Use of stable isotope dimethyl labeling coupled to selected reaction monitoring to enhance throughput by multiplexing relative quantitation of targeted proteins

Options of the Main Derivatization Approaches for Analytical ESI and MALDI Mass Spectrometry

The inclusion of preliminary chemical labeling (derivatization) in the analysis process by such powerful and widespread methods as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is a popular and widely used methodological approach. This is due to the need to remove some fundamental limitations inherent in these powerful analytic methods. Although a number of special reviews has been published discussing the utilization of derivatization approaches, the purpose of the present critical review is to comprehensively summarize, characterize and evaluate most of the previously developed and practically applied, as well as recently proposed representative derivatization reagents for ESI-MS and MALDI-MS platforms in their mostly sensitive positive ion mode and frequently hyphenated with separation techniques. The review is focused on the use of preliminary chemical labeling to facilitate the detection, identification, structure elucidation, quantification, profiling or MS imaging of compounds within complex matrices. Two main derivatization approaches, namely the introduction of permanent charge-fixed or highly proton affinitive residues into analytes are critically evaluated. In situ charge-generation, charge-switch and charge-transfer derivatizations are considered separately. The potential of using reactive matrices in MALDI-MS and chemical labeling in MS-based omics sciences is given.

Methods and Algorithms for Quantitative Proteomics by Mass Spectrometry

Protein quantitation by mass spectrometry has always been a resourceful technique in protein discovery, and more recently it has leveraged the advent of clinical proteomics. A single mass spectrometry analysis experiment provides identification and quantitation of proteins as well as information on posttranslational modifications landscape. By contrast, protein array technologies are restricted to quantitation of targeted proteins and their modifications. Currently, there are an overwhelming number of quantitative mass spectrometry methods for protein and peptide quantitation. The aim here is to provide an overview of the most common mass spectrometry methods and algorithms used in quantitative proteomics and discuss the computational aspects to obtain reliable quantitative measures of proteins, peptides and their posttranslational modifications. The development of a pipeline using commercial or freely available software is one of the main challenges in data analysis of many experimental projects. Recent developments of R statistical programming language make it attractive to fully develop pipelines for quantitative proteomics. We discuss concepts of quantitative proteomics that together with current R packages can be used to build highly customizable pipelines.

Absolute two-point quantification of proteins using dimethylated proteotypic peptides

For absolute quantification of target proteins by LC-MS, adding two versions of spike-in peptides can be used as a quality control against each other.

New ionization tags based on the structure of the 5-azoniaspiro[4.4]nonyl tag for a sensitive peptide sequencing by mass spectrometry

Quaternary ammonium salts (QAS), both linear and bicyclic, are often utilized to improve the mass spectrometry (MS) analysis of peptides by fixing a permanent positive charge on the analyzed molecule. However, during collision-induced dissociation (CID) experiments, QAS undergo unwanted side reactions-Hofmann elimination as well as a tertiary amine loss- rendering the data interpretation complicated. In this work, we present 2-thia- and 2-oxa-5-azoniaspiro[4.4]nonyl groups as heterocyclic derivatives of the highly stable ionization group, 5-azoniaspiro[4.4]nonyl, for a sensitive peptide analysis by MS. Due to the permanent positive charge, labeled peptides are characterized by enhanced ionization efficiency during electrospray mass spectrometry (ESI-MS) conditions. Moreover, interpretation of the CID fragmentation of labeled peptides is facilitated since a series of generated fragmentation ions enable a complete sequence coverage. Introduction of a heteroatom into the 5-azoniaspiro[4.4]nonyl scaffold allows for liberation of a stable reporter ion which could be used in selected reaction monitoring (SRM)-targeted quantification experiments. Additionally, we synthesized a deuterated analog of the tag for LC-SRM-targeted quantitative analysis. The obtained results indicate the general usefulness of the proposed heterocyclic quaternary ammonium ionization tag for sequencing and quantification of peptides. Graphical abstract New reagents based on the structure of the 5-azoniaspiro[4.4]nonyl tag for peptide analysis by tandem mass spectrometry.

