Evaluation of Data-Dependent versus Targeted Shotgun Proteomic Approaches for Monitoring Transcription Factor Expression in Breast Cancer

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

Panomics for Precision Medicine

Medicine is poised to undergo a digital transformation. High-throughput platforms are creating terabytes of genomic, transcriptomic, proteomic, and metabolomic data. The challenge is to interpret these data in a meaningful manner - to uncover relationships that are not readily apparent between molecular profiles and states of health or disease. This will require the development of novel data pipelines and computational tools. The combined analysis of multi-dimensional data is referred to as 'panomics'. The ultimate hope of integrative panomics is that it will lead to the discovery and application of novel markers and targeted therapeutics that drive forward a new era of 'precision medicine' where inter-individual variation is accounted for in the treatment of patients.

A Timely Shift from Shotgun to Targeted Proteomics and How It Can Be Groundbreaking for Cancer Research

The fact that cancer is a leading cause of death all around the world has naturally sparked major efforts in the pursuit of novel and more efficient biomarkers that could better serve as diagnostic tools, prognostic predictors, or therapeutical targets in the battle against this type of disease. Mass spectrometry-based proteomics has proven itself as a robust and logical alternative to the immuno-based methods that once dominated the field. Nevertheless, intrinsic limitations of classic proteomic approaches such as the natural gap between shotgun discovery-based methods and clinically applicable results have called for the implementation of more direct, hypothesis-based studies such as those made available through targeted approaches, that might be able to streamline biomarker discovery and validation as a means to increase survivability of affected patients. In fact, the paradigm shifting potential of modern targeted proteomics applied to cancer research can be demonstrated by the large number of advancements and increasing examples of new and more useful biomarkers found during the course of this review in different aspects of cancer research. Out of the many studies dedicated to cancer biomarker discovery, we were able to devise some clear trends, such as the fact that breast cancer is the most common type of tumor studied and that most of the research for any given type of cancer is focused on the discovery diagnostic biomarkers, with the exception of those that rely on samples other than plasma and serum, which are generally aimed toward prognostic markers. Interestingly, the most common type of targeted approach is based on stable isotope dilution-selected reaction monitoring protocols for quantification of the target molecules. Overall, this reinforces that notion that targeted proteomics has already started to fulfill its role as a groundbreaking strategy that may enable researchers to catapult the number of viable, effective, and validated biomarkers in cancer clinical practice.

Targeted quantitative phosphoproteomic analysis of erythrocyte membranes during blood bank storage

One of the hallmarks of blood bank stored red blood cells (RBCs) is the irreversible transition from a discoid to a spherocyte-like morphology with membrane perturbation and cytoskeleton disorders. Therefore, identification of the storage-associated modifications in the protein-protein interactions between the cytoskeleton and the lipid bilayer may contribute to enlighten the molecular mechanisms involved in the alterations of mechanical properties of stored RBCs. Here we report the results obtained analyzing RBCs after 0, 21 and 35 days of storage under standard blood banking conditions by label free mass spectrometry (MS)-based experiments. We could quantitatively measure changes in the phosphorylation level of crucial phosphopeptides belonging to β-spectrin, ankyrin-1, α-adducin, dematin, glycophorin A and glycophorin C proteins. Data have been validated by both western blotting and pseudo-Multiple Reaction Monitoring (MRM). Although each phosphopeptide showed a distinctive trend, a sharp increase in the phosphorylation level during the storage duration was observed. Phosphopeptide mapping and structural modeling analysis indicated that the phosphorylated residues localize in protein functional domains fundamental for the maintenance of membrane structural integrity. Along with previous morphological evidence acquired by electron microscopy, our results seem to indicate that 21-day storage may represent a key point for the molecular processes leading to the erythrocyte deformability reduction observed during blood storage. These findings could therefore be helpful in understanding and preventing the morphology-linked mechanisms responsible for the post-transfusion survival of preserved RBCs.

