Selected reaction monitoring for quantitative proteomics: a tutorial

Wideband PRM: Highly Accurate and Sensitive Method for High-Throughput Targeted Proteomics

Targeted mass spectrometry (MS) approaches, which are powerful methods for uniquely and confidently quantifying a specific panel of proteins in complex biological samples, play a crucial role in validating and clinically translating protein biomarkers discovered through global proteomic profiling. Common targeted MS methods, such as multiple reaction monitoring (MRM) and parallel-reaction monitoring (PRM), employ specific mass spectrometric technologies to quantify protein levels by comparing the transitions of surrogate endogenous (ENDO) peptides with those of stable isotope-labeled (SIL) peptide counterparts. These methods utilizing amino acid analyzed (AAA) SIL peptides warrant sensitive and precise measurements required for targeted MS assays. Compared with MRM, PRM provides higher experimental throughput by simultaneously acquiring all transitions of the target peptides and thereby compensates for different ion suppressions among transitions of a target peptide. However, PRM still suffers different ion suppressions between ENDO and SIL peptides due to spray instability, as the ENDO and SIL peptides were monitored at different liquid chromatography (LC) retention times. Here we introduce a new targeted MS method, termed wideband PRM (WBPRM), that is designed for high-throughput targeted MS analysis. WBPRM employs a wide isolation window for simultaneous fragmentation of both ENDO and SIL peptides along with multiplexed single ion monitoring (SIM) scans for enhanced MS sensitivity of the target peptides. Compared with PRM, WBPRM was demonstrated to provide increased sensitivity, precision, and reproducibility of quantitative measurements of target peptides with increased throughput, allowing more target peptide measurements in a shortened experiment time. WBPRM is a straightforward adaptation to a manufacturer-provided MS method, making it an easily implementable technique, particularly in complex biological samples where the demand for higher precision, sensitivity, and efficiency is paramount.

A workflow for targeted proteomics assay development using a versatile linear ion trap

Advances in proteomics and mass spectrometry have enabled the study of limited cell populations, such as single-cell proteomics, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive nominal resolution measurements, these instruments are effectively limited to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. We demonstrate a workflow using a newly released, hybrid quadrupole-LIT instrument for developing targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Gas-phase fraction-based DIA enables rapid target library generation in the same background chemical matrix as each quantitative injection. Using a new software tool embedded within EncyclopeDIA for scheduling parallel reaction monitoring assays, we show consistent quantification across three orders of magnitude of input material. Using this approach, we demonstrate measuring peptide quantitative linearity down to 25x dilution in a background of only a 1 ng proteome without requiring stable isotope labeled standards. At 1 ng total protein on column, we found clear consistency between immune cell populations measured using flow cytometry and immune markers measured using LIT-based proteomics. We believe hybrid quadrupole-LIT instruments represent an economic solution to democratizing mass spectrometry in a wide variety of laboratory settings.

Applicability of selected reaction monitoring for precise screening tests

INTRODUCTION

The proactive identification of diseases through screening tests has long been endorsed as a means to preempt symptomatic onset. However, such screening endeavors are fraught with complications, such as diagnostic inaccuracies, procedural risks, and patient unease during examinations. These challenges are amplified when screenings for multiple diseases are administered concurrently. Selected Reaction Monitoring (SRM) offers a unique advantage, allowing for the high-throughput quantification of hundreds of analytes with minimal interferences.

AREAS COVERED

Our research posits that SRM-based assays, traditionally tailored for single-disease biomarker profiling, can be repurposed for multi-disease screening. This innovative approach has the potential to substantially alleviate time, labor, and cost demands on healthcare systems and patients alike. Nonetheless, there are formidable methodological hurdles to overcome. These include difficulties in detecting low-abundance proteins and the risk of model overfitting due to the multiple functionalities of single proteins across different disease spectrums - issues especially pertinent in blood-based assays where detection sensitivity is constrained. As we move forward, technological strides in sample preparation, online extraction, throughput, and automation are expected to ameliorate these limitations.

EXPERT OPINION

The maturation of mass spectrometry's integration into clinical laboratories appears imminent, positioning it as an invaluable asset for delivering highly sensitive, reproducible, and precise diagnostic results.

Sterol Derivatives Specifically Increase Anti-Inflammatory Oxylipin Formation in M2-like Macrophages by LXR-Mediated Induction of 15-LOX

The understanding of the role of LXR in the regulation of macrophages during inflammation is emerging. Here, we show that LXR agonist T09 specifically increases 15-LOX abundance in primary human M2 macrophages. In time- and dose-dependent incubations with T09, an increase of 3-fold for ALOX15 and up to 15-fold for 15-LOX-derived oxylipins was observed. In addition, LXR activation has no or moderate effects on the abundance of macrophage marker proteins such as TLR2, TLR4, PPARγ, and IL-1RII, as well as surface markers (CD14, CD86, and CD163). Stimulation of M2-like macrophages with FXR and RXR agonists leads to moderate ALOX15 induction, probably due to side activity on LXR. Finally, desmosterol, 24(S),25-Ep cholesterol and 22(R)-OH cholesterol were identified as potent endogenous LXR ligands leading to an ALOX15 induction. LXR-mediated ALOX15 regulation is a new link between the two lipid mediator classes sterols, and oxylipins, possibly being an important tool in inflammatory regulation through anti-inflammatory oxylipins.

