Proteomic profiling of H-Ras-G12V induced hypertrophic cardiomyopathy in transgenic mice using comparative LC-MS analysis of thin fresh-frozen tissue sections.

Determination of disease-relevant proteomic profiles from limited tissue specimens, such as pathological biopsies and tissues from small model organisms, remains an analytical challenge and a much needed clinical goal. In this study, a transgenic mouse disease model of cardiac-specific H-Ras-G12V induced hypertrophic cardiomyopathy provided a system to explore the potential of using mass spectrometry (MS)-based proteomics to obtain a disease-relevant molecular profile from amount-limited specimens that are routinely used in pathological diagnosis. Our method employs a two-stage methanol-assisted solubilization to digest lysates prepared from 8-µm-thick fresh-frozen histological tissue sections of diseased/experimental and normal/control hearts. Coupling this approach with a nanoflow reversed-phase liquid chromatography (LC) and a hybrid linear ion trap/Fourier transform-ion cyclotron resonance MS resulted in the identification of 704 and 752 proteins in hypertrophic and wild-type (control) myocardium, respectively. The disease driving H-Ras protein along with vimentin were unambiguously identified by LC-MS in hypertrophic myocardium and cross-validated by immunohistochemistry and western blotting. The pathway analysis involving proteins identified by MS showed strong association of proteomic data with cardiovascular disease. More importantly, the MS identification and subsequent cross-validation of Wnt3a and ß-catenin, in conjunction with IHC identification of phosphorylated GSK-3ß and nuclear localization of ß-catenin, provided evidence of Wnt/ß-catenin canonical pathway activation secondary to Ras activation in the course of pathogenic myocardial hypertrophic transformation. Our method yields results indicating that the described proteomic approach permits molecular discovery and assessment of differentially expressed proteins regulating H-Ras induced hypertrophic cardiomyopathy. Selected proteins and pathways can be further investigated using immunohistochemical techniques applied to serial tissue sections of similar or different origin.

An Efficient Stochastic Search for Bayesian Variable Selection with High-Dimensional Correlated Predictors.

We present a Bayesian variable selection method for the setting in which the number of independent variables or predictors in a particular dataset is much larger than the available sample size. While most existing methods allow some degree of correlations among predictors but do not consider these correlations for variable selection, our method accounts for correlations among the predictors in variable selection. Our correlation-based stochastic search (CBS) method, the hybrid-CBS algorithm, extends a popular search algorithm for high-dimensional data, the stochastic search variable selection (SSVS) method. Similar to SSVS, we search the space of all possible models using variable addition, deletion or swap moves. However, our moves through the model space are designed to accommodate correlations among the variables. We describe our approach for continuous, binary, ordinal, and count outcome data. The impact of choices of prior distributions and hyper-parameters is assessed in simulation studies. We also examined performance of variable selection and prediction as the correlation structure of the predictors varies. We found that the hybrid-CBS resulted in lower prediction errors and better identified the true outcome associated predictors than SSVS when predictors were moderately to highly correlated. We illustrate the method on data from a proteomic profiling study of melanoma, a skin cancer.

Optimized method for computing (18)O/(16)O ratios of differentially stable-isotope labeled peptides in the context of postdigestion (18)O exchange/labeling.

Differential (18)O/(16)O stable isotope labeling of peptides that relies on enzyme-catalyzed oxygen exchange at their carboxyl termini in the presence of H(2)(18)O has been widely used for relative quantitation of peptides/proteins. The role of tryptic proteolysis in bottom-up shotgun proteomics and low reagent costs have made trypsin-catalyzed (18)O postdigestion exchange a convenient and affordable stable isotope labeling approach. However, it is known that trypsin-catalyzed (18)O exchange at the carboxyl terminus is in many instances inhomogeneous/incomplete. The extent of the (18)O exchange/incorporation fluctuates from peptide to peptide mostly due to variable enzyme-substrate affinity. Thus, accurate calculation and interpretation of peptide ratios are analytically complicated and in some regard deficient. Therefore, a computational approach capable of improved measurement of actual (18)O incorporation for each differentially labeled peptide pair is needed. In this regard, we have developed an algorithmic method that relies on the trapezoidal rule to integrate peak intensities of all detected isotopic species across a particular peptide ion over the retention time, which fits the isotopic manifold to Poisson distributions. Optimal values for manifold fitting were calculated and then (18)O/(16)O ratios derived via evolutionary programming. The algorithm is tested using trypsin-catalyzed (18)O postdigestion exchange to differentially label bovine serum albumin (BSA) at a priori determined ratios. Both accuracy and precision are improved utilizing this rigorous mathematical approach. We further demonstrate the effectiveness of this method to accurately calculate (18)O/(16)O ratios in a large scale proteomic quantitation of detergent resistant membrane microdomains (DRMMs) isolated from cells expressing wild-type HIV-1 Gag and its nonmyristylated mutant.

