{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T01:42:35Z","timestamp":1790905355615,"version":"4.1.0"},"reference-count":32,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":23,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Current Protocols"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Plant\u2010based multiomics approaches provide powerful tools for elucidating metabolic regulation, biochemical diversity, and functional responses to genetic and environmental variation. However, plant matrices pose unique analytical challenges due to their chemical complexity, high levels of secondary metabolites, and strong matrix effects that can compromise reproducibility if workflows are not carefully standardized. This article presents a comprehensive and integrated set of protocols for untargeted plant metabolomics, lipidomics, and proteomics, coupled with robust data processing, statistical analysis, and multiomics integration strategies.<\/jats:p>\n                  <jats:p>The protocols describe harmonized workflows for sample collection, preparation, and analysis using gas chromatography\u2010mass spectrometry (GC\u2010MS)\u2010based metabolomics, liquid chromatography\u2010quadrupole time\u2010of\u2010flight mass spectrometry (LC\u2010qTOF\u2010MS)\u2010based metabolomics, liquid chromatography\u2010mass spectrometry (LC\u2010MS)\u2010based lipidomics, and microflow LC\u2010MS\/MS\u2010based proteomics. Emphasis is placed on critical parameters specific to plant matrices, including complete solvent removal prior to GC\u2010MS derivatization, optimized MS\/MS acquisition strategies for high\u2010confidence annotation, and quality control\u2010driven experimental design. Detailed guidance is provided for instrument maintenance, QC strategies, and prevention of analytical artifacts.<\/jats:p>\n                  <jats:p>In addition, the article outlines best practices for data preprocessing, metabolite and lipid annotation, statistical analysis, pathway mapping, and integration of metabolomics with proteomics data to support biologically meaningful interpretation. Collectively, these protocols enable reproducible, high\u2010quality plant multiomics studies and are suitable for both method development and large\u2010scale comparative analyses across plant species, tissues, and experimental conditions. \u00a9 2026 Wiley Periodicals LLC.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Basic Protocol 1<\/jats:bold>\n                    : Plant material collection, handling, and extraction processing\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Support Protocol 1<\/jats:bold>\n                    : Soxhlet extraction\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Alternate Protocol 1<\/jats:bold>\n                    : Dichloromethane (DME)\u2010based sample preparation for lipidomics\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Alternate Protocol 2<\/jats:bold>\n                    : Methyl\u2010tert\u2010butyl ether (MTBE)\u2010based sample preparation for lipidomics\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Basic Protocol 2<\/jats:bold>\n                    : GC\u2010MS\u2010based metabolomics analysis\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Basic Protocol 3<\/jats:bold>\n                    : LC\u2010qTOF\u2010MS\u2010based metabolomics analysis\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Basic Protocol 4<\/jats:bold>\n                    : LC\u2010MS\u2010based lipidomics analysis\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Basic Protocol 5<\/jats:bold>\n                    : Microflow LC\u2010MS\/MS\u2010based proteomics analysis\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Basic Protocol 6<\/jats:bold>\n                    : Multiomics data integration and statistical analysis\n                  <\/jats:p>","DOI":"10.1002\/cpz1.70334","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:02:24Z","timestamp":1773000144000},"source":"Crossref","is-referenced-by-count":2,"title":["Mass Spectrometry\u2010Based Integrated Multiomics Platforms for Medicinal Plant Analysis"],"prefix":"10.1002","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3353-2869","authenticated-orcid":false,"given":"Cemil Can","family":"Eylem","sequence":"first","affiliation":[{"name":"Faculty of Pharmacy, Department of Analytical Chemistry Hacettepe University  Ankara T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6503-0142","authenticated-orcid":false,"given":"Sevilay","family":"Erdo\u011fan Kablan","sequence":"additional","affiliation":[{"name":"Faculty of Pharmacy, Department of Analytical Chemistry Hacettepe University  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