NeuCode labels for relative protein quantification

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

Cells sense and respond to stressful environments by utilizing complex signaling networks that integrate diverse signals to coordinate a multi-faceted physiological response. Much of this response is controlled by post-translational protein phosphorylation. Although many regulators that mediate changes in protein phosphorylation are known, how these regulators inter-connect in a single regulatory network that can transmit cellular signals is not known. It is also unclear how regulators that promote growth and regulators that activate the stress response interconnect to reorganize resource allocation during stress. Here, we developed an integrated experimental and computational workflow to infer the signaling network that regulates phosphorylation changes during osmotic stress in the budding yeast Saccharomyces cerevisiae. The workflow integrates data measuring protein phosphorylation changes in response to osmotic stress with known physical interactions between yeast proteins from large-scale datasets, along with other information about how regulators recognize their targets. The resulting network suggested new signaling connections between regulators and pathways, including those involved in regulating growth and defense, and predicted new regulators involved in stress defense. Our work highlights the power of using network inference to deliver new insight on how cells coordinate a diverse adaptive strategy to stress.

Multiplexed proteome analysis with neutron-encoded stable isotope labeling in cells and mice

We describe a protocol for multiplexed proteomic analysis using neutron-encoded (NeuCode) stable isotope labeling of amino acids in cells (SILAC) or mice (SILAM). This method currently enables simultaneous comparison of up to nine treatment and control proteomes. Another important advantage over traditional SILAC/SILAM is that shorter labeling times are required. Exploiting the small mass differences that correspond to subtle differences in the neutron-binding energies of different isotopes, the amino acids used in NeuCode SILAC/SILAM differ in mass by just a few milliDaltons. Isotopologs of lysine are introduced into cells or mammals, via the culture medium or diet, respectively, to metabolically label the proteome. Labeling time is ∼2 weeks for cultured cells and 3-4 weeks for mammals. The proteins are then extracted, relevant samples are combined, and these are enzymatically digested with lysyl endopeptidase (Lys-C). The resultant peptides are chromatographically separated and then mass analyzed. During mass spectrometry (MS) data acquisition, high-resolution MS¹ spectra (≥240,000 resolving power at m/z = 400) reveal the embedded isotopic signatures, enabling relative quantification, while tandem mass spectra, collected at lower resolutions, provide peptide identities. Both types of spectra are processed using NeuCode-enabled MaxQuant software. In total, the approximate completion time for the protocol is 3-5 weeks.

Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues

Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating six 2D LC-MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.

Mass Defect-Based Dimethyl Pyrimidinyl Ornithine (DiPyrO) Tags for Multiplex Quantitative Proteomics

We have developed a novel amine-reactive mass defect-based chemical tag, dimethyl pyrimidinyl ornithine (DiPyrO), that is compact in size, is suitable for various biological samples, and enables highly multiplexed quantification of peptides at the MS1 level without increasing mass spectral complexity. The DiPyrO tag structure incorporates heavy isotopes in a variety of configurations to impart as much as 45.3 mDa or as little as 5.8 mDa per tag between labeled peptides. Notably, peptides containing lysine are labeled with two tags, doubling the imparted mass defect to up to 90.6 mDa for the duplex tags and effectively reducing the resolving power requirement compared to previously reported mass defect-based quantification approaches. This permits current and previous generation LTQ-Orbitrap platforms to perform confident quantitative analyses of two DiPyrO-labeled samples at 100K resolving power, whereas 3-plex and 6-plex quantifications are possible at 240K and 480K resolving powers, respectively. In this work, we discuss the design and synthesis of the DiPyrO tag, characterize its effect on labeled proteome analysis by nanoLC-MS2, and demonstrate proof-of-principle applications of the duplex and triplex tags for quantitative proteomics using high-resolution MS acquisition on the Orbitrap Elite and Orbitrap Fusion Lumos.

A Split-Abl Kinase for Direct Activation in Cells

To dissect the cellular roles of individual kinases, it is useful to design tools for their selective activation. We describe the engineering of a split-cAbl kinase (sKin-Abl) that is rapidly activated in cells with rapamycin and allows temporal, dose, and compartmentalization control. Our design strategy involves an empirical screen in mammalian cells and identification of split site in the N lobe. This split site leads to complete loss of activity, which can be restored upon small-molecule-induced dimerization in cells. Remarkably, the split site is transportable to the related Src Tyr kinase and the distantly related Ser/Thr kinase, AKT, suggesting broader applications to kinases. To quantify the fold induction of phosphotyrosine (pTyr) modification, we employed quantitative proteomics, NeuCode SILAC. We identified a number of known Abl substrates, including autophosphorylation sites and novel pTyr targets, 432 pTyr sites in total. We believe that this split-kinase technology will be useful for direct activation of protein kinases in cells.

