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G protein-coupled receptors (GPCRs) are the largest family of membrane receptors in humans. They mediate nearly all aspects of human physiology and thus are of high therapeutic interest. GPCR signaling is regulated in space and time by receptor phosphorylation. It is believed that different phosphorylation states are possible for a single receptor, and each encodes for unique signaling outcomes. Methods to determine the phosphorylation status of GPCRs are critical for understanding receptor physiology and signaling properties of GPCR ligands and therapeutics. However, common proteomic techniques have provided limited quantitative information regarding total receptor phosphorylation stoichiometry, relative abundances of isomeric modification states, and temporal dynamics of these parameters. Here, we report a novel middle-down proteomic strategy and parallel reaction monitoring (PRM) to quantify the phosphorylation states of the C-terminal tail of metabotropic glutamate receptor 2 (mGluR2). By this approach, we found that mGluR2 is subject to both basal and agonist-induced phosphorylation at up to four simultaneous sites with varying probability. Using a PRM tandem mass spectrometry methodology, we localized the positions and quantified the relative abundance of phosphorylations following treatment with an agonist. Our analysis showed that phosphorylation within specific regions of the C-terminal tail of mGluR2 is sensitive to receptor activation, and subsequent site-directed mutagenesis of these sites identified key regions which tune receptor sensitivity. This study demonstrates that middle-down purification followed by label-free quantification is a powerful, quantitative, and accessible tool for characterizing phosphorylation states of GPCRs and other challenging proteins.

The high-throughput quantification of intact proteoforms using a label-free approach is typically performed on proteins in the 0-30 kDa mass range extracted from whole cell or tissue lysates. Unfortunately, even when high-resolution separation of proteoforms is achieved by either high performance liquid chromatography or capillary electrophoresis, the number of proteoforms that can be identified and quantified is inevitably limited by the inherent sample complexity. Here we benchmark label-free quantification of proteoforms of Escherichia coli by applying gas-phase fractionation (GPF) via field asymmetric ion mobility spectrometry (FAIMS). Recent advances in Orbitrap instrumentation have enabled the acquisition of high-quality intact and fragmentation mass spectra without the need for averaging time-domain transients prior to Fourier transform. The resulting speed improvements allowed for the application of multiple FAIMS compensation voltages in the same liquid chromatography-tandem mass spectrometry experiment without increasing the overall data acquisition cycle. As a result, the application of FAIMS to label-free quantification based on intact mass spectra substantially increases the number of both identified and quantified proteoforms without penalizing quantification accuracy in comparison to traditional label-free experiments that do not adopt GPF.

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.

The Human Proteoform Atlas (HPfA) is a web-based repository of experimentally verified human proteoforms on-line at http://human-proteoform-atlas.org and is a direct descendant of the Consortium of Top-Down Proteomics’ (CTDP) Proteoform Atlas. Proteoforms are the specific forms of protein molecules expressed by our cells and include the unique combination of post-translational modifications (PTMs), alternative splicing and other sources of variation deriving from a specific gene. The HPfA uses a FAIR system to assign persistent identifiers to proteoforms which allows for redundancy calling and tracking from prior and future studies in the growing community of proteoform biology and measurement. The HPfA is organized around open ontologies and enables flexible classification of proteoforms. To achieve this, a public registry of experimentally verified proteoforms was also created. Submission of new proteoforms can be processed through email vianrtdphelp@northwestern.edu, and future iterations of these proteoform atlases will help to organize and assign function to proteoforms, their PTMs and their complexes in the years ahead.

Imaging of proteoforms in human tissues is hindered by low molecular specificity and limited proteome coverage. Here, we introduce proteoform imaging mass spectrometry (PiMS), which increases the size limit for proteoform detection and identification by fourfold compared to reported methods and reveals tissue localization of proteoforms at

Protein fragmentation is a critical component of top-down proteomics, enabling gene-specific protein identification and full proteoform characterization. The factors that influence protein fragmentation include precursor charge, structure, and primary sequence, which have been explored extensively for collision-induced dissociation (CID). Recently, noticeable differences in CID-based fragmentation were reported for native versus denatured proteins, motivating the need for scoring metrics that are tailored specifically to native top-down mass spectrometry (nTDMS). To this end, position and intensity were tracked for 10,252 fragment ions produced by higher-energy collisional dissociation (HCD) of 159 native monomers and 70 complexes. We used published structural data to explore the relationship between fragmentation and protein topology and revealed that fragmentation events occur at a large range of relative residue solvent accessibility. Additionally, our analysis found that fragment ions at sites with an N-terminal aspartic acid or a C-terminal proline make up on average 40 and 27%, respectively, of the total matched fragment ion intensity in nTDMS. Percent intensity contributed by each amino acid was determined and converted into weights to (1) update the previously published C-score and (2) construct a native Fragmentation Propensity Score. Both scoring systems showed an improvement in protein identification or characterization in comparison to traditional methods and overall increased confidence in results with fewer matched fragment ions but with high probability nTDMS fragmentation patterns. Given the rise of nTDMS as a tool for structural mass spectrometry, we forward these scoring metrics as new methods to enhance analysis of nTDMS data.

