Unraveling Immunogenomic Diversity in Single-Cell Data
Ahmad Al Ajami,Katharina Imkeller
University Hospital Frankfurt
Abstract
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 hyperpolymorphic – showing high allelic diversity across human populations. In addition, typical immune molecules are polygenic, which means that multiple functionally similar genes encode the same protein subunit. However, 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 comparability, which ultimately obscures immunological information contained in the data. In our poster, we introduce the idea behind our new computational approach that enhances information derived from single-cell studies by accurately addressing bioinformatic challenges that arise from human immunogenetic diversity. We summarize our aim, which is to implement a precise quantification of immune gene expression at the allele, gene, and functional level. Since the identification of drug targets or establishment of cell-based therapies is the ultimate goal of most tumor immunologists, a well-defined, flexible, and allele-aware immune gene quantification workflow that facilitates immunological discovery will be relevant and essential for such studies. We anticipate our work to be a starting point for more precise immunological analysis of multi-omics data.