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

Area of Science:

• Immunology
• Biotechnology
• Proteomics

Background:

• Autoantibodies play a significant role in human health, impacting autoimmune diseases and cancer diagnostics.
• Identifying specific autoantigens is essential for advancing diagnostics, understanding disease pathogenesis, and developing novel therapies.

Purpose of the Study:

• To review established and emerging high-throughput methods for autoantigen detection.
• To provide a guideline for selecting appropriate autoantigen detection methods based on application needs.

Main Methods:

• 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)
• Rapid extracellular antigen profiling (REAP)

Main Results:

• The review covers a range of established and novel techniques for parallelized autoantigen detection.
• Advantages and limitations of each method are discussed to guide researchers.

Conclusions:

• Accurate autoantigen detection is a critical step towards improved diagnostics and therapeutics for autoimmune conditions and cancer.
• The choice of method depends on the specific research application and desired throughput.