Quality assessment for clinical proteomics

Abstract

Proteomics has emerged from the labs of technologists to enter widespread application in clinical contexts. This transition, however, has been hindered by overstated early claims of accuracy, concerns about reproducibility, and the challenges of handling batch effects properly. New efforts have produced sets of performance metrics and measurements of variability that establish sound expectations for experiments in clinical proteomics. As researchers begin incorporating these metrics in a quality by design paradigm, the variability of individual steps in experimental pipelines will be reduced, regularizing overall outcomes. This review discusses the evolution of quality assessment in 2D gel electrophoresis, mass spectrometry-based proteomic profiling, tandem mass spectrometry-based protein inventories, and proteomic quantitation. Taken together, the advances in each of these technologies are establishing databases that will be increasingly useful for decision-making in clinical experimentation.

Figure 1

Clinical proteomics workflows include diverse methods, any of which can contribute variability. Quality by design tunes and monitors each of these steps to control variation.

Figure 2

Liquid chromatography-mass spectrometry experiments measure thousands of mass spectra over the space of an hour or more. Each mass spectrum is a list of m/z values associated with observed intensities. In an LC-MS/MS experiment, these mass spectra are interspersed with tandem mass spectra that enumerate the fragment ions produced by the dissociation of a particular peptide.

Figure 3

An extracted ion chromatogram (XIC) visualizes the intensity associated with a particular m/z value over a range of retention times from liquid chromatography. An XIC is typically integrated to produce a peak area that measures the relative abundance of an ion. Chromatographic resolution for an XIC is frequently expressed as the full width at half maximum (FWHM), the peak width in retention time where the shoulders of the peak are half the maximum observed. Signal-to-noise evaluates the extent to which a peak stands out from local intensity fluctuations.

Figure 4

Many configuration choices during the bioinformatics steps of protein identification can significantly alter the peptides and proteins identified from an experimental data set. This Ishikawa diagram relates some of the most prominent sources of variation. For example, converting peak profiles from a Time-of-Flight mass analyzer to a list of peaks will result in very different lists when different tools are employed. The file format to which these peak lists are written may contain many types of metadata (as in the mzML format) or almost none (as in PKL or DTA format).