Single-Cell Analysis Reveals Heterogeneity of Virus Infection, Pathogenicity, and Host Responses: HIV as a Pioneering Example

Literature Cited

1. 1. 

   Telenti A, Goldstein DB. 2006. Genomics meets HIV-1. Nat. Rev. Microbiol. 4:865–73
   [Google Scholar]
2. 2. 

   Fellay J, Shianna KV, Ge D, Colombo S, Ledergerber B et al. 2007. A whole-genome association study of major determinants for host control of HIV-1. Science 317:944–47
   [Google Scholar]
3. 3. 

   McLaren PJ, Carrington M. 2015. The impact of host genetic variation on infection with HIV-1. Nat. Immunol. 16:577–83
   [Google Scholar]
4. 4. 

   Bartha I, McLaren PJ, Ciuffi A, Fellay J, Telenti A 2014. GuavaH: a compendium of host genomic data in HIV biology and disease. Retrovirology 11:6
   [Google Scholar]
5. 5. 

   Bartha I, McLaren PJ, Brumme C, Harrigan R, Telenti A, Fellay J 2017. Estimating the respective contributions of human and viral genetic variation to HIV control. PLOS Comput. Biol. 13:e1005339
   [Google Scholar]
6. 6. 

   Cristinelli S, Ciuffi A. 2018. The use of single-cell RNA-Seq to understand virus-host interactions. Curr. Opin. Virol. 29:39–50
   [Google Scholar]
7. 7. 

   Venter JC, Adams MD, Myers EW, Li PW, Mural RJ et al. 2001. The sequence of the human genome. Science 291:1304–51
   [Google Scholar]
8. 8. 

   Tang F, Barbacioru C, Wang Y, Nordman E, Lee C et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6:377–82
   [Google Scholar]
9. 9. 

   Gross A, Schoendube J, Zimmermann S, Steeb M, Zengerle R, Koltay P 2015. Technologies for single-cell isolation. Int. J. Mol. Sci. 16:16897–919
   [Google Scholar]
10. 10. 

    Mincarelli L, Lister A, Lipscombe J, Macaulay IC 2018. Defining cell identity with single-cell omics. Proteomics 18:e1700312
    [Google Scholar]
11. 11. 

    Svensson V, Vento-Tormo R, Teichmann SA 2018. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13:599–604
    [Google Scholar]
12. 12. 

    Hu P, Zhang W, Xin H, Deng G 2016. Single cell isolation and analysis. Front. Cell Dev. Biol. 4:116
    [Google Scholar]
13. 13. 

    Tanay A, Regev A. 2017. Scaling single-cell genomics from phenomenology to mechanism. Nature 541:331–38
    [Google Scholar]
14. 14. 

    Gawad C, Koh W, Quake SR 2016. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17:175–88
    [Google Scholar]
15. 15. 

    Vitak SA, Torkenczy KA, Rosenkrantz JL, Fields AJ, Christiansen L et al. 2017. Sequencing thousands of single-cell genomes with combinatorial indexing. Nat. Methods 14:302–8
    [Google Scholar]
16. 16. 

    Wen L, Tang F. 2018. Single cell epigenome sequencing technologies. Mol. Aspects Med. 59:62–69
    [Google Scholar]
17. 17. 

    Mulqueen RM, Pokholok D, Norberg SJ, Torkenczy KA, Fields AJ et al. 2018. Highly scalable generation of DNA methylation profiles in single cells. Nat. Biotechnol. 36:428–31
    [Google Scholar]
18. 18. 

    Karemaker ID, Vermeulen M. 2018. Single-cell DNA methylation profiling: technologies and biological applications. Trends Biotechnol 36:952–65
    [Google Scholar]
19. 19. 

    Kanter I, Kalisky T. 2015. Single cell transcriptomics: methods and applications. Front. Oncol. 5:53
    [Google Scholar]
20. 20. 

    Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A et al. 2017. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65:631–43
    [Google Scholar]
21. 21. 

    Kulkarni A, Anderson AG, Merullo DP, Konopka G 2019. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr. Opin. Biotechnol. 58:129–36
    [Google Scholar]
22. 22. 

    Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C et al. 2017. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357:661–67
    [Google Scholar]
23. 23. 

    Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P et al. 2018. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360:176–82
    [Google Scholar]
24. 24. 

    Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X 2015. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348:aaa6090
    [Google Scholar]
25. 25. 

