To address the challenges associated with differential expression proteomics, label-free mass spectrometric protein quantification methods have been developed as alternatives to array-based, gel-based, and stable isotope tag or label-based approaches. In this paper, we focus on the issues associated with label-free methods that rely on quantitation based on peptide ion peak area measurement. These issues include chromatographic alignment, peptide qualification for quantitation, and normalization. In addressing these issues, we present various approaches, assembled in a recently developed label-free quantitative mass spectrometry platform, that overcome these difficulties and enable comprehensive, accurate, and reproducible protein quantitation in highly complex protein mixtures from experiments with many sample groups. As examples of the utility of this approach, we present a variety of cases where the platform was applied successfully to assess differential protein expression or abundance in body fluids, in vitro nanotoxicology models, tissue proteomics in genetic knock-in mice, and cell membrane proteomics. 1. Introduction Protein quantification for differential expression analysis or expression profiling represents the most challenging aspect in proteomics technology. This task is typically carried out through array-based [1], two-dimensional-electrophoretic (2-DE-) based [2] or mass-spectrometry- (MS-) based approaches [3, 4]. MS-based approaches are normally referred to as “bottom-up” rather than “top-down,” because the top-down approach has not yet reached its full potential. In bottom-up quantitative approaches, complex protein mixtures are digested enzymatically, peptides from each protein are separated by liquid chromatography (LC) and detected by MS, and protein quantification is completed at the peptide level and then combined to calculate a summarized value for the protein from which they come. Early in the evolution of quantitative MS-based proteomic technology, stable isotope labeling methods were developed [5–8]. Following that premise, many new label-based methods have arisen. However, all of these suffer from several limitations: (i) additional sample processing steps in the experimental workflow, (ii) high cost of the labeling reagents, (iii) variable labeling efficiency, and (iv) difficulty in analyzing low-abundance peptides in multiple samples, especially when numerous experimental groups are studied [9]. Following the development of label-based approaches, label-free approaches emerged to overcome the drawbacks associated with
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