Calliope DendrouView profile
Associate Professor
Calliope Dendrou is an Associate Professor in Clinical Pathology and Inflammation at the Kennedy Institute of Rheumatology (KIR), University of Oxford, leading the Immune Disease Multiomics Laboratory. She previously held a Wellcome & Royal Society Sir Henry Dale Fellowship at the University of Oxford’s Centre for Human Genetics before joining KIR in 2023. Her research focuses on immune disease mechanisms using multiomics approaches, including genomic profiling to identify therapeutic targets across tissues and immune-mediated diseases. She co-leads large-scale projects like the Oxford-J&J Cartography Consortium and the Chan Zuckerberg Initiative’s LEGACY Network, and teaches on the MSc in Genomic Medicine program. Educational Background: BSc (Biology, Imperial College London, 2005; Forbes Memorial Medal Winner); PhD in Infection & Immunity (University of Cambridge, 2010). Postdoctoral training at the Weatherall Institute of Molecular Medicine under Prof. Lars Fugger. Research interests include immunogenetics, cytokine signaling pathways, drug repositioning, and cross-disease pathophysiology. Her work integrates single-cell and spatial transcriptomics to dissect immune-cell interactions in diseases like rheumatoid arthritis, inflammatory bowel disease, and celiac disease. Recent articles highlight her contributions to understanding vaccine adjuvant responses, Th17 cell roles in spondyloarthritis, and immune-epithelial networks in celiac disease. Collaborations emphasize multi-omic data analysis (e.g., Panpipes pipeline) and translational studies toward precision medicine. Awards: Forbes Memorial Medal (BSc), Wellcome & Royal Society Sir Henry Dale Fellowship. Leadership roles include Equality, Diversity, and Inclusion Champion and 'Single-Cell & Spatial Omics for Precision Medicine' Module Lead. Lab & Teams: Immune Disease Multiomics Lab at KIR. Active in collaborative initiatives such as the LEGACY Network, focusing on large-scale immune profiling in ancestrally diverse populations.