    Kolodziejczyk AA, Lonnberg T. 2018. Global and targeted approaches to single-cell transcriptome characterization. Brief Funct. Genom. 17:209–19
    [Google Scholar]
26. 26. 

    Wu M, Singh AK. 2012. Single-cell protein analysis. Curr. Opin. Biotechnol. 23:83–88
    [Google Scholar]
27. 27. 

    Su Y, Shi Q, Wei W 2017. Single cell proteomics in biomedicine: high-dimensional data acquisition, visualization, and analysis. Proteomics 17:1600267
    [Google Scholar]
28. 28. 

    Levy E, Slavov N. 2018. Single cell protein analysis for systems biology. Essays Biochem 62:595–605
    [Google Scholar]
29. 29. 

    Macaulay IC, Ponting CP, Voet T 2017. Single-cell multiomics: multiple measurements from single cells. Trends Genet 33:155–68
    [Google Scholar]
30. 30. 

    Stuart T, Satija R. 2019. Integrative single-cell analysis. Nat. Rev. Genet. 20:257–72
    [Google Scholar]
31. 31. 

    Chen W, Li S, Kulkarni AS, Huang L, Cao J et al. 2020. Single cell omics: from assay design to biomedical application. Biotechnol. J. 15:e1900262
    [Google Scholar]
32. 32. 

    Angermueller C, Clark SJ, Lee HJ, Macaulay IC, Teng MJ et al. 2016. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13:229–32
    [Google Scholar]
33. 33. 

    Hu Y, Huang K, An Q, Du G, Hu G et al. 2016. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol 17:88
    [Google Scholar]
34. 34. 

    Bock C, Farlik M, Sheffield NC 2016. Multi-omics of single cells: strategies and applications. Trends Biotechnol 34:605–8
    [Google Scholar]
35. 35. 

    Hu Y, An Q, Sheu K, Trejo B, Fan S, Guo Y 2018. Single cell multi-omics technology: methodology and application. Front. Cell Dev. Biol. 6:28
    [Google Scholar]
36. 36. 

    Ziegenhain C, Vieth B, Parekh S, Hellmann I, Enard W 2018. Quantitative single-cell transcriptomics. Brief Funct. Genom. 17:220–32
    [Google Scholar]
37. 37. 

    Vieth B, Parekh S, Ziegenhain C, Enard W, Hellmann I 2019. A systematic evaluation of single cell RNA-seq analysis pipelines. Nat. Commun. 10:4667
    [Google Scholar]
38. 38. 

    Choi YH, Kim JK. 2019. Dissecting cellular heterogeneity using single-cell RNA sequencing. Mol. Cells 42:189–99
    [Google Scholar]
39. 39. 

    van Dijk D, Ertaylan G, Boucher CA, Sloot PM 2010. Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks. BMC Syst. Biol. 4:96
    [Google Scholar]
40. 40. 

    Ptak RG, Fu W, Sanders-Beer BE, Dickerson JE, Pinney JW et al. 2008. Cataloguing the HIV type 1 human protein interaction network. AIDS Res. Hum. Retroviruses 24:1497–502
    [Google Scholar]
41. 41. 

    Bushman FD, Malani N, Fernandes J, D'Orso I, Cagney G et al. 2009. Host cell factors in HIV replication: meta-analysis of genome-wide studies. PLOS Pathog 5:e1000437
    [Google Scholar]
42. 42. 

    Yin X, Langer S, Zhang Z, Herbert KM, Yoh S et al. 2020. Sensor sensibility—HIV-1 and the innate immune response. Cells 9:254
    [Google Scholar]
43. 43. 

    Schoggins JW. 2019. Interferon-stimulated genes: What do they all do. ? Annu. Rev. Virol. 6:567–84
    [Google Scholar]
44. 44. 

    Bergantz L, Subra F, Deprez E, Delelis O, Richetta C 2019. Interplay between intrinsic and innate immunity during HIV infection. Cells 8:922
    [Google Scholar]
45. 45. 

    Sauter D, Kirchhoff F. 2019. Key viral adaptations preceding the AIDS pandemic. Cell Host Microbe 25:27–38
    [Google Scholar]
46. 46. 

    Razooky BS, Gutierrez E, Terry VH, Spina CA, Groisman A, Weinberger LS 2012. Microwell devices with finger-like channels for long-term imaging of HIV-1 expression kinetics in primary human lymphocytes. Lab. Chip 12:4305–12
    [Google Scholar]
47. 47. 

