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

PLoS Pathogens

Volumn 13, Issue 10, 2017, Pages

  Rato, Sylvie ^a   Rausell, Antonio ^b,c   Muñoz, Miguel ^a   Telenti, Amalio ^d   Ciuffi, Angela ^a  

^a LAUSANNE UNIVERSITY HOSPITAL   (Switzerland)

^b IMAGINE INSTITUTE   (France)

^c UNIVERSITÉ PARIS DESCARTES   (France)

^d J CRAIG VENTER INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; CD2 ANTIGEN; CD22 ANTIGEN; CD28 ANTIGEN; CD298 ANTIGEN; CD3 ANTIGEN; CD317 ANTIGEN; CD48 ANTIGEN; CD63 ANTIGEN; CD71 ANTIGEN; CD74 ANTIGEN; CELL MARKER; CHEMOKINE RECEPTOR CXCR4; DIPEPTIDYL PEPTIDASE IV; HEPATITIS A VIRUS CELLULAR RECEPTOR 2; INTERLEUKIN 2 RECEPTOR ALPHA; LENTIVIRUS VECTOR; LYMPHOCYTE FUNCTION ASSOCIATED ANTIGEN 3; MEMBRANE ANTIGEN; T LYMPHOCYTE RECEPTOR; TUMOR NECROSIS FACTOR RELATED APOPTOSIS INDUCING LIGAND; UNCLASSIFIED DRUG;

ANTIBODY SCREENING; ARTICLE; ATTITUDE; CD4 LYMPHOCYTE COUNT; CD4+ T LYMPHOCYTE; CELL POPULATION; CORRELATION ANALYSIS; FLUORESCENCE ACTIVATED CELL SORTING; FLUORESCENCE SPECTROSCOPY; GENE EXPRESSION ASSAY; GENETIC HETEROGENEITY; GENETIC TRANSFECTION; HUMAN; HUMAN CELL; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; MRNA EXPRESSION ASSAY; NONHUMAN; NORMAL HUMAN; PRINCIPAL COMPONENT ANALYSIS; PROTEIN EXPRESSION; RNA SEQUENCE; SINGLE CELL ANALYSIS; STATISTICAL ANALYSIS; STRUCTURAL BIOINFORMATICS; T LYMPHOCYTE ACTIVATION; CELL CULTURE; HUMAN IMMUNODEFICIENCY VIRUS 1; IMMUNOLOGY; LYMPHOCYTE ACTIVATION; PHYSIOLOGY; PROCEDURES; VIRUS REPLICATION;

CD4-POSITIVE T-LYMPHOCYTES; CELLS, CULTURED; HIV INFECTIONS; HIV-1; HUMANS; LYMPHOCYTE ACTIVATION; SINGLE-CELL ANALYSIS; VIRUS REPLICATION;

EID: 85033226512     PISSN: 15537366     EISSN: 15537374     Source Type: Journal    
DOI: 10.1371/journal.ppat.1006678     Document Type: Article

References (52)

