FLASHDeconv: Ultrafast, High-Quality Feature Deconvolution for Top-Down Proteomics

Top-Down Proteomics: Why and When?

Manifold biological processes at all levels of transcription and translation can lead to the formation of a high number of different protein species (i.e., proteoforms), which outnumber the sequences encoded in the genome by far. Due to the large number of protein molecules formed in this way, which span an enormous range of different physicochemical properties, proteoforms are the functional drivers of all biological processes, creating the need for powerful analytical approaches to decipher this language of life. While bottom-up proteomics has become the most widely used approach, providing features such as high sensitivity, depth of analysis, and throughput, it has its limitations when it comes to identifying, quantifying, and characterizing proteoforms. In particular, the major bottleneck is to assign peptide-level information to the original proteoforms. In contrast, top-down proteomics (TDP) targets the direct analysis of intact proteoforms. Despite being characterized by a number of technological challenges, the TDP community has established numerous protocols that allow easy implementation in any proteomics laboratory. In this viewpoint, we compare both approaches, argue that it is worth embedding TDP experiments, and show fields of research in which TDP can be successfully implemented to perform integrative multi-level proteoformics.

Advancing non-target analysis of emerging environmental contaminants with machine learning: Current status and future implications

Emerging environmental contaminants (EECs) such as pharmaceuticals, pesticides, and industrial chemicals pose significant challenges for detection and identification due to their structural diversity and lack of analytical standards. Traditional targeted screening methods often fail to detect these compounds, making non-target analysis (NTA) using high-resolution mass spectrometry (HRMS) essential for identifying unknown or suspected contaminants. However, interpreting the vast datasets generated by HRMS is complex and requires advanced data processing techniques. Recent advancements in machine learning (ML) models offer great potential for enhancing NTA applications. As such, we reviewed key developments, including optimizing workflows using computational tools, improved chemical structure identification, advanced quantification methods, and enhanced toxicity prediction capabilities. It also discusses challenges and future perspectives in the field, such as refining ML tools for complex mixtures, improving inter-laboratory validation, and further integrating computational models into environmental risk assessment frameworks. By addressing these challenges, ML-assisted NTA can significantly enhance the detection, quantification, and evaluation of EECs, ultimately contributing to more effective environmental monitoring and public health protection.

Full Window Data-Independent Acquisition Method for Deeper Top-Down Proteomics

Top-down proteomics (TDP) is emerging as a vital tool for the comprehensive characterization of proteoforms. However, as its core technology, top-down mass spectrometry (TDMS) still faces significant analytical challenges. While data-independent acquisition (DIA) has revolutionized bottom-up proteomics and metabolomics, they are rarely employed in TDP. The unique feature of protein ions in an electrospray mass spectrum as well as the data complexity require the development of new DIA strategies. This study introduces a machine learning-assisted Full Window DIA (FW-DIA) method that eliminates precursor ion isolation, making it compatible with a wide range of commercial mass spectrometers. Moreover, FW-DIA leverages all precursor protein ions to generate high-quality tandem mass spectra, enhancing signal intensities by ∼50-fold and protein sequence coverage by 3-fold in a modular protein analysis. The method was successfully applied to the analysis of a five-protein mixture under native conditions and Escherichia coli ribosomal proteoform characterization.

Establishing a Top-Down Proteomics Platform on a Time-of-Flight Instrument with Electron-Activated Dissociation

Top-down proteomics is the study of intact proteins and their post-translational modifications with mass spectrometry. Historically, this field is more challenging than its bottom-up counterpart because the species are much bigger and have a larger number of possible combinations of sequences and modifications; thus, there is a great need for technological development. With improvements in instrumentation and a multiplicity of fragmentation modes available, top-down proteomics is quickly gaining in popularity with renewed attention on increasing confidence in identification and quantification. Here, we systematically evaluated the Sciex ZenoTOF 7600 system for top-down proteomics, applying standards in the field to validate the platform and further experimenting with its capabilities in electron-activated dissociation and post-translational modification site localization. The instrument demonstrated robustness in standard proteins for platform QC, as aided by zeno trapping. We were also able to apply this to histone post-translational modifications, achieving high sequence coverage that allowed PTM's site localization across protein sequences with optimized EAD fragmentation. We demonstrated the ability to analyze proteins spanning the mass range and included analysis of glycosylated proteins. This is a reference point for future top-down proteomics experiments to be conducted on the ZenoTOF 7600 system.

Cysteine-Directed Isobaric Labeling Combined with GeLC-FAIMS-MS for Quantitative Top-Down Proteomics

The quantification of proteoforms, i.e., all molecular forms in which proteins can be present, by top-down proteomics provides essential insights into biological processes at the molecular level. Isobaric labeling-based quantification strategies are suitable for multidimensional separation strategies and allow for multiplexing of the samples. Here, we investigated cysteine-directed isobaric labeling by iodoTMT in combination with a gel- and gas-phase fractionation (GeLC-FAIMS-MS) for in-depth quantitative proteoform analysis. We optimized the acquisition workflow (i.e., the FAIMS compensation voltages, isolation windows, acquisition strategy, and fragmentation method) using a two-proteome mix to increase the number of quantified proteoforms and reduce ratio compression. Additionally, we implemented a mass feature-based quantification strategy in the widely used deconvolution algorithm FLASHDeconv, which improves and facilitates data analysis. The optimized iodoTMT GeLC-FAIMS-MS workflow was applied to quantitatively analyze the proteome of Escherichia coli grown under glucose or acetate as the sole carbon source, resulting in the identification of 726 differentially abundant proteoforms.