    Petravic J, Ellenberg P, Chan ML, Paukovics G, Smyth RP et al. 2014. Intracellular dynamics of HIV infection. J. Virol. 88:1113–24
    [Google Scholar]
48. 48. 

    Holmes M, Zhang F, Bieniasz PD 2015. Single-cell and single-cycle analysis of HIV-1 replication. PLOS Pathog 11:e1004961
    [Google Scholar]
49. 49. 

    Marini B, Kertesz-Farkas A, Ali H, Lucic B, Lisek K et al. 2015. Nuclear architecture dictates HIV-1 integration site selection. Nature 521:227–31
    [Google Scholar]
50. 50. 

    Boullé M, Muller TG, Dahling S, Ganga Y, Jackson L et al. 2016. HIV cell-to-cell spread results in earlier onset of viral gene expression by multiple infections per cell. PLOS Pathog 12:e1005964
    [Google Scholar]
51. 51. 

    Francis AC, Melikyan GB. 2018. Single HIV-1 imaging reveals progression of infection through CA-dependent steps of docking at the nuclear pore, uncoating, and nuclear transport. Cell Host Microbe 23:536–48
    [Google Scholar]
52. 52. 

    Puray-Chavez M, Tedbury PR, Huber AD, Ukah OB, Yapo V et al. 2017. Multiplex single-cell visualization of nucleic acids and protein during HIV infection. Nat. Commun. 8:1882
    [Google Scholar]
53. 53. 

    Lucic B, Chen HC, Kuzman M, Zorita E, Wegner J et al. 2019. Spatially clustered loci with multiple enhancers are frequent targets of HIV-1 integration. Nat. Commun. 10:4059
    [Google Scholar]
54. 54. 

    Suspène R, Meyerhans A. 2012. Quantification of unintegrated HIV-1 DNA at the single cell level in vivo. PLOS ONE 7:5e36246
    [Google Scholar]
55. 55. 

    Stultz RD, Cenker JJ, McDonald D 2017. Imaging HIV-1 genomic DNA from entry through productive infection. J. Virol. 91:9e00034-17
    [Google Scholar]
56. 56. 

    Pocock GM, Zimdars LL, Yuan M, Eliceiri KW, Ahlquist P, Sherer NM 2016. Diverse activities of viral cis-acting RNA regulatory elements revealed using multicolor, long-term, single-cell imaging. Mol. Biol. Cell 28:3476–86
    [Google Scholar]
57. 57. 

    Cavrois M, Banerjee T, Mukherjee G, Raman N, Hussien R et al. 2017. Mass cytometric analysis of HIV entry, replication, and remodeling in tissue CD4⁺ T cells. Cell Rep 20:984–98
    [Google Scholar]
58. 58. 

    Rato S, Rausell A, Munoz M, Telenti A, Ciuffi A 2017. Single-cell analysis identifies cellular markers of the HIV permissive cell. PLOS Pathog 13:e1006678
    [Google Scholar]
59. 59. 

    Durand CM, Blankson JN, Siliciano RF 2012. Developing strategies for HIV-1 eradication. Trends Immunol 33:554–62
    [Google Scholar]
60. 60. 

    Spivak AM, Planelles V. 2016. HIV-1 eradication: early trials (and tribulations). Trends Mol. Med. 22:10–27
    [Google Scholar]
61. 61. 

    Rothenberger MK, Keele BF, Wietgrefe SW, Fletcher CV, Beilman GJ et al. 2015. Large number of rebounding/founder HIV variants emerge from multifocal infection in lymphatic tissues after treatment interruption. PNAS 112:E1126–1126
    [Google Scholar]
62. 62. 

    Colby DJ, Trautmann L, Pinyakorn S, Leyre L, Pagliuzza A et al. 2018. Rapid HIV RNA rebound after antiretroviral treatment interruption in persons durably suppressed in Fiebig I acute HIV infection. Nat. Med. 24:923–26
    [Google Scholar]
63. 63. 

    Van Lint C, Bouchat S, Marcello A 2013. HIV-1 transcription and latency: an update. Retrovirology 10:67
    [Google Scholar]
64. 64. 

    Whitney JB, Hill AL, Sanisetty S, Penaloza-MacMaster P, Liu J et al. 2014. Rapid seeding of the viral reservoir prior to SIV viraemia in rhesus monkeys. Nature 512:74–77
    [Google Scholar]
65. 65. 

    Whitney JB, Lim SY, Osuna CE, Kublin JL, Chen E et al. 2018. Prevention of SIVmac251 reservoir seeding in rhesus monkeys by early antiretroviral therapy. Nat. Commun. 9:5429
    [Google Scholar]
66. 66. 

