Deciphering the autoreactome: Massively parallelized methods for autoantibody detection in humans

Abstract

Autoantibodies have a substantial impact on human health ranging from autoimmune diseases to cancer diagnostics. Knowledge of the antigens recognized can allow for more accurate diagnostics, a better understanding of pathogeneses and thus improved prevention, as well as laying the foundation for the development of new therapies. A critical step to acquire this knowledge is to detect the exact self-antigens targeted by autoantibodies out of the pool of 20,000 human proteins against which reactivities could be observed. Here, we review established and emerging methods for highly parallelized autoantigen detection such as human proteome microarrays, serological identification of antigens by screening of cDNA expression libraries (SEREX), serological proteome analysis (SERPA), phage display immunoprecipitation sequencing (PhIP-Seq), parallel analysis of translated ORFs (PLATO), and rapid extracellular antigen profiling (REAP). We highlight advantages and limitations of these methods, aiming to give a guideline to choose the appropriate method for a certain application.

Keywords: Autoantibody-detection; Autoimmunity; High-throughput; Serology.