Proteoforms as the next proteomics currency

Native Mass Spectrometry at the Convergence of Structural Biology and Compositional Proteomics

ConspectusBiology is driven by a vast set of molecular interactions that evolved over billions of years. Just as covalent modifications like acetylations and phosphorylations can change a protein's function, so too can noncovalent interactions with metals, small molecules, and other proteins. However, much of the language of protein-level biology is left either undiscovered or inferred, as traditional methods used in the field of proteomics use conditions that dissociate noncovalent interactions and denature proteins.Just in the past few years, mass spectrometry (MS) has evolved the capacity to preserve and subsequently characterize the complete composition of endogenous protein complexes. Using this "native" type of mass spectrometry, a complex can be activated to liberate some or all of its subunits, typically via collisions with neutral gas or solid surfaces and isolated before further characterization ("Native Top-Down MS," or nTDMS). The subunit mass, the parent ion mass, and the fragment ions of the activated subunits can be used to piece together the precise molecular composition of the parent complex. When performed en masse in discovery mode (i.e., "native proteomics"), the interactions of life─including protein modifications─will eventually be clarified and linked to dysfunction in human disease states.In this Account, we describe the current and future components of the native MS toolkit, covering the challenges the field faces to characterize ever larger bioassemblies. Each of the three pillars of native proteomics are addressed: (i) separations, (ii) top-down mass spectrometry, and (iii) integration with structural biology. Complexes such as endogenous nucleosomes can be targeted now using nTDMS, whereas virus particles, exosomes, and high-density lipoprotein particles will be tackled in the coming few years. The future work to adequately address the size and complexity of mega- to gigadalton complexes will include native separations, single ion mass spectrometry, and new data types. The use of nTDMS in discovery mode will incorporate native-compatible separation techniques to maximize the number of proteoforms in complexes identified. With a new wave of innovations, both targeted and discovery mode nTDMS will expand to include extremely scarce and exceedingly heterogeneous bioassemblies. Understanding the proteinaceous interactions of life and how they go wrong (e.g., misfolding, forming complexes in dysfunctional stoichiometries and configurations) will not only inform the development of life-restoring therapeutics but also deepen our understanding of life at the molecular level.

Tandem Trapped Ion Mobility Spectrometry/Mass Spectrometry (tTIMS/MS) Reveals Sequence-Specific Determinants of Top-Down Protein Fragment Ion Cross Sections

Top-down proteomics provides a straightforward approach to the level of proteoforms but remains technologically challenging. Using ion mobility spectrometry/mass spectrometry (IMS/MS) to separate top-down fragment ions improves signal/noise and dynamic range. Such applications, however, do not yet leverage the primary information obtained from IMS/MS, which is the characterization of the fragment ion structure by the measured momentum transfer cross sections. Here, we perform top-down analysis of intact proteins and assemblies using our tandem trapped ion mobility spectrometer/mass spectrometer (tTIMS/MS) and compile over 1400 cross section values of fragment ions. Our analysis reveals that most fragment ions exhibit multiple, stable conformations similar to those of intact polypeptides and proteins. The data further indicate that the conformational heterogeneity is strongly influenced by the amino acid sequences of the fragment ions. Moreover, time-resolved tTIMS/MS experiments reveal that conformations of top-down fragment ions can be metastable on the timescale of ion mobility measurements. Taken together, our analysis indicates that top-down fragment ions undergo a folding process in the gas phase and that this folding process can lead to kinetic trapping of intermediate states in ion mobility measurements. Hence, because the folding free energy surface of a polypeptide ion is encoded by its amino acid sequence and charge state, our analysis suggests that cross sections can be exploited as sequence-specific determinants of top-down fragment ions.

Tandem-trapped ion mobility spectrometry/mass spectrometry (tTIMS/MS): a promising analytical method for investigating heterogenous samples

Ion mobility spectrometry/mass spectrometry (IMS/MS) is widely used to study various levels of protein structure. Here, we review the current state of affairs in tandem-trapped ion mobility spectrometry/mass spectrometry (tTIMS/MS). Two different tTIMS/MS instruments are discussed in detail: the first tTIMS/MS instrument, constructed from coaxially aligning two TIMS devices; and an orthogonal tTIMS/MS configuration that comprises an ion trap for irradiation of ions with UV photons. We discuss the various workflows the two tTIMS/MS setups offer and how these can be used to study primary, tertiary, and quaternary structures of protein systems. We also discuss, from a more fundamental perspective, the processes that lead to denaturation of protein systems in tTIMS/MS and how to soften the measurement so that biologically meaningful structures can be characterised with tTIMS/MS. We emphasize the concepts underlying tTIMS/MS to underscore the opportunities tandem-ion mobility spectrometry methods offer for investigating heterogeneous samples.

Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry

Reversed-phase liquid chromatography (RPLC) and capillary zone electrophoresis (CZE) are two primary proteoform separation methods in mass spectrometry (MS)-based top-down proteomics. Proteoform retention time (RT) prediction in RPLC and migration time (MT) prediction in CZE provide additional information for accurate proteoform identification and quantification. While existing methods are mainly focused on peptide RT and MT prediction in bottom-up MS, there is still a lack of methods for proteoform RT and MT prediction in top-down MS. We systematically evaluated eight machine learning models and a transfer learning method for proteoform RT prediction and five models and the transfer learning method for proteoform MT prediction. Experimental results showed that a gated recurrent unit (GRU)-based model with transfer learning achieved a high accuracy (R = 0.978) for proteoform RT prediction and that the GRU-based model and a fully connected neural network model obtained a high accuracy of R = 0.982 and 0.981 for proteoform MT prediction, respectively.

Mass spectrometry-based technologies for probing the 3D world of plant proteins

Over the past two decades, mass spectrometric (MS)-based proteomics technologies have facilitated the study of signaling pathways throughout biology. Nowhere is this needed more than in plants, where an evolutionary history of genome duplications has resulted in large gene families involved in posttranslational modifications and regulatory pathways. For example, at least 5% of the Arabidopsis thaliana genome (ca. 1,200 genes) encodes protein kinases and protein phosphatases that regulate nearly all aspects of plant growth and development. MS-based technologies that quantify covalent changes in the side-chain of amino acids are critically important, but they only address one piece of the puzzle. A more crucially important mechanistic question is how noncovalent interactions-which are more difficult to study-dynamically regulate the proteome's 3D structure. The advent of improvements in protein 3D technologies such as cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography has allowed considerable progress to be made at this level, but these methods are typically limited to analyzing proteins, which can be expressed and purified in milligram quantities. Newly emerging MS-based technologies have recently been developed for studying the 3D structure of proteins. Importantly, these methods do not require protein samples to be purified and require smaller amounts of sample, opening the wider proteome for structural analysis in complex mixtures, crude lysates, and even in intact cells. These MS-based methods include covalent labeling, crosslinking, thermal proteome profiling, and limited proteolysis, all of which can be leveraged by established MS workflows, as well as newly emerging methods capable of analyzing intact macromolecules and the complexes they form. In this review, we discuss these recent innovations in MS-based "structural" proteomics to provide readers with an understanding of the opportunities they offer and the remaining challenges for understanding the molecular underpinnings of plant structure and function.

