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

Deep Profiling of Plasma Proteoforms with Engineered Nanoparticles for Top-down Proteomics

The dynamic range challenge for detection of proteins and their proteoforms in human plasma has been well documented. Here, we use the nanoparticle protein corona approach to enrich low-abundant proteins selectively and reproducibly from human plasma and use top-down proteomics to quantify differential enrichment for the 2841 detected proteoforms from 114 proteins. Furthermore, nanoparticle enrichment allowed top-down detection of proteoforms between ∼1 µg/mL and ∼10 pg/mL in absolute abundance, providing up to 10 5 -fold increase in proteome depth over neat plasma in which only proteoforms from abundant proteins (>1 µg/mL) were detected. The ability to monitor medium and some low abundant proteoforms through reproducible enrichment significantly extends the applicability of proteoform research by adding depth beyond albumin, immunoglobins and apolipoproteins to uncover many involved in immunity and cell signaling. As proteoforms carry unique information content relative to peptides, this report opens the door to deeper proteoform sequencing in clinical proteomics of disease or aging cohorts.

Are Internal Fragments Observable in Electron Based Top-Down Mass Spectrometry?

Protein tandem mass spectrometry (MS/MS) often generates sequence-informative fragments from backbone bond cleavages near the termini. This lack of fragmentation in the protein interior is particularly apparent in native top-down MS. Improved sequence coverage, critical for reliable annotation of posttranslational modifications (PTMs) and sequence variants, may be obtained from internal fragments generated by multiple backbone cleavage events. However, internal fragment assignments can be error prone due to isomeric/isobaric fragments from different parts of a protein sequence. Also, internal fragment generation propensity depends on the chosen MS/MS activation strategy. Here, we examine internal fragment formation in electron capture dissociation (ECD) and electron transfer dissociation (ETD) following native and denaturing MS, as well as liquid chromatography (LC)/MS of several proteins. Experiments were undertaken on multiple instruments, including Q-ToF, Orbitrap, and high-field FT-ICR across four laboratories. ECD was performed at both ultrahigh vacuum and at similar pressure to ETD conditions. Two complementary software packages were used for data analysis. When feasible, ETD-higher-energy collision dissociation (ETD-HCD) MS3 was performed to validate/refute potential internal fragment assignments, including differentiating MS3 fragmentation behavior of radical vs. even-electron primary fragments. We show that, under typical operating conditions, internal fragments cannot be confidently assigned in ECD, nor ETD. On the other hand, such fragments, along with some b-type terminal fragments (not typically observed in ECD/ETD spectra) appear at atypical ECD operating conditions, suggesting they originate from a separate ion-electron activation process. Furthermore, atypical fragment ion types, e.g., x ions, are observed at such conditions as well as upon EThcD, presumably due to vibrational activation of radical z-type ions.

Top-Down Proteomics Identifies Plasma Proteoform Signatures of Liver Cirrhosis Progression

Cirrhosis, advanced liver disease, affects 2-5 million Americans. While most patients have compensated cirrhosis and may be fairly asymptomatic, many decompensate and experience life-threatening complications such as gastrointestinal bleeding, confusion (hepatic encephalopathy), and ascites, reducing life expectancy from 12 to less than 2 years. Among patients with compensated cirrhosis, identifying patients at high risk of decompensation is critical to optimize care and reduce morbidity and mortality. Therefore, it is important to preferentially direct them towards specialty care which cannot be provided to all patients with cirrhosis. We used discovery Top-down Proteomics (TDP) to identify differentially expressed proteoforms (DEPs) in the plasma of patients with progressive stages of liver cirrhosis with the ultimate goal to identify candidate biomarkers of disease progression. In this pilot study, we identified 209 DEPs across three stages of cirrhosis (compensated, compensated with portal hypertension, and decompensated), of which 115 derived from proteins enriched in the liver at a transcriptional level and discriminated the three stages of cirrhosis. Enrichment analyses demonstrated DEPs are involved in several metabolic and immunological processes known to be impacted by cirrhosis progression. We have preliminarily defined the plasma proteoform signatures of cirrhosis patients, setting the stage for ongoing discovery and validation of biomarkers for early diagnosis, risk stratification, and disease monitoring.

