Liquid Chromatography-mass Spectrometry Workflows Research Articles

OptiPRM: A Targeted Immunopeptidomics LC-MS Workflow With Ultra-High Sensitivity for the Detection of Mutation-Derived Tumor Neoepitopes From Limited Input Material

Personalized cancer immunotherapies such as therapeutic vaccines and adoptive transfer of T cell receptor (TCR)-transgenic T cells rely on the presentation of tumor-specific peptides by human leukocyte antigen (HLA) class I molecules to cytotoxic T cells. Such neoepitopes can for example arise from somatic mutations and their identification is crucial for the rational design of new therapeutic interventions. Liquid chromatography mass spectrometry (LC-MS)-based immunopeptidomics is the only method to directly prove actual peptide presentation and we have developed a parameter optimization workflow to tune targeted assays for maximum detection sensitivity on a per peptide basis, termed optiPRM. Optimization of collision energy using optiPRM allows for improved detection of low abundant peptides that are very hard to detect using standard parameters. Applying this to immunopeptidomics, we detected a neoepitope in a patient-derived xenograft (PDX) from as little as 2.5×106 cells input. Application of the workflow on small patient tumor samples allowed for the detection of five mutation-derived neoepitopes in three patients. One neoepitope was confirmed to be recognized by patient T cells. In conclusion, optiPRM, a targeted MS workflow reaching ultra-high sensitivity by per peptide parameter optimization, which makes the identification of actionable neoepitopes possible from sample sizes usually available in the clinic.

MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

Development of a Rapid Adeno-Associated Virus (AAV) Identity Testing Platform through Comprehensive Intact Mass Analysis of Full-Length AAV Capsid Proteins.

Adeno-associated viruses (AAVs) are commonly used as vectors for the delivery of gene therapy targets. Characterization of AAV capsid proteins (VPs) and their post-translational modifications (PTMs) have become a critical attribute monitored to evaluate product quality. Liquid chromatography-mass spectrometry (LC-MS) analysis of intact AAV VPs provides both quick and reliable serotype identification as well as proteoform information on each VP. Incorporating these analytical strategies into rapid good manufacturing practice (GMP)-compliant workflows containing robust, but simplified, data processing methods is necessary to ensure effective product quality control (QC) during production. Here, we present a GMP-compliant LC-MS workflow for the rapid identification and in-depth characterization of AAVs. Hydrophilic interaction liquid chromatography (HILIC) MS with difluoroacetic acid as a mobile phase modifier is utilized to achieve the intact separation and identification of AAV VPs and their potential proteoforms. Peptide mapping is performed to confirm PTMs identified during intact VP analysis and for in-depth PTM characterization. The intact separations platform is then incorporated into a data processing workflow developed using GMP-compliant software capable of rapid AAV serotype identification and, if desired, specific serotype PTM monitoring and characterization. Such a platform provides product QC capabilities that are easily accessible in a regulatory setting.

LipidOA: A Machine-Learning and Prior-Knowledge-Based Tool for Structural Annotation of Glycerophospholipids.

The Paternò-Büchi (PB) reaction is a carbon-carbon double bond (C═C)-specific derivatization reaction that can be used to pinpoint the location(s) of C═C(s) in unsaturated lipids and quantitate the location of isomers when coupled with tandem mass spectrometry (MS/MS). As the data of PB-MS/MS are increasingly generated, the establishment of a corresponding data analysis tool is highly needed. Herein, LipidOA, a machine-learning and prior-knowledge-based data analysis tool, is developed to analyze PB-MS/MS data generated by liquid chromatography-mass spectrometry workflows. LipidOA consists of four key functional modules to realize an annotation of glycerophospholipid (GPL) structures at the fatty acyl-specific C═C location level. These include (1) data preprocessing, (2) picking C═C diagnostic ions, (3) de novo annotation, and (4) result ranking. Importantly, in the result-ranking module, the reliability of structural annotation is sorted via the use of a machine learning classifier and comparison to the total fatty acid database generated from the same sample. LipidOA is trained and validated by four PB-MS/MS data sets acquired using different PB reagents on mass spectrometers of different resolutions and of different biological samples. Overall, LipidOA provides high precision (higher than 0.9) and a wide coverage for structural annotations of GPLs. These results demonstrate that LipidOA can be used as a robust and flexible tool for annotating PB-MS/MS data collected under different experimental conditions using different lipidomic workflows.

