Benchmarking stable isotope labeling based quantitative proteomics

Collision energies: Optimization strategies for bottom-up proteomics

Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.

Integrated Proteomics to Understand the Role of Neuritin (NRN1) as a Mediator of Cognitive Resilience to Alzheimer's Disease

The molecular mechanisms and pathways enabling certain individuals to remain cognitively normal despite high levels of Alzheimer's disease (AD) pathology remain incompletely understood. These cognitively normal people with AD pathology are described as preclinical or asymptomatic AD (AsymAD) and appear to exhibit cognitive resilience to the clinical manifestations of AD dementia. Here we present a comprehensive network-based approach from cases clinically and pathologically defined as asymptomatic AD to map resilience-associated pathways and extend mechanistic validation. Multiplex tandem mass tag MS (TMT-MS) proteomic data (n = 7787 proteins) was generated on brain tissue from Brodmann area 6 and Brodmann area 37 (n = 109 cases, n = 218 total samples) and evaluated by consensus weighted gene correlation network analysis. Notably, neuritin (NRN1), a neurotrophic factor previously linked to cognitive resilience, was identified as a hub protein in a module associated with synaptic biology. To validate the function of NRN1 with regard to the neurobiology of AD, we conducted microscopy and physiology experiments in a cellular model of AD. NRN1 provided dendritic spine resilience against amyloid-β (Aβ) and blocked Aβ-induced neuronal hyperexcitability in cultured neurons. To better understand the molecular mechanisms of resilience to Aβ provided by NRN1, we assessed how exogenous NRN1 alters the proteome by TMT-MS (n = 8238 proteins) of cultured neurons and integrated the results with the AD brain network. This revealed overlapping synapse-related biology that linked NRN1-induced changes in cultured neurons with human pathways associated with cognitive resilience. Collectively, this highlights the utility of integrating the proteome from the human brain and model systems to advance our understanding of resilience-promoting mechanisms and prioritize therapeutic targets that mediate resilience to AD.

An overview of glioblastoma multiforme and temozolomide resistance: can LC-MS-based proteomics reveal the fundamental mechanism of temozolomide resistance?

Glioblastoma multiforme (GBM) is a primary type of lethal brain tumor. Over the last two decades, temozolomide (TMZ) has remained the primary chemotherapy for GBM. However, TMZ resistance in GBM constitutes an underlying factor contributing to high rates of mortality. Despite intense efforts to understand the mechanisms of therapeutic resistance, there is currently a poor understanding of the molecular processes of drug resistance. For TMZ, several mechanisms linked to therapeutic resistance have been proposed. In the past decade, significant progress in the field of mass spectrometry-based proteomics has been made. This review article discusses the molecular drivers of GBM, within the context of TMZ resistance with a particular emphasis on the potential benefits and insights of using global proteomic techniques.

Absolute Quantitative Targeted Proteomics Assays for Plasma Proteins

Preclinical and clinical trials require rapid, precise, and multiplexed analytical methods to characterize the complex samples and to allow high-throughput biomarker monitoring with low consumption of sample material. Targeted proteomics has been used to address these challenges when quantifying protein abundances in complex biological matrices. In many of these studies, blood plasma is collected either as the main research or diagnostic sample or in combination with other specimens. Mass spectrometry (MS)-based targeted proteomics using multiple reaction monitoring (MRM) or parallel reaction monitoring (PRM) with stable isotope-labeled internal standard (SIS) peptides allows robust characterization of blood plasma protein via absolute quantification. Compared to other commonly used technologies like enzyme-linked immunosorbent assay (ELISA), targeted proteomics is faster, more sensitive, and more cost-effective. Here we describe a protocol for the quantification of proteins in blood plasma using targeted MRM proteomics with heavy-labeled internal standards. The 270-protein panel allows rapid and robust absolute quantitative proteomic characterization of blood plasma in a 1 h gradient. The method we describe here works for non-depleted plasma, which makes it simple and easy to implement. Moreover, the protocol works with the two most commonly used blood plasma collection methods used in practice, namely, either K₂EDTA or sodium citrate as anticoagulants.

Optimization of Higher-Energy Collisional Dissociation Fragmentation Energy for Intact Protein-Level Tandem Mass Tag Labeling

Isobaric chemical tag labeling (e.g., TMT) is a commonly used approach in quantitative proteomics, and quantification is enabled through detection of low-mass reporter ions generated after MS2 fragmentation. Recently, we have introduced and optimized an intact protein-level TMT labeling platform that demonstrated >90% labeling efficiency in complex samples with top-down proteomics. Higher-energy collisional dissociation (HCD) is commonly utilized for isobaric tag-labeled peptide fragmentation because it produces accurate reporter ion intensities and avoids loss of low mass ions. HCD energies have been optimized for isobaric tag labeled-peptides but have not been systematically evaluated for isobaric tag-labeled intact proteins. In this study, we report a systematic evaluation of normalized HCD fragmentation energies (NCEs) on TMT-labeled HeLa cell lysate using top-down proteomics. Our results suggested that reporter ions often result in higher ion intensities at higher NCEs. Optimal fragmentation of intact proteins for identification, however, required relatively lower NCE. We further demonstrated that a stepped NCE scheme with energies from 30% to 50% resulted in optimal quantification and identification of TMT-labeled HeLa proteins. These parameters resulted in an average reporter ion intensity of ∼4E4 and average proteoform spectrum matches (PrSMs) of >1000 per RPLC-MS/MS run with a 1% false discovery rate (FDR) cutoff.