    Bachmann N, von Siebenthal C, Vongrad V, Turk T, Neumann K et al. 2019. Determinants of HIV-1 reservoir size and long-term dynamics during suppressive ART. Nat. Commun. 10:3193
    [Google Scholar]
67. 67. 

    Falcinelli SD, Ceriani C, Margolis DM, Archin NM 2019. New frontiers in measuring and characterizing the HIV reservoir. Front. Microbiol. 10:2878
    [Google Scholar]
68. 68. 

    Pitman MC, Lau JSY, McMahon JH, Lewin SR 2018. Barriers and strategies to achieve a cure for HIV. Lancet HIV 5:e317–317
    [Google Scholar]
69. 69. 

    Sengupta S, Siliciano RF. 2018. Targeting the latent reservoir for HIV-1. Immunity 48:872–95
    [Google Scholar]
70. 70. 

    Ait-Ammar A, Kula A, Darcis G, Verdikt R, De Wit S et al. 2019. Current status of latency reversing agents facing the heterogeneity of HIV-1 cellular and tissue reservoirs. Front. Microbiol. 10:3060
    [Google Scholar]
71. 71. 

    Bohn-Wippert K, Tevonian EN, Lu Y, Huang MY, Megaridis MR, Dar RD 2018. Cell size-based decision-making of a viral gene circuit. Cell Rep 25:3844–57
    [Google Scholar]
72. 72. 

    Bradley T, Ferrari G, Haynes BF, Margolis DM, Browne EP 2018. Single-cell analysis of quiescent HIV infection reveals host transcriptional profiles that regulate proviral latency. Cell Rep 25:107–17
    [Google Scholar]
73. 73. 

    Cohn LB, da Silva IT, Valieris R, Huang AS, Lorenzi JCC et al. 2018. Clonal CD4⁺ T cells in the HIV-1 latent reservoir display a distinct gene profile upon reactivation. Nat. Med. 24:604–9
    [Google Scholar]
74. 74. 

    Golumbeanu M, Cristinelli S, Rato S, Munoz M, Cavassini M et al. 2018. Single-cell RNA-Seq reveals transcriptional heterogeneity in latent and reactivated HIV-infected cells. Cell Rep 23:942–50
    [Google Scholar]
75. 75. 

    Ukah OB, Puray-Chavez M, Tedbury PR, Herschhorn A, Sodroski JG, Sarafianos SG 2018. Visualization of HIV-1 RNA transcription from integrated HIV-1 DNA in reactivated latently infected cells. Viruses 10:534
    [Google Scholar]
76. 76. 

    Telwatte S, Morón-López S, Aran D, Kim P, Hsieh C et al. 2019. Heterogeneity in HIV and cellular transcription profiles in cell line models of latent and productive infection: implications for HIV latency. Retrovirology 16:132
    [Google Scholar]
77. 77. 

    Bui JK, Mellors JW, Cillo AR 2016. HIV-1 virion production from single inducible proviruses following T-cell activation ex vivo. J. Virol. 90:31673–76
    [Google Scholar]
78. 78. 

    Baxter AE, Niessl J, Fromentin R, Richard J, Porichis F et al. 2016. Single-cell characterization of viral translation-competent reservoirs in HIV-infected individuals. Cell Host Microbe 20:3368–80
    [Google Scholar]
79. 79. 

    Coindre S, Tchitchek N, Alaoui L, Vaslin B, Bourgeois C et al. 2018. Mass cytometry analysis reveals the landscape and dynamics of CD32a⁺ CD4⁺ T cells from early HIV infection to effective cART. Front. Immunol. 9:1217
    [Google Scholar]
80. 80. 

    Martrus G, Niehrs A, Cornelis R, Rechtien A, García-Beltran W et al. 2016. Kinetics of HIV-1 latency reversal quantified on the single-cell level using a novel flow-based technique. J. Virol. 90:209018–28
    [Google Scholar]
81. 81. 

    Grau-Expósito J, Luque-Ballesteros L, Navarro J, Curran A, Burgos J et al. 2019. Latency reversal agents affect differently the latent reservoir present in distinct CD4⁺ T subpopulations. PLOS Pathog 15:e1007991
    [Google Scholar]
82. 82. 

    Passaes CP, Bruel T, Decalf J, David A, Angin M et al. 2017. Ultrasensitive HIV-1 p24 assay detects single infected cells and differences in reservoir induction by latency reversal agents. J. Virol. 91:6e02296-16
    [Google Scholar]
83. 83. 