Hypoxia increases methylated histones to prevent histone clipping and heterochromatin redistribution during Raf-induced senescence

Hypoxia enhances histone methylation by inhibiting oxygen- and α-ketoglutarate-dependent demethylases, resulting in increased methylated histones. This study reveals how hypoxia-induced methylation affects histone clipping and the reorganization of heterochromatin into senescence-associated heterochromatin foci (SAHF) during oncogene-induced senescence (OIS) in IMR90 human fibroblasts. Notably, using top-down proteomics, we discovered specific cleavage sites targeted by Cathepsin L (CTSL) in H3, H2B and H4 during Raf activation, identifying novel sites in H2B and H4. Hypoxia counteracts CTSL-mediated histone clipping by promoting methylation without affecting CTSL's activity. This increase in methylation under hypoxia protects against clipping, reshaping the epigenetic landscape and influencing chromatin accessibility, as shown by ATAC-seq analysis. These insights underscore the pivotal role of hypoxia-induced histone methylation in protecting chromatin from significant epigenetic shifts during cellular aging.

PEPPI-MS: gel-based sample pre-fractionation for deep top-down and middle-down proteomics

Top-down analysis of intact proteins and middle-down analysis of proteins subjected to limited digestion require efficient detection of traces of proteoforms in samples, necessitating the reduction of sample complexity by thorough pre-fractionation of the proteome components in the sample. SDS-PAGE is a simple and inexpensive high-resolution protein-separation technique widely used in biochemical and molecular biology experiments. Although its effectiveness for sample preparation in bottom-up proteomics has been proven, establishing a method for highly efficient recovery of intact proteins from the gel matrix has long been a challenge for its implementation in top-down and middle-down proteomics. As a much-awaited solution to this problem, we present an experimental protocol for efficient proteoform fractionation from complex biological samples using passively eluting proteins from polyacrylamide gels as intact species for mass spectrometry (PEPPI-MS), a rapid method for extraction of intact proteins separated by SDS-PAGE. PEPPI-MS allows recovery of proteins below 100 kDa separated by SDS-PAGE in solution with a median efficiency of 68% within 10 min and, unlike conventional electroelution methods, requires no special equipment, contributing to a remarkably economical implementation. The entire protocol from electrophoresis to protein purification can be performed in <5 h. By combining the resulting PEPPI fraction with other protein-separation techniques, such as reversed-phase liquid chromatography and ion mobility techniques, multidimensional proteome separations for in-depth proteoform analysis can be easily achieved.

Intact protein barcoding enables one-shot identification of CRISPRi strains and their metabolic state

Detecting strain-specific barcodes with mass spectrometry can facilitate the screening of genetically engineered bacterial libraries. Here, we introduce intact protein barcoding, a method to measure protein-based library barcodes and metabolites using flow injection mass spectrometry (FI-MS). Protein barcodes are based on ubiquitin with N-terminal tags of six amino acids. We demonstrate that FI-MS detects intact ubiquitin proteins and identifies the mass of N-terminal barcodes. In the same analysis, we measured relative concentrations of primary metabolites. We constructed six ubiquitin-barcoded CRISPR interference (CRISPRi) strains targeting metabolic enzymes and analyzed their metabolic profiles and ubiquitin barcodes. FI-MS detected barcodes and distinct metabolome changes in CRISPRi-targeted pathways. We demonstrate the scalability of intact protein barcoding by measuring 132 ubiquitin barcodes in microtiter plates. These results show that intact protein barcoding enables fast and simultaneous detection of library barcodes and intracellular metabolites, opening up new possibilities for mass spectrometry-based barcoding.

Improving Top-Down Sequence Coverage with Targeted Fragment Matching

Top-down mass spectrometry (TDMS) of intact proteins and antibodies enables direct determination of truncations, sequence variants, post-translational modifications, and disulfides without the need for any proteolytic cleavage. While mass deconvolution of top-down tandem mass spectra is typically used to identify fragment masses for matching to candidate proteoforms, larger molecules such as monoclonal antibodies can produce many fragment ions, making spectral interpretation challenging. Here, we explore an alternative approach for proteoform spectral matching that is better suited for larger protein analysis. This workflow uses direct matching of theoretical proteoform isotopic distributions to TDMS spectra, avoiding drawbacks of mass deconvolution such as poor sensitivity and problems differentiating overlapping distributions. Using a data set that analyzed an intact NIST monoclonal antibody across different fragmentation modes, we show that this isotope fitting strategy increased the sequence coverage of both light and heavy chain sequences >3-fold. We further found that isotope fitting is particularly amenable to identifying large fragments, including those near the hinge region that have been traditionally difficult to analyze by top-down methods. These advances in proteoform spectral matching can greatly increase the power of top-down analyses for intact biotherapeutics and other large molecules.

Influence of different sample preparation approaches on proteoform identification by top-down proteomics

Top-down proteomics using mass spectrometry facilitates the identification of intact proteoforms, that is, all molecular forms of proteins. Multiple past advances have lead to the development of numerous sample preparation workflows. Here we systematically investigated the influence of different sample preparation steps on proteoform and protein identifications, including cell lysis, reduction and alkylation, proteoform enrichment, purification and fractionation. We found that all steps in sample preparation influence the subset of proteoforms identified (for example, their number, confidence, physicochemical properties and artificially generated modifications). The various sample preparation strategies resulted in complementary identifications, substantially increasing the proteome coverage. Overall, we identified 13,975 proteoforms from 2,720 proteins of human Caco-2 cells. The results presented can serve as suggestions for designing and adapting top-down proteomics sample preparation strategies to particular research questions. Moreover, we expect that the sampling bias and modifications identified at the intact protein level will also be useful in improving bottom-up proteomics approaches.

