Unraveling the functional diversity of B cells in health and disease

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The role of B cells in infectious disease, autoimmunity, and allergy is critical. Modern sequencing technologies, such as single-cell RNA sequencing (scRNAseq) and spatial transcriptomics, have emerged as powerful techniques for studying the transcriptional states of individual B cells in a variety of biological contexts. These technologies generate massive amounts of complex data that necessitate use of powerful, sophisticated computational methods. The analysis of such data is hampered by numerous technical and biological biases embedded in the data. In scRNAseq, for example, the non-uniform capture of cells along some developmental trajectory, as well as the expression of multiple concurrent transcriptional programs, pose a challenge to current single cell clustering and trajectory inference methods. These biases are exacerbated when studying B cell compartments with complex dynamics, such as those found in lymphoid tissues. To address these issues, we propose a novel toolbox of algorithms for modeling B cell activity that combines prior, validated biological knowledge with computational algorithm design. In Aim 1, we develop tools to elucidate temporal B cell developmental processes. And in Aim 2, we develop tools to elucidate B cell spatial transcriptional programs. In Aim 3, apply our tools to a variety of important clinical scenarios, such as mapping the immune correlates of higher affinity antibodies and characterizing the heterogeneity observed in IBD. Overall, our research will create much-needed computational tools for analyzing immune signals in scRNAseq and spatial transcriptomics data, as well as show that incorporating prior knowledge greatly improves the ability of computational algorithms to reveal the full spectrum of immune system changes that occur in response to vaccination, infection, and immune-mediated diseases.