Providing octane degradation capability to Pseudomonas putida KT2440 through the horizontal acquisition of oct genes located on an integrative and conjugative element

The extensive use of petrochemicals has produced serious environmental pollution problems; fortunately, bioremediation is considered an efficient way to fight against pollution. In line with Synthetic Biology is that robust microbial chassis with an expanded ability to remove environmental pollutants are desirable. Pseudomonas putida KT2440 is a robust lab microbe that has preserved the ability to survive in the environment and is the natural host for the self-transmissible TOL plasmid, which allows metabolism of toluene and xylenes to central metabolism. We show that the P. putida KT2440 (pWW0) acquired the ability to use octane as the sole C-source after acquisition of an almost 62-kb ICE from a microbial community that harbours an incomplete set of octane metabolism genes. The ICE bears genes for an alkane monooxygenase, a PQQ-dependent alcohol dehydrogenase and aldehyde dehydrogenase but lacks the electron donor enzymes required for the monooxygenase to operate. Host rubredoxin and rubredoxin reductase allow metabolism of octane to octanol. Proteomic assays and mutants unable to grow on octane or octanoic acid revealed that metabolism of octane is mediated by redundant host and ICE enzymes. Octane is oxidized to octanol, octanal and octanoic acid, the latter is subsequently acylated and oxidized to yield acetyl-CoA that is assimilated via the glyoxylate shunt; in fact, a knockout mutant in the aceA gene, encoding isocitrate lyase was unable to grow on octane or octanoic acid.

Radiation-Induced Intestinal Normal Tissue Toxicity: Implications for Altered Proteome Profile

Radiation-induced toxicity to healthy/normal intestinal tissues, especially during radiotherapy, limits the radiation dose necessary to effectively eradicate tumors of the abdomen and pelvis. Although the pathogenesis of intestinal radiation toxicity is highly complex, understanding post-irradiation alterations in protein profiles can provide crucial insights that make radiotherapy safer and more efficient and allow for increasing the radiation dose during cancer treatment. Recent preclinical and clinical studies have advanced our current understanding of the molecular changes associated with radiation-induced intestinal damage by assessing changes in protein expression with mass spectrometry-based approaches and 2-dimensional difference gel electrophoresis. Studies by various groups have demonstrated that proteins that are involved in the inflammatory response, the apoptotic pathway, reactive oxygen species scavenging, and cell proliferation can be targeted to develop effective radiation countermeasures. Moreover, altered protein profiles serve as a crucial biomarkers for intestinal radiation damage. In this review, we present alterations in protein signatures following intestinal radiation damage as detected by proteomics approaches in preclinical and clinical models with the aim of providing a better understanding of how to accomplish intestinal protection against radiation damage.

Retention Time Prediction for TMT-Labeled Peptides in Proteomic LC-MS Experiments

We present the first detailed study of chromatographic behavior of peptides labeled with tandem mass tags (TMT and TMTpro) in 2D LC for proteomic applications. Carefully designed experimental procedures have permitted generating data sets of over 100,000 nonlabeled and TMT-labeled peptide pairs for the low pH RP in the second separation dimension and data sets of over 10,000 peptide pairs for high-pH RP, HILIC (amide and silica), and SCX separations in the first separation dimension. The average increase in peptide RPLC (0.1% formic acid) retention upon TMT labeling was found to be 3.3% acetonitrile (linear water/acetonitrile gradients), spanning a range of -4 to 10.3%. In addition to the bulk peptide properties such as length, hydrophobicity, and the number of labeled residues, we found several sequence-dependent features mostly associated with differences in N-terminal chemistry. The behavior of TMTpro-labeled peptides was found to be very similar except for a slightly higher hydrophobicity: an average retention shift of 3.7% acetonitrile. The respective versions of the sequence-specific retention calculator (SSRCalc) model have been developed to accommodate both TMT chemistries, showing identical prediction accuracy (R² ∼ 0.98) for labeled and nonlabeled peptides. Higher retention for TMT-labeled peptides was observed for high-pH RP and HILIC separations, while SCX selectivity remained virtually unchanged.

The Interleukin-1 (IL-1) Superfamily Cytokines and Their Single Nucleotide Polymorphisms (SNPs)

Interleukins (ILs)—which are important members of cytokines—consist of a vast group of molecules, including a wide range of immune mediators that contribute to the immunological responses of many cells and tissues. ILs are immune-glycoproteins, which directly contribute to the growth, activation, adhesion, differentiation, migration, proliferation, and maturation of immune cells; and subsequently, they are involved in the pro and anti-inflammatory responses of the body, by their interaction with a wide range of receptors. Due to the importance of immune system in different organisms, the genes belonging to immune elements, such as ILs, have been studied vigorously. The results of recent investigations showed that the genes pertaining to the immune system undergo progressive evolution with a constant rate. The occurrence of any mutation or polymorphism in IL genes may result in substantial changes in their biology and function and may be associated with a wide range of diseases and disorders. Among these abnormalities, single nucleotide polymorphisms (SNPs) can represent as important disruptive factors. The present review aims at concisely summarizing the current knowledge available on the occurrence, properties, role, and biological consequences of SNPs within the IL-1 family members.