FLASHQuant: A Fast Algorithm for Proteoform Quantification in Top-Down Proteomics

Accurate quantification of individual proteoforms is a crucial step in identifying proteome-wide alterations in different biological conditions. Intact proteoforms have been analyzed predominantly by liquid chromatography-mass spectrometry (LC-MS)-based top-down proteomics (TDP) and quantified primarily by the label-free quantification (LFQ) method, as it requires no additional costly labeling. In TDP, due to frequent coelution and complex signal structures, overlapping signals deriving from multiple proteoforms complicate accurate quantification. Here, we introduce FLASHQuant for MS1-level LFQ analysis in TDP, which is capable of automatically resolving and quantifying coeluting proteoforms. In benchmark tests performed with both spike-in proteins and proteome-level mixture data sets, FLASHQuant was shown to perform highly accurate and reproducible quantification in short runtimes of just a few minutes per LC-MS run. In particular, it was demonstrated that resolving overlapping proteoforms boosts the quantification accuracy. FLASHQuant is publicly available as platform-independent open-source software at https://openms.org/flashquant/, accompanied by the simple alignment algorithm ConsensusFeatureGroupDetector for multiple LC-MS runs.

pyMS-Vis, an Open-Source Python Application for Visualizing and Investigating Deconvoluted Top-Down Mass Spectrometric Experiments: A Histone Proteoform Case Study

We report the development of an open-source Python application that provides quantitative and qualitative information from deconvoluted liquid-chromatography top-down mass spectrometry (LC-TDMS) data sets. This simple-to-use program allows users to search masses-of-interest across multiple LC-TDMS runs and provides visualization of their ion intensities and elution characteristics while quantifying their abundances relative to one another. Focusing on proteoform-rich histone proteins from the green microalga Chlamydomonas reinhardtii, we were able to quantify proteoform abundances across different growth conditions and replicates in minutes instead of hours typically needed for manual spreadsheet-based analysis. This resulted in extending previously published qualitive observations on Chlamydomonas histone proteoforms into quantitative ones, leading to an exciting new discovery on alpha-amino termini processing exclusive to histone H2A family members. Lastly, the script was intentionally developed with readability and customizability in mind so that fellow mass spectrometrists can modify the code to suit their lab-specific needs.

Exploring snake venoms beyond the primary sequence: From proteoforms to protein-protein interactions

Snakebite envenomation has been a long-standing global issue that is difficult to treat, largely owing to the flawed nature of current immunoglobulin-based antivenom therapy and the complexity of snake venoms as sophisticated mixtures of bioactive proteins and peptides. Comprehensive characterisation of venom compositions is essential to better understanding snake venom toxicity and inform effective and rationally designed antivenoms. Additionally, a greater understanding of snake venom composition will likely unearth novel biologically active proteins and peptides that have promising therapeutic or biotechnological applications. While a bottom-up proteomic workflow has been the main approach for cataloguing snake venom compositions at the toxin family level, it is unable to capture snake venom heterogeneity in the form of protein isoforms and higher-order protein interactions that are important in driving venom toxicity but remain underexplored. This review aims to highlight the importance of understanding snake venom heterogeneity beyond the primary sequence, in the form of post-translational modifications that give rise to different proteoforms and the myriad of higher-order protein complexes in snake venoms. We focus on current top-down proteomic workflows to identify snake venom proteoforms and further discuss alternative or novel separation, instrumentation, and data processing strategies that may improve proteoform identification. The current higher-order structural characterisation techniques implemented for snake venom proteins are also discussed; we emphasise the need for complementary and higher resolution structural bioanalytical techniques such as mass spectrometry-based approaches, X-ray crystallography and cryogenic electron microscopy, to elucidate poorly characterised tertiary and quaternary protein structures. We envisage that the expansion of the snake venom characterisation "toolbox" with top-down proteomics and high-resolution protein structure determination techniques will be pivotal in advancing structural understanding of snake venoms towards the development of improved therapeutic and biotechnology applications.

Top-down Proteomics for the Characterization and Quantification of Calreticulin Arginylation

Arginylation installed by arginyltransferase 1 (ATE1) features an addition of arginine (Arg) to the reactive amino acids (e.g., Glu and Asp) at the protein N-terminus or side chain. Systemic removal of arginylation after ATE1 knockout (KO) in mouse models resulted in heart defects leading to embryonic lethality. The biological importance of arginylation has motivated the discovery of arginylation sites on proteins using bottom-up approaches. While bottom-up proteomics is powerful in localizing peptide arginylation, it lacks the ability to quantify proteoforms at the protein level. Here we developed a top-down proteomics workflow for characterizing and quantifying calreticulin (CALR) arginylation. To generate fully arginylated CALR (R-CALR), we have inserted an R residue after the signaling peptide (AA1-17). Upon overexpression in ATE1 KO cells, CALR and R-CALR were purified by affinity purification and analyzed by LCMS in positive mode. Both proteoforms showed charge states ranging from 27-68 with charge 58 as the most intense charge state. Their MS2 spectra from electron-activated dissociation (EAD) showed preferential fragmentation at the protein N-terminals which yielded sufficient c ions facilitating precise localization of the arginylation sites. The calcium-binding domain (CBD) gave minimum characteristic ions possibly due to the abundant presence of >100 D and E residues. Ultraviolet photodissociation (UVPD) compared with EAD and ETD significantly improved the sequence coverage of CBD. This method can identify and quantify CALR arginylation at absence, endogenous (low), and high levels. To our knowledge, our work is the first application of top-down proteomics in characterizing post-translational arginylation in vitro and in vivo.

FREE: Enhanced Feature Representation for Isotopic Envelope Evaluation in Top-Down Mass Spectra Deconvolution

The aim of deconvolution of top-down mass spectra is to recognize monoisotopic peaks from the experimental envelopes in raw mass spectra. So accurate assessment of similarity between theoretical and experimental envelopes is a critical step in mass spectra data deconvolution. Existing evaluation methods primarily rely on intensity differences and m/z similarity, potentially lacking a comprehensive assessment. To overcome this constraint and facilitate a comprehensive and refined assessment of the similarity between theoretical and experimental envelopes, there exists an imperative to systematically explore and identify increasingly efficacious features for assessing this correspondence. We present enhanced feature representation for isotopic envelope evaluation (FREE) that derives diverse feature representations, encapsulating fundamental physical attributes of envelopes, including peak intensity and envelope shape. We trained FREE and evaluated its performance on both the ovarian tumor (OT) (human OT cells) data set and zebrafish (ZF) (brain in mature female ZF) data set. Specifically, comparing the state-of-art method, FREE demonstrates higher performance in multiple evaluation metrics across both the OT and ZF data sets, with a particular emphasis on precision, and it demonstrates accurate predictions of a greater number of positive envelopes among the top-ranked envelopes based on their scores. Moreover, within a cross-species data set of ZF, FREE identified a higher number of proteoform-spectrum matches (PrSMs), increasing the count from 50,795 to 52,927 compared to EnvCNN, the amalgamation of FREE with TopFD also exhibits a commendable capacity to discern 117,883 fragment ions, thus surpassing the 97,554 fragment ions identified through the application of EnvCNN in conjunction with TopFD. To further validate the performance of FREE, we have tested 10 a cross-species top-down proteomes containing 36 subdata set from ProteomeXchange. The results reveal that, after deconvolution with TopFD + FREE, TopPIC identifies more PrSMs across these 10 data sets in both the first and second rounds of experiments. These findings underscore the robustness and generalization capabilities of the FREE approach in diverse proteomes.

