The intrinsic variation between mass spectrometry data collected from replicate samples is a major hurdle to realizing the full potential of shotgun proteomics. Griffin et al. present a simple method to compare label-free quantifications between technical and biological replicates. Replicate mass spectrometry (MS) measurements and the use of multiple analytical methods can greatly expand the comprehensiveness of shotgun proteomic profiling of biological samples1,2,3,4,5. However, the inherent biases and variations in such data create computational and statistical challenges for quantitative comparative analysis6. We developed and tested a normalized, label-free quantitative method termed the normalized spectral index (SIN), which combines three MS abundance features: peptide count, spectral count and fragment-ion (tandem MS or MS/MS) intensity. SIN 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.

Lipid membranes structurally define the outer surface and internal organelles of cells. The multitude of proteins embedded in lipid bilayers are clearly functionally important, yet they remain poorly defined. Even today, integral membrane proteins represent a special challenge for current large scale shotgun proteomics methods. Here we used endothelial cell plasma membranes isolated directly from lung tissue to test the effectiveness of four different mass spectrometry-based methods, each with multiple replicate measurements, to identify membrane proteins. In doing so, we substantially expanded this membranome to 1,833 proteins, including >500 lipid-embedded proteins. The best method combined SDS-PAGE prefractionation with trypsin digestion of gel slices to generate peptides for seamless and continuous two-dimensional LC/MS/MS analysis. This three-dimensional separation method outperformed current widely used two-dimensional methods by significantly enhancing protein identifications including single and multiple pass transmembrane proteins; >30% are lipid-embedded proteins. It also profoundly improved protein coverage, sensitivity, and dynamic range of detection and substantially reduced the amount of sample and the number of replicate mass spectrometry measurements required to achieve 95% analytical completeness. Such expansion in comprehensiveness requires a trade-off in heavy instrument time but bodes well for future advancements in truly defining the ever important membranome with its potential in network-based systems analysis and the discovery of disease biomarkers and therapeutic targets. This analytical strategy can be applied to other subcellular fractions and should extend the comprehensiveness of many future organellar proteomics pursuits.