Is label-free LC-MS/MS ready for biomarker discovery?

Label-free LC-MS methods are attractive for high-throughput quantitative proteomics, as the sample processing is straightforward and can be scaled to a large number of samples. Label-free methods therefore facilitate biomarker discovery in studies involving dozens of clinical samples. However, despite the increased popularity of label-free workflows, there is a hesitance in the research community to use it in clinical proteomics studies. Therefore, we here discuss pros and cons of label free LC-MS/MS for biomarker discovery, and delineate the main prerequisites for its successful employment. Furthermore, we cite studies where label-free LC-MS/MS was successfully used to identify novel biomarkers, and foresee an increased acceptance of label-free techniques by the proteomics community in the near future. This article is protected by copyright. All rights reserved

Mass Spectrometry-Based Label-Free Quantitative Proteomics

In order to study the differential protein expression in complex biological samples, strategies for rapid, highly reproducible and accurate quantification are necessary. Isotope labeling and fluorescent labeling techniques have been widely used in quantitative proteomics research. However, researchers are increasingly turning to label-free shotgun proteomics techniques for faster, cleaner, and simpler results. Mass spectrometry-based label-free quantitative proteomics falls into two general categories. In the first are the measurements of changes in chromatographic ion intensity such as peptide peak areas or peak heights. The second is based on the spectral counting of identified proteins. In this paper, we will discuss the technologies of these label-free quantitative methods, statistics, available computational software, and their applications in complex proteomics studies

Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling

<b>Background:</b>  Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues.<p></p> <b>Methods:</b>  Fresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis.<p></p> <b>Results:</b>  A total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value <0.05). Of these, 596 proteins were identified as differentially expressed between cancer and noncancer tissues. Two upregulated proteins in tumour samples (adipose differentiation-related protein and Coronin 1A) were further validated by immunohistochemistry. Pathway analysis using IPA, KOBAS 2.0, DAVID functional annotation and FLink tools showed enrichment of many cancer-related biological processes and pathways such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways.<p></p> <b>Conclusions:<b>  Our study identified a number of differentially expressed proteins and pathways using label-free proteomics approach in RCC compared with normal tissue samples. Two proteins validated in this study are the focus of on-going research in a large cohort of patients.<p></p&gt

Latent protein trees

Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing is a collection of features and their associated intensities for each sample. Subsets of features within the data are from the same peptide, subsets of peptides are from the same protein, and subsets of proteins are in the same biological pathways, therefore, there is the potential for very complex and informative correlational structure inherent in these data. Recent attempts to utilize this data often focus on the identification of single features that are associated with a particular phenotype that is relevant to the experiment. However, to date, there have been no published approaches that directly model what we know to be multiple different levels of correlation structure. Here we present a hierarchical Bayesian model which is specifically designed to model such correlation structure in unbiased, label-free proteomics. This model utilizes partial identification information from peptide sequencing and database lookup as well as the observed correlation in the data to appropriately compress features into latent proteins and to estimate their correlation structure. We demonstrate the effectiveness of the model using artificial/benchmark data and in the context of a series of proteomics measurements of blood plasma from a collection of volunteers who were infected with two different strains of viral influenza.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS639 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

RIBAR and xRIBAR: methods for reproducible relative MS/MS-based label-free protein quantification

Mass spectrometry-driven proteomics is increasingly relying on quantitative analyses for biological discoveries. As a result, different methods and algorithms have been developed to perform relative or absolute quantification based on mass spectrometry data. One of the most popular quantification methods are the so-called label-free approaches, which require no special sample processing, and can even be applied retroactively to existing data sets. Of these label-free methods, the MS/MS-based approaches are most often applied, mainly because of their inherent simplicity as compared to MS-based methods. The main application of these approaches is the determination of relative protein amounts between different samples, expressed as protein ratios. However, as we demonstrate here, there are some issues with the reproducibility across replicates of these protein ratio sets obtained from the various, MS/MS-based label-free methods, indicating that the existing methods are not optimally robust. We therefore present two new Methods (called RIBAR and xRIBAR) that use the available MS/MS data more effectively, achieving increased robustness. Both the accuracy and the precision of our novel methods are analyzed and compared to the existing methods to illustrate the increased robustness of our new methods over existing ones

Label-free proteomics identifies Calreticulin and GRP75/Mortalin as peripherally accessible protein biomarkers for spinal muscular atrophy