IS-PRM-based peptide targeting informed by long-read sequencing for alternative proteome detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of pre-defined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (LR RNAseq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNAseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This LR RNA seq-informed Tomahto targeted approach, called LRP-IS-PRM, is a new modality for generating protein-level evidence of alternative isoforms - a critical first step in designing functional studies and eventually clinical assays.

SWATH-MS as a strategy for CHO host cell protein identification and quantification supporting the characterization of mAb purification platforms

Host cell proteins (HCPs) are process-related impurities expressed by the host cells during biotherapeutics' manufacturing, such as monoclonal antibodies (mAbs). Some challenging HCPs evade clearance during the downstream processing and can be co-purified with the molecule of interest, which may impact product stability, efficacy, and safety. Therefore, HCP content is a critical quality attribute to monitor and quantify across the bioprocess. Here we explored a mass spectrometry (MS)-based proteomics tool, the sequential window acquisition of all theoretical fragment-ion spectra (SWATH) strategy, as an orthogonal method to traditional ELISA. The SWATH workflow was applied for high-throughput individual HCP identification and quantification, supporting characterization of a mAb purification platform. The design space of HCP clearance of two polishing resins was evaluated through a design of experiment study. Absolute quantification of high-risk HCPs was achieved (reaching 1.8 and 4.2 ppm limits of quantification, for HCP A and B respectively) using HCP-specific synthetic heavy labeled peptide calibration curves. Profiling of other HCPs was also possible using an average calibration curve (using labeled peptides from different HCPs). The SWATH approach is a powerful tool for HCP assessment during bioprocess development enabling simultaneous monitoring and quantification of different individual HCPs and improving process understanding of their clearance.

Post-translational modifications of proteins in cardiovascular diseases examined by proteomic approaches

Over 400 different types of post-translational modifications (PTMs) have been reported and over 200 various types of PTMs have been discovered using mass spectrometry (MS)-based proteomics. MS-based proteomics has proven to be a powerful method capable of global PTM mapping with the identification of modified proteins/peptides, the localization of PTM sites and PTM quantitation. PTMs play regulatory roles in protein functions, activities and interactions in various heart related diseases, such as ischemia/reperfusion injury, cardiomyopathy and heart failure. The recognition of PTMs that are specific to cardiovascular pathology and the clarification of the mechanisms underlying these PTMs at molecular levels are crucial for discovery of novel biomarkers and application in a clinical setting. With sensitive MS instrumentation and novel biostatistical methods for precise processing of the data, low-abundance PTMs can be successfully detected and the beneficial or unfavorable effects of specific PTMs on cardiac function can be determined. Moreover, computational proteomic strategies that can predict PTM sites based on MS data have gained an increasing interest and can contribute to characterization of PTM profiles in cardiovascular disorders. More recently, machine learning- and deep learning-based methods have been employed to predict the locations of PTMs and explore PTM crosstalk. In this review article, the types of PTMs are briefly overviewed, approaches for PTM identification/quantitation in MS-based proteomics are discussed and recently published proteomic studies on PTMs associated with cardiovascular diseases are included.

M-protein diagnostics in multiple myeloma patients using ultra-sensitive targeted mass spectrometry and an off-the-shelf calibrator

OBJECTIVES

Minimal residual disease status in multiple myeloma is an important prognostic biomarker. Recently, personalized blood-based targeted mass spectrometry (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to measure minimal residual disease. However, quantification of MS-MRD requires a unique calibrator for each patient. The use of patient-specific stable isotope labelled (SIL) peptides is relatively costly and time-consuming, thus hindering clinical implementation. Here, we introduce a simplification of MS-MRD by using an off-the-shelf calibrator.

METHODS

SILuMAB-based MS-MRD was performed by spiking a monoclonal stable isotope labeled IgG, SILuMAB-K1, in the patient serum. The abundance of both M-protein-specific peptides and SILuMAB-specific peptides were monitored by mass spectrometry. The relative ratio between M-protein peptides and SILuMAB peptides allowed for M-protein quantification. We assessed linearity, sensitivity and reproducibility of SILuMAB-based MS-MRD in longitudinally collected sera from the IFM-2009 clinical trial.

RESULTS

A linear dynamic range was achieved of over 5 log scales, allowing for M-protein quantification down to 0.001 g/L. The inter-assay CV of SILuMAB-based MS-MRD was on average 11 %. Excellent concordance between SIL- and SILuMAB-based MS-MRD was shown (R2>0.985). Additionally, signal intensity of spiked SILuMAB can be used for quality control purpose to assess system performance and incomplete SILuMAB digestion can be used as quality control for sample preparation.

CONCLUSIONS

Compared to SIL peptides, SILuMAB-based MS-MRD improves the reproducibility, turn-around-times and cost-efficacy of MS-MRD without diminishing its sensitivity and specificity. Furthermore, SILuMAB can be used as a MS-MRD quality control tool to monitor sample preparation efficacy and assay performance.

Analytical techniques for screening of cannabis and derivatives from human hair specimens

Cannabis and associated substances are some of the most frequently abused drugs across the globe, mainly due to their anxiolytic and euphorigenic properties. Nowadays, the analysis of hair samples has been given high importance in forensic and analytical sciences and in clinical studies because they are associated with a low risk of infection, do not require complicated storage conditions, and offer a broad window of non-invasive detection. Analysis of hair samples is very easy compared to the analysis of blood, urine, and saliva samples. This review places particular emphasis on methodologies of analyzing hair samples containing cannabis, with a special focus on the preparation of samples for analysis, which involves screening and extraction techniques, followed by confirmatory assays. Through this manuscript, we have presented an overview of the available literature on the screening of cannabis using mass spectroscopy techniques. We have presented a detailed overview of the advantages and disadvantages of this technique, to establish it as a suitable method for the analysis of cannabis from hair samples.

