A Mass Spectrometry Strategy for Protein Quantification Based on the Differential Alkylation of Cysteines Using Iodoacetamide and Acrylamide.

Mass spectrometry has become the most prominent yet evolving technology in quantitative proteomics. Today, a number of label-free and label-based approaches are available for the relative and absolute quantification of proteins and peptides. However, the label-based methods rely solely on the employment of stable isotopes, which are expensive and often limited in availability. Here we propose a label-based quantification strategy, where the mass difference is identified by the differential alkylation of cysteines using iodoacetamide and acrylamide. The alkylation reactions were performed under identical experimental conditions; therefore, the method can be easily integrated into standard proteomic workflows. Using high-resolution mass spectrometry, the feasibility of this approach was assessed with a set of tryptic peptides of human serum albumin. Several critical questions, such as the efficiency of labeling and the effect of the differential alkylation on the peptide retention and fragmentation, were addressed. The concentration of the quality control samples calculated against the calibration curves were within the ±20% acceptance range. It was also demonstrated that heavy labeled peptides exhibit a similar extraction recovery and matrix effect to light ones. Consequently, the approach presented here may be a viable and cost-effective alternative of stable isotope labeling strategies for the quantification of cysteine-containing proteins.

Insights into modifiers effects in differential mobility spectrometry: A data science approach for metabolomics and peptidomics.

Utilizing a data-driven approach, this study investigates modifier effects on compensation voltage in differential mobility spectrometry-mass spectrometry (DMS-MS) for metabolites and peptides. Our analysis uncovers specific factors causing signal suppression in small molecules and pinpoints both signal suppression mechanisms and the analytes involved. In peptides, machine learning models discern a relationship between molecular weight, topological polar surface area, peptide charge, and proton transfer-induced signal suppression. The models exhibit robust performance, offering valuable insights for the application of DMS to metabolites and tryptic peptides analysis by DMS-MS.

A Workflow for Improved Analysis of Cross-Linking Mass Spectrometry Data Integrating Parallel Accumulation-Serial Fragmentation with MeroX and Skyline.

Cross-linking mass spectrometry (XL-MS) has evolved into a pivotal technique for probing protein interactions. This study describes the implementation of Parallel Accumulation-Serial Fragmentation (PASEF) on timsTOF instruments, enhancing the detection and analysis of protein interactions by XL-MS. Addressing the challenges in XL-MS, such as the interpretation of complex spectra, low abundant cross-linked peptides, and a data acquisition bias, our current study integrates a peptide-centric approach for the analysis of XL-MS data and presents the foundation for integrating data-independent acquisition (DIA) in XL-MS with a vendor-neutral and open-source platform. A novel workflow is described for processing data-dependent acquisition (DDA) of PASEF-derived information. For this, software by Bruker Daltonics is used, enabling the conversion of these data into a format that is compatible with MeroX and Skyline software tools. Our approach significantly improves the identification of cross-linked products from complex mixtures, allowing the XL-MS community to overcome current analytical limitations.

A Module for Analyzing Interactomes via APEX-MS Integrated into PatternLab for Proteomics.

Proximity labeling techniques, such as APEX-MS, provide valuable insights into proximal interactome mapping; however, the verification of biotinylated peptides is not straightforward. With this as motivation, we present a new module integrated into PatternLab for proteomics to enable APEX-MS data interpretation by targeting diagnostic fragment ions associated with APEX modifications. We reanalyzed a previously published APEX-MS data set and report a significant number of biotinylated peptides and, consequently, a confident set of proximal proteins. As the module is part of the widely adopted PatternLab for proteomics software suite, it offers users a comprehensive, easy, and integrated solution for data analysis. Given the broad utility of the APEX-MS technique in various biological contexts, we anticipate that our module will be a valuable asset to researchers, facilitating and enhancing interactome studies. PatternLab's APEX, including a usage protocol, is available at http://patternlabforproteomics.org/apex.