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.

Orbitrap Mass Spectrometry and High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Enable the in-Depth Analysis of Human Serum Proteoforms

Blood serum and plasma are arguably the most commonly analyzed clinical samples, with dozens of proteins serving as validated biomarkers for various human diseases. Top-down proteomics may provide additional insights into disease etiopathogenesis since this approach focuses on protein forms, or proteoforms, originally circulating in blood, potentially providing access to information about relevant post-translational modifications, truncations, single amino acid substitutions, and many other sources of protein variation. However, the vast majority of proteomic studies on serum and plasma are carried out using peptide-centric, bottom-up approaches that cannot recapitulate the original proteoform content of samples. Clinical laboratories have been slow to adopt top-down analysis, also due to higher sample handling requirements. In this study, we describe a straightforward protocol for intact proteoform sample preparation based on the depletion of albumin and immunoglobulins, followed by simplified protein fractionation via polyacrylamide gel electrophoresis. After molecular weight-based fractionation, we supplemented the traditional liquid chromatography-tandem mass spectrometry (LC-MS2) data acquisition with high-field asymmetric waveform ion mobility spectrometry (FAIMS) to further simplify serum proteoform mixtures. This LC-FAIMS-MS2 method led to the identification of over 1000 serum proteoforms < 30 kDa, outperforming traditional LC-MS2 data acquisition and more than doubling the number of proteoforms identified in previous studies.

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.

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.

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.

Validation of Top-Down Proteomics Data by Bottom-Up-Based N-Terminomics Reveals Pitfalls in Top-Down-Based Terminomics Workflows

Bottom-up proteomics (BUP)-based N-terminomics techniques have become standard to identify protein N-termini. While these methods rely on the identification of N-terminal peptides only, top-down proteomics (TDP) comes with the promise to provide additional information about post-translational modifications and the respective C-termini. To evaluate the potential of TDP for terminomics, two established TDP workflows were employed for the proteome analysis of the nematode Caenorhabditis elegans. The N-termini of the identified proteoforms were validated using a BUP-based N-terminomics approach. The TDP workflows used here identified 1658 proteoforms, the N-termini of which were verified by BUP in 25% of entities only. Caveats in both the BUP- and TDP-based workflows were shown to contribute to this low overlap. In BUP, the use of trypsin prohibits the detection of arginine-rich or arginine-deficient N-termini, while in TDP, the formation of artificially generated termini was observed in particular in a workflow encompassing sample treatment with high acid concentrations. Furthermore, we demonstrate the applicability of reductive dimethylation in TDP to confirm biological N-termini. Overall, our study shows not only the potential but also current limitations of TDP for terminomics studies and also presents suggestions for future developments, for example, for data quality control, allowing improvement of the detection of protein termini by TDP.

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.

Data acquisition suitable for top-down proteomics (TDP) has the potential to significantly improve proteoform analysis. Here, the authors present FLASHIda, an intelligent online data acquisition algorithm for TDP that nearly doubles the number of proteoform-level identifications in complex samples.

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.

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

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

Reassembling protein complexes after controlled disassembly by top-down mass spectrometry in native mode

The combined use of electrospray ionization run in so-called "native mode" with top-down mass spectrometry (nTDMS) is enhancing both structural biology and discovery proteomics by providing three levels of information in a single experiment: the intact mass of a protein or complex, the masses of its subunits and non-covalent cofactors, and fragment ion masses from direct dissociation of subunits that capture the primary sequence and combinations of diverse post-translational modifications (PTMs). While intact mass data are readily deconvoluted using well-known software options, the analysis of fragmentation data that result from a tandem MS experiment - essential for proteoform characterization - is not yet standardized. In this tutorial, we offer a decision-tree for the analysis of nTDMS experiments on protein complexes and diverse bioassemblies. We include an overview of strategies to navigate this type of analysis, provide example data sets, and highlight software for the hypothesis-driven interrogation of fragment ions for localization of PTMs, metals, and cofactors on native proteoforms. Throughout we have emphasized the key features (deconvolution, search mode, validation, other) that the reader can consider when deciding upon their specific experimental and data processing design using both open-access and commercial software.

