Large-scale, unbiased proteomics studies are constrained by the complexity of the plasma proteome. Here we report a highly parallel protein quantitation platform integrating nanoparticle (NP) protein coronas with liquid chromatography-mass spectrometry for efficient proteomic profiling. A protein corona is a protein layer adsorbed onto NPs upon contact with biofluids. Varying the physicochemical properties of engineered NPs translates to distinct protein corona patterns enabling differential and reproducible interrogation of biological samples, including deep sampling of the plasma proteome. Spike experiments confirm a linear signal response. The median coefficient of variation was 22%. We screened 43 NPs and selected a panel of 5, which detect more than 2,000 proteins from 141 plasma samples using a 96-well automated workflow in a pilot non-small cell lung cancer classification study. Our streamlined workflow combines depth of coverage and throughput with precise quantification based on unique interactions between proteins and NPs engineered for deep and scalable quantitative proteomic studies.

Microarrays have been the primary means for large-scale analyses of genes implicated in cancer progression. However, more recently a need has been recognized for investigating cancer development directly at the protein level. In this report, we have applied a comparative proteomic technique to the study of metastatic prostate cancer. This technology, termed stable isotope labeling with amino acids in cell culture (SILAC), has recently gained popularity for its ability to compare the expression levels of hundreds of proteins in a single experiment. SILAC makes use of 12 C-and 13 C-labeled amino acids added to the growth media of separately cultured cell lines, giving rise to cells containing either "light" or "heavy" proteins, respectively. Upon mixing lysates collected from these cells, proteins can be identified by tandem mass spectrometry. The incorporation of stable isotopes also allows for a quantitative comparison between the two samples. Using this method, we compared the expression levels for more than 440 proteins in the microsomal fractions of prostate cancer cells with varying metastatic potential. Of these, 60 were found elevated greater than 3-fold in the highly metastatic cells, whereas 22 were reduced by equivalent amounts. Western blotting provided further confirmation of the mass spectrometry-based quantification. Our results demonstrate the applicability of this novel approach toward the study of cancer progression using defined cell lines. Molecular & Cellular Proteomics 3:729 -735, 2004.

Recently, mass spectrometry has been employed in many studies to provide unbiased, reproducible, and quantitative protein abundance information on a proteome-wide scale. However, how instruments' limited dynamic ranges impact the accuracy of such measurements has remained largely unexplored, especially in the context of complex mixtures. Here, we examined the distribution of peptide signal versus background noise (S/N) and its correlation with quantitative accuracy. With the use of metabolically labeled Jurkat cell lysate, over half of all confidently identified peptides had S/N ratios less than 10 when examined using both hybrid linear ion trap-Fourier transform ion cyclotron resonance and Orbitrap mass spectrometers. Quantification accuracy was also highly correlated with S/N. We developed a mass precision algorithm that significantly reduced measurement variance at low S/N beyond the use of highly accurate mass information alone and expanded it into a new software suite, Vista. We also evaluated the interplay between mass measurement accuracy and S/N; finding a balance between both parameters produced the greatest identification and quantification rates. Finally, we demonstrate that S/N can be a useful surrogate for relative abundance ratios when only a single species is detected.