4.6

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  • ISSN 1674-8301
  • CN 32-1810/R
Qianqian Chen, Chunmei Hu, Wei Lu, Tianxing Hang, Yan Shao, Cheng Chen, Yanli Wang, Nan Li, Linling Jin, Wei Wu, Hong Wang, Xiaoning Zeng, Weiping Xie. Characteristics of alveolar macrophages in bronchioalveolar lavage fluids from active tuberculosis patients identified by single-cell RNA sequencing[J]. The Journal of Biomedical Research, 2022, 36(3): 167-180. DOI: 10.7555/JBR.36.20220007
Citation: Qianqian Chen, Chunmei Hu, Wei Lu, Tianxing Hang, Yan Shao, Cheng Chen, Yanli Wang, Nan Li, Linling Jin, Wei Wu, Hong Wang, Xiaoning Zeng, Weiping Xie. Characteristics of alveolar macrophages in bronchioalveolar lavage fluids from active tuberculosis patients identified by single-cell RNA sequencing[J]. The Journal of Biomedical Research, 2022, 36(3): 167-180. DOI: 10.7555/JBR.36.20220007

Characteristics of alveolar macrophages in bronchioalveolar lavage fluids from active tuberculosis patients identified by single-cell RNA sequencing

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  • Corresponding author:

    Weiping Xie, Xiaoning Zeng, and Hong Wang. Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, Jiangsu 210029, China. Tel/Fax: +86-25-68306030/+86-25-68306030. E-mails: wpxie@njmu.edu.cn, zeng_xiao_ning@hotmail.com, and hongwang@njmu.edu.cn

  • Received Date: January 07, 2022
  • Revised Date: April 05, 2022
  • Accepted Date: April 10, 2022
  • Available Online: May 27, 2022
  • Tuberculosis (TB), is an infectious disease caused by Mycobacterium tuberculosis (M. tuberculosis), and presents with high morbidity and mortality. Alveolar macrophages play an important role in TB pathogenesis although there is heterogeneity and functional plasticity. This study aimed to show the characteristics of alveolar macrophages from bronchioalveolar lavage fluid (BALF) in active TB patients. Single-cell RNA sequencing (scRNA-seq) was performed on BALF cells from three patients with active TB and additional scRNA-seq data from three healthy adults were established as controls. Transcriptional profiles were analyzed and compared by differential geneexpression and functional enrichment analysis. We applied pseudo-temporal trajectory analysis to investigate correlations and heterogeneity within alveolar macrophage subclusters. Alveolar macrophages from active TB patients at the single-cell resolution are described. We found that TB patients have higher cellular percentages in five macrophage subclusters. Alveolar macrophage subclusters with increased percentages were involved in inflammatory signaling pathways as well as the basic macrophage functions. The TB-increased alveolar macrophage subclusters might be derived from M1-like polarization state, before switching to an M2-like polarization state with the development of M. tuberculosis infection. Cell-cell communications of alveolar macrophages also increased and enhanced in active TB patients. Overall, our study demonstrated the characteristics of alveolar macrophages from BALF in active TB patients by using scRNA-seq.
  • [1]
    Achtman M. How old are bacterial pathogens?[J]. Proc Roy Soc B Biol Sci, 2016, 283(1836): 20160990. doi: 10.1098/rspb.2016.0990
    [2]
    Furin J, Cox H, Pai M. Tuberculosis[J]. Lancet, 2019, 393(10181): 1642–1656. doi: 10.1016/S0140-6736(19)30308-3
    [3]
    World Health Organization. Global tuberculosis report 2021[EB/OL]. [2021-10-14]. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2021.