Enhanced Sample Multiplexing of Tissues Using Combined Precursor Isotopic Labeling and Isobaric Tagging (cPILOT)

There is an increasing demand to analyze many biological samples for disease understanding and biomarker discovery. Quantitative proteomics strategies that allow simultaneous measurement of multiple samples have become widespread and greatly reduce experimental costs and times. Our laboratory developed a technique called combined precursor isotopic labeling and isobaric tagging (cPILOT), which enhances sample multiplexing of traditional isotopic labeling or isobaric tagging approaches. Global cPILOT can be applied to samples originating from cells, tissues, bodily fluids, or whole organisms and gives information on relative protein abundances across different sample conditions. cPILOT works by 1) using low pH buffer conditions to selectively dimethylate peptide N-termini and 2) using high pH buffer conditions to label primary amines of lysine residues with commercially-available isobaric reagents (see Table of Materials/Reagents). The degree of sample multiplexing available is dependent on the number of precursor labels used and the isobaric tagging reagent. Here, we present a 12-plex analysis using light and heavy dimethylation combined with six-plex isobaric reagents to analyze 12 samples from mouse tissues in a single analysis. Enhanced multiplexing is helpful for reducing experimental time and cost and more importantly, allowing comparison across many sample conditions (biological replicates, disease stage, drug treatments, genotypes, or longitudinal time-points) with less experimental bias and error. In this work, the global cPILOT approach is used to analyze brain, heart, and liver tissues across biological replicates from an Alzheimer's disease mouse model and wild-type controls. Global cPILOT can be applied to study other biological processes and adapted to increase sample multiplexing to greater than 20 samples.

Strategies in relative and absolute quantitative mass spectrometry based proteomics

Quantitative mass spectrometry approaches are used for absolute and relative quantification in global proteome studies. To date, relative and absolute quantification techniques are available that differ in quantification accuracy, proteome coverage, complexity and robustness. This review focuses on most common relative or absolute quantification strategies exemplified by three experimental studies. A label-free relative quantification approach was performed for the investigation of the membrane proteome of sensory cilia to the depth of olfactory receptors in Mus musculus. A SILAC-based relative quantification approach was successfully applied for the identification of core components and transient interactors of the peroxisomal importomer in Saccharomyces cerevisiae. Furthermore, AQUA using stable isotopes was exemplified to unraveling the prenylome influenced by novel prenyltransferase inhibitors. Characteristic enrichment and fragmentation strategies for a robust quantification of the prenylome are also summarized.

Mass Defect-Based N,N-Dimethyl Leucine Labels for Quantitative Proteomics and Amine Metabolomics of Pancreatic Cancer Cells

Mass spectrometry-based stable isotope labeling has become a key technology for protein and small-molecule analyses. We developed a multiplexed quantification method for simultaneous proteomics and amine metabolomics analyses via nano reversed-phase liquid chromatography-tandem mass spectrometry (nanoRPLC-MS/MS), called mass defect-based N,N-dimethyl leucine (mdDiLeu) labeling. The duplex mdDiLeu reagents were custom-synthesized with a mass difference of 20.5 mDa, arising from the subtle variation in nuclear binding energy between the two DiLeu isotopologues. Optimal MS resolving powers were determined to be 240K for labeled peptides and 120K for labeled metabolites on the Orbitrap Fusion Lumos instrument. The mdDiLeu labeling does not suffer from precursor interference and dynamic range compression, providing excellent accuracy for MS¹-centric quantification. Quantitative information is only revealed at high MS resolution without increasing spectrum complexity and overlapping isotope distribution. Chromatographic performance of polar metabolites was dramatically improved by mdDiLeu labeling with modified hydrophobicity, enhanced ionization efficiency, and picomole levels of detection limits. Paralleled proteomics and amine metabolomics analyses using mdDiLeu were systematically evaluated and then applied to pancreatic cancer cells.

