Single-cell characterization of transcriptomic heterogeneity in lymphoblastoid cell lines

Abstract

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 revealed substantial phenotypic heterogeneity within and across LCLs with respect to immunoglobulin isotype; virus-modulated host pathways involved in survival, proliferation, 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.

Expression Profiling Identifies TWIST2 Target Genes in Setleis Syndrome Patient Fibroblast and Lymphoblast Cells

“…In addition, since LCLs arise after Epstein-Barr viral infection, virus-induced cellular changes due to the transformation process may impact the resulting expression profiles. Single cell RNA sequencing has revealed that LCLs may exhibit substantial phenotypic diversity [ 50 ]. Hence, the variability we observed in the microarray results from three different passages of PR SS and PR control LCLs is not unexpected.…”

Comparing instance segmentation methods for analyzing clonal growth of single cells in microfluidic chips

“…Such populations can comprise individual cells with highly variant phenotypes that fluctuate over time, or rare phenotypes that can be identified in a background of more abundant phenotypes. [18][19][20] Whereas technical capabilities have been developed to capture single-timepoint genetic and transcriptomic profiles of individual cells in a sample, [21][22][23] methods for characterizing single-cell phenotypes in a high-throughput, time-resolved fashion have lagged because of the need to maintain and repeatedly measure the same cell over time. To address this technical gap, Celldom TM recently developed and reported a microfluidic platform that achieves highly parallel single-cell culture.…”