A new Orbitrap-based ion analysis procedure is shown to be possible by determining the direct charge for numerous individual protein ions to generate true mass spectra. The deployment of an Orbitrap system for charge detection enables the characterization of highly complicated mixtures of proteoforms and their complexes in both denatured and native modes of operation, revealing information not obtainable by traditional measurement of an ensemble of ions.

Top-down proteomics studies intact proteoform mixtures and offers important advantages over more common bottom-up proteomics technologies, as it avoids the protein inference problem. However, achieving complete molecular characterization of investigated proteoforms using existing technologies remains a fundamental challenge for top-down proteomics. Here, we benchmark the performance of ultraviolet photodissociation (UVPD) using 213 nm photons generated by a solid-state laser applied to the study of intact proteoforms from three organisms. Notably, the described UVPD setup applies multiple laser pulses to induce ion dissociation, and this feature can be used to optimize the fragmentation outcome based on the molecular weight of the analyzed biomolecule. When applied to complex proteoform mixtures in high-throughput top-down proteomics, 213 nm UVPD demonstrated a high degree of complementarity with the most employed fragmentation method in proteomics studies, higher-energy collisional dissociation (HCD). UVPD at 213 nm offered higher average proteoform sequence coverage and degree of proteoform characterization (including localization of post-translational modifications) than HCD. However, previous studies have shown limitations in applying database search strategies developed for HCD fragmentation to UVPD spectra which contains up to nine fragment ion types. We therefore performed an analysis of the different UVPD product ion type frequencies. From these data, we developed an ad hoc fragment matching strategy and determined the influence of each possible ion type on search outcomes. By paring down the number of ion types considered in high-throughput UVPD searches from all types down to the four most abundant, we were ultimately able to achieve deeper proteome characterization with UVPD. Lastly, our detailed product ion analysis also revealed UVPD cleavage propensities and determined the presence of a product ion produced specifically by 213 nm photons. All together, these observations could be used to better elucidate UVPD dissociation mechanisms and improve the utility of the technique for proteomic applications.

The effectiveness of any proteomics database search depends on the theoretical candidate information contained in the protein database. Unfortunately, candidate entries from protein databases such as UniProt rarely contain all the post-translational modifications (PTMs), disulfide bonds, or endogenous cleavages of interest to researchers. These omissions can limit discovery of novel and biologically important proteoforms. Conversely, searching for a specific proteoform becomes a computationally difficult task for heavily modified proteins. Both situations require updates to the database through user-annotated entries. Unfortunately, manually creating properly formatted UniProt Extensible Markup Language (XML) files is tedious and prone to errors. ProSight Annotator solves these issues by providing a graphical interface for adding user-defined features to UniProt-formatted XML files for better informed proteoform searches. It can be downloaded from http://prosightannotator.northwestern.edu.

There is the need to represent in a standard manner all the possible variations of a protein or peptide primary sequence, including both artefactual and post-translational modifications of peptides and proteins. With that overall aim, here, the Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has developed a notation, called ProForma 2.0, which is a substantial extension of the original ProForma notation, developed by the Consortium for Top-Down Proteomics (CTDP). ProForma 2.0 aims to unify the representation of proteoforms and peptidoforms. Therefore, this notation supports use cases needed for bottom-up and middle/topdown proteomics approaches and allows the encoding of highly modified proteins and peptides using a human and machine-readable string. ProForma 2.0 covers encoding protein modification names and accessions, cross-linking reagents including disulfides, glycans, modifications encoded using mass shifts and/or via chemical formulas, labile and C or N-terminal modifications, ambiguity in the modification position and representation of atomic isotopes, among other use cases. Notational conventions are based on public controlled vocabularies and ontologies. Detailed information about the notation and existing implementations are available at http://www.psidev.info/proforma and at the corresponding GitHub repository (https://github.com/HUPO-PSI/proforma).

Unraveling the complexity of biological systems relies on the development of new approaches for spatially resolved proteoform-specific analysis of the proteome. Top-down proteomics is a powerful tool, which has been used for the identification of thousands of proteoforms in biological samples. Herein, we present a first spatially resolved top-down proteomics analysis of biological tissues using nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI MSI). Nano-DESI generates multiply charged protein ions, which is advantageous for their structural characterization using tandem mass spectrometry (MS/MS). Proof-of-concept experiments demonstrate that the nano-DESI MSI combined with on-tissue top-down proteomics is ideally suited for the proteoform-selective imaging of thin tissue sections. Using rat brain tissue as a model system, we provide the first evidence of the differential proteoform expression in different regions of the brain.

Imaging of proteoforms in human tissues is hindered by low molecular specificity and limited proteome coverage. Here, we introduce proteoform imaging mass spectrometry (PiMS), which increases the size limit for proteoform detection and identification by 4-fold compared to reported methods, and reveals tissue localization of proteoforms at 2MS). We demonstrate the first proteoform imaging of human kidney, identifying 169 of 400 proteoforms TeaserNano-DESI combined with individual ion mass spectrometry generates images of proteoforms up to 70 kDa.

Unraveling the complexity of biological systems relies on the development of new approaches for spatially resolved proteoform-specific analysis of the proteome. Top-down proteomics is a powerful tool, which has been used for the identification of thousands of proteoforms in biological samples. Herein, we present a first spatially resolved top-down proteomics analysis of biological tissues using nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI MSI). Nano-DESI generates multiply charged protein ions, which is advantageous for their structural characterization using tandem mass spectrometry (MS/MS). Proof-of-concept experiments demonstrate that the nano-DESI MSI combined with on-tissue top-down proteomics is ideally suited for the proteoform-selective imaging of thin tissue sections. Using rat brain tissue as a model system, we provide the first evidence of the differential proteoform expression in different regions of the brain.

