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Quantitative analysis of protein levels is important for the system-based understanding of the molecular function of each protein component and is expected to provide insights into molecular mechanisms of various biological processes and systems. Quantification is the determination of proteins' abundances in a sample. Since the absolute abundance is difficult to determine, and not all that useful, it usually involves comparison between two or more samples to examine how much protein is in one sample as compared to others. Quantification is typically achieved by comparison of an unlabeled or 'light' peptide (comprised of naturally abundant stable isotopes) to an internal standard that is chemically identical with the exception of atoms that are enriched with a 'heavy' stable isotope . Although the stable-isotope labeling approach has been the most commonly used over the past several years, label-free approaches have been gaining momentum recently because of their inherent simplicity, increased throughput and low cost.
A mass spectrometer works by using magnetic and electric ï¬elds to exert forces on charged particles (ions) in a vacuum. Therefore, a compound must be charged or ionized to be analyzed and the ions must be introduced in the gas phase into the vacuum system of the mass spectrometer. Ionisation methods include Atmospheric Pressure Chemical Ionisation (APCI), Chemical Ionisation (CI), Electron Impact (EI), Electrospray Ionisation (ESI), Fast Atom Bombardment (FAB), Field Desorption / Field Ionisation (FD/FI), Matrix Assisted Laser Desorption Ionisation (MALDI), and Thermospray Ionisation (TSP). However, ESI and MALDI are widely used, as they allow efficient sequencing of peptides derived from proteolytic digests of protein complexes.
Most of the MS-based quantification methods employ differential stable isotope labeling to create a specific mass tag that can be recognized by a mass spectrometer and at the same time provide the basis for quantification. These mass tags can be introduced into proteins or peptides metabolically, by chemical means, enzymatically, or provided by spiked synthetic peptide standards. In contrast, label-free quantification approaches aim to correlate the mass spectrometric signal of intact proteolytic peptides or the number of peptide sequencing events with the relative or absolute protein quantity directly .
There are several principal techniques, falling into one of three categories:
Isotopic labeling techniques where relative abundances are measured in the MS/MS (fragment) scan, iTRAQ and ExacTag
Isotopic labeling techniques where relative abundances are measured in the MS (survey) scan SILAC and ICAT
Techniques where no label is used (No Label Quantitation)
Fig.1- Strategies for quantitative proteomic profiling:- 2DE: two dimensional gel electrophoresis; SILAC: stable isotope labeling with amino acids in cell culture; iTRAQ: isobaric tags for relative and absolute quantitation; ICAT: isotope-coded affinity tags; NIT: N-terminal isotope encoded tagging; MCAT: mass-coded abundance tagging. 
A novel, MS-based approach for the relative quantification of proteins, relying on the derivatization of primary amino groups in intact proteins using isobaric tag for relative and absolute quantitation (iTRAQ) was presented in 2005-06. The methodology was established by analyzing defined mixtures of iTRAQ-labeled proteins via ESI- as well as MALDI-MS/MS following proteolytic digestion . The iTRAQ uses isotopic labeling to enable relative quantification comparisons. Up to eight different proteomic samples can be labeled using eight different isobaric tags. The data analysis stage of iTRAQ quantification relies on accurate computation of the intensities of reporter ion peaks in MS/MS spectra and rigorous statistical analysis of relative reporter ion intensities from multiple peptides in computation of protein expression ratios.
Data analysis is very simple, since it does not require integration of MS and MS/MS information.
Up to 8 samples can be compared at once (this is called multiplexing), since the new iTRAQ has 8 types of labels producing 8 reporter ions
Most commonly used technique
Using ExacTag protein labeling early in the workflow minimizes experimental error
Protein labeling permits enrichment by standar fractionation methods and reduction of complexity of the biological sample
It is the least accurate technique:
because MS/MS scans have to be done faster, so are usually not as accurate as an MS1 can be.
because not all peptides can be scanned in MS/MS, some are missed.
because it is impossible to get MS/MS information over the whole time window in which a peptide is eluting.
Sometimes the reporter ion does not fragment off resulting in belaboured ID and inaccurate ratios
MS/MS scans are often stored in centroided form, so analysts must trust the instrument's preprocessor
Cost for iTRAQ is quite expensive
The isotope-coded affinity tag (ICAT) reagent method is prototypical method to generate quantitative protein profiles based on stable isotope affinity tagging and MS. The reagents consist of a cysteine-reactive group, a linker that contains either heavy or light isotopes, and a biotin affinity tag. The ICAT reagent method involves in vitro derivatization of cysteine residues with the isotopically heavy or light form of the reagent, respectively, then enzymatic digestion of the combined sample and isolation and mass spectrometric analysis of labeled peptides. In the process the complexity of the peptide sample mixture is significantly reduced and each peptide is isotopically tagged to facilitate quantitative mass spectrometric measurements.
Stable isotope labeling with amino acids in cell culture (SILAC) is a method to metabolically label proteins for relative quantification comparison. One cell population is fed amino acids of normal isotopic composition; the other cell population is fed amino acids labeled with heavier isotopes. The heavy amino acids are incorporated into newly synthesized proteins, eventually completely replacing the cells' proteins, such that labeling efficiency is near 100%. The cell populations are then mixed together and digested for MS analysis to determine differential protein abundances.
Overview of the technical parameters of the different workflows for quantitative proteomics: 
Nature of quantitation
Number of samples to compare
2 or 3
MS or MS2
Ion intensities (PCP)
Many (depends on reproducibility of chromatography)
Within sample/many samples
Derived indices (APEX, emPAI)
3 or 4
Within sample/many samples
1Absolute quantitation is possible only through relative comparison to a spiked "known" standard.
2Absolute quantitation is possible only through empirical features and back-calculation using the molecular weight of the protein and total protein amount in the sample.
SILAC and ICAT
More accurate than iTRAQ since quantitation is done based on MS peaks
Does not suffer from complications arising when trying to find the same peak in two separate LC runs. The labelled peaks appear in the same MS scan
Only works on cell cultures, so it cannot be used for clinical samples.
Only two or three samples can be compared at once analysis is more complex than iTRAQ, requiring both MS and MS/MS data
Label Free quantification relies on the changes in analyte signals directly reflecting their concentrations in one sample relative to another. This technology employs overall spectral intensity normalization by interpreting signals of molecules that do not change concentration from sample to sample. Label free quantitative proteomics analysis is a flexible approach enabling the profiling of protein expression across different datasets. The success of this approach relies not only on the efficient detection of peptides over a wide range of ion abundance but also on the capability of correlating their precise coordinates in different LC-MS runs. Several approaches have been previously studied to achieve these goals including the use of normalized LC retention time for data acquired on high resolution mass spectrometry instruments.
Economical. No labels to buy.
The most sensitive technique.
It is not even necessary to identify a peptide to note its change in abundance. This lends the technique well to biomarker studies.
Requires more instrument time
Analysis is by far the most complex, requiring integration of MS data, MS/MS data, LC data, and multiple data files.