001     291925
005     20241030142645.0
024 7 _ |a 10.3390/metabo14070370
|2 doi
024 7 _ |a pmid:39057693
|2 pmid
024 7 _ |a pmc:PMC11279291
|2 pmc
024 7 _ |a altmetric:165860480
|2 altmetric
037 _ _ |a DKFZ-2024-01537
041 _ _ |a English
082 _ _ |a 540
100 1 _ |a Kipura, Tobias
|0 0009-0007-4733-893X
|b 0
245 _ _ |a Automated Liquid Handling Extraction and Rapid Quantification of Underivatized Amino Acids and Tryptophan Metabolites from Human Serum and Plasma Using Dual-Column U(H)PLC-MRM-MS and Its Application to Prostate Cancer Study.
260 _ _ |a Basel
|c 2024
|b MDPI
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1722340138_32764
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Amino acids (AAs) and their metabolites are important building blocks, energy sources, and signaling molecules associated with various pathological phenotypes. The quantification of AA and tryptophan (TRP) metabolites in human serum and plasma is therefore of great diagnostic interest. Therefore, robust, reproducible sample extraction and processing workflows as well as rapid, sensitive absolute quantification are required to identify candidate biomarkers and to improve screening methods. We developed a validated semi-automated robotic liquid extraction and processing workflow and a rapid method for absolute quantification of 20 free, underivatized AAs and six TRP metabolites using dual-column U(H)PLC-MRM-MS. The extraction and sample preparation workflow in a 96-well plate was optimized for robust, reproducible high sample throughput allowing for transfer of samples to the U(H)PLC autosampler directly without additional cleanup steps. The U(H)PLC-MRM-MS method, using a mixed-mode reversed-phase anion exchange column with formic acid and a high-strength silica reversed-phase column with difluoro-acetic acid as mobile phase additive, provided absolute quantification with nanomolar lower limits of quantification within 7.9 min. The semi-automated extraction workflow and dual-column U(H)PLC-MRM-MS method was applied to a human prostate cancer study and was shown to discriminate between treatment regimens and to identify metabolites responsible for discriminating between healthy controls and patients on active surveillance.
536 _ _ |a 312 - Funktionelle und strukturelle Genomforschung (POF4-312)
|0 G:(DE-HGF)POF4-312
|c POF4-312
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a LC-MS
|2 Other
650 _ 7 |a amino acids
|2 Other
650 _ 7 |a automation
|2 Other
650 _ 7 |a mixed-mode chromatography
|2 Other
650 _ 7 |a prostate cancer
|2 Other
650 _ 7 |a tryptophan metabolites analysis
|2 Other
700 1 _ |a Hotze, Madlen
|0 0009-0001-1816-2089
|b 1
700 1 _ |a Hofer, Alexa
|b 2
700 1 _ |a Egger, Anna-Sophia
|b 3
700 1 _ |a Timpen, Lea E
|0 0009-0003-3207-0230
|b 4
700 1 _ |a Opitz, Christiane
|0 P:(DE-He78)14aa02d2ca0515d0c53f1d6678e3ca34
|b 5
|u dkfz
700 1 _ |a Townsend, Paul A
|0 0000-0001-8956-9508
|b 6
700 1 _ |a Gethings, Lee A
|b 7
700 1 _ |a Thedieck, Kathrin
|b 8
700 1 _ |a Kwiatkowski, Marcel
|0 0000-0002-5804-6031
|b 9
773 _ _ |a 10.3390/metabo14070370
|g Vol. 14, no. 7, p. 370 -
|0 PERI:(DE-600)2662251-8
|n 7
|p 370
|t Metabolites
|v 14
|y 2024
|x 2218-1989
856 4 _ |u https://inrepo02.dkfz.de/record/291925/files/metabolites-14-00370.pdf
856 4 _ |u https://inrepo02.dkfz.de/record/291925/files/metabolites-14-00370.pdf?subformat=pdfa
|x pdfa
909 C O |o oai:inrepo02.dkfz.de:291925
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 5
|6 P:(DE-He78)14aa02d2ca0515d0c53f1d6678e3ca34
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-312
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Funktionelle und strukturelle Genomforschung
|x 0
914 1 _ |y 2024
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b METABOLITES : 2022
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2023-04-12T15:01:13Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2023-04-12T15:01:13Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2023-04-12T15:01:13Z
915 _ _ |a Creative Commons Attribution CC BY (No Version)
|0 LIC:(DE-HGF)CCBYNV
|2 V:(DE-HGF)
|b DOAJ
|d 2023-04-12T15:01:13Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2023-10-26
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2023-10-26
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2023-10-26
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2023-10-26
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2023-10-26
920 1 _ |0 I:(DE-He78)B350-20160331
|k B350
|l Metabolischer Crosstalk bei Krebserkrankungen
|x 0
920 1 _ |0 I:(DE-He78)HD01-20160331
|k HD01
|l DKTK HD zentral
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)B350-20160331
980 _ _ |a I:(DE-He78)HD01-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21