User

Adaptive immune recognition plays a crucial role in both health and disease but remains challenging to study given the incredibly personalized and diverse nature underlying immune responses. Advances in deep sequencing, microfluidics, multi-omics, and artificial intelligence are allowing us to investigate and quantify the incredibly complex and personalized nature of adaptive immune repertoires. We are leveraging and developing such technologies to computationally and functionally profile adaptive immune responses in pre-clinical and clinical settings including vaccination and infection (e.g. SARS-CoV-2, Influenza, RSV), disease (e.g., Inflammatory bowel disease, multiple sclerosis, cancer), and under homeostatic conditions. This additionally includes the development of a comprehensive computational immunology ecosystem for immunogenomics data analysis that consists of a web-based platform, R and python software, educational vignettes, and an integrative database that helps elucidate selection patterns of adaptive immune repertoires. Furthermore, the high-dimensional sequence space of B and T cell repertories provides feature-rich data that is well suited for machine learning and artificial intelligence algorithms. We are therefore developing and implementing novel computational and deep learning algorithms to learn representations of adaptive immune repertoires, to predict functional properties of B and T cell receptors, and to discover personalized immune signatures. Together, this fundamental and translational investigation into the language of the adaptive immune system holds the potential to improve the in silico design of adaptive immune therapeutics.