BACKGROUND: Spinal muscular atrophy (SMA) is a neuromuscular disease resulting from mutations in the survival motor neuron 1 (SMN1) gene. Recent breakthroughs in preclinical research have highlighted several potential novel therapies for SMA, increasing the need for robust and sensitive clinical trial platforms for evaluating their effectiveness in human patient cohorts. Given that most clinical trials for SMA are likely to involve young children, there is a need for validated molecular biomarkers to assist with monitoring disease progression and establishing the effectiveness of therapies being tested. Proteomics technologies have recently been highlighted as a potentially powerful tool for such biomarker discovery. METHODS: We utilized label-free proteomics to identify individual proteins in pathologically-affected skeletal muscle from SMA mice that report directly on disease status. Quantitative fluorescent western blotting was then used to assess whether protein biomarkers were robustly changed in muscle, skin and blood from another mouse model of SMA, as well as in a small cohort of human SMA patient muscle biopsies. RESULTS: By comparing the protein composition of skeletal muscle in SMA mice at a pre-symptomatic time-point with the muscle proteome at a late-symptomatic time-point we identified increased expression of both Calreticulin and GRP75/Mortalin as robust indicators of disease progression in SMA mice. We report that these protein biomarkers were consistently modified in different mouse models of SMA, as well as across multiple skeletal muscles, and were also measurable in skin biopsies. Furthermore, Calreticulin and GRP75/Mortalin were measurable in muscle biopsy samples from human SMA patients. CONCLUSIONS: We conclude that label-free proteomics technology provides a powerful platform for biomarker identification in SMA, revealing Calreticulin and GRP75/Mortalin as peripherally accessible protein biomarkers capable of reporting on disease progression in samples of muscle and skin

Characterization of Novel Cellular Systems Using Labelfree Quantitative Proteomics

Proteomics involves the systematic identification of the protein complement within an array of biological systems. Quantitative proteomics is an extension of proteomics that aims to characterize the changes in protein abundance between different sample states. Mass spectrometry, which measures the mass of ionized molecules in the gas phase, is the predominant analytical tool featured in quantitative proteomic studies. As more genomes are becoming annotated and publicly available, mass spectrometry-based approaches to proteomics have increased in feasibility. Furthermore, the orthogonal detection of all precursor ions and precursor ion fragments, known as data independent acquisition mass spectrometry, has allowed for high-throughput instruments to identify low and high abundant proteins without bias. Data-independent acquisition, in combination with ion mobility, has encouraged the enhancement of protein resolution by further separating ions based on their size, shape and charge. Together, the technological innovations of today’s mass spectrometers and advancement of genomic libraries has extended the boundaries for deeper proteome coverage. The option of using label-free standards for quantitative applications in proteomics is a non-invasive and cost-effective method for measuring protein abundance. However, early label-free strategies suffered from poor resolution and sensitivity issues when analyzing complex mixtures during quantitative studies. By optimizing the use of data-independent acquisition mass spectrometry and ion mobility separation, label-free strategies have joined the toolbox of reliable proteomic platforms for conducting quantitative analysis. In the following thesis, we present the successful application of using data-independent acquisition mass spectrometry employing orthogonal ion mobility separation to three unique biological systems including Daphnia pulex with the supplementation of using label-free quantitative techniques to explore the protein alterations in the seminal plasma of Chinook salmon, and the synaptosomes of a SMA mouse model. For the Daphnia pulex profiling study, our optimized methods have progressed sample preparation methods for the Daphnia model system and suggests label free mass spectrometry techniques will be applicable in utilizing the Daphnia model in assays that monitor aquatic health dynamics. With regard to the Chinook salmon project, we elucidated statistically significant protein abundance differences between hooknose and jack male tactics. Proteins involved in membrane remodeling, proteolysis, hormonal transport, redox regulation, immunomodulation, and ATP metabolism were among the proteins reproducibly identified at different levels and represent putative factors influencing sperm competition between jack and hooknose males. This study represents the largest seminal plasma proteome from teleost fish and the first reported for Chinook salmon. Lastly, Label-free quantitative proteomics on isolated synaptosomes from spinal cords of a SMA mouse model identified 2030 protein groups. Statistical data analysis revealed 65 specific alterations in the proteome of the central synapses at the early onset stage of disease. Functional analysis of the dysregulated proteins indicated a significant enrichment of proteins associated with mitochondrial dynamics, cholesterol biogenesis, and protein clearance. These pathways represent potential targets for therapy development with the goal of providing stability to the central synapses, thereby preserving neuronal integrity in the context of SMA disease