    [4]
    Zumla A, George A, Sharma V, et al. The WHO 2014 global tuberculosis report-further to go[J]. Lancet Glob Health, 2015, 3(1): e10–e12. doi: 10.1016/S2214-109X(14)70361-4
    [5]
    Cohen SB, Gern BH, Urdahl KB. The tuberculous granuloma and preexisting immunity[J]. Annu Rev Immunol, 2022, 40: 589–614. doi: 10.1146/annurev-immunol-093019-125148
    [6]
    Wallis RS, Kim P, Cole S, et al. Tuberculosis biomarkers discovery: developments, needs, and challenges[J]. Lancet Infect Dis, 2013, 13(4): 362–372. doi: 10.1016/S1473-3099(13)70034-3
    [7]
    Cohen SB, Gern BH, Delahaye JL, et al. Alveolar macrophages provide an early mycobacterium tuberculosis niche and initiate dissemination[J]. Cell Host Microbe, 2018, 24(3): 439–446.e4. doi: 10.1016/j.chom.2018.08.001
    [8]
    Khan A, Singh VK, Hunter RL, et al. Macrophage heterogeneity and plasticity in tuberculosis[J]. J Leukoc Biol, 2019, 106(2): 275–282. doi: 10.1002/JLB.MR0318-095RR
    [9]
    Murray PJ. Macrophage polarization[J]. Annu Rev Physiol, 2017, 79: 541–566. doi: 10.1146/annurev-physiol-022516-034339
    [10]
    Murray PJ, Allen JE, Biswas SK, et al. Macrophage activation and polarization: nomenclature and experimental guidelines[J]. Immunity, 2014, 41(1): 14–20. doi: 10.1016/j.immuni.2014.06.008
    [11]
    Redente EF, Higgins DM, Dwyer-Nield LD, et al. Differential polarization of alveolar macrophages and bone marrow-derived monocytes following chemically and pathogen-induced chronic lung inflammation[J]. J Leukoc Biol, 2010, 88(1): 159–168. doi: 10.1189/jlb.0609378
    [12]
    Mily A, Kalsum S, Loreti MG, et al. Polarization of M1 and M2 human monocyte-derived cells and analysis with flow cytometry upon Mycobacterium tuberculosis infection[J]. J Vis Exp, 2020, 163: e61807. doi: 10.3791/61807
    [13]
    Mould KJ, Moore CM, Mcmanus SA, et al. Airspace macrophages and monocytes exist in transcriptionally distinct subsets in healthy adults[J]. Am J Respir Crit Care Med, 2021, 203(8): 946–956. doi: 10.1164/rccm.202005-1989OC
    [14]
    Kwan PKW, Periaswamy B, De Sessions PF, et al. A blood RNA transcript signature for TB exposure in household contacts[J]. BMC Infect Dis, 2020, 20(1): 403. doi: 10.1186/s12879-020-05116-1
    [15]
    Dheda K, Lenders L, Srivastava S, et al. Spatial network mapping of pulmonary multidrug-resistant tuberculosis cavities using RNA sequencing[J]. Am J Respir Crit Care Med, 2019, 200(3): 370–380. doi: 10.1164/rccm.201807-1361OC
    [16]
    Yuan GC, Cai L, Elowitz M, et al. Challenges and emerging directions in single-cell analysis[J]. Genome Biol, 2017, 18(1): 84. doi: 10.1186/s13059-017-1218-y
    [17]
    Cai Y, Dai Y, Wang Y, et al. Single-cell transcriptomics of blood reveals a natural killer cell subset depletion in tuberculosis[J]. eBioMedicine, 2020, 53: 102686. doi: 10.1016/j.ebiom.2020.102686
    [18]
    Nathan A, Beynor JI, Baglaenko Y, et al. Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease[J]. Nat Immunol, 2021, 22(6): 781–793. doi: 10.1038/s41590-021-00933-1
    [19]
    Cronan MR, Hughes EJ, Brewer WJ, et al. A non-canonical type 2 immune response coordinates tuberculous granuloma formation and epithelialization[J]. Cell, 2021, 184(7): 1757–1774.e14. doi: 10.1016/j.cell.2021.02.046
    [20]
    Morrison H, McShane H. Local pulmonary immunological biomarkers in tuberculosis[J]. Front Immunol, 2021, 12: 640916. doi: 10.3389/fimmu.2021.640916
    [21]
