Minimal Information About an Immuno‐Peptidomics Experiment (MIAIPE)

Lill, Jennie R.; Veelen, Peter A. van; Admon, Arie; Caron, Étienne; Elias, Joshua E.; Heck, Albert J. R.; Marcilla, Miguel; Marino, Fabio; Müller, Markus; Peters, Bjoern; Purcell, Anthony W.; Sette, Alessandro; Sturm, Theo; Ternette, Nicola; Vizcaíno, Juan Antonio; Bassani‐Sternberg, Michal

doi:10.1002/pmic.201800110

Cited by 21 publications

(12 citation statements)

References 47 publications

(40 reference statements)

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“…This will provide unique opportunities to further improve our understanding of the rules of antigen presentation. To this end, it will be crucial that raw MS data are made publicly available, and that the reporting of HLA peptidomics data will comply with the recent minimal information about an Immuno-Peptidomics Experiment (MIAIPE) guidelines ( 178 ). Databases such as IEDB ( 25 ), PRIDE ( 179 ), or the SysteMHC Atlas ( 180 ) play a key role in this process, and it is our hope that soon all journals publishing HLA peptidomics studies will require deposition of the raw MS data in PRIDE and unfiltered lists of peptides in appropriate databases, or at least accessible in supplementary datasets.…”

Section: Discussionmentioning

confidence: 99%

Predicting Antigen Presentation—What Could We Learn From a Million Peptides?

2018

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Antigen presentation lies at the heart of immune recognition of infected or malignant cells. For this reason, important efforts have been made to predict which peptides are more likely to bind and be presented by the human leukocyte antigen (HLA) complex at the surface of cells. These predictions have become even more important with the advent of next-generation sequencing technologies that enable researchers and clinicians to rapidly determine the sequences of pathogens (and their multiple variants) or identify non-synonymous genetic alterations in cancer cells. Here, we review recent advances in predicting HLA binding and antigen presentation in human cells. We argue that the very large amount of high-quality mass spectrometry data of eluted (mainly self) HLA ligands generated in the last few years provides unprecedented opportunities to improve our ability to predict antigen presentation and learn new properties of HLA molecules, as demonstrated in many recent studies of naturally presented HLA-I ligands. Although major challenges still lie on the road toward the ultimate goal of predicting immunogenicity, these experimental and computational developments will facilitate screening of putative epitopes, which may eventually help decipher the rules governing T cell recognition.

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Section: Discussionmentioning

confidence: 99%

Predicting Antigen Presentation—What Could We Learn From a Million Peptides?

2018

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“…72 A key need in proteomic workflows is format and data analysis standardization; an example is the Minimal Information About an Immuno-Peptidomics Experiment (MIAIPE) guidelines. 73 Finally, an interlaboratory study to assess the performance of glycoproteomics software for automated intact N- and O-glycopeptide identification from high resolution MS/MS spectral data is ongoing.…”

Section: Highlights Of Progress From the B/d-hppmentioning

confidence: 99%

Progress on Identifying and Characterizing the Human Proteome: 2019 Metrics from the HUPO Human Proteome Project

et al. 2019

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“…Until now, the W6/32 antibody remains the only widely used and commercially available monoclonal antibody in the field. In addition, no protocol for MHC peptide isolation have been standardized across the community (1, 2, 12, 62). Thus, immunopeptidomics is of great scientific and clinical relevance but suffers from a lack of standardized protocols and highly efficient and accessible antibodies for the analysis of MHC class I and II immunopeptidomes in mouse, rat, monkey, and human.…”

Section: Discussionmentioning

confidence: 99%

Immunopeptidomics for Dummies: Detailed Experimental Protocols and Rapid, User-Friendly Visualization of MHC I and II Ligand Datasets with MhcVizPipe

Sirois

Caron

Kovalchik

et al. 2020

Preprint

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Immunopeptidomics refers to the science of investigating the composition and dynamics of peptides presented by major histocompatibility complex (MHC) class I and class II molecules using mass spectrometry (MS). Here, we aim to provide a technical report to any non-expert in the field wishing to establish and/or optimize an immunopeptidomic workflow with relatively limited computational knowledge and resources. To this end, we thoroughly describe step-by-step instructions to isolate MHC class I and II-associated peptides from various biological sources, including mouse and human biospecimens. Most notably, we created MhcVizPipe (MVP) (https://github.com/CaronLab/MhcVizPipe), a new and easy-to-use open-source software tool to rapidly assess the quality and the specific enrichment of immunopeptidomic datasets upon the establishment of new workflows. In fact, MVP enables intuitive visualization of multiple immunopeptidomic datasets upon testing sample preparation protocols and new antibodies for the isolation of MHC class I and II peptides. In addition, MVP enables the identification of unexpected binding motifs and facilitates the analysis of non-canonical MHC peptides. We anticipate that the experimental and bioinformatic resources provided herein will represent a great starting point for any non-expert and will therefore foster the accessibility and expansion of the field to ultimately boost its maturity and impact.

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Minimal Information About an Immuno‐Peptidomics Experiment (MIAIPE)

Cited by 21 publications

References 47 publications

Predicting Antigen Presentation—What Could We Learn From a Million Peptides?

Predicting Antigen Presentation—What Could We Learn From a Million Peptides?

Progress on Identifying and Characterizing the Human Proteome: 2019 Metrics from the HUPO Human Proteome Project

Immunopeptidomics for Dummies: Detailed Experimental Protocols and Rapid, User-Friendly Visualization of MHC I and II Ligand Datasets with MhcVizPipe

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