Quantitative proteomics

Proteomics has developed into a powerful technique for biology and biochemistry by providing the means to reliably identify many proteins in complex mixtures in a rel-atively small time frame. However, information about the presence or absence of a protein in a sample is in most cases not enough to draw valid biological conclusions. It is more important to obtain quantitative information on proteins, as often a change in protein abundance rather than their appearance or disappearanceper seis responsible for a biological effect. Mass spectrometry can be used to obtain not only qualitative, but also this quantitative information. There are two principle types of quantification.

Absolute quantification aims to determine the absolute amount of a protein in a solu-tion or a cell system and yields concentrasolu-tions or copy numbers per cell. In relative quantification, only the abundance difference between two samples is determined.

Two basic strategies can be applied to obtain quantitative information: label-free and stable isotope labeling approaches (Figure 1.2.6). Whereas label-free approaches sim-ply prepare and measure the samples separately, labeling approaches introduce stable isotopes which generate a mass difference. Stable isotopes can be introduced at dif-ferent stages during the experiment (Figure 1.2.6). Difdif-ferently labeled samples can be distinguished in the mass spectrometer and this allows to combine proteins (or pep-tides) and analyze them together. Parallel sample processing steps and, to an even greater extend, separate measurements as in label-free approaches, introduce variabil-ity which reduces the precision of the quantification. A more accurate quantification allows more reliable identification of significantly changed protein hits from the ob-served protein population. The earlier samples can be combined, the more accurate the quantification will be. In principle, metabolic labeling produces the most accurate quantification, whereas label-free approaches are more prone to accumulate variability and demand more replicates and more sophisticated statistical analysis.

Relative quantification by stable isotope labeling

Isotopic labeling strategies are always based on introducing defined stable isotopes into a sample to make it distinguishable from another sample by mass spectrometry.

It can be either achieved by chemical derivatization of an unlabeled sample or by ex-ploiting metabolic pathways to incorporate heavy isotopes.

Chemical labeling: Chemical labeling strategies use a reactive group on a polypep-tide to fuse it to an isotopic label. All samples that are to be compared are treated in

Spiked

peptides Chemical

modification Metabolic

labeling Label free

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Purification or fractionation

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MS sample optional fractionation

Figure 1.2.6: Labeling strategies and their impact on quantitative accuracyThe scheme de-picts typical stable isotope labeling and label-free workflows. Empty boxes represent samples without a label which cannot be distinguished in the mass spectrometer. Once samples are isotopically labeled (represented by colored boxes) they can be distinguished in the mass spec-trometer and are pooled. The earlier the samples are pooled, the less variability is introduced during the sample workflow (modified from [176]).

the same manner, but using isotopically different reagents. For quantification at the MS1 level, reagents are used that introduce a mass difference between the peptides.

The advantage of these methods is high quantification accuracy; however, samples in-crease in complexity by a factor of two for double labeling. The ICAT (isotope-coded affinity tag) reagent consists of a thiol specific reactive group, a linker which contains the isotope label and a biotin group for affinity enrichment [76]. Only cysteine contain-ing peptides can be labeled and are subsequently enriched via the biotin moiety prior to MS analysis. Although this approach is very specific and reduces complexity, only a subset of peptides can be labeled and quantification of many proteins will rely on very few data points. Another method to introduce a mass shift is dimethyl labeling

[20]. Up to three different isotopomeres of formaldehyde react with alpha and epsilon amino groups to form dimethyl amines. This reaction adds two methyl groups to all lysine side chains and all free N-termini and achieves labeling of all peptides present.

A different approach performs quantification on the MS2 level. TMT (tandem mass tag) [241] and iTRAQ (isobaric Tag for Relative and Absolute Quantitation) [203] use isobaric tags. Isobaric tags consist of a reporter group and a balancing group, which add up to the same mass for all tags, hence the name isobaric. Pooled samples which have been treated with isobaric reagents generate a single ion cluster in the MS1 space for every peptide. Upon fragmentation, the different reporter ions are released and their intensity is used for relative quantification. This approach can be easily multi-plexed, without concomitant increase in the complexity in the MS1 space. However, quantification at the MS2 level can have some disadvantages. Standard collision in-duced fragmentation in the ion trap does typically not cover fragments in the low mass region. Instead, pulsed Q dissociation (PQD) or a triple quadrupole like fragmentation (HCD) must be used. Furthermore, every peptide quantification is based on a single observation in a fragmentation event, whereas in MS1 based methods a peptide is ob-served during consecutive full scans allowing several quantification events. Finally, co-eluting peptides, which are in the fragmentation window, also contribute their re-porter ions which leads to ratio dampening [151].

