Targeted Quantification of Proteoforms in Complex Samples by Proteoform Reaction Monitoring.

Existing mass spectrometric assays used for sensitive and specific measurements of target proteins across multiple samples, such as selected/multiple reaction monitoring (SRM/MRM) or parallel reaction monitoring (PRM), are peptide-based methods for bottom-up proteomics. Here, we describe an approach based on the principle of PRM for the measurement of intact proteoforms by targeted top-down proteomics, termed proteoform reaction monitoring (PfRM). We explore the ability of our method to circumvent traditional limitations of top-down proteomics, such as sensitivity and reproducibility. We also introduce a new software program, Proteoform Finder (part of ProSight Native), specifically designed for the easy analysis of PfRM data. PfRM was initially benchmarked by quantifying three standard proteins. The linearity of the assay was shown over almost 3 orders of magnitude in the femtomole range, with limits of detection and quantification in the low femtomolar range. We later applied our multiplexed PfRM assay to complex samples to quantify biomarker candidates in peripheral blood mononuclear cells (PBMCs) from liver-transplanted patients, suggesting their possible translational applications. These results demonstrate that PfRM has the potential to contribute to the accurate quantification of protein biomarkers for diagnostic purposes and to improve our understanding of disease etiology at the proteoform level.

Automated imaging and identification of proteoforms directly from ovarian cancer tissue.

The molecular identification of tissue proteoforms by top-down mass spectrometry (TDMS) is significantly limited by throughput and dynamic range. We introduce AutoPiMS, a single-ion MS based multiplexed workflow for top-down tandem MS (MS2) directly from tissue microenvironments in a semi-automated manner. AutoPiMS directly off human ovarian cancer sections allowed for MS2 identification of 73 proteoforms up to 54 kDa at a rate of <1 min per proteoform. AutoPiMS is directly interfaced with multifaceted proteoform imaging MS data modalities for the identification of proteoform signatures in tumor and stromal regions in ovarian cancer biopsies. From a total of ~1000 proteoforms detected by region-of-interest label-free quantitation, we discover 303 differential proteoforms in stroma versus tumor from the same patient. 14 of the top proteoform signatures are corroborated by MSI at 20 micron resolution including the differential localization of methylated forms of CRIP1, indicating the importance of proteoform-enabled spatial biology in ovarian cancer.

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.

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.

Native top-down proteomics reveals EGFR-ERα signaling crosstalk in breast cancer cells dissociates NUTF2 dimers to modulate ERα signaling and cell growth.

Oligomerization of proteins and their modified forms (proteoforms) produces functional protein complexes 1,2. Complexoforms are complexes that consist of the same set of proteins with different proteoforms 3. The ability to characterize these assemblies within cells is critical to understanding the molecular mechanisms involved in disease and to designing effective drugs. An outstanding biological question is how proteoforms drive function and oligomerization of complexoforms. However, tools to define endogenous proteoform-proteoform/ligand interactions are scarce 4. Here, we present a native top-down proteomics (nTDP) strategy that combines size-exclusion chromatography, nano liquid-chromatography in direct infusion mode, field asymmetric ion mobility spectrometry, and multistage mass spectrometry to identify protein assemblies (≤70 kDa) in breast cancer cells and in cells that overexpress EGFR, a resistance model of estrogen receptor-α (ER-α) targeted therapies. By identifying ~104 complexoforms from 17 protein complexes, our nTDP approach revealed several molecular features of the breast cancer proteome, including EGFR-induced dissociation of nuclear transport factor 2 (NUTF2) assemblies that modulate ER activity. Our findings show that the K4 and K55 posttranslational modification sites discovered with nTDP differentially impact the effects of NUTF2 on the inhibition of the ER signaling pathway. By characterizing endogenous proteoform-proteoform/ligand interactions, we reveal the molecular diversity of complexoforms, which allows us to propose a model for ER drug discovery in the context of designing effective inhibitors to selectively bind and disrupt the actions of targeted ER complexoforms.

ProSight Native: Defining Protein Complex Composition from Native Top-Down Mass Spectrometry Data.

Native mass spectrometry has recently moved alongside traditional structural biology techniques in its ability to provide clear insights into the composition of protein complexes. However, to date, limited software tools are available for the comprehensive analysis of native mass spectrometry data on protein complexes, particularly for experiments aimed at elucidating the composition of an intact protein complex. Here, we introduce ProSight Native as a start-to-finish informatics platform for analyzing native protein and protein complex data. Combining mass determination via spectral deconvolution with a top-down database search and stoichiometry calculations, ProSight Native can determine the complete composition of protein complexes. To demonstrate its features, we used ProSight Native to successfully determine the composition of the homotetrameric membrane complex Aquaporin Z. We also revisited previously published spectra and were able to decipher the composition of a heterodimer complex bound with two noncovalently associated ligands. In addition to determining complex composition, we developed new tools in the software for validating native mass spectrometry fragment ions and mapping top-down fragmentation data onto three-dimensional protein structures. Taken together, ProSight Native will reduce the informatics burden on the growing field of native mass spectrometry, enabling the technology to further its reach.

