Tools for Label-free Peptide Quantification

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.Over recent decades, mass spectrometry has become the analytical method of choice in most proteomics studies (e.g. Refs. 1–4). A standard mass spectrometric workflow allows for both protein identification and protein quantification (5) in some form. For a long time, the technology has been used mainly for qualitative assessments of protein mixtures, namely, to assess whether a specific protein is in the sample or not. However, for the majority of interesting research questions, especially in the field of systems biology, this binary information (present or not) is not sufficient (6). The necessity of more detailed information on protein expression levels drives the field of quantitative proteomics (7, 8), which enables the integration of proteomics data with other data sources and allows network-centered studies, as reviewed in Ref. 9. Recent studies show that mass-spectrometry-based quantitative proteomics experiments can provide quantitative information (relative or absolute) for large parts, if not the entire set, of expressed proteins (10–12).Since the isotope-coded affinity tag protocol was first published in 1999 (13), numerous labeling strategies have found their way into the field of quantitative proteomics (14). These include isotope-coded protein labeling (15), metabolic labeling (16, 17), and isobaric tags (18, 19). Comprehensive overviews of different quantification strategies can be found in Refs. 20 and 21. Because of the shortcomings of labeling strategies, label-free methods are increasingly gaining the interest of proteomics researchers (22, 23). In label-free quantification, no label is introduced to either of the samples. All samples are analyzed in separate LC/MS experiments, and the individual peptide properties of the individual measurements are then compared. Regardless of the quantification strategy, computational approaches for data analyses have become the critical final step of the proteomics workflow. Overviews of existing computational approaches in proteomics are provided in Refs. 24 and 25. The computational label-free quantification workflow in visualized in Fig. 1. Comparing peptide quantities using mass spectrometry remains a difficult task, because mass spectrometers have different response values for different chemical entities, and thus a direct comparison of different peptides is not possible. The computational analysis of a label-free quantitative data set consists of several steps that are mainly split in raw data signal processing and quantification. Signal processing steps comprise data reduction procedures such as baseline removal, denoising, and centroiding.Open in a separate windowFig. 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.These steps can be accomplished in modular building blocks, or the entire analysis can be performed using monolithic analysis software. Recently, it has been shown that it is beneficial to combine modular blocks from different software tools to a consensus pipeline (26). The same study also illustrates the diversity of methods that are modularized by different software tools. In another recent publication, monolithic software packages are compared (27). In that study, the authors identify a set of seven metrics: detection sensitivity, detection consistency, intensity consistency, intensity accuracy, detection accuracy, statistical capability, and quantification accuracy. Despite the missing independence of these metrics and the loose reporting of software parameter settings, such comparative studies are of great interest to the field of quantitative proteomics. A general conclusion from these studies is that the choice of software might, to a certain degree, affect the final results of the study.Absolute quantification of peptides and proteins using intensity-based label-free methods is possible and can be done with excellent accuracy, if standard addition is used. With the help of known concentrations, calibration lines can be drawn, and absolute protein quantities can be directly inferred from these calibration measurements (28). Furthermore, it has been suggested that peptide peak intensities can be predicted and absolute quantities can be derived from these predictions (29). However, the limited accuracy of predictions or the need for peptides of known concentrations limits these approaches to selected proteins/peptides only and prevents their use on a proteome-wide scale.Spectral counting methods have also been used for the estimation of absolute concentrations on a global scale (30), albeit at drastically reduced accuracy relative to intensity-based methods. In one study, the authors used a mixture of 48 proteins with known concentrations and predicted the absolute copy number amounts of thousands of proteins based on that mixture. Despite the fact that large, proteome-wide data sets will dilute the effects of different peptide detectabilities on the individual protein level, such methods will always be limited in their accuracy of quantification.The generic nature of label-free quantification is not restricted to any model system and can also be employed with tissue or body fluids (31, 32). However, the label-free approach is more sensitive to technical deviations between LC/MS runs as information is compared between different measurements. Therefore, the reproducibility of the analytical platform is crucial for successful label-free quantification. The recent success of label-free quantification could only be accomplished through significant improvements of algorithms (33–36). An increasingly large collection of software tools for label-free proteomics have been published as open source applications or have entered the market as commercially available packages. This review aims at outlining the computational methods that are generally implemented by these software tools. Furthermore, we illustrate strengths and weaknesses of different tools. The review provides an information resource for the broad proteomics audience and does not illustrate all algorithmic details of the individual tools.