Absolute quantitation of human wild-type DNAI1 protein in lung tissue using a nanoLC-PRM-MS-based targeted proteomics approach coupled with immunoprecipitation

BACKGROUND

Dynein axonemal intermediate chain 1 protein (DNAI1) plays an essential role in cilia structure and function, while its mutations lead to primary ciliary dyskinesia (PCD). Accurate quantitation of DNAI1 in lung tissue is crucial for comprehensive understanding of its involvement in PCD, as well as for developing the potential PCD therapies. However, the current protein quantitation method is not sensitive enough to detect the endogenous level of DNAI1 in complex biological matrix such as lung tissue.

METHODS

In this study, a quantitative method combining immunoprecipitation with nanoLC-MS/MS was developed to measure the expression level of human wild-type (WT) DNAI1 protein in lung tissue. To our understanding, it is the first immunoprecipitation (IP)-MS based method for absolute quantitation of DNAI1 protein in lung tissue. The DNAI1 quantitation was achieved through constructing a standard curve with recombinant human WT DNAI1 protein spiked into lung tissue matrix.

RESULTS

This method was qualified with high sensitivity and accuracy. The lower limit of quantitation of human DNAI1 was 4 pg/mg tissue. This assay was successfully applied to determine the endogenous level of WT DNAI1 in human lung tissue.

CONCLUSIONS

The results clearly demonstrate that the developed assay can accurately quantitate low-abundance WT DNAI1 protein in human lung tissue with high sensitivity, indicating its high potential use in the drug development for DNAI1 mutation-caused PCD therapy.

A Comprehensive Immunocapture-LC-MS/MS Bioanalytical Approach in Support of a Biotherapeutic Ocular PK Study

BI-X, a therapeutic protein under development for the treatment of human ocular disease via intravitreal administration, binds to its therapeutic targets and endogenous albumin in the vitreous humor. A monkey ocular pharmacokinetic (PK) study following BI-X administration was conducted to measure drug and albumin levels in plasma, the vitreous humor, the aqueous humor, and retina tissue at various timepoints post-dose. A comprehensive bioanalytical approach was implemented in support of this study. Five immunocapture-LC-MS/MS assays were developed and qualified for quantitating BI-X in different matrices, while ELISA was used for albumin measurement. Immunocapture at the protein or peptide level was evaluated to achieve adequate assay sensitivity. Drug and albumin assays were applied for the analysis of the monkey study samples.

Ameliorative effect of Alangium chinense (Lour.) Harms on rheumatoid arthritis by reducing autophagy with targeting regulate JAK3-STAT3 and COX-2 pathways

ETHNOPHARMACOLOGICAL RELEVANCE

Alangium chinense has been used as traditional folk medicine for centuries to treat rheumatoid arthritis (RA) by Guizhou Miao nationality with remarkable clinical effect. But the mechanism of its anti-RA is not fully clarified.

AIM OF THE STUDY

To explore the effect and underlying mechanism of A. chinense against RA.

MATERIAL AND METHODS

RA rats were induced by CII/IFA, and oral administrated with or without ethyl acetate extracts of Alangium chinense (ACEE) and tripterygium glycosides (GTW). Then arthritis scores, inflammatory factors in serum and histological evaluation were evaluated to assess the degree of joints disease. Proteomics were conducted via LC-MS/MS to clarify the mechanism of ACEE preliminarily, and further examined by immunohistochemistry, immunofluorescence, western botting, and molecular docking.

RESULTS

ACEE decreased joints swelling, cell abscission and necrosis of joint tissues arthropathy of RA rats, and attenuated expression of TNF-α, IL-1β, IL-6, PGE2, TGF-β. Meanwhile, differentially expressed proteins in the ACEE treated groups were observed, which were involved in RA, spliceosome, cell adhesion molecules, phagosome and lysosome signaling pathways. Moreover, ACEE significantly ameliorated arthropathy, suppressed JAK-STAT pathway (JAK3, p-JAK3, STAT3, iNOS, RANKL), COX-2 pathway (COX-2, TNF-α, IL-6I, L-1β, 5-LOX), and autophagic signaling pathway (LC3-Ⅰ, LC3-Ⅱ, p62, mTOR). But it showed little effect on the expression of COX-1, JAK1, JAK2, TyK2.

CONCLUSION

It is the first evidence that A. chinense significantly ameliorates RA, and the underlying immune mechanism involves reducing autophagy with targeting regulate JAK3-STAT3 and COX-2 pathways.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

Present and future perspectives on mass spectrometry for clinical microbiology

In the last decade, MALDI-TOF Mass Spectrometry (MALDI-TOF MS) has been introduced and broadly accepted by clinical laboratory laboratories throughout the world as a powerful and efficient tool for rapid microbial identification. During the MALDI-TOF MS process, microbes are identified using either intact cells or cell extracts. The process is rapid, sensitive, and economical in terms of both labor and costs involved. Whilst MALDI-TOF MS is currently the gold-standard, it suffers from several shortcomings such as lack of direct information on antibiotic resistance, poor depth of analysis and insufficient discriminatory power for the distinction of closely related bacterial species or for reliably sub-differentiating isolates to the level of clones or strains. Thus, new approaches targeting proteins and allowing a better characterization of bacterial strains are strongly needed, if possible, on a very short time scale after sample collection in the hospital. Bottom-up proteomics (BUP) is a nice alternative to MALDI-TOF MS, offering the possibility for in-depth proteome analysis. Top-down proteomics (TDP) provides the highest molecular precision in proteomics, allowing the characterization of proteins at the proteoform level. A number of studies have already demonstrated the potential of these techniques in clinical microbiology. In this review, we will discuss the current state-of-the-art of MALDI-TOF MS for the rapid microbial identification and detection of resistance to antibiotics and describe emerging approaches, including bottom-up and top-down proteomics as well as ambient MS technologies.

