Activated Ion-Electron Transfer Dissociation Enables Comprehensive Top-Down Protein Fragmentation

Here we report the first demonstration of near-complete sequence coverage of intact proteins using activated ion-electron transfer dissociation (AI-ETD), a method that leverages concurrent infrared photoactivation to enhance electron-driven dissociation. AI-ETD produces mainly c/z-type product ions and provides comprehensive (77–97%) protein sequence coverage, outperforming HCD, ETD, and EThcD for all proteins investigated. AI-ETD also maintains this performance across precursor ion charge states, mitigating charge-state dependence that limits traditional approaches

Full-Featured Search Algorithm for Negative Electron-Transfer Dissociation

Negative electron-transfer dissociation (NETD) has emerged as a premier tool for peptide anion analysis, offering access to acidic post-translational modifications and regions of the proteome that are intractable with traditional positive-mode approaches. Whole-proteome scale characterization is now possible with NETD, but proper informatic tools are needed to capitalize on advances in instrumentation. Currently only one database search algorithm (OMSSA) can process NETD data. Here we implement NETD search capabilities into the Byonic platform to improve the sensitivity of negative-mode data analyses, and we benchmark these improvements using 90 min LC–MS/MS analyses of tryptic peptides from human embryonic stem cells. With this new algorithm for searching NETD data, we improved the number of successfully identified spectra by as much as 80% and identified 8665 unique peptides, 24 639 peptide spectral matches, and 1338 proteins in activated-ion NETD analyses, more than doubling identifications from previous negative-mode characterizations of the human proteome. Furthermore, we reanalyzed our recently published large-scale, multienzyme negative-mode yeast proteome data, improving peptide and peptide spectral match identifications and considerably increasing protein sequence coverage. In all, we show that new informatics tools, in combination with recent advances in data acquisition, can significantly improve proteome characterization in negative-mode approaches

Implementation of Activated Ion Electron Transfer Dissociation on a Quadrupole-Orbitrap-Linear Ion Trap Hybrid Mass Spectrometer

Using concurrent IR photoactivation during electron transfer dissociation (ETD) reactions, i.e., activated ion ETD (AI-ETD), significantly increases dissociation efficiency resulting in improved overall performance. Here we describe the first implementation of AI-ETD on a quadrupole-Orbitrap-quadrupole linear ion trap (QLT) hybrid MS system (Orbitrap Fusion Lumos) and demonstrate the substantial benefits it offers for peptide characterization. First, we show that AI-ETD can be implemented in a straightforward manner by fastening the laser and guiding optics to the instrument chassis itself, making alignment with the trapping volume of the QLT simple and robust. We then characterize the performance of AI-ETD using standard peptides in addition to a complex mixtures of tryptic peptides using LC–MS/MS, showing not only that AI-ETD can nearly double the identifications achieved with ETD alone but also that it outperforms the other available supplemental activation methods (ETcaD and EThcD). Finally, we introduce a new activation scheme called AI-ETD+ that combines AI-ETD in the high pressure cell of the QLT with a short infrared multiphoton dissociation (IRMPD) activation in the low-pressure cell. This reaction scheme introduces no addition time to the scan duty cycle but generates MS/MS spectra rich in b/y-type and c/z^•-type product ions. The extensive generation of fragment ions in AI-ETD+ substantially increases peptide sequence coverage while also improving peptide identifications over all other ETD methods, making it a valuable new tool for hybrid fragmentation approaches

Top-Down Characterization of Proteins with Intact Disulfide Bonds Using Activated-Ion Electron Transfer Dissociation

