeted metabolomics [J]. BioMed Research International, 2021, 2021: 9984357.
[99] YE G Z, LIU Y, YIN P Y, et al. Study of induction chemotherapy efficacy in oral squamous cell carcinoma using pseudotargeted metabolomics [J]. Journal of Proteome Research, 2014, 13(4): 1994-2004. doi: 10.1021/pr4011298
[100] ZHAO Y N, ZHAO C X, LU X, et al. Investigation of the relationship between the metabolic profile of tobacco leaves in different planting regions and climate factors using a pseudotargeted method based on gas chromatography/mass spectrometry [J]. Journal of Proteome Research, 2013, 12(11): 5072-5083. doi: 10.1021/pr400799a
[101] ZHAO Y N, ZHANG L, ZHAO C X, et al. Metabolic responses of rice leaves and seeds under transgenic backcross breeding and pesticide stress by pseudotargeted metabolomics [J]. Metabolomics, 2015, 11(6): 1802-1814. doi: 10.1007/s11306-015-0834-3
[102] ZHANG J, WU X F, QIU J Q, et al. Comprehensive comparison on the chemical profile of Guang Chen pi at different ripeness stages using untargeted and pseudotargeted metabolomics [J]. Journal of Agricultural and Food Chemistry, 2020, 68(31): 8483-8495. doi: 10.1021/acs.jafc.0c02904
[103] LI L L, WANG Y, LI Y X, et al. A pseudotargeted method based on sequential window acquisition of all theoretical spectra mass spectrometry acquisition and its application in quality assessment of traditional Chinese medicine preparation-Yuanhu Zhitong Tablet [J]. Journal of Separation Science, 2022, 45(2): 650-658. doi: 10.1002/jssc.202100611
[104] XIAO J M, SONG J Y, SA Y H, et al. The mechanisms of improving IVF outcomes of Liu-Wei-di-Huang pill acting on DOR patients [J]. Evidence-Based Complementary and Alternative Medicine, 2020, 2020: 5183017.
[105] WANG F D, ZHANG H J, GENG N B, et al. A metabolomics strategy to assess the combined toxicity of polycyclic aromatic hydrocarbons (PAHs) and short-chain chlorinated paraffins (SCCPs) [J]. Environmental Pollution, 2018, 234: 572-580. doi: 10.1016/j.envpol.2017.11.073
[106] WANG F D, ZHANG H J, GENG N B, et al. New insights into the cytotoxic mechanism of hexabromocyclododecane from a metabolomic approach [J]. Environmental Science & Technology, 2016, 50(6): 3145-3153.
[107] LIU D Y, YANG J N, JIN W B, et al. A high coverage pseudotargeted lipidomics method based on three-phase liquid extraction and segment data-dependent acquisition using UHPLC-MS/MS with application to a study of depression rats [J]. Analytical and Bioanalytical Chemistry, 2021, 413(15): 3975-3986. doi: 10.1007/s00216-021-03349-w
[108] XU S L, LV X, WU B F, et al. Pseudotargeted lipidomics strategy enabling comprehensive profiling and precise lipid structural elucidation of polyunsaturated lipid-rich Echium oil [J]. Journal of Agricultural and Food Chemistry, 2021, 69(32): 9012-9024. doi: 10.1021/acs.jafc.0c07268
[109] XUAN Q H, HU C X, YU D, et al. Development of a high coverage pseudotargeted lipidomics method based on ultra-high performance liquid chromatography-mass spectrometry [J]. Analytical Chemistry, 2018, 90(12): 7608-7616. doi: 10.1021/acs.analchem.8b01331
[110] LUO P, YIN P Y, ZHANG W J, et al. Optimization of large-scale pseudotargeted metabolomics method based on liquid chromatography-mass spectrometry [J]. Journal of Chromatography A, 2016, 1437: 127-136. doi: 10.1016/j.chroma.2016.01.078
[111] ZHAO J J, GUO X M, WANG X C, et al. A chemometric strategy to automatically screen selected ion monitoring ions for gas chromatography-mass spectrometry-based pseudotargeted metabolomics [J]. Journal of Chromatography A, 2022, 1664: 462801. doi: 10.1016/j.chroma.2021.462801
[112] MOUNICOU S, SZPUNAR J, LOBINSKI R. Metallomics: the concept and methodology [J]. Chemical Society Reviews, 2009, 38(4): 1119-1138. doi: 10.1039/b713633c
[113] CHEN C J, HSUEH Y M, LAI M S, et al. Increased prevalence of hypertension and long-term arsenic exposure [J]. Hypertension, 1995, 25(1): 53-60. doi: 10.1161/01.HYP.25.1.53
[114] FARZAN S F, CHEN Y, WU F, et al. Blood pressure changes in relation to arsenic exposure in a US pregnancy cohort [J]. Environmental Health Perspectives, 2015, 123(10): 999-1006. doi: 10.1289/ehp.1408472
[115] NAVAS-ACIEN A. Arsenic exposure and prevalence of type 2 diabetes in US adults [J]. JAMA, 2008, 300(7): 814. doi: 10.1001/jama.300.7.814
