High Sensitivity Quantitative Proteomics Using Automated Multidimensional Nano-flow Chromatography and Accumulated Ion Monitoring on Quadrupole-Orbitrap-Linear Ion Trap Mass Spectrometer. Academic Article uri icon

Overview

abstract

  • Quantitative proteomics using high-resolution and accuracy mass spectrometry promises to transform our understanding of biological systems and disease. Recent development of parallel reaction monitoring (PRM) using hybrid instruments substantially improved the specificity of targeted mass spectrometry. Combined with high-efficiency ion trapping, this approach also provided significant improvements in sensitivity. Here, we investigated the effects of ion isolation and accumulation on the sensitivity and quantitative accuracy of targeted proteomics using the recently developed hybrid quadrupole-Orbitrap-linear ion trap mass spectrometer. We leveraged ultrahigh efficiency nano-electrospray ionization under optimized conditions to achieve yoctomolar sensitivity with more than seven orders of linear quantitative accuracy. To enable sensitive and specific targeted mass spectrometry, we implemented an automated, two-dimensional (2D) ion exchange-reversed phase nanoscale chromatography system. We found that automated 2D chromatography improved the sensitivity and accuracy of both PRM and an intact precursor scanning mass spectrometry method, termed accumulated ion monitoring (AIM), by more than 100-fold. Combined with automated 2D nano-scale chromatography, AIM achieved subattomolar limits of detection of endogenous proteins in complex biological proteomes. This allowed quantitation of absolute abundance of the human transcription factor MEF2C at ∼100 molecules/cell, and determination of its phosphorylation stoichiometry from as little as 1 μg of extracts isolated from 10,000 human cells. The combination of automated multidimensional nano-scale chromatography and targeted mass spectrometry should enable ultrasensitive high-accuracy quantitative proteomics of complex biological systems and diseases.

publication date

  • August 18, 2017

Research

keywords

  • Peptides
  • Proteomics

Identity

PubMed Central ID

  • PMC5672005

Scopus Document Identifier

  • 85032615221

Digital Object Identifier (DOI)

  • 10.1074/mcp.RA117.000023

PubMed ID

  • 28821601

Additional Document Info

volume

  • 16

issue

  • 11