Exoproteome analysis of Pseudomonas aeruginosa response to high alkane stress

Microbial biodegradation serves as an effective approach to treat oil pollution. However, the application of such methods for the degrading long-chain alkanes still encounters significant challenges. Comparative proteomics has extensively studied the intracellular proteins of bacteria that degrade short- and medium-chain alkanes, but the role and mechanism of extracellular proteins in many microorganism remain unclear. To enhance our understanding of the roles of extracellular proteins in the adaptation to long-chain alkanes, a label-free LC-MS/MS strategy was applied for the relative quantification of extracellular proteins of Pseudomonas aeruginosa SJTD-1-M (ProteomeXchange identifier PXD014638). 444 alkane-sentitive proteins were acquired and their cell localization analysis was performed using the Pseudomonas Genome Database. Among them, 111 proteins were found to be located in extracellular or Outer Membrane Vesicles (OMVs). The alkane-induced abundance of 11 extracellular or OMV target proteins was confirmed by parallel reaction monitoring (PRM). Furthermore, we observed that the expression levels of three proteins (Pra, PA2815, and FliC) were associated with the carbon chain length of the added alkane in the culture medium. The roles of these proteins in cell mobility, alkane emulsification, assimilation, and degradation were further discussed. OMVs were found to contain a number of enzymes involved in alkane metabolism, fatty acid beta-oxidation, and the TCA cycle, suggesting their potential as sites for facilitated alkane degradation. In this sense, this exoproteome analysis contributes to a better understanding of the role of extracellular proteins in the hydrocarbon treatment process.

Probing Protein Complexes Composition, Stoichiometry, and Interactions by Peptide-Based Mass Spectrometry

The characterization of a protein complex by mass spectrometry can be conducted at different levels. Initial steps regard the qualitative composition of the complex and subunit identification. After that, quantitative information such as stoichiometric ratios and copy numbers for each subunit in a complex or super-complex is acquired. Peptide-based LC-MS/MS offers a wide number of methods and protocols for the characterization of protein complexes. This chapter concentrates on the applications of peptide-based LC-MS/MS for the qualitative, quantitative, and structural characterization of protein complexes focusing on subunit identification, determination of stoichiometric ratio and number of subunits per complex as well as on cross-linking mass spectrometry and hydrogen/deuterium exchange as methods for the structural investigation of the biological assemblies.

Utilizing proteomics to identify and optimize microalgae strains for high-quality dietary protein: a review

Algae-derived protein has immense potential to provide high-quality protein foods for the expanding human population. To meet its potential, a broad range of scientific tools are required to identify optimal algal strains from the hundreds of thousands available and identify ideal growing conditions for strains that produce high-quality protein with functional benefits. A research pipeline that includes proteomics can provide a deeper interpretation of microalgal composition and biochemistry in the pursuit of these goals. To date, proteomic investigations have largely focused on pathways that involve lipid production in selected microalgae species. Herein, we report the current state of microalgal proteome measurement and discuss promising approaches for the development of protein-containing food products derived from algae.

Comprehensive quantitative modeling of translation efficiency in a genome-reduced bacterium

Translation efficiency has been mainly studied by ribosome profiling, which only provides an incomplete picture of translation kinetics. Here, we integrated the absolute quantifications of tRNAs, mRNAs, RNA half-lives, proteins, and protein half-lives with ribosome densities and derived the initiation and elongation rates for 475 genes (67% of all genes), 73 with high precision, in the bacterium Mycoplasma pneumoniae (Mpn). We found that, although the initiation rate varied over 160-fold among genes, most of the known factors had little impact on translation efficiency. Local codon elongation rates could not be fully explained by the adaptation to tRNA abundances, which varied over 100-fold among tRNA isoacceptors. We provide a comprehensive quantitative view of translation efficiency, which suggests the existence of unidentified mechanisms of translational regulation in Mpn.

The Breast Cancer Proteome and Precision Oncology

The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Assessment and Prediction of Human Proteotypic Peptide Stability for Proteomics Quantification

Mass spectrometry coupled to liquid chromatography is one of the most powerful technologies for proteome quantification in biomedical samples. In peptide-centric workflows, protein mixtures are enzymatically digested to peptides prior their analysis. However, proteome-wide quantification studies rarely identify all potential peptides for any given protein, and targeted proteomics experiments focus on a set of peptides for the proteins of interest. Consequently, proteomics relies on the use of a limited subset of all possible peptides as proxies for protein quantitation. In this work, we evaluated the stability of the human proteotypic peptides during 21 days and trained a deep learning model to predict peptide stability directly from tryptic sequences, which together constitute a resource of broad interest to prioritize and select peptides in proteome quantification experiments.