Emerging power of proteomics for delineation of intrinsic tumor subtypes and resistance mechanisms to anti-cancer therapies

INTRODUCTION

Despite extreme genetic heterogeneity, tumors often show similar alterations in the expression, stability, and activation of proteins important in oncogenic signaling pathways. Thus, classifying tumor samples according to shared proteomic features may help facilitate the identification of cancer subtypes predictive of therapeutic responses and prognostic for patient outcomes. Meanwhile, understanding mechanisms of intrinsic and acquired resistance to anti-cancer therapies at the protein level may prove crucial to devising reversal strategies. Areas covered: Herein, we review recent advances in quantitative proteomic technology and their applications in studies to identify intrinsic tumor subtypes of various tumors, to illuminate mechanistic aspects of pharmacological and oncogenic adaptations, and to highlight interaction targets for anti-cancer compounds and cancer-addicted proteins. Expert commentary: Quantitative proteomic technologies are being successfully employed to classify tumor samples into distinct intrinsic subtypes, to improve existing DNA/RNA based classification methods, and to evaluate the activation status of key signaling pathways.

Measurement of Histone Methylation Dynamics by One-Carbon Metabolic Isotope Labeling and High-energy Collisional Dissociation Methylation Signature Ion Detection

Accumulating evidence suggests that cellular metabolites and nutrition levels control epigenetic modifications, including histone methylation. However, it is not currently possible to measure the metabolic control of histone methylation. Here we report a novel detection method to monitor methyl transfer from serine to histones through the one-carbon metabolic pathway, using stable-isotope labeling and detection of lysine methylation signature ions generated in high-energy-dissociation (HCD) tandem mass spectrometry. This method is a long-needed tool to study the metabolic control of histone methylation.

NeuCode Proteomics Reveals Bap1 Regulation of Metabolism

We introduce neutron-encoded (NeuCode) amino acid labeling of mice as a strategy for multiplexed proteomic analysis in vivo. Using NeuCode, we characterize an inducible knockout mouse model of Bap1, a tumor suppressor and deubiquitinase whose in vivo roles outside of cancer are not well established. NeuCode proteomics revealed altered metabolic pathways following Bap1 deletion, including profound elevation of cholesterol biosynthetic machinery coincident with reduced expression of gluconeogenic and lipid homeostasis proteins in liver. Bap1 loss increased pancreatitis biomarkers and reduced expression of mitochondrial proteins. These alterations accompany a metabolic remodeling with hypoglycemia, hypercholesterolemia, hepatic lipid loss, and acinar cell degeneration. Liver-specific Bap1 null mice present with fully penetrant perinatal lethality, severe hypoglycemia, and hepatic lipid deficiency. This work reveals Bap1 as a metabolic regulator in liver and pancreas, and it establishes NeuCode as a reliable proteomic method for deciphering in vivo biology.

PyQuant: A Versatile Framework for Analysis of Quantitative Mass Spectrometry Data

Quantitative mass spectrometry data necessitates an analytical pipeline that captures the accuracy and comprehensiveness of the experiments. Currently, data analysis is often coupled to specific software packages, which restricts the analysis to a given workflow and precludes a more thorough characterization of the data by other complementary tools. To address this, we have developed PyQuant, a cross-platform mass spectrometry data quantification application that is compatible with existing frameworks and can be used as a stand-alone quantification tool. PyQuant supports most types of quantitative mass spectrometry data including SILAC, NeuCode, (15)N, (13)C, or (18)O and chemical methods such as iTRAQ or TMT and provides the option of adding custom labeling strategies. In addition, PyQuant can perform specialized analyses such as quantifying isotopically labeled samples where the label has been metabolized into other amino acids and targeted quantification of selected ions independent of spectral assignment. PyQuant is capable of quantifying search results from popular proteomic frameworks such as MaxQuant, Proteome Discoverer, and the Trans-Proteomic Pipeline in addition to several standalone search engines. We have found that PyQuant routinely quantifies a greater proportion of spectral assignments, with increases ranging from 25-45% in this study. Finally, PyQuant is capable of complementing spectral assignments between replicates to quantify ions missed because of lack of MS/MS fragmentation or that were omitted because of issues such as spectra quality or false discovery rates. This results in an increase of biologically useful data available for interpretation. In summary, PyQuant is a flexible mass spectrometry data quantification platform that is capable of interfacing with a variety of existing formats and is highly customizable, which permits easy configuration for custom analysis.