New advances in quantitative proteomics research and current applications in asthma

INTRODUCTION

Asthma is the most common chronic respiratory disease and has been declared a global public health problem by the World Health Organization. Due to the high heterogeneity and complexity, asthma can be classified into different 'phenotypes' and it is still difficult to assess the phenotypes and stages of asthma by traditional methods. In recent years, mass spectrometry-based proteomics studies have made significant progress in sensitivity and accuracy of protein identification and quantitation, and are able to obtain differences in protein expression across samples, which provides new insights into the mechanisms and classification of asthma.

AREAS COVERED

In this article, we summarize research strategies in quantitative proteomics, including labeled, label-free and targeted quantification, and highlight the advantages and disadvantages of each. In addition, new applications of quantitative proteomics and the current status of research in asthma have also been discussed. In this study, online resources such as PubMed and Google Scholar were used for literature retrieval.

EXPERT OPINION

The application of quantitative proteomics in asthma has an important role in identifying asthma subphenotypes, revealing potential pathogenesis and therapeutic targets. But the proteomic studies on asthma are not sufficient, as most of them are in the phase of biomarker discovery.

Spinal Muscular Atrophy Patient iPSC-Derived Motor Neurons Display Altered Proteomes at Early Stages of Differentiation

Spinal muscular atrophy (SMA) is an autosomal recessive neurodegenerative disorder characterized by loss of motor neurons (MN) in the spinal cord leading to progressive muscle atrophy and weakness. SMA is caused by mutations in the survival motor neuron 1 (SMN1) gene, resulting in reduced levels of survival motor neuron (SMN) protein. The mechanisms that link SMN deficiency to selective motor neuron dysfunction in SMA remain largely unknown. We present here, for the first time, a comprehensive quantitative TMT-10plex proteomics analysis that covers the development of induced pluripotent stem cell-derived MNs from both healthy individuals and SMA patients. We show that the proteomes of SMA samples segregate from controls already at early stages of neuronal differentiation. The altered proteomic signature in SMA MNs is associated with mRNA splicing, ribonucleoprotein biogenesis, organelle organization, cellular biogenesis, and metabolic processes. We highlight several known SMN-binding partners and evaluate their expression changes during MN differentiation. In addition, we compared our study to human and mouse in vivo proteomic studies revealing distinct and similar signatures. Altogether, our work provides a comprehensive resource of molecular events during early stages of MN differentiation, containing potentially therapeutically interesting protein expression profiles for SMA.

Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.

NHS-Ester Tandem Labeling in One Pot Enables 48-Plex Quantitative Proteomics

Stable-isotope labeling strategies are extensively used for multiplex quantitative proteomics. Hybrid-isotope labeling strategies that combine the use of isotopic mass difference labeling and isobaric tags can greatly increase sample multiplexity. In this work, we present a novel hybrid-isotope labeling approach that we termed NHS-ester tandem labeling in one pot (NETLOP). We first optimized 16-plex isobaric TMTpro labeling of lysine residues followed by 2-plex or 3-plex isotopic mTRAQ labeling of peptide N-termini, both of which with commercially available NHS-ester reactive reagents. We then demonstrated the utility of the NETLOP approach by labeling HeLa cell samples and performing proof-of-principle quantitative 32-plex and 48-plex proteomic analyses, each in a single LC-MS/MS experiment. Compared to current hybrid-isotope labeling methods, our NETLOP approach requires no sample cleanup between different labeling steps to minimize sample loss, induces no retention time shifts that compromise quantification accuracy, can be adapted to other NHS-ester isotopic labeling reagents to further increase multiplexity, and is compatible with samples from any origin in a wide array of biological and clinical proteomics applications.

Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells

INTRODUCTION

Many pathologies, including cancer, have been associated with aberrant phosphorylation-mediated signaling networks that drive altered cell proliferation, migration, metabolic regulation, and can lead to systemic inflammation. Phosphoproteomics, the large-scale analysis of protein phosphorylation sites, has emerged as a powerful tool to define signaling network regulation and dysregulation in normal and pathological conditions.

AREAS COVERED

We provide an overview of methodology for global phosphoproteomics as well as enrichment of specific subsets of the phosphoproteome, including phosphotyrosine and phospho-motif enrichment of kinase substrates. We review quantitative methods, advantages and limitations of different mass spectrometry acquisition formats, and computational approaches to extract biological insight from phosphoproteomics data. Throughout, we discuss various applications and their challenges in implementation.

EXPERT OPINION

Over the past 20 years the field of phosphoproteomics has advanced to enable deep biological and clinical insight through the quantitative analysis of signaling networks. Future areas of development include Clinical Laboratory Improvement Amendments (CLIA)-approved methods for analysis of clinical samples, continued improvements in sensitivity to enable analysis of small numbers of rare cells and tissue microarrays, and computational methods to integrate data resulting from multiple systems-level quantitative analytical methods.

Subcellular proteomics

The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.