Methods of antibody detection are used to assess exposure or immunity to a pathogen. Here, we presentIg-MS, a novel serological readout that captures the immunoglobulin (Ig) repertoire at molecular resolution, including entire variable regions in Ig light and heavy chains. Ig-MS uses recent advances in protein mass spectrometry (MS) for multi-parametric readout of antibodies, with new metrics like Ion Titer (IT) and Degree of Clonality (DoC) capturing the heterogeneity and relative abundance of individual clones without sequencing of B cells. We apply Ig-MS to plasma from subjects with severe & mild COVID-19, using the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2 as the bait for antibody capture. Importantly, we report a new data type for human serology, with compatibility to any recombinant antigen to gauge our immune responses to vaccination, pathogens, or autoimmune disorders.

Within the last several years, top-down proteomics has emerged as a high throughput technique for protein and proteoform identification. This technique has the potential to identify and characterize thousands of proteoforms within a single study, but the absence of accurate false discovery rate (FDR) estimation could hinder the adoption and consistency of top-down proteomics in the future. In automated identification and characterization of proteoforms, FDR calculation strongly depends on the context of the search. The context includes MS data quality, the database being interrogated, the search engine, and the parameters of the search. Particular to top-down proteomics-there are four molecular levels of study: proteoform spectral match (PrSM), protein, isoform, and proteoform. Here, a context-dependent framework for calculating an accurate FDR at each level was designed, implemented, and validated against a manually curated training set with 546 confirmed proteoforms. We examined several search contexts and found that an FDR calculated at the PrSM level under-reported the true FDR at the protein level by an average of 24-fold. We present a new open-source tool, the TDCD_FDR_Calculator, which provides a scalable, context-dependent FDR calculation that can be applied post-search to enhance the quality of results in top-down proteomics from any search engine.

High-throughput top-down proteomic experiments directly identify proteoforms in complex mixtures, making high quality tandem mass spectra necessary to deeply characterize proteins with many sources of variation. Collision-based dissociation methods offer expedient data acquisition, but often fail to extensively fragment proteoforms for thorough analysis. Electron-driven dissociation methods are a popular alternative approach, especially for precursor ions with high charge density. Combining infrared photo-activation concurrent with electron transfer dissociation (ETD) reactions, i.e., activated ion ETD (AI-ETD), can significantly improve ETD characterization of intact proteins, but benefits of AI-ETD have yet to be quantified in high-throughput top-down proteomics. Here we report the first application of AI-ETD to LC-MS/MS characterization of intact proteins (

Recent progress in top-down proteomics has driven the demand for chromatographic methods compatible with mass spectrometry (MS) that can separate intact proteins. Hydrophilic interaction liquid chromatography (HILIC) has recently shown good potential for the characterization of glycoforms of intact proteins. In the present study, we demonstrate that HILIC can separate a wide range of proteins exhibiting orthogonal selectivity with respect to reversed-phase LC (RPLC). However, the application of HILIC to the analysis of low abundance proteins (e.g., in proteomics analysis) is hampered by low volume loadability, hindering down-scaling of the method to column diameters below 2.1 mm. Moreover, HILIC-MS sensitivity is decreased due to ion suppression from the trifluoroacetic acid (TFA) often used as the ion-pair agent to improve the selectivity and efficiency in the analysis of glycoproteins. Here, we introduce a capillary-based HILIC-MS method that overcomes these problems. Our method uses RPLC trap-columns to load and inject the sample, circumventing issues of protein solubility and volume loadability in capillary columns (200 μm ID). The low flow rates and use of a dopant gas in the electrospray interface improve protein-ionization efficiencies and reduce suppression by TFA. Overall, this allows the separation and detection of small protein quantities (down to 5 ng injected on column) as indicated by the analysis of a mixture of model proteins. The potential of the new capillary HILIC-MS is demonstrated by the analysis of a complex cell lysate.

Top-down proteomics (TDP) allows precise determination/characterization of the different proteoforms derived from the expression of a single gene. In this study, we targeted apolipoprotein A-I (ApoA-I), a mediator of high-density-lipoprotein cholesterol efflux (HDL-E), which is inversely associated with coronary heart disease risk. Absolute ApoA-I concentration and allelic variation only partially explain interindividual HDL-E variation. Therefore, we hypothesize that differences in HDL-E are associated with the abundances of different ApoA-I proteoforms. Here, we present a targeted TDP methodology to characterize ApoA-I proteoforms in serum samples and compare their abundances between individuals. We characterized 18 ApoA-I proteoforms using selected-ion monitoring coupled to electron-transfer dissociation mass spectrometry. We then compared the abundances of these proteoforms between two groups of four participants, representing the individuals with highest and lowest HDL-E values within the Chicago Healthy Aging Study ( n = 420). Six proteoforms showed significantly ( p0.0005) higher intensity in high HDL-E individuals: canonical ApoA-I [fold difference (fd) = 1.17], carboxymethylated ApoA-I (fd = 1.24) and, with highest difference, four fatty acylated forms: palmitoylated (fd = 2.16), oleoylated (fd = 2.08), arachidonoylated (fd = 2.31) and one bearing two modifications: palmitoylation and truncation (fd = 2.13). These results demonstrate translational potential for targeted TDP in revealing, with high sensitivity, associations between interindividual proteoform variation and physiological differences underlying disease risk.

