Intact-Mass Analysis Facilitating the Identification of Large Human Heart Proteoforms

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

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.

Small-Scale Serial Size Exclusion Chromatography (s3SEC) for High Sensitivity Top-Down Proteomics of Large Proteoforms

Top-down-mass spectrometry (MS)-based proteomics has emerged as a premier technology to examine proteins at the proteoform level, enabling characterization of genetic mutations, alternative splicing, and post-translational modifications. However, significant challenges that remain in top-down proteomics include the analysis of large proteoforms and the sensitivity required to examine proteoforms from minimal amounts of sample. To address these challenges, we have developed a new method termed "small-scale serial Size Exclusion Chromatography" (s3SEC), which incorporates a small-scale protein extraction (1 mg of tissue) and serial SEC without postfractionation sample handling, coupled with online high sensitivity capillary reversed-phase liquid chromatography tandem MS (RPLC-MS/MS) for analysis of large proteoforms. The s3SEC-RPLC-MS/MS method significantly enhanced the sensitivity and reduced the proteome complexity across the fractions, enabling the detection of high MW proteoforms previously undetected in one-dimensional (1D)-RPLC analysis. Importantly, we observed a drastic improvement in the signal intensity of high MW proteoforms in early fractions when using the s3SEC-RPLC method. Moreover, we demonstrate that this s3SEC-RPLC-MS/MS method also allows the analysis of lower MW proteoforms in subsequent fractions without significant alteration in proteoform abundance and equivalent or improved fragmentation efficiency to that of the 1D-RPLC approach. Although this study focuses on the use of cardiac tissue, the s3SEC-RPLC-MS/MS method could be broadly applicable to other systems with limited sample inputs.

ProSight Native: Defining Protein Complex Composition from Native Top-Down Mass Spectrometry Data

Native mass spectrometry has recently moved alongside traditional structural biology techniques in its ability to provide clear insights into the composition of protein complexes. However, to date, limited software tools are available for the comprehensive analysis of native mass spectrometry data on protein complexes, particularly for experiments aimed at elucidating the composition of an intact protein complex. Here, we introduce ProSight Native as a start-to-finish informatics platform for analyzing native protein and protein complex data. Combining mass determination via spectral deconvolution with a top-down database search and stoichiometry calculations, ProSight Native can determine the complete composition of protein complexes. To demonstrate its features, we used ProSight Native to successfully determine the composition of the homotetrameric membrane complex Aquaporin Z. We also revisited previously published spectra and were able to decipher the composition of a heterodimer complex bound with two noncovalently associated ligands. In addition to determining complex composition, we developed new tools in the software for validating native mass spectrometry fragment ions and mapping top-down fragmentation data onto three-dimensional protein structures. Taken together, ProSight Native will reduce the informatics burden on the growing field of native mass spectrometry, enabling the technology to further its reach.

Size Exclusion Chromatography Strategies and MASH Explorer for Large Proteoform Characterization

Top-down mass spectrometry (MS)-based analysis of larger proteoforms (>50 kDa) is typically challenging due to an exponential decay in the signal-to-noise ratio with increasing protein molecular weight (MW) and coelution with low-MW proteoforms. Size exclusion chromatography (SEC) fractionates proteins based on their size, separating larger proteoforms from those of smaller size in the proteome. In this protocol, we initially describe the use of SEC to fractionate high-MW proteoforms from low-MW proteoforms. Subsequently, the SEC fractions containing the proteoforms of interest are subjected to reverse-phase liquid chromatography (RPLC) coupled online with high-resolution MS. Finally, proteoforms are characterized using MASH Explorer, a user-friendly software environment for in-depth proteoform characterization.