Ethane-Bridged Hybrid Monolithic Column with Large Mesopores for Boosting Top-Down Proteomic Analysis

Top-down proteomics is challenged by the high complexity of biological samples. The coelution of intact proteins results in overlapped mass spectra, and hence, an increased peak capacity for protein separation is needed. Herein, ethane-bridged hybrid monoliths with well-defined large mesopores were successfully prepared based on the sol-gel condensation of 1,2-bis(trimethoxysilyl)ethane and tetramethoxysilane, followed by two-step base etching of the Si-O-Si domain while maintaining the Si-C-C-Si domain in the structure. Relatively homogeneous macropores of 1.1 μm and large mesopores of 24 nm were obtained, permitting fast mass transfer of large molecules and efficient diffusion without obstruction. The use of less hydrophobic C1 ligand further sharpened the peak shape and improved peak capacity. A 120 cm-long capillary column was used for top-down proteomic analysis of E. coli lysates under low backpressure with 16 MPa. High peak capacity of 646 was achieved within 240 min gradient. With MS/MS analysis, 959 proteoforms corresponding to 263 proteins could be unambiguously identified from E. coli lysates in a single run. Furthermore, to illustrate the separation performance for large proteoforms, such monoliths were applied to top-down analysis of the SEC fraction of E. coli lysates with Mw ranging from 30 to 70 kDa. With highly effective separation, 347 large proteoforms with Mw higher than 30 kDa were detected in the single 75 min run. These results showed great potential for top-down proteomic analysis in complex samples.

Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics?

Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. Commonly, a single quantitative value is reported for each protein-coding gene by aggregating peptide quantities into protein groups following protein inference or parsimony. However, given the complexity of both RNA splicing and post-translational protein modification, it is overly simplistic to assume that all peptides that map to a singular protein-coding gene will demonstrate the same quantitative response. By assuming that all peptides from a protein-coding sequence are representative of the same protein, we may miss the discovery of important biological differences. To capture the contributions of existing proteoforms, we need to reconsider the practice of aggregating protein values to a single quantity per protein-coding gene.

Fourier-transform ion cyclotron resonance mass spectrometry for characterizing proteoforms

Proteoforms contribute functional diversity to the proteome and aberrant proteoforms levels have been implicated in biological dysfunction and disease. Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), with its ultrahigh mass-resolving power, mass accuracy, and versatile tandem MS capabilities, has empowered top-down, middle-down, and native MS-based approaches for characterizing proteoforms and their complexes in biological systems. Herein, we review the features which make FT-ICR MS uniquely suited for measuring proteoform mass with ultrahigh resolution and mass accuracy; obtaining in-depth proteoform sequence coverage with expansive tandem MS capabilities; and unambiguously identifying and localizing post-translational and noncovalent modifications. We highlight examples from our body of work in which we have quantified and comprehensively characterized proteoforms from cardiac and skeletal muscle to better understand conditions such as chronic heart failure, acute myocardial infarction, and sarcopenia. Structural characterization of monoclonal antibodies and their proteoforms by FT-ICR MS and emerging applications, such as native top-down FT-ICR MS and high-throughput top-down FT-ICR MS-based proteomics at 21 T, are also covered. Historically, the information gleaned from FT-ICR MS analyses have helped provide biological insights. We predict FT-ICR MS will continue to enable the study of proteoforms of increasing size from increasingly complex endogenous mixtures and facilitate the benchmarking of sensitive and specific assays for clinical diagnostics. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Laser capture microdissection-capillary zone electrophoresis-tandem mass spectrometry (LCM-CZE-MS/MS) for spatially resolved top-down proteomics: a pilot study of zebrafish brain

Mass spectrometry (MS)-based spatially resolved top-down proteomics (TDP) of tissues is crucial for understanding the roles played by microenvironmental heterogeneity in the biological functions of organs and for discovering new proteoform biomarkers of diseases. There are few published spatially resolved TDP studies. One of the challenges relates to the limited performance of TDP for the analysis of spatially isolated samples using, for example, laser capture microdissection (LCM) because those samples are usually mass-limited. We present the first pilot study of LCM-capillary zone electrophoresis (CZE)-MS/MS for spatially resolved TDP and used zebrafish brain as the sample. The LCM-CZE-MS/MS platform employed a non-ionic detergent and a freeze-thaw method for efficient proteoform extraction from LCM isolated brain sections followed by CZE-MS/MS without any sample cleanup step, ensuring high sensitivity. Over 400 proteoforms were identified in a CZE-MS/MS analysis of one LCM brain section via consuming the protein content of roughly 250 cells. We observed drastic differences in proteoform profiles between two LCM brain sections isolated from the optic tectum (Teo) and telencephalon (Tel) regions. Proteoforms of three proteins (npy, penkb, and pyya) having neuropeptide hormone activity were exclusively identified in the isolated Tel section. Proteoforms of reticulon, myosin, and troponin were almost exclusively identified in the isolated Teo section, and those proteins play essential roles in visual and motor activities. The proteoform profiles accurately reflected the main biological functions of the Teo and Tel regions of the brain. Additionally, hundreds of post-translationally modified proteoforms were identified.

Roles of E3 Ubiquitin Ligases in Plant Responses to Abiotic Stresses

Plants are frequently exposed to a variety of abiotic stresses, such as those caused by salt, drought, cold, and heat. All of these stressors can induce changes in the proteoforms, which make up the proteome of an organism. Of the many different proteoforms, protein ubiquitination has attracted a lot of attention because it is widely involved in the process of protein degradation; thus regulates many plants molecular processes, such as hormone signal transduction, to resist external stresses. Ubiquitin ligases are crucial in substrate recognition during this ubiquitin modification process. In this review, the molecular mechanisms of plant responses to abiotic stresses from the perspective of ubiquitin ligases have been described. This information is critical for a better understanding of plant molecular responses to abiotic stresses.

