Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Commentary
  • Published:

Toward effective sharing of high-dimensional immunology data

Nature Biotechnology volume 32, pages 755–759 (2014)Cite this article

Subjects

Immunology is on the cusp of a 'big data'–driven breakthrough, but strategies for standardizing and sharing high-dimensional data from independent laboratories are needed to ensure that data support the formation of new and robust hypotheses.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Procedures needed for effective decentralized large-scale data sharing.
Figure 2

References

  1. Baker, M. Big biology: the 'omes puzzle. Nature 494, 416–419 (2013).

    Article  CAS  Google Scholar 

  2. Bammler, T. Standardizing global gene expression analysis between laboratories and across platforms. Nat. Methods 2, 351–356 (2005).

    Article  Google Scholar 

  3. Varjosalo, M. et al. Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS. Nat. Methods 10, 307–314 (2013).

    Article  CAS  Google Scholar 

  4. Gautier, E.L. et al. Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages. Nat. Immunol. 13, 1118–1128 (2012).

    Article  CAS  Google Scholar 

  5. Miller, J.C. et al. Deciphering the transcriptional network of the dendritic cell lineage. Nat. Immunol. 13, 888–899 (2012).

    Article  CAS  Google Scholar 

  6. Han, A. et al. Dietary gluten triggers concomitant activation of CD4+ and CD8+ alphabeta T cells and gammadelta T cells in celiac disease. Proc. Natl. Acad. Sci. USA 110, 13073–13078 (2013).

    Article  CAS  Google Scholar 

  7. Obermoser, G. et al. Systems scale interactive exploration reveals quantitative and qualitative differences in response to influenza and pneumococcal vaccines. Immunity 38, 831–844 (2013).

    Article  CAS  Google Scholar 

  8. Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782–795 (2013).

    Article  CAS  Google Scholar 

  9. Heinäniemi, M. et al. Gene-pair expression signatures reveal lineage control. Nat. Methods 10, 577–583 (2013).

    Article  Google Scholar 

  10. Khatri, P. et al. A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation. J. Exp. Med. 210, 2205–2221 (2013).

    Article  CAS  Google Scholar 

  11. Li, S. et al. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat. Immunol. 15, 195–204 (2013).

    Article  Google Scholar 

  12. Brazma, A. et al. Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat. Genet. 29, 365–371 (2001).

    Article  CAS  Google Scholar 

  13. Barrett, T. et al. NCBI GEO: mining tens of millions of expression profiles—database and tools update. Nucleic Acids Res. 35, D760–D765 (2007).

    Article  CAS  Google Scholar 

  14. Parkinson, H. et al. ArrayExpress update—from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res. 37, D868–D872 (2009).

    Article  CAS  Google Scholar 

  15. Chattopadhyay, P.K., Gierahn, T.M., Roederer, M. & Love, J.C. Single-cell technologies for monitoring immune systems. Nat. Immunol. 15, 128–135 (2014).

    Article  CAS  Google Scholar 

  16. Georgiou, G. et al. The promise and challenge of high-throughput sequencing of the antibody repertoire. Nat. Biotechnol. 32, 158–168 (2014).

    Article  CAS  Google Scholar 

  17. Newell, E.W. & Davis, M.M. Beyond model antigens: high-dimensional methods for the analysis of antigen-specific T cells. Nat. Biotechnol. 32, 149–157 (2014).

    Article  CAS  Google Scholar 

  18. Woodsworth, D.J., Castellarin, M. & Holt, R.A. Sequence analysis of T-cell repertoires in health and disease. Genome Med. 5, 98 (2013).

    Article  Google Scholar 

  19. Kidd, B.A., Peters, L.A., Schadt, E.E. & Dudley, J.T. Unifying immunology with informatics and multiscale biology. Nat. Immunol. 15, 118–127 (2014).

    Article  CAS  Google Scholar 

  20. Kerrien, S. et al. The IntAct molecular interaction database in 2012. Nucleic Acids Res. 40, D841–D846 (2012).

    Article  CAS  Google Scholar 

  21. Taylor, C.F. et al. The minimum information about a proteomics experiment (MIAPE). Nat. Biotechnol. 25, 887–893 (2007).

    Article  CAS  Google Scholar 

  22. Maecker, H.T., McCoy, J.P. & Nussenblatt, R. Standardizing immunophenotyping for the human immunology project. Nat. Rev. Immunol. 12, 191–200 (2012).

    Article  CAS  Google Scholar 

  23. Pachón, G., Caragol, I. & Petriz, J. Subjectivity and flow cytometric variability. Nat. Rev. Immunol. 12, 396–396 (2012).

    Article  Google Scholar 

  24. Valle, A., Maugeri, N., Manfredi, A.A. & Battaglia, M. Standardization in flow cytometry: correct sample handling as a priority. Rev. Immunol. 12, 191–200 (2012).

    Article  Google Scholar 

  25. Amir, E.-D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).

    Article  CAS  Google Scholar 

  26. Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886–891 (2011).

    Article  CAS  Google Scholar 

  27. Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796–804 (2012).

    Article  CAS  Google Scholar 

  28. Snijder, B., Liberali, P., Frechin, M., Stoeger, T. & Pelkmans, L. Predicting functional gene interactions with the hierarchical interaction score. Nat. Methods 10, 1089–1092 (2013).

    Article  CAS  Google Scholar 

  29. Vizcaíno, J.A. et al. The Proteomics Identifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063–D1069 (2013).

    Article  Google Scholar 

  30. Piwowar, H. Altmetrics: value all research products. Nature 493, 159 (2013).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank the members of the Superti-Furga lab, and S.H. Friend for critically reading the manuscript and helpful discussions. This work was supported by a Swiss National Science Foundation fellowship (P300P3_147897) to B.S., by a European Molecular Biology Organization long-term fellowship to R.K.K. (ALTF 314-2012), and by the Austrian Academy of Sciences and the European Research Council grant iFIVE to G.S.-F.

Author information

Author notes

  1. Berend Snijder and Richard Kumaran Kandasamy: These authors contributed equally to this work.

Authors and Affiliations

  1. The CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

    Berend Snijder, Richard Kumaran Kandasamy & Giulio Superti-Furga

Authors
  1. Berend Snijder
    View author publications

    Search author on:PubMed Google Scholar

  2. Richard Kumaran Kandasamy
    View author publications

    Search author on:PubMed Google Scholar

  3. Giulio Superti-Furga
    View author publications

    Search author on:PubMed Google Scholar

Corresponding authors

Correspondence to Berend Snijder or Giulio Superti-Furga.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Snijder, B., Kandasamy, R. & Superti-Furga, G. Toward effective sharing of high-dimensional immunology data. Nat Biotechnol 32, 755–759 (2014). https://doi.org/10.1038/nbt.2974

Download citation

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/nbt.2974

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research