In summary, chemical labeling provides a possibility to perform isotope labeling based quantification on material that was initially unlabeled . These chemical labeling meth-ods have the disadvantage of additional processing steps that can introduce variability and artifacts.

Metabolic labeling: Metabolic labeling strategies already introduce the isotopic atoms through growth medium or food. This can be done in a global manner, e.g. by replac-ing all nitrogen atoms by heavy nitrogen [170]. Unfortunately, this approach produces broad isotope distributions which are complicated to analyze and it is therefore only used for specialized applications in plant and bacterial biology. A very defined incor-poration can be achieved by replacing essential amino acids in the growth medium with their heavy counterpart, an approach termed SILAC (stable isotope labeling with amino acids in cell culture) [175]. Labeling all proteins with heavy arginine and lysine in combination with usage of the protease trypsin which cleaves C-terminal to these amino acids [173] for digestion ensures that every peptide contains at least one labeled amino acid (except the C-terminal peptide of the protein). Two isotope clusters can be observed for every peptide, forming a so-called SILAC pair. From the intensities

of the SILAC pair, a ratio can be directly assigned to the identified peptide. In princi-ple, nearly every cell line can be SILAC-labeled, including cell lines that demand more sophisticated culturing like mouse and human embryonic stem cells [72, 199]. More-over, whole organisms are also amenable to SILAC labeling. A lysine auxotroph Sac-charomyces cerevisiaestrain [74],Drosophila melanogaster [233],Mus musculus[116] and Caenorhabditis elegans[123] were successfully labeled.

By spiking in a heavy-labeled human cell line to human samples (e.g. tumour biop-sies), the high-accuracy SILAC based quantification can be applied for human samples which are otherwise not accessible for metabolic labeling. As the internal standard (the heavy labeled cell line) is the same in all samples, the “ratio of ratios” allows a direct comparison of protein abundance between different samples. Combining several rep-resentative cell lines to a super-SILAC mix further enhances quantification accuracy [67].

Relative quantification by label-free approaches

Label-free proteomics aims at performing quantification without the introduction of stable isotopes. In general, these approaches have to cope with higher variability be-cause sample preparation and measurement are performed separately. As a conse-quence, a more complex statistical analysis is required. Comparing the number of peptide spectra recorded for a protein in two samples is the most straightforward rel-ative quantification. This spectral counting approach [134] correctly classifies highly regulated proteins. However, especially proteins with few sequenced peptides cannot be quantified accurately and this approach is generally prone to a high false negative rate. Better results can be obtained by an intensity-based label-free quantification as it is computed in the MaxQuant software platform [40, 136]. To overcome experimentally introduced variability, the algorithm contains several normalization steps.

Absolute quantification

To obtain absolute protein concentrations, a defined amount of standard, in most ap-proaches a heavy-isotope-labeled reference, needs to be spiked into the sample. La-beled synthetic peptides can be used for this purpose in an approach often referred to as AQUA (for absolute quantification) [108]. To control for variability introduced during sample preparation (mainly missed cleavages and protein adsorption), heavy protein fragments or full length proteins can be spiked in before digestion [78, 269].

The abovementioned methods can provide very accurate quantification, but their high-throughput capability is severely limited as for every protein to be quantified a separate

standard has to be spiked in.

Although the intensity between two different peptides cannot be directly used to in-fer quantitative information, approaches were developed that can estimate absolute amounts without using isotope labeled spike in standards. The empirical abundance index (emPAI), for example, is computed as ten to the power of the number of observed peptides divided by the number of theoretical peptides minus one. Interestingly, the emPAI shows direct proportionality to the absolute protein amount [92].

A more refined method is to calculate a so-called iBAC (intensity-based absolute quan-tification) intensity [213]. In this method the intensities of all identified peptides of a protein are summed up, divided by the number of theoretically observed peptides and log transformed. To perform absolute quantification, a non-labeled standard of accu-rately quantified proteins is spiked into the sample before sample preparation. Using iBAC intensities for the standard proteins, the absolute protein amount of all identified proteins can be estimated using a linear regression [213].