A peptidoform based proteomic strategy for studying functions of post-translational modifications

Protein post-translational modifications (PTMs) generate an enormous, but as yet undetermined, expansion of the produced proteoforms. In this Viewpoint, we firstly reviewed the concepts of proteoform and peptidoform. We show that many of the current PTM biological investigation and annotation studies largely follow a PTM site-specific rather than proteoform-specific approach. We further illustrate a potentially useful matching strategy in which a particular "modified peptidoform" is matched to the corresponding "unmodified peptidoform" as a reference for the quantitative analysis between samples and conditions. We suggest this strategy has the potential to provide more directly relevant information to learn the PTM site-specific biological functions. Accordingly, we advocate for the wider use of the nomenclature "peptidoform" in future bottom-up proteomic studies.

Characterisation of a new online nanoLC-CZE-MS platform and application for the glycosylation profiling of alpha-1-acid glycoprotein

The ever-increasing complexity of biological samples to be analysed by mass spectrometry has led to the necessity of sophisticated separation techniques, including multidimensional separation. Despite a high degree of orthogonality, the coupling of liquid chromatography (LC) and capillary zone electrophoresis (CZE) has not gained notable attention in research. Here, we present a heart-cut nanoLC-CZE-ESI-MS platform to analyse intact proteins. NanoLC and CZE-MS are coupled using a four-port valve with an internal nanoliter loop. NanoLC and CZE-MS conditions were optimised independently to find ideal conditions for the combined setup. The valve setup enables an ideal transfer efficiency between the dimensions while maintaining good separation conditions in both dimensions. Due to the higher loadability, the nanoLC-CZE-MS setup exhibits a 280-fold increased concentration sensitivity compared to CZE-MS. The platform was used to characterise intact human alpha-1-acid glycoprotein (AGP), an extremely heterogeneous N-glycosylated protein. With the nanoLC-CZE-MS approach, 368 glycoforms can be assigned at a concentration of 50 μg/mL as opposed to the assignment of only 186 glycoforms from 1 mg/mL by CZE-MS. Additionally, we demonstrate that glycosylation profiling is accessible for dried blood spot analysis (25 μg/mL AGP spiked), indicating the general applicability of our setup to biological matrices. The combination of high sensitivity and orthogonal selectivity in both dimensions makes the here-presented nanoLC-CZE-MS approach capable of detailed characterisation of intact proteins and their proteoforms from complex biological samples and in physiologically relevant concentrations.

The Blood Proteoform Atlas: A reference map of proteoforms in human hematopoietic cells

Human biology is tightly linked to proteins, yet most measurements do not precisely determine alternatively spliced sequences or posttranslational modifications. Here, we present the primary structures of ~30,000 unique proteoforms, nearly 10 times more than in previous studies, expressed from 1690 human genes across 21 cell types and plasma from human blood and bone marrow. The results, compiled in the Blood Proteoform Atlas (BPA), indicate that proteoforms better describe protein-level biology and are more specific indicators of differentiation than their corresponding proteins, which are more broadly expressed across cell types. We demonstrate the potential for clinical application, by interrogating the BPA in the context of liver transplantation and identifying cell and proteoform signatures that distinguish normal graft function from acute rejection and other causes of graft dysfunction.

Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research

The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.

Top-Down Proteomics by Capillary Zone Electrophoresis-Tandem Mass Spectrometry for Large-Scale Characterization of Proteoforms in Complex Samples

Capillary zone electrophoresis (CZE) is a fundamentally simple and highly efficient separation technique based on differences in electrophoretic mobilities of analytes. CZE-mass spectrometry (MS) has become an important analytical tool in top-down proteomics which aims to delineate proteoforms in cells comprehensively, because of the improvement of capillary coatings, sample stacking methods, and CE-MS interfaces. Here, we present a CZE-MS/MS-based top-down proteomics procedure for the characterization of a standard protein mixture and an Escherichia coli (E. coli) cell lysate using linear polyacrylamide-coated capillaries, a dynamic pH junction sample stacking method, a commercialized electro-kinetically pumped sheath flow CE-MS interface and an Orbitrap mass spectrometer. CZE-MS/MS can identify hundreds of proteoforms routinely from the E. coli sample with a 1% proteoform-level false discovery rate (FDR).

Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Post-translational modifications (PTMs) regulate complex biological processes through the modulation of protein activity, stability, and localization. Insights into the specific modification type and localization within a protein sequence can help ascertain functional significance. Computational models are increasingly demonstrated to offer a low-cost, high-throughput method for comprehensive PTM predictions. Algorithms are optimized using existing experimental PTM data, thus accurate prediction performance relies on the creation of robust datasets. Herein, advancements in mass spectrometry-based proteomics technologies to maximize PTM coverage are reviewed. Further, requisite experimental validation approaches for PTM predictions are explored to ensure that follow-up mechanistic studies are focused on accurate modification sites.

The promise of organoids for unraveling the proteomic landscape of the developing human brain

Cerebral organoids offer an opportunity to bioengineer experimental avatars of the developing human brain and have already begun garnering relevant insights into complex neurobiological processes and disease. Thus far, investigations into their heterogeneous cellular composition and developmental trajectories have been largely limited to transcriptional readouts. Recent advances in global proteomic technologies have enabled a new range of techniques to explore dynamic and non-overlapping spatiotemporal protein-level programs operational in these humanoid neural structures. Here we discuss these early protein-based studies and their potentially essential role for unraveling critical secreted paracrine signals, processes with poor proteogenomic correlations, or neurodevelopmental proteins requiring post-translational modification for biological activity. Integrating emerging proteomic tools with these faithful human-derived neurodevelopmental models could transform our understanding of complex neural cell phenotypes and neurobiological processes, not exclusively driven by transcriptional regulation. These insights, less accessible by exclusive RNA-based approaches, could reveal new knowledge into human brain development and guide improvements in neural regenerative medicine efforts.

Host Plant Adaptation Drives Changes in Diaphorina citri Proteome Regulation, Proteoform Expression, and Transmission of 'Candidatus Liberibacter asiaticus', the Citrus Greening Pathogen

The Asian citrus psyllid (Diaphorina citri) is a pest of citrus and the primary insect vector of the bacterial pathogen, 'Candidatus Liberibacter asiaticus' (CLas), which is associated with citrus greening disease. The citrus relative Murraya paniculata (orange jasmine) is a host plant of D. citri but is more resistant to CLas compared with all tested Citrus genotypes. The effect of host switching of D. citri between Citrus medica (citron) and M. paniculata plants on the acquisition and transmission of CLas was investigated. The psyllid CLas titer and the proportion of CLas-infected psyllids decreased in the generations after transfer from CLas-infected citron to healthy M. paniculata plants. Furthermore, after several generations of feeding on M. paniculata, pathogen acquisition (20 to 40% reduction) and transmission rates (15 to 20% reduction) in psyllids transferred to CLas-infected citron were reduced compared with psyllids continually maintained on infected citron. Top-down (difference gel electrophoresis) and bottom-up (shotgun MS/MS) proteomics methods were used to identify changes in D. citri protein expression resulting from host plant switching between Citrus macrophylla and M. paniculata. Changes in expression of insect metabolism, immunity, and cytoskeleton proteins were associated with host plant switching. Both transient and sustained feeding on M. paniculata induced distinct patterns of protein expression in D. citri compared with psyllids reared on C. macrophylla. The results point to complex interactions that affect vector competence and may lead to strategies to control the spread of citrus greening disease.