Complementary proteomic approaches reveal mitochondrial dysfunction, immune and inflammatory dysregulation in a mouse model of Gulf War Illness

Purpose

Long‐term consequences of combined pyridostigmine bromide (PB) and permethrin (PER) exposure in C57BL6/J mice using a well‐characterized mouse model of exposure to these Gulf War (GW) agents were explored at the protein level.

Experimental design

We used orthogonal proteomic approaches to identify pathways that are chronically impacted in the mouse CNS due to semiacute GW agent exposure early in life. These analyses were performed on soluble and membrane‐bound protein fractions from brain samples using two orthogonal isotopic labeling LC‐MS/MS proteomic approaches—stable isotope dimethyl labeling and iTRAQ.

Results

The use of these approaches allowed for greater coverage of proteins than was possible by either one alone and revealed both distinct and overlapping datasets. This combined analysis identified changes in several mitochondrial, as well as immune and inflammatory pathways after GW agent exposure.

Conclusions and clinical relevance

The work discussed here provides insight into GW agent exposure dependent mechanisms that adversely affect mitochondrial function and immune and inflammatory regulation. Collectively, our work identified key pathways which were chronically impacted in the mouse CNS following acute GW agent exposure, this may lead to the identification of potential targets for therapeutic intervention in the future.

Long‐term consequences of combined PB and PER exposure in C57BL6/J mice using a well‐characterized mouse model of exposure to these GW agents were explored at the protein level. Expanding on earlier work, we used orthogonal proteomic approaches to identify pathways that are chronically impacted in the mouse CNS due to semiacute GW agent exposure early in life. These analyses were performed on soluble and membrane‐bound protein fractions from brain samples using two orthogonal isotopic labeling LC‐MS/MS proteomic approaches—stable isotope dimethyl labeling and iTRAQ. The use of these approaches allowed for greater coverage of proteins than was possible by either one alone and revealed both distinct and overlapping datasets. This combined analysis identified changes in several mitochondrial, as well as immune and inflammatory pathways after GW agent exposure. The work discussed here provides insight into GW agent exposure dependent mechanisms that adversely affect mitochondrial function and immune and inflammatory regulation at 5 months postexposure to PB + PER.

Neucode Labels for Multiplexed, Absolute Protein Quantification

We describe a new method to accomplish multiplexed, absolute protein quantification in a targeted fashion. The approach draws upon the recently developed neutron encoding (NeuCode) metabolic labeling strategy and parallel reaction monitoring (PRM). Since PRM scanning relies upon high-resolution tandem mass spectra for targeted protein quantification, incorporation of multiple NeuCode labeled peptides permits high levels of multiplexing that can be accessed from high-resolution tandem mass spectra. Here we demonstrate this approach in cultured cells by monitoring a viral infection and the corresponding viral protein production over many infection time points in a single experiment. In this context the NeuCode PRM combination affords up to 30 channels of quantitative information in a single MS experiment.

A new dimethyl labeling-based SID-MRM-MS method and its application to three proteases involved in insulin maturation

The absolute quantification of target proteins in proteomics involves stable isotope dilution coupled with multiple reactions monitoring mass spectrometry (SID-MRM-MS). The successful preparation of stable isotope-labeled internal standard peptides is an important prerequisite for the SID-MRM absolute quantification methods. Dimethyl labeling has been widely used in relative quantitative proteomics and it is fast, simple, reliable, cost-effective, and applicable to any protein sample, making it an ideal candidate method for the preparation of stable isotope-labeled internal standards. MRM mass spectrometry is of high sensitivity, specificity, and throughput characteristics and can quantify multiple proteins simultaneously, including low-abundance proteins in precious samples such as pancreatic islets. In this study, a new method for the absolute quantification of three proteases involved in insulin maturation, namely PC1/3, PC2 and CPE, was developed by coupling a stable isotope dimethyl labeling strategy for internal standard peptide preparation with SID-MRM-MS quantitative technology. This method offers a new and effective approach for deep understanding of the functional status of pancreatic β cells and pathogenesis in diabetes.

