The Human Proteoform Atlas: a FAIR community resource for experimentally derived proteoforms.

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.

Amyloid ß Proteoforms Elucidated by Quantitative LC/MS in the 5xFAD Mouse Model of Alzheimer's Disease.

Numerous Aß proteoforms, identified in the human brain, possess differential neurotoxic and aggregation propensities. These proteoforms contribute in unknown ways to the conformations and resultant pathogenicity of oligomers, protofibrils, and fibrils in Alzheimer's disease (AD) manifestation owing to the lack of molecular-level specificity to the exact chemical composition of underlying protein products with widespread interrogating techniques, like immunoassays. We evaluated Aß proteoform flux using quantitative top-down mass spectrometry (TDMS) in a well-studied 5xFAD mouse model of age-dependent Aß-amyloidosis. Though the brain-derived Aß proteoform landscape is largely occupied by Aß1-42, 25 different forms of Aß with differential solubility were identified. These proteoforms fall into three natural groups defined by hierarchical clustering of expression levels in the context of mouse age and proteoform solubility, with each group sharing physiochemical properties associated with either N/C-terminal truncations or both. Overall, the TDMS workflow outlined may hold tremendous potential for investigating proteoform-level relationships between insoluble fibrils and soluble Aß, including low-molecular-weight oligomers hypothesized to serve as the key drivers of neurotoxicity. Similarly, the workflow may also help to validate the utility of AD-relevant animal models to recapitulate amyloidosis mechanisms or possibly explain disconnects observed in therapeutic efficacy in animal models vs humans.

Anatomic nomenclature and 3-dimensional regional model of the human ovary: call for a new paradigm.

The ovaries are the female gonads that are crucial for reproduction, steroid production, and overall health. Historically, the ovary was broadly divided into regions defined as the cortex, medulla, and hilum. This current nomenclature lacks specificity and fails to consider the significant anatomic variations in the ovary. Recent technological advances in imaging modalities and high-resolution omic analyses have brought about the need for revision of the existing definitions, which will facilitate the integration of generated data and enable the characterization of organ subanatomy and function at the cellular level. The creation of these high-resolution multimodal maps of the ovary will enhance collaboration and communication among disciplines and between clinicians and researchers. Beginning in March 2021, the Pediatric and Adolescent Gynecology Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development invited subject-matter experts to participate in a series of workshops and meetings to standardize ovarian nomenclature and define the organ's features. The goal was to develop a spatially defined and semantically consistent terminology of the ovary to support collaborative, team science-based endeavors aimed at generating reference atlases of the human ovary. The group recommended a standardized, 3-dimensional description of the ovary and an ontological approach to the subanatomy of the ovary and definition of follicles. This new greater precision in nomenclature and mapping will better reflect the ovary's heterogeneous composition and function, support the standardization of tissue collection, facilitate functional analyses, and enable clinical and research collaborations. The conceptualization process and outcomes of the effort, which spanned the better part of 2021 and early 2022, are introduced in this article. The institute and the workshop participants encourage researchers and clinicians to adopt the new systems in their everyday work to advance the overarching goal of improving human reproductive health.

ProSight Annotator: Complete control and customization of protein entries in UniProt XML files.

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.

Proteomics Standards Initiative's ProForma 2.0: Unifying the Encoding of Proteoforms and Peptidoforms.

It is important for the proteomics community to have a standardized manner to represent all possible variations of a protein or peptide primary sequence, including natural, chemically induced, and artifactual modifications. The Human Proteome Organization Proteomics Standards Initiative in collaboration with several members of the Consortium for Top-Down Proteomics (CTDP) has developed a standard notation called ProForma 2.0, which is a substantial extension of the original ProForma notation developed by the CTDP. ProForma 2.0 aims to unify the representation of proteoforms and peptidoforms. ProForma 2.0 supports use cases needed for bottom-up and middle-/top-down proteomics approaches and allows the encoding of highly modified proteins and peptides using a human- and machine-readable string. ProForma 2.0 can be used to represent protein modifications in a specified or ambiguous location, designated by mass shifts, chemical formulas, or controlled vocabulary terms, including cross-links (natural and chemical) and atomic isotopes. Notational conventions are based on public controlled vocabularies and ontologies. The most up-to-date full specification document and information about software implementations are available at http://psidev.info/proforma.

Next-Generation Serology by Mass Spectrometry: Readout of the SARS-CoV-2 Antibody Repertoire.

Methods of antibody detection are used to assess exposure or immunity to a pathogen. Here, we present Ig-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 multiparametric 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 applied Ig-MS to plasma from subjects with severe and mild COVID-19 and immunized subjects after two vaccine doses, 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, that could use other antigens of interest to gauge immune responses to vaccination, pathogens, or autoimmune disorders.

