AB1190 EXTENDED POLY-DIMENSIONAL IMMUNOME CHARACTERIZATION (EPIC): A WEB-BASED IMMUNE REFERENCE ATLAS OF THE HEALTHY HUMAN IMMUNOME AND A TOOL FOR TRANSLATIONAL MEDICINE. (June 2019)

• Record Type: Journal Article
• Authors: Yeo, Joo Guan; Pan, Lu; Wasser, Martin; Kumar, Pavanish; Arkachaisri, Thaschawee; Poh, Su LI; Ally, Fauziah; Leong, Jing Yao; Yeo, Kee Thai; Lai, Liyun; June Tan, Angela Yun; Albani, Salvatore
• Is Part Of: Annals of the rheumatic diseases. Volume 78(2019)Supplement 2
• Page Start: 2056
• Page End: 2056
• Publication Date: 2019-06
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2019-eular.4557
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: An atlas of the developing immune system will not only improve our understanding of normal immune ontogenesis but more importantly, aid in our identification of disease-associated cell subsets. However, such a resource is still unavailable despite accessibility to technologies like mass cytometry due to the general focus on specific cell subsets or ages. There is a critical unmet need for standardized datasets depicting at single cell level and with high dimensionality the entire developmental gradient of the healthy immune system from the neonatal to adult age. Objectives: We aim to provide a detailed depiction of the architecture of the human healthy Immunome across an entire age gradient. Methods: We have created a high dimensional atlas of the healthy human immunome (EPIC: Extended Poly-dimensional Immunome Characterization) by interrogating the peripheral blood mononuclear cells (PBMC) of over 200 healthy subjects, ranging from cord blood to adult age, with 63 unique mechanistic and phenotypic markers per cell by mass cytometry (CyTOF). The EPIC analytical and visualization pipeline is based on an open source web-based R Shiny bioinformatics toolkit that allows it to be easily accessible to the research community. Results: EPIC can be mined in various ways, for instance to follow developmental changes of any given cell subset or to depict the architecture of the Immunome at any given age range. For example, transition developmental milestones were observed in the TNFα+ CD4+ T cells where the size of its memory subset would exceed its naive subset at 8 year old. There was a significant reduction and increase in the frequency of the naive and memory TNFα+ CD4+ T cells with a Spearman's correlation coefficient, rho, of -0.4662 and 0.4164 respectively. More importantly, we have built and will keep developing datasets from various immune mediated diseases using the same approach. Consequently, by providing the healthy standard, EPIC enables the depiction and dissection of disease-dependent perturbations of the Immunome architecture. Conclusion: EPIC provides a transformational conceptual advance in Translational Immunology from individual subset focused to immune architecture based approach for the understating of physiology and pathogenesis of immune mediated mechanisms. We intend to make EPIC available to the entire community in its full capacity. References: None Disclosure of Interests: Joo Guan Yeo: None declared, Lu Pan: None declared, Martin Wasser: None declared, Pavanish Kumar: None declared, Thaschawee Arkachaisri Speakers bureau: Abbvie Pte, Ltd, Su Li Poh: None declared, Fauziah Ally: None declared, Jing yao Leong: None declared, Kee Thai Yeo: None declared, Liyun Lai: None declared, Angela Yun June Tan: None declared, Salvatore Albani: None declared