Combined blood/tissue analysis for cancer biomarker discovery: application to renal cell carcinoma.

A method that relies on subtractive tissue-directed shot-gun proteomics to identify tumor proteins in the blood of a patient newly diagnosed with cancer is described. To avoid analytical and statistical biases caused by physiologic variability of protein expression in the human population, this method was applied on clinical specimens obtained from a single patient diagnosed with nonmetastatic renal cell carcinoma (RCC). The proteomes extracted from tumor, normal adjacent tissue and preoperative plasma were analyzed using 2D-liquid chromatography-mass spectrometry (LC-MS). The lists of identified proteins were filtered to discover proteins that (i) were found in the tumor but not normal tissue, (ii) were identified in matching plasma, and (iii) whose spectral count was higher in tumor tissue than plasma. These filtering criteria resulted in identification of eight tumor proteins in the blood. Subsequent Western-blot analysis confirmed the presence of cadherin-5, cadherin-11, DEAD-box protein-23, and pyruvate kinase in the blood of the patient in the study as well as in the blood of four other patients diagnosed with RCC. These results demonstrate the utility of a combined blood/tissue analysis strategy that permits the detection of tumor proteins in the blood of a patient diagnosed with RCC.

Proteomic analysis of traumatic brain injury: the search for biomarkers.

Although there are a number of causes of traumatic brain injury (TBI), the armed conflict in Iraq and Afghanistan has brought this disorder to the attention of the global community. A biomarker that would enable army medics to rapidly diagnose the severity of TBI on the battle-field would be a huge asset. Unfortunately, the study of TBI has not historically attracted the proteomic research community's interest as other disorders have, such as cancer. On the positive side, however, many of the analytical and technological challenges that were overcome in the development of biofluid proteomic methods are now being applied to the study of TBI. In this review, we discuss and highlight select examples of discovery-driven proteomic studies focused on finding effective biomarkers for TBI.

Protocol for the Analysis of Laser Capture Microdissected Fresh-Frozen Tissue Homogenates by Silver-Stained 1D SDS-PAGE.

The heterogeneity present in solid tumors adds significant difficulty to scientific analysis and improved understanding. Fundamentally, solid tumor formation consists of cancer cells proper along with stromal elements. The burgeoning malignant process is dependent upon modified stromal elements. Collectively, the stroma forms an essential microenvironment, which is indispensable for the survival and growth of the malignant neoplasm. This cellular heterogeneity makes molecular profiling of solid tumors via mass spectrometry (MS)-based proteomics a daunting task. Laser capture microdissection (LCM) is commonly used to obtain distinct histological cell types (e.g., tumor parenchymal cells, stromal cells) from tumor tissue and attempt to address the tumor heterogeneity interference with downstream liquid chromatography (LC) MS analysis. To provide optimal LC-MS analysis of micro-scale and/or nano-scale tissue sections, we modified and optimized a silver-stained one-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis (1D-SDS-PAGE) protocol for the LC-MS analysis of LCM-procured fresh-frozen tissue specimens. Presented is a detailed in-gel digestion protocol adjusted specifically to maximize the proteome coverage of amount-limited LCM samples, and facilitate in-depth molecular profiling. Following LCM, targeted tissue sections are further fractionated using silver-stained 1D-SDS-PAGE to resolve and visualize tissue proteins prior to in-gel digestion and subsequent LC-MS analysis.

Integrated proteotranscriptomics of breast cancer reveals globally increased protein-mRNA concordance associated with subtypes and survival.