Improved assay development of pharmaceutical modalities using feedback-controlled liquid chromatography optimization.

Development of meaningful and reliable analytical assays in the (bio)pharmaceutical industry can often be challenging, involving tedious trial and error experimentation. In this work, an automated analytical workflow using an AI-based algorithm for streamlined method development and optimization is presented. Chromatographic methods are developed and optimized from start to finish by a feedback-controlled modeling approach using readily available LC instrumentation and software technologies, bypassing manual user intervention. With the use of such tools, the time requirement of the analyst is drastically minimized in the development of a method. Herein key insights on chromatography system control, automatic optimization of mobile phase conditions, and final separation landscape for challenging multicomponent mixtures are presented (e.g., small molecules drug, peptides, proteins, and vaccine products) showcased by a detailed comparison of a chiral method development process. The work presented here illustrates the power of modern chromatography instrumentation and AI-based software to accelerate the development and deployment of new separation assays across (bio)pharmaceutical modalities while yielding substantial cost-savings, method robustness, and fast analytical turnaround.

Improved Mass Accuracy and Precision for Multi-Attribute Methods Using a New Internally Calibrated High Resolution Orbitrap Mass Detector.

In the development of biotherapeutics, a thorough understanding of a molecule's product quality attributes (PQAs) and their effect on structure-function relationships and long-term stability is essential for ensuring the safety and efficacy of the product. First published in 2015, the multi-attribute method (MAM), based on LC-MS peptide mapping and automation principles, can be used to support biotherapeutic process and product development. The MAM provides simultaneous site-specific detection, identification, quantitation, and quality control monitoring of selected PQAs. In this article, a low-maintenance MAM-ready mass detector with a small footprint was evaluated for its ability to monitor PQAs on proteolytically digested proteins with high mass accuracy and precision. Optimized source parameters enable robust relative quantitation of attributes with high sensitivity and minimal in-source fragmentation. A combination of a built-in one-point mass calibration procedure prior to data acquisition and Scan-to-Scan on-the-fly mass correction allows monitoring of most peptides for at least 54 days with sub-1 ppm mass accuracies at high-resolution (180,000 at m/z 200). This enables the use of <3 ppm mass tolerances for peptide monitoring, supporting high method specificity and robustness. LC-MS based MAM data from this instrument compares well to data collected by earlier MAM systems and conventional HPLC profile-based drug substance release assays.

Synthetic Knockout Protein Standard for Evaluating Interference in Tandem Mass Tag-Based Proteomics.

Isobaric labeling is widely used for unbiased, proteome-wide studies, and it provides several advantages, such as fewer missing values among samples and higher quantitative precision. However, ion interference may lead to compressed or distorted observed ratios due to the coelution and coanalysis of peptides. Here, we introduced a synthetic KnockOut standard (sKO) for evaluating interference in tandem mass tags-based proteomics. sKO is made by mixing TMTpro-labeled tryptic peptides derived from four nonhuman proteins and a whole human proteome as background at different proportions. We showcased the utility of the sKO standard by exploring ion interference at different peptide concentrations (up to a 30-fold change in abundance) and using a variety of mass spectrometer data acquisition strategies. We also demonstrated that the sKO standard could provide valuable information for the rational design of acquisition strategies to achieve optimal data quality and discussed its potential applications for high-throughput proteomics workflows development.

Investigation of liquid chromatography-mass spectrometry analysis of a peptide aldehyde SJA6017 with identifying its hemiacetal, gem-diol, and enol ether.

