Article ; Online: Developing quantitative assays for six urinary glycoproteins using parallel reaction monitoring, data-independent acquisition, and TMT-based data-dependent acquisition.
2023 Volume 23, Issue 7-8, Page(s) e2200072
Abstract: Quantitative approaches encompassing parallel reaction monitoring (PRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) are commonly used to investigate protein expression profiles. However, analytical performances of assays ... ...
Abstract | Quantitative approaches encompassing parallel reaction monitoring (PRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) are commonly used to investigate protein expression profiles. However, analytical performances of assays developed using PRM, DIA, and Tandem Mass Tag (TMT)-based DDA for quantitative proteomics have yet not been investigated. Here, we developed assays for glycopeptides identified from six glycoproteins, including Leucine-rich alpha-2-glycoprotein (LRG1), Prostaglandin-H2 D-isomerase (PTGDS), Aminopeptidase N (ANPEP), CD63 antigen (CD63), Clusterin (CLU), and Prostatic acid phosphatase (ACPP), using PRM, DDA, and DIA and evaluated the analytical performances of each assay using the different acquisition modes. We also compared assays in each acquisition mode on three different orbitrap instruments: Thermo Fisher Q Exactive, Exploris 480, and Lumos. We found that DIA showed the largest linear range, highest sensitivity, and most reproducibility. We then applied our developed DIA assays to urine samples from non-aggressive (n = 48) and aggressive (n = 35) prostate cancer patients. In conclusion, we developed assays for the six glycoproteins, evaluated the analytical performances of each assay in DIA, PRM, and PRM acquisition modes on three types of mass spectrometry instruments, and chose the DIA assays for the quantitative analysis of urine samples from patients with aggressive and non-aggressive prostate cancer. |
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MeSH term(s) | Male ; Humans ; Reproducibility of Results ; Mass Spectrometry/methods ; Proteomics/methods ; Glycoproteins |
Chemical Substances | Glycoproteins |
Language | English |
Publishing date | 2023-01-11 |
Publishing country | Germany |
Document type | Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural |
ZDB-ID | 2032093-0 |
ISSN | 1615-9861 ; 1615-9853 |
ISSN (online) | 1615-9861 |
ISSN | 1615-9853 |
DOI | 10.1002/pmic.202200072 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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