Profiling SARS-CoV-2 HLA-I peptidome reveals T cell epitopes from out-of-frame ORFs

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inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction

Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase peptide spectrum match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example, when dealing with nonspecific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identification. Here, we present inSPIRE (in silico Spectral Predictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spectral prediction, which is compatible with common database search engines. inSPIRE allows large-scale rescoring with data from multiple MS search files, increases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immunopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.     

A Targeted LC‐MS Strategy for Low‐Abundant HLA Class‐I‐Presented Peptide Detection Identifies Novel Human Papillomavirus T‐Cell Epitopes

For rational design of therapeutic vaccines, detailed knowledge about target epitopes that are endogenously processed and truly presented on infected or transformed cells is essential. Many potential target epitopes (viral or mutation‐derived), are presented at low abundance. Therefore, direct detection of these peptides remains a challenge. This study presents a method for the isolation and LC‐MS³‐based targeted detection of low‐abundant human leukocyte antigen (HLA) class‐I‐presented peptides from transformed cells. Human papillomavirus (HPV) was used as a model system, as the HPV oncoproteins E6 and E7 are attractive therapeutic vaccination targets and expressed in all transformed cells, but present at low abundance due to viral immune evasion mechanisms. The presented approach included preselection of target antigen‐derived peptides by in silico predictions and in vitro binding assays. The peptide purification process was tailored to minimize contaminants after immunoprecipitation of HLA‐peptide complexes, while keeping high isolation yields of low‐abundant target peptides. The subsequent targeted LC‐MS³ detection allowed for increased sensitivity, which resulted in successful detection of the known HLA‐A2‐restricted epitope E7_11–19 and ten additional E7‐derived peptides on the surface of HPV16‐transformed cells. T‐cell reactivity was shown for all the 11 detected peptides in ELISpot assays, which shows that detection by our approach has high predictive value for immunogenicity. The presented strategy is suitable for validating even low‐abundant candidate epitopes to be true immunotherapy targets.     

Peptide Presentation to T Cells: Solving the Immunogenic Puzzle

The vertebrate immune system uses an impressive arsenal of mechanisms to combat harmful cellular states such as infection. One way is via cells delivering real-time snapshots of their protein content to the cell surface in the form of short peptides. Specialized immune cells (T cells) sample these peptides and assess whether they are foreign, warranting an action such as destruction of the infected cell. The delivery of peptides to the cell surface is termed antigen processing and presentation, and decades of research have provided unprecedented understanding of this process. However, predicting the capacity for a given peptide to be immunogenic—to elicit a T cell response—has remained both enigmatic and a long sought-after goal. In the era of big data, a point is being approached where the steps of antigen processing and presentation can be quantified and assessed against peptide immunogenicity in order to build predictive models. This review presents new findings in this area and contemplates challenges ahead.     

NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions

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Highlights

• •NNAlign_MA enables full deconvolution of single MHC specificities from MS assays.
• •NNAlign_MA expands MHC allelic coverage, improving identification of T-cell epitopes.
• •NNAlign_MA was benchmarked on MHC classes I and II, outperforming current methods.
• •NNAlign_MA offers a universal solution to analyze and exploit MHC peptidomics data.

     

Employing proteomics in the study of antigen presentation: an update

Introduction: Our immune system discriminates self from non-self by examining the peptide cargo of human leukocyte antigen (HLA) molecules displayed on the cell surface. Successful recognition of HLA-bound non-self peptides can induce T cell responses leading to, for example, the destruction of infected cells. Today, largely due to advances in technology, we have an unprecedented capability to identify the nature of these presented peptides and unravel the true complexity of antigen presentation.

Areas covered: In addition to conventional linear peptides, HLA molecules also present post-translationally modified sequences comprising a wealth of chemical and structural modifications, including a novel class of noncontiguous spliced peptides. This review focuses on these emerging themes in antigen presentation and how mass spectrometry in particular has contributed to a new view of the antigenic landscape that is presented to the immune system.

Expert Commentary: Advances in the sensitivity of mass spectrometers and use of hybrid fragmentation technologies will provide more information-rich spectra of HLA bound peptides leading to more definitive identification of T cell epitopes. Coupled with improvements in sample preparation and new informatics workflows, studies will access novel classes of peptide antigen and allow interrogation of rare and clinically relevant samples.     

MS2Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates

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Data‐Independent Acquisition of HLA Class I Peptidomes on the Q Exactive Mass Spectrometer Platform

The characterization of peptides presented by human leukocyte antigen (HLA) class I molecules is crucial for understanding immune processes, biomarker discovery, and the development of novel immunotherapies or vaccines. Mass spectrometry allows the direct identification of thousands of HLA‐bound peptides from cell lines, blood, or tissue. In recent years, data‐independent acquisition (DIA) mass spectrometry methods have evolved, promising to increase reproducibility and sensitivity over classical data‐dependent acquisition (DDA) workflows. Here, we describe a DIA setup on the Q Exactive mass spectrometer, optimized regarding the unique properties of HLA class I peptides. The methodology enables sensitive and highly reproducible characterization of HLA peptidomes from individual cell lines. From up to 16 DDA analyses of 100 million human cells, more than 10 000 peptides could be confidently identified, serving as basis for the generation of spectral libraries. This knowledge enabled the subsequent interrogation of DIA data, leading to the identification of peptide sets with >90% overlap between replicate samples, a prerequisite for the comparative study of closely related specimens. Furthermore, >3000 peptides could be identified from just one million cells after DIA analysis using a library generated from 300 million cells. The reduction in sample quantity and the high reproducibility of DIA‐based HLA peptidome analysis should facilitate personalized medicine applications.     

MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Datasets

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The Choice of HLA‐Associated Peptide Enrichment and Purification Strategy Affects Peptide Yields and Creates a Bias in Detected Sequence Repertoire

Understanding the most appropriate workflow for biochemical human leukocyte antigen (HLA)‐associated peptide enrichment prior to ligand sequencing is essential to achieve optimal sensitivity in immunopeptidomics experiments. The use of different detergents for HLA solubilization as well as complementary workflows to separate HLA‐bound peptides from HLA protein complex components after their immunoprecipitation including HPLC, C18 cartridge, and 5 kDa filter are described. It is observed that all solubilization approaches tested led to similar peptide ligand identification rates; however, a higher number of peptides are identified in samples lysed with CHAPS compared with other methods. The HPLC method is superior in terms of HLA‐I peptide recovery compared with 5 kDa filter and C18 cartridge peptide purification methods. Most importantly, it is observed that both the choice of detergent and peptide purification strategy creates a significant bias for the identified peptide sequences, and that allele‐specific peptide repertoires are affected depending on the workflow of choice. The results highlight the importance of employing a suitable strategy for HLA peptide enrichment and that the obtained peptide repertoires do not necessarily reflect the true distributions of peptide sequences in the sample.