Applying label-free quantitation to top down proteomics

Ioanna Ntai, Kyunggon Kim, Ryan T. Fellers, Owen S. Skinner, Archer D. Smith, Bryan P. Early, John P. Savaryn, Richard D. Leduc, Paul Martin Thomas, Neil L Kelleher*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

Abstract

With the prospect of resolving whole protein molecules into their myriad proteoforms on a proteomic scale, the question of their quantitative analysis in discovery mode comes to the fore. Here, we demonstrate a robust pipeline for the identification and stringent scoring of abundance changes of whole protein forms <30 kDa in a complex system. The input is ∼100-400 μg of total protein for each biological replicate, and the outputs are graphical displays depicting statistical confidence metrics for each proteoform (i.e., a volcano plot and representations of the technical and biological variation). A key part of the pipeline is the hierarchical linear model that is tailored to the original design of the study. Here, we apply this new pipeline to measure the proteoform-level effects of deleting a histone deacetylase (rpd3) in S. cerevisiae. Over 100 proteoform changes were detected above a 5% false positive threshold in WT vs the Δrpd3 mutant, including the validating observation of hyperacetylation of histone H4 and both H2B isoforms. Ultimately, this approach to label-free top down proteomics in discovery mode is a critical technical advance for testing the hypothesis that whole proteoforms can link more tightly to complex phenotypes in cell and disease biology than do peptides created in shotgun proteomics.

Original languageEnglish (US)
Pages (from-to)4961-4968
Number of pages8
JournalAnalytical Chemistry
Volume86
Issue number10
DOIs
StatePublished - May 20 2014

ASJC Scopus subject areas

  • Analytical Chemistry

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