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

Panda-UV Unlocks Deeper Protein Characterization with Internal Fragments in Ultraviolet Photodissociation Mass Spectrometry

Ultraviolet photodissociation (UVPD) mass spectrometry unlocks insights into the protein structure and sequence through fragmentation patterns. While N- and C-terminal fragments are traditionally relied upon, this work highlights the critical role of internal fragments in achieving near-complete sequencing of protein. Previous limitations of internal fragment utilization, owing to their abundance and potential for random matching, are addressed here with the development of Panda-UV, a novel software tool combining spectral calibration, and Pearson correlation coefficient scoring for confident fragment assignment. Panda-UV showcases its power through comprehensive benchmarks on three model proteins. The inclusion of internal fragments boosts identified fragment numbers by 26% and enhances average protein sequence coverage to a remarkable 93% for intact proteins, unlocking the hidden region of the largest protein carbonic anhydrase II in model proteins. Notably, an average of 65% of internal fragments can be identified in multiple replicates, demonstrating the high confidence of the fragments Panda-UV provided. Finally, the sequence coverages of mAb subunits can be increased up to 86% and the complementary determining regions (CDRs) are nearly completely sequenced in a single experiment. The source codes of Panda-UV are available at https://github.com/PHOENIXcenter/Panda-UV.

Puzzle of Proteoform Variety-Where Is a Key?

One of the human proteome puzzles is an imbalance between the theoretically calculated and experimentally measured amounts of proteoforms. Considering the possibility of combinations of different post-translational modifications (PTMs), the quantity of possible proteoforms is huge. An estimation gives more than a million different proteoforms in each cell type. But, it seems that there is strict control over the production and maintenance of PTMs. Although the potential complexity of proteoforms due to PTMs is tremendous, available information indicates that only a small part of it is being implemented. As a result, a protein could have many proteoforms according to the number of modification sites, but because of different systems of personal regulation, the profile of PTMs for a given protein in each organism is slightly different.

The need for open and FAIR data in top-down proteomics

In recent years, there has been a tremendous evolution in the high-throughput, tandem mass spectrometry-based analysis of intact proteins, also known as top-down proteomics (TDP). Both hardware and software have developed to the point that the technique has largely entered the mainstream, and large-scale, ambitious, multi-laboratory initiatives have started to make their appearance in the literature. For this, however, more convenient and robust data sharing and reuse will be required. Walzer et al. have created TopDownApp, a customisable, open platform for visualisation and analysis of TDP data, which they hope will be a step in this direction. As they point out, other benefits of such data sharing and interoperability would include reanalysis of published datasets, as well as the prospect of using large amounts of data to train machine learning algorithms. In time, this work could prove to be a valuable resource in the move towards a future of greater TDP data findability, accessibility, interoperability and reusability.

TopDownApp: An open and modular platform for analysis and visualisation of top-down proteomics data

Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise their adoption. In this context, there are numerous improvements that are possible in the area of open science practices, including a greater application of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. These include, for example, increased data sharing practices and readily available open data standards. Additionally, the field would benefit from the development of open data analysis workflows that can enable data reuse of public datasets, something that is increasingly common in other proteomics fields.

Top-Down Proteomics and the Challenges of True Proteoform Characterization

Top-down proteomics (TDP) aims to identify and profile intact protein forms (proteoforms) extracted from biological samples. True proteoform characterization requires that both the base protein sequence be defined and any mass shifts identified, ideally localizing their positions within the protein sequence. Being able to fully elucidate proteoform profiles lends insight into characterizing proteoform-unique roles, and is a crucial aspect of defining protein structure-function relationships and the specific roles of different (combinations of) protein modifications. However, defining and pinpointing protein post-translational modifications (PTMs) on intact proteins remains a challenge. Characterization of (heavily) modified proteins (>∼30 kDa) remains problematic, especially when they exist in a population of similarly modified, or kindred, proteoforms. This issue is compounded as the number of modifications increases, and thus the number of theoretical combinations. Here, we present our perspective on the challenges of analyzing kindred proteoform populations, focusing on annotation of protein modifications on an "average" protein. Furthermore, we discuss the technical requirements to obtain high quality fragmentation spectral data to robustly define site-specific PTMs, and the fact that this is tempered by the time requirements necessary to separate proteoforms in advance of mass spectrometry analysis.

