Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks

Abstract

Although recent developments in MS have enabled the identification and quantification of hundreds of phosphorylation sites from a given biological sample, phosphoproteome analysis by MS has been plagued by inconsistent reproducibility arising from automated selection of precursor ions for fragmentation, identification, and quantification. To address this challenge, we have developed a new MS-based strategy, based on multiple reaction monitoring of stable isotope-labeled peptides, that enables highly reproducible quantification of hundreds of nodes (phosphorylation sites) within a signaling network and across multiple conditions simultaneously. We have applied this strategy to quantify temporal phosphorylation profiles of 222 tyrosine phosphorylated peptides across seven time points following EGF treatment, including 31 tyrosine phosphorylation sites not previously known to be regulated by EGF stimulation. With this approach, 88% of the signaling nodes were reproducibly quantified in four analyses, as compared with only 34% by typical information-dependent analysis. As a result of the improved reproducibility, full temporal phosphorylation profiles were generated for an additional 104 signaling nodes with the multiple reaction monitoring strategy, an 88% increase in our coverage of the signaling network. This method is broadly applicable to multiple signaling networks and to a variety of samples, including quantitative analysis of signaling networks in clinical samples. Using this approach, it should now be possible to routinely monitor the phosphorylation status of hundreds of nodes across multiple biological conditions. epidermal growth factor receptor ͉ mass spectrometry ͉ signal transduction ͉ tyrosine phosphorylation L igand binding to cell surface receptors activates multiple protein tyrosine phosphorylation-mediated signaling cascades that regulate many cell biological processes, including proliferation, differentiation, migration, and cell death (1-3). To mechanistically define the relationship between signaling networks and downstream biological responses, it is necessary to quantify the dynamics of protein phosphorylation sites across multiple cell states and to correlate this information to quantitative phenotypic measurements. Until recently, antibody-based assays (e.g., FACS, Western blots, tissue microarrays) have been favored for most signaling network studies. These assays provide excellent quantitative information on the phosphorylation status of selected nodes within the network, but require a priori knowledge of the proteins and phosphorylation sites to be studied and are limited by the need to have high-quality, non-cross-reactive antibodies recognizing specific sites within the network. By comparison, analysis of protein phosphorylation by MS provides the capability of identifying novel phosphorylation sites on novel proteins within the network, requires minimal a priori knowledge, and is therefore compatible with both well and poorly characterized signaling networks. Recent developments in M...

Applications of targeted proteomics in systems biology and translational medicine

“…Darwanto and coworkers quantified H2B ubiquitination and H3 K79 methylation in the U937 human leukemia cell line and proposed a crosstalk regulatory mechanism between these two modifications 65. Wolf‐Yadlin and coworkers examined an EFGR network of 222 tyrosine phosphopeptides across seven time points following EGF stimulation of 184A1 HMEC cells, demonstrating excellent sensitivity, robust quantitation, and throughput 66. A useful case study and tutorial for targeted proteomics with enrichment is presented in Rardin and coworkers, in which they detail a method for measuring lysine acetylated peptides from mitochondria in mouse liver and targeted quantitation of a lysine acetylation site in succinate dehydrogenase A 67.…”

A comprehensive analysis of the Streptococcus pyogenes and human plasma protein interaction network

“…To omit the protein fractionation step and still maintain the same level of sensitivity we turned to SRM. SRM is associated with high sensitivity and specificity enabling quantification of the majority of the target proteins in 1D--LC--SRM--MS. 30 By pooling high, medium and low abundant proteins in separate transitions sets the sample load was optimized for the individual transitions sets. To reduce the number 8 of target proteins certain proteins were removed prior to the SRM analysis.…”

Network‐based drugs and biomarkers

“…In particular, we have recently shown that, integrating systems genetics data [19] with phospho-proteomics data and computational models of cellular kinase specificity [11], we could derive an integrative network model of JNK regulation in the fruit fly [12]. This network can now serve as a framework for future targeted proteomics studies [20,21] in cancer cells (in vitro and in vivo) to define how the network evolves and changes during cancer progression. We are now actively pursuing the integration of data from deep sequencing, functional genomics and extreme-throughput microscopy and mass spectrometry to perform network medicine and systems-level modelling of cancer metastasis [4,18].…”

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