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For: Rampler E, Abiead YE, Schoeny H, Rusz M, Hildebrand F, Fitz V, Koellensperger G. Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput. Anal Chem 2021;93:519-45. [PMID: 33249827 DOI: 10.1021/acs.analchem.0c04698] [Cited by in Crossref: 49] [Cited by in F6Publishing: 56] [Article Influence: 16.3] [Reference Citation Analysis]
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6 Qu X, Ma J, Gao H, Zhang Y, Zhai J, Gong J, Song Y, Hu T. Integration of metabolomics and proteomics analysis to explore the mechanism of neurotoxicity induced by receipt of isoniazid and rifampicin in mice. Neurotoxicology 2023;94:24-34. [PMID: 36347327 DOI: 10.1016/j.neuro.2022.11.004] [Reference Citation Analysis]
7 Machado S, Barreiros L, Graça AR, Páscoa RN, Segundo MA, Lopes JA. A data mining tool for untargeted biomarkers analysis: Grapes ripening application. Chemometrics and Intelligent Laboratory Systems 2022. [DOI: 10.1016/j.chemolab.2022.104745] [Reference Citation Analysis]
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9 Flasch M, Fitz V, Rampler E, Ezekiel CN, Koellensperger G, Warth B. Integrated Exposomics/Metabolomics for Rapid Exposure and Effect Analyses. JACS Au 2022. [DOI: 10.1021/jacsau.2c00433] [Reference Citation Analysis]
10 Rakusanova S, Fiehn O, Cajka T. Toward building mass spectrometry-based metabolomics and lipidomics atlases for biological and clinical research. TrAC Trends in Analytical Chemistry 2022. [DOI: 10.1016/j.trac.2022.116825] [Reference Citation Analysis]
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12 Li P, Lämmerhofer M. Generation of 13C-Labeled Inositol and Inositol Phosphates by Stable Isotope Labeling Cell Culture for Quantitative Metabolomics. Anal Chem 2022. [DOI: 10.1021/acs.analchem.2c02819] [Reference Citation Analysis]
13 Hohenwallner K, Troppmair N, Panzenboeck L, Kasper C, El Abiead Y, Koellensperger G, Lamp LM, Hartler J, Egger D, Rampler E. Decoding Distinct Ganglioside Patterns of Native and Differentiated Mesenchymal Stem Cells by a Novel Glycolipidomics Profiling Strategy. JACS Au 2022. [DOI: 10.1021/jacsau.2c00230] [Reference Citation Analysis]
14 Jensen-kroll J, Demetrowitsch T, Clawin-rädecker I, Klempt M, Waschina S, Schwarz K. Microbiota independent effects of oligosaccharides on Caco-2 cells -A semi-targeted metabolomics approach using DI-FT-ICR-MS coupled with pathway enrichment analysis. Front Mol Biosci 2022;9. [DOI: 10.3389/fmolb.2022.968643] [Reference Citation Analysis]
15 Mishra AK, Sudalaimuthuasari N, Hazzouri KM, Saeed EE, Shah I, Amiri KMA. Tapping into Plant–Microbiome Interactions through the Lens of Multi-Omics Techniques. Cells 2022;11:3254. [DOI: 10.3390/cells11203254] [Reference Citation Analysis]
16 Cerrato A, Capriotti AL, Cavaliere C, Montone CM, Piovesana S, Laganà A. Novel Aza-Paternò-Büchi Reaction Allows Pinpointing Carbon-Carbon Double Bonds in Unsaturated Lipids by Higher Collisional Dissociation. Anal Chem 2022. [PMID: 36121000 DOI: 10.1021/acs.analchem.2c02549] [Reference Citation Analysis]
17 Song Y, Qu X, Tao L, Gao H, Zhang Y, Zhai J, Gong J, Hu T. Exploration of the underlying mechanisms of isoniazid/rifampicin‐induced liver injury in mice using an integrated proteomics and metabolomics approach. J Biochem & Molecular Tox. [DOI: 10.1002/jbt.23217] [Reference Citation Analysis]
18 Zhang J, Zhang M, Zhang W, Zhu Q, Huo X, Sun C, Ma X, Xiao H. Total flavonoids of Inula japonica alleviated the inflammatory response and oxidative stress in LPS-induced acute lung injury via inhibiting the sEH activity: Insights from lipid metabolomics. Phytomedicine 2022. [DOI: 10.1016/j.phymed.2022.154380] [Reference Citation Analysis]
19 Kirkwood KI, Pratt BS, Shulman N, Tamura K, MacCoss MJ, MacLean BX, Baker ES. Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions. Nat Protoc 2022. [PMID: 35831612 DOI: 10.1038/s41596-022-00714-6] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