    Shan L, Deng K, Gao H, Xing S, Capoferri AA et al. 2017. Transcriptional reprogramming during effector-to-memory transition renders CD4⁺ T cells permissive for latent HIV-1 infection. Immunity 47:4766–75
    [Google Scholar]
84. 84. 

    Zhang W, Svensson Akusjärvi S, Sönnerborg A, Neogi U 2018. Characterization of inducible transcription and translation-competent HIV-1 using the RNAscope ISH technology at a single-cell resolution. Front. Microbiol. 9:2358
    [Google Scholar]
85. 85. 

    Bruner KM, Wang Z, Simonetti FR, Bender AM, Kwon KJ et al. 2019. A quantitative approach for measuring the reservoir of latent HIV-1 proviruses. Nature 566:7742120–25
    [Google Scholar]
86. 86. 

    Grau-Expósito J, Serra-Peinado C, Miguel L, Navarro J, Curran A et al. 2017. A novel single-cell FISH-flow assay identifies effector memory CD4⁺ T cells as a major niche for HIV-1 transcription in HIV-infected patients. mBio 8:4e00876-17
    [Google Scholar]
87. 87. 

    Bushman FD, Craigie R. 1990. Sequence requirements for integration of Moloney murine leukemia virus DNA in vitro. J. Virol. 64:5645–48
    [Google Scholar]
88. 88. 

    Pardons M, Baxter AE, Massanella M, Pagliuzza A, Fromentin R et al. 2019. Single-cell characterization and quantification of translation-competent viral reservoirs in treated and untreated HIV infection. PLOS Pathog 15:e1007619
    [Google Scholar]
89. 89. 

    Yucha RW, Hobbs KS, Hanhauser E, Hogan LE, Nieves W et al. 2017. High-throughput characterization of HIV-1 reservoir reactivation using a single-cell-in-droplet PCR assay. EBioMedicine 20:217–29
    [Google Scholar]
90. 90. 

    Josefsson L, Palmer S, Faria NR, Lemey P, Casazza J et al. 2013. Single cell analysis of lymph node tissue from HIV-1 infected patients reveals that the majority of CD4⁺ T-cells contain one HIV-1 DNA molecule. PLOS Pathog 9:6e1003432
    [Google Scholar]
91. 91. 

    Lee GQ, Reddy K, Einkauf KB, Gounder K, Chevalier JM et al. 2019. HIV-1 DNA sequence diversity and evolution during acute subtype C infection. Nat. Commun. 10:12737
    [Google Scholar]
92. 92. 

    Wiegand A, Spindler J, Hong FF, Shao W, Cyktor JC et al. 2017. Single-cell analysis of HIV-1 transcriptional activity reveals expression of proviruses in expanded clones during ART. PNAS 114:E3659–3659
    [Google Scholar]
93. 93. 

    Chen HC, Martinez JP, Zorita E, Meyerhans A, Filion GJ 2017. Position effects influence HIV latency reversal. Nat. Struct. Mol. Biol. 24:47–54
    [Google Scholar]
94. 94. 

    Lusic M, Marini B, Ali H, Lucic B, Luzzati R, Giacca M 2013. Proximity to PML nuclear bodies regulates HIV-1 latency in CD4⁺ T cells. Cell Host Microbe 13:665–77
    [Google Scholar]
95. 95. 

    Einkauf KB, Lee GQ, Gao C, Sharaf R, Sun X et al. 2019. Intact HIV-1 proviruses accumulate at distinct chromosomal positions during prolonged antiretroviral therapy. J. Clin. Invest. 129:3988–98
    [Google Scholar]
96. 96. 

    Razooky BS, Pai A, Aull K, Rouzine IM, Weinberger LS 2015. A hardwired HIV latency program. Cell 160:990–1001
    [Google Scholar]
97. 97. 

    Aull KH, Tanner EJ, Thomson M, Weinberger LS 2017. Transient thresholding: a mechanism enabling noncooperative transcriptional circuitry to form a switch. Biophys. J. 112:2428–38
    [Google Scholar]
98. 98. 

    Hansen MMK, Wen WY, Ingerman E, Razooky BS, Thompson CE et al. 2018. A post-transcriptional feedback mechanism for noise suppression and fate stabilization. Cell 173:1609–21
    [Google Scholar]
99. 99. 