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 Biologist's Field Guide to Multiplexed Quantitative Proteomics

High-throughput genomic and proteomic studies have generated near-comprehensive catalogs of biological constituents within many model systems. Nevertheless, static catalogs are often insufficient to fully describe the dynamic processes that drive biology. Quantitative proteomic techniques address this need by providing insight into closely related biological states such as the stages of a therapeutic response or cellular differentiation. The maturation of quantitative proteomics in recent years has brought about a variety of technologies, each with their own strengths and weaknesses. It can be difficult for those unfamiliar with this evolving landscape to match the experiment at hand with the best tool for the job. Here, we outline quantitative methods for proteomic mass spectrometry and discuss their benefits and weaknesses from the perspective of the biologist aiming to generate meaningful data and address mechanistic questions.

Multiplexed, Scheduled, High-Resolution Parallel Reaction Monitoring on a Full Scan QqTOF Instrument with Integrated Data-Dependent and Targeted Mass Spectrometric Workflows

Recent advances in commercial mass spectrometers with higher resolving power and faster scanning capabilities have expanded their functionality beyond traditional data-dependent acquisition (DDA) to targeted proteomics with higher precision and multiplexing. Using an orthogonal quadrupole time-of flight (QqTOF) LC-MS system, we investigated the feasibility of implementing large-scale targeted quantitative assays using scheduled, high resolution multiple reaction monitoring (sMRM-HR), also referred to as parallel reaction monitoring (sPRM). We assessed the selectivity and reproducibility of PRM, also referred to as parallel reaction monitoring, by measuring standard peptide concentration curves and system suitability assays. By evaluating up to 500 peptides in a single assay, the robustness and accuracy of PRM assays were compared to traditional SRM workflows on triple quadrupole instruments. The high resolution and high mass accuracy of the full scan MS/MS spectra resulted in sufficient selectivity to monitor 6-10 MS/MS fragment ions per target precursor, providing flexibility in postacquisition assay refinement and optimization. The general applicability of the sPRM workflow was assessed in complex biological samples by first targeting 532 peptide precursor ions in a yeast lysate, and then 466 peptide precursors from a previously generated candidate list of differentially expressed proteins in whole cell lysates from E. coli. Lastly, we found that sPRM assays could be rapidly and efficiently developed in Skyline from DDA libraries when acquired on the same QqTOF platform, greatly facilitating their successful implementation. These results establish a robust sPRM workflow on a QqTOF platform to rapidly transition from discovery analysis to highly multiplexed, targeted peptide quantitation.

NeuCode Labeling in Nematodes: Proteomic and Phosphoproteomic Impact of Ascaroside Treatment in Caenorhabditis elegans

The nematode Caenorhabditis elegans is an important model organism for biomedical research. We previously described NeuCode stable isotope labeling by amino acids in cell culture (SILAC), a method for accurate proteome quantification with potential for multiplexing beyond the limits of traditional stable isotope labeling by amino acids in cell culture. Here we apply NeuCode SILAC to profile the proteomic and phosphoproteomic response of C. elegans to two potent members of the ascaroside family of nematode pheromones. By consuming labeled E. coli as part of their diet, C. elegans nematodes quickly and easily incorporate the NeuCode heavy lysine isotopologues by the young adult stage. Using this approach, we report, at high confidence, one of the largest proteomic and phosphoproteomic data sets to date in C. elegans: 6596 proteins at a false discovery rate ≤ 1% and 6620 phosphorylation isoforms with localization probability ≥75%. Our data reveal a post-translational signature of pheromone sensing that includes many conserved proteins implicated in longevity and response to stress.

Subtyping of breast cancer using reverse phase protein arrays

Reverse phase protein arrays (RPPAs) present a robust and sensitive high capacity platform for targeted proteomics that relies on highly specific antibodies to obtain a quantitative readout regarding phosphorylation state and abundance of proteins of interest. This review summarizes the current state of RPPA-based proteomic profiling of breast cancer in the context of existing preanalytical strategies and sample preparation protocols. RPPA-based subtypes identified so far are compared to those obtained by other approaches such as immunohistochemistry, genomics and transcriptomics. Special attention is given to discussing the potential of RPPA for biomarker discovery and biomarker validation.