1. Rausell A, Munoz M, Martinez R, Roger T, Telenti A, Ciuffi A, Innate immune defects in HIV permissive cell lines. Retrovirology. 2016;13(1):43. doi: 10.1186/s12977-016-0275-8 27350062.
2. Mohammadi P, Desfarges S, Bartha I, Joos B, Zangger N, Munoz M, et al. 24 hours in the life of HIV-1 in a T cell line. PLoS pathogens. 2013;9(1):e1003161. doi: 10.1371/journal.ppat.1003161 23382686.
3. Ciuffi A, Bleiber G, Munoz M, Martinez R, Loeuillet C, Rehr M, et al. Entry and transcription as key determinants of differences in CD4 T-cell permissiveness to human immunodeficiency virus type 1 infection. Journal of virology. 2004;78(19):10747–54. doi: 10.1128/JVI.78.19.10747-10754.2004 15367641.
4. Weiss L, Chevalier MF, Assoumou L, Didier C, Girard PM, Piketty C, et al. T-cell activation positively correlates with cell-associated HIV-DNA level in viremic patients with primary or chronic HIV-1 infection. AIDS. 2014;28(11):1683–7. doi: 10.1097/QAD.0000000000000319 24841127.
5. Pan X, Baldauf HM, Keppler OT, Fackler OT, Restrictions to HIV-1 replication in resting CD4+ T lymphocytes. Cell Res. 2013;23(7):876–85. doi: 10.1038/cr.2013.74 23732522.
6. Stevenson M, Stanwick TL, Dempsey MP, Lamonica CA, HIV-1 replication is controlled at the level of T cell activation and proviral integration. EMBO J. 1990;9(5):1551–60. 2184033.
7. Gowda SD, Stein BS, Mohagheghpour N, Benike CJ, Engleman EG, Evidence that T cell activation is required for HIV-1 entry in CD4+ lymphocytes. J Immunol. 1989;142(3):773–80. 2521508.
8. Imbeault M, Giguere K, Ouellet M, Tremblay MJ, Exon level transcriptomic profiling of HIV-1-infected CD4(+) T cells reveals virus-induced genes and host environment favorable for viral replication. PLoS pathogens. 2012;8(8):e1002861. doi: 10.1371/journal.ppat.1002861 22876188.
9. Douek DC, Picker LJ, Koup RA, T cell dynamics in HIV-1 infection. Annu Rev Immunol. 2003;21:265–304. doi: 10.1146/annurev.immunol.21.120601.141053 12524385.
10. Gosselin A, Monteiro P, Chomont N, Diaz-Griffero F, Said EA, Fonseca S, et al. Peripheral blood CCR4+CCR6+ and CXCR3+CCR6+CD4+ T cells are highly permissive to HIV-1 infection. J Immunol. 2010;184(3):1604–16. doi: 10.4049/jimmunol.0903058 20042588.
11. Ciuffi A, Rato S, Telenti A, Single-Cell Genomics for Virology. Viruses. 2016;8(5). doi: 10.3390/v8050123 27153082.
12. Rato S, Golumbeanu M, Telenti A, Ciuffi A, Exploring viral infection using single-cell sequencing. Virus Res. 2016. doi: 10.1016/j.virusres.2016.10.016 27816430.
13. Giladi A, Amit I, Immunology, one cell at a time. Nature. 2017;547(7661):27–9. doi: 10.1038/547027a 28682352.
14. Becattini S, Latorre D, Mele F, Foglierini M, De Gregorio C, Cassotta A, et al. T cell immunity. Functional heterogeneity of human memory CD4(+) T cell clones primed by pathogens or vaccines. Science. 2015;347(6220):400–6. Epub 2014/12/06. doi: 10.1126/science.1260668 25477212.
15. Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nature biotechnology. 2015;33(2):155–60. Epub 2015/01/20. doi: 10.1038/nbt.3102 25599176.
16. Han A, Glanville J, Hansmann L, Davis MM, Linking T-cell receptor sequence to functional phenotype at the single-cell level. Nature biotechnology. 2014;32(7):684–92. Epub 2014/06/24. doi: 10.1038/nbt.2938 24952902.
17. Pyndiah N, Telenti A, Rausell A, Evolutionary genomics and HIV restriction factors. Current opinion in HIV and AIDS. 2015;10(2):79–83. Epub 2015/01/17. doi: 10.1097/COH.0000000000000138 25594315.