Mass spectrometry-intensive top-down proteomics: an update on technology advancements and biomedical applications

Proteoforms are all forms of protein molecules from the same gene because of variations at the DNA, RNA, and protein levels, e.g., alternative splicing and post-translational modifications (PTMs). Delineation of proteins in a proteoform-specific manner is crucial for understanding their biological functions. Mass spectrometry (MS)-intensive top-down proteomics (TDP) is promising for comprehensively characterizing intact proteoforms in complex biological systems. It has achieved substantial progress in technological development, including sample preparation, proteoform separations, MS instrumentation, and bioinformatics tools. In a single TDP study, thousands of proteoforms can be identified and quantified from a cell lysate. It has also been applied to various biomedical research to better our understanding of protein function in regulating cellular processes and to discover novel proteoform biomarkers of diseases for early diagnosis and therapeutic development. This review covers the most recent technological development and biomedical applications of MS-intensive TDP.

Development of a top-down MS assay for specific identification of human periostin isoforms

Periostin is a matricellular protein encoded by the POSTN gene that is alternatively spliced to produce ten different periostin isoforms with molecular weights ranging from 78 to 91 kDa. It is known to promote fibrillogenesis, organize the extracellular matrix, and bind integrin-receptors to induce cell signaling. As well as being a key component of the wound healing process, it is also known to participate in the pathogenesis of different diseases including atopic dermatitis, asthma, and cancer. In both health and disease, the functions of the different periostin isoforms are largely unknown. The ability to precisely determine the isoform profile of a given human sample is fundamental for characterizing their functional significance. Identification of periostin isoforms is most often carried out at the transcriptional level using RT-PCR based approaches, but due to high sequence homogeneity, identification on the protein level has always been challenging. Top-down proteomics, where whole proteins are measured by mass spectrometry, offers a fast and reliable method for isoform identification. Here we present a fully developed top-down mass spectrometry assay for the characterization of periostin splice isoforms at the protein level.

Top-down proteomics

Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.

MSModDetector: a tool for detecting mass shifts and post-translational modifications in individual ion mass spectrometry data

MOTIVATION

Post-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs on the same protein molecule, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even PTMs at distant sites to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein.

RESULTS

We developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I2MS) data. I2MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I2MS data and experimental I2MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a protein's PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/marjanfaizi/MSModDetector.

Spectral averaging with outlier rejection algorithms to increase identifications in top-down proteomics

The identification of proteoforms by top-down proteomics requires both high quality fragmentation spectra and the neutral mass of the proteoform from which the fragments derive. Intact proteoform spectra can be highly complex and may include multiple overlapping proteoforms, as well as many isotopic peaks and charge states. The resulting lower signal-to-noise ratios for intact proteins complicates downstream analyses such as deconvolution. Averaging multiple scans is a common way to improve signal-to-noise, but mass spectrometry data contains artifacts unique to it that can degrade the quality of an averaged spectra. To overcome these limitations and increase signal-to-noise, we have implemented outlier rejection algorithms to remove outlier measurements efficiently and robustly in a set of MS1 scans prior to averaging. We have implemented averaging with rejection algorithms in the open-source, freely available, proteomics search engine MetaMorpheus. Herein, we report the application of the averaging with rejection algorithms to direct injection and online liquid chromatography mass spectrometry data. Averaging with rejection algorithms demonstrated a 45% increase in the number of proteoforms detected in Jurkat T cell lysate. We show that the increase is due to improved spectral quality, particularly in regions surrounding isotopic envelopes.

Precursor deconvolution error estimation: The missing puzzle piece in false discovery rate in top-down proteomics

Top-down proteomics (TDP) directly analyzes intact proteins and thus provides more comprehensive qualitative and quantitative proteoform-level information than conventional bottom-up proteomics (BUP) that relies on digested peptides and protein inference. While significant advancements have been made in TDP in sample preparation, separation, instrumentation, and data analysis, reliable and reproducible data analysis still remains one of the major bottlenecks in TDP. A key step for robust data analysis is the establishment of an objective estimation of proteoform-level false discovery rate (FDR) in proteoform identification. The most widely used FDR estimation scheme is based on the target-decoy approach (TDA), which has primarily been established for BUP. We present evidence that the TDA-based FDR estimation may not work at the proteoform-level due to an overlooked factor, namely the erroneous deconvolution of precursor masses, which leads to incorrect FDR estimation. We argue that the conventional TDA-based FDR in proteoform identification is in fact protein-level FDR rather than proteoform-level FDR unless precursor deconvolution error rate is taken into account. To address this issue, we propose a formula to correct for proteoform-level FDR bias by combining TDA-based FDR and precursor deconvolution error rate.

GeLC-FAIMS-MS workflow for in-depth middle-down proteomics

Middle-down proteomics (MDP) is an analytical approach in which protein samples are digested with proteases such as Glu-C to generate large peptides (>3 kDa) that are analyzed by mass spectrometry (MS). This method is useful for characterizing high-molecular-weight proteins that are difficult to detect by top-down proteomics (TDP), in which intact proteins are analyzed by MS. In this study, we applied GeLC-FAIMS-MS, a multidimensional separation workflow that combines gel-based prefractionation with LC-FAIMS MS, for deep MDP. Middle-down peptides generated by optimized limited Glu-C digestion conditions were first size-fractionated by polyacrylamide gel electrophoresis, followed by C4 reversed-phase liquid chromatography separation and additional ion mobility fractionation, resulting in a significant increase in peptide length detectable by MS. In addition to global analysis, the GeLC-FAIMS-MS concept can also be applied to targeted MDP, where only proteins in the desired molecular weight range are gel-fractionated and their Glu-C digestion products are analyzed, as demonstrated by targeted analysis of integrins in exosomes. In-depth MDP achieved by global and targeted GeLC-FAIMS-MS supports the exploration of proteoform information not covered by conventional TDP by increasing the number of detectable protein groups or post-translational modifications (PTMs) and improving the sequence coverage.

