bioRxivImmunologyPreprint
Serum proteomics expands on high-affinity antibodies in immunized rabbits than deep B-cell repertoire sequencing alone
S. R. Bonissone, T. Lima, K. Harris, L. Davison, B. Avanzino, N. Trinklein, N. Castellana, A. Patel
Abstract
AO_SCPLOWBSTRACTC_SCPLOWRabbits are a model for immunology studies, and monoclonal antibodies developed from rabbits have been sought after to empower immunoassays in a variety of applications. High-throughput characterization of circulating serum antibodies in response to specific antigens is highly impactful for both humoral immunology studies and antibody development. A combination of high throughput sequencing of antibody transcripts from B cells and proteomic analysis of serum antibodies, an approach referred to as immunoproteogenomics, is applied to profile the immune response of rabbits to {beta}-galactosidase (Beta-gal) in both recombinant antigen and peptide antigen immunization formats. The use of intact protein antigen resulted in observing 56.3% more heavy chains CDR3s in serum than immunization with peptide antigens. Additionally, sampling peripheral blood mononuclear cells (PBMCs) for B-cell repertoire sequencing at different time points throughout the immunization was found to capture 47.8%-72.8% of total proteomically observed heavy chain CDR3s, and would serve well in replacing sequencing the B cell rich, but more difficult to access spleen or bone marrow compartments. Despite B-cell repertoire sequencing to depths of 2M to 10M reads, we found proteomic evidence supporting at least 10% of serum antibodies are still missed. Further improvements to proteomic analysis techniques would enable more precise characterization of antibodies circulating in serum and determine antibody protein sequences missed by repertoire sequencing.
- organism: rat
- assay: proteomics
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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