20 Ramalingam V, Narendra Kumar N, Harshavardhan M, Sampath Kumar HM, Tiwari AK, Suresh Babu K, Mudiam MKR. Chemical profiling of marine seaweed Halimeda gracilis using UPLC-ESI-Q-TOF-MSE and evaluation of anticancer activity targeting PI3K/AKT and intrinsic apoptosis signaling pathway. Food Research International 2022;157:111394. [DOI: 10.1016/j.foodres.2022.111394] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
21 Petrick LM, Shomron N. AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications. Cell Reports Physical Science 2022;3:100978. [DOI: 10.1016/j.xcrp.2022.100978] [Reference Citation Analysis]
22 Chen X, Peng X, Sun X, Pan L, Shi J, Gao Y, Lei Y, Jiang F, Li R, Liu Y, Xu YJ. Development and Application of Feature-Based Molecular Networking for Phospholipidomics Analysis. J Agric Food Chem 2022;70:7815-25. [PMID: 35709392 DOI: 10.1021/acs.jafc.2c01770] [Reference Citation Analysis]
23 Hoffmann N, Mayer G, Has C, Kopczynski D, Al Machot F, Schwudke D, Ahrends R, Marcus K, Eisenacher M, Turewicz M. A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics. Metabolites 2022;12:584. [DOI: 10.3390/metabo12070584] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Gao H, Song Y, Ma J, Zhai J, Zhang Y, Qu X. Untargeted metabolomics analysis of omeprazole-enhanced chemosensitivity to cisplatin in mice with non-small cell lung cancer. Chem Biol Interact 2022;360:109933. [PMID: 35447140 DOI: 10.1016/j.cbi.2022.109933] [Reference Citation Analysis]
25 Tose LV, Ramirez CE, Michalkova V, Nouzova M, Noriega FG, Fernandez-Lima F. Coupling Stable Isotope Labeling and Liquid Chromatography-Trapped Ion Mobility Spectrometry-Time-of-Flight-Tandem Mass Spectrometry for De Novo Mosquito Ovarian Lipid Studies. Anal Chem 2022. [PMID: 35420029 DOI: 10.1021/acs.analchem.1c05090] [Reference Citation Analysis]
26 Mohd Kamal K, Mahamad Maifiah MH, Abdul Rahim N, Hashim YZH, Abdullah Sani MS, Azizan KA, Gutiérrez-méndez N. Bacterial Metabolomics: Sample Preparation Methods. Biochemistry Research International 2022;2022:1-14. [DOI: 10.1155/2022/9186536] [Reference Citation Analysis]
27 Hohenwallner K, Troppmair N, Panzenboeck L, Kasper C, El Abiead Y, Koellensperger G, Lamp LM, Hartler J, Egger D, Rampler E. Fatty sweet symphony: Decoding distinct ganglioside patterns of native and differentiated mesenchymal stem cells by a novel glycolipidomics profiling strategy.. [DOI: 10.1101/2022.04.11.487866] [Reference Citation Analysis]
28 Gloaguen Y, Kirwan JA, Beule D. Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics. Anal Chem 2022. [PMID: 35290737 DOI: 10.1021/acs.analchem.1c02220] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
29 Carnevale Neto F, Clark TN, Lopes NP, Linington RG. Evaluation of Ion Mobility Spectrometry for Improving Constitutional Assignment in Natural Product Mixtures. J Nat Prod 2022. [PMID: 35235328 DOI: 10.1021/acs.jnatprod.1c01048] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 Costanzo M, Caterino M, Fedele R, Cevenini A, Pontillo M, Barra L, Ruoppolo M. COVIDomics: The Proteomic and Metabolomic Signatures of COVID-19. Int J Mol Sci 2022;23:2414. [PMID: 35269564 DOI: 10.3390/ijms23052414] [Cited by in Crossref: 18] [Cited by in F6Publishing: 19] [Article Influence: 18.0] [Reference Citation Analysis]
31 Bertho G, Lordello L, Chen X, Lucas-Torres C, Oumezziane IE, Caradeuc C, Baudin M, Nuan-Aliman S, Thieblemont C, Baud V, Giraud N. Ultrahigh-Resolution NMR with Water Signal Suppression for a Deeper Understanding of the Action of Antimetabolic Drugs on Diffuse Large B-Cell Lymphoma. J Proteome Res 2022. [PMID: 35119866 DOI: 10.1021/acs.jproteome.1c00914] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
32 Xia Y, Sun J. Statistical Data Analysis of Microbiomes and Metabolomics. ACS In Focus 2022. [DOI: 10.1021/acsinfocus.7e5035] [Reference Citation Analysis]