Identification of proteoforms by top-down proteomics using two-dimensional low/low pH reversed-phase liquid chromatography-mass spectrometry

In top-down (TD) proteomics, efficient proteoform separation is crucial to reduce the sample complexity and increase the depth of the analysis. Here, we developed a two-dimensional low pH/low pH reversed-phase liquid chromatography separation scheme for TD proteomics. The first dimension for offline fractionation was performed using a polymeric reversed-phase (PLRP-S) column with trifluoroacetic acid as ion-pairing reagent. The second dimension, a C4 nanocolumn with formic acid as ion-pairing reagent, was coupled online with a high-field asymmetric ion mobility spectrometry (FAIMS) Orbitrap Tribrid mass spectrometer. For both dimensions several parameters were optimized, such as the adaption of the LC gradients in the second dimension according to the elution time (i.e., fraction number) in the first dimension. Avoidance of elevated temperatures and prolonged exposure to acidic conditions minimized cleavage of acid labile aspartate-proline peptide bonds. Furthermore, a concatenation strategy was developed to reduce the total measurement time. We compared our low/low pH with a previously published high pH (C4, ammonium formate)/low pH strategy and found that both separation strategies led to complementary proteoform identifications, mainly below 20 kDa, with a higher number of proteoforms identified by the low/low pH separation. With the optimized separation scheme, more than 4900 proteoforms from 1250 protein groups were identified in Caco-2 cells.

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.

Advancing Intact Protein Quantitation with Updated Deconvolution Routines

Analysis of intact proteins by mass spectrometry enables direct quantitation of the specific proteoforms present in a sample and is an increasingly important tool for biopharmaceutical and academic research. Interpreting and quantifying intact protein species from mass spectra typically involves many challenges including mass deconvolution and peak processing as well as determining optimal spectral averaging parameters and matching masses to theoretical proteoforms. Each of these steps can present informatic hurdles, as parameters often need to be tailored specifically to the data sets. To reduce intact mass deconvolution data analysis burdens, we built upon the widely used "sliding window" mass deconvolution technique with several additional concepts. First, we found that how spectra are averaged and the overlap in spectral windows can be tuned to favor either sensitivity or speed. A multiple window averaging approach was found to be the most effective way to increase mass detection and yielded a >2-fold increase in the number of masses detected. We also developed a targeted feature-finding routine that boosted sensitivity by >2-fold, decreased coefficient of variation across replicates by 50%, and increased the quality of mass elution profiles through 3-fold more detected time points. Lastly, we furthered existing approaches for annotating detected masses with potential proteoforms through spectral fitting for possible proteoform family modifications and network viewing. These proteoform annotation approaches ultimately produced a more accurate way of finding related, but previously unknown proteoforms from intact mass-only data. Together, these quantitation workflow improvements advance the information obtainable from intact protein mass spectrometry analyses.

MSModDetector: A Tool for Detecting Mass Shifts and Post-Translational Modifications in Individual Ion Mass Spectrometry Data

Motivation

Post-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even widely separated PTMs to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein.

Results

We developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I MS) data. I MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I MS data and experimental I MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a protein's PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes.

Availability

The source code is available at https://github.com/marjanfaizi/MSModDetector together with the scripts used for analyses and to generate the figures presented in this study.

MASH Native: a unified solution for native top-down proteomics data processing

MOTIVATION

Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking.

RESULTS

We have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a "one-stop shop" for characterizing both native protein complexes and proteoforms.

AVAILABILITY AND IMPLEMENTATION

The MASH Native app, video tutorials, written tutorials, and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHSoftware.php. All data files shown in user tutorials are included with the MASH Native software in the download .zip file.

Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection

Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.

TopFD: A Proteoform Feature Detection Tool for Top-Down Proteomics

Top-down liquid chromatography-mass spectrometry (LC-MS) analyzes intact proteoforms and generates mass spectra containing peaks of proteoforms with various isotopic compositions, charge states, and retention times. An essential step in top-down MS data analysis is proteoform feature detection, which aims to group these peaks into peak sets (features), each containing all peaks of a proteoform. Accurate protein feature detection enhances the accuracy in MS-based proteoform identification and quantification. Here, we present TopFD, a software tool for top-down MS feature detection that integrates algorithms for proteoform feature detection, feature boundary refinement, and machine learning models for proteoform feature evaluation. We performed extensive benchmarking of TopFD, ProMex, FlashDeconv, and Xtract using seven top-down MS data sets and demonstrated that TopFD outperforms other tools in feature accuracy, reproducibility, and feature abundance reproducibility.

Current Methods for Identifying Plasma Membrane Proteins as Cancer Biomarkers

Plasma membrane proteins are a special class of biomolecules present on the cellular membrane. They provide the transport of ions, small molecules, and water in response to internal and external signals, define a cell's immunological identity, and facilitate intra- and intercellular communication. Since they are vital to almost all cellular functions, their mutants, or aberrant expression is linked to many diseases, including cancer, where they are a part of cancer cell-specific molecular signatures and phenotypes. In addition, their surface-exposed domains make them exciting biomarkers for targeting by imaging agents and drugs. This review looks at the challenges in identifying cancer-related cell membrane proteins and the current methodologies that solve most of the challenges. We classified the methodologies as biased, i.e., search cells for the presence of already known membrane proteins. Second, we discuss the unbiased methods that can identify proteins without prior knowledge of what they are. Finally, we discuss the potential impact of membrane proteins on the early detection and treatment of cancer.

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.

