1. Computational and Systems Biology
  2. Immunology and Inflammation
Download icon

An open-source computational and data resource to analyze digital maps of immunopeptidomes

  1. Etienne Caron  Is a corresponding author
  2. Lucia Espona
  3. Daniel J Kowalewski
  4. Heiko Schuster
  5. Nicola Ternette
  6. Adán Alpízar
  7. Ralf B Schittenhelm
  8. Sri H Ramarathinam
  9. Cecilia S Lindestam Arlehamn
  10. Ching Chiek Koh
  11. Ludovic C Gillet
  12. Armin Rabsteyn
  13. Pedro Navarro
  14. Sangtae Kim
  15. Henry Lam
  16. Theo Sturm
  17. Miguel Marcilla
  18. Alessandro Sette
  19. David S Campbell
  20. Eric W Deutsch
  21. Robert L Moritz
  22. Anthony W Purcell
  23. Hans-Georg Rammensee
  24. Stefan Stevanovic
  25. Ruedi Aebersold
  1. ETH Zürich, Switzerland
  2. University of Tübingen, Germany
  3. University of Oxford, United Kingdom
  4. Spanish National Biotechnology Centre, Spain
  5. Monash University, Australia
  6. Monash University, United States
  7. La Jolla Institute for Allergy and Immunology, United States
  8. University Medical Center of the Johannes Gutenberg University Mainz, Germany
  9. Pacific Northwest National Laboratory, United States
  10. Hong Kong University of Science and Technology, China
  11. Institute for Systems Biology, United States
Tools and Resources
  • Cited 57
  • Views 5,482
  • Annotations
Cite this article as: eLife 2015;4:e07661 doi: 10.7554/eLife.07661

Abstract

We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies.

Article and author information

Author details

  1. Etienne Caron

    Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
    For correspondence
    caron@imsb.biol.ethz.ch
    Competing interests
    The authors declare that no competing interests exist.
  2. Lucia Espona

    Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  3. Daniel J Kowalewski

    Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Heiko Schuster

    Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  5. Nicola Ternette

    Target Discovery Institute Mass Spectrometry Laboratory, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Adán Alpízar

    Proteomics Unit, Spanish National Biotechnology Centre, Madrid, Spain
    Competing interests
    The authors declare that no competing interests exist.
  7. Ralf B Schittenhelm

    Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia
    Competing interests
    The authors declare that no competing interests exist.
  8. Sri H Ramarathinam

    Department of Biochemistry and Molecular Biology, Monash University, Clayton, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Cecilia S Lindestam Arlehamn

    La Jolla Institute for Allergy and Immunology, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Ching Chiek Koh

    Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  11. Ludovic C Gillet

    Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  12. Armin Rabsteyn

    Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  13. Pedro Navarro

    Institute for Immunology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
    Competing interests
    The authors declare that no competing interests exist.
  14. Sangtae Kim

    Pacific Northwest National Laboratory, Richland, United States
    Competing interests
    The authors declare that no competing interests exist.
  15. Henry Lam

    Division of Biomedical Engineering and Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Hong Kong, China
    Competing interests
    The authors declare that no competing interests exist.
  16. Theo Sturm

    Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  17. Miguel Marcilla

    Proteomics Unit, Spanish National Biotechnology Centre, Madrid, Spain
    Competing interests
    The authors declare that no competing interests exist.
  18. Alessandro Sette

    La Jolla Institute for Allergy and Immunology, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  19. David S Campbell

    Institute for Systems Biology, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  20. Eric W Deutsch

    Institute for Systems Biology, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  21. Robert L Moritz

    Institute for Systems Biology, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  22. Anthony W Purcell

    Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia
    Competing interests
    The authors declare that no competing interests exist.
  23. Hans-Georg Rammensee

    Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  24. Stefan Stevanovic

    Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  25. Ruedi Aebersold

    Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
    Competing interests
    The authors declare that no competing interests exist.

Ethics

Human subjects: Informed consent was obtained in accordance with the Declaration of Helsinki protocol. The study was performed according to the guidelines of the local ethics committee (University of Tubingen, Germany).

Reviewing Editor

  1. Arup K Chakraborty, Massachusetts Institute of Technology, United States

Publication history

  1. Received: March 23, 2015
  2. Accepted: July 7, 2015
  3. Accepted Manuscript published: July 8, 2015 (version 1)
  4. Version of Record published: July 20, 2015 (version 2)

Copyright

© 2015, Caron et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 5,482
    Page views
  • 1,148
    Downloads
  • 57
    Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Cell Biology
    2. Computational and Systems Biology
    Taraneh Zarin et al.
    Research Advance Updated

    In previous work, we showed that intrinsically disordered regions (IDRs) of proteins contain sequence-distributed molecular features that are conserved over evolution, despite little sequence similarity that can be detected in alignments (Zarin et al., 2019). Here, we aim to use these molecular features to predict specific biological functions for individual IDRs and identify the molecular features within them that are associated with these functions. We find that the predictable functions are diverse. Examining the associated molecular features, we note some that are consistent with previous reports and identify others that were previously unknown. We experimentally confirm that elevated isoelectric point and hydrophobicity, features that are positively associated with mitochondrial localization, are necessary for mitochondrial targeting function. Remarkably, increasing isoelectric point in a synthetic IDR restores weak mitochondrial targeting. We believe feature analysis represents a new systematic approach to understand how biological functions of IDRs are specified by their protein sequences.

    1. Cancer Biology
    2. Computational and Systems Biology
    Kenichi Shimada et al.
    Tools and Resources Updated

    Individual cancers rely on distinct essential genes for their survival. The Cancer Dependency Map (DepMap) is an ongoing project to uncover these gene dependencies in hundreds of cancer cell lines. To make this drug discovery resource more accessible to the scientific community, we built an easy-to-use browser, shinyDepMap (https://labsyspharm.shinyapps.io/depmap). shinyDepMap combines CRISPR and shRNA data to determine, for each gene, the growth reduction caused by knockout/knockdown and the selectivity of this effect across cell lines. The tool also clusters genes with similar dependencies, revealing functional relationships. shinyDepMap can be used to (1) predict the efficacy and selectivity of drugs targeting particular genes; (2) identify maximally sensitive cell lines for testing a drug; (3) target hop, that is, navigate from an undruggable protein with the desired selectivity profile, such as an activated oncogene, to more druggable targets with a similar profile; and (4) identify novel pathways driving cancer cell growth and survival.