    Rouzine IM, Weinberger AD, Weinberger LS 2015. An evolutionary role for HIV latency in enhancing viral transmission. Cell 160:51002–12
    [Google Scholar]
100. 100. 

     Walker BD, Yu XG. 2013. Unravelling the mechanisms of durable control of HIV-1. Nat. Rev. Immunol. 13:487–98
     [Google Scholar]
101. 101. 

     McBrien JB, Kumar NA, Silvestri G 2018. Mechanisms of CD8⁺ T cell-mediated suppression of HIV/SIV replication. Eur. J. Immunol. 48:898–914
     [Google Scholar]
102. 102. 

     Porichis F, Hart MG, Griesbeck M, Everett HL, Hassan M et al. 2014. High-throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry. Nat. Commun. 5:5641
     [Google Scholar]
103. 103. 

     Corneau A, Cosma A, Even S, Katlama C, Le Grand R et al. 2017. Comprehensive mass cytometry analysis of cell cycle, activation, and coinhibitory receptors expression in CD4 T cells from healthy and HIV-infected individuals. Cytometry B Clin. Cytom. 92:21–32
     [Google Scholar]
104. 104. 

     Bengsch B, Ohtani T, Khan O, Setty M, Manne S et al. 2018. Epigenomic-guided mass cytometry profiling reveals disease-specific features of exhausted CD8 T cells. Immunity 48:1029–45
     [Google Scholar]
105. 105. 

     Nguyen S, Deleage C, Darko S, Ransier A, Truong DP et al. 2019. Elite control of HIV is associated with distinct functional and transcriptional signatures in lymphoid tissue CD8⁺ T cells. Sci. Transl. Med. 11:523eaax4077
     [Google Scholar]
106. 106. 

     Angin M, Volant S, Passaes C, Lecuroux C, Monceaux V et al. 2019. Metabolic plasticity of HIV-specific CD8⁺ T cells is associated with enhanced antiviral potential and natural control of HIV-1 infection. Nat. Metab. 1:704–16
     [Google Scholar]
107. 107. 

     de Armas LR, Pallikkuth S, Pan L, Rinaldi S, Cotugno N et al. 2019. Single cell profiling reveals PTEN overexpression in influenza-specific B cells in aging HIV-infected individuals on anti-retroviral therapy. Sci. Rep. 9:2482
     [Google Scholar]
108. 108. 

     Han Q, Bradley T, Williams WB, Cain DW, Montefiori DC et al. 2020. Neonatal rhesus macaques have distinct immune cell transcriptional profiles following HIV envelope immunization. Cell Rep 30:1553–69
     [Google Scholar]
109. 109. 

     Farhadian SF, Mehta SS, Zografou C, Robertson K, Price RW et al. 2018. Single-cell RNA sequencing reveals microglia-like cells in cerebrospinal fluid during virologically suppressed HIV. JCI Insight 3:e121718
     [Google Scholar]
110. 110. 

     Coindre S, Tchitchek N, Alaoui L, Vaslin B, Bourgeois C et al. 2019. Mass cytometry analysis reveals complex cell-state modifications of blood myeloid cells during HIV infection. Front. Immunol. 10:2677
     [Google Scholar]
111. 111. 

     Martin-Gayo E, Cole MB, Kolb KE, Ouyang Z, Cronin J et al. 2018. A reproducibility-based computational framework identifies an inducible, enhanced antiviral state in dendritic cells from HIV-1 elite controllers. Genome Biol 19:10
     [Google Scholar]
112. 112. 

     Zanini F, Robinson ML, Croote D, Sahoo MK, Sanz AM et al. 2018. Virus-inclusive single-cell RNA sequencing reveals the molecular signature of progression to severe dengue. PNAS 115:52E12363–12363
     [Google Scholar]
113. 113. 

     Wyler E, Franke V, Menegatti J, Kocks C, Boltengagen A et al. 2019. Single-cell RNA-sequencing of herpes simplex virus 1-infected cells connects NRF2 activation to an antiviral program. Nat. Commun. 10:4878
     [Google Scholar]
114. 114. 

     Zhang J, Liu J, Yuan Y, Huang F, Ma R et al. 2020. Two waves of pro-inflammatory factors are released during the influenza A virus (IAV)-driven pulmonary immunopathogenesis. PLOS Pathog 16:2e1008334
     [Google Scholar]
115. 115. 

     Bolton DL, McGinnis K, Finak G, Chattopadhyay P, Gottardo R et al. 2017. Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells. PLOS Pathog 13:6e1006445
     [Google Scholar]