Project 467172

The immune response to infectious molecules is highly dependent on the evolution of key immune cells. For instance, binding of a pathogenic molecule to a B cell elicits a cellular response to produce antibodies specific to the pathogen. This pathway follows the principles of competitive evolution, as antibodies with greater affinity for the targeted molecule are selected for cellular expansion. Furthermore, during the early stages of the immune response, antibodies are also marked for differentiation into memory B cells. However, how selection occurs remains unknown, as it is difficult to track antibody evolution throughout the vaccination process. Due to complications with current screening techniques, high-quality sequencing data is difficult to produce, which hinders our understanding of B-cell evolution. I hypothesize that the success of a vaccine can be measured by studying the competitive evolution of B-cell lineages, and enable us to visualize the memory B cell selection process. I propose the use of single-cell split sequencing for capturing the entirety of the antibody sequence to produce high-quality sequencing data. This method would resolve current issues surrounding RNA-sequencing of antibodies, and allow us to quantitatively observe B-cell evolution in a time-dependent response to vaccination. I will then compare B-cell evolution to different vaccines against Neisseria meningitidis B by frequent sampling of mouse blood prior to and after vaccination. Mice will also be challenged with the pathogen to study their response to infection, and memory B cells extracted from the spleen will be sequenced. This would enable us to accordingly visualize B-cell evolution and memory B cell selection to help predict vaccine success prior to clinical trials.