18. Rand U, Rinas M, Schwerk J, Nohren G, Linnes M, Kroger A, et al. Multi-layered stochasticity and paracrine signal propagation shape the type-I interferon response. Molecular systems biology. 2012;8:584. Epub 2012/05/24. doi: 10.1038/msb.2012.17 22617958.
19. Rausell A, McLaren PJ, Telenti A, HIV and innate immunity—a genomics perspective. F1000prime reports. 2013;5:29. doi: 10.12703/P5-29 23967380.
20. Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D, Lu D, et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature. 2014;510(7505):363–9. Epub 2014/06/12. doi: 10.1038/nature13437 24919153.
21. Raj A, van Oudenaarden A, Nature, nurture, or chance: stochastic gene expression and its consequences. Cell. 2008;135(2):216–26. Epub 2008/10/30. doi: 10.1016/j.cell.2008.09.050 18957198.
22. Borel C, Ferreira PG, Santoni F, Delaneau O, Fort A, Popadin KY, et al. Biased allelic expression in human primary fibroblast single cells. American journal of human genetics. 2015;96(1):70–80. Epub 2015/01/06. doi: 10.1016/j.ajhg.2014.12.001 25557783.
23. Deng Q, Ramskold D, Reinius B, Sandberg R, Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science. 2014;343(6167):193–6. Epub 2014/01/11. doi: 10.1126/science.1245316 24408435.
24. Usoskin D, Furlan A, Islam S, Abdo H, Lonnerberg P, Lou D, et al. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nature neuroscience. 2015;18(1):145–53. Epub 2014/11/25. doi: 10.1038/nn.3881 25420068.
25. Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature. 2013;498(7453):236–40. Epub 2013/05/21. doi: 10.1038/nature12172 23685454.
26. Kippner LE, Kim J, Gibson G, Kemp ML, Single cell transcriptional analysis reveals novel innate immune cell types. PeerJ. 2014;2:e452. Epub 2014/07/16. doi: 10.7717/peerj.452 25024920.
27. Julia M, Telenti A, Rausell A, Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq. Bioinformatics. 2015. doi: 10.1093/bioinformatics/btv368 26099264.
28. Fazekas de St Groth B, Smith AL, Higgins CA, T cell activation: in vivo veritas. Immunol Cell Biol. 2004;82(3):260–8. doi: 10.1111/j.0818-9641.2004.01243.x 15186257.
29. Biancotto A, Iglehart SJ, Vanpouille C, Condack CE, Lisco A, Ruecker E, et al. HIV-1 induced activation of CD4+ T cells creates new targets for HIV-1 infection in human lymphoid tissue ex vivo. Blood. 2008;111(2):699–704. doi: 10.1182/blood-2007-05-088435 17909079.
30. Chachage M, Pollakis G, Kuffour EO, Haase K, Bauer A, Nadai Y, et al. CD25+ FoxP3+ Memory CD4 T Cells Are Frequent Targets of HIV Infection In Vivo. Journal of virology. 2016;90(20):8954–67. doi: 10.1128/JVI.00612-16 27384654.
31. Ramilo O, Bell KD, Uhr JW, Vitetta ES, Role of CD25+ and CD25-T cells in acute HIV infection in vitro. J Immunol. 1993;150(11):5202–8. 8496611.
32. Love MI, Huber W, Anders S, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology. 2014;15(12):550. Epub 2014/12/18. doi: 10.1186/s13059-014-0550-8 25516281.
33. Zack JA, Kim SG, Vatakis DN, HIV restriction in quiescent CD4(+) T cells. Retrovirology. 2013;10:37. doi: 10.1186/1742-4690-10-37 23557201.
34. Pfistershammer K, Majdic O, Stockl J, Zlabinger G, Kirchberger S, Steinberger P, et al. CD63 as an activation-linked T cell costimulatory element. J Immunol. 2004;173(10):6000–8. 15528334.
35. Li G, Endsley MA, Somasunderam A, Gbota SL, Mbaka MI, Murray JL, et al. The dual role of tetraspanin CD63 in HIV-1 replication. Virol J. 2014;11:23. doi: 10.1186/1743-422X-11-23 24507450.
