PhIP-Seq

PhIP-Seq, or phage immunoprecipitation sequencing, is a high-throughput method used to identify and characterize antibodies and their cognate antigens. The technology combines phage display with massively parallel DNA sequencing to comprehensively profile antibody repertoires and map antibody-antigen interactions.

In a typical PhIP-Seq experiment, a library of peptides or proteins is displayed on the surface of bacteriophages. These phage-displayed libraries are then incubated with a sample containing antibodies, such as serum from a patient or an immunized animal. Antibodies that bind to the phage-displayed peptides/proteins are captured through immunoprecipitation. The bound phages are then eluted, and their DNA is amplified and sequenced using next-generation sequencing (NGS).

The resulting sequence data allows for the identification of the peptides or proteins that were specifically bound by the antibodies in the sample. By counting the reads for each peptide/protein sequence, the relative abundance of antibodies targeting each antigen can be quantified. This provides a detailed snapshot of the antibody repertoire within the sample.

PhIP-Seq has a wide range of applications in immunology, including:

• Autoantibody Discovery: Identifying autoantibodies in autoimmune diseases.
• Vaccine Development: Monitoring antibody responses to vaccines.
• Cancer Immunology: Characterizing tumor-associated antigens and antibody responses to cancer.
• Infectious Disease: Identifying antibodies targeting pathogens.
• Biomarker Discovery: Identifying antibody signatures associated with specific diseases or conditions.

Compared to traditional methods for antibody discovery, PhIP-Seq offers several advantages, including high throughput, the ability to screen large and diverse antigen libraries, and the capability to identify novel antigens. However, it is important to note that PhIP-Seq primarily identifies linear epitopes and may not capture conformational epitopes that rely on the three-dimensional structure of the antigen. Furthermore, data analysis and interpretation can be complex, requiring specialized bioinformatics tools.