Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions.

SignificanceDeep profiling of the plasma proteome at scale has been a challenge for traditional approaches. We achieve superior performance across the dimensions of precision, depth, and throughput using a panel of surface-functionalized superparamagnetic nanoparticles in comparison to conventional workflows for deep proteomics interrogation. Our automated workflow leverages competitive nanoparticle-protein binding equilibria that quantitatively compress the large dynamic range of proteomes to an accessible scale. Using machine learning, we dissect the contribution of individual physicochemical properties of nanoparticles to the composition of protein coronas. Our results suggest that nanoparticle functionalization can be tailored to protein sets. This work demonstrates the feasibility of deep, precise, unbiased plasma proteomics at a scale compatible with large-scale genomics enabling multiomic studies.

Quantitative proteomics analysis reveals molecular networks regulated by epidermal growth factor receptor level in head and neck cancer.

Epidermal growth factor receptor (EGFR) is overexpressed in up to 90% of head and neck cancer (HNC), where increased expression levels of EGFR correlate with poor prognosis. To date, EGFR expression levels have not predicted the clinical response to the EGFR-targeting therapies. Elucidation of the molecular mechanisms underlying anti-EGFR-induced antitumor effects may shed some light on the mechanisms of HNC resistance to EGFR-targeting therapeutics and provide novel targets for improving the treatment of HNC. Here, we conducted a quantitative proteomics analysis to determine the molecular networks regulated by EGFR levels in HNC by specifically knocking-down EGFR and employing stable isotope labeling with amino acids in cell culture (SILAC). Following data normalization to minimize systematic errors and Western blotting validation, 12 proteins (e.g., p21, stratifin, and maspin) and 24 proteins (e.g., cdc2 and MTA2) were found to be significantly upregulated or downregulated by EGFR knockdown, respectively. Bioinformatic analysis revealed that these proteins were mainly involved in long-chain fatty acid biosynthesis and beta-oxidation, cholesterol biosynthesis, cell proliferation, DNA replication, and apoptosis. Cell cycle analysis confirmed that G(2)/M phase progression was significantly inhibited by EGFR knockdown, a hypothesis generated from network modeling. Further investigation of these molecular networks may not only enhance our understanding of the antitumor mechanisms of EGFR targeting but also improve patient selection and provide novel targets for better therapeutics.

A large-scale quantitative proteomic approach to identifying sulfur mustard-induced protein phosphorylation cascades.

Sulfur mustard [SM, bis-(2-chloroethyl) sulfide] is a potent alkylating agent and chemical weapon. While there are no effective treatments for SM-induced injury, current research focuses on understanding the molecular changes upon SM exposure. Indeed, efforts that seek a more comprehensive analysis of proteins and post-translational modifications are critical for understanding SM-induced toxicity on a more global scale. Furthermore, these studies can uncover proteins previously uncharacterized in SM-exposed cells, which in turn leads to potential targets for therapeutic intervention. Here, we apply a quantitative proteomic approach termed stable isotope-labeling with amino acids in cell culture combined with immobilized metal affinity chromatography to study the large-scale protein phosphorylation changes resulting from SM exposure in a human keratinocyte cell culture model. This resulted in the characterization of over 2300 nonredundant phosphorylation sites, many of which exhibit altered levels in response to SM. Our results point toward several proteins previously implicated in SM-induced toxicity as well as many additional proteins previously uncharacterized. Further de novo analysis of these phosphoproteins using interaction mapping software revealed both known and novel pathways that may serve as future therapeutic targets of SM exposure.

Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona.

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.

Proteomics in tumor progression and metastasis.

With the ultimate goal of systematically identifying and characterizing proteins within an organism, the field of proteomics has generated much excitement in the past few years. Coupled with mass spectrometry, various quantitative and functional techniques are now available that allow for large-scale analyses of proteins implicated in cancer. New techniques are just now being applied to identifying the temporal changes in protein levels associated with tumor development. This review will focus on the use and promise of proteomic technologies as they apply to the study of tumor progression and metastasis.

Genomics and proteomics in chemical warfare agent research: recent studies and future applications.

Medical research on the effects of chemical warfare agents (CWAs) has been ongoing for nearly 100 years, yet these agents continue to pose a serious threat to deployed military forces and civilian populations. CWAs are extremely toxic, relatively inexpensive, and easy to produce, making them a legitimate weapon of choice for terrorist organizations. While the mechanisms of action for many CWAs have been known for years, questions about their molecular effects following acute and chronic exposure remain largely unanswered. Global approaches that can pinpoint which cellular pathways are altered in response to CWAs and characterize long-term toxicity have not been widely used. Fortunately, innovations in genomics and proteomics technologies now allow for thousands of genes and proteins to be identified and subsequently quantified in a single experiment. Advanced bioinformatics software can also help decipher large-scale changes observed, leading to mapping of signaling pathways, functional characterization, and identification of potential therapeutic targets. Here we present an overview of how genomics and proteomics technologies have been applied to CWA research and also provide a series of questions focused on how these techniques could further our understanding of CWA toxicity.

The impact of peptide abundance and dynamic range on stable-isotope-based quantitative proteomic analyses.

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.

Assessing enzyme activities using stable isotope labeling and mass spectrometry.

Activity-based protein profiling has emerged as a valuable technology for labeling, enriching, and assessing protein activities from complex mixtures. This is primarily accomplished via a two-step identification and quantification process. Here we show a highly quantitative and streamlined method, termed catch-and-release activity profiling of enzymes (CAPE), which reduces this procedure to a single step. Furthermore the CAPE approach has the ability to detect small quantitative changes that may have been missed by alternative mass spectrometry-based techniques.

Enhanced analysis of metastatic prostate cancer using stable isotopes and high mass accuracy instrumentation.

The primary goal of proteomics is to gain a better understanding of biological function at the protein expression level. As the field matures, numerous technologies are being developed to aid in the identification, quantification and characterization of protein expression and post-translational modifications on a near-global scale. Stable isotope labeling by amino acids in cell culture is one such technique that has shown broad biological applications. While we have recently shown the application of this technology to a model of metastatic prostate cancer, we now report a substantial improvement in quantitative analysis using a linear ion-trap Fourier transform ion cyclotron resonance mass spectrometer (LTQ FT) and novel quantification software. This resulted in the quantification of nearly 1400 proteins, a greater than 3-fold increase in comparison to our earlier study. This dramatic increase in proteome coverage can be attributed to (1) use of a double-labeling strategy, (2) greater sensitivity, speed and mass accuracy provided by the LTQ FT mass spectrometer, and (3) more robust quantification software. Finally, by using a concatenated target/decoy protein database for our peptide searches, we now report these data in the context of an estimated false-positive rate of one percent.

Quantitative cancer proteomics: stable isotope labeling with amino acids in cell culture (SILAC) as a tool for prostate cancer research.

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.