Intact Protein Mass Spectrometry for Therapeutic Protein Quantitation, Pharmacokinetics, and Biotransformation in Preclinical and Clinical Studies: An Industry Perspective

Recent advancements in immunocapture methods and mass spectrometer technology have enabled intact protein mass spectrometry to be applied for the characterization of antibodies and other large biotherapeutics from in-life studies. Protein molecules have not been traditionally studied by intact mass or screened for catabolites in the same manner as small molecules, but the landscape has changed. Researchers have presented methods that can be applied to the drug discovery and development stages, and others are exploring the possibilities of the new approaches. However, a wide variety of options for assay development exists without clear recommendation on best practice, and data processing workflows may have limitations depending on the vendor. In this perspective, we share experiences and recommendations for current and future application of mass spectrometry for biotherapeutic molecule monitoring from preclinical and clinical studies.

Quantitative Top-Down Proteomics by Isobaric Labeling with Thiol-Directed Tandem Mass Tags

While identification-centric (qualitative) top-down proteomics (TDP) has seen rapid progress in the recent past, the quantification of intact proteoforms within complex proteomes is still challenging. The by far mostly applied approach is label-free quantification, which, however, provides limited multiplexing capacity, and its use in combination with multidimensional separation is encountered with a number of problems. Isobaric labeling, which is a standard quantification approach in bottom-up proteomics, circumvents these limitations. Here, we introduce the application of thiol-directed isobaric labeling for quantitative TDP. For this purpose, we analyzed the labeling efficiency and optimized tandem mass spectrometry parameters for optimal backbone fragmentation for identification and reporter ion formation for quantification. Two different separation schemes, gel-eluted liquid fraction entrapment electrophoresis × liquid chromatography-mass spectrometry (LC-MS) and high/low-pH LC-MS, were employed for the analyses of either Escherichia coli (E. coli) proteomes or combined E. coli/yeast samples (two-proteome interference model) to study potential ratio compression. While the thiol-directed labeling introduces a bias in the quantifiable proteoforms, being restricted to Cys-containing proteoforms, our approach showed excellent accuracy in quantification, which is similar to that achievable in bottom-up proteomics. For example, 876 proteoforms could be quantified with high accuracy in an E. coli lysate. The LC-MS data were deposited to the ProteomeXchange with the dataset identifier PXD026310.

Top-Down Proteomics of Endogenous Membrane Proteins Enabled by Cloud Point Enrichment and Multidimensional Liquid Chromatography-Mass Spectrometry

Although top-down proteomics has emerged as a powerful strategy to characterize proteins in biological systems, the analysis of endogenous membrane proteins remains challenging due to their low solubility, low abundance, and the complexity of the membrane subproteome. Here, we report a simple but effective enrichment and separation strategy for top-down proteomics of endogenous membrane proteins enabled by cloud point extraction and multidimensional liquid chromatography coupled to high-resolution mass spectrometry (MS). The cloud point extraction efficiently enriched membrane proteins using a single extraction, eliminating the need for time-consuming ultracentrifugation steps. Subsequently, size-exclusion chromatography (SEC) with an MS-compatible mobile phase (59% water, 40% isopropanol, 1% formic acid) was used to remove the residual surfactant and fractionate intact proteins (6-115 kDa). The fractions were separated further by reversed-phase liquid chromatography (RPLC) coupled with MS for protein characterization. This method was applied to human embryonic kidney cells and cardiac tissue lysates to enable the identification of 188 and 124 endogenous integral membrane proteins, respectively, some with as many as 19 transmembrane domains.

Construction of Human Proteoform Families from 21 Tesla Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Top-Down Proteomic Data

