Immobilized Metal Affinity Chromatography Coupled to Multiple Reaction Monitoring Enables Reproducible Quantification of Phospho-signaling
Year: 2015
Type: article
Abstract: A major goal in cell signaling research is the quantification of phosphorylation pharmacodynamics following perturbations. Traditional methods of studying cellular phospho-signaling measure one analyte at a time with poor standardization, rendering them inadequate for interrogating network biology and contributing to the irreproducibility of preclinical research. In this study, we test the feasibility of circumventing these issues by coupling immobilized metal affinity chromatography (IMAC)-based enrichment of phosphopeptides with targeted, multiple reaction monitoring (MRM) mass spectrometry to achieve precise, specific, standardized, multiplex quantification of phospho-signaling responses. A multiplex immobilized metal affinity chromatography- multiple reaction monitoring assay targeting phospho-analytes responsive to DNA damage was configured, analytically characterized, and deployed to generate phospho-pharmacodynamic curves from primary and immortalized human cells experiencing genotoxic stress. The multiplexed assays demonstrated linear ranges of ≥3 orders of magnitude, median lower limit of quantification of 0.64 fmol on column, median intra-assay variability of 9.3%, median inter-assay variability of 12.7%, and median total CV of 16.0%. The multiplex immobilized metal affinity chromatography- multiple reaction monitoring assay enabled robust quantification of 107 DNA damage-responsive phosphosites from human cells following DNA damage. The assays have been made publicly available as a resource to the community. The approach is generally applicable, enabling wide interrogation of signaling networks.
Source: Molecular & Cellular Proteomics
Institutions: Fred Hutch Cancer Center, University of Washington
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,430
OpenAlex ID: W2189178602
