Secondary or Specialized Metabolites, or Natural Products: A Case Study of Untargeted LC–QTOF Auto-MS/MS Analysis
The large structural diversity of specialized metabolites represents a substantial challenge in untargeted metabolomics. Modern LC–QTOF instruments can provide three- to four-digit numbers of auto-MS/MS spectra from sample sets. This case study utilizes twelve structurally closely related flavonol g...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Cells |
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| Online Access: | https://www.mdpi.com/2073-4409/11/6/1025 |
| _version_ | 1797472360317583360 |
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| author | Franz Hadacek |
| author_facet | Franz Hadacek |
| author_sort | Franz Hadacek |
| collection | DOAJ |
| description | The large structural diversity of specialized metabolites represents a substantial challenge in untargeted metabolomics. Modern LC–QTOF instruments can provide three- to four-digit numbers of auto-MS/MS spectra from sample sets. This case study utilizes twelve structurally closely related flavonol glycosides, characteristic specialized metabolites of plant tissues, some of them isomeric and isobaric, to illustrate the possibilities and limitations of their identification. This process requires specific software tools that perform peak picking and feature alignment after spectral deconvolution and facilitate molecular structure base searching with subsequent <i>in silico</i> fragmentation to obtain initial ideas about possible structures. The final assignment of a putative identification, so long as spectral databases are not complete enough, requires structure searches in a chemical reference database, such as SciFinder<sup>n</sup>, in attempts to obtain additional information about specific product ions of a metabolite candidate or check its feasibility. The highlighted problems in this process not only apply to specialized metabolites in plants but to those occurring in other organisms as well. This case study is aimed at providing guidelines for all researchers who obtain data from such analyses but are interested in deeper information than just Venn diagrams of the feature distribution in their sample groups. |
| first_indexed | 2024-03-09T20:00:45Z |
| format | Article |
| id | doaj.art-32cef64f3e6a402aa35ead6730ba7283 |
| institution | Directory Open Access Journal |
| issn | 2073-4409 |
| language | English |
| last_indexed | 2024-03-09T20:00:45Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cells |
| spelling | doaj.art-32cef64f3e6a402aa35ead6730ba72832023-11-24T00:44:46ZengMDPI AGCells2073-44092022-03-01116102510.3390/cells11061025Secondary or Specialized Metabolites, or Natural Products: A Case Study of Untargeted LC–QTOF Auto-MS/MS AnalysisFranz Hadacek0Plant Biochemistry, Albrecht-von-Haller Institute for Plant Sciences, Georg-August University of Goettingen, 37073 Goettingen, GermanyThe large structural diversity of specialized metabolites represents a substantial challenge in untargeted metabolomics. Modern LC–QTOF instruments can provide three- to four-digit numbers of auto-MS/MS spectra from sample sets. This case study utilizes twelve structurally closely related flavonol glycosides, characteristic specialized metabolites of plant tissues, some of them isomeric and isobaric, to illustrate the possibilities and limitations of their identification. This process requires specific software tools that perform peak picking and feature alignment after spectral deconvolution and facilitate molecular structure base searching with subsequent <i>in silico</i> fragmentation to obtain initial ideas about possible structures. The final assignment of a putative identification, so long as spectral databases are not complete enough, requires structure searches in a chemical reference database, such as SciFinder<sup>n</sup>, in attempts to obtain additional information about specific product ions of a metabolite candidate or check its feasibility. The highlighted problems in this process not only apply to specialized metabolites in plants but to those occurring in other organisms as well. This case study is aimed at providing guidelines for all researchers who obtain data from such analyses but are interested in deeper information than just Venn diagrams of the feature distribution in their sample groups.https://www.mdpi.com/2073-4409/11/6/1025untargeted metabolomicsauto-MS/MS spectraspecialized metabolitesidentification |
| spellingShingle | Franz Hadacek Secondary or Specialized Metabolites, or Natural Products: A Case Study of Untargeted LC–QTOF Auto-MS/MS Analysis Cells untargeted metabolomics auto-MS/MS spectra specialized metabolites identification |
| title | Secondary or Specialized Metabolites, or Natural Products: A Case Study of Untargeted LC–QTOF Auto-MS/MS Analysis |
| title_full | Secondary or Specialized Metabolites, or Natural Products: A Case Study of Untargeted LC–QTOF Auto-MS/MS Analysis |
| title_fullStr | Secondary or Specialized Metabolites, or Natural Products: A Case Study of Untargeted LC–QTOF Auto-MS/MS Analysis |
| title_full_unstemmed | Secondary or Specialized Metabolites, or Natural Products: A Case Study of Untargeted LC–QTOF Auto-MS/MS Analysis |
| title_short | Secondary or Specialized Metabolites, or Natural Products: A Case Study of Untargeted LC–QTOF Auto-MS/MS Analysis |
| title_sort | secondary or specialized metabolites or natural products a case study of untargeted lc qtof auto ms ms analysis |
| topic | untargeted metabolomics auto-MS/MS spectra specialized metabolites identification |
| url | https://www.mdpi.com/2073-4409/11/6/1025 |
| work_keys_str_mv | AT franzhadacek secondaryorspecializedmetabolitesornaturalproductsacasestudyofuntargetedlcqtofautomsmsanalysis |