Unraveling Immunogenomic Diversity in Single-Cell Data
Author(s): Ahmad Al Ajami,Annekathrin Silvia Ludt,Federico Marini,Katharina Imkeller
Affiliation(s): Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Germany
Immune molecules such as B and T cell receptors, human leukocyte antigens (HLAs), or killer Ig-like receptors (KIRs) are encoded in the most genetically 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 consider 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 order to enhance information derived from single-cell studies and address bioinformatic challenges that arise from human immunogenetic diversity, we developed a workflow to detect and quantify allele-specific immune gene expression. Our preliminary results showed quantification of different allele groups of HLA in both amplicon-based and whole transcriptome amplification (WTA) single-cell RNA-sequencing datasets of multiple cancer patients. Our ultimate aim is to implement a precise quantification of immune gene expression by building a multi-layer data structure for immune gene representation in single-cell data at the allele, gene, and functional level. We anticipate our work to be a starting point for more precise immunological analysis of multi-omics data, especially those focusing on inferring allele-specific interactions.