Precise O-Glycosylation Site Localization of CD24Fc by LC-MS Workflows.

CD24Fc is a homodimeric recombinant Fc-fusion protein comprised of human CD24 connected to immunoglobulin G1 (IgG1) Fc fragment. CD24 is heavily glycosylated, and its biological function is considered mainly mediated by its glycosylation. Identification of the O-glycosylation sites would facilitate an in-depth understanding of the functional role of O-glycans in CD24. However, the presence of clustered mucin-type O-glycans together with N-glycans makes the determination of O-glycosylation sites and abundance very challenging. In this study, two sets of liquid chromatography-mass spectrometry (LC-MS) workflows were developed for the comprehensive characterization of O-glycosylation in CD24: (1) Fractionation and collision-induced dissociation (CID) workflow involving multienzyme digestion, fractionation, OpeRATOR/SialEXO digestion, and CID analysis; (2) Direct OpeRATOR/SialEXO digestion followed by electron-transfer/higher-energy collision dissociation (EThcD) analysis. The precise O-glycosylation sites were identified in CD24 for the first time, and the site occupancy was assessed. A total of 12 O-glycosylation sites were identified. Seven glycosylation sites were identified by both workflows, and five additional sites were identified only by the EThcD workflow. The predominant O-glycosylation site in CD24 was Thr25 followed by Thr15. The CID workflow provided an overall relative quantitation of O-glycoforms at the CD24 level and site localization for singly O-glycosylated peptides. The EThcD workflow directly identified glycosylation sites by tandem mass spectrometry (MS/MS) for singly, doubly, and triply O-glycosylated peptides. Together, both workflows validated each other's results and can be applied to a complex mucin-type O-glycosylation site analysis of other glycoproteins and Fc-fusion therapeutics.

MD-UPLC-MS/MS: Next Generation of mAb Characterization by Multidimensional Ultraperformance Liquid Chromatography-Mass Spectrometry and Parallel On-Column LysC and Trypsin Digestion.

For the past few years, multidimensional liquid chromatography-mass spectrometry (LC-MS) systems have been commonly used to characterize post-translational modifications (PTMs) of therapeutic antibodies (mAbs). In most cases, this is performed by fractionation of charge variants by ion-exchange chromatography and subsequent online LC-MS peptide mapping analysis. In this study, we developed a multidimensional ultra-performance-liquid-chromatography-mass spectrometry system (mD-UPLC-MS/MS) for PTM characterization and quantification, allowing both rapid analysis and decreased risk of artificial modifications during sample preparation. We implemented UPLC columns for peptide mapping analysis, facilitating the linkage between mD-LC and routine LC-MS workflows. Furthermore, the introduced system incorporates a novel in-parallel trypsin and LysC on-column digestion setup, followed by a combined peptide mapping analysis. This parallel digestion with different enzymes enhances characterization by generating two distinct peptides. Using this approach, a low retentive ethylene oxide adduct of a bispecific antibody was successfully characterized within this study. In summary, our approach allows versatile and rapid analysis of PTMs, enabling efficient characterization of therapeutic molecules.

Molecular networking and collision cross section prediction for structural isomer and unknown compound identification in plant metabolomics: a case study applied to Zhanthoxylum heitzii extracts

