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The Extended Polydimensional Immunome Characterization (EPIC) web-based reference and discovery tool for cytometry data

Nature Biotechnology volume 38, pages 679–684 (2020)Cite this article

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A Publisher Correction to this article was published on 28 May 2020

This article has been updated

To the Editor — The proliferation of single-cell datasets from mass cytometry has driven the need for an open registry capable of hosting data from different laboratories, correcting for batch effects, and interrogating a broad array of markers that can aid in inferring the underlying mechanistic significance of cell subsets identified under certain conditions. Here, we present EPIC (https://epicimmuneatlas.org), a web-based analytical and discovery platform for hosting and analyzing mass cytometry data from immune cells in a standardized manner. Upon request for access, a unique user identifier and password will be assigned to each investigator. A file-based user and password authentication system is used and passwords are encrypted for further protection. The platform provides a pipeline for the construction of immune maps that integrate thematically grouped mass cytometry data with clustering outputs, cell-type annotations, demographic metadata and predictive models to facilitate multidimensional data analysis (Supplementary Fig. 1b).

For benchmarking, users can upload their dataset to EPIC for comparison and mapping with an internally generated and curated (177 individuals) single-cell high-dimensional dataset that represents the healthy human immunome across a spread of ages (Supplementary Fig. 1b; see Supplementary Methods for details on data upload). Data are obtained from two antibody panels (A and B), which have been carefully selected and validated to encompass a total of 63 individual non-redundant functional and phenotypical variables per cell (Supplementary Fig. 1a).

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Fig. 1: EPIC bio-informatics toolkit.
Fig. 2: Exploration of EPIC data and user-generated data.
Fig. 3: EPIC data analytics using an elderly cohort and user uploaded data.

Data availability

The EPIC web portal and its associated database and analytic tools will be made available for investigation as described in the manuscript to researchers who do not belong to a for-profit entity.

Code availability

The web applications Sci-Atlas Miner and Discovery Tool can be freely accessed on all modern web browsers at https://www.epicimmuneatlas.org. The source code of the R Shiny applications and R scripts for the creation of Immune Atlas data files are available on request for academic use.

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Acknowledgements

This research is supported by the National Research Foundation Singapore under its NMRC Centre Grant Programme (NMRC/CG/M003/2017) and administered by the Singapore Ministry of Health’s National Medical Research Council. Other grant support from NMRC (NMRC/TA/0059/2017, NMRC/STaR/020/2013, NMRC/MOHIAFCAT2/2/08, MOHIAFCAT2/0001/2014, NMRC MOHIAFCAT1-6003, TCR15Jun006, NMRC/CIRG/ 1460/2016, MH 095:003\016-0002), Duke-NUS, A*STAR-BMRC (IAF311020) and BMRC (SPF2014/005) is gratefully acknowledged. The pediatric patients were recruited with the strong support of Yew Nam Siow and the Department of Anaesthesia, KK Women’s and Children’s Hospital. Testing of EPIC and feedback by Antonio La Cava, Femke van Wijk, Eveline Delemarre, Antonio Bertoletti, Michelle Hong Li Wen, Matthew Kirkey and Rosa Bacchetta are greatly appreciated. We also acknowledge Loshinidevi Thana Bathi for processing the large number of samples used in EPIC.

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Author notes

  1. These authors contributed equally: Joo Guan Yeo, Martin Wasser.

Authors and Affiliations

  1. Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore

    Joo Guan Yeo, Martin Wasser, Pavanish Kumar, Lu Pan, Su Li Poh, Fauziah Ally, Thaschawee Arkachaisri, Amanda Jin Mei Lim, Jing Yao Leong, Liyun Lai, Camillus Jian Hui Chua, Bhairav Suryakant Paleja, Charles-Antoine Dutertre, Katherine Nay Yaung, Florent Ginhoux & Salvatore Albani

  2. KK Women’s and Children’s Hospital, Singapore, Singapore

    Joo Guan Yeo, Thaschawee Arkachaisri, Kee Thai Yeo, Elene Seck Choon Lee, Chee Fu Yung, Angela Yun June Tan, Shu Ying Lee & Salvatore Albani

  3. Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Anis Larbi, Jinmiao Chen & Florent Ginhoux

  4. National University of Singapore, Singapore, Singapore

    Ma Shwe Zin Nyunt & Tze Pin Ng

  5. IRCCS Istituto Giannina Gaslini, Ospedale Pediatrico Gaslini, Genoa, Italy

    Sabrina Chiesa, Marco Gattorno & Alberto Martini

  6. Rheumatology Section, Department of Pediatrics, Selayang Hospital, Kuala Lumpur, Malaysia

    Swee Ping Tang & Sue Kheng Ng

Authors
  1. Joo Guan Yeo
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Contributions

S.A. conceived and oversaw the study. E.S.C.L., K.T.Y., A.Y.J.T., S.Y.L., A.L., T.A., M.S.Z.N., T.P.N., S.C., M.G., A.M., S.P.T., S.K.N. and C.F.Y. oversaw the ethics approval, patients screening and recruitment. J.G.Y., S.L.P., F.A., L.L., A.J.M.L. and K.N.Y. performed the experiments. S.L.P. oversaw and coordinated all the mass cytometry staining. P.L. and J.G.Y. performed the initial bioinformatics analysis with input from J.C., F.G., C.-A.D., B.S.P. and J.L. M.W. developed the R-Shiny-based Sci-Atlas Miner tool and P.K. developed the Discovery tool. P.K. and C.J.H.C. constructed the web-based interface and server system. S.A., J.G.Y., M.W. and P.K. wrote the manuscript with input from all other authors.

Corresponding author

Correspondence to Salvatore Albani.

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The authors declare no competing interests.

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Yeo, J.G., Wasser, M., Kumar, P. et al. The Extended Polydimensional Immunome Characterization (EPIC) web-based reference and discovery tool for cytometry data. Nat Biotechnol 38, 679–684 (2020). https://doi.org/10.1038/s41587-020-0532-1

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