BACKGROUND: Transcriptome analysis of breast cancer discovered distinct disease subtypes of clinical significance. However, it remains a challenge to define disease biology solely based on gene expression because tumor biology is often the result of protein function. Here, we measured global proteome and transcriptome expression in human breast tumors and adjacent non-cancerous tissue and performed an integrated proteotranscriptomic analysis. METHODS: We applied a quantitative liquid chromatography/mass spectrometry-based proteome analysis using an untargeted approach and analyzed protein extracts from 65 breast tumors and 53 adjacent non-cancerous tissues. Additional gene expression data from Affymetrix Gene Chip Human Gene ST Arrays were available for 59 tumors and 38 non-cancerous tissues in our study. We then applied an integrated analysis of the proteomic and transcriptomic data to examine relationships between them, disease characteristics, and patient survival. Findings were validated in a second dataset using proteome and transcriptome data from "The Cancer Genome Atlas" and the Clinical Proteomic Tumor Analysis Consortium. RESULTS: We found that the proteome describes differences between cancerous and non-cancerous tissues that are not revealed by the transcriptome. The proteome, but not the transcriptome, revealed an activation of infection-related signal pathways in basal-like and triple-negative tumors. We also observed that proteins rather than mRNAs are increased in tumors and show that this observation could be related to shortening of the 3' untranslated region of mRNAs in tumors. The integrated analysis of the two technologies further revealed a global increase in protein-mRNA concordance in tumors. Highly correlated protein-gene pairs were enriched in protein processing and disease metabolic pathways. The increased concordance between transcript and protein levels was additionally associated with aggressive disease, including basal-like/triple-negative tumors, and decreased patient survival. We also uncovered a strong positive association between protein-mRNA concordance and proliferation of tumors. Finally, we observed that protein expression profiles co-segregate with a Myc activation signature and separate breast tumors into two subgroups with different survival outcomes. CONCLUSIONS: Our study provides new insights into the relationship between protein and mRNA expression in breast cancer and shows that an integrated analysis of the proteome and transcriptome has the potential of uncovering novel disease characteristics.

Preparation and Immunoaffinity Depletion of Fresh Frozen Tissue Homogenates for Mass Spectrometry-Based Proteomics in the Context of Drug Target/Biomarker Discovery.

The discovery of novel drug targets and biomarkers via mass spectrometry (MS)-based proteomic analysis of clinical specimens has proven to be challenging. The wide dynamic range of protein concentration in clinical specimens and the high background/noise originating from highly abundant proteins in tissue homogenates and serum/plasma encompass two major analytical obstacles. Immunoaffinity depletion of highly abundant blood-derived proteins from serum/plasma is a well-established approach adopted by numerous researchers; however, the utilization of this technique for immunodepletion of tissue homogenates obtained from fresh frozen clinical specimens is lacking. We first developed immunoaffinity depletion of highly abundant blood-derived proteins from tissue homogenates, using renal cell carcinoma as a model disease, and followed this study by applying it to different tissue types. Tissue homogenate immunoaffinity depletion of highly abundant proteins may be equally important as is the recognized need for depletion of serum/plasma, enabling more sensitive MS-based discovery of novel drug targets, and/or clinical biomarkers from complex clinical samples. Provided is a detailed protocol designed to guide the researcher through the preparation and immunoaffinity depletion of fresh frozen tissue homogenates for two-dimensional liquid chromatography, tandem mass spectrometry (2D-LC-MS/MS)-based molecular profiling of tissue specimens in the context of drug target and/or biomarker discovery.

Proinflammatory and lipid biomarkers mediate metabolically healthy obesity: A proteomics study.

OBJECTIVE: The metabolically healthy obesity (MHO) phenotype is an important obesity subtype in which obesity is not accompanied by any metabolic comorbidity. However, the underlying molecular mechanisms remain elusive. In this study, a shotgun proteomics approach to identify circulating biomolecules and pathways associated with MHO was used. METHODS: The subjects were 20 African-American women: 10 MHO cases and 10 metabolically abnormal individuals with obesity (MAO) controls. Serum proteins were detected and quantified using label-free proteomics. Differential expression of proteins between the two groups was analyzed, and the list of differentially expressed proteins was analyzed to determine enriched biological pathways. RESULTS: Twenty proteins were differentially expressed between MHO and controls. These proteins included: hemoglobin subunits (HBA1, P = 6.00 × 10(-18) ), haptoglobin-related protein (HPR, P = 1.2 × 10(-15) ), apolipoproteins (APOB-100, P = 1.50 × 10(-40) ; APOA4, P = 1.1 × 10(-14) ), retinol-binding protein 4 (RBP4, P = 7.1 × 10(-08) ), and CRP (P = 2.0 × 10(-04) ). MHO was associated with lower levels of proinflammatory and higher levels of anti-inflammatory biomarkers when compared with MAO. Pathway analysis showed enrichment of lipids and inflammatory pathways, including LXR/RXR and FXR/RXR activation, and acute phase response signaling. CONCLUSIONS: These findings suggested that protection from dysregulated inflammatory and lipid processes were primary molecular hallmarks of MHO. The candidate biomarkers (AHSG, RBP4, and APOA4) identified in this study are potential prognostic markers for MHO.