Mutations of the KRAS gene are found in human cancers with high frequency and result in the constitutive activation of its protein products. This leads to aberrant regulation of downstream pathways, promoting cell survival, proliferation, and tumorigenesis that drive cancer progression and negatively affect treatment outcomes. Here, we describe a workflow that can detect and quantify mutation-specific consequences of KRAS biochemistry, namely linked changes in posttranslational modifications (PTMs). We combined immunoaffinity enrichment with detection by top-down mass spectrometry to discover and quantify proteoforms with or without the Gly13Asp mutation (G13D) specifically in the KRAS4b isoform. The workflow was applied first to isogenic KRAS colorectal cancer (CRC) cell lines and then to patient CRC tumors with matching KRAS genotypes. In two cellular models, a direct link between the knockout of the mutant G13D allele and the complete nitrosylation of cysteine 118 of the remaining WT KRAS4b was observed. Analysis of tumor samples quantified the percentage of mutant KRAS4b actually present in cancer tissue and identified major differences in the levels of C-terminal carboxymethylation, a modification critical for membrane association. These data from CRC cells and human tumors suggest mechanisms of posttranslational regulation that are highly context-dependent and which lead to preferential production of specific KRAS4b proteoforms.

Over the past decade, advances in mass spectrometry-based proteomics have accelerated brain proteome research aimed at studying the expression, dynamic modification, interaction and function of proteins in the nervous system that are associated with physiological and behavioral processes. With the latest hardware and software improvements in top-down mass spectrometry, the technology has expanded from mere protein profiling to high-throughput identification and quantification of intact proteoforms. Murine systems are broadly used as models to study human diseases. Neuroscientists specifically study the mouse brain from inbred strains to help understand how strain-specific genotype and phenotype affect development, functioning, and disease progression. This work describes the first application of label-free quantitative top-down proteomics to the analysis of the mouse brain proteome. Operating in discovery mode, we determined physiochemical differences in brain tissue from four healthy inbred strains, C57BL/6J, DBA/2J, FVB/NJ, and BALB/cByJ, after probing their intact proteome in the 3.5–30 kDa mass range. We also disseminate these findings using a new tool for top-down proteomics, TDViewer and cataloged them in a newly established Mouse Brain Proteoform Atlas. The analysis of brain tissues from the four strains identified 131 gene products leading to the full characterization of 343 of the 593 proteoforms identified. Within the results, singly and doubly phosphorylated ARPP-21 proteoforms, known to inhibit calmodulin, were differentially expressed across the four strains. Gene ontology (GO) analysis for detected differentially expressed proteoforms also helps to illuminate the similarities and dissimilarities in phenotypes among these inbred strains.

Biomarker profiles of acute rejection in liver transplant recipients could enhance the diagnosis and management of recipients. Our aim was to identify diagnostic proteoform signatures of acute rejection in circulating immune cells, using an emergent "top-down" proteomics methodology. We prepared differentially processed and cryopreserved cell lysates from 26 nonviral liver transplant recipients by molecular weight-based fractionation and analyzed them by mass spectrometry of whole proteins in three steps: (i) Nanocapillary liquid chromatography coupled with high-resolution tandem mass spectrometry; (ii) database searching to identify and characterize intact proteoforms; (iii) data processing through a hierarchical linear model matching the study design to quantify proteoform fold changes in patients with rejection versus normal liver function versus acute dysfunction without rejection. Differentially expressed proteoforms were seen in patients with rejection versus normal and nonspecific controls, most evidently in the cell preparations stored in traditional serum-rich media. Mapping analysis of these proteins back to genes through gene ontology and pathway analysis tools revealed multiple signaling pathways, including inflammation mediated by cytokines and chemokines. Larger studies are needed to validate these novel rejection signatures and test their predictive value for use in clinical management.

The analysis of intact proteins (top-down strategy) by mass spectrometry has great potential to elucidate proteoform variation, including patterns of post-translational modifications (PTMs), which may not be discernable by analysis of peptides alone (bottom-up approach). To maximize sequence coverage and localization of PTMs, various fragmentation modes have been developed to produce fragment ions from deep within intact proteins. Ultraviolet photodissociation (UVPD) has recently been shown to produce high sequence coverage and PTM retention on a variety of proteins, with increasing evidence of efficacy on a chromatographic time scale. However, utilization of UVPD for high-throughput top-down analysis to date has been limited by bioinformatics. Here, we detected 153 proteins and 489 proteoforms using UVPD and 271 proteins and 982 proteoforms using higher-energy collisional dissociation (HCD) in a comparative analysis of HeLa whole-cell lysate by qualitative top-down proteomics. Of the total detected proteoforms, 286 overlapped between the UVPD and HCD datasets, with 68% of proteoforms having C-scores greater than 40 for UVPD and 63% for HCD. The average sequence coverage (28% ± 20% for UVPD versus 17% ± 8% for HCD, p < 0.0001) found to be higher for UVPD than HCD, and with a trend toward improvement in q-value for the UVPD dataset. This study demonstrates the complementarity of UVPD and HCD for more extensive protein profiling and proteoform characterization.