Cand1 promotes assembly of new SCF complexes through dynamic exchange of F box proteins

The modular SCF (Skp1, cullin, and F box) ubiquitin ligases feature a large family of F box protein substrate receptors that enable recognition of diverse targets. However, how the repertoire of SCF complexes is sustained remains unclear. Real-time measurements of formation and disassembly indicate that SCF(Fbxw7) is extraordinarily stable, but, in the Nedd8-deconjugated state, the cullin-binding protein Cand1 augments its dissociation by one-million-fold. Binding and ubiquitylation assays show that Cand1 is a protein exchange factor that accelerates the rate at which Cul1-Rbx1 equilibrates with multiple F box protein-Skp1 modules. Depletion of Cand1 from cells impedes recruitment of new F box proteins to pre-existing Cul1 and profoundly alters the cellular landscape of SCF complexes. We suggest that catalyzed protein exchange may be a general feature of dynamic macromolecular machines and propose a hypothesis for how substrates, Nedd8, and Cand1 collaborate to regulate the cellular repertoire of SCF complexes.

Targeted protein identification, quantification and reporting for high-resolution nanoflow targeted peptide monitoring

Mass spectrometry-based targeted proteomic assays are experiencing a surge in awareness due to the diverse possibilities arising from the re-application of traditional LC-SRM technology. The FDA-approved quantitative LC-SRM-pipeline in drug discovery motivates the use to quantitatively validate putative proteomic biomarkers. However, complexity of biological specimens bears a huge challenge to identify, in parallel, specific peptides and proteins of interest from large biomarker candidate lists. Methods have been devised to increase scan speeds, improve detection specificity and verify quantitative SRM-features. In contrast, high-resolution mass spectrometers could be used to improve reliability and precision of targeted proteomics assays. Here, we present a new method for identifying, quantifying and reporting peptides in high-resolution targeted proteomics experiments performed on an orbitrap hybrid instrument using stable isotope-labeled internal reference peptides. This high precision targeted peptide monitoring (TPM) method has unique advantages over existing techniques, including the need to only detect the most abundant product ion of a given target for confident peptide identification using a scoring function that evaluates assay performance based on 1) m/z-mass accuracy, 2) retention time accuracy of observed species relative to prediction, and 3) retention time accuracy relative to internal reference peptides. Further, we show management of multiplexed precision TPM-assays using sentinel peptide standards. This article is part of a Special Issue entitled: From protein structures to clinical applications.

Cancer proteomics

Cancer presents high mortality and morbidity globally, largely due to its complex and heterogenous nature, and lack of biomarkers for early diagnosis. A proteomics study of cancer aims to identify and characterize functional proteins that drive the transformation of malignancy, and to discover biomarkers to detect early-stage cancer, predict prognosis, determine therapy efficacy, identify novel drug targets, and ultimately develop personalized medicine. The various sources of human samples such as cell lines, tissues, and plasma/serum are probed by a plethora of proteomics tools to discover novel biomarkers and elucidate mechanisms of tumorigenesis. Innovative proteomics technologies and strategies have been designed for protein identification, quantitation, fractionation, and enrichment to delve deeper into the oncoproteome. In addition, there is the need for high-throughput methods for biomarker validation, and integration of the various platforms of oncoproteome data to fully comprehend cancer biology.

The functional network of the Arabidopsis plastoglobule proteome based on quantitative proteomics and genome-wide coexpression analysis

Plastoglobules (PGs) in chloroplasts are thylakoid-associated monolayer lipoprotein particles containing prenyl and neutral lipids and several dozen proteins mostly with unknown functions. An integrated view of the role of the PG is lacking. Here, we better define the PG proteome and provide a conceptual framework for further studies. The PG proteome from Arabidopsis (Arabidopsis thaliana) leaf chloroplasts was determined by mass spectrometry of isolated PGs and quantitative comparison with the proteomes of unfractionated leaves, thylakoids, and stroma. Scanning electron microscopy showed the purity and size distribution of the isolated PGs. Compared with previous PG proteome analyses, we excluded several proteins and identified six new PG proteins, including an M48 metallopeptidase and two Absence of bc1 complex (ABC1) atypical kinases, confirmed by immunoblotting. This refined PG proteome consisted of 30 proteins, including six ABC1 kinases and seven fibrillins together comprising more than 70% of the PG protein mass. Other fibrillins were located predominantly in the stroma or thylakoid and not in PGs; we discovered that this partitioning can be predicted by their isoelectric point and hydrophobicity. A genome-wide coexpression network for the PG genes was then constructed from mRNA expression data. This revealed a modular network with four distinct modules that each contained at least one ABC1K and/or fibrillin gene. Each module showed clear enrichment in specific functions, including chlorophyll degradation/senescence, isoprenoid biosynthesis, plastid proteolysis, and redox regulators and phosphoregulators of electron flow. We propose a new testable model for the PGs, in which sets of genes are associated with specific PG functions.