Proteogenomic insights suggest druggable pathways in endometrial carcinoma

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Proteomic analysis by mass spectrometry of postmortem muscle protein degradation for PMI estimation: A pilot study

The determination of the postmortem interval is a topic of great forensic interest. The possibility of using new technologies has allowed the study of postmortem decay of biomolecules in the determination of PMI. Skeletal muscle proteins are promising candidates because skeletal muscle exhibits slower postmortem decay compared to other internal organs and nervous tissues, while its degradation is faster than cartilage and bone. In this pilot study, skeletal muscle tissue from pigs was degraded at two different controlled temperatures, 21 °C and 6 °C, and analysed at predefined times points: 0, 24, 48, 72, 96, and 120 h. The obtained samples were analysed by mass spectrometry proteomics approach for qualitative and quantitative evaluation of proteins and peptides. Immunoblotting validation was performed for the candidate proteins. The results obtained appeared significant and identified several proteins useful for possible postmortem interval estimation. Of these proteins, PDLIM7, TPM1, and ATP2A2 were validated by immunoblotting at a larger number of experimental points and at different temperatures. The results obtained are in agreement with those observed in similar works. In addition, the use of a mass spectrometry approach increased the number of protein species identified, providing a larger panel of proteins for PMI assessment.

DPHL v.2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins

A comprehensive pan-human spectral library is critical for biomarker discovery using mass spectrometry (MS)-based proteomics. DPHL v.1, a previous pan-human library built from 1,096 data-dependent acquisition (DDA) MS data of 16 human tissue types, allows quantifying of 10,943 proteins. Here, we generated DPHL v.2 from 1,608 DDA-MS data. The data included 586 DDA-MS data acquired from 18 tissue types, while 1,022 files were derived from DPHL v.1. DPHL v.2 thus comprises data from 24 sample types, including several cancer types (lung, breast, kidney, and prostate cancer, among others). We generated four variants of DPHL v.2 to include semi-tryptic peptides and protein isoforms. DPHL v.2 was then applied to two colorectal cancer cohorts. The numbers of identified and significantly dysregulated proteins increased by at least 21.7% and 14.2%, respectively, compared with DPHL v.1. Our findings show that the increased human proteome coverage of DPHL v.2 provides larger pools of potential protein biomarkers.

A single protein to multiple peptides: Investigation of protein-peptide correlations using targeted alpha-2-macroglobulin analysis

Recent advances in proteomics technologies have enabled the analysis of thousands of proteins in a high-throughput manner. Mass spectrometry (MS) based proteomics uses a peptide-centric approach where biological samples undergo specific proteolytic digestion and then only unique peptides are used for protein identification and quantification. Considering the fact that a single protein may have multiple unique peptides and a number of different forms, it becomes essential to understand dynamic protein-peptide relationships to ensure robust and reliable peptide-centric protein analysis. In this study, we investigated the correlation between protein concentration and corresponding unique peptide responses under a conventional proteolytic digestion condition. Protein-peptide correlation, digestion efficiency, matrix-effect, and concentration-effect were evaluated. Twelve unique peptides of alpha-2-macroglobulin (A2MG) were monitored using a targeted MS approach to acquire insights into protein-peptide dynamics. Although the peptide responses were reproducible between replicates, protein-peptide correlation was moderate in protein standards and low in complex matrices. The results suggest that reproducible peptide signal could be misleading in clinical studies and a peptide selection could dramatically change the outcome at protein level. This is the first study investigating quantitative protein-peptide correlations in biological samples using all unique peptides representing the same protein and opens a discussion on peptide-based proteomics.

An isotope dilution mass spectrometry assay to track Norovirus-like particles in vaccine process intermediates by quantifying capsid protein VP1

The coronavirus disease (COVID-19) pandemic shows the rapid pace at which vaccine development can occur which highlights the need for more fast and efficient analytical methodologies to track and characterize candidate vaccines during manufacturing and purification processes. The candidate vaccine in this work comprises plant-derived Norovirus-like particles (NVLPs) which are structures that mimic the virus but lack any infectious genetic material. Presented here is a liquid chromatography-tandem mass spectrometry (LC-MS/MS) methodology for the quantification of viral protein VP1, the main component of the NVLPs in this study. It combines isotope dilution mass spectrometry (IDMS) with multiple reaction monitoring (MRM) to quantify targeted peptides in process intermediates. Multiple MRM transitions (precursor/product ion pairs) for VP1 peptides were tested with varying MS source conditions and collision energies. Final parameter selection for quantification includes three peptides with two MRM transitions each offering maximum detection sensitivity under optimized MS conditions. For quantification, a known concentration of the isotopically labeled version of the peptides to be quantified was added into working standard solutions to serve as an internal standard (IS); calibration curves were generated for concentration of native peptide vs. the peak area ratio of native-to-isotope labeled peptide. VP1 peptides in samples were quantified with labeled versions of the peptides added at the same level as that of the standards. Peptides were quantified with limit of detection (LOD) as low as 1.0 fmol μL-1 and limit of quantitation (LOQ) as low as 2.5 fmol μL-1. NVLP preparations spiked with known quantities of either native peptides or drug substance (DS) comprising assembled NVLPs produced recoveries indicative of minimal matrix effects. Overall, we report a fast, specific, selective, and sensitive LC-MS/MS strategy to track NVLPs through the purification steps of the DS of a Norovirus candidate vaccine. To the best of our knowledge, this is the first application of an IDMS method to track virus-like particles (VLPs) produced in plants as well as measurements performed with VP1, a Norovirus capsid protein.

