Elimination of Systematic Mass Measurement Errors in Liquid Chromatography−Mass Spectrometry Based Proteomics Using Regression Models and a Priori Partial Knowledge of the Sample Content

Abstract

The high mass measurement accuracy and precision available with recently developed mass spectrometers is increasingly used in proteomics analyses to confidently identify tryptic peptides from complex mixtures of proteins, as well as post-translational modifications and peptides from non-annotated proteins. To take full advantage of high mass measurement accuracy instruments it is necessary to limit systematic mass measurement errors. It is well known that errors in the measurement of m/z can be affected by experimental parameters that include e.g., outdated calibration coefficients, ion intensity, and temperature changes during the measurement. Traditionally, these variations have been corrected through the use of internal calibrants (well-characterized standards introduced with the sample being analyzed). In this paper we describe an alternative approach where the calibration is provided through the use of a priori knowledge of the sample being analyzed. Such an approach has previously been demonstrated based on the dependence of systematic error on m/z alone. To incorporate additional explanatory variables, we employed multidimensional, nonparametric regression models, which were evaluated using several commercially available instruments. The applied approach is shown to remove any noticeable biases from the overall mass measurement errors, and decreases the overall standard deviation of the mass measurement error distribution by 1.2-to 2-fold, depending on instrument type. Subsequent reduction of the random errors based on multiple measurements over consecutive spectra further improves accuracy and results in an overall decrease of the standard deviation by 1.8-to 3.7-fold. This new procedure will decrease the false discovery rates for peptide identifications using high accuracy mass measurements.

DtaRefinery, a Software Tool for Elimination of Systematic Errors from Parent Ion Mass Measurements in Tandem Mass Spectra Data Sets

“…Multiple sources of variation can cause systematic errors in mass measurements; for example, power supply voltage drift over time, space charge effects, differing ion compositions within the cell, ion intensity variation, and outdated calibration coefficients (for a review, see Ref. 13). …”

“…Internal calibrants well capture scan to scan variations and correct for time and/or total ion current (TIC)-dependent systematic errors, which are associated with the entire MS scan. Technically, it is also possible to correct for intensity-related dependence of MME, which is quite prominent on a certain type of instrument (13). However, it will require an increase in the number of calibrants to cover the entire dynamic range of the mass spectrometer.…”

“…Instead of spiked or co-injected calibrants, they make use of putative peptide identifications as internal calibrants. Initially, such recalibration approaches have been limited mostly to peptide identifications based on either high accuracy measurements of peptide masses alone (21) or in combination with LC retention times (13,20). Recently, we and others have proposed that partial sample knowledge can also be utilized for recalibrating parent ion masses in MS/MS data sets obtained on hybrid instrumentation (12,13,(23)(24)(25)(26).…”

“…Initially, such recalibration approaches have been limited mostly to peptide identifications based on either high accuracy measurements of peptide masses alone (21) or in combination with LC retention times (13,20). Recently, we and others have proposed that partial sample knowledge can also be utilized for recalibrating parent ion masses in MS/MS data sets obtained on hybrid instrumentation (12,13,(23)(24)(25)(26). In one implementation, described as "postexperiment monoisotopic mass filtering and refinement" (26), the parent ion masses in the .dta files were replaced with the mass of the ion averaged over all scans in which it was observed followed by a simple recalibration.…”

“…There is another implementation that resembles this concept (23,25) that is based on using putative peptide identifications as calibrants from neighboring MS/MS scans, that is scans either immediately before or after the MS scan chosen for recalibration. We reviewed this approach earlier (13) and suggested that it has a potential benefit, although it does have a few potential limitations that need to be addressed, e.g. disregarding individual ion intensity information, use of only linear calibration functions, and lack of control of the m/z range covered by putative calibrants.…”