SuperQuant: A Data Processing Approach to Increase Quantitative Proteome Coverage

SuperQuant is a quantitative proteomics data processing approach that uses complementary fragment ions to identify multiple coisolated peptides in tandem mass spectra allowing for their quantification. This approach can be applied to any shotgun proteomics data set acquired with high mass accuracy for quantification at the MS(1) level. The SuperQuant approach was developed and implemented as a processing node within the Thermo Proteome Discoverer 2.x. The performance of the developed approach was tested using dimethyl-labeled HeLa lysate samples having a ratio between channels of 10(heavy):4(medium):1(light). Peptides were fragmented with collision-induced dissociation using isolation windows of 1, 2, and 4 Th while recording data both with high-resolution and low-resolution. The results obtained using SuperQuant were compared to those using the conventional ion trap-based approach (low mass accuracy MS(2) spectra), which is known to achieve high identification performance. Compared to the common high-resolution approach, the SuperQuant approach identifies up to 70% more peptide-spectrum matches (PSMs), 40% more peptides, and 20% more proteins at the 0.01 FDR level. It identifies more PSMs and peptides than the ion trap-based approach. Improvements in identifications resulted in up to 10% more PSMs, 15% more peptides, and 10% more proteins quantified on the same raw data. The developed approach does not affect the accuracy of the quantification and observed coefficients of variation between replicates of the same proteins were close to the values typical for other precursor ion-based quantification methods. The raw data is deposited to ProteomeXchange (PXD001907). The developed node is available for testing at https://github.com/caetera/SuperQuantNode.

Potential Peripartum Markers of Infectious-Inflammatory Complications in Spontaneous Preterm Birth

Spontaneous preterm birth significantly contributes to the overall neonatal morbidity associated with preterm deliveries. Nearly 50% of cases are associated with microbial invasion of the amniotic cavity followed by an inflammatory response. Robust diagnostic tools for neonates jeopardized by infection and inflammation may thus decrease the overall neonatal morbidity substantially. Amniotic fluid retrieved during labor retains fetal and pregnancy-related protein fingerprint and its sampling does not place any unwanted stress on women. Using exploratory and targeted methods we analyzed proteomes of amniotic fluid sampled at the end of spontaneous preterm labor prior to delivery from women with and without infection and inflammation. Exploratory data indicated several amniotic fluid proteins to be associated with infectious-inflammatory complications in spontaneous preterm birth. LC-SRM analysis subsequently verified statistically significant changes in lipocalin-1 (P = 0.047 and AUC = 0.67, P = 0.046), glycodelin (P = 0.013 and AUC = 0.73, P = 0.013), and nicotinamide phosphoribosyltransferase (P = 0.018 and AUC = 0.71, P = 0.01).

Targeted proteomics: a bridge between discovery and validation

New technologies in mass spectrometry are beginning to mature and show unique advantages for the identification and quantitation of proteins. In recent years, one of the significant goals of clinical proteomics has been to identify biomarkers that can be used for clinical diagnosis. As technology has progressed, the list of potential biomarkers has grown. However, the verification and validation of these potential biomarkers is increasingly challenging and require high-throughput quantitative assays, targeting specific candidates. Targeted proteomics bridges the gap between biomarker discovery and the development of clinically applicable biomarker assays.