Proteomic Analysis of Honey: Peptide Profiling as a Novel Approach for New Zealand Mānuka (Leptospermum scoparium) Honey Authentication

New Zealand mānuka (Leptospermum scoparium) honey is a premium food product. Unfortunately, its high demand has led to "not true to label" marketed mānuka honey. Robust methods are therefore required to determine authenticity. We previously identified three unique nectar-derived proteins in mānuka honey, detected as twelve tryptic peptide markers, and hypothesized these could be used to determine authenticity. We invoked a targeted proteomic approach based on parallel reaction-monitoring (PRM) to selectively monitor relative abundance of these peptides in sixteen mānuka and twenty six non-mānuka honey samples of various floral origin. We included six tryptic peptide markers derived from three bee-derived major royal jelly proteins as potential internal standards. The twelve mānuka-specific tryptic peptide markers were present in all mānuka honeys with minor regional variation. By comparison, they had negligible presence in non-mānuka honeys. Bee-derived peptides were detected in all honeys with similar relative abundance but with sufficient variation precluding their utility as internal standards. Mānuka honeys displayed an inverse relationship between total protein content and the ratio between nectar- to bee-derived peptide abundance. This trend reveals an association between protein content on possible nectar processing time by bees. Overall, these findings demonstrate the first successful application of peptide profiling as an alternative and potentially more robust approach for mānuka honey authentication.

A Facile and High-Sensitivity Method for Determining Proteinogenic Amino Acid Enantiomers by Integrating Chiral Phosphinate Derivatizing, 31P NMR and Parallel Reaction Monitoring

Here, we have documented a new protocol to determine d/l-amino acids by derivatizing amino acids via a chiral phosphinate. (R_P)-l-Menthyl phenylphosphinate was able to bond both primary and secondary amines, as well as improve the sensitivity of analytes in MS. Eighteen pairs of amino acids were successfully labeled except for Cys which has a thiol group on the side chain, and the chirality of amino acids can be discriminated by ³¹P NMR. Seventeen pairs of amino acids were separated by a C18 column within 45 min of elution, and resolution values ranged from 2.01 to 10.76. The lowest limit of detection was 10 pM acquired at parallel reaction monitoring, in which two factors collectively contributed that the ability of protonation of phosphine oxide and the sensitivity of parallel reaction monitoring. Chiral phosphine oxides might be a promising tool in future chiral metabolomics.

Mass spectrometry-based retina proteomics

A subfield of neuroproteomics, retina proteomics has experienced a transformative growth since its inception due to methodological advances in enabling chemical, biochemical, and molecular biology techniques. This review focuses on mass spectrometry's contributions to facilitate mammalian and avian retina proteomics to catalog and quantify retinal protein expressions, determine their posttranslational modifications, as well as its applications to study the proteome of the retina in the context of biology, health and diseases, and therapy developments.

Discovery Proteomics and Absolute Protein Quantification Can Be Performed Simultaneously on an Orbitrap-Based Mass Spectrometer

Mass spectrometry (MS) has steadily moved into the forefront of quantification-centered protein research. Protein cleavage isotope dilution MS is a proven way for quantifying proteins by using an isotope-labeled analogue of a peptide fragment of the parent protein as an internal standard. Parallel reaction monitoring (PRM) has become the go-to approach for such quantification on an Orbitrap-based instrument as it is assumed that the instrument sensitivity is enhanced. We performed a comparative study on data-dependent acquisition (DDA) and PRM-based workflows to quantify egg yolk protein precursors or vitellogenins (VTGs) Aa, Ab, and C in striped bass (Morone saxatilis). VTG proportions serve as a developmental measure of egg quality, possibly changing with the environment, and have been studied as an indicator of the health of North Carolina stocks. Based on single-factor analysis of variance comparisons of mean VTG amounts across fish from the same sample groupings, our results indicate that there is no statistical difference between MS1-based and MS2-based VTG quantification. We further conclude that DDA is able to deliver both discovery data and absolute quantification data in the same experiment.

Peptidomics

Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.

Updates on the study of lysosomal protein dynamics: possibilities for the clinic

INTRODUCTION

The lysosome is the main degradative organelle of almost all mammalian cells, fulfilling important functions in macromolecule recycling, metabolism, and signaling. Lysosomal dysfunction is connected to a continuously growing number of pathologic conditions, and lysosomal proteins present potential biomarkers for a variety of diseases. Therefore, there is an increasing interest in their analysis in patient samples.

AREAS COVERED

We provide an overview of OMICs studies which identified lysosomal proteins as potential biomarkers for pathological conditions, covering proteomics, genomics, and transcriptomics approaches, identified through PubMed searches. With respect to discovery proteomics analyses, mainly lysosomal luminal and associated proteins were detected, while membrane proteins were found less frequently. Comprehensive coverage of the lysosomal proteome was only achieved by ultra-deep-coverage studies, but targeted approaches allowed for the reproducible quantification of lysosomal proteins in diverse sample types.

EXPERT OPINION

The low abundance of lysosomal proteins complicates their reproducible analysis in patient samples. Whole proteome shotgun analyses fail in many instances to cover the lysosomal proteome, which is due to under-sampling and/or a lack of sensitivity. With the current state of the art, targeted proteomics assays provide the best performance for the characterization of lysosomal proteins in patient samples.

Automated proteomic sample preparation: The key component for high throughput and quantitative mass spectrometry analysis

Sample preparation for mass spectrometry-based proteomics has many tedious and time-consuming steps that can introduce analytical errors. In particular, the steps around the proteolytic digestion of protein samples are prone to inconsistency. One route for reliable sample processing is the development and optimization of a workflow utilizing an automated liquid handling workstation. Diligent assessment of the sample type, protocol design, reagents, and incubation conditions can significantly improve the speed and consistency of preparation. When combining robust liquid chromatography-mass spectrometry with either discovery or targeted methods, automated sample preparation facilitates increased throughput and reproducible quantitation of biomarker candidates. These improvements in analysis are also essential to process the large patient cohorts necessary to validate a candidate biomarker for potential clinical use. This article reviews the steps in the workflow, optimization strategies, and known applications in clinical, pharmaceutical, and research fields that demonstrate the broad utility for improved automation of sample preparation in the proteomic field.

