Unraveling Immunogenomic Diversity in Single-Cell Data

Immune molecules such as B and T cell receptors, human leukocyte antigens (HLAs) or killer Ig-like receptors (KIRs) are encoded in the genetically most diverse loci of the human genome. Many of these immune genes are hyper polymorphic, showing high allelic diversity across human populations. In addition, typical immune molecules are polygenic, meaning multiple functionally similar genes encode the same protein subunit. Integrative single-cell methods commonly used to analyze immune cells in large patient cohorts do not take into account this polygeny and allelic diversity. This leads to erroneous quantification of important immune mediators and impaired inter-donor compatibility. It ultimately obscures immunological information contained in the data. 

In this project, the team will implement a new computational approach that enhances information derived from single-cell studies by accurately addressing bioinformatic challenges that arise from human immunogenetic diversity. The project aims to implement a precise quantification of immune gene expression at the allele, gene, and functional level. As a core functionality for interactive and reproducible data exploration, the team will implement dedicated panels to leverage these different annotation layers. By extending standardized containers, the team will guarantee the applicability of its method at a broad scale, making it interoperable with many existing methods and software (such as Bioconductor, Scanpy, Seurat, and existing data exploration portals). This work will contribute to accelerated and more precise immunological analysis of single-cell data.