Liquid Tissue: proteomic profiling of formalin-fixed tissues.

Identification and quantitation of candidate biomarker proteins in large numbers of individual tissues is required to validate specific proteins, or panels of proteins, for clinical use as diagnostic, prognostic, toxicological, or therapeutic markers. Mass spectrometry (MS) provides an exciting analytical methodology for this purpose. Liquid Tissue MS protein preparation allows researchers to utilize the vast, already existing, collections offormalin-fixed paraffin-embedded (FFPE) tissues for the procurement of peptides and the analysis across a variety of MS platforms.

Proteomic patterns: their potential for disease diagnosis.

Alterations in proteins abundance, structure, or function, act as useful indicators of pathological abnormalities prior to development of clinical symptoms and as such are often useful diagnostic and prognostic biomarkers. The underlying mechanism of diseases such as cancer are, however, quite complicated in that often multiple dysregulated proteins are involved. It is for this reason that recent hypotheses suggest that detection of panels of biomarkers may provide higher sensitivities and specificities for disease diagnosis than is afforded with single markers. Recently, a novel approach based on the analysis of protein patterns has emerged that may provide a more effective means to diagnose diseases, such as ovarian and prostate cancer. The method is based on the use of surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry (TOF-MS) to detect differentially captured proteins from clinical samples, such as serum and plasma. This analysis results in the detection of "proteomic" patterns that have been shown in recent investigations to distinguish diseased and unaffected subjects to varying degrees. This review will discuss the basics of SELDI protein chip technology and highlight its recent applications in disease biomarker discovery with emphasis on cancer diagnosis.

Mammalian cell transient expression, non-affinity purification, and characterization of human recombinant IGFBP7, an IGF-1 targeting therapeutic protein.

Targeted inhibiting insulin-like growth factor 1 is an effective approach for cancer therapy. Insulin-like growth factor binding protein 7 (IGFBP7) is considered as a potential therapeutic protein. However, producing high quality of such non-IgG proteins in mammalian cells is still a challenge in biopharmaceutical development. Here, we report a rapid production process by using transient gene transfection in HEK 293E cells. A set of constructs combining several expression promoters, leader sequences, and 5' un-translated regions were generated and optimized, from which the best vector with expression level at ~50mg/L was selected for production at 2L cell culture scale. Comparison study in downstream purification methods led to development of a scalable, non-affinity chromatography strategy through Super Q, Fast Flow Q, and Heparin columns. The product was characterized in purity (99%), isoelectric point, molecule weight, glycosylation, and stability by using SEC-HPLC, SDS-PAGE, isoelectric focusing and mass spectrometry. The highly purified product shows IGF-1 binding activity and inhibits IGF-1-induced cell proliferation. This process not only provides a remarkable high expression at ~50mg/L and pure glycosylated mammalian rhIGFBP7, also highlights that transient gene expression technology is practical to be used for production and early development of recombinant non-IgG therapeutic proteins.

Proteomic patterns for early cancer detection.

The advent of proteomics has brought with it the hope of discovering novel biomarkers that can be used to diagnose diseases, predict susceptibility, and monitor progression. Much of this effort has focused on the mass spectral identification of the thousands of proteins that populate complex biosystems such as serum and tissues. A revolutionary approach in proteomic pattern analysis has emerged as an effective method for the early diagnosis of diseases such as ovarian, breast, and prostate cancer. This technology is capable of analyzing hundreds of clinical samples per day and has the potential to be a novel, highly sensitive diagnostic tool for the early detection of diseases, or as a predictor of response to therapy.

Telomere length and heavy-chain mutation status in familial chronic lymphocytic leukemia.

We examined whether telomere lengths of peripheral blood mononuclear cells are associated with immunoglobulin gene usage in 21 familial chronic lymphocytic leukemia (CLL) patients. Subjects with unmutated V genes tended to have shorter telomeres than those with somatic mutations, especially after adjusting for age. Unlike V(H) mutation status, telomere length was not predictive for survival. Our results suggest that telomere length is associated with V(H) gene mutation status and provides further evidence that the biological basis of familial B-CLL is similar to that of sporadic patients.