[116] WANG X X, MU X L, ZHANG J, et al. Serum metabolomics reveals that arsenic exposure disrupted lipid and amino acid metabolism in rats: A step forward in understanding chronic arsenic toxicity [J]. Metallomics, 2015, 7(3): 544-552. doi: 10.1039/C5MT00002E
[117] LI H, WANG M, LIANG Q D, et al. Urinary metabolomics revealed arsenic exposure related to metabolic alterations in general Chinese pregnant women [J]. Journal of Chromatography A, 2017, 1479: 145-152. doi: 10.1016/j.chroma.2016.12.007
[118] SPRATLEN M J, GRAU-PEREZ M, UMANS J G, et al. Targeted metabolomics to understand the association between arsenic metabolism and diabetes-related outcomes: Preliminary evidence from the Strong Heart Family Study [J]. Environmental Research, 2019, 168: 146-157. doi: 10.1016/j.envres.2018.09.034
[119] CHEN S, ZHANG M Y, BO L, et al. Metabolomic analysis of the toxic effect of chronic exposure of cadmium on rat urine [J]. Environmental Science and Pollution Research International, 2018, 25(4): 3765-3774. doi: 10.1007/s11356-017-0774-8
[120] ZENG T, LIANG Y S, CHEN J Y, et al. Urinary metabolic characterization with nephrotoxicity for residents under cadmium exposure [J]. Environment International, 2021, 154: 106646. doi: 10.1016/j.envint.2021.106646
[121] EDWARDS M. Fetal death and reduced birth rates associated with exposure to lead-contaminated drinking water [J]. Environmental Science & Technology, 2014, 48(1): 739-746.
[122] MIELKE H W, ZAHRAN S. The urban rise and fall of air lead (Pb) and the latent surge and retreat of societal violence [J]. Environment International, 2012, 43: 48-55. doi: 10.1016/j.envint.2012.03.005
[123] EGUCHI A, NOMIYAMA K, SAKURAI K, et al. Alterations in urinary metabolomic profiles due to lead exposure from a lead-acid battery recycling site [J]. Environmental Pollution, 2018, 242: 98-105. doi: 10.1016/j.envpol.2018.06.071
[124] KELLY R S, BAYNE H, SPIRO A II, et al. Metabolomic signatures of lead exposure in the VA Normative Aging Study [J]. Environmental Research, 2020, 190: 110022. doi: 10.1016/j.envres.2020.110022
[125] SUN R L, XU K, JI S B, et al. Benzene exposure induces gut microbiota dysbiosis and metabolic disorder in mice [J]. Science of the Total Environment, 2020, 705: 135879. doi: 10.1016/j.scitotenv.2019.135879
[126] JI H N, SONG N N, REN J, et al. Metabonomics reveals bisphenol A affects fatty acid and glucose metabolism through activation of LXR in the liver of male mice [J]. Science of the Total Environment, 2020, 703: 134681. doi: 10.1016/j.scitotenv.2019.134681
[127] LAW K L, THOMPSON R C. Microplastics in the seas [J]. Science, 2014, 345(6193): 144-145. doi: 10.1126/science.1254065
[128] HUANG W, WANG X H, CHEN D Y, et al. Toxicity mechanisms of polystyrene microplastics in marine mussels revealed by high-coverage quantitative metabolomics using chemical isotope labeling liquid chromatography mass spectrometry [J]. Journal of Hazardous Materials, 2021, 417: 126003. doi: 10.1016/j.jhazmat.2021.126003
[129] YANG L X, LIU Y P, CUI Z, et al. Metabolomic mechanisms of short chain chlorinated paraffins toxicity in rats [J]. Environmental Research, 2021, 197: 111060. doi: 10.1016/j.envres.2021.111060
[130] WANG Z H, ZHENG Y J, ZHAO B X, et al. Human metabolic responses to chronic environmental polycyclic aromatic hydrocarbon exposure by a metabolomic approach [J]. Journal of Proteome Research, 2015, 14(6): 2583-2593. doi: 10.1021/acs.jproteome.5b00134
[131] MIDGETT K, PEDEN-ADAMS M M, GILKESON G S, et al. In vitro evaluation of the effects of perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) on IL-2 production in human T-cells [J]. Journal of Applied Toxicology, 2015, 35(5): 459-465. doi: 10.1002/jat.3037
[132] LI R, GUO C, TSE W K F, et al. Metabolomic analysis reveals metabolic alterations of human peripheral blood lymphocytes by perfluorooctanoic acid [J]. Chemosphere, 2020, 239: 124810. doi: 10.1016/j.chemosphere.2019.124810
[133] XU M Y, WANG P, SUN Y J, et al. Metabolomic analysis for combined hepatotoxicity of chlorpyrifos and cadmium in rats [J]. Toxicology, 2017, 384: 50-58. doi: 10.1016/j.tox.2017.04.008
[134] LIU F F, CHEN X L, LIU Y S, et al. Serum cardiovascular-related metabolites disturbance exposed to different heavy metal exposure scenarios [J]. Journal of Hazardous Materials, 2021, 415: 125590. doi: 10.1016/j.jhazmat.2021.125590