Reproducibility Assessment of Relative Quantitation Strategies for LC−MS Based Proteomics

TGFBR3 supports anoikis through suppressing ATF4 signaling

Epithelial morphogenesis and oncogenic transformation can cause loss of cell adhesion, and detached cells are eliminated by anoikis. Here, we reveal that transforming growth factor beta receptor 3 (TGFBR3) acts as an anoikis mediator through the coordination of activating transcription factor 4 (ATF4). In breast cancer, TGFBR3 is progressively lost, but elevated TGFBR3 is associated with a histologic subtype characterized by cellular adhesion defects. Dissecting the impact of extracellular matrix (ECM) deprivation, we demonstrate that ECM loss promotes TGFBR3 expression, which in turn differentiates cell aggregates to a prosurvival phenotype and drives the intrinsic apoptotic pathway. We demonstrate that inhibition of TGFBR3 impairs epithelial anoikis by activating ATF4 signaling. These preclinical findings provide a rationale for therapeutic inhibition of ATF4 in the subgroup of breast cancer patients with low TGFBR3 expression.

Recent advances in phosphoproteomics and application to neurological diseases

Phosphorylation has an incredible impact on the biological behavior of proteins, altering everything from intrinsic activity to cellular localization and complex formation. It is no surprise then that this post-translational modification has been the subject of intense study and that, with the advent of faster, more accurate instrumentation, the number of large-scale mass spectrometry-based phosphoproteomic studies has swelled over the past decade. Recent developments in sample preparation, phosphorylation enrichment, quantification, and data analysis strategies permit both targeted and ultra-deep phosphoproteome profiling, but challenges remain in pinpointing biologically relevant phosphorylation events. We describe here technological advances that have facilitated phosphoproteomic analysis of cells, tissues, and biofluids and note applications to neuropathologies in which the phosphorylation machinery may be dysregulated, much as it is in cancer.

Reproducibility of Molecular Phenotypes after Long-Term Differentiation to Human iPSC-Derived Neurons: A Multi-Site Omics Study

Reproducibility in molecular and cellular studies is fundamental to scientific discovery. To establish the reproducibility of a well-defined long-term neuronal differentiation protocol, we repeated the cellular and molecular comparison of the same two iPSC lines across five distinct laboratories. Despite uncovering acceptable variability within individual laboratories, we detect poor cross-site reproducibility of the differential gene expression signature between these two lines. Factor analysis identifies the laboratory as the largest source of variation along with several variation-inflating confounders such as passaging effects and progenitor storage. Single-cell transcriptomics shows substantial cellular heterogeneity underlying inter-laboratory variability and being responsible for biases in differential gene expression inference. Factor analysis-based normalization of the combined dataset can remove the nuisance technical effects, enabling the execution of robust hypothesis-generating studies. Our study shows that multi-center collaborations can expose systematic biases and identify critical factors to be standardized when publishing novel protocols, contributing to increased cross-site reproducibility.

Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues

Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating six 2D LC-MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.

Conservation of protein abundance patterns reveals the regulatory architecture of the EGFR-MAPK pathway

Various genetic mutations associated with cancer are known to alter cell signaling, but it is not clear whether they dysregulate signaling pathways by altering the abundance of pathway proteins. Using a combination of RNA sequencing and ultrasensitive targeted proteomics, we defined the primary components-16 core proteins and 10 feedback regulators-of the epidermal growth factor receptor (EGFR)-mitogen-activated protein kinase (MAPK) pathway in normal human mammary epithelial cells and then quantified their absolute abundance across a panel of normal and breast cancer cell lines as well as fibroblasts. We found that core pathway proteins were present at very similar concentrations across all cell types, with a variance similar to that of proteins previously shown to display conserved abundances across species. In contrast, EGFR and transcriptionally controlled feedback regulators were present at highly variable concentrations. The absolute abundance of most core proteins was between 50,000 and 70,000 copies per cell, but the adaptors SOS1, SOS2, and GAB1 were found at far lower amounts (2000 to 5000 copies per cell). MAPK signaling showed saturation in all cells between 3000 and 10,000 occupied EGFRs, consistent with the idea that adaptors limit signaling. Our results suggest that the relative stoichiometry of core MAPK pathway proteins is very similar across different cell types, with cell-specific differences mostly restricted to variable amounts of feedback regulators and receptors. The low abundance of adaptors relative to EGFR could be responsible for previous observations that only a fraction of total cell surface EGFR is capable of rapid endocytosis, high-affinity binding, and mitogenic signaling.