Nucleoid-enriched proteomes in developing plastids and chloroplasts from maize leaves: a new conceptual framework for nucleoid functions

Plastids contain multiple copies of the plastid chromosome, folded together with proteins and RNA into nucleoids. The degree to which components of the plastid gene expression and protein biogenesis machineries are nucleoid associated, and the factors involved in plastid DNA organization, repair, and replication, are poorly understood. To provide a conceptual framework for nucleoid function, we characterized the proteomes of highly enriched nucleoid fractions of proplastids and mature chloroplasts isolated from the maize (Zea mays) leaf base and tip, respectively, using mass spectrometry. Quantitative comparisons with proteomes of unfractionated proplastids and chloroplasts facilitated the determination of nucleoid-enriched proteins. This nucleoid-enriched proteome included proteins involved in DNA replication, organization, and repair as well as transcription, mRNA processing, splicing, and editing. Many proteins of unknown function, including pentatricopeptide repeat (PPR), tetratricopeptide repeat (TPR), DnaJ, and mitochondrial transcription factor (mTERF) domain proteins, were identified. Strikingly, 70S ribosome and ribosome assembly factors were strongly overrepresented in nucleoid fractions, but protein chaperones were not. Our analysis strongly suggests that mRNA processing, splicing, and editing, as well as ribosome assembly, take place in association with the nucleoid, suggesting that these processes occur cotranscriptionally. The plastid developmental state did not dramatically change the nucleoid-enriched proteome but did quantitatively shift the predominating function from RNA metabolism in undeveloped plastids to translation and homeostasis in chloroplasts. This study extends the known maize plastid proteome by hundreds of proteins, including more than 40 PPR and mTERF domain proteins, and provides a resource for targeted studies on plastid gene expression. Details of protein identification and annotation are provided in the Plant Proteome Database.

The workflow for quantitative proteome analysis of chloroplast development and differentiation, chloroplast mutants, and protein interactions by spectral counting

This chapter outlines a quantitative proteomics workflow using a label-free spectral counting technique. The workflow has been tested on different aspects of chloroplast biology in maize and Arabidopsis, including chloroplast mutant analysis, cell-type specific chloroplast differentiation, and the proplastid-to-chloroplast transition. The workflow involves one-dimensional SDS-PAGE of the proteomes of leaves or chloroplast subfractions, tryptic digestions, online LC-MS/MS using a mass spectrometer with high mass accuracy and duty cycle, followed by semiautomatic data processing. The bioinformatics analysis can effectively select best gene models and deals with quantification of closely related proteins; the workflow avoids overidentification of proteins and results in more accurate protein quantification. The final output includes pairwise comparative quantitative analysis, as well as hierarchical clustering for discovery of temporal and spatial patterns of protein accumulation. A brief discussion about potential pitfalls, as well as the advantages and disadvantages of spectral counting, is provided.

Analysis of NF-kappaB signaling pathways by proteomic approaches

NF-kappaB is a transcription factor that plays important roles in the regulation of apoptosis and inflammation as well as innate and adaptive immunity. Consequently, dysregulations in the NF-kappaB activation cascade have been associated with the pathogenesis of several diseases such as cancer, atherosclerosis and rheumatoid arthritis. Although NF-kappaB signaling pathways have been extensively investigated in this context, its varying components and targets are far from being completely elucidated. There is still an urgent need for the detection of novel NF-kappaB target proteins, novel interaction partners and novel regulators in the activation cascade, in particular with regard to its role in the aforementioned diseases. Therefore, several groups have performed different proteomic approaches to further investigate NF-kappaB signal transduction pathways. Most of these studies have been carried out in the area of cancer research; however, there are also several analyses in the field of inflammatory or autoimmune diseases. Furthermore, there have been a number of basic investigations that principally examined binding partners or so far unknown target proteins of NF-kappaB-related proteins. With these approaches, a number of novel and interesting proteins have been found that interfere with NF-kappaB signal transduction and might have an impact on NF-kappaB-related diseases. The results of these studies are summarized and discussed in this review.