    Guler R, Ozturk M, Sabeel S, et al. Targeting molecular inflammatory pathways in granuloma as host-directed therapies for tuberculosis[J]. Front Immunol, 2021, 12: 733853. doi: 10.3389/fimmu.2021.733853
    [22]
    Yang L, Hu X, Chai X, et al. Opportunities for overcoming tuberculosis: emerging targets and their inhibitors[J]. Drug Discov Today, 2022, 27(1): 326–336. doi: 10.1016/j.drudis.2021.09.003
    [23]
    Eurosurveillance Editorial Team. WHO revised definitions and reporting framework for tuberculosis[J]. Euro Surveill, 2013, 18(16): 20455. doi: 10.2807/ese.18.16.20455-en
    [24]
    Hardoon DR, Szedmak S, Shawe-Taylor J. Canonical correlation analysis: an overview with application to learning methods[J]. Neural Comput, 2004, 16(12): 2639–2664. doi: 10.1162/0899766042321814
    [25]
    Liao M, Liu Y, Yuan J, et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19[J]. Nat Med, 2020, 26(6): 842–844. doi: 10.1038/s41591-020-0901-9
    [26]
    Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data[J]. Cell, 2019, 177(7): 1888–1902.e21. doi: 10.1016/j.cell.2019.05.031
    [27]
    Becht E, Mcinnes L, Healy J, et al. Dimensionality reduction for visualizing single-cell data using UMAP[J]. Nat Biotechnol, 2019, 37(1): 38–44. doi: 10.1038/nbt.4314
    [28]
    Finak G, McDavid A, Yajima M, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data[J]. Genome Biol, 2015, 16: 278. doi: 10.1186/s13059-015-0844-5
    [29]
    Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes[J]. Nucleic Acids Res, 2000, 28(1): 27–30. doi: 10.1093/nar/28.1.27
    [30]
    Huang D, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[J]. Nat Protoc, 2009, 4(1): 44–57. doi: 10.1038/nprot.2008.211
    [31]
    Qiu X, Mao Q, Tang Y, et al. Reversed graph embedding resolves complex single-cell trajectories[J]. Nat Methods, 2017, 14(10): 979–982. doi: 10.1038/nmeth.4402
    [32]
    Mao Q, Wang L, Tsang IW, et al. Principal graph and structure learning based on reversed graph embedding[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(11): 2227–2241. doi: 10.1109/TPAMI.2016.2635657
    [33]
    Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using CellChat[J]. Nat Commun, 2021, 12(1): 1088. doi: 10.1038/s41467-021-21246-9
    [34]
    Zhang J, Guan M, Wang Q, et al. Single-cell transcriptome-based multilayer network biomarker for predicting prognosis and therapeutic response of gliomas[J]. Brief Bioinform, 2020, 21(3): 1080–1097. doi: 10.1093/bib/bbz040
    [35]
    Cheng J, Zhang J, Wu Z, et al. Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19[J]. Brief Bioinform, 2021, 22(2): 988–1005. doi: 10.1093/bib/bbaa327
    [36]
    Zhao F, Xuan Z, Liu L, et al. TRED: a Transcriptional regulatory element database and a platform for in silico gene regulation studies[J]. Nucleic Acids Res, 2005, 33(S1): D103–D107. doi: 10.1093/nar/gki004
    [37]
    Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life[J]. Nucleic Acids Res, 2015, 43(D1): D447–D452. doi: 10.1093/nar/gku1003
    [38]
    Türei D, Korcsmáros T, Saez-Rodriguez J. OmniPath: guidelines and gateway for literature-curated signaling pathway resources[J]. Nat Methods, 2016, 13(12): 966–967. doi: 10.1038/nmeth.4077
    [39]
    Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks[J]. Genome Res, 2003, 13(11): 2498–2504. doi: 10.1101/gr.1239303
    [40]
    Wauters E, Van Mol P, Garg AD, et al. Discriminating mild from critical COVID-19 by innate and adaptive immune single-cell profiling of bronchoalveolar lavages[J]. Cell Res, 2021, 31(3): 272–290. doi: 10.1038/s41422-020-00455-9