Fragmentation of intact proteins in the gas phase is influenced by amino acid composition, the mass and charge of precursor ions, higher order structure, and the dissociation technique used. The likelihood of fragmentation occurring between a pair of residues is referred to as the fragmentation propensity and is calculated by dividing the total number of assigned fragmentation events by the total number of possible fragmentation events for each residue pair. Here, we describe general fragmentation propensities when performing top-down mass spectrometry (TDMS) using denaturing or native electrospray ionization. A total of 5311 matched fragmentation sites were collected for 131 proteoforms that were analyzed over 165 experiments using native top-down mass spectrometry (nTDMS). These data were used to determine the fragmentation propensities for 399 residue pairs. In comparison to denatured top-down mass spectrometry (dTDMS), the fragmentation pathways occurring either N-terminal to proline or C-terminal to aspartic acid were even more enhanced in nTDMS compared with other residues. More generally, 257/399 (64%) of the fragmentation propensities were significantly altered (P ≤ 0.05) when using nTDMS compared with dTDMS, and of these, 123 were altered by 2-fold or greater. The most notable enhancements of fragmentation propensities for TDMS in native versus denatured mode occurred (1) C-terminal to aspartic acid, (2) between phenylalanine and tryptophan (F|W), and (3) between tryptophan and alanine (W|A). The fragmentation propensities presented here will be of high value in the development of tailored scoring systems used in nTDMS of both intact proteins and protein complexes. Graphical Abstract ᅟ.

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 identified on the liquid chromatography time scale.

Cocaine addiction afflicts nearly 1 million adults in the United States, and to date, there are no known treatments approved for this psychiatric condition. Women are particularly vulnerable to developing a cocaine use disorder and suffer from more serious cardiac consequences than men when using cocaine. Estrogen is one biological factor contributing to the increased risk for females to develop problematic cocaine use. Animal studies have demonstrated that estrogen (17β-estradiol or E2) enhances the rewarding properties of cocaine. Although E2 affects the dopamine system, the molecular and cellular mechanisms of E2-enhanced cocaine reward have not been characterized. In this study, quantitative top-down proteomics was used to measure intact proteins in specific regions of the female mouse brain after mice were trained for cocaine-conditioned place preference, a behavioral test of cocaine reward. Several proteoform changes occurred in the ventral tegmental area after combined cocaine and E2 treatments, with the most numerous proteoform alterations on myelin basic protein, indicating possible changes in white matter structure. There were also changes in histone H4, protein phosphatase inhibitors, cholecystokinin, and calmodulin proteoforms. These observations provide insight into estrogen signaling in the brain and may guide new approaches to treating women with cocaine use disorder.

International audience; To the editorThe term proteoform, introduced in Nature Methods in 2013 (ref. 1), has rapidly gained acceptance in the proteomics community. The challenge and importance of comprehensively identifying proteoforms in complex samples has been recognized, and reports have begun to appear of new platforms towards that end2,3,4,5. However, one interesting central ambiguity has emerged, namely determining precisely what is meant by a ‘proteoform identification’. At present, the only practical approaches for establishing the exact primary structure of a proteoform employ mass spectrometry (MS), and a wide range of MS results claim proteoform identifications6. This seemingly small matter has significant impact, as the ambiguity in what is meant by an ‘identification’ makes it difficult to compare results from different laboratories and approaches. This situation hinders the ability of the community to evaluate technological progress and to efficiently expand biological knowledge.

A new Orbitrap-based single ion analysis procedure is shown to be possible by determining the direct charge on numerous measurements of individual protein ions to generate true mass spectra. The deployment of an Orbitrap system for charge detection enables the characterization of highly complicated mixtures of proteoforms and their complexes in both denatured and native modes of operation, revealing information not obtainable by traditional measurement of an ensemble of ions.

A proteoform is a defined form of a protein derived from a given gene with a specific amino acid sequence and localized post-translational modifications. In top-down proteomic analyses, proteoforms are identified and quantified through mass spectrometric analysis of intact proteins. Recent technological developments have enabled comprehensive proteoform analyses in complex samples, and an increasing number of laboratories are adopting top-down proteomic workflows. In this review, some recent advances are outlined and current challenges and future directions for the field are discussed.

Recent studies utilizing transcriptomics, metabolomics, and bottom up proteomics have identified molecular signatures of kidney allograft pathology. Although these results make significant progress toward non-invasive differential diagnostics of dysfunction of a transplanted kidney, they provide little information on the intact, often modified, protein molecules present during progression of this pathology. Because intact proteins underpin diverse biological processes, measuring the relative abundance of their modified forms promises to advance mechanistic understanding, and might provide a new class of biomarker candidates. Here, we used top down proteomics to inventory the modified forms of whole proteins in peripheral blood mononuclear cells (PBMCs) taken at the time of kidney biopsy for 40 kidney allograft recipients either with healthy transplants or those suffering acute rejection. Supported by gas-phase fragmentation of whole protein ions during tandem mass spectrometry, we identified 344 proteins mapping to 2,905 distinct molecular forms (proteoforms). Using an initial implementation of a label-free approach to quantitative top down proteomics, we obtained evidence suggesting relative abundance changes in 111 proteoforms between the two patient groups. Collectively, our work is the first to catalog intact protein molecules in PBMCs and suggests differentially abundant proteoforms for further analysis.

