(11c) Signal-to-Noise and Quantitative Proteomics Using Difference Gel Electrophoresis (DIGE)

The complex methodology used in many large-scale quantitative proteomics experiments often dictates an experimental design with small sample size and limited repetition. Variation in a complex dataset may hopefully arise from changes caused by the experimental condition(s), but could also arise due to technical noise (poor sample prep, run-to-run variation) and biological noise (normal differences between samples, especially present in clinical samples). Difference Gel Electrophoresis (DIGE) is particularly well-suited for quantitative proteomics studies. DIGE uses a common pooled-sample internal standard to coordinate multiple 2D gels, each of which also contains two multiplexed samples from a large sample set representing independent (biological) replicates from multiple conditions. Multivariate statistical analyses such as Principle Component Analysis (PCA) and unsupervised Hierarchical Clustering (HC) can now be applied to complex DIGE datasets. These tools provide a global perspective on multivariable DIGE datasets that can assess whether the variation in the system describes the biological signal, rather than being derived from technical/biological noise whereby ?significant' changes may arise stochastically. Examples will be shown from experiments containing samples from micro-organisms, tissue culture and clinical samples, where these tools were instrumental in demonstrating sample outliers, fouled samples, as well as variation in sample preparation that overrides the variation from biological treatment (despite standard biological tests for sample validity).