2-D Gel Technology
Although not generally considered avant-garde technology, in reality, 2-D gel electrophoresis remains the best of all available protein separation approaches, and recent commercial refinements to the strategy have improved its potential. Discovery proteomics based on 2-D gel electrophoresis begins by separating the complex protein mixture present in a biological sampleusually a biological fluid or tissue specimenon an SDS-polyacrylamide gel (SDS-PAGE).
There are two discrete separation steps in this process, both driven by the application of an electric current. Separation in the first (x) dimension, isoelectric focusing, is based on protein charge (isoelectric point, pI); separation in the second (y) dimension, gel electrophoresis, is based on molecular weight.
A single 2-D gel can resolve thousands of proteins which, when stained, form a pattern of spots. A comparison of the intensity of individual spots yields information on the relative amounts of each protein in the sample. 2-D gels can be used to define both qualitative and quantitative changes in expression levels between biological samples, such as comparing healthy versus disease tissue samples or samples derived from the same patient at different stages of disease progression.
Once a mixture of proteins is separated in 2-D space, robotic devices can then be used to excise the protein spots of interest from the gel. While still resident in the gel plug, the proteins are then digested with a protease, usually trypsin, to yield a set of peptides unique to each protein. This mixture of peptides can then be analyzed directly by MS. Typically, MALDI-ToF MS is employed, but other options can be adopted.
Single-stage MS analysis generates a mass spectruma plot of mass-to-charge (m/z) ratios versus intensityand this is effectively a mass fingerprint of the principal protein component in the sample. A comparison of the experimentally determined MS spectrum to MS spectra generated in silico from primary sequence databases reveals the identity of the protein. Conventional 2-D gel-MS techniques provide fairly broad coverage of proteins with isoelectric points between 311 and molecular weights in the range of ca. 10100 kDa.
Frequently touted limitations of the 2-D gel-MS paradigm include the under-representation of proteins at the extremes of pI (very acidic or basic proteins), and poor representation of hydrophobic proteins, in particular those at extremes of the molecular weight range (low- and high-mass proteins). However, arguably the key limitation of the 2-D gel approach has been the generally poor reproducibility of gel runs, making it difficult to compare results across gels.
Therefore, with traditional 2-D gel technology, when comparing a patient sample to a control, or when comparing two patient samples, each sample would have to be run on its own gel and the gel patterns compared. Because of the intrinsic variability in the gel-preparation process, the same protein may not migrate to the same location on two different gels due to differences in gel composition or run conditions, and this presents problems when attempting to overlay gel spot patterns and determine which spot on one gel corresponds to the same protein on another.
We adopt a second-generation 2-D gel paradigm routinely in our laboratory called difference gel electrophoresis (DIGE). In its simplest form, the DIGE approach, now commercialized by GE Healthcare (www.gehealthcare.com), involves labeling two distinct protein mixtures with two different cyanine dyes, each of which fluoresces at a distinct wavelength. The labeled protein samples are then separated on a single 2-D gel. The size- and charge-matched dyes allow for co-migration of identical proteins.
DIGE reduces experimental variation and enables direct comparisons of protein expression between samples. A third cyanine dye can also be used to label a pooled sample, which acts as an internal standard to control for inter-gel comparisons and improve quantitative precision across gels. This mixture is run side-by-side with the various samples on every gel.
Fluorescent laser scanning using two or three excitation and emission wavelengths detects the signals emitted by the dyes and generates distinct images that can then be super-imposed and aligned through pixel matching. This approach minimizes experimental variability and leads to both qualitative and quantitative advantages.
In particular, the power of DIGE lies in its ability to reduce system variability and accurately represent differences in protein abundance, yielding statistically meaningful results that unmask true biological variability.