33 Baesu A, Audet C, Bayen S. Evaluation of different extractions for the metabolite identification of malachite green in brook trout and shrimp. Food Chem 2022;369:130567. [PMID: 34492611 DOI: 10.1016/j.foodchem.2021.130567] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
34 Schoeny H, Rampler E, Binh Chu D, Schoeberl A, Galvez L, Blaukopf M, Kosma P, Koellensperger G. Achieving Absolute Molar Lipid Concentrations: A Phospholipidomics Cross-Validation Study. Anal Chem 2022. [PMID: 35025205 DOI: 10.1021/acs.analchem.1c03743] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 Carcelén JN, Marchante-gayón JM, Rodríguez-gonzález P, Ballesteros A, González JM, Cocho de Juan JÁ, García Alonso JI. Determination of 3-monoiodotyrosine and 3,5-diiodotyrosine in newborn urine and dried urine spots by isotope dilution tandem mass spectrometry. Analyst 2022;147:1329-1340. [DOI: 10.1039/d1an02203b] [Reference Citation Analysis]
36 da Silva Zandonadi F, dos Santos EAF, Marques MS, Sussulini A. Metabolomics: A Powerful Tool to Understand the Schizophrenia Biology. Advances in Experimental Medicine and Biology 2022. [DOI: 10.1007/978-3-030-97182-3_8] [Reference Citation Analysis]
37 Pelle J, Castelli FA, Rudler M, Alioua I, Colsch B, Fenaille F, Junot C, Thabut D, Weiss N. Metabolomics in the understanding and management of hepatic encephalopathy. Anal Biochem 2022;636:114477. [PMID: 34808106 DOI: 10.1016/j.ab.2021.114477] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Arnett LP, Forbes MW, Keunen R, Winnik MA. Scratching the Surface (Modification): Developing a Quantitative Liquid Chromatography-Tandem Mass Spectrometry Method for the Investigation of PEGylated and Non-PEGylated Lipid Mixtures on Lipid-Coated Lanthanide Nanoparticles. Langmuir 2021;37:14605-13. [PMID: 34879202 DOI: 10.1021/acs.langmuir.1c02260] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
39 Kawai T, Matsumori N, Otsuka K. Recent advances in microscale separation techniques for lipidome analysis. Analyst 2021;146:7418-30. [PMID: 34787600 DOI: 10.1039/d1an00967b] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
40 Damont A, Legrand A, Cao C, Fenaille F, Tabet JC. Hydrogen/deuterium exchange mass spectrometry in the world of small molecules. Mass Spectrom Rev 2021;:e21765. [PMID: 34859466 DOI: 10.1002/mas.21765] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
41 Leiser OP, Hobbs EC, Sims AC, Korch GW, Taylor KL. Beyond the List: Bioagent-Agnostic Signatures Could Enable a More Flexible and Resilient Biodefense Posture Than an Approach Based on Priority Agent Lists Alone. Pathogens 2021;10:1497. [PMID: 34832652 DOI: 10.3390/pathogens10111497] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
42 Cerrato A, Aita SE, Capriotti AL, Cavaliere C, Montone CM, Piovesana S, Laganà A. Fully Automatized Detection of Phosphocholine-Containing Lipids through an Isotopically Labeled Buffer Modification Workflow. Anal Chem 2021;93:15042-8. [PMID: 34726396 DOI: 10.1021/acs.analchem.1c02944] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
43 Wang Y, Liu Y, Chen R, Qiao L. Metabolomic Characterization of Cerebrospinal Fluid from Intracranial Bacterial Infection Pediatric Patients: A Pilot Study. Molecules 2021;26:6871. [PMID: 34833963 DOI: 10.3390/molecules26226871] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
44 Bowen JK, Chaparro JM, McCorkle AM, Palumbo E, Prenni JE. The impact of extraction protocol on the chemical profile of cannabis extracts from a single cultivar. Sci Rep 2021;11:21801. [PMID: 34750475 DOI: 10.1038/s41598-021-01378-0] [Reference Citation Analysis]
45 Chen J, Tang M, Xu D. Integrated microfluidic chip coupled to mass spectrometry: A minireview of chip pretreatment methods and applications. Journal of Chromatography Open 2021;1:100021. [DOI: 10.1016/j.jcoa.2021.100021] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
