Network properties derived from deep sequencing of human B-cell receptor repertoires delineate B-cell populations.

The adaptive immune response selectively expands B- and T-cell clones following antigen recognition by B- and T-cell receptors (BCR and TCR), respectively. Next-generation sequencing is a powerful tool for dissecting the BCR and TCR populations at high resolution, but robust computational analyses are required to interpret such sequencing. Here, we develop a novel computational approach for BCR repertoire analysis using established next-generation sequencing methods coupled with network construction and population analysis. BCR sequences organize into networks based on sequence diversity, with differences in network connectivity clearly distinguishing between diverse repertoires of healthy individuals and clonally expanded repertoires from individuals with chronic lymphocytic leukemia (CLL) and other clonal blood disorders. Network population measures defined by the Gini Index and cluster sizes quantify the BCR clonality status and are robust to sampling and sequencing depths. BCR network analysis therefore allows the direct and quantifiable comparison of BCR repertoires between samples and intra-individual population changes between temporal or spatially separated samples and over the course of therapy.