Benchmarking Non-Targeted Metabolomics Using Yeast-Derived Libraries

Non-targeted analysis by high-resolution mass spectrometry (HRMS) is an essential discovery tool in metabolomics. To date, standardization and validation remain a challenge. Community-wide accepted cost-effective benchmark materials are lacking. In this work, we propose yeast (Pichia pastoris) extracts derived from fully controlled fermentations for this purpose. We established an open-source metabolite library of >200 identified metabolites based on compound identification by accurate mass, matching retention times, and MS/MS, as well as a comprehensive literature search. The library includes metabolites from the classes of (1) organic acids and derivatives (2) nucleosides, nucleotides, and analogs, (3) lipids and lipid-like molecules, (4) organic oxygen compounds, (5) organoheterocyclic compounds, (6) organic nitrogen compounds, and (7) benzoids at expected concentrations ranges of sub-nM to µM. As yeast is a eukaryotic organism, key regulatory elements are highly conserved between yeast and all annotated metabolites were also reported in the human metabolome database (HMDB). Orthogonal state-of-the-art reversed-phase (RP-) and hydrophilic interaction chromatography mass spectrometry (HILIC-MS) non-targeted analysis and authentic standards revealed that 104 out of the 206 confirmed metabolites were reproducibly recovered and stable over the course of three years when stored at −80 °C. Overall, 67 out of these 104 metabolites were identified with comparably stable areas over all three yeast fermentation and are the ideal starting point for benchmarking experiments. The provided yeast benchmark material enabled not only to test for the chemical space and coverage upon method implementation and developments but also allowed in-house routines for instrumental performance tests. Transferring the quality control strategy of proteomics workflows based on the number of protein identification in HeLa extracts, metabolite IDs in the yeast benchmarking material can be used as metabolomics quality control. Finally, the benchmark material opens new avenues for batch-to-batch corrections in large-scale non-targeted metabolomics studies.

Quantitative Mass Spectrometry-Based Proteomics: An Overview

In recent decades, mass spectrometry has moved more than ever before into the front line of protein-centered research. After being established at the qualitative level, the more challenging question of quantification of proteins and peptides using mass spectrometry has become a focus for further development. In this chapter, we discuss and review actual strategies and problems of the methods for the quantitative analysis of peptides, proteins, and finally proteomes by mass spectrometry. The common themes, the differences, and the potential pitfalls of the main approaches are presented in order to provide a survey of the emerging field of quantitative, mass spectrometry-based proteomics.

Defining the carrier proteome limit for single-cell proteomics

Single-cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method to quantify multiplexed single-cell proteomes. While this technique has generated great excitement, the underlying technologies (isobaric labeling and mass spectrometry (MS)) have technical limitations with the potential to affect data quality and biological interpretation. These limitations are particularly relevant when a carrier proteome, a sample added at 25-500× the amount of a single-cell proteome, is used to enable peptide identifications. Here we perform controlled experiments with increasing carrier proteome amounts and evaluate quantitative accuracy, as it relates to mass analyzer dynamic range, multiplexing level and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy. Lastly, we introduce Single-Cell Proteomics Companion (SCPCompanion), a software tool that enables rapid evaluation of single-cell proteomic data and recommends instrument and data analysis parameters for improved data quality.

Comparison of quantitation methods in proteomics to define relevant toxicological information on AhR activation of HepG2 cells by BaP

The application of quantitative proteomics provides a new and promising tool for standardized toxicological research. However, choosing a suitable quantitative method still puzzles many researchers because the optimal method needs to be determined. In this study, we investigated the advantages and limitations of two of the most commonly used global quantitative proteomics methods, namely label-free quantitation (LFQ) and tandem mass tags (TMT). As a case study, we exposed hepatocytes (HepG2) to the environmental contaminant benzo[a]pyrene (BaP) using a concentration of 2 μM. Our results revealed that both methods yield a similar proteome coverage, in which for LFQ a wider range of fold changes was observed but with less significant p-values compared to TMT. We detected 37 and 47 significantly enriched pathways by LFQ and TMT, respectively, with 17 overlapping pathways. To define the minimally required effort in proteomics as a benchmark, we artificially reduced the LFQ, and TMT data sets stepwise and compared the pathway enrichment. Thereby, we found that fewer proteins are necessary for detecting significant enrichment of pathways in TMT compared to LFQ, which might be explained by the higher reproducibility of the TMT data that was observed. In summary, we showed that the TMT approach is the preferable one when investigating toxicological questions because it offers a high reproducibility and sufficient proteome coverage in a comparably short time.

Global analysis of RNA-binding protein dynamics by comparative and enhanced RNA interactome capture

Interactions between RNA-binding proteins (RBPs) and RNAs are critical to cell biology. However, methods to comprehensively and quantitatively assess these interactions within cells were lacking. RNA interactome capture (RIC) uses in vivo UV crosslinking, oligo(dT) capture, and proteomics to identify RNA-binding proteomes. Recent advances have empowered RIC to quantify RBP responses to biological cues such as metabolic imbalance or virus infection. Enhanced RIC exploits the stronger binding of locked nucleic acid (LNA)-containing oligo(dT) probes to poly(A) tails to maximize RNA capture selectivity and efficiency, profoundly improving signal-to-noise ratios. The subsequent analytical use of SILAC and TMT proteomic approaches, together with high-sensitivity sample preparation and tailored statistical data analysis, substantially improves RIC's quantitative accuracy and reproducibility. This optimized approach is an extension of the original RIC protocol. It takes 3 d plus 2 weeks for proteomics and data analysis and will enable the study of RBP dynamics under different physiological and pathological conditions.

