Correlation of Ecom₅₀ values between mass spectrometers: effect of collision cell radiofrequency voltage on calculated survival yield

Abstract

RATIONALE The determination of the center-of-mass energy at which 50% of a precursor ion decomposes (Ecom50) during collision-induced dissociation (CID) is dependent on the chemical structure of the ion as well as the physical and electrical characteristics of the collision cell. The current study was designed to identify variables influencing Ecom50 values measured on four different mass spectrometers. METHODS Fifteen test compounds were protonated using +ve electrospray ionization and the resulting ions were fragmented across a range of collision energies by CID. Survival yield versus collision energy curves were then used to calculate Ecom50 values for each of these [M+H]+ ions on four different mass spectrometers. In addition, the relative recovery of the [M+H]+ ions of eight compounds ranging in molecular weight from 46 to 854 Da were determined at collision cell RF voltages ranging from 0 to 600 V. RESULTS Ecom50 values determined on the four instruments were highly correlated (r2 values ranged from 0.953 to 0.992). Although these overall correlations were high, we found different maximum ion recoveries depending on collision cell RF voltage. High mass ions had greater recovery at higher collision cell RF voltages, whereas low mass ions had greater recovery at lower collision cell RF voltages as well as a broader range of ion recoveries. CONCLUSIONS Ecom50 values measured on four different instruments correlated surprisingly well given the differences in electrical and physical characteristics of the collision cells. However, our results suggest caution when comparing Ecom50 values or CID spectra between instruments without correcting for the effects of RF voltage on ion transfer efficiency.

Correction of precursor and product ion relative abundances in order to standardize CID spectra and improve Ecom50 accuracy for non-targeted metabolomics

“…The hexapole collision cell uses six stainless steel rods 160.5 mm in length arranged in a circle 9 mm in diameter, and 2 mm entrance and exit slits. (Hill et al 2012). Masslynx 4.1 (Waters Associates, Milford, MA) was used to process mass spectral data.…”

“…Thus, higher collision energy or a heavier collision gas increases the probability of precursor ion fragmentation as more energy is absorbed during the collision (Hill et al 2012). In addition, increased collision gas pressure increases the probability of multiple collisions resulting in greater fragmentation of the precursor ion.…”

“…As a result, the gas pressure and collision energy must be optimized based on the m/z of the sample ions. In addition, collision cell dimensions affect ion resident time which in turn affect the probability that an ion that has absorbed sufficient energy will dissociate before it exits the collision cell (Hill et al 2012). Relative precursor and product ion abundances are therefore related to internal bond energies, the conditions of the experiment, and the physical characteristics of the instrument.…”

“…the energy required to fragment 50% of a precursor molecule, between instruments when comparable RF voltages in the collision cells were used (Hill et al 2012). The Ecom 50 value of a compound depends on its ‘survival yield’ (ratio of precursor ion to sum of product and precursor ions) and as such, is affected by all ion peak intensities in CID spectra.…”

Optimizing Artificial Neural Network Models for Metabolomics and Systems Biology: an Example Using HPLC Retention Index Data

“…MolFind/BioSM applies five additional orthogonal filters to reduce the number of false positives returned from the exact mass search [2–6]. Filters are: HPLC retention index (RI) [3, 6–7], Ecom 50 [2,3] drift index (DI) [5], biological/nonbiological (BioSM) [8] and CID (MS/MS) spectra [5, 9]. In filtering, predicted values for RI, Ecom 50 and DI are made for candidate compounds using computational models.…”