Mass Spectrometry-Based Proteomic Technology and Its Application to Study Skeletal Muscle Cell Biology

Voluntary striated muscles are characterized by a highly complex and dynamic proteome that efficiently adapts to changed physiological demands or alters considerably during pathophysiological dysfunction. The skeletal muscle proteome has been extensively studied in relation to myogenesis, fiber type specification, muscle transitions, the effects of physical exercise, disuse atrophy, neuromuscular disorders, muscle co-morbidities and sarcopenia of old age. Since muscle tissue accounts for approximately 40% of body mass in humans, alterations in the skeletal muscle proteome have considerable influence on whole-body physiology. This review outlines the main bioanalytical avenues taken in the proteomic characterization of skeletal muscle tissues, including top-down proteomics focusing on the characterization of intact proteoforms and their post-translational modifications, bottom-up proteomics, which is a peptide-centric method concerned with the large-scale detection of proteins in complex mixtures, and subproteomics that examines the protein composition of distinct subcellular fractions. Mass spectrometric studies over the last two decades have decisively improved our general cell biological understanding of protein diversity and the heterogeneous composition of individual myofibers in skeletal muscles. This detailed proteomic knowledge can now be integrated with findings from other omics-type methodologies to establish a systems biological view of skeletal muscle function.

Considerations for defining +80 Da mass shifts in mass spectrometry-based proteomics: phosphorylation and beyond

Post-translational modifications (PTMs) are ubiquitous and key to regulating protein function. Understanding the dynamics of individual PTMs and their biological roles requires robust characterisation. Mass spectrometry (MS) is the method of choice for the identification and quantification of protein modifications. This article focusses on the MS-based analysis of those covalent modifications that induce a mass shift of +80 Da, notably phosphorylation and sulfation, given the challenges associated with their discrimination and pinpointing the sites of modification on a polypeptide chain. Phosphorylation in particular is highly abundant, dynamic and can occur on numerous residues to invoke specific functions, hence robust characterisation is crucial to understanding biological relevance. Showcasing our work in the context of other developments in the field, we highlight approaches for enrichment and site localisation of phosphorylated (canonical and non-canonical) and sulfated peptides, as well as modification analysis in the context of intact proteins (top down proteomics) to explore combinatorial roles. Finally, we discuss the application of native ion-mobility MS to explore the effect of these PTMs on protein structure and ligand binding.

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform

Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples-based sample preparation system and an laser capture microdissection-based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry-based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

Seeing the complete picture: proteins in top-down mass spectrometry

Top-down protein mass spectrometry can provide unique insights into protein sequence and structure, including precise proteoform identification and study of protein–ligand and protein–protein interactions. In contrast with the commonly applied bottom-up approach, top-down approaches do not include digestion of the protein of interest into small peptides, but instead rely on the ionization and subsequent fragmentation of intact proteins. As such, it is fundamentally the only way to fully characterize the composition of a proteoform. Here, we provide an overview of how a top-down protein mass spectrometry experiment is performed and point out recent applications from the literature to the reader. While some parts of the top-down workflow are broadly applicable, different research questions are best addressed with specific experimental designs. The most important divide is between studies that prioritize sequence information (i.e., proteoform identification) versus structural information (e.g., conformational studies, or mapping protein–protein or protein–ligand interactions). Another important consideration is whether to work under native or denaturing solution conditions, and the overall complexity of the sample also needs to be taken into account, as it determines whether (chromatographic) separation is required prior to MS analysis. In this review, we aim to provide enough information to support both newcomers and more experienced readers in the decision process of how to answer a potential research question most efficiently and to provide an overview of the methods that exist to answer these questions.