Proteoforms expand the world of microproteins and short open reading frame-encoded peptides

Microproteins and short open reading frame-encoded peptides (SEPs) can, like all proteins, carry numerous posttranslational modifications. Together with posttranscriptional processes, this leads to a high number of possible distinct protein molecules, the proteoforms, out of a limited number of genes. The identification, quantification, and molecular characterization of proteoforms possess special challenges to established, mainly bottom-up proteomics (BUP) based analytical approaches. While BUP methods are powerful, proteins have to be inferred rather than directly identified, which hampers the detection of proteoforms. An alternative approach is top-down proteomics (TDP) which allows to identify intact proteoforms. This perspective article provides a brief overview of modified microproteins and SEPs, introduces the proteoform terminology, and compares present BUP and TDP workflows highlighting their major advantages and caveats. Necessary future developments in TDP to fully accentuate its potential for proteoform-centric analytics of microproteins and SEPs will be discussed.

Impact of Internal Fragments on Top-Down Analysis of Intact Proteins by 193 nm UVPD

193 nm ultraviolet photodissociation (UVPD) allows high sequence coverage to be obtained for intact proteins using terminal fragments alone. However, internal fragments, those that contain neither N- nor C- terminus, are typically ignored, neglecting their potential to bolster characterization of intact proteins. Here, we explore internal fragments generated by 193 nm UVPD for proteins ranging in size from 17-47 kDa and using the ClipsMS algorithm to facilitate searches for internal fragments. Internal fragments were only retained if identified in multiple replicates in order to reduce spurious assignments and to explore the reproducibility of internal fragments generated by UVPD. Inclusion of internal fragment improved sequence coverage by an average of 18% and 32% for UVPD and HCD, respectively, across all proteins and charge states studied. However, only an average of 18% of UVPD internal fragments were identified in two out of three replicates relative to the average number identified across all replicates for all proteins studied. Conversely, for HCD, an average of 63% of internal fragments were retained across replicates. These trends reflect an increased risk of false-positive identifications and a need for caution when considering internal fragments for UVPD. Additionally, proton-transfer charge reduction (PTCR) reactions were performed following UVPD or HCD to assess the impact on internal fragment identifications, allowing up to 20% more fragment ions to be retained across multiple replicates. At this time, it is difficult to recommend the inclusion of the internal fragment when searching UVPD spectra without further work to develop strategies for reducing the possibilities of false-positive identifications. All mass spectra are available in the public repository jPOST with the accession number JPST001885.

MASH Native: A Unified Solution for Native Top-Down Proteomics Data Processing

Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking. Herein, we have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a one-stop shop for characterizing both native protein complexes and proteoforms. The MASH Native app, video tutorials, written tutorials and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHNativeSoftware.php . All data files shown in user tutorials are included with the MASH Native software in the download .zip file.

Increasing confidence in proteomic spectral deconvolution through mass defect

MOTIVATION

Confident deconvolution of proteomic spectra is critical for several applications such as de novo sequencing, cross-linking mass spectrometry and handling chimeric mass spectra.

RESULTS

In general, all deconvolution algorithms may eventually report mass peaks that are not compatible with the chemical formula of any peptide. We show how to remove these artifacts by considering their mass defects. We introduce Y.A.D.A. 3.0, a fast deconvolution algorithm that can remove peaks with unacceptable mass defects. Our approach is effective for polypeptides with less than 10 kDa, and its essence can be easily incorporated into any deconvolution algorithm.

AVAILABILITY AND IMPLEMENTATION

Y.A.D.A. 3.0 is freely available for academic use at http://patternlabforproteomics.org/yada3.

SUPPLEMENTARY INFORMATION

Supplementary information is available at Bioinformatics online.

Size-Based Proteome Fractionation through Polyacrylamide Gel Electrophoresis Combined with LC-FAIMS-MS for In-Depth Top-Down Proteomics

The combination of liquid chromatography (LC) and gas-phase separation by field-asymmetric ion mobility spectrometry (FAIMS) is a powerful proteoform separation system for top-down proteomics. Here, we present an in-depth top-down proteomics workflow, GeLC-FAIMS-MS, in which a molecular-weight-based proteome fractionation approach using SDS-polyacrylamide gel electrophoresis is performed prior to LC-FAIMS-MS. Since individual bands and their corresponding mass ranges require different compensating voltages (CVs), the MS parameters for each gel band and CV were optimized to increase the number and reliability of proteoform identifications further. We developed an easy-to-implement and inexpensive procedure combining the earlier established Passively Eluting Proteins from Polyacrylamide gels as Intact species (PEPPI) protocol with an optimized Anion-Exchange disk-assisted Sequential sample Preparation (AnExSP) method for the removal of stains and SDS. The protocol was compared with a methanol-chloroform-water (MCW)-based protein precipitation protocol. The results show that the PEPPI-AnExSP procedure is better suited for the identification of low-molecular-weight proteoforms, whereas the MCW-based protocol showed advantages for higher-molecular-weight proteoforms. Moreover, complementary results were observed with the two methods in terms of hydrophobicity and isoelectric points of the identified proteoforms. In total, 8500 proteoforms could be identified in a human proteome standard, showing the effectiveness of the gel-based sample fractionation approaches in combination with LC-FAIMS-MS.

Challenges and opportunities for proteomics and the improvement of bread wheat quality

Wheat remains a critical global food source, pressured by climate change and the need to maximize yield, improve processing and nutritional quality and ensure safety. An enormous amount of research has been conducted to understand gluten protein composition and structure in relation to end-use quality, yet progress has become stagnant. This is mainly due to the need and inability to biochemically characterize the intact functional glutenin polymer in order to correlate to quality, necessitating reduction to monomeric subunits and a loss of contextual information. While some individual gluten proteins might have a positive or negative influence on gluten quality, it is the sum total of these proteins, their relative and absolute expression, their sub-cellular trafficking, the amount and size of glutenin polymers, and ratios between gluten protein classes that define viscoelasticity of gluten. The sub-cellular trafficking of gluten proteins during seed maturation is still not completely clear and there is evidence of dual pathways and therefore different destinations for proteins, either constitutively or temporally. The trafficking of proteins is also unclear in endosperm cells as they undergo programmed cell death; Golgi disappear around 12 DPA but protein filling continues at least to 25 DPA. Modulation of the timing of cellular events will invariably affect protein deposition and therefore gluten strength and function. Existing and emerging proteomics technologies such as proteoform profiling and top-down proteomics offer new tools to study gluten protein composition as a whole system and identify compositional patterns that can modify gluten structure with improved functionality.