Improved Label-Free Quantification of Intact Proteoforms Using Field Asymmetric Ion Mobility Spectrometry.

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

High-Throughput Deconvolution of Intact Protein Mass Spectra for the Screening of Covalent Inhibitors.

Deconvolution from intact protein mass-to-charge spectra to mass spectra is essential to generate interpretable data for mass spectrometry (MS) platforms coupled to ionization sources that produce multiply charged species. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) can be used to analyze intact proteins in multiwell microtiter plates with speed matching small molecule analyses (at least 1 Hz). However, the lack of compatible deconvolution software has limited its use in high-throughput screening applications. Most existing automated deconvolution software packages work best for data generated from LC-MS, and to the best of our knowledge, there is no software capable of performing fast plate-based mass spectral deconvolution. Herein we present the use of a new workflow in ProSight Native for the deconvolution of protein spectra from entire well plates that can be completed within 3 s. First, we successfully demonstrated the potential increased throughput benefits produced by the combined IR-MALDESI-MS - ProSight Native platform using protein standards. We then conducted a screen for Bruton's tyrosine kinase (BTK) covalent binders against a well-annotated compound collection consisting of 2232 compounds and applied ProSight Native to deconvolute the protein spectra. Seventeen hits including five known BTK covalent inhibitors in the compound set were identified. By alleviating the data processing bottleneck using ProSight Native, it may be feasible to analyze and report covalent screening results for >200,000 samples in a single day.

Highly multiplexed, label-free proteoform imaging of tissues by individual ion mass spectrometry.

Imaging of proteoforms in human tissues is hindered by low molecular specificity and limited proteome coverage. Here, we introduce proteoform imaging mass spectrometry (PiMS), which increases the size limit for proteoform detection and identification by fourfold compared to reported methods and reveals tissue localization of proteoforms at <80-µm spatial resolution. PiMS advances proteoform imaging by combining ambient nanospray desorption electrospray ionization with ion detection using individual ion mass spectrometry. We demonstrate highly multiplexed proteoform imaging of human kidney, annotating 169 of 400 proteoforms of <70 kDa using top-down MS and a database lookup of ~1000 kidney candidate proteoforms, including dozens of key enzymes in primary metabolism. PiMS images reveal distinct spatial localizations of proteoforms to both anatomical structures and cellular neighborhoods in the vasculature, medulla, and cortex regions of the human kidney. The benefits of PiMS are poised to increase proteome coverage for label-free protein imaging of tissues.

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

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

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.

Quantitation of Plasma Membrane Drug Transporters in Kidney Tissue and Cell Lines Using a Novel Proteomic Approach Enabled a Prospective Prediction of Metformin Disposition.

The successful prospective incorporation of in vitro transporter kinetics in physiologically based pharmacokinetic (PBPK) models to describe drug disposition remains challenging. Although determination of scaling factors to extrapolate in vitro to in vivo transporter kinetics has been facilitated by quantitative proteomics, no robust assessment comparing membrane recoveries between different cells/tissues has been made. HEK293 cells overexpressing OCT2, MATE1, and MATE2K or human kidney cortex were homogenized and centrifuged to obtain the total membrane fractions, which were subsequently subjected to liquid-liquid extraction followed by centrifugation and precipitation to isolate plasma membrane fractions. Plasma membrane recoveries determined by quantitation of the marker Na+/K+-ATPase in lysate and plasma membrane fractions were ≤20% but within 3-fold across different cells and tissues. A separate study demonstrated that recoveries are comparable between basolateral and apical membranes of renal proximal tubules, as measured by Na+/K+-ATPase and γ-glutamyl transpeptidase 1, respectively. The plasma membrane expression of OCT2, MATE1, and MATE2K was quantified and relative expression factors (REFs) were determined as the ratio between the tissue and cell concentrations. Corrections using plasma membrane recovery had minimal impact on REF values (<2-fold). In vitro transporter kinetics of metformin were extrapolated to in vivo using the corresponding REFs in a PBPK model. The simulated metformin exposures were within 2-fold of clinical exposure. These results demonstrate that transporter REFs based on plasma membrane expression enable a prediction of transporter-mediated drug disposition. Such REFs may be estimated without the correction of plasma membrane recovery when the same procedure is applied between different matrices. SIGNIFICANCE STATEMENT: Transporter REFs based on plasma membrane expression enable in vitro-in vivo extrapolation of transporter kinetics. Plasma membrane recoveries as determined by the quantification of sodium-potassium adenosine triphosphatase were comparable between the in vitro and in vivo systems used in the present study, and therefore had minimal impact on the transporter REF values.

Sequential Ion-Ion Reactions for Enhanced Gas-Phase Sequencing of Large Intact Proteins in a Tribrid Orbitrap Mass Spectrometer.