Quantifying Positional Isomers (QPI) by Top-Down Mass Spectrometry

Proteomics has exposed a plethora of posttranslational modifications, but demonstrating functional relevance requires new approaches. Top-down proteomics of intact proteins has the potential to fully characterize protein modifications in terms of amount, site(s), and the order in which they are deposited on the protein; information that so far has been elusive to extract by shotgun proteomics. Data acquisition and analysis of intact multimodified proteins have however been a major challenge, in particular for positional isomers that carry the same number of modifications at different sites. Solutions were previously proposed to extract this information from fragmentation spectra, but these have so far mainly been limited to peptides and have entailed a large degree of manual interpretation. Here, we apply high-resolution Orbitrap fusion top-down analyses in combination with bioinformatics approaches to attempt to characterize multiple modified proteins and quantify positional isomers. Automated covalent fragment ion type definition, detection of mass precision and accuracy, and extensive use of replicate spectra increase sequence coverage and drive down false fragment assignments from 10% to 1.5%. Such improved performance in fragment assignment is key to localize and quantify modifications from fragment spectra. The method is tested by investigating positional isomers of Ubiquitin mixed in known concentrations, which results in quantification of high ratios at very low standard errors of the mean (<5%), as well as with synthetic phosphorylated peptides. Application to multiphosphorylated Bora provides an estimation of the so far unknown stoichiometry of the known set of phosphosites and uncovers new sites from hyperphosphorylated Bora.

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ETD fragmentation reveals the presence of positional isomers.

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For proteins up to 40 kDa these positional isomers can accurately be quantified.

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For in-vitro phosphorylated BoraNT a wide array of positional isomers is revealed.

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Use of Fragment ion FDR levels improve the quality of extracted stoichiometries.

Optimization of a Top-Down Proteomics Platform for Closely Related Pathogenic Bacterial Discrimination

The current technique used for microbial identification in hospitals is matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). However, it suffers from important limitations, in particular, for closely related species or when the database used for the identification lacks the appropriate reference. In this work, we set up a liquid chromatography (LC)-MS/MS top-down proteomics platform, which aims at discriminating closely related pathogenic bacteria through the identification of specific proteoforms. Using Escherichia coli as a model, all steps of the workflow were optimized: protein extraction, on-line LC separation, MS method, and data analysis. Using optimized parameters, about 220 proteins, corresponding to more than 500 proteoforms, could be identified in a single run. We then used this platform for the discrimination of enterobacterial pathogens undistinguishable by MALDI-TOF, although leading to very different clinical outcomes. For each pathogen, we identified specific proteoforms that could potentially be used as biomarkers. We also improved the characterization of poorly described bacterial strains. Our results highlight the advantage of addressing proteoforms rather than peptides for accurate bacterial characterization and qualify top-down proteomics as a promising tool in clinical microbiology. Data are available via ProteomeXchange with the identifier PXD019247.

How Do the Different Proteomic Strategies Cope with the Complexity of Biological Regulations in a Multi-Omic World? Critical Appraisal and Suggestions for Improvements

In this second decade of the 21st century, we are lucky enough to have different types of proteomic analyses at our disposal. Furthermore, other functional omics such as transcriptomics have also undergone major developments, resulting in mature tools. However, choice equals questions, and the major question is how each proteomic strategy is fit for which purpose. The aim of this opinion paper is to reposition the various proteomic strategies in the frame of what is known in terms of biological regulations in order to shed light on the power, limitations, and paths for improvement for the different proteomic setups. This should help biologists to select the best-suited proteomic strategy for their purposes in order not to be driven by raw availability or fashion arguments but rather by the best fitness for purpose. In particular, knowing the limitations of the different proteomic strategies helps in interpreting the results correctly and in devising the validation experiments that should be made downstream of the proteomic analyses.

