Protein expression profiling using MALDI-TOF-MS is a widely used technique for a variety of studies including microbial typing [1
], semi-quantitative comparison [2
], imaging MS [3
], etc. While advances in signal processing and instrumentation improve the ability to resolve spectral features, the reproducibility of such spectra remains a major limitation to the precision of MALDI-TOF-MS. Precise time measurement is critical to both protein identification and pattern recognition in mass spectra. In order to achieve high precision, it is necessary to align (or synchronize) spectra so that characteristic features occur at the same time in all spectra being analyzed.
Instrument precision can be reduced by both systematic and random errors. Although it is hard to avoid random errors, systematic errors can be reduced or eliminated from individual spectra, provided they can be characterized. The most important sources of systematic instrumental error in MALDI spectra are variations in the triggering time from spectrum to spectrum and small variations in the accelerating voltage. Since these errors appear as linear effects in the TOF data, it should be straightforward to remove such errors using corrections to uncalibrated TOF data.
There are various approaches to aligning TOF data including the use of: frequent calibration [4
]; clustering or re-binning [6
]; cross-correlations [14
]; minimizing entropy [16
]; and others [17
]. Of these, only three are based in the time domain, either directly [24
] or indirectly by adjusting calibration parameters [16
]. Corrections made in the time domain should be inherently more accurate since m
values are derived from equations obtained by a quadratic fit to a few calibration peaks in time data. (Corrections to data after calibration in effect fit predicted values rather than measured values.)
In this study, we reanalyze the raw TOF data obtained during a multi-lab reproducibility study [25
] using our high resolution peak detection and label-free alignment methods to show an improvement of more than an order of magnitude in precision. In this paper, label-free alignment refers to aligning time domain data by using the most commonly occurring peaks, without regard to the identity of those peaks. While we have used naturally occurring peaks, it would be possible to use markers that were added for calibration. However, even for added calibration markers, the actual m/z
values would not be used for this alignment. This method of label-free alignment should be broadly applicable and have significant impact for researchers using TOF data for expression pattern analysis, imaging MS, and improved MS/MS protein ID.