1. Immunology and Inflammation
  2. Microbiology and Infectious Disease

Single-cell RNA-seq reveals transcriptomic heterogeneity mediated by host-pathogen dynamics in lymphoblastoid cell lines

  1. Elliott D SoRelle
  2. Joanne Dai
  3. Emmanuela N Bonglack
  4. Emma M Heckenberg
  5. Jeffrey Y Zhou
  6. Stephanie N Giamberardino
  7. Jeffrey A Bailey
  8. Simon G Gregory
  9. Cliburn Chan
  10. Micah A Luftig  Is a corresponding author
  1. Duke University School of Medicine, United States
  2. University of Massachusetts Medical School, United States
  3. Brown University, United States
https://doi.org/10.7554/eLife.62586
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  1. Elliott D SoRelle
  2. Joanne Dai
  3. Emmanuela N Bonglack
  4. Emma M Heckenberg
  5. Jeffrey Y Zhou
  6. Stephanie N Giamberardino
  7. Jeffrey A Bailey
  8. Simon G Gregory
  9. Cliburn Chan
  10. Micah A Luftig
(2021)
Single-cell RNA-seq reveals transcriptomic heterogeneity mediated by host-pathogen dynamics in lymphoblastoid cell lines
eLife 10:e62586.
https://doi.org/10.7554/eLife.62586
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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 (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.

Data availability

Raw sequencing data for the three previously unpublished samples (LCL_777_B958, LCL_777_M81, and LCL_461_B958) are deposited in the NCBI Sequence Read Archive (SRA) and can be accessed along with processed data from the NCBI Gene Expression Omnibus (GEO, Series Accession: GSE158275).

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Elliott D SoRelle

    Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, United States
    Competing interests
    No competing interests declared.

  2. Joanne Dai

    Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, United States
    Competing interests
    Joanne Dai, Joanne Dai is affiliated with Amgen Inc. The author has no financial interests to declare..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9879-4704

  3. Emmanuela N Bonglack

    Molecular Genetics and Microbiology, Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, United States
    Competing interests
    No competing interests declared.

  4. Emma M Heckenberg

    Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, United States
    Competing interests
    No competing interests declared.

  5. Jeffrey Y Zhou

    Medicine, University of Massachusetts Medical School, Worcester, United States
    Competing interests
    No competing interests declared.

  6. Stephanie N Giamberardino

    Department of Neurology, Duke University School of Medicine, Durham, United States
    Competing interests
    No competing interests declared.

  7. Jeffrey A Bailey

    Department of Pathology and Laboratory Medicine, Brown University, Providence, United States
    Competing interests
    No competing interests declared.

  8. Simon G Gregory

    Department of Neurology, Duke University School of Medicine, Durham, United States
    Competing interests
    No competing interests declared.

  9. Cliburn Chan

    Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, United States
    Competing interests
    No competing interests declared.

  10. Micah A Luftig

    Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, United States
    For correspondence
    micah.luftig@duke.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2964-1907

Funding

National Institute of Dental and Craniofacial Research (R01-DE025994)

  • Micah A Luftig

National Cancer Institute (T32-CA009111)

  • Elliott D SoRelle
  • Joanne Dai
  • Micah A Luftig

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, SoRelle et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Elliott D SoRelle
  2. Joanne Dai
  3. Emmanuela N Bonglack
  4. Emma M Heckenberg
  5. Jeffrey Y Zhou
  6. Stephanie N Giamberardino
  7. Jeffrey A Bailey
  8. Simon G Gregory
  9. Cliburn Chan
  10. Micah A Luftig
(2021)
Single-cell RNA-seq reveals transcriptomic heterogeneity mediated by host-pathogen dynamics in lymphoblastoid cell lines
eLife 10:e62586.
https://doi.org/10.7554/eLife.62586

Share this article

https://doi.org/10.7554/eLife.62586

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