Application of single-cell transcriptome sequencing technology in tuberculosis research

doi:10.19982/j.issn.1000-6621.20240512

Abstract

Abstract:

Tuberculosis, caused by Mycobacterium tuberculosis (MTB) infection, is one of the infectious diseases with the highest mortality rates globally, posing a severe threat to public health. The cellular immune response mechanisms triggered by MTB infection in the host are not yet fully understood. Single-cell transcriptome sequencing technology can reveal cellular heterogeneity at the resolution of individual cells, which can help researchers better understand the pathogenesis of tuberculosis and provide strong support for the development of targeted drugs, new diagnostic markers, and vaccines. This article reviewed the application of this technology in the exploration of tuberculosis pathogenesis, the analysis of cell subgroup heterogeneity in different clinical samples, and the disclosure of immune microenvironment, and discussed the limitations and future challenges of this technology, and looked forward to its potential application in tuberculosis research.

Key words: Tuberculosis, RNA, Immunity, cellular, Single cell RNA sequencing

CLC Number:

Shi Hongyu, Zhang Guoliang, Xiao Guohui. Application of single-cell transcriptome sequencing technology in tuberculosis research[J]. Chinese Journal of Antituberculosis, 2025, 47(3): 362-368. doi: 10.19982/j.issn.1000-6621.20240512