Obtaining extensive sequencing of an intact protein is essential in order to simultaneously determine both the nature and exact localization of chemical and genetic modifications which distinguish different proteoforms arising from the same gene. To effectively achieve such characterization, it is necessary to take advantage of the analytical potential offered by the top-down mass spectrometry approach to protein sequence analysis. However, as a protein increases in size, its gas-phase dissociation produces overlapping, low signal-to-noise fragments. The application of advanced ion dissociation techniques such as electron transfer dissociation (ETD) and ultraviolet photodissociation (UVPD) can improve the sequencing results compared to slow-heating techniques such as collisional dissociation; nonetheless, even ETD- and UVPD-based approaches have thus far fallen short in their capacity to reliably enable extensive sequencing of proteoforms ≥30 kDa. To overcome this issue, we have applied proton transfer charge reduction (PTCR) to limit signal overlap in tandem mass spectra (MS2) produced by ETD (alone or with supplemental ion activation, EThcD). Compared to conventional MS2 experiments, following ETD/EThcD MS2 with PTCR MS3 prior to m/z analysis of deprotonated product ions in the Orbitrap mass analyzer proved beneficial for the identification of additional large protein fragments (≥10 kDa), thus improving the overall sequencing and in particular the coverage of the central portion of all four analyzed proteins spanning from 29 to 56 kDa. Specifically, PTCR-based data acquisition led to 39% sequence coverage for the 56 kDa glutamate dehydrogenase, which was further increased to 44% by combining fragments obtained via HCD followed by PTCR MS3.

Using 10,000 Fragment Ions to Inform Scoring in Native Top-down Proteomics.

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

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

An Orbitrap-based ion analysis procedure determines the direct charge for numerous individual protein ions to generate true mass spectra. This individual ion mass spectrometry (I2MS) method 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 typical measurements of ensembles of ions.

Individual Ion Mass Spectrometry Enhances the Sensitivity and Sequence Coverage of Top-Down Mass Spectrometry.

Charge detection mass spectrometry (CDMS) is mainly utilized to determine the mass of intact molecules. Previous applications of CDMS have determined the mass-to-charge ratio and the charge of large polymers, DNA molecules, and native protein complexes, from which corresponding mass values could be assigned. Recent advances have demonstrated that CDMS using an Orbitrap mass analyzer yields the reliable assignment of integer charge states that enables individual ion mass spectrometry (I2MS) and spectral output directly into the mass domain. Here I2MS analysis was extended to isotopically resolved fragment ions from intact proteoforms for the first time. With a radically different bias for ion readout, I2MS identified low-abundance fragment ions containing many hundreds of residues that were undetectable by standard Orbitrap measurements, leading to a doubling in the sequence coverage of triosephosphate isomerase. Thus MS/MS with the detection of individual ions (MS/I2MS) provides a far greater ability to detect high mass fragment ions and exhibits strong complementarity to traditional spectral readout in this, its first application to top-down mass spectrometry.

Effects of microtubule-inhibiting small molecule and antibody-drug conjugate treatment on differentially-sized A431 squamous carcinoma spheroids.

Multicellular tumor spheroids have been increasingly used by researchers to produce more physiologically relevant experimental environments. However, tracking of spheroid growth and treatment-induced volume reduction has not been readily adopted. Here, squamous carcinoma cells were seeded at different starting cell numbers with growth and reduction kinetics monitored using live cell imaging. Following the initial growth phase, spheroids were treated with auristatin as small molecule (MMAE) or as antibody-drug conjugate containing non-cleavable auristatin drug payload (033-F). Compared to cells in monolayers, 033-F had notably weaker potency against spheroids despite potency levels of MMAE being similar against monolayers and spheroids. Accumulation of released payload from 033-F was reduced in higher volume spheroids, likely contributing to the potency differences. Despite lowered potency towards spheroids with 033-F, spheroid volume was still readily reduced by 033-F in a dose-dependent fashion, with >85% volume reductions at the highest concentrations for all spheroid sizes. Additionally, the core of the larger spheroids showed more resiliency towards microtubule inhibition. Overall, this work highlights how various in-vivo 'features' such as tumor penetration, cell interactions, and increased resistance to therapeutics can be integrated into a spheroid model and tracked over time by automated imaging technology.

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.

STORI Plots Enable Accurate Tracking of Individual Ion Signals.

Charge detection mass spectrometry (CDMS) of low-level signals is currently limited to the analysis of individual ions that generate a persistent signal during the entire observation period. Ions that disintegrate during the observation period produce reduced frequency domain signal amplitudes, which lead to an underestimation of the ion charge state, and thus the ion mass. The charge assignment can only be corrected through an accurate determination of the time of ion disintegration. The traditional mechanisms for temporal signal analysis have severe limitations for temporal resolution, spectral resolution, and signal-to-noise ratios. Selective Temporal Overview of Resonant Ions (STORI) plots provide a new framework to accurately analyze low-level time domain signals of individual ions. STORI plots allow for complete correction of intermittent signals, the differentiation of single and multiple ions at the same frequency, and the association of signals that spontaneously change frequency.