The quantitative analysis of SJA6017, a peptide aldehyde inhibitor of calpain (Calpain Inhibitor VI), has encountered challenges in preclinical drug studies. The complex reverse-phase HPLC chromatographic behavior exhibits two peaks, each containing multiple species. An liquid chromatography-mass spectrometry (LC-MS/MS) study proposed an explanation for this phenomenon, caused by the amide aldehyde structure of SJA6017. Four chemical species corresponding to the two HPLC peaks have been identified as SJA6017 and its methyl hemiacetal, methyl enol ether, and gem-diol. In many instances of preclinical studies, methanol is favored as a substitute for DMSO. The hemiacetal is formed when the amide-activated peptide aldehyde reacts with methanol, which can then be further dehydrated in the mass spectrometer ion source under high temperature to form the methyl enol ether. The hemiacetal and gem-diol can also be decomposed to SJA6017 in the ion source. Additionally, the amide-activated peptide aldehyde can easily hydrate to the gem-diol of SJA6017 during sample incubation or sample preparation. The hemiacetal and gem-diol of SJA6017 are stable enough to have different retention times in the liquid chromatography, which explains why SJA6017 appears as two peaks, each containing multiple species. An LC-MS/MS tandem quadrupole mass spectrometer quantitative analysis method is proposed, enabling the analysis of these types of samples. This work serves as both an illustrative example and a cautionary note for mass analysis, sample incubations, and sample preparations involving compounds of peptide aldehyde, including similar aldehyde-containing metabolites, especially when methanol is present. This study provides the information needed to understand peptide aldehyde behavior at various steps of preclinical in vitro studies in the presence of methanol. It has assisted in the development of the SJA6017 bioanalysis method and will also aid in the development of bioanalysis methods for similar peptide aldehydes.

Proteogenomic Gene Structure Validation in the Pineapple Genome.

MD2 pineapple (Ananas comosus) is the second most important tropical crop that preserves crassulacean acid metabolism (CAM), which has high water-use efficiency and is fast becoming the most consumed fresh fruit worldwide. Despite the significance of environmental efficiency and popularity, until very recently, its genome sequence has not been determined and a high-quality annotated proteome has not been available. Here, we have undertaken a pilot proteogenomic study, analyzing the proteome of MD2 pineapple leaves using liquid chromatography-mass spectrometry (LC-MS/MS), which validates 1781 predicted proteins in the annotated F153 (V3) genome. In addition, a further 603 peptide identifications are found that map exclusively to an independent MD2 transcriptome-derived database but are not found in the standard F153 (V3) annotated proteome. Peptide identifications derived from these MD2 transcripts are also cross-referenced to a more recent and complete MD2 genome annotation, resulting in 402 nonoverlapping peptides, which in turn support 30 high-quality gene candidates novel to both pineapple genomes. Many of the validated F153 (V3) genes are also supported by an independent proteomics data set collected for an ornamental pineapple variety. The contigs and peptides have been mapped to the current F153 genome build and are available as bed files to display a custom gene track on the Ensembl Plants region viewer. These analyses add to the knowledge of experimentally validated pineapple genes and demonstrate the utility of transcript-derived proteomics to discover both novel genes and genetic structure in a plant genome, adding value to its annotation.

Inserting Pre-analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics with Sample Multiplexing.

GoDig, a platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standards, is a powerful, flexible, and easy-to-use method that uses tandem mass tags to increase sample throughput up to 18-fold relative to label-free methods. Though the protein-level success rates of GoDig are high, the peptide-level success rates are more limited, hampering assays of harder-to-quantify proteins and site-specific phenomena. To guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates. In this mode, two or more chromatographic priming runs are automatically performed to improve the accuracy and precision of target elution orders, followed by analytical runs which quantify targets. Using priming runs, success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy expression profiles across four human cell lines.

Improved Detection of Tryptic Peptides from Tissue Sections Using Desorption Electrospray Ionization Mass Spectrometry Imaging.