APPLIED QUANTITATIVE PROTEOMICS ANALYSIS

En esta tesis se ha aplicado el estado del arte en análisis cuantitativo en proteómica. Los datos analizados en este trabajo, provenientes de tres proyectos distintos, fueron obtenidos usando tres de las técnicas más utilizadas en proteómica: cuantificación label-free, marcaje isobárico y SWATH. Los resultados obtenidos en los diferentes proyectos son también interpretados mediante múltiples herramientas bioinformáticas. La cuantificación label-free es utilizada aquí para obtener la combinación óptima de software y parámetros usando un conjunto de datos públicos. El marcaje isobárico, usando TMT, se emplea en el estudio de los diferentes perfiles de expresión proteica, obtenidos con dos modelos de hipoxia de diferente severidad en cerebros de rata. La técnica SWATH se busca en la búsqueda de biomarcadores de síndorme de ovario poliquístico en plasma. Por último, los elementos necesarios para la implantación de una plataforma de análisis proteómica , en términos de software y hardware, se describen en forma detallada. In this thesis, the state of the art in quantitative proteomics analysis has been applied. The data analyzed in this work, coming from three different projects, were acquired using three of the most used techniques in proteomics: label-free, isobaric labeling and SWATH. The results obtained in the different projects are also interpreted using multiple bioinformatics tools. The label-free quantization is used here to asses the optimal combination of software and parameters using a public data set. Isobaric labeling, using TMT, is employed to study the different profiles in protein expression when two hypoxic models, with different severity, are applied in rat brains. The SWATH technique is used in the search of biomarkers for polycystic ovary syndrome in plasma. Finally, the elements required for setting up a platform for proteomics analysis, both in terms of hardware and software, are comprehensively described.Tesis Univ. Jaén. Departamento de Biología Experimenta

Quantitative Mass Spectrometry-based Proteomics

Mass spectrometry-based proteomics, the large-scale analysis of proteins by mass spectrometry, has emerged as a powerful technology over the past decade and has become an indispensable tool in many biomedical laboratories. Many strategies for differential proteomics have been developed in recent years, which involve either the incorporation of heavy stable isotopes or are based on label-free comparisons and their statistical assessment, and each of these has specific strengths and limitations. This chapter gives an overview of the current state-of-the-art in quantitative or differential proteomics and will be illustrated by several examples

Tissue Proteomes: Quantitative Mass Spectrometry of Murine Liver and Ovarian Endometrioma

A human genome contains more than 20 000 protein-encoding genes. A human proteome, instead, has been estimated to be much more complex and dynamic. The most powerful tool to study proteins today is mass spectrometry (MS). MS based proteomics is based on the measurement of the masses of charged peptide ions in a gas-phase. The peptide amino acid sequence can be deduced, and matching proteins can be found, using software to correlate MS-data with sequence database information. Quantitative proteomics allow the estimation of the absolute or relative abundance of a certain protein in a sample. The label-free quantification methods use the intrinsic MS-peptide signals in the calculation of the quantitative values enabling the comparison of peptide signals from numerous patient samples. In this work, a quantitative MS methodology was established to study aromatase overexpressing (AROM+) male mouse liver and ovarian endometriosis tissue samples. The workflow of label-free quantitative proteomics was optimized in terms of sensitivity and robustness, allowing the quantification of 1500 proteins with a low coefficient of variance in both sample types. Additionally, five statistical methods were evaluated for the use with label-free quantitative proteomics data. The proteome data was integrated with other omics datasets, such as mRNA microarray and metabolite data sets. As a result, an altered lipid metabolism in liver was discovered in male AROM+ mice. The results suggest a reduced beta oxidation of long chain phospholipids in the liver and increased levels of pro-inflammatory fatty acids in the circulation in these mice. Conversely, in the endometriosis tissues, a set of proteins highly specific for ovarian endometrioma were discovered, many of which were under the regulation of the growth factor TGF-β1. This finding supports subsequent biomarker verification in a larger number of endometriosis patient samples.Siirretty Doriast