4.7 Article

Rapid Optimization of MRM-MS Instrument Parameters by Subtle Alteration of Precursor and Product m/z Targets

Journal

JOURNAL OF PROTEOME RESEARCH
Volume 8, Issue 7, Pages 3746-3751

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/pr801122b

Keywords

multiple reaction monitoring (MRM); selective reaction monitoring (SRM); optimization; collision energy; cone voltage; triple quadrupole; targeted proteomics

Funding

  1. NCI NIH HHS [5R21CA126216, R21 CA126216-02, R21 CA126216] Funding Source: Medline
  2. NHLBI NIH HHS [N01HV28179, N01-HV-28179] Funding Source: Medline
  3. NIGMS NIH HHS [P50GM076547, P50 GM076547, P50 GM076547-03] Funding Source: Medline

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Multiple reaction monitoring (MRM) is a highly sensitive method of targeted mass spectrometry (MS) that can be used to selectively detect and quantify peptides based on the screening of specified precursor peptide-to-fragment ion transitions. MRM-MS sensitivity depends critically on the tuning of instrument parameters, such as collision energy and cone voltage, for the generation of maximal product ion signal. Although generalized equations and values exist for such instrument parameters, there is no clear indication that optimal signal can be reliably produced for all types of MRM transitions using such an algorithmic approach. To address this issue, we have devised a workflow functional on both Waters Quattro Premier and ABI 4000 QTRAP triple quadrupole instruments that allows rapid determination of the optimal value of any programmable instrument parameter for each MRM transition. Here, we demonstrate the strategy for the optimizations of collision energy and cone voltage, but the method could be applied to other instrument parameters, such as declustering potential, as well. The workflow makes use of the incremental adjustment of the precursor and product m/z values at the hundredth decimal place to create a series of MRM targets at different collision energies that can be cycled through in rapid succession within a single run, avoiding any run-to-run variability in execution or comparison. Results are easily visualized and quantified using the MRM software package Mr. M to determine the optimal instrument parameters for each transition.

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