Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model.

Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS2PIP model for tryptic and non-tryptic peptides and implement it in MS2Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.

Modulation of cellular transcriptome and proteome composition by azidohomoalanine-implications on click chemistry-based secretome analysis.

The analysis of the secretome provides important information on proteins defining intercellular communication and the recruitment and behavior of cells in specific tissues. Especially in the context of tumors, secretome data can support decisions for diagnosis and therapy. The mass spectrometry-based analysis of cell-conditioned media is widely used for the unbiased characterization of cancer secretomes in vitro. Metabolic labeling using azide-containing amino acid analogs in combination with click chemistry facilitates this type of analysis in the presence of serum, preventing serum starvation-induced effects. The modified amino acid analogs, however, are less efficiently incorporated into newly synthesized proteins and may perturb protein folding. Combining transcriptome and proteome analysis, we elucidate in detail the effects of metabolic labeling with the methionine analog azidohomoalanine (AHA) on gene and protein expression. Our data reveal that 15-39% of the proteins detected in the secretome displayed changes in transcript and protein expression induced by AHA labeling. Gene Ontology (GO) analyses indicate that metabolic labeling using AHA leads to induction of cellular stress and apoptosis-related pathways and provide first insights on how this affects the composition of the secretome on a global scale. KEY MESSAGES: Azide-containing amino acid analogs affect gene expression profiles. Azide-containing amino acid analogs influence cellular proteome. Azidohomoalanine labeling induces cellular stress and apoptotic pathways. Secretome consists of proteins with dysregulated expression profiles.

Enhancing Sensitivity of Microflow-Based Bottom-Up Proteomics through Postcolumn Solvent Addition.

The introduction of more sensitive mass spectrometers allows researchers to adapt front-end liquid chromatography (LC) to individual needs for the analysis of complex proteomes. Where absolute sensitivity is not paramount, it is advantageous to switch from a highly sensitive nanoflow-LC setup, the de facto standard platform in mass-spectrometry (MS)-based discovery proteomics, to a more robust, high-throughput-compatible microflow or conventional-flow setup. To enhance the microflow-LC-MS electrospray process of complex proteomic samples, we tested the effects of different solvents, including 2-propanol, methanol, and acetonitrile, pure or as mixture with dimethyl sulfoxide, which were added postcolumn to the eluting sample. Postcolumn addition of organic solvents strongly enhanced the electrospray efficiency in microflow-LC-MS experiments and improved the sensitivity across the entire gradient and for early eluting peptides by up to 10-fold. Postcolumn solvent addition did not negatively affect chromatographic performance and resulted in an overall 28-36% increase in identifications at both the protein and peptide levels. The presented microflow-LC-MS workflow, including postcolumn solvent addition, can be easily adopted on any LC-MS/MS platform.

Estimation of Rates of Reactions Triggered by Electron Transfer in Top-Down Mass Spectrometry.

Electron transfer dissociation (ETD) is a versatile technique used in mass spectrometry for the high-throughput characterization of proteins. It consists of several concurrent reactions triggered by the transfer of an electron from its anion source to sample cations. Transferring an electron causes peptide backbone cleavage while leaving labile post-translational modifications intact. The obtained fragmentation spectra provide valuable information for sequence and structure analyses. In this study, we propose a formal mathematical model of the ETD fragmentation process in the form of a system of stochastic differential equations describing its joint dynamics. Parameters of the model correspond to the rates of occurring reactions. Their estimates for various experimental settings give insight into the dynamics of the ETD process. We estimate the model parameters from the relative quantities of fragmentation products in a given mass spectrum by solving a nonlinear optimization problem. The cost function penalizes for the differences between the analytically derived average number of reaction products and their experimental counterparts. The presented method proves highly robust to noise in silico. Moreover, the model can explain a considerable amount of experimental results for a wide range of instrumentation settings. The implementation of the presented workflow, code-named ETDetective, is freely available under the two-clause BSD license.

Conformational Space and Stability of ETD Charge Reduction Products of Ubiquitin.

Owing to its versatility, electron transfer dissociation (ETD) has become one of the most commonly utilized fragmentation techniques in both native and non-native top-down mass spectrometry. However, several competing reactions-primarily different forms of charge reduction-occur under ETD conditions, as evidenced by the distorted isotope patterns usually observed. In this work, we analyze these isotope patterns to compare the stability of nondissociative electron transfer (ETnoD) products, specifically noncovalent c/z fragment complexes, across a range of ubiquitin conformational states. Using ion mobility, we find that more extended states are more prone to fragment release. We obtain evidence that for a given charge state, populations of ubiquitin ions formed either directly by electrospray ionization or through collapse of more extended states upon charge reduction, span a similar range of collision cross-sections. Products of gas-phase collapse are, however, less stabilized towards unfolding than the native conformation, indicating that the ions retain a memory of previous conformational states. Furthermore, this collapse of charge-reduced ions is promoted if the ions are 'preheated' using collisional activation, with possible implications for the kinetics of gas-phase compaction. Graphical Abstract ᅟ.