An insight into iTRAQ: where do we stand now?

Transcriptome and proteome quantification of a tumor model provides novel insights into post-transcriptional gene regulation

Background

Genome‐wide transcriptome analyses have given systems‐level insights into gene regulatory networks. Due to the limited depth of quantitative proteomics, however, our understanding of post‐transcriptional gene regulation and its effects on protein‐complex stoichiometry are lagging behind.

Results

Here, we employ deep sequencing and the isobaric tag for relative and absolute quantification (iTRAQ) technology to determine transcript and protein expression changes of a Drosophila brain tumor model at near genome‐wide resolution. In total, we quantify more than 6,200 tissue‐specific proteins, corresponding to about 70% of all transcribed protein‐coding genes. Using our integrated data set, we demonstrate that post‐transcriptional gene regulation varies considerably with biological function and is surprisingly high for genes regulating transcription. We combine our quantitative data with protein‐protein interaction data and show that post‐transcriptional mechanisms significantly enhance co‐regulation of protein‐complex subunits beyond transcriptional co‐regulation. Interestingly, our results suggest that only about 11% of the annotated Drosophila protein complexes are co‐regulated in the brain. Finally, we refine the composition of some of these core protein complexes by analyzing the co‐regulation of potential subunits.

Conclusions

Our comprehensive transcriptome and proteome data provide a valuable resource for quantitative biology and offer novel insights into understanding post‐transcriptional gene regulation in a tumor model.

Immunodepletion plasma proteomics by tripleTOF 5600 and Orbitrap elite/LTQ-Orbitrap Velos/Q exactive mass spectrometers

Plasma proteomic experiments performed rapidly and economically using several of the latest high-resolution mass spectrometers were compared. Four quantitative hyperfractionated plasma proteomics experiments were analyzed in replicates by two AB SCIEX TripleTOF 5600 and three Thermo Scientific Orbitrap (Elite/LTQ-Orbitrap Velos/Q Exactive) instruments. Each experiment compared two iTRAQ isobaric-labeled immunodepleted plasma proteomes, provided as 30 labeled peptide fractions, and 480 LC-MS/MS runs delivered >250 GB of data in 2 months. Several analysis algorithms were compared. At 1% false discovery rate, the relative comparative findings concluded that the Thermo Scientific Q Exactive Mass Spectrometer resulted in the highest number of identified proteins and unique sequences with iTRAQ quantitation. The confidence of iTRAQ fold-change for each protein is dependent on the overall ion statistics (Mascot Protein Score) attainable by each instrument. The benchmarking also suggested how to further improve the mass spectrometry parameters and HPLC conditions. Our findings highlight the special challenges presented by the low abundance peptide ions of iTRAQ plasma proteome because the dynamic range of plasma protein abundance is uniquely high compared with cell lysates, necessitating high instrument sensitivity.

Comparison of SILAC and mTRAQ quantification for phosphoproteomics on a quadrupole orbitrap mass spectrometer

Advances in mass spectrometric methodology and instrumentation have promoted a continuous increase in analytical performance in the field of phosphoproteomics. Here, we employed the recently introduced quadrupole Orbitrap (Q Exactive) mass spectrometer for quantitative signaling analysis to a depth of more than 15 000 phosphorylation sites. In parallel to the commonly used SILAC approach, we evaluated the nonisobaric chemical labeling reagent mTRAQ as an alternative quantification technique. Both enabled high phosphoproteome coverage in H3122 lung cancer cells. Replicate quantifications by mTRAQ identified almost as many significant phosphorylation changes upon treatment with ALK kinase inhibitor crizotinib as found by SILAC quantification. Overall, mTRAQ was slightly less precise than SILAC as evident from a somewhat higher variance of replicate phosphosite ratios. Direct comparison of SILAC- and mTRAQ-quantified phosphosites revealed that the majority of changes were detected by either quantification techniques, but also highlighted the aspect of false negative identifications in quantitative proteomics applications. Further inspection of crizotinib-regulated phosphorylation changes unveiled interference with multiple antioncogenic mechanisms downstream of ALK fusion kinase in H3122 cells. In conclusion, our results demonstrate a strong analytical performance of the Q Exactive in global phosphoproteomics, and establish mTRAQ quantification as a useful alternative to metabolic isotope labeling.

Drug target inference through pathway analysis of genomics data

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues in this field, covering methodological developments, their pros and cons, as well as future research directions.

Targeting proteomics to investigate metastasis-associated mitochondrial proteins

Mitochondria are essential organelles in eukaryotic cells and are responsible for regulating energy metabolism, ROS production, and cell survival. Recently, various cellular pathogeneses, including tumorigenesis and metastasis, have been reported to be associated with mitochondrial homeostasis. Consequently, exploiting the correlation between dysfunctional mitochondria and tumor progression has been implicated in the understanding of tumorigenesis, tumor metastasis, and chemoresistance, along with novel strategies to develop cancer therapeutics. To comprehensively understand the role of the mitochondria in cancer metastasis, it is necessary to resolve thousands of mitochondrial proteins and their post-translational modifications with high-throughput global assessments. We introduce mitochondrial proteomic strategies in this review and a discussion on their recent findings related to cancer metastasis. Additionally, the mitochondrial respiratory chain is believed to be a major site for ROS production, and elevated ROS is likely a key source to trigger dysfunctional mitochondria and impaired mitochondrial metabolism that subsequently contribute to the development of cancer progression. Equipment-based metabolomic analysis now allows the monitoring of disease progression and diagnosis. These newly emerging techniques, including proteomics, redox-proteomics, and metabolomics, are described in this review.