A rapid, reproducible, on-the-fly orthogonal array optimization method for targeted protein quantification by LC/MS and its application for accurate and sensitive quantification of carbonyl reductases in human liver

Liquid chromatography (LC)/mass spectrometry (MS) in selected-reactions-monitoring (SRM) mode provides a powerful tool for targeted protein quantification. However, efficient, high-throughput strategies for proper selection of signature peptides (SP) for protein quantification and accurate optimization of their SRM conditions remain elusive. Here we describe an on-the-fly, orthogonal array optimization (OAO) approach that enables rapid, comprehensive, and reproducible SRM optimization of a large number of candidate peptides in a single nanoflow-LC/MS run. With the optimized conditions, many peptide candidates can be evaluated in biological matrixes for selection of the final SP. The OAO strategy employs a systematic experimental design that strategically varies product ions, declustering energy, and collision energy in a cycle of 25 consecutive SRM trials, which accurately reveals the effects of these factors on the signal-to-noise ratio of a candidate peptide and optimizes each. As proof of concept, we developed a highly sensitive, accurate, and reproducible method for the quantification of carbonyl reductases CBR1 and CBR3 in human liver. Candidate peptides were identified by nano-LC/LTQ/Orbitrap, filtered using a stringent set of criteria, and subjected to OAO. After evaluating both sensitivity and stability of the candidates, two SP were selected for quantification of each protein. As a result of the accurate OAO of assay conditions, sensitivities of 80 and 110 amol were achieved for CBR1 and CBR3, respectively. The method was validated and used to quantify the CBRs in 33 human liver samples. The mean level of CBR1 was 93.4 +/- 49.7 (range: 26.2-241) ppm of total protein, and of CBR3 was 7.69 +/- 4.38 (range: 1.26-17.9) ppm. Key observations of this study: (i) evaluation of peptide stability in the target matrix is essential for final selection of the SP; (ii) utilization of two unique SP contributes to high reliability of target protein quantification; (iii) it is beneficial to construct calibration curves using standard proteins of verified concentrations to avoid severe biases that may result if synthesized peptides alone are used. Overall, the OAO method is versatile and adaptable to high-throughput quantification of validated biomarkers identified by proteomic discovery experiments.

Synthetic peptide arrays for pathway-level protein monitoring by liquid chromatography-tandem mass spectrometry

Effective methods to detect and quantify functionally linked regulatory proteins in complex biological samples are essential for investigating mammalian signaling pathways. Traditional immunoassays depend on proprietary reagents that are difficult to generate and multiplex, whereas global proteomic profiling can be tedious and can miss low abundance proteins. Here, we report a target-driven liquid chromatography-tandem mass spectrometry (LC-MS/MS) strategy for selectively examining the levels of multiple low abundance components of signaling pathways which are refractory to standard shotgun screening procedures and hence appear limited in current MS/MS repositories. Our stepwise approach consists of: (i) synthesizing microscale peptide arrays, including heavy isotope-labeled internal standards, for use as high quality references to (ii) build empirically validated high density LC-MS/MS detection assays with a retention time scheduling system that can be used to (iii) identify and quantify endogenous low abundance protein targets in complex biological mixtures with high accuracy by correlation to a spectral database using new software tools. The method offers a flexible, rapid, and cost-effective means for routine proteomic exploration of biological systems including "label-free" quantification, while minimizing spurious interferences. As proof-of-concept, we have examined the abundance of transcription factors and protein kinases mediating pluripotency and self-renewal in embryonic stem cell populations.