The use of urine proteomic and metabonomic patterns for the diagnosis of interstitial cystitis and bacterial cystitis.

The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance (1H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and 1H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precise m/z or chemical shift values plotted in n-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84%.

Mass spectrometry in cancer biomarker research: a case for immunodepletion of abundant blood-derived proteins from clinical tissue specimens.

The discovery of clinically relevant cancer biomarkers using mass spectrometry (MS)-based proteomics has proven difficult, primarily because of the enormous dynamic range of blood-derived protein concentrations and the fact that the 22 most abundant blood-derived proteins constitute approximately 99% of the total plasma protein mass. Immunodepletion of clinical body fluid specimens (e.g., serum/plasma) for the removal of highly abundant proteins is a reasonable and reproducible solution. Often overlooked, clinical tissue specimens also contain a formidable amount of highly abundant blood-derived proteins present in tissue-embedded networks of blood/lymph capillaries and interstitial fluid. Hence, the dynamic range impediment to biomarker discovery remains a formidable obstacle, regardless of clinical sample type (solid tissue and/or body fluid). Thus, we optimized and applied simultaneous immunodepletion of blood-derived proteins from solid tissue and peripheral blood, using clear cell renal cell carcinoma as a model disease. Integrative analysis of data from this approach and genomic data obtained from the same type of tumor revealed concordant key pathways and protein targets germane to clear cell renal cell carcinoma. This includes the activation of the lipogenic pathway characterized by increased expression of adipophilin (PLIN2) along with 'cadherin switching', a phenomenon indicative of transcriptional reprogramming linked to renal epithelial dedifferentiation. We also applied immunodepletion of abundant blood-derived proteins to various tissue types (e.g., adipose tissue and breast tissue) showing unambiguously that the removal of abundant blood-derived proteins represents a powerful tool for the reproducible profiling of tissue proteomes. Herein, we show that the removal of abundant blood-derived proteins from solid tissue specimens is of equal importance to depletion of body fluids and recommend its routine use in the context of biological discovery and/or cancer biomarker research. Finally, this perspective presents the background, rationale and strategy for using tissue-directed high-resolution/accuracy MS-based shotgun proteomics to detect genuine tumor proteins in the peripheral blood of a patient diagnosed with nonmetastatic cancer, employing concurrent liquid chromatography-MS analysis of immunodepleted clinical tissue and blood specimens.

MYC-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis.

Metabolic profiling of cancer cells has recently been established as a promising tool for the development of therapies and identification of cancer biomarkers. Here we characterized the metabolomic profile of human breast tumors and uncovered intrinsic metabolite signatures in these tumors using an untargeted discovery approach and validation of key metabolites. The oncometabolite 2-hydroxyglutarate (2HG) accumulated at high levels in a subset of tumors and human breast cancer cell lines. We discovered an association between increased 2HG levels and MYC pathway activation in breast cancer, and further corroborated this relationship using MYC overexpression and knockdown in human mammary epithelial and breast cancer cells. Further analyses revealed globally increased DNA methylation in 2HG-high tumors and identified a tumor subtype with high tissue 2HG and a distinct DNA methylation pattern that was associated with poor prognosis and occurred with higher frequency in African-American patients. Tumors of this subtype had a stem cell-like transcriptional signature and tended to overexpress glutaminase, suggestive of a functional relationship between glutamine and 2HG metabolism in breast cancer. Accordingly, 13C-labeled glutamine was incorporated into 2HG in cells with aberrant 2HG accumulation, whereas pharmacologic and siRNA-mediated glutaminase inhibition reduced 2HG levels. Our findings implicate 2HG as a candidate breast cancer oncometabolite associated with MYC activation and poor prognosis.

Proteomic analysis of frozen tissue samples using laser capture microdissection.

