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  1. J. G. Yeo1,2,
  2. M. Wasser1,
  3. P. Kumar1,
  4. L. Pan1,
  5. S. L. Poh1,
  6. F. Ally1,
  7. T. Arkachaisri1,2,
  8. A. J. M. Lim1,
  9. J. Y. Leong1,
  10. K. T. Yeo1,2,
  11. L. Lai1,
  12. E. S. C. Lee2,
  13. C. Chua1,
  14. B. Paleja1,
  15. S. P. Tang3,
  16. S. K. Ng4,
  17. A. Y. J. Tan2,
  18. S. Y. Lee2,
  19. F. Ginhoux1,5,
  20. T. P. Ng6,
  21. A. Larbi5,
  22. S. Albani1,2
  1. 1Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
  2. 2KK Women’s and Children’s Hospital, Singapore, Singapore
  3. 3Selayang Hospital, Paediatric Rheumatology Unit, Department of Pediatrics, Kuala Lumpur, Malaysia
  4. 3Selayang Hospital, Paediatric Rheumatology Unit, Department of Pediatrics, Kuala Lumpur, Malaysia
  5. 5Agency for Science, Technology and Research, Singapore Immunology Network (SIgN), Singapore, Singapore
  6. 6National University of Singapore, Singapore, Singapore


Background: We created a high dimensionality healthy human Immunome atlas by interrogating the peripheral blood mononuclear cells (PBMC) of >200 healthy subjects (cord blood to adult) with 63 unique mechanistic and phenotypic markers per cell by mass cytometry (CyTOF). This database is built with an open source, web-based bioinformatics toolkit, enabling its mining and uploading of datasets for comparison with the EPIC healthy database.

Objectives: Here, we demonstrate the platform’s ability to identify the immunological differences of mechanistically important cell subsets in the uploaded data in comparison with EPIC.

Methods: CyTOF data from 37 healthy elderly (>60 years old) was uploaded onto the EPIC Discovery tool where down-sampling, normalising and FlowSOM (Flow analysis with Self-Organising Maps) clustering were done with the EPIC database for comparison. Online visualisation outputs include cluster frequency boxplots, correspondence analysis (CA) plot and markers expression heat-map. The CA 2-dimensional plot depicts the global differences in immune cells composition between subjects with proximity between points (subjects) denoting similarity. Kruskal-Wallis test was done to identify age groups differences.

Results: Increasing distances on the CA plot with age were observed with the elderly being farthest from the new-borns. Notably, we observed significant changes in naive CD4+ IL8+ T cells (p<1×10-20), memory CD4+ IL17A+ T cells (p<1×10-20) and type 2 innate lymphoid cells (ILC2) (Lin- CD7+ CD25+ CD127+ CD161+, p<1×10-17) with increasing age. The naive CD4+ IL8+ T cells (median: 0.68%, interquartile range: 0.415 to 1.055% of CD45+ PBMC) and ILC2 (0.09%, 0.065 to 0.12%) were lowest and memory IL17A+ T cells (0.58%, 0.41 to 0.905%) highest in the elderly. Significantly, the memory IL17A+ T cells and ILC2 have been implicated in the pathogenesis of auto-immune conditions1,2.

Conclusion: With EPIC, we have created an online tool enabling data uploading for comparison to a healthy database, allowing the holistic characterisation of immunological changes in different clinical scenarios. Using it, we were able to identify mechanistically important differences in immune cells composition in a distinct clinical cohort (elderly) compared to the younger ages. Translationally, the EPIC platform can be utilised similarly to catalyse the discovery process in auto-immune diseases interrogated with the EPIC antibody panels.

References: [1]Fasching P, Stradner M, Graninger W, Dejaco C, Fessler J. Therapeutic Potential of Targeting the Th17/Treg Axis in Autoimmune Disorders. Molecules. 2017 Jan 14;22(1). pii: E134.

[2]Klose CS, Artis D. Innate lymphoid cells as regulators of immunity, inflammation and tissue homeostasis. Nat Immunol. 2016 Jun 21; 17(7): 765-74.

Disclosure of Interests: None declared

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