Mass spectrometry-based plant metabolomics allow large-scale analysis of a wide range of compounds and the discovery of potential new active metabolites with minimal sample preparation. Despite recent tools for molecular networking, many metabolites remain unknown. Our objective is to show the complementarity of collision cross section (CCS) measurements and calculations for metabolite annotation in a real case study. Thus, a systematic and high-throughput investigation of root, bark, branch, and leaf of the Gabonese plant Zhanthoxylum heitzii was performed through ultra-high performance liquid chromatography high-resolution tandem mass spectrometry (UHPLC-QTOF/MS). A feature-based molecular network (FBMN) was employed to study the distribution of metabolites in the organs of the plants and discover potential new components. In total, 143 metabolites belonging to the family of alkaloids, lignans, polyphenols, fatty acids, and amino acids were detected and a semi-quantitative analysis in the different organs was performed. A large proportion of medical plant phytochemicals is often characterized by isomerism and, in the absence of reference compounds, an additional dimension of gas phase separation can result in improvements to both quantitation and compound annotation. The inclusion of ion mobility in the ultra-high performance liquid chromatography mass spectrometry workflow (UHPLC-IMS-MS) has been used to collect experimental CCS values in nitrogen and helium (CCSN2 and CCSHe) of Zhanthoxylum heitzii features. Due to a lack of reference data, the investigation of predicted collision cross section has enabled comparison with the experimental values, helping in dereplication and isomer identification. Moreover, in combination with mass spectra interpretation, the comparison of experimental and theoretical CCS values allowed annotation of unknown features. The study represents a practical example of the potential of modern mass spectrometry strategies in the identification of medicinal plant phytochemical components.

Localization of Intrachain Modifications in Bacterial Lipids Via Radical-Directed Dissociation.

Intrachain modifications of membrane glycerophospholipids (GPLs) due to formation of the carbon-carbon double bond (C═C), cyclopropane ring, and methyl branching are crucial for bacterial membrane homeostasis. Conventional collision-induced dissociation (CID) of even-electron ions of GPL favors charge-directed fragmentation channels, and thus little structurally informative fragments can be detected for locating intrachain modifications. In this study, we report a radical-directed dissociation (RDD) approach for characterization of the intrachain modifications within phosphoethanolamines (PEs), a major lipid component in bacterial membrane. In this method, a radical precursor that can produce benzyl or pyridine methyl radical upon low-energy CID at high efficiency is conjugated onto the amine group of PEs. The carbon-centered radical ions subsequently initiate RDD along the fatty acyl chain, producing fragment patterns key to the assignment and localization of intrachain modifications including C═C, cyclopropane rings, and methyl branching. Besides intrachain fragmentation, RDD on the glycerol backbone produces fatty acyl loss as radicals, allowing one to identify the fatty acyl chain composition of PE. Moreover, RDD of lyso-PEs produces radical losses for distinguishing the sn-isomers. The above RDD approach has been incorporated onto a liquid chromatography-mass spectrometry workflow and applied for the analysis of lipid extracts from Escherichia coli and Bacillus subtilis.

Characterization of Proteinaceous Particles in Monoclonal Antibody Drug Products Using Mass Spectrometry.

In recent years, monoclonal antibodies (mAb) have become one of the most important classes of therapeutic proteins. Among many of the quality attributes monitored and controlled throughout therapeutic antibody development, particulate matter is one of the critical quality attributes (CQAs) for drug products. Visible and subvisible particulates in drug products may pose safety and immunogenicity risks to patients and therefore are tightly controlled and regulated. Characterization of the particle composition in drug products is essential to understand the origin of particulates and their mechanism of formation. In this study, we developed a liquid chromatography-mass spectrometry (LC-MS) based method and integrated it into the typical particulate characterization workflow to identify and quantify the composition of proteinaceous particles isolated from a therapeutic mAb drug product. The LC-MS workflow provides a useful tool to study particle formation and monitor the protein composition of particulates during therapeutic mAb development.

Characterization of Synthetic Peptide Therapeutics Using Liquid Chromatography-Mass Spectrometry: Challenges, Solutions, Pitfalls, and Future Perspectives.

Synthetic peptides represent an important and expanding class of therapeutics. Despite having a relatively small size as compared to monoclonal antibodies and other proteins, synthetic peptides are subject to many complex structural modifications originating from the starting materials, manufacturing process, and storage conditions. Although mass spectrometry has been increasingly used to characterize impurities of synthetic peptides, systematic review of this field is scarce. In this paper, an overview of the impurities in synthetic peptide therapeutics is provided in the context of how the knowledge from detailed characterization of the impurities using liquid chromatography-mass spectrometry (LC-MS) can be used to develop the manufacturing process and control strategy for synthetic peptide therapeutics following the critical quality attribute (CQA)-driven and risk-based approach. The thresholds for identifying and controlling the impurities are discussed based on currently available regulatory guidance. Specific LC-MS techniques for identification of various types of impurities based on their structural characteristics are discussed with the focus on structural isomers and stereoisomers (i.e., peptide epimers). Absolute and relative quantitation methods for the peptide impurities are critiqued. Potential pitfalls in characterization of synthetic peptide therapeutics using LC-MS are discussed. Finally, a systematic LC-MS workflow for characterizing the impurities in synthetic peptide therapeutics is proposed, and future perspectives on applying emerging LC-MS techniques to address the remaining challenges in the development of synthetic peptide therapeutics are presented.

