Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis.

Abstract

Replicate mass spectrometry (MS) measurements and the use of multiple analytical methods can greatly expand the comprehensiveness of shotgun proteomic profiling of biological samples. However, the inherent biases and variations in such data create computational and statistical challenges for quantitative comparative analysis. We developed and tested a normalized, label-free quantitative method termed the normalized spectral index (SI(N)), which combines three MS abundance features: peptide count, spectral count and fragment-ion (tandem MS or MS/MS) intensity. SI(N) largely eliminated variances between replicate MS measurements, permitting quantitative reproducibility and highly significant quantification of thousands of proteins detected in replicate MS measurements of the same and distinct samples. It accurately predicts protein abundance more often than the five other methods we tested. Comparative immunoblotting and densitometry further validate our method. Comparative quantification of complex data sets from multiple shotgun proteomics measurements is relevant for systems biology and biomarker discovery.