Fast proteomic protocol for biomarker fingerprinting in cancerous cells

The advance of novel technologies that will enable the detection of large sets of biomarker proteins, to greatly improve the sensitivity and specificity of an assay, represents a major objective in biomedical research. To demonstrate the power of mass spectrometry (MS) detection for large-scale biomarker screening in cancer research, a simple, one-step approach for fast biomarker fingerprinting in complex cellular extracts is described. MCF-7 breast cancer cells were used as a model system. Fast proteomic profiling of whole cellular extracts was achieved on a linear trap quadrupole (LTQ) mass spectrometer by one of the following techniques: (a) data-dependent liquid chromatography (LC)-MS/MS of un-labeled cell extracts, (b) data-dependent LC-MS/MS with pulsed Q dissociation (PQD) detection of iTRAQ labeled samples, and (c) multiple reaction monitoring (MRM)-MS of low abundant proteins that could not be detected with data-dependent MS/MS. The data-dependent LC-MS/MS analysis of MCF-7 cells enabled the identification of 796 proteins (p<0.001) and the simultaneous detection of 156 previously reported putative cancer biomarkers. PQD detection of iTRAQ labeled cells resulted in the detection of 389 proteins and 64 putative biomarkers. MRM-MS analysis enabled the successful monitoring of a panel of low-abundance proteins in one single experiment, highlighting the utility of this technique for targeted analysis in cancer investigations. These results demonstrate that MS-based technologies relying on a one-step separation protocol have the potential to revolutionize biomarker research and screening applications by enabling fast, sensitive and reliable detection of large panels of putative biomarkers. To further stimulate the exploration of proteins that have been previously reported in the literature to be differentially expressed in a variety of cancers, an extensive list of approximately 1100 candidate biomarkers has been compiled and included in the manuscript.

Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses

A major unmet need in LC-MS/MS-based proteomics analyses is a set of tools for quantitative assessment of system performance and evaluation of technical variability. Here we describe 46 system performance metrics for monitoring chromatographic performance, electrospray source stability, MS1 and MS2 signals, dynamic sampling of ions for MS/MS, and peptide identification. Applied to data sets from replicate LC-MS/MS analyses, these metrics displayed consistent, reasonable responses to controlled perturbations. The metrics typically displayed variations less than 10% and thus can reveal even subtle differences in performance of system components. Analyses of data from interlaboratory studies conducted under a common standard operating procedure identified outlier data and provided clues to specific causes. Moreover, interlaboratory variation reflected by the metrics indicates which system components vary the most between laboratories. Application of these metrics enables rational, quantitative quality assessment for proteomics and other LC-MS/MS analytical applications.

Experimental advances in the characterization of metazoan gene regulatory networks

Gene regulatory networks (GRNs) play a vital role in metazoan development and function, and deregulation of these networks is often implicated in disease. GRNs depict the dynamic interactions between genomic and regulatory state components. The genomic components comprise genes and their associated cis-regulatory elements. The regulatory state components consist primarily of transcriptional complexes that bind the latter elements. With the availability of complete genome sequences, several approaches have recently been developed which promise to significantly enhance our ability to identify either the genomic or regulatory state components, or the interactions between these two. In this review, I provide an in-depth overview of these approaches and detail how each contributes to a more comprehensive understanding of GRN composition and function.

Unique ion signature mass spectrometry, a deterministic method to assign peptide identity

The growing use of selected reaction monitoring (SRM) mass spectrometry in proteomic analyses led us to investigate how to identify peptides by SRM using only a minimal number of fragment ions. By using a computational model of the SRM work flow we computed the potential interferences from other peptides in a given proteome. From these results, we selected the deterministic SRM addresses that contained sufficient information to confer peptide and protein identity that we termed unique ion signatures (UIS). We computationally showed that UIS comprised of only two transitions are diagnostic for >99% of Escherichia coli proteins and >96% of human proteins that possess a sequence-unique peptide. We demonstrated an example of experimental use of UIS using a modified SRM methodology to profile the E. coli tricarboxylic acid cycle from a single injection of cell lysate. In addition, we showed the potential of UIS to form the first functionally orthogonal approach to validate peptide assignments obtained from conventional analyses of MS/MS spectra. The UIS methodology is a novel deterministic peptide identification method for MS/MS spectra based on information content. These robust theoretical assays will have widespread use when integrated with previously collected MS/MS data and conventional proteomics technologies.

Global screening of human cord blood proteomes for biomarkers of toxic exposure and effect

BACKGROUND

Exposures of pregnant women to natural and manmade chemicals can lead to negative health effects in the baby, ranging from low birth weight to developmental defects. In some cases, diseases were postulated to have their basis in toxic exposure in utero or in early childhood. Therefore, an understanding of fetal responses to environmental exposures is essential. To that end, cord blood is a readily accessible biofluid whose proteomic makeup remains mostly unexplored when compared with that of adults.

OBJECTIVES

Our goal was an initial global assessment of the fetal serum proteome and for the identification of protein biomarkers indicative of toxic in utero exposures related to maternal cigarette smoking.