Here we report the fragmentation of disulfide linked intact proteins using activated-ion electron transfer dissociation (AI-ETD) for top-down protein characterization. This fragmentation method is then compared to the alternative methods of beam-type collisional activation (HCD), electron transfer dissociation (ETD), and electron transfer and higher-energy collision dissociation (EThcD). We analyzed multiple precursor charge states of the protein standards bovine insulin, α-lactalbumin, lysozyme, β-lactoglobulin, and trypsin inhibitor. In all cases, we found that AI-ETD provides a boost in protein sequence coverage information and the generation of fragment ions from within regions enclosed by disulfide bonds. AI-ETD shows the largest improvement over the other techniques when analyzing highly disulfide linked and low charge density precursor ions. This substantial improvement is attributed to the concurrent irradiation of the gas phase ions while the electron-transfer reaction is taking place, mitigating nondissociative electron transfer, helping unfold the gas phase protein during the electron transfer event, and preventing disulfide bond reformation. We also show that AI-ETD is able to yield comparable sequence coverage information when disulfide bonds are left intact relative to proteins that have been reduced and alkylated. This work demonstrates that AI-ETD is an effective fragmentation method for the analysis of proteins with intact disulfide bonds, dramatically enhancing sequence ion generation and total sequence coverage compared to HCD and ETD

Infrared Multiphoton Dissociation for Quantitative Shotgun Proteomics

We modified a dual-cell linear ion trap mass spectrometer to perform infrared multiphoton dissociation (IRMPD) in the low-pressure trap of a dual-cell quadrupole linear ion trap (dual-cell QLT) and perform large-scale IRMPD analyses of complex peptide mixtures. Upon optimization of activation parameters (precursor <i>q</i>-value, irradiation time, and photon flux), IRMPD subtly, but significantly, outperforms resonant-excitation collisional-activated dissociation (CAD) for peptides identified at a 1% false-discovery rate (FDR) from a yeast tryptic digest (95% confidence, <i>p</i> = 0.019). We further demonstrate that IRMPD is compatible with the analysis of isobaric-tagged peptides. Using fixed QLT rf amplitude allows for the consistent retention of reporter ions, but necessitates the use of variable IRMPD irradiation times, dependent upon precursor mass to charge (<i>m</i>/<i>z</i>). We show that IRMPD activation parameters can be tuned to allow for effective peptide identification and quantitation simultaneously. We thus conclude that IRMPD performed in a dual-cell ion trap is an effective option for the large-scale analysis of both unmodified and isobaric-tagged peptides

Automated Gas-Phase Purification for Accurate, Multiplexed Quantification on a Stand-Alone Ion-Trap Mass Spectrometer

Isobaric tagging enables the acquisition of highly multiplexed proteome quantification, but it is hindered by the pervasive problem of precursor interference. The elimination of coisolated contaminants prior to reporter tag generation can be achieved through the use of gas-phase purification via proton transfer ion/ion reactions (QuantMode); however, the original QuantMode technique was implemented on the high-resolution linear ion-trap–Orbitrap hybrid mass spectrometer enabled with electron transfer dissociation (ETD). Here we extend this technology to stand-alone linear ion-trap systems (trapQuantMode, trapQM). Facilitated by the use of inlet beam-type activation (i.e., trapHCD) for production and observation of the low mass-to-charge reporter region, this scan sequence comprises three separate events to maximize peptide identifications, minimize duty cycle requirements, and increase quantitative accuracy, precision, and dynamic range. Significant improvements in quantitative accuracy were attained over standard methods when using trapQM to analyze an interference model system comprising tryptic peptides of yeast that we contaminated with human peptides. Finally, we demonstrate practical benefits of this method by analysis of the proteomic changes that occur during mouse skeletal muscle myoblast differentiation. While the reduced duty cycle of trapQM led to the identification of fewer proteins than conventional operation (4050 vs 2964), trapQM identified more significant differences (>1.5 fold, 1362 vs 1132, respectively; <i>p</i> < 0.05) between the proteomes of undifferentiated myoblasts and differentiated myotubes and nearly 10-fold more differences with changes greater than 5-fold (96 vs 12). We further show that our trapQM dataset is superior for identifying changes in protein abundance that are consistent with the metabolic and structural changes known to accompany myotube formation