FLASHIda enables intelligent data acquisition for top-down proteomics to boost proteoform identification counts

The detailed analysis and structural characterization of proteoforms by top-down proteomics (TDP) has gained a lot of interest in biomedical research. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complexity of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. Here, we present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In an analysis of E. coli lysate, FLASHIda increases the number of unique proteoform level identifications from 800 to 1500 or generates a near-identical number of identifications in one third of the instrument time when compared to standard DDA mode. Furthermore, FLASHIda enables sensitive mapping of post-translational modifications and detection of chemical adducts. As a software extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.

High-Resolution Native Mass Spectrometry

Native mass spectrometry (MS) involves the analysis and characterization of macromolecules, predominantly intact proteins and protein complexes, whereby as much as possible the native structural features of the analytes are retained. As such, native MS enables the study of secondary, tertiary, and even quaternary structure of proteins and other biomolecules. Native MS represents a relatively recent addition to the analytical toolbox of mass spectrometry and has over the past decade experienced immense growth, especially in enhancing sensitivity and resolving power but also in ease of use. With the advent of dedicated mass analyzers, sample preparation and separation approaches, targeted fragmentation techniques, and software solutions, the number of practitioners and novel applications has risen in both academia and industry. This review focuses on recent developments, particularly in high-resolution native MS, describing applications in the structural analysis of protein assemblies, proteoform profiling of─among others─biopharmaceuticals and plasma proteins, and quantitative and qualitative analysis of protein-ligand interactions, with the latter covering lipid, drug, and carbohydrate molecules, to name a few.

Improving the Center Section Sequence Coverage of Large Proteins Using Stepped-Fragment Ion Protection Ultraviolet Photodissociation

Ultraviolet photodissociation (UVPD) mass spectrometry has gained attention in recent years for its ability to provide high sequence coverage of intact proteins. However, secondary dissociation of fragment ions, in which fragment ions subjected to multiple laser pulses decompose into small products, is a common phenomenon during UVPD that contributes to limited coverage in the midsection of protein sequences. To counter secondary dissociation, a method involving the application of notched waveforms to modulate the trajectories of fragment ions away from the laser beam, termed fragment ion protection (FIP), was previously developed to reduce the probability of secondary dissociation. This, in turn, increased the number of identified large fragment ions. In the present study, FIP was applied to UVPD of large proteins ranging in size from 29 to 55 kDa, enhancing the abundances of large fragment ions. A stepped-FIP strategy was implemented in which UVPD mass spectra were collected using multiple different amplitudes of the FIP waveforms and then the results from the mass spectra were combined. By using stepped-FIP, the number of fragment ions in the midsections of the sequences increased for all proteins. For example, whereas no fragment ions were identified in the middle section of the sequence for glutamate dehydrogenase (55 kDa, 55+ charge state), 10 sequence ions were identified by using UVPD-FIP.

Improved Identification of Proteoforms in Top-Down Proteomics Using FAIMS with Internal CV Stepping

In top-down (TD) proteomics, prefractionation prior to mass spectrometric (MS) analysis is a crucial step for both the high confidence identification of proteoforms and increased proteome coverage. In addition to liquid-phase separations, gas-phase fractionation strategies such as field asymmetric ion mobility spectrometry (FAIMS) have been shown to be highly beneficial in TD proteomics. However, so far, only external compensation voltage (CV) stepping has been demonstrated for TD proteomics, i.e., single CVs were applied for each run. Here, we investigated the use of internal CV stepping (multiple CVs per acquisition) for single-shot TD analysis, which has huge advantages in terms of measurement time and the amount of sample required. In addition, MS parameters were optimized for the individual CVs since different CVs target certain mass ranges. For example, small proteoforms identified mainly with more negative CVs can be identified with lower resolution and number of microscans than larger proteins identified primarily via less negative CVs. We investigated the optimal combination and number of CVs for different gradient lengths and validated the optimized settings with the low-molecular-weight proteome of CaCo-2 cells obtained using a range of different sample preparation techniques. Compared to measurements without FAIMS, both the number of identified protein groups (+60-94%) and proteoforms (+46-127%) and their confidence were significantly increased, while the measurement time remained identical. In total, we identified 684 protein groups and 2675 proteoforms from CaCo-2 cells in less than 24 h using the optimized multi-CV method.

Proteoform Analysis and Construction of Proteoform Families in Proteoform Suite

Proteoform Suite is an interactive software program for the identification and quantification of intact proteoforms from mass spectrometry data. Proteoform Suite identifies proteoforms observed by intact-mass (MS1) analysis. In intact-mass analysis, unfragmented experimental proteoforms are compared to a database of known proteoform sequences and to one another, searching for mass differences corresponding to well-known post-translational modifications or amino acids. Intact-mass analysis enables proteoforms observed in the MS1 data without MS/MS (MS2) fragmentation to be identified. Proteoform Suite further facilitates the construction and visualization of proteoform families, which are the sets of proteoforms derived from individual genes. Bottom-up peptide identifications and top-down (MS2) proteoform identifications can be integrated into the Proteoform Suite analysis to increase the sensitivity and accuracy of the analysis. Proteoform Suite is open source and freely available at https://github.com/smith-chem-wisc/proteoform-suite .

Mass Deconvolution of Top-Down Mass Spectrometry Datasets by FLASHDeconv

Mass deconvolution, the determination of proteoform precursor and fragment masses, is crucial for top-down proteomics data analysis. Here we describe the detailed procedure to run FLASHDeconv, an ultrafast, high-quality mass deconvolution tool. Both spectrum- and feature-level deconvolution results are obtainable in various output formats by FLASHDeconv. FLASHDeconv is runnable in different environments such as the command line and OpenMS workflows.