Effects of blood contamination and the rostro-caudal gradient on the human cerebrospinal fluid proteome

Over the last years there has been an increased focus on the importance of knowing the effect of pre-analytical influence on the proteomes under study, particularly in the field of biomarker discovery. We present three proteomics studies examining the effect of blood contamination and the rostro-caudal gradient (RCG) on the cerebrospinal fluid (CSF) proteome, in addition to plasma/CSF protein ratios. The studies showed that the central nervous system (CNS) derived proteins appeared to be unaffected by the RCG, while the plasma-derived proteins showed an increase in concentration towards the lumbar area. This implies that the concentration of the plasma-derived proteins in CSF will vary depending on the volume of CSF that is collected. In the CSF samples spiked with blood, 262 of 814 quantified proteins showed an abundance increase of more than 1.5 fold, while 403 proteins had a fold change of less than 1.2 and appeared to be unaffected by blood contamination. Proteins with a high plasma/CSF ratio appeared to give the largest effect on the CSF proteome upon blood contamination. The results give important background information on how factors like blood contamination, RCG and blood-CNS-barrier influences the CSF proteome. This information is particularly important in the field of biomarker discovery, but also for routine clinical measurements. The data from the blood contamination and RCG discovery studies have been deposited to the ProteomeXchange with identifier PXD000401.

Comprehensive quantitative comparison of the membrane proteome, phosphoproteome, and sialiome of human embryonic and neural stem cells

Human embryonic stem cells (hESCs) can differentiate into neural stem cells (NSCs), which can further be differentiated into neurons and glia cells. Therefore, these cells have huge potential as source for treatment of neurological diseases. Membrane-associated proteins are very important in cellular signaling and recognition, and their function and activity are frequently regulated by post-translational modifications such as phosphorylation and glycosylation. To obtain information about membrane-associated proteins and their modified amino acids potentially involved in changes of hESCs and NSCs as well as to investigate potential new markers for these two cell stages, we performed large-scale quantitative membrane-proteomic of hESCs and NSCs. This approach employed membrane purification followed by peptide dimethyl labeling and peptide enrichment to study the membrane subproteome as well as changes in phosphorylation and sialylation between hESCs and NSCs. Combining proteomics and modification specific proteomics we identified a total of 5105 proteins whereof 57% contained transmembrane domains or signal peptides. The enrichment strategy yielded a total of 10,087 phosphorylated peptides in which 78% of phosphopeptides were identified with ≥99% confidence in site assignment and 1810 unique formerly sialylated N-linked glycopeptides. Several proteins were identified as significantly regulated in hESCs and NSC, including proteins involved in the early embryonic and neural development. In the latter group of proteins, we could identify potential NSC markers as Crumbs 2 and several novel proteins. A motif analysis of the altered phosphosites showed a sequence consensus motif (R-X-XpS/T) significantly up-regulated in NSC. This motif is among other kinases recognized by the calmodulin-dependent protein kinase-2, emphasizing a possible importance of this kinase for this cell stage. Collectively, this data represent the most diverse set of post-translational modifications reported for hESCs and NSCs. This study revealed potential markers to distinguish NSCs from hESCs and will contribute to improve our understanding on the differentiation process.

Methods and algorithms for quantitative proteomics by mass spectrometry

Protein quantitation by mass spectrometry (MS) is attractive since it is possible to obtain both identification and quantitative values of proteins and their posttranslational modifications in a single experiment. In contrast, protein arrays only provide quantitative values of targeted proteins and their modifications. There are an overwhelming number of quantitative MS methods for protein and peptide quantitation. The aim here is to provide an overview of the most common MS methods and algorithms used in quantitative proteomics and discuss the computational algorithms needed to reliably quantitate proteins, peptides, and their posttranslational modifications. One of the main challenges in data analysis of many experimental projects is to pipe together a number of software solutions that are either commercial or freely available. The aim of this chapter is to provide a good set of algorithms, ideas, and resources that can easily be implemented in scripting language like R, Python, or Perl. By understanding the algorithmic ideas presented here, data from any instrument or modified experimental protocol can be analyzed and is therefore in the authors' opinion more valuable than a black box concept.