DESI-MSI is an ambient ionization technique used frequently for the detection of lipids, small molecules, and drug targets. Until recently, DESI had only limited use for the detection of proteins and peptides due to the setup and needs around deconvolution of data resulting in a small number of species being detected at lower spatial resolution. There are known differences in the ion species detected using DESI and MALDI for nonpeptide molecules, and here, we identify that this extends to proteomic species. DESI MS images were obtained for tissue sections of mouse and rat brain using a precommercial heated inlet (approximately 450 °C) to the mass spectrometer. Ion mobility separation resolved spectral overlap of peptide ions and significantly improved the detection of multiply charged species. The images acquired were of pixel size 100 µm (rat brain) and 50 µm (mouse brain), respectively. Observed tryptic peptides were filtered against proteomic target lists, generated by LC-MS, enabling tentative protein assignment for each peptide ion image. Precise localizations of peptide ions identified by DESI and MALDI were found to be comparable. Some spatially localized peptides ions were observed in DESI that were not found in the MALDI replicates, typically, multiply charged species with a low mass to charge ratio. This method demonstrates the potential of DESI-MSI to detect large numbers of tryptic peptides from tissue sections with enhanced spatial resolution when compared to previous DESI-MSI studies.

Mass spectrometry imaging protocol for spatial mapping of lipids, N-glycans and peptides in murine lung tissue.

RATIONALE: The application of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to murine lungs is challenging due to the spongy nature of the tissue. Lungs consist of interconnected air sacs (alveoli) lined by a single layer of flattened epithelial cells, which requires inflation to maintain its natural structure. Therefore, a protocol that is compatible with both lung instillation and high spatial resolution is essential to enable multi-omic studies on murine lung disease models using MALDI-MSI. METHODS AND RESULTS: To maintain the structural integrity of the tissue, murine lungs were inflated with 8% (w/v) gelatin for lipid MSI of fresh frozen tissues or 4% (v/v) paraformaldehyde neutral buffer for N-glycan and peptide MSI of FFPE tissues. Tissues were sectioned and prepared for enzymatic digestion and/or matrix deposition. Glycerol-free PNGase F was applied for N-glycan MSI, while Trypsin Gold was applied for peptide MSI using the iMatrixSpray and ImagePrep Station, respectively. For lipid, N-glycan and peptide MSI, α-cyano-4-hydroxycinnamic acid matrix was deposited using the iMatrixSpray. MS data were acquired with 20 µm spatial resolution using a timsTOF fleX MS instrument followed by MS fragmentation of lipids, N-glycans and peptides. For lipid MSI, trapped ion mobility spectrometry was used to separate isomeric/isobaric lipid species. SCiLS™ Lab was used to visualize all MSI data. For analyte identification, MetaboScape®, GlycoMod and Mascot were used to annotate MS fragmentation spectra of lipids, N-glycans and tryptic peptides, respectively. CONCLUSIONS: Our protocol provides instructions on sample preparation for high spatial resolution MALDI-MSI, MS/MS data acquisition and lipid, N-glycan and peptide annotation and identification from murine lungs. This protocol will allow non-biased analyses of diseased lungs from preclinical murine models and provide further insight into disease models.

Development of an isotope dilution mass spectrometry assay for the quantification of insulin based on signature peptide analysis.

An isotope dilution mass spectrometry (IDMS) method that involves peptide-based protein analysis was developed to accurately quantify insulin. In this study, a signature peptide (GFFYTPK) obtained from tryptic digestion of insulin was selected as a surrogate for insulin. Then, the optimal conditions for signature peptide analysis through mass spectrometry detection and enzymatic digestion were determined. The analytical performance of this method was assessed and validated using porcine insulin-certified reference material. The linear range of the insulin calibration curve ranged from 0.05 ~ 2 mass ratios, with recoveries ranging from 96.15 to approximately 101.15%. The limit of detection was 0.19 ng/mL, and the limit of quantification was 0.63 ng/mL. The quantitative results corresponded well with a certified value that was obtained from measuring a porcine insulin reference material with amino acid-based IDMS. In addition, the target peptide GFFYTPK can be found in other species of insulin. This method was also applied for the quantification of human insulin-certified reference material. Finally, we applied the method to quantify the concentrations of simulated serum insulin. These findings suggested that this signature peptide-based IDMS approach can accurately quantify insulin levels, can assign a certified value to insulin reference materials, and has the potential to quantify serum insulin with traceable measurements.