MSTopDiff: A Tool for the Visualization of Mass Shifts in Deconvoluted Top-Down Proteomics Data for the Database-Independent Detection of Protein Modifications

Top-down proteomics analyzes intact proteoforms with all of their post-translational modifications and genetic and RNA splice variants. In addition, modifications introduced either deliberately or inadvertently during sample preparation, that is, via oxidation, alkylation, or labeling reagents, or through the formation of noncovalent adducts (e.g., detergents) further increase the sample complexity. To facilitate the recognition of protein modifications introduced during top-down analysis, we developed MSTopDiff, a software tool with a graphical user interface written in Python, which allows one to detect protein modifications by calculating and visualizing mass differences in top-down data without the prerequisite of a database search. We demonstrate the successful application of MSTopDiff for the detection of artifacts originating from oxidation, formylation, overlabeling during isobaric labeling, and adduct formation with cations or sodium dodecyl sulfate. MSTopDiff offers several modes of data representation using deconvoluted MS1 or MS2 spectra. In addition to artificial modifications, the tool enables the visualization of biological modifications such as phosphorylation and acetylation. MSTopDiff provides an overview of the artificial and biological modifications in top-down proteomics samples, which makes it a valuable tool in quality control of standard workflows and for parameter evaluation during method development.

An Improved Top-Down Mass Spectrometry Characterization of Chlamydomonas reinhardtii Histones and Their Post-translational Modifications

We present an updated analysis of the linker and core histone proteins and their proteoforms in the green microalga Chlamydomonas reinhardtii by top-down mass spectrometry (TDMS). The combination of high-resolution liquid chromatographic separation, robust fragmentation, high mass spectral resolution, the application of a custom search algorithm, and extensive manual analysis enabled the characterization of 86 proteoforms across all four core histones H2A, H2B, H3, and H4 and the linker histone H1. All canonical H2A paralogs, which vary in their C-termini, were identified, along with the previously unreported noncanonical variant H2A.Z that had high levels of acetylation and C-terminal truncations. Similarly, a majority of the canonical H2B paralogs were identified, along with a smaller noncanonical variant, H2B.v1, that was highly acetylated. Histone H4 exhibited a novel acetylation profile that differs significantly from that found in other organisms. A majority of H3 was monomethylated at K4 with low levels of co-occuring acetylation, while a small fraction of H3 was trimethylated at K4 with high levels of co-occuring acetylation.

Mesh Fragmentation Improves Dissociation Efficiency in Top-down Proteomics

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

TopMSV: A Web-Based Tool for Top-Down Mass Spectrometry Data Visualization

Top-down mass spectrometry (MS) investigates intact proteoforms for proteoform identification, characterization, and quantification. Data visualization plays an essential role in top-down MS data analysis because proteoform identification and characterization often involve manual data inspection to determine the molecular masses of highly charged ions and validate unexpected alterations in identified proteoforms. While many software tools have been developed for MS data visualization, there is still a lack of web-based visualization software designed for top-down MS. Here, we present TopMSV, a web-based tool for top-down MS data processing and visualization. TopMSV provides interactive views of top-down MS data using a web browser. It integrates software tools for spectral deconvolution and proteoform identification and uses analysis results of the tools to annotate top-down MS data.

Novel Strategies to Address the Challenges in Top-Down Proteomics

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

Top-down proteomics: challenges, innovations, and applications in basic and clinical research

Introduction- A better understanding of the underlying molecular mechanism of diseases is critical for developing more effective diagnostic tools and therapeutics toward precision medicine. However, many challenges remain to unravel the complex nature of diseases. Areas covered- Changes in protein isoform expression and post-translation modifications (PTMs) have gained recognition for their role in underlying disease mechanisms. Top-down mass spectrometry (MS)-based proteomics is increasingly recognized as an important method for the comprehensive characterization of proteoforms that arise from alternative splicing events and/or PTMs for basic and clinical research. Here, we review the challenges, technological innovations, and recent studies that utilize top-down proteomics to elucidate changes in the proteome with an emphasis on its use to study heart diseases. Expert opinion- Proteoform-resolved information can substantially contribute to the understanding of the molecular mechanisms underlying various diseases and for the identification of novel proteoform targets for better therapeutic development . Despite the challenges of sequencing intact proteins, top-down proteomics has enabled a wealth of information regarding protein isoform switching and changes in PTMs. Continuous developments in sample preparation, intact protein separation, and instrumentation for top-down MS have broadened its capabilities to characterize proteoforms from a range of samples on an increasingly global scale.

Transient-Mediated Simulations of FTMS Isotopic Distributions and Mass Spectra to Guide Experiment Design and Data Analysis

Fourier transform mass spectrometry (FTMS) applications require accurate analysis of extremely complex mixtures of species in wide mass and charge state ranges. To optimize the related FTMS data analysis accuracy, parameters for data acquisition and the allied data processing should be selected rationally, and their influence on the data analysis outcome is to be understood. To facilitate this selection process and to guide the experiment design and data processing workflows, we implemented the underlying algorithms in a software tool with a graphical user interface, FTMS Isotopic Simulator. This tool computes FTMS data via time-domain data (transient) simulations for user-defined molecular species of interest and FTMS instruments, including diverse Orbitrap FTMS models, followed by user-specified FT processing steps. Herein, we describe implementation and benchmarking of this tool for analysis of a wide range of compounds as well as compare simulated and experimentally generated FTMS data. In particular, we discuss the use of this simulation tool for narrowband, broadband, and low- and high-resolution analysis of small molecules, peptides, and proteins, up to the level of their isotopic fine structures. By demonstrating the allied FT processing artifacts, we raise awareness of a proper selection of FT processing parameters for modern applications of FTMS, including intact mass analysis of proteoforms and top-down proteomics. Overall, the described transient-mediated approach to simulate FTMS data has proven useful for supporting contemporary FTMS applications. We also find its utility in fundamental FTMS studies and creating didactic materials for FTMS teaching.