Quantifying ubiquitin signaling

Ubiquitin (UB)-driven signaling systems permeate biology, and are often integrated with other types of post-translational modifications (PTMs), including phosphorylation. Flux through such pathways is dictated by the fractional stoichiometry of distinct modifications and protein assemblies as well as the spatial organization of pathway components. Yet, we rarely understand the dynamics and stoichiometry of rate-limiting intermediates along a reaction trajectory. Here, we review how quantitative proteomic tools and enrichment strategies are being used to quantify UB-dependent signaling systems, and to integrate UB signaling with regulatory phosphorylation events, illustrated with the PINK1/PARKIN pathway. A key feature of ubiquitylation is that the identity of UB chain linkage types can control downstream processes. We also describe how proteomic and enzymological tools can be used to identify and quantify UB chain synthesis and linkage preferences. The emergence of sophisticated quantitative proteomic approaches will set a new standard for elucidating biochemical mechanisms of UB-driven signaling systems.

Proteomic approaches for site-specific O-GlcNAcylation analysis

O-GlcNAcylation is a dynamic protein post-translational modification of serine or threonine residues by an O-linked monosaccharide N-acetylglucosamine (O-GlcNAc). O-GlcNAcylation was discovered three decades ago and its significance has been implicated in several disease states, such as metabolic diseases, cancer and neurological diseases. Yet it remains technically challenging to characterize comprehensively and quantitatively because of its low abundance, low stoichiometry and extremely labile nature under conventional collision-induced dissociation tandem MS conditions. Herein, we review the recent advances addressing these challenges in developing proteomic approaches for site-specific O-GlcNAcylation analysis, including specific enrichment of O-GlcNAc peptides/proteins, unambiguous site-determination of O-GlcNAc modification and quantitative analysis of O-GlcNAcylation.

Exploring G protein-coupled receptor signaling networks using SILAC-based phosphoproteomics

The type 1 parathyroid hormone receptor (PTH1R) is a key regulator of calcium homeostasis and bone turnover. Here, we employed SILAC-based quantitative mass spectrometry and bioinformatic pathways analysis to examine global changes in protein phosphorylation following short-term stimulation of endogenously expressed PTH1R in osteoblastic cells in vitro. Following 5min exposure to the conventional agonist, PTH(1-34), we detected significant changes in the phosphorylation of 224 distinct proteins. Kinase substrate motif enrichment demonstrated that consensus motifs for PKA and CAMK2 were the most heavily upregulated within the phosphoproteome, while consensus motifs for mitogen-activated protein kinases were strongly downregulated. Signaling pathways analysis identified ERK1/2 and AKT as important nodal kinases in the downstream network and revealed strong regulation of small GTPases involved in cytoskeletal rearrangement, cell motility, and focal adhesion complex signaling. Our data illustrate the utility of quantitative mass spectrometry in measuring dynamic changes in protein phosphorylation following GPCR activation.

Development and characterization of novel 8-plex DiLeu isobaric labels for quantitative proteomics and peptidomics

RATIONALE

Relative quantification of proteins via their enzymatically digested peptide products determines disease biomarker candidate lists in discovery studies. Isobaric label-based strategies using TMT and iTRAQ allow for up to 10 samples to be multiplexed in one experiment, but their expense limits their use. The demand for cost-effective tagging reagents capable of multiplexing many samples led us to develop an 8-plex version of our isobaric labeling reagent, DiLeu.

METHODS

The original 4-plex DiLeu reagent was extended to an 8-plex set by coupling isotopic variants of dimethylated leucine to an alanine balance group designed to offset the increasing mass of the label's reporter group. Tryptic peptides from a single protein digest, a protein mixture digest, and Saccharomyces cerevisiae lysate digest were labeled with 8-plex DiLeu and analyzed via nanospray liquid chromatography/tandem mass spectrometry (nanoLC/MS(2) ) on a Q-Exactive Orbitrap mass spectrometer. Characteristics of 8-plex DiLeu-labeled peptides, including quantitative accuracy and fragmentation, were examined.

RESULTS

An 8-plex set of DiLeu reagents with 1 Da spaced reporters was synthesized at a yield of 36%. The average cost to label eight 100 µg peptide samples was calculated to be approximately $15. Normalized collision energy tests on the Q-Exactive revealed that a higher-energy collisional dissociation value of 27 generated the optimum number of high-quality spectral matches. Relative quantification of DiLeu-labeled peptides yielded normalized median ratios accurate to within 12% of their expected values.