Comparison of MS2, synchronous precursor selection MS3, and real-time search MS3 methodologies for lung proteomes of hydrogen sulfide treated swine

Tandem mass tags (TMTs) have increasingly become an attractive technique for global proteomics. However, its effectiveness for multiplexed quantitation by traditional tandem mass spectrometry (MS²) suffers from ratio distortion. Synchronous precursor selection (SPS) MS³ has been widely accepted for improved quantitation accuracy, but concurrently decreased proteome coverage. Recently, a Real-Time Search algorithm has been integrated with the SPS MS³ pipeline (RTS MS³) to provide accurate quantitation and improved depth of coverage. In this mechanistic study of the impact of exposure to hydrogen sulfide (H₂S) on the respiration of swine, we used TMT-based comparative proteomics of lung tissues from control and H₂S-treated subjects as a test case to evaluate traditional MS², SPS MS³, and RTS MS³ acquisition methods on both the Orbitrap Fusion and Orbitrap Eclipse platforms. Comparison of the results obtained by the MS² with those of SPS MS³ and RTS MS³ methods suggests that the MS³-driven quantitative strategies provided a more accurate global-scale quantitation; however, only RTS MS³ provided proteomic coverage that rivaled that of traditional MS² analysis. RTS MS³ not only yields more productive MS³ spectra than SPS MS³ but also appears to focus the analysis more effectively on unique peptides. Furthermore, pathway enrichment analyses of the H₂S-altered proteins demonstrated that an additional apoptosis pathway was discovered exclusively by RTS MS³. This finding was verified by RT-qPCR, western blotting, and TUNEL staining experiments. We conclude that RTS MS³ workflow enables simultaneous improvement of quantitative accuracy and proteome coverage over alternative approaches (MS² and SPS MS³). Graphical abstract.

New label-free methods for protein relative quantification applied to the investigation of an animal model of Huntington Disease

Spectral Counts approaches (SpCs) are largely employed for the comparison of protein expression profiles in label-free (LF) differential proteomics applications. Similarly, to other comparative methods, also SpCs based approaches require a normalization procedure before Fold Changes (FC) calculation. Here, we propose new Complexity Based Normalization (CBN) methods that introduced a variable adjustment factor (f), related to the complexity of the sample, both in terms of total number of identified proteins (CBN(P)) and as total number of spectral counts (CBN(S)). Both these new methods were compared with the Normalized Spectral Abundance Factor (NSAF) and the Spectral Counts log Ratio (Rsc), by using standard protein mixtures. Finally, to test the robustness and the effectiveness of the CBNs methods, they were employed for the comparative analysis of cortical protein extract from zQ175 mouse brains, model of Huntington Disease (HD), and control animals (raw data available via ProteomeXchange with identifier PXD017471). LF data were also validated by western blot and MRM based experiments. On standard mixtures, both CBN methods showed an excellent behavior in terms of reproducibility and coefficients of variation (CVs) in comparison to the other SpCs approaches. Overall, the CBN(P) method was demonstrated to be the most reliable and sensitive in detecting small differences in protein amounts when applied to biological samples.

Quantitative proteomic analysis of Rett iPSC-derived neuronal progenitors

Background

Rett syndrome (RTT) is a progressive neurodevelopmental disease that is characterized by abnormalities in cognitive, social, and motor skills. RTT is often caused by mutations in the X-linked gene encoding methyl-CpG binding protein 2 (MeCP2). The mechanism by which impaired MeCP2 induces the pathological abnormalities in the brain is not understood. Both patients and mouse models have shown abnormalities at molecular and cellular level before typical RTT-associated symptoms appear. This implies that underlying mechanisms are already affected during neurodevelopmental stages.

Methods

To understand the molecular mechanisms involved in disease onset, we used an RTT patient induced pluripotent stem cell (iPSC)-based model with isogenic controls and performed time-series of proteomic analysis using in-depth high-resolution quantitative mass spectrometry during early stages of neuronal development.

Results

We provide mass spectrometry-based quantitative proteomic data, depth of about 7000 proteins, at neuronal progenitor developmental stages of RTT patient cells and isogenic controls. Our data gives evidence of proteomic alteration at early neurodevelopmental stages, suggesting alterations long before the phase that symptoms of RTT syndrome become apparent. Significant changes are associated with the GO enrichment analysis in biological processes cell-cell adhesion, actin cytoskeleton organization, neuronal stem cell population maintenance, and pituitary gland development, next to protein changes previously associated with RTT, i.e., dendrite morphology and synaptic deficits. Differential expression increased from early to late neural stem cell phases, although proteins involved in immunity, metabolic processes, and calcium signaling were affected throughout all stages analyzed.