36. Li G, Dziuba N, Friedrich B, Murray JL, Ferguson MR, A post-entry role for CD63 in early HIV-1 replication. Virology. 2011;412(2):315–24. doi: 10.1016/j.virol.2011.01.017 21315401.
37. Chen H, Dziuba N, Friedrich B, von Lindern J, Murray JL, Rojo DR, et al. A critical role for CD63 in HIV replication and infection of macrophages and cell lines. Virology. 2008;379(2):191–6. doi: 10.1016/j.virol.2008.06.029 18682304.
38. von Lindern JJ, Rojo D, Grovit-Ferbas K, Yeramian C, Deng C, Herbein G, et al. Potential role for CD63 in CCR5-mediated human immunodeficiency virus type 1 infection of macrophages. Journal of virology. 2003;77(6):3624–33. doi: 10.1128/JVI.77.6.3624-3633.2003 12610138.
39. Neil SJ, Zang T, Bieniasz PD, Tetherin inhibits retrovirus release and is antagonized by HIV-1 Vpu. Nature. 2008;451(7177):425–30. doi: 10.1038/nature06553 18200009.
40. Malik N, Canfield VA, Beckers MC, Gros P, Levenson R, Identification of the mammalian Na,K-ATPase 3 subunit. J Biol Chem. 1996;271(37):22754–8. 8798450.
41. Chruewkamlow N, Pata S, Mahasongkram K, Laopajon W, Kasinrerk W, Chiampanichayakul S, beta3 subunit of Na,K ATPase regulates T cell activation with no involvement of Na,K ATPase activity. Immunobiology. 2015;220(5):634–40. doi: 10.1016/j.imbio.2014.11.017 25678464.
42. Nishitsuji H, Sugiyama R, Abe M, Takaku H, ATP1B3 Protein Modulates the Restriction of HIV-1 Production and Nuclear Factor kappa Light Chain Enhancer of Activated B Cells (NF-kappaB) Activation by BST-2. J Biol Chem. 2016;291(9):4754–62. doi: 10.1074/jbc.M115.679357 26694617.
43. Ciuffi A, Llano M, Poeschla E, Hoffmann C, Leipzig J, Shinn P, et al. A role for LEDGF/p75 in targeting HIV DNA integration. Nature medicine. 2005;11(12):1287–9. doi: 10.1038/nm1329 16311605.
44. Hotta A, Cheung AY, Farra N, Vijayaragavan K, Seguin CA, Draper JS, et al. Isolation of human iPS cells using EOS lentiviral vectors to select for pluripotency. Nat Methods. 2009;6(5):370–6. doi: 10.1038/nmeth.1325 19404254.
45. Naldini L, Blomer U, Gallay P, Ory D, Mulligan R, Gage FH, et al. In vivo gene delivery and stable transduction of nondividing cells by a lentiviral vector. Science. 1996;272(5259):263–7. 8602510.
46. Trinite B, Chan CN, Lee CS, Mahajan S, Luo Y, Muesing MA, et al. Suppression of Foxo1 activity and down-modulation of CD62L (L-selectin) in HIV-1 infected resting CD4 T cells. PLoS One. 2014;9(10):e110719. doi: 10.1371/journal.pone.0110719 25330112.
47. Mohammadi P, di Iulio J, Munoz M, Martinez R, Bartha I, Cavassini M, et al. Dynamics of HIV latency and reactivation in a primary CD4+ T cell model. PLoS pathogens. 2014;10(5):e1004156. doi: 10.1371/journal.ppat.1004156 24875931.
48. Martin M, Cutadapt removes adapter sequences from high-throughput sequencing reads2015 [cited 17 1]. 10–2].
49. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. Epub 2012/10/30. doi: 10.1093/bioinformatics/bts635 23104886.
50. Grant GR, Farkas MH, Pizarro AD, Lahens NF, Schug J, Brunk BP, et al. Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM). Bioinformatics. 2011;27(18):2518–28. Epub 2011/07/22. doi: 10.1093/bioinformatics/btr427 21775302.
51. Anders S, Pyl PT, Huber W, HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–9. Epub 2014/09/28. doi: 10.1093/bioinformatics/btu638 25260700.
52. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45(D1):D362–D8. doi: 10.1093/nar/gkw937 27924014.