CONCLUSIONS

Cost-effective 8-plex DiLeu reagents can be synthesized and applied to relative peptide and protein quantification. These labels increase the multiplexing capacity of our previous 4-plex implementation without requiring high-resolution instrumentation to resolve reporter ion signals.

Global cPILOT analysis of the APP/PS-1 mouse liver proteome

PURPOSE

A quantitative proteomics strategy called combined precursor isotopic labeling and isobaric tagging (cPILOT) was designed to discover alterations in the amyloid precursor protein/presenilin-1 (APP/PS-1) mouse liver proteome. The multiplexing strategy allows simultaneous quantitation of 12 samples in a single experiment.

EXPERIMENTAL DESIGN

For cPILOT samples, six APP/PS-1 and six heterozygous mouse livers were modified using precursor dimethylation (pH 2.5) followed by isobaric tagging (pH 8.0). Samples were pooled, fractioned with strong cation exchange, and analyzed using RPLC-MS(3) for protein identification and relative quantitation. In order to increase proteome coverage, a two-tiered data collection strategy was employed. Six duplex precursor dimethylation experiments were also performed to verify cPILOT protein quantitation.

RESULTS

The combination of cPILOT with precursor dimethylation data resulted in 2437 total liver proteins identified and 77 differentially expressed proteins in APP/PS-1 liver. Differentially expressed proteins are involved in metabolic processes such as B-oxidation, pyruvate metabolism, and glucose regulation.

CONCLUSIONS AND CLINICAL RELEVANCE

cPILOT expands protein quantitation using isobaric tags and can be applied to any clinical laboratory interested in enhanced multiplexing strategies. Differentially expressed proteins in APP/PS-1 mouse liver suggest the potential use of ketone bodies to alleviate metabolic dysregulation in Alzheimer's disease brain. Our work also suggests alterations in the alanine cycle potentially leading to hyperammonia production, may contribute to Alzheimer's disease pathogenesis.

Multiplexed quantification for data-independent acquisition

Data-independent acquisition (DIA) strategies provide a sensitive and reproducible alternative to data-dependent acquisition (DDA) methods for large-scale quantitative proteomic analyses. Unfortunately, DIA methods suffer from incompatibility with common multiplexed quantification methods, specifically stable isotope labeling approaches such as isobaric tags and stable isotope labeling of amino acids in cell culture (SILAC). Here we expand the use of neutron-encoded (NeuCode) SILAC to DIA applications (NeuCoDIA), producing a strategy that enables multiplexing within DIA scans without further convoluting the already complex MS(2) spectra. We demonstrate duplex NeuCoDIA analysis of both mixed-ratio (1:1 and 10:1) yeast and mouse embryo myogenesis proteomes. Analysis of the mixed-ratio yeast samples revealed the strong accuracy and precision of our NeuCoDIA method, both of which were comparable to our established MS(1)-based quantification approach. NeuCoDIA also uncovered the dynamic protein changes that occur during myogenic differentiation, demonstrating the feasibility of this methodology for biological applications. We consequently establish DIA quantification of NeuCode SILAC as a useful and practical alternative to DDA-based approaches.

Quantitative proteomics of kinase inhibitor targets and mechanisms

Small molecule inhibitors of protein kinases are key tools for signal transduction research and represent a major class of targeted drugs. Recent developments in quantitative proteomics enable an unbiased view on kinase inhibitor selectivity and modes of action in the biological context. While chemical proteomics techniques utilizing quantitative mass spectrometry interrogate both target specificity and affinity in cellular extracts, proteome-wide phosphorylation analyses upon kinase inhibitor treatment identify signal transduction pathway and network regulation in an unbiased manner. Thus, critical information is provided to promote new insights into mechanisms of kinase signaling and their relevance for kinase inhibitor drug discovery.

Ion coalescence in Fourier transform mass spectrometry: should we worry about this in shotgun proteomics?