Middle-Down Mass Spectrometry Reveals Activity-Modifying Phosphorylation Barcode in a Class C G Protein-Coupled Receptor.

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.

Accurate Estimation of Context-Dependent False Discovery Rates in Top-Down Proteomics.

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.

Precise characterization of KRAS4b proteoforms in human colorectal cells and tumors reveals mutation/modification cross-talk.

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.

Defining the NSD2 interactome: PARP1 PARylation reduces NSD2 histone methyltransferase activity and impedes chromatin binding.

NSD2 is a histone methyltransferase that specifically dimethylates histone H3 lysine 36 (H3K36me2), a modification associated with gene activation. Dramatic overexpression of NSD2 in t(4;14) multiple myeloma (MM) and an activating mutation of NSD2 discovered in acute lymphoblastic leukemia are significantly associated with altered gene activation, transcription, and DNA damage repair. The partner proteins through which NSD2 may influence critical cellular processes remain poorly defined. In this study, we utilized proximity-based labeling (BioID) combined with label-free quantitative MS to identify high confidence NSD2 interacting partners in MM cells. The top 24 proteins identified were involved in maintaining chromatin structure, transcriptional regulation, RNA pre-spliceosome assembly, and DNA damage. Among these, an important DNA damage regulator, poly(ADP-ribose) polymerase 1 (PARP1), was discovered. PARP1 and NSD2 have been found to be recruited to DNA double strand breaks upon damage and H3K36me2 marks are enriched at damage sites. We demonstrate that PARP1 regulates NSD2 via PARylation upon oxidative stress. In vitro assays suggest the PARylation significantly reduces NSD2 histone methyltransferase activity. Furthermore, PARylation of NSD2 inhibits its ability to bind to nucleosomes and further get recruited at NSD2-regulated genes, suggesting PARP1 regulates NSD2 localization and H3K36me2 balance. This work provides clear evidence of cross-talk between PARylation and histone methylation and offers new directions to characterize NSD2 function in DNA damage response, transcriptional regulation, and other pathways.

Identification and Quantification of Proteoforms by Mass Spectrometry.

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.

Multidimensional Top-Down Proteomics of Brain-Region-Specific Mouse Brain Proteoforms Responsive to Cocaine and Estradiol.

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 (17ß-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.

An informatic framework for decoding protein complexes by top-down mass spectrometry.

Efforts to map the human protein interactome have resulted in information about thousands of multi-protein assemblies housed in public repositories, but the molecular characterization and stoichiometry of their protein subunits remains largely unknown. Here, we report a computational search strategy that supports hierarchical top-down analysis for precise identification and scoring of multi-proteoform complexes by native mass spectrometry.

ProForma: A Standard Proteoform Notation.

The Consortium for Top-Down Proteomics (CTDP) proposes a standardized notation, ProForma, for writing the sequence of fully characterized proteoforms. ProForma provides a means to communicate any proteoform by writing the amino acid sequence using standard one-letter notation and specifying modifications or unidentified mass shifts within brackets following certain amino acids. The notation is unambiguous, human-readable, and can easily be parsed and written by bioinformatic tools. This system uses seven rules and supports a wide range of possible use cases, ensuring compatibility and reproducibility of proteoform annotations. Standardizing proteoform sequences will simplify storage, comparison, and reanalysis of proteomic studies, and the Consortium welcomes input and contributions from the research community on the continued design and maintenance of this standard.

The Value of Activated Ion Electron Transfer Dissociation for High-Throughput Top-Down Characterization of Intact Proteins.

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 photoactivation 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 (<20 kDa), highlighting improved proteoform identification the method offers over higher energy-collisional dissociation (HCD), standard ETD, and ETD followed by supplemental HCD activation (EThcD). We identified 935 proteoforms from 295 proteins from human colorectal cancer cell line HCT116 using AI-ETD compared to 1014 proteoforms, 915 proteoforms, and 871 proteoforms with HCD, ETD, and EThcD, respectively. Importantly, AI-ETD outperformed each of the three other methods in MS/MS success rates and spectral quality metrics (e.g., sequence coverage achieved and proteoform characterization scores). In all, this four-method analysis offers the most extensive comparisons to date and demonstrates that AI-ETD both increases identifications over other ETD methods and improves proteoform characterization via higher sequence coverage, positioning it as a premier method for high-throughput top-down proteomics.

Top-Down Proteomics Enables Comparative Analysis of Brain Proteoforms Between Mouse Strains.

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.

Integrated Bottom-Up and Top-Down Proteomics of Patient-Derived Breast Tumor Xenografts.

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.

Advancing Top-down Analysis of the Human Proteome Using a Benchtop Quadrupole-Orbitrap Mass Spectrometer.

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.