Reproducibility of Differential Proteomic Technologies in CPTAC Fractionated Xenografts

The NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) employed a pair of reference xenograft proteomes for initial platform validation and ongoing quality control of its data collection for The Cancer Genome Atlas (TCGA) tumors. These two xenografts, representing basal and luminal-B human breast cancer, were fractionated and analyzed on six mass spectrometers in a total of 46 replicates divided between iTRAQ and label-free technologies, spanning a total of 1095 LC-MS/MS experiments. These data represent a unique opportunity to evaluate the stability of proteomic differentiation by mass spectrometry over many months of time for individual instruments or across instruments running dissimilar workflows. We evaluated iTRAQ reporter ions, label-free spectral counts, and label-free extracted ion chromatograms as strategies for data interpretation (source code is available from http://homepages.uc.edu/~wang2x7/Research.htm ). From these assessments, we found that differential genes from a single replicate were confirmed by other replicates on the same instrument from 61 to 93% of the time. When comparing across different instruments and quantitative technologies, using multiple replicates, differential genes were reproduced by other data sets from 67 to 99% of the time. Projecting gene differences to biological pathways and networks increased the degree of similarity. These overlaps send an encouraging message about the maturity of technologies for proteomic differentiation.

Blood-based lung cancer biomarkers identified through proteomic discovery in cancer tissues, cell lines and conditioned medium

Background

Support for early detection of lung cancer has emerged from the National Lung Screening Trial (NLST), in which low-dose computed tomography (LDCT) screening reduced lung cancer mortality by 20 % relative to chest x-ray. The US Preventive Services Task Force (USPSTF) recently recommended annual screening for the high-risk population, concluding that the benefits (life years gained) outweighed harms (false positive findings, abortive biopsy/surgery, radiation exposure). In making their recommendation, the USPSTF noted that the moderate net benefit of screening was dependent on the resolution of most false-positive results without invasive procedures. Circulating biomarkers may serve as a valuable adjunctive tool to imaging.

Results

We developed a broad-based proteomics discovery program, integrating liquid chromatography/mass spectrometry (LC/MS) analyses of freshly resected lung tumor specimens (n = 13), lung cancer cell lines (n = 17), and conditioned media collected from tumor cell lines (n = 7). To enrich for biomarkers likely to be found at elevated levels in the peripheral circulation of lung cancer patients, proteins were prioritized based on predicted subcellular localization (secreted, cell-membrane associated) and differential expression in disease samples. 179 candidate biomarkers were identified. Several markers selected for further validation showed elevated levels in serum collected from subjects with stage I NSCLC (n = 94), relative to healthy smoker controls (n = 189). An 8-marker model was developed (TFPI, MDK, OPN, MMP2, TIMP1, CEA, CYFRA 21–1, SCC) which accurately distinguished subjects with lung cancer (n = 50) from high risk smokers (n = 50) in an independent validation study (AUC = 0.775).

Conclusions

Integrating biomarker discovery from multiple sample types (fresh tissue, cell lines and conditioned medium) has resulted in a diverse repertoire of candidate biomarkers. This unique collection of biomarkers may have clinical utility in lung cancer detection and diagnoses.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9090-9) contains supplementary material, which is available to authorized users.

Quantitation of the phosphoproteome using the library-assisted extracted ion chromatogram (LAXIC) strategy

Phosphorylation is a key posttranslational modification that regulates many signaling pathways, but quantifying changes in phosphorylation between samples can be challenging due to its low stoichiometry within cells. We have introduced a mass spectrometry-based label-free quantitation strategy termed LAXIC for the analysis of the phosphoproteome. This method uses a spiked-in synthetic peptide library designed to elute across the entire chromatogram for local normalization of phosphopeptides within complex samples. Normalization of phosphopeptides by library peptides that co-elute within a small time frame accounts for fluctuating ion suppression effects, allowing more accurate quantitation even when LC-MS performance varies. Here we explain the premise of LAXIC, the design of a suitable peptide library, and how the LAXIC algorithm can be implemented with software developed in-house.