Limitations

The limitation of our study is the number of RTT patients analyzed. As the aim of our study was to investigate a large number of proteins, only one patient was considered, of which 3 different RTT iPSC clones and 3 isogenic control iPSC clones were included. Even though this approach allowed the study of mutation-induced alterations due to the usage of isogenic controls, results should be validated on different RTT patients to suggest common disease mechanisms.

Conclusions

During early neuronal differentiation, there are consistent and time-point specific proteomic alterations in RTT patient cells carrying exons 3–4 deletion in MECP2. We found changes in proteins involved in pathway associated with RTT phenotypes, including dendrite morphology and synaptogenesis. Our results provide a valuable resource of proteins and pathways for follow-up studies, investigating common mechanisms involved during early disease stages of RTT syndrome.

Deciphering the Proteome Dynamics during Development of Neurons Derived from Induced Pluripotent Stem Cells

Neuronal development is a complex multistep process that shapes neurons by progressing though several typical stages, including axon outgrowth, dendrite formation, and synaptogenesis. Knowledge of the mechanisms of neuronal development is mostly derived from the study of animal models. Advances in stem cell technology now enable us to generate neurons from human induced pluripotent stem cells (iPSCs). Here we provide a mass spectrometry-based quantitative proteomic signature of human iPSC-derived neurons, i.e., iPSC-derived induced glutamatergic neurons and iPSC-derived motor neurons, throughout neuronal differentiation. Tandem mass tag 10-plex labeling was carried out to perform proteomic profiling of cells at different time points. Our analysis reveals significant expression changes (FDR < 0.001) of several key proteins during the differentiation process, e.g., proteins involved in the Wnt and Notch signaling pathways. Overall, our data provide a rich resource of information on protein expression during human iPSC neuron differentiation.

MS1-Level Proteome Quantification Platform Allowing Maximally Increased Multiplexity for SILAC and In Vitro Chemical Labeling

Quantitative proteomic platforms based on precursor intensity in mass spectrometry (MS1-level) uniquely support in vivo metabolic labeling with superior quantification accuracy but suffer from limited multiplexity (≤3-plex) and frequent missing quantities. Here we present a new MS1-level quantification platform that allows maximal multiplexing with high quantification accuracy and precision for the given labeling scheme. The platform currently comprises 6-plex in vivo SILAC or in vitro diethylation labeling with a dedicated algorithm and is also expandable to higher multiplexity (e.g., nine-plex for SILAC). For complex samples with broad dynamic ranges such as total cell lysates, our platform performs highly accurately and free of missing quantities. Furthermore, we successfully applied our method to measure protein synthesis rate under heat shock response in human cells by 6-plex pulsed SILAC experiments, demonstrating the unique biological merits of our in vivo platform to disclose translational regulations for cellular response to stress.

Photoprotective Acclimation of the Arabidopsis thaliana Leaf Proteome to Fluctuating Light

Plants are subjected to strong fluctuations in light intensity in their natural growth environment, caused both by unpredictable changes due to weather conditions and movement of clouds and upper canopy leaves and predictable changes during day-night cycle. The mechanisms of long-term acclimation to fluctuating light (FL) are still not well understood. Here, we used quantitative mass spectrometry to investigate long-term acclimation of low light-grown Arabidopsis thaliana to a FL condition that induces mild photooxidative stress. On the third day of exposure to FL, young and mature leaves were harvested in the morning and at the end of day for proteome analysis using a stable isotope labeling approach. We identified 2,313 proteins, out of which 559 proteins exhibited significant changes in abundance in at least one of the four experimental groups (morning-young, morning-mature, end-of-day-young, end-of-day-mature). A core set of 49 proteins showed significant responses to FL in three or four experimental groups, which included enhanced accumulation of proteins involved in photoprotection, cyclic electron flow around photosystem I, photorespiration, and glycolysis, while specific glutathione transferases and proteins involved in translation and chlorophyll biosynthesis were reduced in abundance. In addition, we observed pathway- and protein-specific changes predominantly at the end of day, whereas few changes were observed exclusively in the morning. Comparison of the proteome data with the matching transcript data revealed gene- and protein-specific responses, with several chloroplast-localized proteins decreasing in abundance despite increased gene expression under FL. Together, our data shows moderate but widespread alterations of protein abundance during acclimation to FL and suggests an important role of post-transcriptional regulation of protein abundance.

Quantitative Microproteomics Based Characterization of the Central and Peripheral Nervous System of a Mouse Model of Krabbe Disease

Krabbe disease is a rare, childhood lysosomal storage disorder caused by a deficiency of galactosylceramide beta-galactosidase (GALC). The major effect of GALC deficiency is the accumulation of psychosine in the nervous system and widespread degeneration of oligodendrocytes and Schwann cells, causing rapid demyelination. The molecular mechanisms of Krabbe disease are not yet fully elucidated and a definite cure is still missing. Here we report the first in-depth characterization of the proteome of the Twitcher mouse, a spontaneous mouse model of Krabbe disease, to investigate the proteome changes in the Central and Peripheral Nervous System. We applied a TMT-based workflow to compare the proteomes of the corpus callosum, motor cortex and sciatic nerves of littermate homozygous Twitcher and wild-type mice. More than 400 protein groups exhibited differences in expression and included proteins involved in pathways that can be linked to Krabbe disease, such as inflammatory and defense response, lysosomal proteins accumulation, demyelination, reduced nervous system development and cell adhesion. These findings provide new insights on the molecular mechanisms of Krabbe disease, representing a starting point for future functional experiments to study the molecular pathogenesis of Krabbe disease. Data are available via ProteomeXchange with identifier PXD010594.

