B cell Immunoglobin (Ig) repertoire sequencing (Rep-Seq) analysis
Large-scale characterization of B cell immunoglobulin (Ig) repertoires is now feasible in humans. Driven by the dramatic improvements in high-throughput sequencing technologies, these repertoire sequencing (Rep-Seq) projects are opening up exciting avenues of inquiry. Features of the repertoire - including polymorphisms, biased segment usage and diversity - can be correlated with clinically relevant outcomes, such as susceptibility to infection, vaccination or treatment responses and cancer prognosis. These data have broad applications not only for understanding the adaptive immune response to pathogens, but can also provide insights into the role of somatic hypermutation in autoimmunity and B cell cancers. Achieving these goals requires effective frameworks to manage and analyze such “Big Data”.
We have developed many widely used methods for the processing and analysis of large-scale B cell immunoglobulin sequencing data sets generated by next-generation sequencing technologies. Many of these methods are available to the wider scientific community as part of two software toolkits. The REpertoire Sequencing TOolkit (pRESTO) handles all stages of sequence processing from raw reads up to the task of V(D)J germline segment assignment, including specific support for single-molecule barcodes. The Change-O toolkit provides several utilities for analyzing B cell repertoire properties, including novel V segment allele detection, subject-specific germline repertoire identification, clone assignment, lineage tree construction, somatic mutation profiling and selection analysis.
Working in close collaboration with experimental and clinical groups, we have been applying our methods to gain biological insights in several systems, including: infection (Salmonella, West Nile virus, HIV), vaccination (influenza), allergy (allergic rhinitis, atopic asthma) and autoimmune disease (Multiple Sclerosis, Myasthenia Gravis).
Immune signatures of human infection and vaccination responses
Individual variations in immune status and function produce significant heterogeneity in infection and vaccination responses. For example, West Nile virus infection is usually asymptomatic, but can cause severe neurological disease and death, particularly in older patients. Our research leverages recent advances in immune profiling methods to characterize diverse states of human immune system (in health and disease, and following infection and vaccination). We have developed several computational methods for large-scale genetic network modeling, including: (1) SPEC, which predicts the specific cellular source (e.g., B cells, T cells, etc.) of a gene expression signature using data from total PBMCs, (2) TIDAL, which integrates genome-wide expression kinetics and time-dependent promoter analysis to reconstruct transcriptional regulatory networks, and (3) QuSAGE, which quantifies pathway activity from gene expression data while accounting for gene-gene correlations.
A major biological focus area for this research is the response to influenza infection and vaccination. As part of the multi-institutional Program for Research on Immune Modeling and Experimentation (PRIME), we are developing data-driven models for the response of human dendritic cell to infection with different strains of influenza (including the infamous 1918 pandemic strain). We also study influenza vaccination responses as part of the NIH/NIAID Human Immunology Project Consortium (HIPC).