Analytical challenges translating mass spectrometry-based phosphoproteomics from discovery to clinical applications

Phosphoproteomics is the systematic study of one of the most common protein modifications in high throughput with the aim of providing detailed information of the control, response, and communication of biological systems in health and disease. Advances in analytical technologies and strategies, in particular the contributions of high-resolution mass spectrometers, efficient enrichments of phosphopeptides, and fast data acquisition and annotation, have catalyzed dramatic expansion of signaling landscapes in multiple systems during the past decade. While phosphoproteomics is an essential inquiry to map high-resolution signaling networks and to find relevant events among the apparently ubiquitous and widespread modifications of proteome, it presents tremendous challenges in separation sciences to translate it from discovery to clinical practice. In this mini-review, we summarize the analytical tools currently utilized for phosphoproteomic analysis (with focus on MS), progresses made on deciphering clinically relevant kinase-substrate networks, MS uses for biomarker discovery and validation, and the potential of phosphoproteomics for disease diagnostics and personalized medicine.

Quantitative proteomics: A strategic ally to map protein interaction networks

Many physiological processes are regulated by dynamic protein interaction networks whose characterization provides valuable information on cell biology. Several strategies can be used to analyze protein-protein interactions. Among them, affinity purification combined with mass spectrometry (AP-MS) is arguably the most widely employed technique, not only owing to its high throughput and sensitivity but also because it can answer critical questions such as where, when, and how protein-protein interactions occur. In AP-MS workflows, both the target protein and its interacting partners are isolated before being identified by MS. The main challenge of this approach is to distinguish bona fide binders from background contaminants. This review focuses on the different strategies designed to circumvent this limitation. In this regard, the combination of quantitative proteomics and affinity purification emerges as one of the most powerful, yet relatively simple, strategies to characterize protein-protein interactions.

Quantitative measurement of phosphoproteome response to osmotic stress in arabidopsis based on Library-Assisted eXtracted Ion Chromatogram (LAXIC)

Global phosphorylation changes in plants in response to environmental stress have been relatively poorly characterized to date. Here we introduce a novel mass spectrometry-based label-free quantitation method that facilitates systematic profiling plant phosphoproteome changes with high efficiency and accuracy. This method employs synthetic peptide libraries tailored specifically as internal standards for complex phosphopeptide samples and accordingly, a local normalization algorithm, LAXIC, which calculates phosphopeptide abundance normalized locally with co-eluting library peptides. Normalization was achieved in a small time frame centered to each phosphopeptide to compensate for the diverse ion suppression effect across retention time. The label-free LAXIC method was further treated with a linear regression function to accurately measure phosphoproteome responses to osmotic stress in Arabidopsis. Among 2027 unique phosphopeptides identified and 1850 quantified phosphopeptides in Arabidopsis samples, 468 regulated phosphopeptides representing 497 phosphosites have shown significant changes. Several known and novel components in the abiotic stress pathway were identified, illustrating the capability of this method to identify critical signaling events among dynamic and complex phosphorylation. Further assessment of those regulated proteins may help shed light on phosphorylation response to osmotic stress in plants.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

Proteomic approaches to unravel the complexity of schizophrenia

Schizophrenia is a debilitating mental disorder that affects approximately 30 million people worldwide. The development and progression of this disease is now thought to be precipitated through a complex interaction between altered gene function and environmental factors. Proteomic analyses have been applied extensively over the past 10 years in studies of several tissues from schizophrenic patients, resulting in increased insight into the affected molecular pathways. In addition, these proteomic approaches have led to the identification of a set of molecular biomarker assays as the first blood-based test to aid in the diagnosis of schizophrenia. Here, we discuss the main outcome of these investigations and suggest a practical means of integrating and translating the findings between the brain and peripheral blood to increase our understanding of schizophrenia pathophysiology.

Mass spectrometry-based approaches toward absolute quantitative proteomics

Mass spectrometry has served as a major tool for the discipline of proteomics to catalogue proteins in an unprecedented scale. With chemical and metabolic techniques for stable isotope labeling developed over the past decade, it is now routinely used as a method for relative quantification to provide valuable information on alteration of protein abundance in a proteome-wide scale. More recently, absolute or stoichiometric quantification of proteome is becoming feasible, in particular, with the development of strategies with isotope-labeled standards composed of concatenated peptides. On the other hand, remarkable progress has been also made in label-free quantification methods based on the number of identified peptides. Here we review these mass spectrometry-based approaches for absolute quantification of proteome and discuss their implications.

