TCRClass: A Novel Method for Identifying T Cell Receptor Specificity and Phenotype from Single Cell Immune Profiling Data

Recent advances in immune cell profiling in single cell resolution allow for antigen-specific T Cell Receptors identification and sequencing via highly specific barcoded multimers.

Here we present a novel method to identify and profile T Cell Receptor specificities in-silico and assess their diversity and phenotype in an integrated and interactive web-based tool using cancer specific antigens using TCR sequencing data and single cell immune profiling assays.

Our method implements metrics of sequence similarity, clonal expansion, gene usage, and defines a novel metric for repertoire diversity based on graph-based network modularity optimization.

We use published antigen-specific TCR sequencing data of MHC-I peptides in mice to extract and engineer CDR3 sequence-based features and train a machine learning classifier for TCR specificity prediction.

Additionally, our method integrates single cell RNA-Seq functional analysis for each tested antigen with its corresponding specificity and repertoire features.