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Single-cell analysis identifies cellular markers of the HIV permissive cell

Author

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  • Sylvie Rato
  • Antonio Rausell
  • Miguel Muñoz
  • Amalio Telenti
  • Angela Ciuffi

Abstract

Cellular permissiveness to HIV infection is highly heterogeneous across individuals. Heterogeneity is also found across CD4+ T cells from the same individual, where only a fraction of cells gets infected. To explore the basis of permissiveness, we performed single-cell RNA-seq analysis of non-infected CD4+ T cells from high and low permissive individuals. Transcriptional heterogeneity translated in a continuum of cell states, driven by T-cell receptor-mediated cell activation and was strongly linked to permissiveness. Proteins expressed at the cell surface and displaying the highest correlation with T cell activation were tested as biomarkers of cellular permissiveness to HIV. FACS sorting using antibodies against several biomarkers of permissiveness led to an increase of HIV cellular infection rates. Top candidate biomarkers included CD25, a canonical activation marker. The combination of CD25 high expression with other candidate biomarkers led to the identification of CD298, CD63 and CD317 as the best biomarkers for permissiveness. CD25highCD298highCD63highCD317high cell population showed an enrichment of HIV-infection of up to 28 fold as compared to the unsorted cell population. The purified hyper-permissive cell subpopulation was characterized by a downregulation of interferon-induced genes and several known restriction factors. Single-cell RNA-seq analysis coupled with functional characterization of cell biomarkers provides signatures of the “HIV-permissive cell”.Author summary: CD4+ T cells are the main target of human immunodeficiency virus (HIV) infection. However, CD4+ T cells are not equally permissive to infection, varying between individuals and across cells isolated from the same individual. We explored cellular heterogeneity by analyzing the transcriptome profile of CD4+ T cells at single-cell level. Results identified T-cell receptor-mediated activation as the major determinant of CD4+ T cell heterogeneity. We identified cell surface proteins that highly correlated with T cell activation and HIV permissiveness. Activated CD4+ T cells expressing CD25, CD298, CD63 and CD317 were highly enriched for HIV permissiveness. The single-cell analysis approach used in this study allowed for the identification of the most HIV-permissive cell.

Suggested Citation

  • Sylvie Rato & Antonio Rausell & Miguel Muñoz & Amalio Telenti & Angela Ciuffi, 2017. "Single-cell analysis identifies cellular markers of the HIV permissive cell," PLOS Pathogens, Public Library of Science, vol. 13(10), pages 1-23, October.
  • Handle: RePEc:plo:ppat00:1006678
    DOI: 10.1371/journal.ppat.1006678
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    References listed on IDEAS

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    1. Alex K. Shalek & Rahul Satija & Joe Shuga & John J. Trombetta & Dave Gennert & Diana Lu & Peilin Chen & Rona S. Gertner & Jellert T. Gaublomme & Nir Yosef & Schraga Schwartz & Brian Fowler & Suzanne W, 2014. "Single-cell RNA-seq reveals dynamic paracrine control of cellular variation," Nature, Nature, vol. 510(7505), pages 363-369, June.
    2. Stuart J. D. Neil & Trinity Zang & Paul D. Bieniasz, 2008. "Tetherin inhibits retrovirus release and is antagonized by HIV-1 Vpu," Nature, Nature, vol. 451(7177), pages 425-430, January.
    3. Alex K. Shalek & Rahul Satija & Xian Adiconis & Rona S. Gertner & Jellert T. Gaublomme & Raktima Raychowdhury & Schraga Schwartz & Nir Yosef & Christine Malboeuf & Diana Lu & John J. Trombetta & Dave , 2013. "Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells," Nature, Nature, vol. 498(7453), pages 236-240, June.
    4. Amir Giladi & Ido Amit, 2017. "Immunology, one cell at a time," Nature, Nature, vol. 547(7661), pages 27-29, July.
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