METHODS

Drawing from a repository of 300 samples, we selected umbilical cord blood sera from 12 babies born to six smokers and six nonsmokers and analyzed both sample pools by tandem mass spectrometry in conjunction with isobaric tags (iTRAQ) for protein quantification.

RESULTS

We identified 203 proteins, 17 of which were differentially expressed between the cigarette smoke-exposed and control populations. Most of the identified candidate biomarkers were biologically plausible, thereby underscoring the feasibility of screening neonates with global proteomic techniques for biomarkers of exposure and early biological effects triggered by in utero chemical exposures.

CONCLUSIONS

This validation study provides an initial view of the proteome of human cord blood sera; it demonstrates the feasibility of identifying therein by use of proteomics, biomarkers of environmental, toxic exposures.

MRM screening/biomarker discovery with linear ion trap MS: a library of human cancer-specific peptides

BACKGROUND

The discovery of novel protein biomarkers is essential in the clinical setting to enable early disease diagnosis and increase survivability rates. To facilitate differential expression analysis and biomarker discovery, a variety of tandem mass spectrometry (MS/MS)-based protein profiling techniques have been developed. For achieving sensitive detection and accurate quantitation, targeted MS screening approaches, such as multiple reaction monitoring (MRM), have been implemented.

METHODS

MCF-7 breast cancer protein cellular extracts were analyzed by 2D-strong cation exchange (SCX)/reversed phase liquid chromatography (RPLC) separations interfaced to linear ion trap MS detection. MS data were interpreted with the Sequest-based Bioworks software (Thermo Electron). In-house developed Perl-scripts were used to calculate the spectral counts and the representative fragment ions for each peptide.

RESULTS

In this work, we report on the generation of a library of 9,677 peptides (p < 0.001), representing approximately 1,572 proteins from human breast cancer cells, that can be used for MRM/MS-based biomarker screening studies. For each protein, the library provides the number and sequence of detectable peptides, the charge state, the spectral count, the molecular weight, the parameters that characterize the quality of the tandem mass spectrum (p-value, DeltaM, Xcorr, DeltaCn, Sp, no. of matching a, b, y ions in the spectrum), the retention time, and the top 10 most intense product ions that correspond to a given peptide. Only proteins identified by at least two spectral counts are listed. The experimental distribution of protein frequencies, as a function of molecular weight, closely matched the theoretical distribution of proteins in the human proteome, as provided in the SwissProt database. The amino acid sequence coverage of the identified proteins ranged from 0.04% to 98.3%. The highest-abundance proteins in the cellular extract had a molecular weight (MW)<50,000.

CONCLUSION

Preliminary experiments have demonstrated that putative biomarkers, that are not detectable by conventional data dependent MS acquisition methods in complex un-fractionated samples, can be reliable identified with the information provided in this library. Based on the spectral count, the quality of a tandem mass spectrum and the m/z values for a parent peptide and its most abundant daughter ions, MRM conditions can be selected to enable the detection of target peptides and proteins.

Critical Evaluation of Product Ion Selection and Spectral Correlation Analysis for Biomarker Screening Using Targeted Peptide Multiple Reaction Monitoring

Introduction

Tandem mass spectrometry (MS/MS) has emerged as a cornerstone of proteomic screens aimed at discovering putative protein biomarkers of disease with potential clinical applications. Systematic validation of lead candidates in large numbers of samples from patient cohorts remains an important challenge. One particularly promising high throughout technique is multiple reaction monitoring (MRM), a targeted form of MS/MS by which precise peptide precursor–product ion combinations, or transitions, are selectively tracked as informative probes. Despite recent progress, however, many important computational and statistical issues remain unresolved. These include the selection of an optimal set of transitions so as to achieve sufficiently high specificity and sensitivity when profiling complex biological specimens, and the corresponding generation of a suitable scoring function to reliably confirm tentative molecular identities based on noisy spectra.

Methods

In this study, we investigate various empirical criteria that are helpful to consider when developing and interpreting MRM-style assays based on the similarity between experimental and annotated reference spectra. We also rigorously evaluate and compare the performance of conventional spectral similarity measures, based on only a few pre-selected representative transitions, with a generic scoring metric, termed T corr, wherein a selected product ion profile is used to score spectral comparisons.

Conclusions

Our analyses demonstrate that T corr is potentially more suitable and effective for detecting biomarkers in complex biological mixtures than more traditional spectral library searches.