Quantitative mass spectrometry

Only two types information are gained from a MS experiment, mass and intensity. Mass information of precursor and fragment ions is used for peptide identification. Additionally, m/z of ions provides to map post translational modifications (PTM), and helps to elucidate of three-dimensional structure of proteins. The intensity information enables researchers to gain more insights about the dynamic nature of the proteome from relative abundance of proteins to absolute protein copy numbers. However, MS is not quantitative per se. Every peptide has

different physicochemical properties. In other words, the MS response of ions is unique and cannot be directly correlated with abundance. Therefore, numerous experimental approaches have been developed to make MS experiment quantitative. Figure 2.4 summarizes current approaches of quantitative mass spectrometry (qMS). Overall, qMS approaches can be separated into two broad categories, relative and absolute quantification. Depending on purpose of experiment, a label-based or label-free approach can be followed. In label-based approaches, chemically identical peptide species are compared at different experimental states. In order to differentiate peptides by MS, a mass tag is introduced into peptides. This is achieved by modifying peptides with stable isotopes, which differs in only m/z.

Figure 2.4 MS-based quantitative approaches.

This figure has been adapted from (Nikolov et al., 2012), see text for details.

Subsequent to MS analysis, relative intensities of the same peptide species can directly be compared. A great advantage is that differential labeled samples are analyzed simultaneously;

which prevents any biases coming from sample handing and MS analysis. Stable isotopes can be introduced either chemically or enzymatically after protein digestion (Boersema et al., 2009;

Mirgorodskaya et al., 2000) , or more preferentially, labeled amino acids can be metabolically incorporated into proteins (Ong et al., 2002). In general, label-based approaches provide high quantification accuracy, but requires an additionally step for labeling. Furthermore, only defined number of samples can be compared within the same workflow. Recent developments in sample preparation have significantly increased this number up to 18 with combination of chemical and metabolic labeling (McAlister et al., 2012). Alternatively, without any prior labeling, label-free approaches can be achieved to make MS experiment quantitative. This has the advantage of

variation that might arise from sample handling and MS analysis. Therefore, quantification accuracy is relatively moderate compare to label-based approaches. Label-free approaches can be modified to obtain absolute abundance information with spiking already known amounts of standard proteins, e.g. iBAQ (Schwanhäusser et al., 2011).

Stable isotope labeling by amino acids in cell culture

One of the most widely used MS-based relative quantification approaches is stable isotope labeling by amino acids in cell culture (SILAC) (Ong et al., 2002). Heavy isotope labeled essential amino acids are incorporated into the proteome metabolically. The principle advantage of SILAC is the earliest incorporation of the label and ability to mix samples earlier than any other labeling method. Therefore, SILAC reduces biases that might arise from any steps of sample handling and MS analysis. A typical workflow of SILAC-based experiment is depicted at Figure 2.5.

Figure 2.5 Schematic representation of SILAC experimental workflow.

See text for details.

Initially, SILAC was performed only in the cell culture, but has now extended to label whole organisms as well, such as E.coli, worm, fly, and mouse (Krüger et al., 2008; Larance et al., 2011;

Soufi and Macek, 2014; Sury et al., 2010). In a standard practice, a population of cell is grown in a medium containing natural light isotope (¹²C,¹⁴N,¹H) containing amino acids and other population is cultured with “heavy“ amino acids (¹³C,¹⁵N,²H). Complete incorporation (> 99%) is achieved after 5-6 generations. Afterwards, experimental set up is separately performed, e.g. differential treatment, overexpression, knockouts, or pull down. Light and heavy samples are mixed, and processed for MS analysis. The most commonly used endopeptidase is trypsin, since it generates widest range of MS-compatible peptides. Trypsin cleaves carboxyl site of arginine and lysine;

hereby those amino acids are the most preferred for metabolic labeling. After trypsinization all

but extreme C-terminal peptide is unlabeled unless the last reside is lysine or arginine. MS analysis of an SILAC experiment is carried out in a similar manner as routine protein identification.

During computational analysis by using defined mass shift between light and heavy peptides, SILAC pairs are detected, and the direct comparison of SILAC pair intensities provides relative ratios of peptides, and subsequently of proteins.

Intensity based absolute quantification (iBAQ)

Aside from the relative quantification of proteins, another common aim of MS-based approaches is to measure absolute amount of proteins. Most commonly used method for this task is Absolute Quantification of Protein (AQUA) (Gerber et al., 2003). This is achieved with spiking of synthetic heavy-isotope labeled reference peptides into sample mixture with known amounts. The combination of AQUA with “selected reaction monitoring (SRM) “ advanced the approach and it became quite successful in measuring the absolute amount of proteins with high accuracy (Picotti et al., 2010). Unfortunately, designing the appropriate references peptides requires substantial time and effort. Therefore this method is not suitable for high throughput applications.

Alternatively, high throughput, more cost effective approaches was developed, such as emPAI, APEX and iBAQ (Ishihama et al., 2005; Lu et al., 2007; Schwanhäusser et al., 2011). Intensity based absolute quantification (iBAQ) relies on the sum of the intensities of all detected peptides of a protein normalized by the number of theoretically observable peptides. iBAQ intensities provide the most accurate absolute abundance of all identified protein in sample (Wilhelm et al., 2014b).

Additionally, to determine absolute amounts of proteins, a reference protein mixture can be spiked into sample. iBAQ intensities of the reference proteins is plotted against known amounts of reference proteins to generate regression curve to estimate absolute abundance of proteins in the sample. This approach has been successfully employed in different studies (Schwanhäusser et al. 2011; Wilhelm et al. 2014). Applicability of any MS-based approaches strongly relies on the availability of computational platform to analyze the generated raw data. One of the main reasons why SILAC and iBAQ are currently method of choice is the availability of complete computational analysis platform, MaxQuant (Cox and Mann, 2008). MaxQuant together with

“sister” software, ”Perseus”, provides complete data analysis platform for raw data processing, database search, peptide and protein quantification, statistical evaluation and data visualization.

3 Material and Methods