    [41]
    Refai A, Gritli S, Barbouche MR, et al. Mycobacterium tuberculosis virulent factor ESAT-6 drives macrophage differentiation toward the pro-inflammatory M1 phenotype and subsequently switches it to the anti-inflammatory M2 phenotype[J]. Front Cell Infect Microbiol, 2018, 8: 327. doi: 10.3389/fcimb.2018.00327
    [42]
    Zilionis R, Engblom C, Pfirschke C, et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species[J]. Immunity, 2019, 50(5): 1317–1334.e10. doi: 10.1016/j.immuni.2019.03.009
    [43]
    Zhang H, Liu Y, Cao X, et al. Nrf2 promotes inflammation in early myocardial ischemia-reperfusion via recruitment and activation of macrophages[J]. Front Immunol, 2021, 12: 763760. doi: 10.3389/fimmu.2021.763760
    [44]
    Zhao Y, Liu P, Xin Z, et al. Biological characteristics of severe combined immunodeficient mice produced by CRISPR/Cas9-mediated Rag2 and IL2rg mutation[J]. Front Genet, 2019, 10: 401. doi: 10.3389/fgene.2019.00401
    [45]
    Leemans JC, Florquin S, Heikens M, et al. CD44 is a macrophage binding site for Mycobacterium tuberculosis that mediates macrophage recruitment and protective immunity against tuberculosis[J]. J Clin Invest, 2003, 111(5): 681–689. doi: 10.1172/JCI200316936
    [46]
    Dubey N, Khan MZ, Kumar S, et al. Mycobacterium tuberculosis peptidyl prolyl isomerase a interacts with host integrin receptor to exacerbate disease progression[J]. J Infect Dis, 2021, 224(8): 1383–1393. doi: 10.1093/infdis/jiab081
    [47]
    Wen D, Cui J, Li P, et al. Syndecan-4 assists Mycobacterium tuberculosis entry into lung epithelial cells by regulating the Cdc42, N-WASP, and Arp2/3 signaling pathways[J]. Microbes Infect, 2022, 104931. doi: 10.1016/j.micinf.2022.104931
    [48]
    Shi L, Eugenin EA, Subbian S. Immunometabolism in tuberculosis[J]. Front Immunol, 2016, 7: 150. doi: 10.3389/fimmu.2016.00150
    [49]
    Yu YA, Hotten DF, Malakhau Y, et al. Flow cytometric analysis of myeloid cells in human blood, bronchoalveolar lavage, and lung tissues[J]. Am J Respir Cell Mol Biol, 2016, 54(1): 13–24. doi: 10.1165/rcmb.2015-0146OC
    [50]
    Mould KJ, Jackson ND, Henson PM, et al. Single cell RNA sequencing identifies unique inflammatory airspace macrophage subsets[J]. JCI Insight, 2019, 4(5): e126556. doi: 10.1172/jci.insight.126556
    [51]
    Lastrucci C, Bénard A, Balboa L, et al. Tuberculosis is associated with expansion of a motile, permissive and immunomodulatory CD16+ monocyte population via the IL-10/STAT3 axis[J]. Cell Res, 2015, 25(12): 1333–1351. doi: 10.1038/cr.2015.123
    [52]
    Weiss G, Schaible UE. Macrophage defense mechanisms against intracellular bacteria[J]. Immunol Rev, 2015, 264(1): 182–203. doi: 10.1111/imr.12266
    [53]
    Kreuger J, Phillipson M. Targeting vascular and leukocyte communication in angiogenesis, inflammation and fibrosis[J]. Nat Rev Drug Discov, 2016, 15(2): 125–142. doi: 10.1038/nrd.2015.2
    [54]
    Gupta S, Rodriguez GM. Mycobacterial extracellular vesicles and host pathogen interactions[J]. Pathog Dis, 2018, 76(4): fty031. doi: 10.1093/femspd/fty031
    [55]
    Arya R, Dabral D, Faruquee HM, et al. Serum small extracellular vesicles proteome of tuberculosis patients demonstrated deregulated immune response[J]. Proteomics Clin Appl, 2020, 14(1): 1900062. doi: 10.1002/prca.201900062
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