Resolving Modifications on Sphingoid Base and N-Acyl Chain of Sphingomyelin Lipids in Complex Lipid Extracts.

Sphingomyelins (SMs) are an essential class of lipids widely existing in different organisms. The sphingoid base and N-acyl chain are two building blocks which can undergo different types of modifications during lipogenesis, including desaturation, hydroxylation, and methyl branching. Current lipidomic analysis methods cannot provide detailed information on these structural motifs. Herein, we developed a tandem mass spectrometric method by harnessing radical-directed dissociation (RDD) from collision-induced dissociation (CID) of the bicarbonate anion adduct of SM ([M + HCO3]-). A major RDD channel produced a high-abundance fragment carrying the intact N-acyl chain, termed as "N-acyl fragment", allowing the assignment of the sphingoid base/N-acyl composition and relative quantitation of compositional isomers of SM at high sensitivity. RDD also produced intrachain fragments in lower abundances, which helped localization of methyl branching and hydroxylation in SM. The acetone Paternò-Büchi (PB) reaction was found to be capable of derivatizing the Δ4 carbon-carbon double bond (C═C) in sphingosine (SPH) base and producing C═C diagnostic ions upon CID, albeit at much lower efficiencies than those of the isolated C═C in alkyl chains. A liquid chromatography-mass spectrometry workflow was developed by incorporating MS2 CID of SM via [M + HCO3]- and PB-MS2 CID. The capability of profiling SM with detailed structural information was demonstrated by analyzing complex lipid extracts from porcine brain and Caenorhabditis elegans. These results provided visualization of the sphingoid base/N-acyl compositional isomers of SM lipids and revealed large structural diversity from each sample. These included identification of the sphingadiene base [d18:1(Δ4,14)], C═C location isomers in N-acyls, C-2 hydroxylation of N-acyls, and iso-methyl branched SPH base.

The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows.

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. Therefore, highly promising recent machine learning developments in proteomics are pointed out in this viewpoint, alongside some of the remaining challenges.

From proof of concept to the routine use of an automated and robust multi-dimensional liquid chromatography mass spectrometry workflow applied for the charge variant characterization of therapeutic antibodies

The identification and quantification of post-translational modifications (PTMs) is a crucial step required during the development of therapeutic proteins. In particular, the characterization of charge variants separated by cation exchange chromatography (CEX) is a tedious process commonly performed with an off-line manual fraction collection followed by peptide mapping. To improve the efficiency of this time-consuming approach, a generic on-line multi-dimensional LC/MS approach was developed for the characterization of various monoclonal antibody (mAb) isotypes and a bi-specific antibody (BsAb). Fractions collected from 1D CEX analysis were consecutively reduced on a 2D reversed phase liquid chromatography (RPLC) column (polyphenyl), digested within 1–2 min using a 3D immobilized trypsin cartridge, and finally the obtained peptides were separated on another 4D RPLC column (C18), and simultaneously identified with a Q Exactive™ mass spectrometer. 2D RPLC columns and 3D trypsin cartridges from different suppliers were compared, as well as the effects of reducing agents. The effect of 2D and 4D RPLC column temperature, and 2D RPLC column mass load were also systematically studied. Under optimal conditions, the multi-dimensional LC/MS system described in this paper is a robust tool for the on-line digestion of proteins and shows high repeatability. Similar levels of oxidation and deamidation were measured using the off-line and on-line approaches for the same stressed samples. The lower amounts of deamidation and isomerization measured at some asparagine and aspartic acid residues by the on-line approach compared to the manual off-line procedure suggest reduced artifacts using the on-line methodology. The multi-dimensional LC/MS described here enables fast, on-line, automated characterization of therapeutic antibodies without the need for off-line fraction collection and sample pre-treatment (manual approach). The entire workflow can be completed within less than one day, compared to weeks with the manual off-line procedure.

