Rapid optimization of MRM-MS instrument parameters by subtle alteration of precursor and product m/z targets.
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.
Sherwood
Carly A
CA
Institute for Systems Biology, 1441 North 34th Street, Seattle, Washington 98103, USA.
Eastham
Ashley
A
Lee
Lik Wee
LW
Risler
Jenni
J
Mirzaei
Hamid
H
Falkner
Jayson A
JA
Martin
Daniel B
DB
D000465:Algorithms; D000595:Amino Acid Sequence; D019540:Area Under Curve; D015415:Biomarkers / Q000737:chemistry; D019295:Computational Biology / Q000379:methods; D005656:Fungal Proteins / Q000737:chemistry; D006193:Haemophilus influenzae / Q000378:metabolism; D007477:Ions; D013058:Mass Spectrometry / Q000379:methods*; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D012984:Software
D015415:Biomarkers; D005656:Fungal Proteins; D007477:Ions; D010455:Peptides; D020543:Proteome