Identification of Inflammatory Proteomics Networks of Toll-like Receptor 4 through Immunoprecipitation-Based Chemical Cross-Linking Proteomics

Toll-like receptor 4 (TLR4) is a receptor on an immune cell that can recognize the invasion of bacteria through their attachment with bacterial lipopolysaccharides (LPS). Hence, LPS is a pro-immune response stimulus. On the other hand, statins are lipid-lowering drugs and can also lower immune cell responses. We used human embryonic kidney (HEK 293) cells engineered to express HA-tagged TLR-4 upon treatment with LPS, statin, and both statin and LPS to understand the effect of pro- and anti-inflammatory responses. We performed a monoclonal antibody (mAb) directed co-immunoprecipitation (CO-IP) of HA-tagged TLR4 and its interacting proteins in the HEK 293 extracted proteins. We utilized an ETD cleavable chemical cross-linker to capture weak and transient interactions with TLR4 protein. We tryptic digested immunoprecipitated and cross-linked proteins on beads, followed by liquid chromatography–mass spectrometry (LC-MS/MS) analysis of the peptides. Thus, we utilized the label-free quantitation technique to measure the relative expression of proteins between treated and untreated samples. We identified 712 proteins across treated and untreated samples and performed protein network analysis using Ingenuity Pathway Analysis (IPA) software to reveal their protein networks. After filtering and evaluating protein expression, we identified macrophage myristoylated alanine-rich C kinase substrate (MARCKSL1) and creatine kinase proteins as a potential part of the inflammatory networks of TLR4. The results assumed that MARCKSL1 and creatine kinase proteins might be associated with a statin-induced anti-inflammatory response due to possible interaction with the TLR4.

Efficient Detection of the Alternative Spliced Human Proteome Using Translatome Sequencing

Alternative splicing (AS) isoforms create numerous proteoforms, expanding the complexity of the genome. Highly similar sequences, incomplete reference databases and the insufficient sequence coverage of mass spectrometry limit the identification of AS proteoforms. Here, we demonstrated full-length translating mRNAs (ribosome nascent-chain complex-bound mRNAs, RNC-mRNAs) sequencing (RNC-seq) strategy to sequence the entire translating mRNA using next-generation sequencing, including short-read and long-read technologies, to construct a protein database containing all translating AS isoforms. Taking the advantage of read length, short-read RNC-seq identified up to 15,289 genes and 15,906 AS isoforms in a single human cell line, much more than the Ribo-seq. The single-molecule long-read RNC-seq supplemented 4,429 annotated AS isoforms that were not identified by short-read datasets, and 4,525 novel AS isoforms that were not included in the public databases. Using such RNC-seq-guided database, we identified 6,766 annotated protein isoforms and 50 novel protein isoforms in mass spectrometry datasets. These results demonstrated the potential of full-length RNC-seq in investigating the proteome of AS isoforms.

Mapping the Proteoform Landscape of Five Human Tissues

A functional understanding of the human body requires structure-function studies of proteins at scale. The chemical structure of proteins is controlled at the transcriptional, translational, and post-translational levels, creating a variety of products with modulated functions within the cell. The term "proteoform" encapsulates this complexity at the level of chemical composition. Comprehensive mapping of the proteoform landscape in human tissues necessitates analytical techniques with increased sensitivity and depth of coverage. Here, we took a top-down proteomics approach, combining data generated using capillary zone electrophoresis (CZE) and nanoflow reversed-phase liquid chromatography (RPLC) hyphenated to mass spectrometry to identify and characterize proteoforms from the human lungs, heart, spleen, small intestine, and kidneys. CZE and RPLC provided complementary post-translational modification and proteoform selectivity, thereby enhancing the overall proteome coverage when used in combination. Of the 11,466 proteoforms identified in this study, 7373 (64%) were not reported previously. Large differences in the protein and proteoform level were readily quantified, with initial inferences about proteoform biology operative in the analyzed organs. Differential proteoform regulation of defensins, glutathione transferases, and sarcomeric proteins across tissues generate hypotheses about how they function and are regulated in human health and disease.

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 (HUPO) Proteomics Standards Initiative (PSI) 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.