Albumen and Yolk Plasma Peptidomics for the Identification and Quantitation of Bioactive Molecules and the Quality Control of Hen Egg Products.

Hen eggs and the corresponding food products are essential components of human diet. In addition to supplying basic nutrients, they contain functional peptides that are released in vivo within the intact raw material following physiological proteolytic events affecting specific proteins or derive from technological processing of albumen and yolk fractions as a result of the dedicated use of proteases from plant and microbial sources. Besides their potential importance for functional applications, peptides released under physiological conditions in intact egg can be used as markers of product storage and deterioration. Therefore, characterization and quantitation of peptides in egg and egg-derived products can be used to implement evaluation of potential bioactivities as well as to assess food product qualitative characteristics. Here, we provide dedicated information on extraction, identification, and quantitative analysis of peptides from albumen and yolk plasma; nano-liquid chromatography-mass spectrometry combined with bioinformatic analysis of resulting raw data by different software tools allowed to assign molecules based on database searching and to evaluate their relative quantity in different samples.

Deep Learning-Assisted Analysis of Immunopeptidomics Data.

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.

Physicochemical and Sensory Characterization of Whey Protein-Enriched Semihard Cheese.

In view of potential future changes of German food legislation with regard to cheese product quality parameters, this study aimed to evaluate the quality of whey protein-enriched semihard cheese (WPEC). Model WPEC was produced in a pilot plant and on an industrial scale by adding defined amounts of high-heat (HH) milk to the cheese milk and comprehensively analyzed during cheese processing. The dry matter, total protein, pure protein, fat, and sodium chloride content of six-week ripened cheese samples were not significantly different (p < 0.05) when the technologically necessary heating of the curd was adapted to the amount of HH milk. However, the ripening, firmness, and melting behavior of WPEC was different compared to cheese without HH milk. During ripening, no formation of whey protein peptides was observed, but differences in the amount of some bitter peptides deriving from the casein fraction were found. Sensory data suggested a slightly more bitter taste perception by the panelists for the WPEC. Further technological adjustments are recommended to obtain marketable WPEC.

Addressing common challenges of biotherapeutic protein peptide mapping using recombinant trypsin.

Peptide mapping is the key method for characterization of primary structure of biotherapeutic proteins. This method relies on digestion of proteins into peptides that are then analyzed for amino acid sequence and post-translational modifications. Owing to its high activity and cleavage specificity, trypsin is the protease of choice for peptide mapping. In this study, we investigated critical requirements of peptide mapping and how trypsin affects these requirements. We found that the commonly used MS-grade trypsins contained non-specific, chymotryptic-like cleavage activity causing generation of semi-tryptic peptides and degradation of tryptic-specific peptides. Furthermore, MS-grade trypsins contained pre-existing autoproteolytic peptides and, moreover, additional autoproteolytic peptides were resulting from prominent autoproteolysis during digestion. In our long-standing quest to improve trypsin performance, we developed novel recombinant trypsin and evaluated whether it could address major trypsin drawbacks in peptide mapping. The study showed that the novel trypsin was free of detectable non-specific cleavage activity, had negligible level of autoproteolysis and maintained high activity over the course of digestion reaction. Taking advantage of the novel trypsin advanced properties, especially high cleavage specificity, we established the application for use of large trypsin quantities to digest proteolytically resistant protein sites without negative side effects. We also tested trypsin/Lys-C mix comprising the novel trypsin and showed elimination of non-specific cleavages observed in the digests with the commonly used trypsins. In addition, the improved features of the novel trypsin allowed us to establish the method for accurate and efficient non-enzymatic PTM analysis in biotherapeutic proteins.

SpectiCal: m/z Calibration of MS2 Peptide Spectra Using Known Low Mass Ions.