Identification of proteoforms, the different forms of a protein, is important to understand biological processes. A proteoform family is the set of different proteoforms from the same gene. We previously developed the software program Proteoform Suite, which constructs proteoform families and identifies proteoforms by intact-mass analysis. Here, we have applied this approach to top-down proteomic data acquired at the National High Magnetic Field Laboratory 21 tesla Fourier transform ion cyclotron resonance mass spectrometer (data available on the MassIVE platform with identifier MSV000085978). We explored the ability to construct proteoform families and identify proteoforms from the high mass accuracy data that this instrument provides for a complex cell lysate sample from the MCF-7 human breast cancer cell line. There were 2830 observed experimental proteforms, of which 932 were identified, 44 were ambiguous, and 1854 were unidentified. Of the 932 unique identified proteoforms, 766 were identified by top-down MS2 analysis at 1% false discovery rate (FDR) using TDPortal, and 166 were additional intact-mass identifications (∼4.7% calculated global FDR) made using Proteoform Suite. We recently published a proteoform level schema to represent ambiguity in proteoform identifications. We implemented this proteoform level classification in Proteoform Suite for intact-mass identifications, which enables users to determine the ambiguity levels and sources of ambiguity for each intact-mass proteoform identification.

Distinct hypertrophic cardiomyopathy genotypes result in convergent sarcomeric proteoform profiles revealed by top-down proteomics

Hypertrophic cardiomyopathy (HCM) is the most common heritable heart disease. Although the genetic cause of HCM has been linked to mutations in genes encoding sarcomeric proteins, the ability to predict clinical outcomes based on specific mutations in HCM patients is limited. Moreover, how mutations in different sarcomeric proteins can result in highly similar clinical phenotypes remains unknown. Posttranslational modifications (PTMs) and alternative splicing regulate the function of sarcomeric proteins; hence, it is critical to study HCM at the level of proteoforms to gain insights into the mechanisms underlying HCM. Herein, we employed high-resolution mass spectrometry-based top-down proteomics to comprehensively characterize sarcomeric proteoforms in septal myectomy tissues from HCM patients exhibiting severe outflow track obstruction (n = 16) compared to nonfailing donor hearts (n = 16). We observed a complex landscape of sarcomeric proteoforms arising from combinatorial PTMs, alternative splicing, and genetic variation in HCM. A coordinated decrease of phosphorylation in important myofilament and Z-disk proteins with a linear correlation suggests PTM cross-talk in the sarcomere and dysregulation of protein kinase A pathways in HCM. Strikingly, we discovered that the sarcomeric proteoform alterations in the myocardium of HCM patients undergoing septal myectomy were remarkably consistent, regardless of the underlying HCM-causing mutations. This study suggests that the manifestation of severe HCM coalesces at the proteoform level despite distinct genotype, which underscores the importance of molecular characterization of HCM phenotype and presents an opportunity to identify broad-spectrum treatments to mitigate the most severe manifestations of this genetically heterogenous disease.

Proton Transfer Charge Reduction Enables High-Throughput Top-Down Analysis of Large Proteoforms

Despite the recent technological advances in Fourier transform mass spectrometry (FTMS) instrumentation, top-down proteomics (TDP) is currently mostly applied to the characterization of proteoforms <30 kDa due to the poor performance of high-resolution FTMS for the analysis of larger proteoforms and the high complexity of intact proteomes in the 30-60 kDa mass range. Here, we propose a novel data acquisition method based on ion-ion proton transfer, herein termed proton transfer charge reduction (PTCR), to investigate large proteoforms of Pseudomonas aeruginosa in a high-throughput fashion. We designed a targeted data acquisition strategy, named tPTCR, which applies two consecutive gas phase fractionation steps for obtaining intact precursor masses: first, a narrow (1.5 m/z-wide) quadrupole filter m/z transmission window is used to select a subset of charge states from all ionized proteoform cations; second, this aliquot of protein cations is subjected to PTCR in order to reduce their average charge state: upon m/z analysis in an Orbitrap, proteoform mass spectra with minimal m/z peak overlap and easy-to-interpret charge state distributions are obtained, simplifying the proteoform mass calculation. Subsequently, the same quadrupole-selected narrow m/z region of analytes is subjected to collisional dissociation to obtain proteoform sequence information, which used in combination with intact mass information leads to proteoform identification through an off-line database search. The newly proposed method was benchmarked against the previously developed "medium/high" data-dependent acquisition strategy and doubled the number of UniProt entries and proteoforms >30 kDa identified on the liquid chromatography time scale.