Introduction of a Fixed-Charge, Photolabile Derivative for Enhanced Structural Elucidation of Fatty Acids.

Fatty acids are a structurally diverse category of lipids with a myriad of biochemical functions, which includes their role as building blocks of more complex lipids (e.g., glycerophospholipids and triacylglycerols). Increasingly, the analysis of fatty acids is undertaken using liquid chromatography-mass spectrometry (LC-MS), due to its versatility in the detection of lipids across a wide range of concentrations and diversity of molecular structures and masses. Previous work has shown that fixed-charge pyridinium derivatives are effective in enhancing the detection of fatty acids in LC-MS workflows. Herein, we describe the development of two novel pyridinium fixed-charged derivatization reagents that incorporate a photolabile aryl iodide that is selectively activated by laser irradiation inside the mass spectrometer. Photodissociation mass spectra of fatty acids conjugated to 1-(3-(aminomethyl)-4-iodophenyl)pyridin-1-ium (4-I-AMPP+) and 1-(4-(aminomethyl)-3-iodophenyl)pyridin-1-ium (3-I-AMPP+) derivatives reveal structurally diagnostic product ions. These spectra feature radical-directed dissociation of the carbon-carbon bonds within the fatty acyl chain, enabling structural assignments of fatty acids and discrimination of isomers that differ in site(s) of unsaturation, methyl branching or cyclopropanation. These derivatives are shown to be suitable for hyphenated LC-MS methods, and their predictable photodissociation behavior allows de novo identification of unusual fatty acids within a biological context.

Higher Mass Accuracy MALDI-TOF/TOF Lipid Imaging of Human Brain Tissue in Alzheimer's Disease.

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is a well-established technique for elucidating the location and relative abundance of a range of biomolecules. More recently, research into this technique has shifted from simple discovery and demonstration of utility to application in biomedical research. Here, we describe a protocol utilizing MALDI-IMS for the spatial mapping of lipids in brain tissue from normal human brains and brains from patients with Alzheimer's disease, in the context of Alzheimer's disease. Improved accuracy calibration of the instrument from the tissue surface is emphasized, as this allows for significantly improved mass determination in time of flight (TOF)-based instruments enabling more confident preliminary lipid identification. This improved initial result allows MALDI-IMS data to be complemented with additional instrumentation, such as liquid chromatography mass spectrometry workflows or specialized non-TOF systems such as Fourier transform cyclotron resonance instruments. This method is not limited to human tissue and can be applied to virtually any lipid-rich formalin-fixed tissue. © 2019 by John Wiley & Sons, Inc.

Speaq 2.0: A complete workflow for high-throughput 1D NMR spectra processing and quantification.

Nuclear Magnetic Resonance (NMR) spectroscopy is, together with liquid chromatography-mass spectrometry (LC-MS), the most established platform to perform metabolomics. In contrast to LC-MS however, NMR data is predominantly being processed with commercial software. Meanwhile its data processing remains tedious and dependent on user interventions. As a follow-up to speaq, a previously released workflow for NMR spectral alignment and quantitation, we present speaq 2.0. This completely revised framework to automatically analyze 1D NMR spectra uses wavelets to efficiently summarize the raw spectra with minimal information loss or user interaction. The tool offers a fast and easy workflow that starts with the common approach of peak-picking, followed by grouping, thus avoiding the binning step. This yields a matrix consisting of features, samples and peak values that can be conveniently processed either by using included multivariate statistical functions or by using many other recently developed methods for NMR data analysis. speaq 2.0 facilitates robust and high-throughput metabolomics based on 1D NMR but is also compatible with other NMR frameworks or complementary LC-MS workflows. The methods are benchmarked using a simulated dataset and two publicly available datasets. speaq 2.0 is distributed through the existing speaq R package to provide a complete solution for NMR data processing. The package and the code for the presented case studies are freely available on CRAN (https://cran.r-project.org/package=speaq) and GitHub (https://github.com/beirnaert/speaq).