Mass Spectrometry-Based Quantification

Apart from identification of peptides within a sample, mass spectrometry (MS) can be utilized to quantify peptides. MS-based quantification is mainly performed by two main strategies: unlabeled and labeled. Since MS intensities of peptide ions are proportional to their amount in a sample (within the linear dynamic range of the used mass spectrometer), mass spectra from an MS survey (MS1) scan can be used to extract quantitative information about each detected peptide within a sample. Peptides can be distinct from each other in terms of sequence, PTMs or charge state, which results in different ionization efficiencies. Therefore, each peptide is detected by MS with a different efficiency, termed the response factor. MS can accordingly only produce

relative quantities, which only allows the comparison of identical peptides in different samples. Notably, the incorporation of isotope-labeled synthetic peptides of known concentration and with the same sequence and PTMs of the peptides of interest allows measurement of the response factor, and therefore, absolute quantification (Schmidt et al., 2011; Ludwig et al., 2012).

Spectral counting is an unlabeled quantification MS strategy based on the shotgun approach, which allows semi-quantitative analysis (Liu et al., 2004). The quantification strategy is based on the hypothesis that the number of times a peptide precursor ion is selected for fragmentation in a large data set is directly related to the abundance of a peptide represented by its precursor ion in the sample. Due to the low accuracy of this technique, it has been frequently used for the analysis of samples with low to moderate mass resolution.

The increased mass resolution of the new generation of mass spectrometers greatly improved the performance of free quantification strategies towards quantitative analysis of biological samples. In a typical label-free approach, peptides are detected at MS1 level and then integrated across the retention time dimension (m/z-by-RT) and used to reconstruct a chromatographic elution profile of the monoisotopic peptide mass. This allows the computational quantification of the number of detections per peptide, which can be used as a quantitative measurement of the original peptide concentration. Thereby, every peptide signal within the sensitivity range of the mass spectrometer can principally be extracted and incorporated into the quantification process independent of MS2 spectra (Listgarten and Emili, 2005). Contrary to the previously described shotgun approach, the common undersampling problem is greatly reduced by this dynamic range of peptide detection.

Differential stable isotope labeling has proven to be a very precise and robust method for quantitative proteomics. It allows for absolute and relative quantification of peptide concentrations, either by comparing relevant peptides of a sample against their spiked-in isotopic labeled synthetic analogue, or by comparing peptide intensity values between multiple biological samples within a single MS1 measurement (Aebersold and Mann, 2003; Kusmierz et al., 1990; Lill, 2003; Yan and Chen, 2005). Labeling can be performed by metabolic, chemical or enzymatic approaches (Conrads et al., 2002; Gygi et al., 1999; Zhou et al., 2002; Yao et al., 2001). Labeling strategies can be divided into two main approaches, each exploiting a different principle:

The first introduces known mass shifts to the MS1 spectrum of different samples by using stable isotope labels of different mass. Examples for this approach are Isotope Coded Affinity Tag (ICAT) (Gygi et al., 1999), Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) (Ong et al., 2002), Isotope-Code Protein Label (ICPL) (Schmidt et al., 2005), and ¹⁵N or ¹⁸O labeling. The second approach employs isobaric labels which do not produce mass shifts in MS1 spectra, but generate reporter ions of different masses after fragmentation in the MS2 spectrum. Examples for use of this principle are Isobaric Tags for Relative and Absolute Quantification (iTRAQ) (Ross et al., 2004) and Tandem Mass Tag (TMT) (Thompson et al., 2003).

Label quantification approaches have two main advantages over label-free quantification approaches: Firstly, multiple samples can be prepared and measured simultaneously in one MS run, because samples can be

In recent years, SILAC has become increasingly popular as an excellent approach for high-accuracy quantitative proteomics (Ong et al., 2002; Ong and Mann, 2006) (Figure 1.13). It was originally described for mammalian cell culture (Ong et al., 2003) but has been quickly adapted for labeling in S. cerevisiae (Jiang and English, 2002; Gruhler et al., 2005), E. coli (Kerner et al., 2005), Plasmodium falciparum (Nirmalan et al., 2004), and Arabidopsis thaliana (Gruhler et al, 2005). Its application spectrum ranges from detection of specific biological changes in functional proteomics assays (Schulze and Mann, 2004; de Hoog et al., 2004;

Blagoev et al., 2003) to acquiring temporal profiles of protein abundances (Andersen et al., 2005) and changes in modification states of proteins (Blagoev et al., 2004). SILAC can be adapted to practically every cell culture system that uses defined sources of amino acids. It involves culturing of cells in media containing either light (e.g. ¹H, ¹²C and ¹⁴N) or heavy (e.g. ²H, ¹³C and ¹⁵N) labeled amino acids to incorporate the isotopically labeled amino acids into proteins through the metabolic cycle. Trypsin is the most common protease in proteomic workflows, because it possesses a very efficient, yet specific cleaving activity, targeting the carboxyl-termini of lysine and arginine residues (Olsen et al., 2004). Therefore, heavy labeled L-arginine and L-lysine in combination with trypsin digest is an excellent combination for SILAC experiments, since all peptides except the very C-terminal peptide of a protein are principally quantifiable (Ibarrola et al., 2003).

Most media used for cell culturing provide an overabundance of amino acids and other nutrients. This can lead to metabolic conversion of arginine to proline and the formation of additional heavy labeled proline peptide satellites (Ong et al., 2003; Van Hoof, 2007). This conversion usually occurs when an excess of arginine is provided to cells in growth medium. This can be addressed by reducing the concentrations of stable isotope labeled amino acids or using adapted arginine-to-proline converting cell types.

Although S. cerevisiae can normally synthesize all amino acids, SILAC labeling can be performed by using deletion strains in which the biosynthesis pathways of the specific amino acids used for labeling are disrupted (Gruhler et al., 2005).

Figure 1.13: Basic principle of Stable Isotope Labeling with Amino Acids in Cell Culture

Cell cultures are grown in media containing either light or heavy labeled arginine and lysine to incorporate the modified amino acids into proteins through the metabolic cycle. Quantification of differences in protein abundance caused by e.g.

stimuli is accomplished by mixing equal amounts of cells, isolation of proteins and extraction of peptides after trypsin digestion, MS/MS analysis and determination of intensity ratios between specific light and heavy peptide pairs.

When the mixed light and heavy isotope labeled proteins or peptides are analyzed by MS, they are separated

number of labeled amino acid residues in the analyte. Because quantitative information is encoded by the used isotope labeled amino acid, its choice is important and should be targeted specifically to the goals of the experiment. The relative abundance of the proteins is based on the intensities of the light and heavy peptides.

Since differential labeling and combining of the individual samples occurs early on during sample preparation, this approach reduces the introduction of additional error sources through extra experimental sample processing steps (Ong and Mann, 2006). Additionally, modification of proteins or peptides does not require chemical reactions and the level of incorporation is high. New generation mass spectrometers display rapid data acquisition rates which allow the identification and quantification of large amounts of proteins from a single SILAC experiment. Due to the increased mass resolution of these instruments, proteins can be easily identified and quantified by as few as two peptides observed from each protein (Steen and Mann, 2004; Ong et al., 2004).