Multi-dimensional liquid chromatography in proteomics--a review

Proteomics is the large-scale study of proteins, particularly their expression, structures and functions. This still-emerging combination of technologies aims to describe and characterize all expressed proteins in a biological system. Because of upper limits on mass detection of mass spectrometers, proteins are usually digested into peptides and the peptides are then separated, identified and quantified from this complex enzymatic digest. The problem in digesting proteins first and then analyzing the peptide cleavage fragments by mass spectrometry is that huge numbers of peptides are generated that overwhelm direct mass spectral analyses. The objective in the liquid chromatography approach to proteomics is to fractionate peptide mixtures to enable and maximize identification and quantification of the component peptides by mass spectrometry. This review will focus on existing multidimensional liquid chromatographic (MDLC) platforms developed for proteomics and their application in combination with other techniques such as stable isotope labeling. We also provide some perspectives on likely future developments.

Current trends in quantitative proteomics

It was inevitable that as soon as mass spectrometrists were able to tell biologists which proteins were in their samples, the next question would be how much of these proteins were present. This has turned out to be a much more challenging question. In this review, we describe the multiple ways that mass spectrometry has attempted to address this issue, both for relative quantitation and for absolute quantitation of proteins. There is no single method that will work for every problem or for every sample. What we present here is a variety of techniques, with guidelines that we hope will assist the researcher in selecting the most appropriate technique for the particular biological problem that needs to be addressed. We need to emphasize that this is a very active area of proteomics research-new quantitative methods are continuously being introduced and some 'pitfalls' of older methods are just being discovered. However, even though there is no perfect technique--and a better technique may be developed tomorrow--valuable information on biomarkers and pathways can be obtained using these currently available methods.

Immune modulator CD70 as a potential cisplatin resistance predictive marker in ovarian cancer

OBJECTIVE

We have used mass-spectrometry (MS) based proteomics platform to identify cell surface proteins over-expressed on a cisplatin resistant derivative of an ovarian cancer cell line A2780.

METHODS

Membrane associated glycoproteins from A2780 and its cisplatin resistant derivative cell line, A2780cis, were processed for liquid chromatography (LC)-MS based analysis. The expression of proteins found at elevated levels in A2780cis cell line was confirmed using flow cytometry and Taqman analysis. The expression of these proteins was further evaluated in unrelated ovarian cancer cell lines using MS analysis and flow cytometry. Immunohistochemical (IHC) analysis was performed using ovarian tumor tissues to evaluate the protein density on the cell surface. Monoclonal antibodies were used in an alamar blue proliferation assay to examine the cytotoxic effects on cell proliferation in resistant cell lines.

RESULTS

Six proteins were identified by LC-MS as being over-expressed on cell surface of A2780cis cell line. Mass spectrometry and flow cytometry confirmed the over-expression of CD49f, CD70 and Her-2/neu in other cisplatin resistant ovarian cancer cell lines. Immunohistochemical analysis revealed that only CD70 was expressed at moderate levels in ovarian tumors. When cisplatin resistant ovarian cell lines A2780cis and SKOV-3 were treated with antibody against CD70, there was a significant decrease in cell proliferation.

CONCLUSION

Using a MS based proteomics approach we have shown that expression of CD70 is associated with cisplatin resistance in ovarian cancer cell lines. Follow-up examination of these tumor cell line findings in clinical tumor specimens with available pathology staging and cisplatin treatment history is warranted.

Industrialized MS-based proteomics in the search for circulating biomarkers

Proteomics is the study of the expression, structure and function of proteins under a range of cellular conditions. A rapidly evolving component of this field is clinical proteomics, which focuses on proteins involved in human disease and how they are affected by therapeutic intervention. MS is the main analytical technology for identifying and quantifying proteins whose expression is modulated across the normal to disease continuum. Applying this technology to clinical samples, however, is particularly challenging due to high biological variability in the population, a variety of disease stages, nonuniform response to therapy, multiple concomitant treatments and special requirements for handling samples from clinical trials. Given these challenges, an ‘industrialized’ approach is best suited to clinical biomarker development, with its standard operating procedures, process control and ‘chain of custody’. This review will focus, therefore, on MS-based industrialized proteomics for the discovery and verification of circulating candidate clinical protein biomarkers.

