Hyperplexing: A Method for Higher-Order Multiplexed Quantitative Proteomics Provides a Map of the Dynamic Response to Rapamycin in Yeast

A dual-labeling strategy enables high-throughput analysis of multiple samples by mass spectrometry. Improving Throughput Mass spectrometry is a powerful tool for monitoring changes in protein abundance and posttranslational modifications. Dephoure and Gygi describe a mass spectrometry approach that enables the analysis of samples from multiple conditions simultaneously by combining two different labeling methods, which they call “hyperplexing.” Using this method, the authors monitored changes in protein abundance in yeast in response to the drug rapamycin, an inhibitor of the kinase TOR, that has been clinically used as an immunosuppressant and anticancer agent. Rapamycin-induced changes in the abundance of proteins in a six-point time course with biological triplicates were monitored in a single experiment. Statistical analysis provided a high-confidence set of proteins that increased or decreased in abundance in response to this drug. This technique should facilitate the application of quantitative mass spectrometry to the analysis of dynamic cellular events. Large-scale quantitative proteomics can provide a near-global view of cellular protein abundance. Yet, the time, effort, and expertise required to achieve reasonable protein coverage and reliable quantification have limited the broad application of this technology. To fully leverage mass spectrometry for the elucidation of biological systems requires sufficient throughput to monitor dynamic changes across conditions and to enable replicate analysis to provide statistical power. We report a straightforward approach to increase the multiplexing capacity of quantitative mass spectrometry, which provides a platform for the analysis of cellular signaling pathways. Using triplex metabolic labeling and six-plex isobaric tags, we monitored changes in protein abundance from 18 samples simultaneously, performing biological triplicates of a six-point time course of rapamycin-stimulated yeast. The data set provides temporal abundance profiles for thousands of yeast proteins, highlighting the complex cellular roles of the TOR (target of rapamycin) pathway.

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