Removal of Interference MS/MS Spectra for Accurate Quantification in Isobaric Tag-Based Proteomics

Rapid progress in mass spectrometry (MS) has made comprehensive analyses of the proteome possible, but accurate quantification remains challenging. Isobaric tags for relative and absolute quantification (iTRAQ) is widely used as a tool to quantify proteins expressed in different cell types and various cellular conditions. The quantification precision of iTRAQ is quite high, but the accuracy dramatically decreases in the presence of interference peptides that are coeluted and coisolated with the target peptide. Here, we developed "removal of interference mixture MS/MS spectra (RiMS)" to improve the quantification accuracy of isobaric tag approaches. The presence of spectrum interference is judged by examining the overlap in the elution time of all scanned precursor ions. Removal of this interference decreased protein identification (11% loss) but improved quantification accuracy. Further, RiMS does not require any specialized equipment, such as MS³ instruments or an additional ion separation mode. Finally, we demonstrated that RiMS can be used to quantitatively compare human-induced pluripotent stem cells and human dermal fibroblasts, as it revealed differential protein expressions that reflect the biological characteristics of the cells.

Ribosome Stoichiometry: From Form to Function

The existence of eukaryotic ribosomes with distinct ribosomal protein (RP) stoichiometry and regulatory roles in protein synthesis has been speculated for over 60 years. Recent advances in mass spectrometry (MS) and high-throughput analysis have begun to identify and characterize distinct ribosome stoichiometry in yeast and mammalian systems. In addition to RP stoichiometry, ribosomes host a vast array of protein modifications, effectively expanding the number of human RPs from 80 to many thousands of distinct proteoforms. Is it possible that these proteoforms combine to function as a 'ribosome code' to tune protein synthesis? We outline the specific benefits that translational regulation by specialized ribosomes can offer and discuss the means and methodologies available to correlate and characterize RP stoichiometry with function. We highlight previous research with a focus on formulating hypotheses that can guide future experiments and crack the ribosome code.

Quantitative Phosphoproteomic Using Titanium Dioxide Micro-Columns and Label-Free Quantitation

Phosphorylation events are important during cellular function. Analysis of phosphorylation in complex samples has been extensively studied using large-scale phosphopeptide enrichment methods. Quantitative analysis of the enriched phosphopeptides is subsequently performed using label-based methodologies (e.g., SILAC, iTRAQ, and others). Here we describe the protocol for the quantitative analysis of phosphopeptides, enriched with titanium dioxide micro-column, using an intensity-based label-free quantitation.

An Overview of Mass Spectrometry-Based Methods for Functional Proteomics

The mechanism underlying many biological phenotypes remains unknown despite the increasing availability of whole genome and transcriptome sequencing. Direct measurement of changes in protein expression is an attractive alternative and has the potential to reveal novel processes. Mass spectrometry has become the standard method for proteomics, allowing both the confident identification and quantification of thousands of proteins from biological samples. In this review, mass spectrometry-based proteomic methods and their applications are described.

Quantifying Homologous Proteins and Proteoforms

Many proteoforms-arising from alternative splicing, post-translational modifications (PTM), or paralogous genes-have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid these biases, we developed and implemented a first-principles model (HIquant) for quantifying proteoform stoichiometries. We characterized when MS data allow inferring proteoform stoichiometries by HIquant and derived an algorithm for optimal inference. We applied this algorithm to infer proteoform stoichiometries in two experimental systems that supported rigorous bench-marking: alkylated proteoforms spiked-in at known ratios and endogenous histone 3 PTM proteoforms quantified relative to internal heavy standards. When compared with the benchmarks, the proteoform stoichiometries interfered by HIquant without using external standards had relative error of 5-15% for simple proteoforms and 20-30% for complex proteoforms. A HIquant server is implemented at: https://web.northeastern.edu/slavov/2014HIquant/.

Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics

Mass spectrometry-based quantitative proteomics is a powerful tool for gaining insights into function and dynamics of biological systems. However, peptides with different sequences have different ionization efficiencies, and their intensities in a mass spectrum are not correlated with their abundances. Therefore, various label-free or stable isotope label-based quantitation methods have emerged to assist mass spectrometry to perform comparative proteomic experiments, thus enabling nonbiased identification of thousands of proteins differentially expressed in healthy versus diseased cells. Here, we discuss the most widely used label-free and metabolic-, enzymatic-, and chemical labeling-based proteomic strategies for relative and absolute quantitation. We summarize the specific strengths and weaknesses of each technique in terms of quantification accuracy, proteome coverage, multiplexing capability, and robustness. Applications of each strategy for solving specific biological complexities are also presented.