Mass spectrometry-based targeted proteomics for analysis of protein mutations

Cancers are caused by accumulated DNA mutations. This recognition of the central role of mutations in cancer and recent advances in next-generation sequencing, has initiated the massive screening of clinical samples and the identification of 1000s of cancer-associated gene mutations. However, proteomic analysis of the expressed mutation products lags far behind genomic (transcriptomic) analysis. With comprehensive global proteomics analysis, only a small percentage of single nucleotide variants detected by DNA and RNA sequencing have been observed as single amino acid variants due to current technical limitations. Proteomic analysis of mutations is important with the potential to advance cancer biomarker development and the discovery of new therapeutic targets for more effective disease treatment. Targeted proteomics using selected reaction monitoring (also known as multiple reaction monitoring) and parallel reaction monitoring, has emerged as a powerful tool with significant advantages over global proteomics for analysis of protein mutations in terms of detection sensitivity, quantitation accuracy and overall practicality (e.g., reliable identification and the scale of quantification). Herein we review recent advances in the targeted proteomics technology for enhancing detection sensitivity and multiplexing capability and highlight its broad biomedical applications for analysis of protein mutations in human bodily fluids, tissues, and cell lines. Furthermore, we review recent applications of top-down proteomics for analysis of protein mutations. Unlike the commonly used bottom-up proteomics which requires digestion of proteins into peptides, top-down proteomics directly analyzes intact proteins for more precise characterization of mutation isoforms. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale targeted detection and quantification of important protein mutations are discussed.

Resistance prediction in high-grade serous ovarian carcinoma with neoadjuvant chemotherapy using data-independent acquisition proteomics and an ovary-specific spectral library

High-grade serous ovarian carcinoma (HGSOC) is the most common subtype of ovarian cancer with 5-year survival rates below 40%. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is recommended for patients with advanced-stage HGSOC unsuitable for primary debulking surgery (PDS). However, about 40% of patients receiving this treatment exhibited chemoresistance of uncertain molecular mechanisms and predictability. Here, we built a high-quality ovary-specific spectral library containing 130 735 peptides and 10 696 proteins on Orbitrap instruments. Compared to a published DIA pan-human spectral library (DPHL), this spectral library provides 10% more ovary-specific and 3% more ovary-enriched proteins. This library was then applied to analyze data-independent acquisition (DIA) data of tissue samples from an HGSOC cohort treated with NACT, leading to 10 070 quantified proteins, which is 9.73% more than that with DPHL. We further established a six-protein classifier by parallel reaction monitoring (PRM) to effectively predict the resistance to additional chemotherapy after IDS (Log-rank test, P = 0.002). The classifier was validated with 57 patients from an independent clinical center (P = 0.014). Thus, we have developed an ovary-specific spectral library for targeted proteome analysis, and propose a six-protein classifier that could potentially predict chemoresistance in HGSOC patients after NACT-IDS treatment.

Proteomics as a Tool for the Study of Mitochondrial Proteome, Its Dysfunctionality and Pathological Consequences in Cardiovascular Diseases

The focus of this review is on the proteomic approaches applied to the study of the qualitative/quantitative changes in mitochondrial proteins that are related to impaired mitochondrial function and consequently different types of pathologies. Proteomic techniques developed in recent years have created a powerful tool for the characterization of both static and dynamic proteomes. They can detect protein-protein interactions and a broad repertoire of post-translation modifications that play pivotal roles in mitochondrial regulation, maintenance and proper function. Based on accumulated proteomic data, conclusions can be derived on how to proceed in disease prevention and treatment. In addition, this article will present an overview of the recently published proteomic papers that deal with the regulatory roles of post-translational modifications of mitochondrial proteins and specifically with cardiovascular diseases connected to mitochondrial dysfunction.

Resolving omicron sub-variants of SARS CoV-2 coronavirus with MALDI mass spectrometry

Mass mapping using high resolution mass spectrometry has been applied to identify and rapidly distinguish the omicron sub-variants across the BA.1-BA.5 lineages. Lineage-specific protein mutations in the surface spike protein give rise to peptide biomarkers of unique mass that can be confidently and sensitively detected with high resolution mass spectrometry. Those that are most efficiently ionised and detected within the S1 subunit in recombinant forms facilitate their detection in clinical specimens containing other SARS-CoV2 viral proteins and contaminants. A study of five dozen omicron-positive specimens, using a selected ion monitoring approach, detected peptide biomarkers for strains of BA.1, BA.2.75 and BA.4 sub-variants in 23%, 42% and 28% of samples respectively, consistent with their reported levels in the local population. The virus was confidently assigned in over 93% of omicron positive specimens. The ease of detection of the BA.2.75 variant, in particular, is of vital importance given its rapid global spread in late 2022 due to several immune evasive mutations within the receptor-binding domain.