Phosphoproteomics with Activated Ion Electron Transfer Dissociation

The ability to localize phosphosites to specific amino acid residues is crucial to translating phosphoproteomic data into biological meaningful contexts. In a companion manuscript (Anal. Chem. 2017, DOI: 10.1021/acs.analchem.7b00213), we described a new implementation of activated ion electron transfer dissociation (AI-ETD) on a quadrupole-Orbitrap-linear ion trap hybrid MS system (Orbitrap Fusion Lumos), which greatly improved peptide fragmentation and identification over ETD and other supplemental activation methods. Here we present the performance of AI-ETD for identifying and localizing sites of phosphorylation in both phosphopeptides and intact phosphoproteins. Using 90 min analyses we show that AI-ETD can identify 24,503 localized phosphopeptide spectral matches enriched from mouse brain lysates, which more than triples identifications from standard ETD experiments and outperforms ETcaD and EThcD as well. AI-ETD achieves these gains through improved quality of fragmentation and MS/MS success rates for all precursor charge states, especially for doubly protonated species. We also evaluate the degree to which phosphate neutral loss occurs from phosphopeptide product ions due to the infrared photoactivation of AI-ETD and show that modifying phosphoRS (a phosphosite localization algorithm) to include phosphate neutral losses can significantly improve localization in AI-ETD spectra. Finally, we demonstrate the utility of AI-ETD in localizing phosphosites in α-casein, an ∼23.5 kDa phosphoprotein that showed eight of nine known phosphorylation sites occupied upon intact mass analysis. AI-ETD provided the greatest sequence coverage for all five charge states investigated and was the only fragmentation method to localize all eight phosphosites for each precursor. Overall, this work highlights the analytical value AI-ETD can bring to both bottom-up and top-down phosphoproteomics

Segmentation of Precursor Mass Range Using “Tiling” Approach Increases Peptide Identifications for MS¹‑Based Label-Free Quantification

Label-free quantification is a powerful tool for the measurement of protein abundances by mass spectrometric methods. To maximize quantifiable identifications, MS¹-based methods must balance the collection of survey scans and fragmentation spectra while maintaining reproducible extracted ion chromatograms (XIC). Here we present a method which increases the depth of proteome coverage over replicate data-dependent experiments without the requirement of additional instrument time or sample prefractionation. Sampling depth is increased by restricting precursor selection to a fraction of the full MS¹ mass range for each replicate; collectively, the <i>m</i>/<i>z</i> segments of all replicates encompass the full MS¹ range. Although selection windows are narrowed, full MS¹ spectra are obtained throughout the method, enabling the collection of full mass range MS¹ chromatograms such that label-free quantitation can be performed for any peptide in any experiment. We term this approach “binning” or “tiling” depending on the type of <i>m</i>/<i>z</i> window utilized. By combining the data obtained from each segment, we find that this approach increases the number of quantifiable yeast peptides and proteins by 31% and 52%, respectively, when compared to normal data-dependent experiments performed in replicate

Neucode Labels for Multiplexed, Absolute Protein Quantification

We describe a new method to accomplish multiplexed, absolute protein quantification in a targeted fashion. The approach draws upon the recently developed neutron encoding (NeuCode) metabolic labeling strategy and parallel reaction monitoring (PRM). Since PRM scanning relies upon high-resolution tandem mass spectra for targeted protein quantification, incorporation of multiple NeuCode labeled peptides permits high levels of multiplexing that can be accessed from high-resolution tandem mass spectra. Here we demonstrate this approach in cultured cells by monitoring a viral infection and the corresponding viral protein production over many infection time points in a single experiment. In this context the NeuCode PRM combination affords up to 30 channels of quantitative information in a single MS experiment

The Peripheral Blood Eosinophil Proteome

A system-wide understanding of biological processes requires a comprehensive knowledge of the proteins in the biological system. The eosinophil is a type of granulocytic leukocyte specified early in hematopoietic differentiation that participates in barrier defense, innate immunity, and allergic disease. The proteome of the eosinophil is largely unannotated with under 500 proteins identified. We now report a map of the nonstimulated peripheral blood eosinophil proteome assembled using two-dimensional liquid chromatography coupled with high-resolution mass spectrometry. Our analysis yielded 100,892 unique peptides mapping to 7,086 protein groups representing 6,813 genes as well as 4,802 site-specific phosphorylation events. We account for the contribution of platelets that routinely contaminate purified eosinophils and report the variability in the eosinophil proteome among five individuals and proteomic changes accompanying acute activation of eosinophils by interleukin-5. Our deep coverage and quantitative analyses fill an important gap in the existing maps of the human proteome and will enable the strategic use of proteomics to study eosinophils in human diseases