Accurate Proteoform Identification and Quantitation Using pTop 2.0

The remarkable advancement of top-down proteomics in the past decade is driven by the technological development in separation, mass spectrometry (MS) instrumentation, novel fragmentation, and bioinformatics. However, the accurate identification and quantification of proteoforms, all clearly-defined molecular forms of protein products from a single gene, remain a challenging computational task. This is in part due to the complicated mass spectra from intact proteoforms when compared to those from the digested peptides. Herein, pTop 2.0 is developed to fill in the gap between the large-scale complex top-down MS data and the shortage of high-accuracy bioinformatic tools. Compared with pTop 1.0, the first version, pTop 2.0 concentrates mainly on the identification of the proteoforms with unexpected modifications or a terminal truncation. The quantitation based on isotopic labeling is also a new function, which can be carried out by the convenient and user-friendly "one-key operation," integrated together with the qualitative identifications. The accuracy and running speed of pTop 2.0 is significantly improved on the test data sets. This chapter will introduce the main features, step-by-step running operations, and algorithmic developments of pTop 2.0 in order to push the identification and quantitation of intact proteoforms to a higher-accuracy level in top-down proteomics.

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.

Membrane Ultrafiltration-Based Sample Preparation Method and Sheath-Flow CZE-MS/MS for Top-Down Proteomics

Mass spectrometry (MS)-based denaturing top-down proteomics (dTDP) identify proteoforms without pretreatment of enzyme proteolysis. A universal sample preparation method that can efficiently extract protein, reduce sample loss, maintain protein solubility, and be compatible with following up liquid-phase separation, MS, and tandem MS (MS/MS) is vital for large-scale proteoform characterization. Membrane ultrafiltration (MU) was employed here for buffer exchange to efficiently remove the sodium dodecyl sulfate (SDS) detergent in protein samples used for protein extraction and solubilization, followed by capillary zone electrophoresis (CZE)-MS/MS analysis. The MU method showed good protein recovery, minimum protein bias, and nice compatibility with CZE-MS/MS. Single-shot CZE-MS/MS analysis of an Escherichia coli sample prepared by the MU method identified over 800 proteoforms.

Nanostructure stable hydrophilic hierarchical porous metal-organic frameworks for highly efficient enrichment of glycopeptides

Protein glycosylation plays a vital role in many physiological activities in organisms. Due to the low abundance of glycopeptides and the interference of numerous non-glycopeptides in biological samples, selective enrichment of glycopeptides is of great significance for their successful identification. Metal organic frameworks (MOFs) materials are appropriate for glycopeptides enrichment by virtue of their large specific surface area and outstanding hydrophilic properties. However, the instability of hydrophilic MOFs in acidic solutions have severely limited their applications. In this work, a rational facile strategy was established to synthesize a stable hydrophilic hierarchical porous MOF (denoted as HP-MOF-Arg@mSiO₂). This new material improved the selectivity and sensitivity of enrichment for glycopeptides via modification of arginine groups. More importantly, the mesoporous silica layer was introduced to enhance the stability of MOFs in aqueous solution and achieve the size exclusion effect of large-size proteins in complex samples. Overall, owing to the unique hierarchical porous and the hydrophilic modification, the synthesized HP-MOF-Arg@mSiO₂ materials showed excellent hydrophilicity and hydrolytic stability, resulting in outstanding specific separation capacity in glycopeptides enrichment. A total of 521 and 342 glycopeptides were respectively captured from 2 μL human serum digests and mouse testis tissue digests, revealing the potential of the materials in the study of glycoproteomics in complex biological samples.

TDFragMapper: a visualization tool for evaluating experimental parameters in top-down proteomics

MOTIVATION

We present a new software-tool allowing an easy visualization of fragment ions and thus a rapid evaluation of key experimental parameters on the sequence coverage obtained for the MS/MS (tandem mass spectrometry) analysis of intact proteins. Our tool can process data obtained from various deconvolution and fragment assignment software.

RESULTS

We demonstrate that TDFragMapper can rapidly highlight the experimental fragmentation parameters that are critical to the characterization of intact proteins of various size using top-down proteomics.

AVAILABILITY AND IMPLEMENTATION

TDFragMapper, a demonstration video and user tutorial are freely available for academic use at https://msbio.pasteur.fr/tdfragmapper; all data are thus available from the ProteomeXchange consortium (identifier PXD024643).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Human Proteoform Project: Defining the human proteome

Proteins are the primary effectors of function in biology, and thus, complete knowledge of their structure and properties is fundamental to deciphering function in basic and translational research. The chemical diversity of proteins is expressed in their many proteoforms, which result from combinations of genetic polymorphisms, RNA splice variants, and posttranslational modifications. This knowledge is foundational for the biological complexes and networks that control biology yet remains largely unknown. We propose here an ambitious initiative to define the human proteome, that is, to generate a definitive reference set of the proteoforms produced from the genome. Several examples of the power and importance of proteoform-level knowledge in disease-based research are presented along with a call for improved technologies in a two-pronged strategy to the Human Proteoform Project.

Toward improvement of screening through mass spectrometry-based proteomics: ovarian cancer as a case study

Ovarian cancer is one of the leading causes of cancer related deaths affecting United States women. Early-stage detection of ovarian cancer has been linked to increased survival, however, current screening methods, such as biomarker testing, have proven to be ineffective in doing so. Therefore, further developments are necessary to be able to achieve positive patient prognosis. Ongoing efforts are being made in biomarker discovery towards clinical applications in screening for early-stage ovarian cancer. In this perspective, we discuss and provide examples for several workflows employing mass spectrometry-based proteomics towards protein biomarker discovery and characterization in the context of ovarian cancer; workflows include protein identification and characterization as well as intact protein profiling. We also discuss the opportunities to merge these workflows for a multiplexed approach for biomarkers. Lastly, we provide our insight as to future developments that may serve to enhance biomarker discovery workflows while also considering translational potential.

