We demonstrate the implementation of a method for collision energy or cone voltage optimization on the Waters Quattro Premier triple quadrupole mass spectrometer with possible application to instruments from other manufacturers. We sought to create an MRM-MS run that successively collected data from the same transition while varying a voltage parameter such as collision energy or cone voltage. Ideally, we would have liked to program the instrument to successfully ramp the collision energy or cone voltage for the same precursor–product pair; however, during a standard analysis, the CE and CV cannot vary for a single pairing. To defeat this restriction, we used an in-house Perl script that alters the m/z value of each precursor peptide and product ion by 0.01 to represent each change in transition and each change in voltage, respectively. This effort yields a “family” of product ion scans from each precursor–product pair that differ in product ion m/z by less than 1 m/z (see ). For each of these entries, the desired CE or CV value can be entered, enabling an effective CE or CV ramp. Because the mass selection window of the triple quadrupole is ~1 m/z, the subtle differences in the precursor and product ion masses have little impact on the quantity of product ion reaching the detector. The end result of this programming strategy is to generate an MS run in which the same precursor–product pair is sequentially dwelled upon with an increasing CE or CV value. Because the switching time of the mass spectrometer (~10 ms) is much faster than the time scale of chromatographic peak elution (~15 s), the sequential scans produce superimposable elution peaks that can easily be compared with one another graphically or in a tabular format using the analysis portion of the MRM software package Mr. M. An example of such an elution peak plot from Mr. M is given in .
Figure 1 Elution peaks (plotted as intensity vs elution time) for the transition 418.2 → 860.4 acquired at seven different collision energies. The second decimal place of the product m/z values listed in the figure codes for the collision energy used, (more ...)
Using this technique, we have programmed CE and CV optimizations of 22 triply charged peptides from a standard mixture of commercial proteins. Triply charged peptides were chosen for this demonstration because default CE values were known to work poorly for this class of targets, but this optimization method could be applied to any set of transitions. In the first optimization, the collision energy was varied across a precursor m
-dependent range of 12 V, and in the second optimization, the cone voltage was varied across a constant range of 12 V. Each analysis took 1 h to perform and produced optimal CE and CV values for each transition. Excerpts from the tabular data from these analyses are provided in and , respectively, and the full data sets are reported in Supplemental Tables 4 and 6
, respectively. This information is also reported in terms of the percent gain (or loss) in sensitivity for each CE or CV value in Supplemental Tables 5 and 7
. These tables show that the variation of CE produced more substantial gains in signal than the variation in CV, with 59% of transitions experiencing an increase in AUC greater than 30% for the best-performing CE relative to the default CE, while only 35% of transitions experienced this level of increase for CV. A graphical summary of the data is also provided in . Most importantly, this tabular data clearly identifies the optimal CE value and the optimal CV value to use for each individual transition, allowing all of these transitions to be targeted in subsequent analyses with maximal sensitivity.
Excerpt from the Tabulated Results from the CE Optimization Experimenta
Excerpt from the Tabulated Results from the CV Optimization Experimenta
Figure 2 Histograms illustrating the number of times each CE (top) and CV (bottom) was the best-performing CE or CV for a targeted transition. The best-performing CE or CV for each transition was defined as the value that produced the maximum AUC for the given (more ...)
In addition to using the optimized CE and CV values for each transition directly, one can also use these results to determine generalized equations or values for certain classes of peptides or transitions to allow the extrapolation of optimal parameter values for transitions not explicitly optimized in the analysis. In this case, we can use the optimization results to try to determine CE and CV equations or values for triply charged peptides. The data from our optimization runs suggest that cone voltage is fairly generalizable for this class of peptides, as the large majority of transitions were optimized at the default CV (36 V) or 2 V less than the default (34 V). The collision energy, however, is more difficult to generalize, as all seven of the relative collision energies tested were optimal for approximately the same number of transitions. This is not unexpected, since the default CE equation is linearly dependent upon precursor peptide m/z. The optimal CE values determined by our analysis for each transition were therefore plotted against precursor (Q1) m/z (), and the data was fit with a linear curve, yielding the equation
Plot of the experimentally determined optimal CE for each transition versus the corresponding precursor peptide m/z value. The dashed line (y = 0.0241x + 7.2239) represents a linear fit to the data (R2 = 0.5238).
with an R2
value of 0.5238. Three major conclusions can be drawn from this CE generalization: (1) the newly derived equation for collision energy exhibits a linear dependence upon precursor m
, as expected; (2) this equation is substantially different than the default CE equation, suggesting that triply charged peptides indeed require optimization for CE that is different from that used for the more typical doubly charged peptides; and (3) there is considerable variance in the optimal collision energies, suggesting that further studies are required to identify the peptide-specific parameters that determine which transitions follow the equation and which require individual optimization. Further optimization experiments can then be performed—using the same method described here—to elucidate more precise CE equations for any identified subsets of transitions.
Our strategy streamlines the determination of optimal instrument parameters for individual MRM transitions. Although a simplistic, well-defined protein mixture was used for the demonstration of this method, it works just as well for peptides within a complex background, provided the concentration of the given peptide is within the detection limits of the mass spectrometer. We have illustrated this functionality in a supplementary analysis
in which we performed a collision energy optimization of 40 transitions from 10 yeast peptides in a yeast lysate (see the Supplemental Optimization in the Supporting Information
). Using the optimization technique described herein, we determined an optimal CE value for each of the 40 transitions, proving that the method is applicable to biologically relevant samples. Additionally, although this optimization method was developed for the Waters Quattro Premier, we have also successfully performed this same technique using the ABI 4000 QTRAP (methods and data available in the Supplemental Analysis included in the Supporting Information
), and it can potentially be applied to other platforms as well. Finally, a commercial MRM reviewing application (Mr. M) was used for all data analysis; however, our recently published MRMer software12
will also allow evaluation and output of the results, as will the software provided by the instrument vendors.
The primary advantages offered by this optimization method are threefold. First, this technique allows the investigator to perform a CE or CV ramp for one or many precursor–product transitions in a single run, thereby eliminating run-to-run variability. Second, the method permits such an optimization using a protein mixture or complex sample, rather than requiring the infusion of a purified or synthesized peptide. Lastly, the ease of data interpretation and analysis afforded by programs such as Mr. M makes this optimization fast and efficient.