Histones, and their modifications, are critical components of cellular programming and epigenetic inheritance. Recently, cancer genome sequencing has uncovered driver mutations in chromatin modifying enzymes spurring high interest how such mutations change histone modification patterns. Here, we applied Top-Down mass spectrometry for the characterization of combinatorial modifications (i.e. methylation and acetylation) on full length histone H3 from human cell lines derived from multiple myeloma patients with overexpression of the histone methyltransferase MMSET as the result of a t(4;14) chromosomal translocation. Using the latest in Orbitrap-based technology for clean isolation of isobaric proteoforms containing up to 10 methylations and/or up to two acetylations, we provide extensive characterization of histone H3.1 and H3.3 proteoforms. Differential analysis of modifications by electron-based dissociation recapitulated antagonistic crosstalk between K27 and K36 methylation in H3.1, validating that full-length histone H3 (15 kDa) can be analyzed with site-specific assignments for multiple modifications. It also revealed K36 methylation in H3.3 was affected less by the overexpression of MMSET because of its higher methylation levels in control cells. The co-occurrence of acetylation with a minimum of three methyl groups in H3K9 and H3K27 suggested a hierarchy in the addition of certain modifications. Comparative analysis showed that high levels of MMSET in the myeloma-like cells drove the formation of hypermethyled proteoforms containing H3K36me2 co-existent with the repressive marks H3K9me2/3 and H3K27me2/3. Unique histone proteoforms with such “trivalent hypermethylation” (K9me2/3-K27me2/3-K36me2) were not discovered when H3.1 peptides were analyzed by Bottom-Up. Such disease-correlated proteoforms could link tightly to aberrant transcription programs driving cellular proliferation, and their precise description demonstrates that Top-Down mass spectrometry can now decode crosstalk involving up to three modified sites.

Top-down proteomics is capable of identifying and quantitating unique proteoforms through the analysis of intact proteins. We extended the coverage of the label-free technique, achieving differential analysis of whole proteins

Post-translational modifications (PTMs) at multiple sites can collectively influence protein function but the scope of such PTM coding has been challenging to determine. The number of potential combinatorial patterns of PTMs on a single molecule increases exponentially with the number of modification sites and a population of molecules exhibits a distribution of such “modforms”. Estimating these “modform distributions” is central to understanding how PTMs influence protein function. Although mass-spectrometry (MS) has made modforms more accessible, we have previously shown that current MS technology cannot recover the modform distribution of heavily modified proteins. However, MS data yield linear equations for modform amounts, which constrain the distribution within a high-dimensional, polyhedral “modform region”. Here, we show that linear programming (LP) can efficiently determine a range within which each modform value must lie, thereby approximating the modform region. We use this method on simulated data for mitogen-activated protein kinase 1 with the 7 phosphorylations reported on UniProt, giving a modform region in a 128 dimensional space. The exact dimension of the region is determined by the number of linearly independent equations but its size and shape depend on the data. The average modform range, which is a measure of size, reduces when data from bottom-up (BU) MS, in which proteins are first digested into peptides, is combined with data from top-down (TD) MS, in which whole proteins are analysed. Furthermore, when the modform distribution is structured, as might be expected of real distributions, the modform region for BU and TD combined has a more intricate polyhedral shape and is substantially more constrained than that of a random distribution. These results give the first insights into high-dimensional modform regions and confirm that fast LP methods can be used to analyse them. We discuss the problems of using modform regions with real data, when the actual modform distribution will not be known.

SummaryPost-translational modifications (PTMs) are found on most proteins, particularly on “hub” proteins like the tumor suppressor p53, which has over 100 possible PTM sites. Substantial crosstalk between PTM sites underlies the ability of such proteins to integrate diverse signals and coordinate downstream responses. However, disentangling the combinatorial explosion in global PTM patterns across an entire protein (“modforms”) has been challenging, as conventional peptide-based mass spectrometry strategies (so-called “bottom-up” MS) destroy such global correlations. Alternatively, direct analysis of intact and modified proteins using “top-down” MS retains global information. Here, we applied both strategies to recombinant p53 phosphorylatedin vitrowith Chk1 kinase, which exhibited 41 modified sites by bottom-up MS, but no more than 8 modified sites per molecule detected by top-down MS. This observation that many low-abundance modifications comprise relatively few modforms above a 1% threshold indicates that endogenous p53 PTM complexity may be more definable than previously thought.

Bottom-up proteomics relies on the use of proteases and is the method of choice for identifying thousands of protein groups in complex samples. Top-down proteomics has been shown to be robust for direct analysis of small proteins and offers a solution to the "peptide-to-protein" inference problem inherent with bottom-up approaches. Here, we describe the first large-scale integration of genomic, bottom-up and top-down proteomic data for the comparative analysis of patient-derived mouse xenograft models of basal and luminal B human breast cancer, WHIM2 and WHIM16, respectively. Using these well-characterized xenograft models established by the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium, we compared and contrasted the performance of bottom-up and top-down proteomics to detect cancer-specific aberrations at the peptide and proteoform levels and to measure differential expression of proteins and proteoforms. Bottom-up proteomic analysis of the tumor xenografts detected almost 10 times as many coding nucleotide polymorphisms and peptides resulting from novel splice junctions than top-down. For proteins in the range of 0-30 kDa, where quantitation was performed using both approaches, bottom-up proteomics quantified 3,519 protein groups from 49,185 peptides, while top-down proteomics quantified 982 proteoforms mapping to 358 proteins. Examples of both concordant and discordant quantitation were found in a ∼60:40 ratio, providing a unique opportunity for top-down to fill in missing information. The two techniques showed complementary performance, with bottom-up yielding eight times more identifications of 0-30 kDa proteins in xenograft proteomes, but failing to detect differences in certain posttranslational modifications (PTMs), such as phosphorylation pattern changes of alpha-endosulfine. This work illustrates the potency of a combined bottom-up and top-down proteomics approach to deepen our knowledge of cancer biology, especially when genomic data are available.