Petroleum Hydrocarbon Catabolic Pathways as Targets for Metabolic Engineering Strategies for Enhanced Bioremediation of Crude-Oil-Contaminated Environments

Anthropogenic activities and industrial effluents are the major sources of petroleum hydrocarbon contamination in different environments. Microbe-based remediation techniques are known to be effective, inexpensive, and environmentally safe. In this review, the metabolic-target-specific pathway engineering processes used for improving the bioremediation of hydrocarbon-contaminated environments have been described. The microbiomes are characterised using environmental genomics approaches that can provide a means to determine the unique structural, functional, and metabolic pathways used by the microbial community for the degradation of contaminants. The bacterial metabolism of aromatic hydrocarbons has been explained via peripheral pathways by the catabolic actions of enzymes, such as dehydrogenases, hydrolases, oxygenases, and isomerases. We proposed that by using microbiome engineering techniques, specific pathways in an environment can be detected and manipulated as targets. Using the combination of metabolic engineering with synthetic biology, systemic biology, and evolutionary engineering approaches, highly efficient microbial strains may be utilised to facilitate the target-dependent bioprocessing and degradation of petroleum hydrocarbons. Moreover, the use of CRISPR-cas and genetic engineering methods for editing metabolic genes and modifying degradation pathways leads to the selection of recombinants that have improved degradation abilities. The idea of growing metabolically engineered microbial communities, which play a crucial role in breaking down a range of pollutants, has also been explained. However, the limitations of the in-situ implementation of genetically modified organisms pose a challenge that needs to be addressed in future research.

Targeted proteomics using stable isotope labeled protein fragments enables precise and robust determination of total apolipoprotein(a) in human plasma

Lipoprotein(a), also known as Lp(a), is an LDL-like particle composed of apolipoprotein(a) (apo(a)) bound covalently to apolipoprotein B100. Plasma concentrations of Lp(a) are highly heritable and vary widely between individuals. Elevated plasma concentration of Lp(a) is considered as an independent, causal risk factor of cardiovascular disease (CVD). Targeted mass spectrometry (LC-SRM/MS) combined with stable isotope-labeled recombinant proteins provides robust and precise quantification of proteins in the blood, making LC-SRM/MS assays appealing for monitoring plasma proteins for clinical implications. This study presents a novel quantitative approach, based on proteotypic peptides, to determine the absolute concentration of apo(a) from two microliters of plasma and qualified according to guideline requirements for targeted proteomics assays. After optimization, assay parameters such as linearity, lower limits of quantification (LLOQ), intra-assay variability (CV: 4.7%) and inter-assay repeatability (CV: 7.8%) were determined and the LC-SRM/MS results were benchmarked against a commercially available immunoassay. In summary, the measurements of an apo(a) single copy specific peptide and a kringle 4 specific peptide allow for the determination of molar concentration and relative size of apo(a) in individuals.

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.

Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection

Early detection of solid tumors through a simple screening process, such as the proteomic analysis of biofluids, has the potential to significantly alter the management and outcomes of cancers. The application of advanced targeted proteomics measurements and data analysis strategies to uniformly collected serum or plasma samples would enable longitudinal studies of cancer risk, progression, and response to therapy that have the potential to significantly reduce cancer burden in general. In this article, we describe a generalizable workflow combining robust, multiplexed targeted proteomics measurements applied to longitudinal samples from the Department of Defense Serum Repository with a Random Forest machine learning method for developing and initially evaluating the performance of candidate biomarker panels for early detection of cancers. The effectiveness of this approach was demonstrated in a cohort of 175 head and neck squamous cell carcinoma patients. The outlined protocols include methods for sample preparation, instrument analysis, and data analysis and interpretation using this workflow.

Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics

In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.

Typic: A Practical and Robust Tool to Rank Proteotypic Peptides for Targeted Proteomics

The selection of a suitable proteotypic peptide remains a challenge for designing a targeted quantitative proteomics assay. Although the criteria are well-established in the literature, the selection of these peptides is often performed in a subjective and time-consuming manner. Here, we have developed a practical and semiautomated workflow implemented in an open-source program named Typic. Typic is designed to run in a command line and a graphical interface to help selecting a list of proteotypic peptides for targeted quantitation. The tool combines the input data and downloads additional data from public repositories to produce a file per protein as output. Each output file includes relevant information to the selection of proteotypic peptides organized in a table, a colored ranking of peptides according to their potential value as targets for quantitation and auxiliary plots to assist users in the task of proteotypic peptides selection. Taken together, Typic leads to a practical and straightforward data extraction from multiple data sets, allowing the identification of most suitable proteotypic peptides based on established criteria, in an unbiased and standardized manner, ultimately leading to a more robust targeted proteomics assay.

Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways

Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)-also known as multiple reaction monitoring (MRM)-and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.

Prediction of peptide mass spectral libraries with machine learning

The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.

Aggregation of Multimodal ICE-MS Data into Joint Classifier Increases Quality of Brain Cancer Tissue Classification

Mass spectrometry fingerprinting combined with multidimensional data analysis has been proposed in surgery to determine if a biopsy sample is a tumor. In the specific case of brain tumors, it is complicated to obtain control samples, leading to model overfitting due to unbalanced sample cohorts. Usually, classifiers are trained using a single measurement regime, most notably single ion polarity, but mass range and spectral resolution could also be varied. It is known that lipid groups differ significantly in their ability to produce positive or negative ions; hence, using only one polarity significantly restricts the chemical space available for sample discrimination purposes. In this work, we have developed an approach employing mass spectrometry data obtained by eight different regimes of measurement simultaneously. Regime-specific classifiers are trained, then a mixture of experts techniques based on voting or mean probability is used to aggregate predictions of all trained classifiers and assign a class to the whole sample. The aggregated classifiers have shown a much better performance than any of the single-regime classifiers and help significantly reduce the effect of an unbalanced dataset without any augmentation.