Improving qualitative and quantitative performance for MS(E)-based label-free proteomics

Label-free quantitation by data independent methods (for instance MS(E)) is growing in popularity due to the high technical reproducibility of mass spectrometry analysis. The recent introduction of Synapt hybrid instruments capable of incorporating ion mobility separation within mass spectrometry analysis now allows acquisition of high definition MS(E) data (HDMS(E)). HDMS(E) enables deeper proteome coverage and more confident peptide identifications when compared to MS(E), while the latter offers a higher dynamic range for quantitation. We have developed synapter as, a versatile tool to better evaluate the results of data independent acquisitions on Waters instruments. We demonstrate that synapter can be used to combine HDMS(E) and MS(E) data to achieve deeper proteome coverage delivered by HDMS(E) and more accurate quantitation for high intensity peptides, delivered by MS(E). For users who prefer to run samples exclusively in one mode, synapter allows other useful functionality like false discovery rate estimation, filtering on peptide match type and mass error, and filling missing values. Our software integrates with existing tools, thus permitting us to easily combine peptide quantitation information into protein quantitation by a range of different approaches.

iTRAQ-Based and Label-Free Proteomics Approaches for Studies of Human Adenovirus Infections

Both isobaric tags for relative and absolute quantitation (iTRAQ) and label-free methods are widely used for quantitative proteomics. Here, we provide a detailed evaluation of these proteomics approaches based on large datasets from biological samples. iTRAQ-label-based and label-free quantitations were compared using protein lysate samples from noninfected human lung epithelial A549 cells and from cells infected for 24 h with human adenovirus type 3 or type 5. Either iTRAQ-label-based or label-free methods were used, and the resulting samples were analyzed by liquid chromatography (LC) and tandem mass spectrometry (MS/MS). To reduce a possible bias from quantitation software, we applied several software packages for each procedure. ProteinPilot and Scaffold Q+ software were used for iTRAQ-labeled samples, while Progenesis LC-MS and ProgenesisF-T2PQ/T3PQ were employed for label-free analyses. R² correlation coefficients correlated well between two software packages applied to the same datasets with values between 0.48 and 0.78 for iTRAQ-label-based quantitations and 0.5 and 0.86 for label-free quantitations. Analyses of label-free samples showed higher levels of protein up- or downregulation in comparison to iTRAQ-labeled samples. The concentration differences were further evaluated by Western blotting for four downregulated proteins. These data suggested that the label-free method was more accurate than the iTRAQ method.

Sample preparation and biostatistics for integrated genomics approaches

Genomics is based on the ability to determine the transcriptome, proteome, and metabolome of a cell. These technologies only have added value when they are integrated and based on robust and reproducible workflows. This chapter describes the experimental design, sampling, sample pretreatment, data evaluation, integration, and interpretation. The actual generation of the data is not covered in this chapter since it is highly depended on available equipment and infrastructure. The enormous amount of data generated by these technologies are integrated and interpreted inorder to generate leads for strain and process improvement. Biostatistics are becoming very important for the whole work flow therefore, some general recommendations how to set up experimental design and how to use biostatistics in enhancing the quality of the data and the selection of biological relevant leads for strain engineering and target identification are described.

Unraveling the different proteomic platforms

This review is addressed to scientists working outside the field of proteomics and wishes to shed a light on the possibility offered by the latest proteomics strategies. Bottom-up and top-down platforms are critically examined outlining advantages and limitations of their application to qualitative and quantitative investigations. Discovery, directed and targeted proteomics as different options for the management of the MS instrument are defined emphasizing their integration in the experimental plan to accomplish meaningful results. The issue of data validation is analyzed and discussed. The most common qualitative proteomic platforms are described, with a particular emphasis on enrichment methods to elucidate PTMs codes (i.e. ubiquitin and histone codes). Label-free and labeled methods for relative and absolute quantification are critically compared. The possible contribution of proteomics platforms to the transition from structural proteomics to functional proteomics (study of the functional connections between different proteins) and to the challenging system biology (integrated study of all the functional cellular functions) is also briefly discussed.

Label-free quantitative proteomics trends for protein-protein interactions

Understanding protein interactions within the complexity of a living cell is challenging, but techniques coupling affinity purification and mass spectrometry have enabled important progress to be made in the past 15 years. As identification of protein-protein interactions is becoming easier, the quantification of the interaction dynamics is the next frontier. Several quantitative mass spectrometric approaches have been developed to address this issue that vary in their strengths and weaknesses. While isotopic labeling approaches continue to contribute to the identification of regulated interactions, techniques that do not require labeling are becoming increasingly used in the field. Here, we describe the major types of label-free quantification used in interaction proteomics, and discuss the relative merits of data dependent and data independent acquisition approaches in label-free quantification. This article is part of a Special Issue entitled: From protein structures to clinical applications.

Bioinformatic study of the relationship between protein regulation and sequence properties

Although protein expression and regulation have been intensively studied, a complete picture of its mechanisms is still to be drawn. Analysis of high-throughput quantitative proteomics data provides a way to better understand protein regulation. Here, we introduce a bioinformatic analysis method to correlate protein regulation with individual amino acid patterns. We compare the amino acid composition between groups of regulated and unregulated proteins and investigate the correlation between codon usage patterns and protein regulation levels in two Sulfolobus species in "biofilm vs planktonic" experiments. The identified amino acids can then be associated with the regulation of specific gene functions. Strikingly, our analysis shows that functional categories of regulated proteins with similar composition and codon usage pattern of specific amino acids behave similarly. This finding can contribute to a better understanding of protein and gene expression regulation and could find applications in gene optimisation.