Increased Single-Spectrum Top-Down Protein Sequence Coverage in Trapping Mass Spectrometers with Chimeric Ion Loading

Fourier transform mass spectrometers routinely provide high mass resolution, mass measurement accuracy, and mass spectral dynamic range. In this work, we utilize 21 T Fourier transform ion cyclotron resonance (FT-ICR) to analyze product ions derived from the application of multiple dissociation techniques and/or multiple precursor ions within a single transient acquisition. This ion loading technique, which we call, "chimeric ion loading", saves valuable acquisition time, decreases sample consumption, and improves top-down protein sequence coverage. In the analysis of MCF7 cell lysate, we show collision-induced dissociation (CID) and electron-transfer dissociation (ETD) on each precursor on a liquid chromatography-mass spectrometry (LC-MS) timescale and improve mean sequence coverage dramatically (CID-only 15% vs chimeric 33%), even during discovery-based acquisition. This approach can also be utilized to multiplex the acquisition of product ion spectra of multiple charge states from a single protein precursor or multiple ETD/proton-transfer reactions (PTR) reaction periods. The analytical utility of chimeric ion loading is demonstrated for top-down proteomics, but it is also likely to be impactful for tandem mass spectrometry applications in other areas.

Ultraviolet Photodissociation Mass Spectrometry for Analysis of Biological Molecules

The development of new ion-activation/dissociation methods continues to be one of the most active areas of mass spectrometry owing to the broad applications of tandem mass spectrometry in the identification and structural characterization of molecules. This Review will showcase the impact of ultraviolet photodissociation (UVPD) as a frontier strategy for generating informative fragmentation patterns of ions, especially for biological molecules whose complicated structures, subtle modifications, and large sizes often impede molecular characterization. UVPD energizes ions via absorption of high-energy photons, which allows access to new dissociation pathways relative to more conventional ion-activation methods. Applications of UVPD for the analysis of peptides, proteins, lipids, and other classes of biologically relevant molecules are emphasized in this Review.

Predicting Electrophoretic Mobility of Proteoforms for Large-Scale Top-Down Proteomics

Large-scale top-down proteomics characterizes proteoforms in cells globally with high confidence and high throughput using reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS) or capillary zone electrophoresis (CZE)-MS/MS. The false discovery rate (FDR) from the target-decoy database search is typically deployed to filter identified proteoforms to ensure high-confidence identifications (IDs). It has been demonstrated that the FDRs in top-down proteomics can be drastically underestimated. An alternative approach to the FDR can be useful for further evaluating the confidence of proteoform IDs after the database search. We argue that predicting retention/migration time of proteoforms from the RPLC/CZE separation accurately and comparing their predicted and experimental separation time could be a useful and practical approach. Based on our knowledge, there is still no report in the literature about predicting separation time of proteoforms using large top-down proteomics data sets. In this pilot study, for the first time, we evaluated various semiempirical models for predicting proteoforms' electrophoretic mobility (μef) using large-scale top-down proteomics data sets from CZE-MS/MS. We achieved a linear correlation between experimental and predicted μef of E. coli proteoforms (R2 = 0.98) with a simple semiempirical model, which utilizes the number of charges and molecular mass of each proteoform as the parameters. Our modeling data suggest that the complete unfolding of proteoforms during CZE separation benefits the prediction of their μef. Our results also indicate that N-terminal acetylation and phosphorylation both decrease the proteoforms' charge by roughly one charge unit.