Immunoaffinity Capillary Electrophoresis in the Era of Proteoforms, Liquid Biopsy and Preventive Medicine: A Potential Impact in the Diagnosis and Monitoring of Disease Progression

Over the years, multiple biomarkers have been used to aid in disease screening, diagnosis, prognosis, and response to therapy. As of late, protein biomarkers are gaining strength in their role for early disease diagnosis and prognosis in part due to the advancements in identification and characterization of a distinct functional pool of proteins known as proteoforms. Proteoforms are defined as all of the different molecular forms of a protein derived from a single gene caused by genetic variations, alternative spliced RNA transcripts and post-translational modifications. Monitoring the structural changes of each proteoform of a particular protein is essential to elucidate the complex molecular mechanisms that guide the course of disease. Clinical proteomics therefore holds the potential to offer further insight into disease pathology, progression, and prevention. Nevertheless, more technologically advanced diagnostic methods are needed to improve the reliability and clinical applicability of proteomics in preventive medicine. In this manuscript, we review the use of immunoaffinity capillary electrophoresis (IACE) as an emerging powerful diagnostic tool to isolate, separate, detect and characterize proteoform biomarkers obtained from liquid biopsy. IACE is an affinity capture-separation technology capable of isolating, concentrating and analyzing a wide range of biomarkers present in biological fluids. Isolation and concentration of target analytes is accomplished through binding to one or more biorecognition affinity ligands immobilized to a solid support, while separation and analysis are achieved by high-resolution capillary electrophoresis (CE) coupled to one or more detectors. IACE has the potential to generate rapid results with significant accuracy, leading to reliability and reproducibility in diagnosing and monitoring disease. Additionally, IACE has the capability of monitoring the efficacy of therapeutic agents by quantifying companion and complementary protein biomarkers. With advancements in telemedicine and artificial intelligence, the implementation of proteoform biomarker detection and analysis may significantly improve our capacity to identify medical conditions early and intervene in ways that improve health outcomes for individuals and populations.

Refining the N-Termini of the SARS-CoV-2 Spike Protein and Its Discrete Receptor-Binding Domain

Previous work employing five SARS-CoV-2 spike protein receptor-binding domain (RBD) constructs, comprising versions originally developed by Mt. Sinai or the Ragon Institute and later optimized in-house, revealed potential heterogeneity which led to questions regarding variable seropositivity assay performance. Each construct was subjected to N-deglycosylation and subsequent intact mass analysis, revealing significant deviations from predicted theoretical mass for all five proteins. Complementary tandem MS/MS analysis revealed the presence of an additional pyroGlu residue on the N-termini of the two Mt. Sinai RBD constructs, as well as on the N-terminus of the full-length spike protein from which they were derived, thus explaining the observed mass shift and definitively establishing the spike protein N-terminal sequence. Moreover, the observed mass additions for the three Ragon Institute RBD constructs were identified as variable N-terminal cleavage points within the signal peptide sequence employed for recombinant expression. To resolve this issue and minimize heterogeneity for further seropositivity assay development, the best-performing RBD construct was further optimized to exhibit complete homogeneity, as determined by both intact mass and tandem MS/MS analysis. This new RBD construct has been validated for seropositivity assay performance, is available to the greater scientific community, and is recommended for use in future assay development.

Sequential Ion-Ion Reactions for Enhanced Gas-Phase Sequencing of Large Intact Proteins in a Tribrid Orbitrap Mass Spectrometer

Obtaining extensive sequencing of an intact protein is essential in order to simultaneously determine both the nature and exact localization of chemical and genetic modifications which distinguish different proteoforms arising from the same gene. To effectively achieve such characterization, it is necessary to take advantage of the analytical potential offered by the top-down mass spectrometry approach to protein sequence analysis. However, as a protein increases in size, its gas-phase dissociation produces overlapping, low signal-to-noise fragments. The application of advanced ion dissociation techniques such as electron transfer dissociation (ETD) and ultraviolet photodissociation (UVPD) can improve the sequencing results compared to slow-heating techniques such as collisional dissociation; nonetheless, even ETD- and UVPD-based approaches have thus far fallen short in their capacity to reliably enable extensive sequencing of proteoforms ≥30 kDa. To overcome this issue, we have applied proton transfer charge reduction (PTCR) to limit signal overlap in tandem mass spectra (MS²) produced by ETD (alone or with supplemental ion activation, EThcD). Compared to conventional MS² experiments, following ETD/EThcD MS² with PTCR MS³ prior to m/z analysis of deprotonated product ions in the Orbitrap mass analyzer proved beneficial for the identification of additional large protein fragments (≥10 kDa), thus improving the overall sequencing and in particular the coverage of the central portion of all four analyzed proteins spanning from 29 to 56 kDa. Specifically, PTCR-based data acquisition led to 39% sequence coverage for the 56 kDa glutamate dehydrogenase, which was further increased to 44% by combining fragments obtained via HCD followed by PTCR MS³.

Artificial intelligence for proteomics and biomarker discovery

There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have now taken hold in all omics disciplines and increasingly call for multi-omics integration as well as data interpretation by artificial intelligence technologies. Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliability of analytical workflows because experimental results should agree with predictions in a multi-dimensional data landscape. Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we discuss model transparency and explainability and data privacy that are required to deploy MS-based biomarkers in clinical settings.

ProteoCombiner: integrating bottom-up with top-down proteomics data for improved proteoform assessment

MOTIVATION

We present a high-performance software integrating shotgun with top-down proteomic data. The tool can deal with multiple experiments and search engines. Enable rapid and easy visualization, manual validation and comparison of the identified proteoform sequences including the post-translational modification characterization.

RESULTS

We demonstrate the effectiveness of our approach on a large-scale Escherichia coli dataset; ProteoCombiner unambiguously shortlisted proteoforms among those identified by the multiple search engines.

AVAILABILITY AND IMPLEMENTATION

ProteoCombiner, a demonstration video and user tutorial are freely available at https://proteocombiner.pasteur.fr, for academic use; all data are thus available from the ProteomeXchange consortium (identifier PXD017618).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

The ubiquitin proteoform problem

The diversity of ubiquitin modifications is immense. A protein can be monoubiquitylated, multi-monoubiquitylated, and polyubiquitylated with chains varying in size and shape. Ubiquitin itself can be adorned with other ubiquitin-like proteins and smaller functional groups. Considering different combinations of post-translational modifications can give rise to distinct biological outcomes, characterizing ubiquitylated proteoforms of a given protein is paramount. In this Opinion, we review recent advances in detecting and quantifying various ubiquitin proteoforms using mass spectrometry.

