The aim of quantitative proteomics is to obtain quantitative information about all proteins in a sample.[1][2] Rather than just providing lists of proteins identified in a certain sample, quantitative proteomics yields information about differences between samples. For example, this approach can be used to compare samples from healthy and diseased patients. The methods for protein identification are identical to those used in general (i.e. qualitative) proteomics, but include quantification as an additional dimension.
Contents |
Strategies to improve the sensitivity and scope of proteomic analysis often require large sample quantities and multi-dimensional fractionation, which sacrifices throughput. Alternatively, efforts to improve the sensitivity and throughput of protein quantification limit the number of peptides that can be monitored per MS run. For this reason, proteomics research is typically divided into two categories: discovery and targeted proteomics. Discovery proteomics optimizes protein identification by spending more time and effort per sample and reducing the number of samples analyzed. In contrast, targeted proteomics strategies limit the number of features that will be monitored and then optimize the chromatography, instrument tuning and acquisition methods to achieve the highest sensitivity and throughput for hundreds or thousands of samples.
Mass spectrometry is not inherently quantitative because of differences in the ionization efficiency and/or detectability of the many peptides in a given sample, which has sparked the development of methods to determine relative and absolute abundance of proteins in samples. The intensity of a peak in a mass spectrum is not a good indicator of the amount of the analyte in the sample, although differences in peak intensity of the same analyte between multiple samples accurately reflect relative differences in its abundance. One approach for relative quantitation is to separately analyze samples by MS and compare the spectra to determine peptide abundance in one sample relative to another, as in label-free quantitation strategies. An approach for relative quantitation that is more costly and time-consuming, though less sensitive to experimental bias than label-free quantitation, entails labeling the samples with stable isotope labels that allow the mass spectrometer to distinguish between identical proteins in separate samples. One type of label, isotopic tags, consist of stable isotopes incorporated into protein crosslinkers that causes a known mass shift of the labeled protein or peptide in the mass spectrum. Differentially labeled samples are combined and analyzed together, and the differences in the peak intensities of the isotope pairs accurately reflect difference in the abundance of the corresponding proteins. Absolute proteomic quantitation using isotopic peptides entails spiking known concentrations of synthetic, heavy isotopologues of target peptides into an experimental sample and then performing LC-MS/MS. As with relative quantitation using isotopic labels, peptides of equal chemistry co-elute and are analyzed by MS simultaneously. Unlike relative quantitation, though, the abundance of the target peptide in the experimental sample is compared to that of the heavy peptide and back-calculated to the initial concentration of the standard using a pre-determined standard curve to yield the absolute quantitation of the target peptide.
Relative quantitation methods include:
Absolute quantitation is performed using:
MeCAT can be used in combination with element mass spectrometry ICP-MS allowing first-time absolute quantification of the metal bound by MeCAT reagent to a protein or biomolecule. Thus it is possible to determine the absolute amount of protein down to attomol range using external calibration by metal standard solution. It is compatible to protein separation by 2D electrophoresis and chromatography in multiplex experiments. Protein identification and relative quantification can be performed by MALDI-MS/MS and ESI-MS/MS.
Experimental bias can influence the decision to use relative or absolute quantitation strategies. One source of bias is the mass spectrometer itself, which has a limited capacity to detect low-abundance peptides in samples with a high dynamic range. Additionally, the limited duty cycle of mass spectrometers restricts the number of collisions per unit of time, which may result in an undersampling of complex proteomic samples [3] . Another source of bias is variation in sample preparation between experiments or individual samples in single experiments. The greater the number of steps between labeling and sample combination, the greater is the risk of introducing experimental bias. For example, during metabolic labeling, proteins are labeled in live animals or cells and the samples are then immediately combined. Because all subsequent sample preparation and analysis is performed with the combined samples, metabolic labeling has the lowest risk of experimental variation. Conversely, samples that are individually processed and analyzed in label-free quantitation strategies have a greater risk of sample variation and experimental bias.
Modern day gel electrophoresis research often leverages software-based image analysis tools primarily to analyze bio-markers by quantifying individual, as well as showing the separation between one or more protein "spots" on a scanned image of a 2-DE product. Differential staining of gels with fluorescent dyes (difference gel electrophoresis) can also be used to highlight differences in the spot pattern.
|
||||||||