Coupling of motion of the ion clouds with close m/z values is a well-established phenomenon for ion- trapping mass analyzers. In Fourier transform ion cyclotron resonance mass spectrometry it is known as ion coalescence. Recently, ion coalescence was demonstrated and semiquantitatively characterized for the Orbitrap mass analyzer as well. When it occurs, the coalescence negatively affects the basic characteristics of a mass analyzer. Specifically, the dynamic range available for the high resolving power mass measurements reduces. In shotgun proteomics, another potentially adverse effect of ion coalescence is interference of the isotopic envelopes for the coeluting precursor ions of close m/z values, subjected to isolation before fragmentation. In this work we characterize coalescence events for synthetic peptide mixtures with fully and partially overlapping (13)C-isotope envelopes including pairs of peptides with glutamine deamidation. Furthermore, we demonstrate that fragmentation of the otherwise coalesced peptide ion clouds may remove the locking between them owing to the total charge redistribution between more ion species in the mass spectrum. Finally, we estimated the possible scale of the coalescence phenomenon for shotgun proteomics by considering the fraction of coeluted peptide pairs with the close masses using literature data for the yeast proteome. It was found that up to one tenth of all peptide identifications with the relative mass differences of 20 ppm or less in the corresponding pairs may potentially experience the coalescence of the (13)C-isotopic envelopes. However, sample complexity in a real proteomics experiment and precursor ion signal splitting between many channels in tandem mass spectrometry drastically increase the threshold for coalescence, thus leading to practically coalescence-free proteomics based on Fourier transform mass spectrometry.

High-resolution enabled 12-plex DiLeu isobaric tags for quantitative proteomics

Multiplex isobaric tags (e.g., tandem mass tags (TMT) and isobaric tags for relative and absolute quantification (iTRAQ)) are a valuable tool for high-throughput mass spectrometry based quantitative proteomics. We have developed our own multiplex isobaric tags, DiLeu, that feature quantitative performance on par with commercial offerings but can be readily synthesized in-house as a cost-effective alternative. In this work, we achieve a 3-fold increase in the multiplexing capacity of the DiLeu reagent without increasing structural complexity by exploiting mass defects that arise from selective incorporation of ¹³C, ¹⁵N, and ²H stable isotopes in the reporter group. The inclusion of eight new reporter isotopologues that differ in mass from the existing four reporters by intervals of 6 mDa yields a 12-plex isobaric set that preserves the synthetic simplicity and quantitative performance of the original implementation. We show that the new reporter variants can be baseline-resolved in high-resolution higher-energy C-trap dissociation (HCD) spectra, and we demonstrate accurate 12-plex quantitation of a DiLeu-labeled Saccharomyces cerevisiae lysate digest via high-resolution nano liquid chromatography–tandem mass spectrometry (nanoLC–MS²) analysis on an Orbitrap Elite mass spectrometer.

Broader implications of SILAC-based proteomics for dissecting signaling dynamics in cancer

Large-scale transcriptome and epigenome analyses have been widely utilized to discover gene alterations implicated in cancer development at the genetic level. However, mapping of signaling dynamics at the protein level is likely to be more insightful and needed to complement massive genomic data. Stable isotope labeling with amino acids in cell culture (SILAC)-based proteomic analysis represents one of the most promising comparative quantitative methods that has been extensively employed in proteomic research. This technology allows for global, robust and confident identification and quantification of signal perturbations important for the progress of human diseases, particularly malignancies. The present review summarizes the latest applications of in vitro and in vivo SILAC-based proteomics in identifying global proteome/phosphoproteome and genome-wide protein-protein interactions that contribute to oncogenesis, highlighting the recent advances in dissecting signaling dynamics in cancer.

Super-SILAC: current trends and future perspectives

Stable isotope labeling with amino acids in cell culture (SILAC) has risen as a powerful quantification technique in mass spectrometry (MS)-based proteomics in classical and modified forms. Previously, SILAC was limited to cultured cells because of the requirement of active protein synthesis; however, in recent years, it was expanded to model organisms and tissue samples. Specifically, the super-SILAC technique uses a mixture of SILAC-labeled cells as a spike-in standard for accurate quantification of unlabeled samples, thereby enabling quantification of human tissue samples. Here, we highlight the recent developments in super-SILAC and its application to the study of clinical samples, secretomes, post-translational modifications and organelle proteomes. Finally, we propose super-SILAC as a robust and accurate method that can be commercialized and applied to basic and clinical research.

Recent advances in stable isotope labeling based techniques for proteome relative quantification

The large scale relative quantification of all proteins expressed in biological samples under different states is of great importance for discovering proteins with important biological functions, as well as screening disease related biomarkers and drug targets. Therefore, the accurate quantification of proteins at proteome level has become one of the key issues in protein science. Herein, the recent advances in stable isotope labeling based techniques for proteome relative quantification were reviewed, from the aspects of metabolic labeling, chemical labeling and enzyme-catalyzed labeling. Furthermore, the future research direction in this field was prospected.