Most tandem mass spectrometry fragmentation spectra have small calibration errors that can lead to suboptimal interpretation and annotation. We developed SpectiCal, a software tool that can read mzML files from data-dependent acquisition proteomics experiments in parallel, compute m/z calibrations for each file prior to identification analysis based on known low-mass ions, and produce information about frequently observed peaks and their explanations. Using calibration coefficients, the data can be corrected to generate new calibrated mzML files. SpectiCal was tested using five public data sets, creating a table of commonly observed low-mass ions and their identifications. Information about the calibration and individual peaks is written in PDF and TSV files. This includes information for each peak, such as the number of runs in which it appears, the percentage of spectra in which it appears, and a plot of the aggregated region surrounding each peak. SpectiCal can be used to compute MS run calibrations, examine MS runs for artifacts that might hinder downstream analysis, and generate tables of detected low-mass ions for further analysis. SpectiCal is freely available at https://github.com/PlantProteomes/SpectiCal.

Proteomic Analysis of Human Saliva via Solid-Phase Microextraction Coupled with Liquid Chromatography-Mass Spectrometry.

Proteomics of human saliva samples was achieved for the first time via biocompatible solid-phase microextraction (bio-SPME) devices. Upon introduction of a porogen to a conventional C18 coating, porous C18/polyacrylonitrile (PAN) SPME blades were able to extract peptides up to 3.0 kDa and more peptides than commercial SPME blades. Following Trypsin digestion, salivary proteomic analysis was achieved via SPME-LC-MS/MS. Seven endogenous proteins were consistently identified in all saliva samples via bio-SPME. Taking advantage of this strategy, untargeted peptidomics was applied for the comparison of saliva samples between healthy and SARS-CoV-2 positive individuals. The results showed clear peptidomic differences between the viral and healthy saliva samples. This proof-of-concept study demonstrates the potential of bio-SPME-LC-MS/MS for peptidomics and proteomics in biomedical applications.

Evaluation of different isotope dilution mass spectrometry strategies for the characterization of naturally abundant and isotopically labelled peptide standards.

Natural abundance and isotopically labelled tryptic peptides are routinely employed as standards in quantitative proteomics. The certification of the peptide content is usually carried out by amino acid analysis using isotope dilution mass spectrometry (IDMS) after the acid hydrolysis of the peptide. For the validation and traceability of the amino acid analysis procedure, expensive certified peptides must be employed. In this work we evaluate different IDMS alternatives which will reduce the amount of certified peptide required for validation of the amino acid analysis procedure. In this context, the characterization of both natural and isotopically labelled synthetic angiotensin I peptides was carried out. First, we applied a fast procedure for peptide hydrolysis based on microwave-assisted digestion and employed two certified peptide reference materials SRM 998 angiotensin I and CRM 6901-b C-peptide for validation of the hydrolysis procedure. The amino acids proline, leucine, isoleucine, valine, tyrosine, arginine and phenylalanine were evaluated for their suitability for peptide certification by IDMS by both liquid chromatography with tandem mass spectrometry (LC-MS/MS) and gas chromatography with mass spectrometry (GC)-MS/MS. Then, natural angiotensin I and 13C1-labelled angiotensin I were synthesized in-house and purified by preparative liquid chromatography. The concentration of the 13C1-labelled angiotensin I peptide was established by reverse IDMS in its native form using SRM 998 angiotensin I as reference. The concentration of the natural synthesized peptide was determined by IDMS both using the 13C1-labelled peptide in its native form and by amino acid analysis showing comparable results. Finally, the synthetic naturally abundant angiotensin I peptide was employed as "in-house" standard for the validation of subsequent peptide characterization procedures. Therefore, the novelty of this work relies on, first, the development of a faster hydrolysis procedure assisted by focused microwaves, providing complete hydrolysis in 150 min, and secondly, a validation strategy combining GC-MS and LC-MS/MS that allowed us to certify the purity of an in-house-synthesized peptide standard that can be employed as quality control in further experiments.