Reference map for liquid chromatography-mass spectrometry-based quantitative proteomics

The accurate mass and time (AMT) tag strategy has been recognized as a powerful tool for high-throughput analysis in liquid chromatography-mass spectrometry (LC-MS)-based proteomics. Due to the complexity of the human proteome, this strategy requires highly accurate mass measurements for confident identifications. We have developed a method of building a reference map that allows relaxed criteria for mass errors yet delivers high confidence for peptide identifications. The samples used for generating the peptide database were produced by collecting cysteine-containing peptides from T47D cells and then fractionating the peptides using strong cationic exchange chromatography (SCX). LC-tandem mass spectrometry (MS/MS) data from the SCX fractions were combined to create a comprehensive reference map. After the reference map was built, it was possible to skip the SCX step in further proteomic analyses. We found that the reference-driven identification increases the overall throughput and proteomic coverage by identifying peptides with low intensity or complex interference. The use of the reference map also facilitates the quantitation process by allowing extraction of peptide intensities of interest and incorporating models of theoretical isotope distribution.

Halogenated peptides as internal standards (H-PINS): introduction of an MS-based internal standard set for liquid chromatography-mass spectrometry

As the application for quantitative proteomics in the life sciences has grown in recent years, so has the need for more robust and generally applicable methods for quality control and calibration. The reliability of quantitative proteomics is tightly linked to the reproducibility and stability of the analytical platforms, which are typically multicomponent (e.g. sample preparation, multistep separations, and mass spectrometry) with individual components contributing unequally to the overall system reproducibility. Variations in quantitative accuracy are thus inevitable, and quality control and calibration become essential for the assessment of the quality of the analyses themselves. Toward this end, the use of internal standards cannot only assist in the detection and removal of outlier data acquired by an irreproducible system (quality control) but can also be used for detection of changes in instruments for their subsequent performance and calibration. Here we introduce a set of halogenated peptides as internal standards. The peptides are custom designed to have properties suitable for various quality control assessments, data calibration, and normalization processes. The unique isotope distribution of halogenated peptides makes their mass spectral detection easy and unambiguous when spiked into complex peptide mixtures. In addition, they were designed to elute sequentially over an entire aqueous to organic LC gradient and to have m/z values within the commonly scanned mass range (300-1800 Da). In a series of experiments in which these peptides were spiked into an enriched N-glycosite peptide fraction (i.e. from formerly N-glycosylated intact proteins in their deglycosylated form) isolated from human plasma, we show the utility and performance of these halogenated peptides for sample preparation and LC injection quality control as well as for retention time and mass calibration. Further use of the peptides for signal intensity normalization and retention time synchronization for selected reaction monitoring experiments is also demonstrated.

Large-scale multiplexed quantitative discovery proteomics enabled by the use of an (18)O-labeled "universal" reference sample

The quantitative comparison of protein abundances across a large number of biological or patient samples represents an important proteomics challenge that needs to be addressed for proteomics discovery applications. Herein, we describe a strategy that incorporates a stable isotope (18)O-labeled "universal" reference sample as a comprehensive set of internal standards for analyzing large sample sets quantitatively. As a pooled sample, the (18)O-labeled "universal" reference sample is spiked into each individually processed unlabeled biological sample and the peptide/protein abundances are quantified based on (16)O/(18)O isotopic peptide pair abundance ratios that compare each unlabeled sample to the identical reference sample. This approach also allows for the direct application of label-free quantitation across the sample set simultaneously along with the labeling-approach (i.e., dual-quantitation) since each biological sample is unlabeled except for the labeled reference sample that is used as internal standards. The effectiveness of this approach for large-scale quantitative proteomics is demonstrated by its application to a set of 18 plasma samples from severe burn patients. When immunoaffinity depletion and cysteinyl-peptide enrichment-based fractionation with high resolution LC-MS measurements were combined, a total of 312 plasma proteins were confidently identified and quantified with a minimum of two unique peptides per protein. The isotope labeling data was directly compared with the label-free (16)O-MS intensity data extracted from the same data sets. The results showed that the (18)O reference-based labeling approach had significantly better quantitative precision compared to the label-free approach. The relative abundance differences determined by the two approaches also displayed strong correlation, illustrating the complementary nature of the two quantitative methods. The simplicity of including the (18)O-reference for accurate quantitation makes this strategy especially attractive when a large number of biological samples are involved in a study where label-free quantitation may be problematic, for example, due to issues associated with instrument platform robustness. The approach will also be useful for more effectively discovering subtle abundance changes in broad systems biology studies.

