Matches in SemOpenAlex for { <https://semopenalex.org/work/W2097662842> ?p ?o ?g. }
- W2097662842 endingPage "1791" @default.
- W2097662842 startingPage "1779" @default.
- W2097662842 abstract "RATIONALE An ideal method for bioanalytical applications would deliver spatially resolved quantitative information in real time and without sample preparation. In reality these requirements can typically not be met by a single analytical technique. Therefore, we combine different mass spectrometry approaches: chromatographic separation, ambient ionization and imaging techniques, in order to obtain comprehensive information about metabolites in complex biological samples. METHODS Samples were analyzed by laser desorption followed by electrospray ionization (LD-ESI) as an ambient ionization technique, by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging for spatial distribution analysis and by high-performance liquid chromatography/electrospray ionization mass spectrometry (HPLC/ESI-MS) for quantitation and validation of compound identification. All MS data were acquired with high mass resolution and accurate mass (using orbital trapping and ion cyclotron resonance mass spectrometers). Grape berries were analyzed and evaluated in detail, whereas wheat seeds and mouse brain tissue were analyzed in proof-of-concept experiments. RESULTS In situ measurements by LD-ESI without any sample preparation allowed for fast screening of plant metabolites on the grape surface. MALDI imaging of grape cross sections at 20 µm pixel size revealed the detailed distribution of metabolites which were in accordance with their biological function. HPLC/ESI-MS was used to quantify 13 anthocyanin species as well as to separate and identify isomeric compounds. A total of 41 metabolites (amino acids, carbohydrates, anthocyanins) were identified with all three approaches. Mass accuracy for all MS measurements was better than 2 ppm (root mean square error). CONCLUSIONS The combined approach provides fast screening capabilities, spatial distribution information and the possibility to quantify metabolites. Accurate mass measurements proved to be critical in order to reliably combine data from different MS techniques. Initial results on the mycotoxin deoxynivalenol (DON) in wheat seed and phospholipids in mouse brain as a model for mammalian tissue indicate a broad applicability of the presented workflow. Copyright © 2014 John Wiley & Sons, Ltd." @default.
- W2097662842 created "2016-06-24" @default.
- W2097662842 creator A5025334913 @default.
- W2097662842 creator A5048603885 @default.
- W2097662842 creator A5056196765 @default.
- W2097662842 creator A5060169968 @default.
- W2097662842 creator A5070984111 @default.
- W2097662842 creator A5084641762 @default.
- W2097662842 creator A5088499466 @default.
- W2097662842 date "2014-06-30" @default.
- W2097662842 modified "2023-10-16" @default.
- W2097662842 title "A comprehensive high-resolution mass spectrometry approach for characterization of metabolites by combination of ambient ionization, chromatography and imaging methods" @default.
- W2097662842 cites W1563191776 @default.
- W2097662842 cites W1599833391 @default.
- W2097662842 cites W1964817949 @default.
- W2097662842 cites W1968985568 @default.
- W2097662842 cites W1972329860 @default.
- W2097662842 cites W1974267906 @default.
- W2097662842 cites W1974830441 @default.
- W2097662842 cites W1977737260 @default.
- W2097662842 cites W1978748421 @default.
- W2097662842 cites W1979730205 @default.
- W2097662842 cites W1982026768 @default.
- W2097662842 cites W1987525288 @default.
- W2097662842 cites W1995262184 @default.
- W2097662842 cites W1996525310 @default.
- W2097662842 cites W1999046244 @default.
- W2097662842 cites W2007116883 @default.
- W2097662842 cites W2008416430 @default.
- W2097662842 cites W2014160033 @default.
- W2097662842 cites W2018760984 @default.
- W2097662842 cites W2019105733 @default.
- W2097662842 cites W2022220175 @default.
- W2097662842 cites W2023096047 @default.
- W2097662842 cites W2025228136 @default.
- W2097662842 cites W2027866734 @default.
- W2097662842 cites W2028505900 @default.
- W2097662842 cites W2030049097 @default.
- W2097662842 cites W2033278243 @default.
- W2097662842 cites W2037837681 @default.
- W2097662842 cites W2042172323 @default.
- W2097662842 cites W2043705684 @default.
- W2097662842 cites W2044101419 @default.
- W2097662842 cites W2044712639 @default.
- W2097662842 cites W2047305526 @default.
- W2097662842 cites W2048474321 @default.
- W2097662842 cites W2051683517 @default.
- W2097662842 cites W2051775822 @default.
- W2097662842 cites W2054014227 @default.
- W2097662842 cites W2055036923 @default.
- W2097662842 cites W2056171394 @default.
- W2097662842 cites W2066440553 @default.
- W2097662842 cites W2087082980 @default.
- W2097662842 cites W2101004170 @default.
- W2097662842 cites W2105976216 @default.
- W2097662842 cites W2106074866 @default.
- W2097662842 cites W2108618189 @default.
- W2097662842 cites W2112413708 @default.
- W2097662842 cites W2117425034 @default.
- W2097662842 cites W2119716449 @default.
- W2097662842 cites W2121767302 @default.
- W2097662842 cites W2132514608 @default.
- W2097662842 cites W2134003086 @default.
- W2097662842 cites W2140656365 @default.
- W2097662842 cites W2142116755 @default.
- W2097662842 cites W2144713863 @default.
- W2097662842 cites W2150823512 @default.
- W2097662842 cites W2151935868 @default.
- W2097662842 cites W2153882915 @default.
- W2097662842 cites W2153894482 @default.
- W2097662842 cites W2155892228 @default.
- W2097662842 cites W2268793204 @default.
- W2097662842 cites W2301411269 @default.
- W2097662842 cites W4249005591 @default.
- W2097662842 cites W4362231430 @default.
- W2097662842 cites W2644804608 @default.
- W2097662842 doi "https://doi.org/10.1002/rcm.6960" @default.
- W2097662842 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25559448" @default.
- W2097662842 hasPublicationYear "2014" @default.
- W2097662842 type Work @default.
- W2097662842 sameAs 2097662842 @default.
- W2097662842 citedByCount "26" @default.
- W2097662842 countsByYear W20976628422014 @default.
- W2097662842 countsByYear W20976628422015 @default.
- W2097662842 countsByYear W20976628422016 @default.
- W2097662842 countsByYear W20976628422017 @default.
- W2097662842 countsByYear W20976628422018 @default.
- W2097662842 countsByYear W20976628422019 @default.
- W2097662842 countsByYear W20976628422020 @default.
- W2097662842 countsByYear W20976628422021 @default.
- W2097662842 countsByYear W20976628422022 @default.
- W2097662842 crossrefType "journal-article" @default.
- W2097662842 hasAuthorship W2097662842A5025334913 @default.
- W2097662842 hasAuthorship W2097662842A5048603885 @default.
- W2097662842 hasAuthorship W2097662842A5056196765 @default.
- W2097662842 hasAuthorship W2097662842A5060169968 @default.
- W2097662842 hasAuthorship W2097662842A5070984111 @default.
- W2097662842 hasAuthorship W2097662842A5084641762 @default.