46 Peter KT, Phillips AL, Knolhoff AM, Gardinali PR, Manzano CA, Miller KE, Pristner M, Sabourin L, Sumarah MW, Warth B, Sobus JR. Nontargeted Analysis Study Reporting Tool: A Framework to Improve Research Transparency and Reproducibility. Anal Chem 2021;93:13870-9. [PMID: 34618419 DOI: 10.1021/acs.analchem.1c02621] [Cited by in Crossref: 10] [Cited by in F6Publishing: 14] [Article Influence: 5.0] [Reference Citation Analysis]
47 Wasito H, Hermann G, Fitz V, Troyer C, Hann S, Koellensperger G. Yeast-based reference materials for quantitative metabolomics. Anal Bioanal Chem 2021. [PMID: 34642781 DOI: 10.1007/s00216-021-03694-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
48 Niu L, Sulek K, Vasilopoulou CG, Santos A, Wewer Albrechtsen NJ, Rasmussen S, Meier F, Mann M. Defining NASH from a Multi-Omics Systems Biology Perspective. J Clin Med 2021;10:4673. [PMID: 34682795 DOI: 10.3390/jcm10204673] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
49 Letertre MPM, Giraudeau P, de Tullio P. Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives. Front Mol Biosci 2021;8:698337. [PMID: 34616770 DOI: 10.3389/fmolb.2021.698337] [Cited by in Crossref: 8] [Cited by in F6Publishing: 11] [Article Influence: 4.0] [Reference Citation Analysis]
50 Castelli FA, Rosati G, Moguet C, Fuentes C, Marrugo-Ramírez J, Lefebvre T, Volland H, Merkoçi A, Simon S, Fenaille F, Junot C. Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests. Anal Bioanal Chem 2021. [PMID: 34432105 DOI: 10.1007/s00216-021-03586-z] [Cited by in Crossref: 10] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
51 Herrmann HA, Rusz M, Baier D, Jakupec MA, Keppler BK, Berger W, Koellensperger G, Zanghellini J. Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer. Cancers (Basel) 2021;13:4130. [PMID: 34439283 DOI: 10.3390/cancers13164130] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
52 Aristizabal-Henao JJ, Lemas DJ, Griffin EK, Costa KA, Camacho C, Bowden JA. Metabolomic Profiling of Biological Reference Materials using a Multiplatform High-Resolution Mass Spectrometric Approach. J Am Soc Mass Spectrom 2021. [PMID: 34388338 DOI: 10.1021/jasms.1c00194] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
53 Man L, Klare WP, Dale AL, Cain JA, Cordwell SJ. Integrated mass spectrometry-based multi-omics for elucidating mechanisms of bacterial virulence. Biochem Soc Trans 2021:BST20191088. [PMID: 34374408 DOI: 10.1042/BST20191088] [Reference Citation Analysis]
54 Gupta N, Ramakrishnan S, Wajid S. Emerging role of metabolomics in protein conformational disorders. Expert Rev Proteomics 2021;18:395-410. [PMID: 34227444 DOI: 10.1080/14789450.2021.1948330] [Reference Citation Analysis]
55 Barupal DK, Baygi SF, Wright RO, Arora M. Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics. Front Public Health 2021;9:653599. [PMID: 34178917 DOI: 10.3389/fpubh.2021.653599] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
56 Herrmann HA, Rusz M, Baier D, Jakupec MA, Keppler BK, Berger W, Koellensperger G, Zanghellini J. Thermodynamic genome-scale metabolic modeling of metallodrug resistance in colorectal cancer.. [DOI: 10.1101/2021.06.09.447534] [Reference Citation Analysis]
57 Babiy B, Busto R, Pastor Ó. A normalized signal calibration with a long-term reference improves the robustness of RPLC-MRM/MS lipidomics in plasma. Anal Bioanal Chem 2021;413:4077-90. [PMID: 33907864 DOI: 10.1007/s00216-021-03364-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
58 Schoeny H, Rampler E, El Abiead Y, Hildebrand F, Zach O, Hermann G, Koellensperger G. A combined flow injection/reversed-phase chromatography-high-resolution mass spectrometry workflow for accurate absolute lipid quantification with 13C internal standards. Analyst 2021;146:2591-9. [PMID: 33734229 DOI: 10.1039/d0an02443k] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
59 Rampler E, Hermann G, Grabmann G, El Abiead Y, Schoeny H, Baumgartinger C, Köcher T, Koellensperger G. Benchmarking Non-Targeted Metabolomics Using Yeast-Derived Libraries. Metabolites 2021;11:160. [PMID: 33802096 DOI: 10.3390/metabo11030160] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]