A proteomics approach to identify changes in protein profiles in serum of Familial Adenomatous Polyposis patients

Familial adenomatous polyposis (FAP) is one of the most important clinical hereditary forms of inherited susceptibility to colorectal cancer and is characterized by a high degree of phenotypic heterogeneity. We used a mass spectrometry driven-proteomic strategy to identify serum molecules differently expressed in FAP patients. The data obtained were subsequently processed by bioinformatic analysis and confirmed by Western blotting. Significant differences were highlighted in the expression of serum proteins of FAP patients. In particular, two proteins (alpha-2-HS-glycoprotein and apoliprotein D) were down-regulated (about 0.5- and 0.7-fold, respectively) in carpeting versus diffuse FAP patients and healthy donors, while alpha-2-antiplasmin was up-regulated (about 1.4-fold). Moreover, mass spectrometry approach enabled us to identify serum biomarkers specific for two distinct clinical form of FAP, i.e. carpeting and diffuse FAP. In particular, vitronectin was up-regulated (more than 1.4-fold) in diffuse FAP patients versus carpeting FAP and versus healthy donors, and two additional proteins (Haptoglobin and alpha-1-acid glycoprotein 1) were up-regulated in 2 out of 3 carpeting FAP patients. Our study suggests that mass spectrometry combined to a strong bioinformatics analysis is a valuable tool for the identification of quali/quantitative differences in the serum proteome of otherwise indistinguishable FAP phenotypes. Moreover, the definition of a proteomic profile, supported by the supervised classification, is a powerful and highly sensitive approach for the identification molecular signatures that are able to outperform the traditional disease markers and can therefore be efficiently applied for the diagnosis and clinical management of FAP patients.

Significance analysis of microarray for relative quantitation of LC/MS data in proteomics

Background

Although fold change is a commonly used criterion in quantitative proteomics for differentiating regulated proteins, it does not provide an estimation of false positive and false negative rates that is often desirable in a large-scale quantitative proteomic analysis. We explore the possibility of applying the Significance Analysis of Microarray (SAM) method (PNAS 98:5116-5121) to a differential proteomics problem of two samples with replicates. The quantitative proteomic analysis was carried out with nanoliquid chromatography/linear iron trap-Fourier transform mass spectrometry. The biological sample model included two Mycobacterium smegmatis unlabeled cell cultures grown at pH 5 and pH 7. The objective was to compare the protein relative abundance between the two unlabeled cell cultures, with an emphasis on significance analysis of protein differential expression using the SAM method. Results using the SAM method are compared with those obtained by fold change and the conventional t-test.

Results

We have applied the SAM method to solve the two-sample significance analysis problem in liquid chromatography/mass spectrometry (LC/MS) based quantitative proteomics. We grew the pH5 and pH7 unlabelled cell cultures in triplicate resulting in 6 biological replicates. Each biological replicate was mixed with a common ¹⁵N-labeled reference culture cells for normalization prior to SDS/PAGE fractionation and LC/MS analysis. For each biological replicate, one center SDS/PAGE gel fraction was selected for triplicate LC/MS analysis. There were 121 proteins quantified in at least 5 of the 6 biological replicates. Of these 121 proteins, 106 were significant in differential expression by the t-test (p < 0.05) based on peptide-level replicates, 54 were significant in differential expression by SAM with Δ = 0.68 cutoff and false positive rate at 5%, and 29 were significant in differential expression by the t-test (p < 0.05) based on protein-level replicates. The results indicate that SAM appears to overcome the false positives one encounters using the peptide-based t-test while allowing for identification of a greater number of differentially expressed proteins than the protein-based t-test.

Conclusion

We demonstrate that the SAM method can be adapted for effective significance analysis of proteomic data. It provides much richer information about the protein differential expression profiles and is particularly useful in the estimation of false discovery rates and miss rates.