A Novel Lipidomics Workflow for Improved Human Plasma Identification and Quantification Using RPLC-MSn Methods and Isotope Dilution Strategies

Lipid identification and quantification are essential objectives in comprehensive lipidomics studies challenged by the high number of lipids, their chemical diversity, and their dynamic range. In this work, we developed a tailored method for profiling and quantification combining (1) isotope dilution, (2) enhanced isomer separation by C30 fused-core reversed-phase material, and (3) parallel Orbitrap and ion trap detection by the Orbitrap Fusion Lumos Tribid mass spectrometer. The combination of parallelizable ion analysis without time loss together with different fragmentation techniques (HCD/CID) and an inclusion list led to higher quality in lipid identifications exemplified in human plasma and yeast samples. Moreover, we used lipidome isotope-labeling of yeast (LILY)-a fast and efficient in vivo labeling strategy in Pichia pastoris-to produce (nonradioactive) isotopically labeled eukaryotic lipid standards in yeast. We integrated the ¹³C lipids in the LC-MS workflow to enable relative and absolute compound-specific quantification in yeast and human plasma samples by isotope dilution. Label-free and compound-specific quantification was validated by comparison against a recent international interlaboratory study on human plasma SRM 1950. In this way, we were able to prove that LILY enabled quantification leads to accurate results, even in complex matrices. Excellent analytical figures of merit with enhanced trueness, precision and linearity over 4-5 orders of magnitude were observed applying compound-specific quantification with ¹³C-labeled lipids. We strongly believe that lipidomics studies will benefit from incorporating isotope dilution and LC-MSn strategies.

Benchmarking common quantification strategies for large-scale phosphoproteomics

Comprehensive mass spectrometry (MS)-based proteomics is now feasible, but reproducible quantification remains challenging, especially for post-translational modifications such as phosphorylation. Here, we compare the most popular quantification techniques for global phosphoproteomics: label-free quantification (LFQ), stable isotope labeling by amino acids in cell culture (SILAC) and MS²- and MS³-measured tandem mass tags (TMT). In a mixed species comparison with fixed phosphopeptide ratios, we find LFQ and SILAC to be the most accurate techniques. MS²-based TMT yields the highest precision but lowest accuracy due to ratio compression, which MS³-based TMT can partly rescue. However, MS²-based TMT outperforms MS³-based TMT when analyzing phosphoproteome changes in the DNA damage response, since its higher precision and larger identification numbers allow detection of a greater number of significantly regulated phosphopeptides. Finally, we utilize the TMT multiplexing capabilities to develop an algorithm for determining phosphorylation site stoichiometry, showing that such applications benefit from the high accuracy of MS³-based TMT.

Quantitative phosphoproteomics has become a standard method in molecular and cell biology. Here, the authors compare performance and parameters of phosphoproteome quantification by LFQ, SILAC, and MS²-/MS³-based TMT and introduce a TMT-adapted algorithm for calculating phosphorylation site stoichiometry.

ExSTA: External Standard Addition Method for Accurate High-Throughput Quantitation in Targeted Proteomics Experiments

PURPOSE

Targeted proteomics using MRM with stable-isotope-labeled internal-standard (SIS) peptides is the current method of choice for protein quantitation in complex biological matrices. Better quantitation can be achieved with the internal standard-addition method, where successive increments of synthesized natural form (NAT) of the endogenous analyte are added to each sample, a response curve is generated, and the endogenous concentration is determined at the x-intercept. Internal NAT-addition, however, requires multiple analyses of each sample, resulting in increased sample consumption and analysis time.

EXPERIMENTAL DESIGN

To compare the following three methods, an MRM assay for 34 high-to-moderate abundance human plasma proteins is used: classical internal SIS-addition, internal NAT-addition, and external NAT-addition-generated in buffer using NAT and SIS peptides. Using endogenous-free chicken plasma, the accuracy is also evaluated.

RESULTS

The internal NAT-addition outperforms the other two in precision and accuracy. However, the curves derived by internal vs. external NAT-addition differ by only ≈3.8% in slope, providing comparable accuracies and precision with good CV values.

CONCLUSIONS AND CLINICAL RELEVANCE

While the internal NAT-addition method may be "ideal", this new external NAT-addition can be used to determine the concentration of high-to-moderate abundance endogenous plasma proteins, providing a robust and cost-effective alternative for clinical analyses or other high-throughput applications.

Recent advances in applying mass spectrometry and systems biology to determine brain dynamics

INTRODUCTION

Neurological disorders encompass various pathologies which disrupt normal brain physiology and function. Poor understanding of their underlying molecular mechanisms and their societal burden argues for the necessity of novel prevention strategies, early diagnostic techniques and alternative treatment options to reduce the scale of their expected increase. Areas covered: This review scrutinizes mass spectrometry based approaches used to investigate brain dynamics in various conditions, including neurodegenerative and neuropsychiatric disorders. Different proteomics workflows for isolation/enrichment of specific cell populations or brain regions, sample processing; mass spectrometry technologies, for differential proteome quantitation, analysis of post-translational modifications and imaging approaches in the brain are critically deliberated. Future directions, including analysis of cellular sub-compartments, targeted MS platforms (selected/parallel reaction monitoring) and use of mass cytometry are also discussed. Expert commentary: Here, we summarize and evaluate current mass spectrometry based approaches for determining brain dynamics in health and diseases states, with a focus on neurological disorders. Furthermore, we provide insight on current trends and new MS technologies with potential to improve this analysis.

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.