DMIL-IsoFun: predicting isoform function using deep multi-instance learning

MOTIVATION

Alternative splicing creates the considerable proteomic diversity and complexity on relatively limited genome. Proteoforms translated from alternatively spliced isoforms of a gene actually execute the biological functions of this gene, which reflect the functional knowledge of genes at a finer granular level. Recently, some computational approaches have been proposed to differentiate isoform functions using sequence and expression data. However, their performance is far from being desirable, mainly due to the imbalance and lack of annotations at isoform-level, and the difficulty of modeling gene-isoform relations.

RESULT

We propose a deep multi-instance learning based framework (DMIL-IsoFun) to differentiate the functions of isoforms. DMIL-IsoFun firstly introduces a multi-instance learning convolution neural network trained with isoform sequences and gene-level annotations to extract the feature vectors and initialize the annotations of isoforms, and then uses a class-imbalance Graph Convolution Network to refine the annotations of individual isoforms based on the isoform co-expression network and extracted features. Extensive experimental results show that DMIL-IsoFun improves the Smin and Fmax of state-of-the-art solutions by at least 29.6% and 40.8%. The effectiveness of DMIL-IsoFun is further confirmed on a testbed of human multiple-isoform genes, and Maize isoforms related with photosynthesis.

AVAILABILITY

The code and data are available at http://www.sdu-idea.cn/codes.php?name=DMIL-Isofun.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.

Automated Assignment of Proteoform Classification Levels

Proteoform identification is required to fully understand the biological diversity present in a sample. However, these identifications are often ambiguous because of the challenges in analyzing full length proteins by mass spectrometry. A five-level proteoform classification system was recently developed to delineate the ambiguity of proteoform identifications and to allow for comparisons across software platforms and acquisition methods. Widespread adoption of this system requires software tools to provide classification of the proteoform identifications. We describe here an implementation of the five-level classification system in the software program MetaMorpheus, which provides both bottom-up and top-down identifications. Additionally, we developed a stand-alone program called ProteoformClassifier that allows users to classify proteoform results from any search program, provided that the program writes output that includes the information necessary to evaluate proteoform ambiguity. This stand-alone program includes a small test file and database to evaluate if a given program provides sufficient information to evaluate ambiguity. If the program does not, then ProteoformClassifier provides meaningful feedback to assist developers with implementing the classification system. We tested currently available top-down software programs and found that none of them (other than MetaMorpheus) provided sufficient information regarding identification ambiguity to permit classification.

Characterization and Quantification of Selenoprotein P: Challenges to Mass Spectrometry

Selenoprotein P (SELENOP) is an emerging marker of the nutritional status of selenium and of various diseases, however, its chemical characteristics still need to be investigated and methods for its accurate quantitation improved. SELENOP is unique among selenoproteins, as it contains multiple genetically encoded SeCys residues, whereas all the other characterized selenoproteins contain just one. SELENOP occurs in the form of multiple isoforms, truncated species and post-translationally modified variants which are relatively poorly characterized. The accurate quantification of SELENOP is contingent on the availability of specific primary standards and reference methods. Before recombinant SELENOP becomes available to be used as a primary standard, careful investigation of the characteristics of the SELENOP measured by electrospray MS and strict control of the recoveries at the various steps of the analytical procedures are strongly recommended. This review critically discusses the state-of-the-art of analytical approaches to the characterization and quantification of SELENOP. While immunoassays remain the standard for the determination of human and animal health status, because of their speed and simplicity, mass spectrometry techniques offer many attractive and complementary features that are highlighted and critically evaluated.

Mesh Fragmentation Improves Dissociation Efficiency in Top-down Proteomics

Top-down proteomics is a key mass spectrometry-based technology for comprehensive analysis of proteoforms. Proteoforms exhibit multiple high charge states and isotopic forms in full MS scans. The dissociation behavior of proteoforms in different charge states and subjected to different collision energies is highly variable. The current widely employed data-dependent acquisition (DDA) method selects a narrow m/z range (corresponding to a single proteoform charge state) for dissociation from the most abundant precursors. We describe here Mesh, a novel dissociation strategy, to dissociate multiple charge states of one proteoform with multiple collision energies. We show that the Mesh strategy has the potential to generate fragment ions with improved sequence coverage and improve identification ratios in top-down proteomic analyses of complex samples. The strategy is implemented within an open-source instrument control software program named MetaDrive to perform real time deconvolution and precursor selection.

Capillary Zone Electrophoresis-Electron-Capture Collision-Induced Dissociation on a Quadrupole Time-of-Flight Mass Spectrometer for Top-Down Characterization of Intact Proteins

Mass spectrometry (MS)-based denaturing top-down proteomics (dTDP) requires high-capacity separation and extensive gas-phase fragmentation of proteoforms. Herein, we coupled capillary zone electrophoresis (CZE) to electron-capture collision-induced dissociation (ECciD) on an Agilent 6545 XT quadrupole time-of-flight (Q-TOF) mass spectrometer for dTDP for the first time. During ECciD, the protein ions were first fragmented using ECD, followed by further activation and fragmentation by applying a CID potential. In this pilot study, we optimized the CZE-ECciD method for small proteins (lower than 20 kDa) regarding the charge state of protein parent ions for fragmentation and the CID potential applied to maximize the protein backbone cleavage coverage and the number of sequence-informative fragment ions. The CZE-ECciD Q-TOF platform provided extensive backbone cleavage coverage for three standard proteins lower than 20 kDa from only single charge states in a single CZE-MS/MS run in the targeted MS/MS mode, including ubiquitin (97%, +7, 8.6 kDa), superoxide dismutase (SOD, 87%, +17, 16 kDa), and myoglobin (90%, +16, 17 kDa). The CZE-ECciD method produced comparable cleavage coverage of small proteins (i.e., myoglobin) with direct-infusion MS studies using electron transfer dissociation (ETD), activated ion-ETD, and combinations of ETD and collision-based fragmentation on high-end orbitrap mass spectrometers. The results render CZE-ECciD a new tool for dTDP to enhance both separation and gas-phase fragmentation of proteoforms.

Novel Strategies to Address the Challenges in Top-Down Proteomics

Top-down mass spectrometry (MS)-based proteomics is a powerful technology for comprehensively characterizing proteoforms to decipher post-translational modifications (PTMs) together with genetic variations and alternative splicing isoforms toward a proteome-wide understanding of protein functions. In the past decade, top-down proteomics has experienced rapid growth benefiting from groundbreaking technological advances, which have begun to reveal the potential of top-down proteomics for understanding basic biological functions, unraveling disease mechanisms, and discovering new biomarkers. However, many challenges remain to be comprehensively addressed. In this Account & Perspective, we discuss the major challenges currently facing the top-down proteomics field, particularly in protein solubility, proteome dynamic range, proteome complexity, data analysis, proteoform-function relationship, and analytical throughput for precision medicine. We specifically review the major technology developments addressing these challenges with an emphasis on our research group's efforts, including the development of top-down MS-compatible surfactants for protein solubilization, functionalized nanoparticles for the enrichment of low-abundance proteoforms, strategies for multidimensional chromatography separation of proteins, and a new comprehensive user-friendly software package for top-down proteomics. We have also made efforts to connect proteoforms with biological functions and provide our visions on what the future holds for top-down proteomics.