Gaseous fragmentation of intact proteins is multifaceted and can be unpredictable by current theories in the field. Contributing to the complexity is the multitude of precursor ion states and fragmentation channels. Terminal fragment ions can be re-fragmented, yielding product ions containing neither terminus, termed internal fragment ions. In an effort to better understand and capitalize upon this fragmentation process, we collisionally dissociated the high (13+), middle (10+), and low (7+) charge states of electrosprayed ubiquitin ions. Both terminal and internal fragmentation processes were quantified through step-wise increases of voltage potential in the collision cell. An isotope fitting algorithm matched observed product ions to theoretical terminal and internal fragment ions. At optimal energies for internal fragmentation of the 10+, nearly 200 internal fragments were observed; on average each of the 76 residues in ubiquitin was covered by 24.1 internal fragments. A pertinent finding was that formation of internal ions occurs at similar energy thresholds as terminal b- and y-ion types in beam-type activation. This large amount of internal fragmentation is frequently overlooked during top-down mass spectrometry. As such, we present several new approaches to visualize internal fragments through modified graphical fragment maps. With the presented advances of internal fragment ion accounting and visualization, the total percentage of matched fragment ions increased from approximately 40% to over 75% in a typical beam-type MS/MS spectrum. These sequence coverage improvements offer greater characterization potential for whole proteins with no needed experimental changes and could be of large benefit for future high-throughput intact protein analysis.

Amyloid-beta (Aβ) plays a key role in the pathogenesis of Alzheimer’s disease (AD), but little is known about the proteoforms present in AD brain. We used high-resolution mass spectrometry to analyze intact Aβ from soluble aggregates and insoluble material in brains of six cases with severe dementia and pathologically confirmed AD. The soluble aggregates are especially relevant because they are believed to be the most toxic form of Aβ. We found a diversity of Aβ peptides, with 26 unique proteoforms including various N- and C-terminal truncations. N- and C-terminal truncations comprised 73% and 30%, respectively, of the total Aβ proteoforms detected. The Aβ proteoforms segregated between the soluble and more insoluble aggregates with N-terminal truncations predominating in the insoluble material and C- terminal truncations segregating into the soluble aggregates. In contrast, canonical Aβ comprised the minority of the identified proteoforms (15.3%) and did not distinguish between the soluble and more insoluble aggregates. The relative abundance of many truncated Aβ proteoforms did not correlate with post-mortem interval, suggesting they are not artefacts. This heterogeneity of Aβ proteoforms deepens our understanding of AD and offers many new avenues for investigation into pathological mechanisms of the disease, with implications for therapeutic development.

Over the past decade, developments in high resolution mass spectrometry have enabled the high throughput analysis of intact proteins from complex proteomes, leading to the identification of thousands of proteoforms. Several previous reports on top-down proteomics (TDP) relied on hybrid ion trap–Fourier transform mass spectrometers combined with data-dependent acquisition strategies. To further reduce TDP to practice, we use a quadrupole-Orbitrap instrument coupled with software for proteoform-dependent data acquisition to identify and characterize nearly 2000 proteoforms at a 1% false discovery rate from human fibroblasts. By combining a 3 m/z isolation window with short transients to improve specificity and signal-to-noise for proteoforms >30 kDa, we demonstrate improving proteome coverage by capturing 439 proteoforms in the 30–60 kDa range. Three different data acquisition strategies were compared and resulted in the identification of many proteoforms not observed in replicate data-dependent experiments. Notably, the data set is reported with updated metrics and tools including a new viewer and assignment of permanent proteoform record identifiers for inclusion of highly characterized proteoforms (i.e., those with C-scores >40) in a repository curated by the Consortium for Top-Down Proteomics.

We evaluate the impact of carbamylation of the primary amines of the side-chains of Lys and the N-termini on the fragmentation of intact protein ions and the chromatographic properties of a mixture of E. coli ribosomal proteins. The fragmentation patterns of the six unmodified and carbamylated proteins obtained by higher energy collision dissociation (HCD) and ultraviolet photodissociation (UVPD) were compared. Carbamylation significantly reduced the total number of protons retained by the protein owing to the conversion of basic primary amines to non-basic carbamates. Carbamylation caused a significant negative impact on fragmentation of the protein by HCD (i.e., reduced sequence coverage and fewer diagnostic fragment ions) consistent with the mobile proton model, which correlates peptide fragmentation with charge distribution and the opportunity for charge-directed pathways. In addition, fragmentation was enhanced near the N- and C-termini upon HCD of carbamylated proteins. For LCMS/MS analysis of E. coli ribosomal proteins, the retention times increased by 16 min on average upon carbamylation, an outcome attributed to the increased hydrophobicity of the proteins after carbamylation. As noted for both the six model proteins and the ribosomal proteins, carbamylation had relatively little impact on the distribution or types of fragment ions product by UVPD, supporting the proposition that the mechanism of UVPD for intact proteins does not reflect the mobile proton model. Graphical Abstract ᅟ.

