Tools for label-free peptide quantification

Abstract

The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically, and do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data.

Fig. 1.

The sample cohort that can be analyzed via label-free proteomics is not limited in size. Each sample is processed separately through the sample preparation and data acquisition pipeline. For data analysis, the data from the different LC/MS runs are combined.

Fig. 2.

Label-free LC/MS data consist of individual MS spectra accumulated over (retention) time. Stacked side by side, these spectra form two-dimensional maps. In these maps, individual peptides being eluted from the column give rise to sets of peaks across multiple spectra. Feature-finding algorithms can identify features, which can be defined as all mass-spectrometric signals (peaks) caused by the same peptide. Elution profiles have ideally a Gaussian shape, but they can be significantly distorted. The projection of a feature along the m/z axis accordingly corresponds to the isotope profile of the peptide.