The capability to compare and contrast proteomes using techniques of protein expression profiling (1
) can add immensely to our understanding of the integration of biological processes, as well as the identification of biomarkers of disease progression or drug action. The overall objective of expression profiling is to determine comprehensively the identity, abundance, and modification state of proteins in the sets of proteomic samples to be compared. However, accurate and reliable expression profiling remains challenging on the proteomic scale. The approaches generally employed include two-dimensional (2D) gel electrophoresis (3
) or LC/MS-based methods, such as isotope labeling by metabolic incorporation (e.g. SILAC) (4
) and chemical/enzymatic labeling (e.g. ICAT, iTRAQ and 18
), and more recently, label-free protein expression profiling approaches (2
Label-free methods employ a “shotgun” approach that is particularly effective for large-scale protein analysis (15
): samples are digested enzymatically into a large number of small peptide fragments and then subjected to LC/MS analysis without labeling. Because there exists a linear correlation between MS signal intensities and the relative quantity of peptides (16
), the relative quantification of proteins in these samples is carried out by direct comparison of the peak areas of proteolytic peptides among LC/MS runs. A recent evaluation by the Association of Biomolecular Resource Facilities (www.abrf.org/prg
) suggested that label-free approaches provided the best accuracy for relative protein quantification in protein mixtures, compared to other expression profiling methods. In addition to the potential for providing higher quantitative accuracy, the label-free approach holds advantages of (i) applicability to a wide range of proteomic samples, such as those derived from tissues, (ii) the ability to quantify and compare multiple samples, and (iii) simplicity and less expensive sample preparation.
Because the label-free proteomic analysis approach often does not employ internal standards, quantitative and reproducible sample preparation is particularly important for obtaining reliable results (17
). However, processing samples for label-free expression profiling in a quantitative manner, particularly for analyzing tissue proteomes, represents a fundamental challenge for several reasons (18
). First, although tissue samples are believed to provide a highly focused pool of biomarkers (19
), efficient extraction of these marker proteins can be difficult because of the necessity to disrupt tissue structural interactions, as well as the low solubility of some proteins, such as membrane proteins (20
). Therefore, a ‘strong’ solvent that contains significant amounts of detergents often is necessary to dissolve membranes, disrupt protein-lipid interactions, and achieve protein solubilization (20
). However, it is both necessary and a challenge to remove these detergents and other buffer components without incurring uneven loss of proteins, which can severely compromise quantitative LC/MS analysis. Second, endogenous non-protein compounds are co-extracted from tissue samples, and these can co-elute with the proteolytic peptides during LC/MS analysis, causing undesirable effects such as compromise of chromatographic reproducibility, ion suppression, and/or interference. Therefore, elimination of such compounds before analysis is essential to ensure reproducible label-free quantification. Finally, protease inhibitors that were added during protein extraction also must be eliminated.
To address these challenges, numerous sample preparation strategies have been employed for label-free expression profiling of tissue samples, including 1-dimensional SDS/PAGE electrophoresis (1-DE) followed by in-gel digestion (21
), strong cation exchange (SCX) chromatography (18
) and the use of non-detergent extraction buffers (24
). Among these approaches, the 1-DE/in-gel digestion approach is the most prevalent. This method not only fractionates the sample proteins, but also effectively reduces or eliminates detergents, protease inhibitors, and non-protein components, so that the samples are suitable for enzymatic digestion and LC/MS analysis (20
). Nevertheless, 1-DE/in-gel digestion has several limitations. First, the in-gel digestion procedure is susceptible to serious and non-uniform peptide losses, mainly arising from loss of proteins during destaining, and from incomplete peptide extraction from the polyacrylamide gel after proteolysis (28
). Figeys and co-workers reported peptide losses ranging from 15 to 50%, depending on peptide properties and concentrations (29
). Non-uniform peptide losses may adversely affect both the sensitivity and quantitative accuracy of label-free profiling. Efforts to increase the efficiency of peptide extraction from gels generally have achieved only marginal gains in peptide yields (e.g. 5%) (28
). A second problem with 1-DE/in-gel digestion is that many proteins span the borders of gel slices, and therefore will be present in multiple gel fractions. Because the separation of proteins by the 1-DE method is not highly reproducible for parallel samples (20
), artificial quantitative differences in “boundary proteins” thus may be created among proteomic samples. As a result, additional normalizations based on informatics approaches may be necessary to reduce this confounding factor (23
A robust and traditional approach to protein sample cleanup is protein precipitation employing denaturing organic solvents such as acetone, which has long been regarded as an effective means for the removal of detergents, lipids, small molecular-mass nucleic acids, and other contaminant species, while providing high protein recoveries (30
). A significant disadvantage of protein precipitation is that the proteins are denatured and aggregated, rendering the pellet difficult to re-solubilize unless detergent-containing buffers are used. Difficulties in re-dissolution are especially problematic for samples rich in membrane proteins (33
). Therefore, the protein precipitation approach is usually reserved for those applications in which components that aid in re-solubilizing the protein pellet can be tolerated, such as SDS-PAGE or 2-D electrophoresis (33
Here we report the development of a facile, efficient, and reproducible “precipitation/on-pellet digestion” strategy for label-free expression profiling of tissue proteomes. In this approach, a strong, detergent-containing buffer is employed to extract tissue proteins efficiently. The proteins in the extract are then precipitated using acetone to remove detergents, protease inhibitors, and non-protein matrix components. A 2-step enzymatic digestion procedure subsequently brings the precipitated proteins back into solution as soluble, completely-cleaved peptides, without introducing detergents. This method is simple to follow, highly quantitative in protein recovery, and provides a clean peptide mixture suitable for nano-LC/MS analysis. Combined with efficient chromatographic separation and sensitive MS detection, as developed in this study, this strategy enables relatively comprehensive label-free expression profiling.
As a proof of concept, this strategy was applied for large-scale comparative expression profiling of the mitochondrial proteome of myocardium from healthy swine and those with chronic hibernating myocardium. Hibernating myocardium develops as an intrinsic adaptive response to repetitive myocardial ischemia, and is characterized by reduced regional contraction, resting blood flow, and regional oxygen consumption, which is correlated with reductions in multiple mitochondrial proteins involved in oxidative metabolism (34
). Identification of changes in the abundance of specific proteins in hibernating myocardium can shed light upon the mechanisms responsible for compensatory physiological changes, and identify novel cellular adaptations to chronic ischemia (35
). A major challenge for label-free profiling of the mitochondrial proteome of myocardium is the presence of a high percentage of membrane proteins; approx. 80% of the inner mitochondrial membrane mass is comprised of membrane proteins (36
). The approach developed here addresses the need for efficient extraction of membrane proteins from tissue sub-fractions, adequate sample cleanup, and a sufficient chromatographic separation to resolve peptides of this highly complex sample, thus permitting label-free, comparative quantification.