Multiplexed mass spectrometry of individual ions improves measurement of proteoforms and their complexes

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

Thorough Performance Evaluation of 213 nm Ultraviolet Photodissociation for Top-down Proteomics

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

Elucidating Proteoform Dynamics Underlying the Senescence Associated Secretory Phenotype

Primary diploid cells exit the cell cycle in response to exogenous stress or oncogene activation through a process known as cellular senescence. This cell-autonomous tumor-suppressive mechanism is also a major mechanism operative in organismal aging. To date, temporal aspects of senescence remain understudied. Therefore, we use quantitative proteomics to investigate changes following forced HRAS^G12V expression and induction of senescence across 1 week in normal diploid fibroblasts. We demonstrate that global intracellular proteomic changes correlate with the emergence of the senescence-associated secretory phenotype and the switch to robust cell cycle exit. The senescence secretome reinforces cell cycle exit, yet is largely detrimental to tissue homeostasis. Previous studies of secretomes rely on ELISA, bottom-up proteomics or RNA-seq. To date, no study to date has examined the proteoform complexity of secretomes to elucidate isoform-specific, post-translational modifications or regulated cleavage of signal peptides. Therefore, we use a quantitative top-down proteomics approach to define the molecular complexity of secreted proteins <30 kDa. We identify multiple forms of immune regulators with known activities and affinities such as distinct forms of interleukin-8, as well as GROα and HMGA1, and temporally resolve secreted proteoform dynamics. Together, our work demonstrates the complexity of the secretome past individual protein accessions and provides motivation for further proteoform-resolved measurements of the secretome.

Identification of Plasmodium falciparum proteoforms from liver stage models

BACKGROUND

Immunization with attenuated malaria sporozoites protects humans from experimental malaria challenge by mosquito bite. Protection in humans is strongly correlated with the production of T cells targeting a heterogeneous population of pre-erythrocyte antigen proteoforms, including liver stage antigens. Currently, few T cell epitopes derived from Plasmodium falciparum, the major aetiologic agent of malaria in humans are known.

METHODS

In this study both in vitro and in vivo malaria liver stage models were used to sequence host and pathogen proteoforms. Proteoforms from these diverse models were subjected to mild acid elution (of soluble forms), multi-dimensional fractionation, tandem mass spectrometry, and top-down bioinformatics analysis to identify proteoforms in their intact state.

RESULTS

These results identify a group of host and malaria liver stage proteoforms that meet a 5% false discovery rate threshold.

CONCLUSIONS

This work provides proof-of-concept for the validity of this mass spectrometry/bioinformatic approach for future studies seeking to reveal malaria liver stage antigens towards vaccine development.

Proton Transfer Charge Reduction Enables High-Throughput Top-Down Analysis of Large Proteoforms

Despite the recent technological advances in Fourier transform mass spectrometry (FTMS) instrumentation, top-down proteomics (TDP) is currently mostly applied to the characterization of proteoforms <30 kDa due to the poor performance of high-resolution FTMS for the analysis of larger proteoforms and the high complexity of intact proteomes in the 30-60 kDa mass range. Here, we propose a novel data acquisition method based on ion-ion proton transfer, herein termed proton transfer charge reduction (PTCR), to investigate large proteoforms of Pseudomonas aeruginosa in a high-throughput fashion. We designed a targeted data acquisition strategy, named tPTCR, which applies two consecutive gas phase fractionation steps for obtaining intact precursor masses: first, a narrow (1.5 m/z-wide) quadrupole filter m/z transmission window is used to select a subset of charge states from all ionized proteoform cations; second, this aliquot of protein cations is subjected to PTCR in order to reduce their average charge state: upon m/z analysis in an Orbitrap, proteoform mass spectra with minimal m/z peak overlap and easy-to-interpret charge state distributions are obtained, simplifying the proteoform mass calculation. Subsequently, the same quadrupole-selected narrow m/z region of analytes is subjected to collisional dissociation to obtain proteoform sequence information, which used in combination with intact mass information leads to proteoform identification through an off-line database search. The newly proposed method was benchmarked against the previously developed "medium/high" data-dependent acquisition strategy and doubled the number of UniProt entries and proteoforms >30 kDa identified on the liquid chromatography time scale.

Identification and Quantification of Proteoforms by Mass Spectrometry

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