Tools for label-free peptide quantification

Accessing microenvironment compartments in formalin-fixed paraffin-embedded tissues by protein expression analysis

BACKGROUND

Formalin-fixed paraffin-embedded (FFPE) samples are an outstanding source of new information regarding disease evolvements. Current research on new biomarkers and diseases features has recently invested resources in FFPE-related projects.

RESULTS

In order to initiate clinical protein-expression studies using minute amount of biological material, a workflow based on the combination of filter-assisted sample preparation with MS analysis and label-free quantification was developed. Xenograft lung tumor tissue was investigated as a model system. The workflow was optimized and characterized in terms of its reproducibility from a quantitative and qualitative point of view. We proposed a modification of the original filter-assisted sample preparation protocol to improve reproducibility and highlight its potential for the investigation of hydrophobic proteins.

CONCLUSIONS

Altogether the presented workflow allows analysis of FFPE samples with improvements in the analytical time and performance, and we show its application for lung cancer xenograft tissue samples.

Label-free glycopeptide quantification for biomarker discovery in human sera

Glycan moieties of glycoproteins modulate many biological processes in mammals, such as immune response, inflammation, and cell signaling. Numerous studies show that many human diseases are correlated with quantitative alteration of protein glycosylation. In some cases, these changes can occur for certain types of glycans over specific sites in a glycoprotein rather than on the global abundance of the glycoprotein. Conventional analytical techniques that analyze the abundance of glycans cleaved from glycoproteins cannot reveal these subtle effects. Here we present a novel statistical method to quantify the site-specific glycosylation of glycoproteins in complex samples using label-free mass spectrometric techniques. Abundance variations between sites of a glycoprotein as well as different glycoforms, that is, glycopeptides with different glycans attached to the same site, can be detected using these techniques. We applied our method to an esophageal cancer study based on blood serum samples from cancer patients in an attempt to detect potential biomarkers of site-specific N-linked glycosylation. A few glycoproteins, including vitronectin, showed significantly different site-specific glycosylations within cancer/control samples, indicating that our method is ready to be used for the discovery of glycosylated biomarkers.

Quantitative proteomic analysis of Huh-7 cells infected with Dengue virus by label-free LC-MS

Dengue is an important and growing public health problem worldwide with an estimated 100million new clinical cases annually. Currently, no licensed drug or vaccine is available. During natural infection in humans, liver cells constitute one of the main targets of dengue virus (DENV) replication. However, a clear understanding of dengue pathogenesis remains elusive. In order to gain a better reading of the cross talk between virus and host cell proteins, we used a proteomics approach to analyze the host response to DENV infection in a hepatic cell line Huh-7. Differences in proteome expression were assayed 24h post-infection using label-free LC-MS. Quantitative analysis revealed 155 differentially expressed proteins, 64 of which were up-regulated and 91 down-regulated. These results reveal an important decrease in the expression of enzymes involved in the glycolytic pathway, citrate cycle, and pyruvate metabolism. This study provides large-scale quantitative information regarding protein expression in the early stages of infection that should be useful for better compression of the pathogenesis of dengue.

BIOLOGICAL SIGNIFICANCE

Dengue infection involves alterations in the homeostasis of the host cell. Defining the interactions between virus and cell proteins should provide a better understanding of how viruses propagate and cause disease. Here, we present for the first time the proteomic analysis of hepatocytes (Huh-7 cells) infected with DENV-2 by label-free LC-MS.

Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ

Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.

Methylcrotonyl-CoA Carboxylase Regulates Triacylglycerol Accumulation in the Model Diatom Phaeodactylum tricornutum

The model marine diatom Phaeodactylum tricornutum can accumulate high levels of triacylglycerols (TAGs) under nitrogen depletion and has attracted increasing attention as a potential system for biofuel production. However, the molecular mechanisms involved in TAG accumulation in diatoms are largely unknown. Here, we employed a label-free quantitative proteomics approach to estimate differences in protein abundance before and after TAG accumulation. We identified a total of 1193 proteins, 258 of which were significantly altered during TAG accumulation. Data analysis revealed major changes in proteins involved in branched-chain amino acid (BCAA) catabolic processes, glycolysis, and lipid metabolic processes. Subsequent quantitative RT-PCR and protein gel blot analysis confirmed that four genes associated with BCAA degradation were significantly upregulated at both the mRNA and protein levels during TAG accumulation. The most significantly upregulated gene, encoding the β-subunit of methylcrotonyl-CoA carboxylase (MCC2), was selected for further functional studies. Inhibition of MCC2 expression by RNA interference disturbed the flux of carbon (mainly in the form of leucine) toward BCAA degradation, resulting in decreased TAG accumulation. MCC2 inhibition also gave rise to incomplete utilization of nitrogen, thus lowering biomass during the stationary growth phase. These findings help elucidate the molecular and metabolic mechanisms leading to increased lipid production in diatoms.