Traditional bottom-up mass spectrometry-based proteomics relies on the use of an enzyme, often trypsin, to generate small peptides (typically < 25 amino acids long). In top-down proteomics, proteins remain intact and are directly measured within the mass spectrometer. This technique, while inherently simpler than bottom-up proteomics, generates data which must be processed and analyzed using software tools "purpose-built" for the job. In this chapter, we will show the analysis of intact protein spectra through deconvolution, deisotoping, and searching with ProSight Lite, a free, vendor-agnostic tool for the analysis of top-down mass spectrometry data. We will illustrate with two examples of intact protein fragmentation spectra and discuss the iterative use of the software to characterize proteoforms and discover the sites of post-translational modifications.

The automated processing of data generated by top down proteomics would benefit from improved scoring for protein identification and characterization of highly related protein forms (proteoforms). Here we propose the "C-score" (short for Characterization Score), a Bayesian approach to the proteoform identification and characterization problem, implemented within a framework to allow the infusion of expert knowledge into generative models that take advantage of known properties of proteins and top down analytical systems (e.g., fragmentation propensities, "off-by-1 Da" discontinuous errors, and intelligent weighting for site-specific modifications). The performance of the scoring system based on the initial generative models was compared to the current probability-based scoring system used within both ProSightPC and ProSightPTM on a manually curated set of 295 human proteoforms. The current implementation of the C-score framework generated a marked improvement over the existing scoring system as measured by the area under the curve on the resulting ROC chart (AUC of 0.99 versus 0.78).

With the prospect of resolving whole protein molecules into their myriad proteoforms on a proteomic scale, the question of their quantitative analysis in discovery mode comes to the fore. Here, we demonstrate a robust pipeline for the identification and stringent scoring of abundance changes of whole protein forms

Pilot Project #1--the identification and characterization of human histone H4 proteoforms by top-down MS--is the first project launched by the Consortium for Top-Down Proteomics (CTDP) to refine and validate top-down MS. Within the initial results from seven participating laboratories, all reported the probability-based identification of human histone H4 (UniProt accession P62805) with expectation values ranging from 10(-13) to 10(-105). Regarding characterization, a total of 74 proteoforms were reported, with 21 done so unambiguously; one new PTM, K79ac, was identified. Inter-laboratory comparison reveals aspects of the results that are consistent, such as the localization of individual PTMs and binary combinations, while other aspects are more variable, such as the accurate characterization of low-abundance proteoforms harboring2 PTMs. An open-access tool and discussion of proteoform scoring are included, along with a description of general challenges that lie ahead including improved proteoform separations prior to mass spectrometric analysis, better instrumentation performance, and software development.

Top-down proteomics is emerging as a viable method for the routine identification of hundreds to thousands of proteins. In this work we report the largest top-down study to date, with the identification of 1,220 proteins from the transformed human cell line H1299 at a false discovery rate of 1%. Multiple separation strategies were utilized, including the focused isolation of mitochondria, resulting in significantly improved proteome coverage relative to previous work. In all, 347 mitochondrial proteins were identified, including ~50% of the mitochondrial proteome below 30 kDa and over 75% of the subunits constituting the large complexes of oxidative phosphorylation. Three hundred of the identified proteins were found to be integral membrane proteins containing between 1 and 12 transmembrane helices, requiring no specific enrichment or modified LC-MS parameters. Over 5,000 proteoforms were observed, many harboring post-translational modifications, including over a dozen proteins containing lipid anchors (some previously unknown) and many others with phosphorylation and methylation modifications. Comparison between untreated and senescent H1299 cells revealed several changes to the proteome, including the hyperphosphorylation of HMGA2. This work illustrates the burgeoning ability of top-down proteomics to characterize large numbers of intact proteoforms in a high-throughput fashion.

Successful high-throughput characterization of intact proteins from complex biological samples by mass spectrometry requires instrumentation capable of high mass resolving power, mass accuracy, sensitivity, and spectral acquisition rate. These limitations often necessitate the performance of hundreds of LC–MS/MS experiments to obtain reasonable coverage of the targeted proteome, which is still typically limited to molecular weights below 30 kDa. The National High Magnetic Field Laboratory (NHMFL) recently installed a 21 T FT-ICR mass spectrometer, which is part of the NHMFL FT-ICR User Facility and available to all qualified users. Here we demonstrate top-down LC-21 T FT-ICR MS/MS of intact proteins derived from human colorectal cancer cell lysate. We identified a combined total of 684 unique protein entries observed as 3238 unique proteoforms at a 1% false discovery rate, based on rapid, data-dependent acquisition of collision-induced and electron-transfer dissociation tandem mass spectra from just 40 LC–MS/MS experiments. Our identifications included 372 proteoforms with molecular weights over 30 kDa detected at isotopic resolution, which substantially extends the accessible mass range for high-throughput top-down LC–MS/MS.