Department of Molecular Genetics and Microbiology, Center for Virology, Duke University School of Medicine, Durham, United States; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, United States
Department of Molecular Genetics and Microbiology, Center for Virology, Duke University School of Medicine, Durham, United States
Emmanuela N Bonglack
Department of Molecular Genetics and Microbiology, Center for Virology, Duke University School of Medicine, Durham, United States; Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, United States
Emma M Heckenberg
Department of Molecular Genetics and Microbiology, Center for Virology, Duke University School of Medicine, Durham, United States
Jeffrey Y Zhou
Department of Medicine, University of Massachusetts Medical School, Worcester, United States
Stephanie N Giamberardino
Duke Molecular Physiology Institute and Department of Neurology, Duke University School of Medicine, Durham, United States
Jeffrey A Bailey
Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, United States
Simon G Gregory
Duke Molecular Physiology Institute and Department of Neurology, Duke University School of Medicine, Durham, United States
Cliburn Chan
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, United States
Lymphoblastoid cell lines (LCLs) are generated by transforming primary B cells with Epstein–Barr virus (EBV) and are used extensively as model systems in viral oncology, immunology, and human genetics research. In this study, we characterized single-cell transcriptomic profiles of five LCLs and present a simple discrete-time simulation to explore the influence of stochasticity on LCL clonal evolution. Single-cell RNA sequencing (scRNA-seq) revealed substantial phenotypic heterogeneity within and across LCLs with respect to immunoglobulin isotype; virus-modulated host pathways involved in survival, activation, and differentiation; viral replication state; and oxidative stress. This heterogeneity is likely attributable to intrinsic variance in primary B cells and host–pathogen dynamics. Stochastic simulations demonstrate that initial primary cell heterogeneity, random sampling, time in culture, and even mild differences in phenotype-specific fitness can contribute substantially to dynamic diversity in populations of nominally clonal cells.