Global analysis of cellular proteolysis by selective enzymatic labeling of protein N-termini

Proteolysis is a critical modification leading to alteration of protein function with important outcomes in many biological processes. However, for the majority of proteases, we have an incomplete understanding of both cellular substrates and downstream effects. Here, we describe detailed protocols and applications for using the rationally engineered peptide ligase, subtiligase, to specifically label and capture protein N-termini generated by proteases either induced or added to complex biological samples. This method allows identification of the protein targets as well as their precise cleavage locations. This approach has revealed >8000 proteolytic sites in healthy and apoptotic cells including >1700 caspase cleavages. One can further determine substrate preferences through rate analysis with quantitative mass spectrometry, physiological substrate specificities, and even infer the identity of proteases operating in the cell. In this chapter, we also describe how this experimental method can be generalized to investigate proteolysis in any biological sample.

Post-translational modifications and mass spectrometry detection

In this review, we provide a comprehensive bibliographic overview of the role of mass spectrometry and the recent technical developments in the detection of post-translational modifications (PTMs). We briefly describe the principles of mass spectrometry for detecting PTMs and the protein and peptide enrichment strategies for PTM analysis, including phosphorylation, acetylation and oxidation. This review presents a bibliographic overview of the scientific achievements and the recent technical development in the detection of PTMs is provided. In order to ascertain the state of the art in mass spectrometry and proteomics methodologies for the study of PTMs, we analyzed all the PTM data introduced in the Universal Protein Resource (UniProt) and the literature published in the last three years. The evolution of curated data in UniProt for proteins annotated as being post-translationally modified is also analyzed. Additionally, we have undertaken a careful analysis of the research articles published in the years 2010 to 2012 reporting the detection of PTMs in biological samples by mass spectrometry.

Mass spectrometry-based identification of proteins interacting with nucleic acids

The identification of the regulatory proteins that control DNA transcription as well as RNA stability and translation represents a key step in the comprehension of gene expression regulation. Those proteins can be purified by DNA- or RNA-affinity chromatography, followed by identification by mass spectrometry. Although very simple in the concept, this represents a real technological challenge due to the low abundance of regulatory proteins compared to the highly abundant proteins binding to nucleic acids in a nonsequence-specific manner. Here we review the different strategies that have been set up to reach this purpose, discussing the key parameters that should be considered to increase the chances of success. Typically, two categories of biological questions can be distinguished: the identification of proteins that specifically interact with a precisely defined binding site, mostly addressed by quantitative mass spectrometry, and the identification in a non-comparative manner of the protein complexes recruited by a poorly characterized long regulatory region of nucleic acids. Finally, beside the numerous studies devoted to in vitro-assembled nucleic acid-protein complexes, the scarce data reported on proteomic analyses of in vivo-assembled complexes are described, with a special emphasis on the associated challenges.

Bioinformatic progress and applications in metaproteogenomics for bridging the gap between genomic sequences and metabolic functions in microbial communities

Metaproteomics of microbial communities promises to add functional information to the blueprint of genes derived from metagenomics. Right from its beginning, the achievements and developments in metaproteomics were closely interlinked with metagenomics. In addition, the evaluation, visualization, and interpretation of metaproteome data demanded for the developments in bioinformatics. This review will give an overview about recent strategies to use genomic data either from public databases or organismal specific genomes/metagenomes to increase the number of identified proteins obtained by mass spectrometric measurements. We will review different published metaproteogenomic approaches in respect to the used MS pipeline and to the used protein identification workflow. Furthermore, different approaches of data visualization and strategies for phylogenetic interpretation of metaproteome data are discussed as well as approaches for functional mapping of the results to the investigated biological systems. This information will in the end allow a comprehensive analysis of interactions and interdependencies within microbial communities.

Recent advances in quantitative neuroproteomics

The field of proteomics is undergoing rapid development in a number of different areas including improvements in mass spectrometric platforms, peptide identification algorithms and bioinformatics. In particular, new and/or improved approaches have established robust methods that not only allow for in-depth and accurate peptide and protein identification and modification, but also allow for sensitive measurement of relative or absolute quantitation. These methods are beginning to be applied to the area of neuroproteomics, but the central nervous system poses many specific challenges in terms of quantitative proteomics, given the large number of different neuronal cell types that are intermixed and that exhibit distinct patterns of gene and protein expression. This review highlights the recent advances that have been made in quantitative neuroproteomics, with a focus on work published over the last five years that applies emerging methods to normal brain function as well as to various neuropsychiatric disorders including schizophrenia and drug addiction as well as of neurodegenerative diseases including Parkinson's disease and Alzheimer's disease. While older methods such as two-dimensional polyacrylamide electrophoresis continued to be used, a variety of more in-depth MS-based approaches including both label (ICAT, iTRAQ, TMT, SILAC, SILAM), label-free (label-free, MRM, SWATH) and absolute quantification methods, are rapidly being applied to neurobiological investigations of normal and diseased brain tissue as well as of cerebrospinal fluid (CSF). While the biological implications of many of these studies remain to be clearly established, that there is a clear need for standardization of experimental design and data analysis, and that the analysis of protein changes in specific neuronal cell types in the central nervous system remains a serious challenge, it appears that the quality and depth of the more recent quantitative proteomics studies is beginning to shed light on a number of aspects of neuroscience that relates to normal brain function as well as of the changes in protein expression and regulation that occurs in neuropsychiatric and neurodegenerative disorders.