Enhancing Top-Down Proteomics of Brain Tissue with FAIMS

Proteomic investigations of Alzheimer's and Parkinson's disease have provided valuable insights into neurodegenerative disorders. Thus far, these investigations have largely been restricted to bottom-up approaches, hindering the degree to which one can characterize a protein's "intact" state. Top-down proteomics (TDP) overcomes this limitation; however, it is typically limited to observing only the most abundant proteoforms and of a relatively small size. Therefore, fractionation techniques are commonly used to reduce sample complexity. Here, we investigate gas-phase fractionation through high-field asymmetric waveform ion mobility spectrometry (FAIMS) within TDP. Utilizing a high complexity sample derived from Alzheimer's disease (AD) brain tissue, we describe how the addition of FAIMS to TDP can robustly improve the depth of proteome coverage. For example, implementation of FAIMS with external compensation voltage (CV) stepping at -50, -40, and -30 CV could more than double the mean number of non-redundant proteoforms, genes, and proteome sequence coverage compared to without FAIMS. We also found that FAIMS can influence the transmission of proteoforms and their charge envelopes based on their size. Importantly, FAIMS enabled the identification of intact amyloid beta (Aβ) proteoforms, including the aggregation-prone Aβ1-42 variant which is strongly linked to AD. Raw data and associated files have been deposited to the ProteomeXchange Consortium via the MassIVE data repository with data set identifier PXD023607.

Serum N-glycan profiles differ for various breast cancer subtypes

Breast cancer is the most prevalent cancer in women. Early detection of this disease improves survival and therefore population screenings, based on mammography, are performed. However, the sensitivity of this screening modality is not optimal and new screening methods, such as blood tests, are being explored. Most of the analyses that aim for early detection focus on proteins in the bloodstream. In this study, the biomarker potential of total serum N-glycosylation analysis was explored with regard to detection of breast cancer. In an age-matched case-control setup serum protein N-glycan profiles from 145 breast cancer patients were compared to those from 171 healthy individuals. N-glycans were enzymatically released, chemically derivatized to preserve linkage-specificity of sialic acids and characterized by high resolution mass spectrometry. Logistic regression analysis was used to evaluate associations of specific N-glycan structures as well as N-glycosylation traits with breast cancer. In a case-control comparison three associations were found, namely a lower level of a two triantennary glycans and a higher level of one tetraantennary glycan in cancer patients. Of note, various other N-glycomic signatures that had previously been reported were not replicated in the current cohort. It was further evaluated whether the lack of replication of breast cancer N-glycomic signatures could be partly explained by the heterogenous character of the disease since the studies performed so far were based on cohorts that included diverging subtypes in different numbers. It was found that serum N-glycan profiles differed for the various cancer subtypes that were analyzed in this study.

A Mini Review on Capillary Isoelectric Focusing-Mass Spectrometry for Top-Down Proteomics

Mass spectrometry (MS)-based top-down proteomics (TDP) requires high-resolution separation of proteoforms before electrospray ionization (ESI)-MS and tandem mass spectrometry (MS/MS). Capillary isoelectric focusing (cIEF)-ESI-MS and MS/MS could be an ideal method for TDP because cIEF can enable separation of proteoforms based on their isoelectric points (pIs) with ultra-high resolution. cIEF-ESI-MS has been well-recognized for protein characterization since 1990s. However, the widespread adoption of cIEF-MS for the characterization of proteoforms had been impeded by several technical challenges, including the lack of highly sensitive and robust ESI interface for coupling cIEF to MS, ESI suppression of analytes from ampholytes, and the requirement of manual operations. In this mini review, we summarize the technical improvements of cIEF-ESI-MS for characterizing proteoforms and highlight some recent applications to hydrophobic proteins, urinary albumin variants, charge variants of monoclonal antibodies, and large-scale TDP of complex proteomes.

IsoDA: Isoform-Disease Association Prediction by Multiomics Data Fusion

A gene can be spliced into different isoforms by alternative splicing, which contributes to the functional diversity of protein species. Computational prediction of gene-disease associations (GDAs) has been studied for decades. However, the process of identifying the isoform-disease associations (IDAs) at a large scale is rarely explored, which can decipher the pathology at a more granular level. The main bottleneck is the lack of IDAs in current databases and the multilevel omics data fusion. To bridge this gap, we propose a computational approach called Isoform-Disease Association prediction by multiomics data fusion (IsoDA) to predict IDAs. Based on the relationship between a gene and its spliced isoforms, IsoDA first introduces a dispatch and aggregation term to dispatch gene-disease associations to individual isoforms, and reversely aggregate these dispatched associations to their hosting genes. At the same time, it fuses the genome, transcriptome, and proteome data by joint matrix factorization to improve the prediction of IDAs. Experimental results show that IsoDA significantly outperforms the related state-of-the-art methods at both the gene level and isoform level. A case study further shows that IsoDA credibly identifies three isoforms spliced from apolipoprotein E, which have individual associations with Alzheimer's disease, and two isoforms spliced from vascular endothelial growth factor A, which have different associations with coronary heart disease. The codes of IsoDA are available at http://mlda.swu.edu.cn/codes.php?name=IsoDA.

Mapping Isoform Abundance and Interactome of the Endogenous TMPRSS2-ERG Fusion Protein by Orthogonal Immunoprecipitation-Mass Spectrometry Assays

TMPRSS2-ERG gene fusion, a molecular alteration found in nearly half of primary prostate cancer cases, has been intensively characterized at the transcript level. However limited studies have explored the molecular identity and function of the endogenous fusion at the protein level. Here, we developed immunoprecipitation-mass spectrometry assays for the measurement of a low-abundance T1E4 TMPRSS2-ERG fusion protein, its isoforms, and its interactome in VCaP prostate cancer cells. Our assays quantified total ERG (∼27,000 copies/cell) and its four unique isoforms and revealed that the T1E4-ERG isoform accounted for 52 ± 3% of the total ERG protein in VCaP cells, and 50 ± 11% in formalin-fixed paraffin-embedded prostate cancer tissues. For the first time, the N-terminal peptide (methionine-truncated and N-acetylated TASSSSDYGQTSK) unique for the T1/E4 fusion was identified. ERG interactome profiling with the C-terminal, but not the N-terminal, antibodies identified 29 proteins, including mutually exclusive BRG1- and BRM-associated canonical SWI/SNF chromatin remodeling complexes. Our sensitive and selective IP-SRM assays present alternative tools to quantify ERG and its isoforms in clinical samples, thus paving the way for development of more accurate diagnostics of prostate cancer.

Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal

The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number of spectra that can be matched to peptides but also greatly advances the understanding of the biological roles of PTMs through the identification, and the thereby facilitated quantification, of peptidoforms (peptide sequences and their potential PTMs). Whereas the benefits of combining results from multiple protein database search engines have been previously established, similar approaches for OMS results have been missing so far. Here we compare and combine results from three different OMS engines, demonstrating an increase in peptide spectrum matches of 8-18%. The unification of search results furthermore allows for the combined downstream processing of search results, including the mapping to potential PTMs. Finally, we test for the ability of OMS engines to identify glycosylated peptides. The implementation of these engines in the Python framework Ursgal facilitates the straightforward application of the OMS with unified parameters and results files, thereby enabling yet unmatched high-throughput, large-scale data analysis.

Functionally Integrated Top-Down Proteomics for Standardized Assessment of Human Induced Pluripotent Stem Cell-Derived Engineered Cardiac Tissues

Three-dimensional (3D) human induced pluripotent stem cell-derived engineered cardiac tissues (hiPSC-ECTs) have emerged as a promising alternative to two-dimensional hiPSC-cardiomyocyte monolayer systems because hiPSC-ECTs are a closer representation of endogenous cardiac tissues and more faithfully reflect the relevant cardiac pathophysiology. The ability to perform functional and molecular assessments using the same hiPSC-ECT construct would allow for more reliable correlation between observed functional performance and underlying molecular events, and thus is critically needed. Herein, for the first time, we have established an integrated method that permits sequential assessment of functional properties and top-down proteomics from the same single hiPSC-ECT construct. We quantitatively determined the differences in isometric twitch force and the sarcomeric proteoforms between two groups of hiPSC-ECTs that differed in the duration of time of 3D-ECT culture. Importantly, by using this integrated method we discovered a new and strong correlation between the measured contractile parameters and the phosphorylation levels of alpha-tropomyosin between the two groups of hiPSC-ECTs. The integration of functional assessments together with molecular characterization by top-down proteomics in the same hiPSC-ECT construct enables a holistic analysis of hiPSC-ECTs to accelerate their applications in disease modeling, cardiotoxicity, and drug discovery. Data are available via ProteomeXchange with identifier PXD022814.

Quantitative Mass Spectrometry-Based Proteomics: An Overview

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

Precise Characterization of KRAS4B Proteoforms by Combining Immunoprecipitation with Top-Down Mass Spectrometry

The characterization of biologically relevant post-translational modifications (PTMs) on KRAS4B has historically been carried out through methodologies such as immunoblotting with PTM-specific antibodies or peptide-based proteomic methods. While these methods have the potential to identify a given PTM on KRAS4B, they are incapable of characterizing or distinguishing the different molecular forms or proteoforms of KRAS4B from those of related RAS isoforms. We present a method that combines immunoprecipitation of KRAS4B with top-down mass spectrometry (IP-TDMS), thus enabling the precise characterization of intact KRAS4B proteoforms. We provide detailed protocols for the IP, LC-MS/MS, and data analysis comprising a successful IP-TDMS assay in the contexts of cancer cell lines and tissue samples.

Post-transcriptional control of cellular differentiation by the RNA exosome complex

Given the complexity of intracellular RNA ensembles and vast phenotypic remodeling intrinsic to cellular differentiation, it is instructive to consider the role of RNA regulatory machinery in controlling differentiation. Dynamic post-transcriptional regulation of protein-coding and non-coding transcripts is vital for establishing and maintaining proteomes that enable or oppose differentiation. By contrast to extensively studied transcriptional mechanisms governing differentiation, many questions remain unanswered regarding the involvement of post-transcriptional mechanisms. Through its catalytic activity to selectively process or degrade RNAs, the RNA exosome complex dictates the levels of RNAs comprising multiple RNA classes, thereby regulating chromatin structure, gene expression and differentiation. Although the RNA exosome would be expected to control diverse biological processes, studies to elucidate its biological functions and how it integrates into, or functions in parallel with, cell type-specific transcriptional mechanisms are in their infancy. Mechanistic analyses have demonstrated that the RNA exosome confers expression of a differentiation regulatory receptor tyrosine kinase, downregulates the telomerase RNA component TERC, confers genomic stability and promotes DNA repair, which have considerable physiological and pathological implications. In this review, we address how a broadly operational RNA regulatory complex interfaces with cell type-specific machinery to control cellular differentiation.

Automated Capillary Isoelectric Focusing-Tandem Mass Spectrometry for Qualitative and Quantitative Top-Down Proteomics

Top-down proteomics (TDP) aims to delineate proteomes in a proteoform-specific manner, which is vital for accurately understanding protein function in cellular processes. It requires high-capacity separation of proteoforms before mass spectrometry (MS) and tandem MS (MS/MS). Capillary isoelectric focusing (cIEF)-MS has been recognized as a useful tool for TDP in the 1990s because cIEF is capable of high-resolution separation of proteoforms. Previous cIEF-MS studies concentrated on measuring the protein's mass without MS/MS, impeding the confident proteoform identification in complex samples and the accurate localization of post-translational modifications on proteoforms. Herein, for the first time, we present automated cIEF-MS/MS-based TDP for large-scale delineation of proteoforms in complex proteomes. Single-shot cIEF-MS/MS identified 711 proteoforms from an Escherichia coli (E. coli) proteome consuming only nanograms of proteins. Coupling two-dimensional size-exclusion chromatography (SEC)-cIEF to ESI-MS/MS enabled the identification of nearly 2000 proteoforms from the E. coli proteome. Label-free quantitative TDP of zebrafish male and female brains using SEC-cIEF-MS/MS quantified thousands of proteoforms and revealed sex-dependent proteoform profiles in brains. Particularly, we discovered several proteolytic proteoforms of pro-opiomelanocortin and prodynorphin with significantly higher abundance in male zebrafish brains as potential endogenous hormone proteoforms. Multilevel quantitative proteomics (TDP and bottom-up proteomics) of the brains revealed that the majority of proteoforms having statistically significant difference in abundance between genders showed no abundance difference at the protein group level. This work represents the first multilevel quantitative proteomics study of sexual dimorphism of the brain.