The discovery of effective cancer biomarkers is essential for the development of both advanced molecular diagnostics and new therapies/medications. Finding and exploiting useful clinical biomarkers for cancer patients is fundamentally linked to improving outcomes. Towards these aims, the heterogeneous nature of tumors represents a significant problem. Thus, methods establishing an effective functional linkage between laser capture microdissection (LCM) and mass spectrometry (MS) provides for an enhanced molecular profiling of homogenous, specifically targeted cell populations from solid tumors. Utilizing frozen tissue avoids molecular degradation and bias that can be induced by other preservation techniques. Since clinical samples are often of a small quantity, tissue losses must be minimized. Therefore, all steps are carried out in the same single tube. Proteins are identified through peptide sequencing and subsequent matching against a specific proteomic database. Using such an approach enhances clinical biomarker discovery in the following ways. First, LCM allows for the complexity of a solid tumor to be reduced. Second, MS provides for the profiling of proteins, which are the ultimate bio-effectors. Third, by selecting for tumor proper or microenvironment-specific cells from clinical samples, the heterogeneity of individual solid tumors is directly addressed. Finally, since proteins are the targets of most pharmaceuticals, the enriched protein data streams can then be further analyzed for potential biomarkers, drug targets, pathway elucidation, as well as an enhanced understanding of the various pathologic processes under study. Within this context, the following method illustrates in detail a synergy between LCM and MS for an enhanced molecular profiling of solid tumors and clinical biomarker discovery.

Trypsin-mediated ¹8O/¹6O labeling for biomarker discovery.

Differential (18)O/(16)O stable isotopic labeling that relies on post-digestion (18)O exchange is a simple and efficient method for the relative quantitation of proteins in complex mixtures. This method incorporates two (18)O atoms onto the C-termini of proteolytic peptides resulting in a 4 Da mass-tag difference between (18)O- and (16)O-labeled peptides. This allows for wide-range relative quantitation of proteins in complex mixtures using shotgun proteomics. Because of minimal sample consumption and unrestricted peptide tagging, the post-digestion (18)O exchange is suitable for labeling of low-abundance membrane proteins enriched from cancer cell lines or clinical specimens, including tissues and body fluids. This chapter describes a protocol that applies post-digestion (18)O labeling to elucidate putative endogenous tumor hypoxia markers in the plasma membrane fraction enriched from a hypoxia-adapted malignant melanoma cell line. Plasma membrane proteins from hypoxic and normoxic cells were differentially tagged using (18)O/(16)O stable isotopic labeling. The initial tryptic digestion and solubilization of membrane proteins were carried out in a buffer containing 60 % methanol followed by post-digestion (18)O exchange/labeling in buffered 20 % methanol. The differentially labeled peptides were mixed in a 1:1 ratio and fractionated using off-line strong cation exchange (SCX) liquid chromatography followed by on-line reversed-phase nano-flow RPLC-MS identification and quantitation of peptides/proteins in respective SCX fractions. The present protocol illustrates the utility of (18)O/(16)O stable isotope labeling in the context of quantitative shotgun proteomics that provides a basis for the discovery of hypoxia-induced membrane protein markers in malignant melanoma cell lines.

Post-digestion ¹8O exchange/labeling for quantitative shotgun proteomics of membrane proteins.

The role of membrane proteins is critical for regulation of physiologic and pathologic cellular processes. Hence it is not surpassing that membrane proteins make ∼70% of contemporary drug targets. Quantitative profiling of membrane proteins using mass spectrometry (MS)-based proteomics is critical in a quest for disease biomarkers and novel cancer drugs. Post-digestion (18)O exchange is a simple and efficient method for differential (18)O/(16)O stable isotope labeling of two biologically distinct specimens, allowing relative quantitation of proteins in complex mixtures when coupled with shotgun MS-based proteomics. Due to minimal sample consumption and unrestricted peptide tagging, (18)O/(16)O stable isotope labeling is particularly suitable for amount-limited protein specimens typically encountered in membrane and clinical proteomics. This chapter describes a protocol that relies on shotgun proteomics for quantitative profiling of the detergent-insoluble membrane proteins isolated from HeLa cells, differentially transfected with plasmids expressing HIV Gag protein and its myristylation-defective N-terminal mutant. Whilst this protocol depicts solubilization of detergent-insoluble membrane proteins coupled with post-digestion (18)O labeling, it is amenable to any complex membrane protein mixture. Described approach relies on solubilization and tryptic digestion of membrane proteins in a buffer containing 60% (v/v) methanol followed by differential (18)O/(16)O labeling of protein digests in 20% (v/v) methanol buffer. After mixing, the differentially labeled peptides are fractionated using off-line strong cation exchange (SCX) followed by on-line reversed phase nanoflow reversed-phase liquid chromatography (nanoRPLC)-MS identification/quantiation of peptides/proteins. The use of methanol-based buffers in the context of the post-digestion (18)O exchange/labeling eliminates the need for detergents or chaotropes that interfere with LC separations and peptide ionization. Sample losses are minimized because solubilization, digestion, and stable isotope labeling are carried out in a single tube, avoiding any sample transfer or buffer exchange between these steps.