References 38

[1]	Villar-Hernández R, Ghodousi A, Konstantynovska O, et al. Tuberculosis: current challenges and beyond. Breathe(Sheff), 2023, 19(1): 220166. doi:10.1183/20734735.0166-2022.
[2]	World Health Organization. Global tuberculosis report 2024. Geneva: World Health Organization, 2024.
[3]	Tang X, Huang Y, Lei J, et al. The single-cell sequencing: new developments and medical applications. Cell Biosci, 2019, 9: 53. doi:10.1186/s13578-019-0314-y. pmid: 31391919
[4]	Ke M, Elshenawy B, Sheldon H, et al. Single cell RNA-sequencing: A powerful yet still challenging technology to study cellular heterogeneity. Bioessays, 2022, 44(11): e2200084. doi:10.1002/bies.202200084.
[5]	Kashima Y, Sakamoto Y, Kaneko K, et al. Single-cell sequencing techniques from individual to multiomics analyses. Exp Mol Med, 2020, 52(9): 1419-1427. doi:10.1038/s12276-020-00499-2.
[6]	Gupta UD, Katoch VM. Animal models of tuberculosis. Tuberculosis, 2005, 85(5-6): 277-293. doi:10.1016/j.tube.2005.08.008. pmid: 16249122
[7]	Tiwari S, Casey R, Goulding CW, et al. Infect and inject: how Mycobacterium tuberculosis exploits its major virulence-associated type Ⅶ secretion system, ESX-1. Microbiol Spectr, 2019, 7(3): 10.1128/microbiolspec.bai-0024-2019. doi:10.1128/microbiolspec.BAI-0024-2019.
[8]	Zheng W, Borja M, Dorman L, et al. How Mycobacterium tuberculosis builds a home: Single-cell analysis reveals M.tuberculosis ESX-1-mediated accumulation of anti-inflammatory macrophages in infected mouse lungs. bioRxiv, 2024: 2024.04.20.590421. doi:10.1101/2024.04.20.590421.
[9]	Bobba S, Howard NC, Das S, et al. Mycobacterium tuberculosis infection drives differential responses in the bone marrow hematopoietic stem and progenitor cells. Infect Immun, 2023, 91(10): e00201-23. doi:10.1128/iai.00201-23.
[10]	Akter S, Chauhan KS, Dunlap MD, et al. Mycobacterium tuberculosis infection drives a type Ⅰ IFN signature in lung lymphocytes. Cell Rep, 2022, 39(12): 110983. doi:10.1016/j.celrep.2022.110983.
[11]	Zhang X, Zhao Z, Wu Q, et al. Single-cell analysis reveals changes in BCG vaccine-injected mice modeling tuberculous meningitis brain infection. Cell Rep, 2023, 42(3): 112177. doi:10.1016/j.celrep.2023.112177.
[12]	Rubin EJ. The granuloma in tuberculosis—friend or foe?. N Engl J Med, 2009, 360(23): 2471-2473. doi:10.1056/NEJMcibr0902539.
[13]	Scanga CA, Flynn JL. Modeling tuberculosis in nonhuman primates. Cold Spring Harb Perspect Med, 2014, 4(12): a018564. doi:10.1101/cshperspect.a018564.
[14]	Gideon HP, Hughes TK, Tzouanas CN, et al. Multimodal profiling of lung granulomas in macaques reveals cellular correlates of tuberculosis control. Immunity, 2022, 55(5): 827-846.e10. doi:10.1016/j.immuni.2022.04.004. pmid: 35483355
[15]	Hunter L, Ruedas-Torres I, Agulló-Ros I, et al. Comparative pathology of experimental pulmonary tuberculosis in animal models. Front Vet Sci, 2023, 10: 1264833. doi:10.3389/fvets.2023.1264833.
[16]	Williams A, Orme IM. Animal models of tuberculosis: an overview. Microbiol Spectr, 2016, 4(4). doi:10.1128/microbiolspec.TBTB2-0004-2015.
[17]	Alfaro JA, Bohländer P, Dai M, et al. The emerging landscape of single-molecule protein sequencing technologies. Nat Methods, 2021, 18(6): 604-617. doi:10.1038/s41592-021-01143-1. pmid: 34099939
[18]	Pan J, Zhang X, Xu J, et al. Landscape of exhausted T cells in tuberculosis revealed by single-cell sequencing. Microbiol Spectr, 2023, 11(2): e02839-22. doi:10.1128/spectrum.02839-22.
[19]	Jiang J, Cao Z, Li B, et al. Disseminated tuberculosis is associated with impaired T cell immunity mediated by non-canonical NF-κB pathway. J Infect, 2024, 89(3): 106231. doi:10.1016/j.jinf.2024.106231.
[20]	Jiang J, Cao Z, Xiao L, et al. Single-cell profiling identifies T cell subsets associated with control of tuberculosis dissemination. Clin Immunol, 2023, 248: 109266. doi:10.1016/j.clim.2023.109266.
[21]	Pisu D, Huang L, Narang V, et al. Single cell analysis of M.tuberculosis phenotype and macrophage lineages in the infected lung. J Exp Med, 2021, 218(9): e20210615. doi:10.1084/jem.20210615.
[22]	Gouzy A. Use of single-cell technology to improve our understanding of the role of TLR2 in macrophage-Mycobacterium tuberculosis interaction. mSystems, 2023, 8(5): e0073023. doi:10.1128/msystems.00730-23.
[23]	Jani C, Solomon SL, Peters JM, et al. TLR2 is non-redundant in the population and subpopulation responses to Mycobacterium tuberculosis in macrophages and in vivo. mSystems, 2023, 8(4): e0005223. doi:10.1128/msystems.00052-23.
[24]	Shekarkar Azgomi M, Badami GD, Lo Pizzo M, et al. Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data Reveals Memory-like NK Cell Subset Associated with Mycobacterium tuberculosis Latency. Cells, 2024, 13(4): 293. doi:10.3390/cells13040293.
[25]	Cai Y, Dai Y, Wang Y, et al. Single-cell transcriptomics of blood reveals a natural killer cell subset depletion in tuberculosis. EBioMedicine, 2020, 53: 102686. doi:10.1016/j.ebiom.2020.102686.
[26]	Guo Q, Zhong Y, Wang Z, et al. Single-cell transcriptomic landscape identifies the expansion of peripheral blood monocytes as an indicator of HIV-1-TB co-infection. Cell Insight, 2022, 1(1): 100005. doi:10.1016/j.cellin.2022.100005.
[27]	Ahmad M, Ibrahim WH, Sarafandi SA, et al. Diagnostic value of bronchoalveolar lavage in the subset of patients with negative sputum/smear and mycobacterial culture and a suspicion of pulmonary tuberculosis. Int J Infect Dis, 2019, 82: 96-101. doi:10.1016/j.ijid.2019.03.021. pmid: 30904678
[28]	Chen Q, Hu C, Lu W, et al. Characteristics of alveolar macrophages in bronchioalveolar lavage fluids from active tuberculosis patients identified by single-cell RNA sequencing. J Biomed Res, 2022, 36(3): 167-180. doi:10.7555/JBR.36.20220007.
[29]	Yang Q, Qi F, Ye T, et al. The interaction of macrophages and CD 8 T cells in bronchoalveolar lavage fluid is associated with latent tuberculosis infection. Emerg Microbes Infect, 2023, 12(2): 2239940. doi:10.1080/22221751.2023.2239940.
[30]	Xiao G, Huang W, Zhong Y, et al. Uncovering the Bronchoalveolar Single-Cell Landscape of Patients With Pulmonary Tuberculosis With Human Immunodeficiency Virus Type 1 Coinfection. J Infect Dis, 2024, 230(3): e524-e535. doi:10.1093/infdis/jiae042.
[31]	McNally E, Ross C, Gleeson LE. The tuberculous pleural effusion. Breathe(Sheff), 2023, 19(4): 230143. doi:10.1183/20734735.0143-2023.
[32]	Cai Y, Wang Y, Shi C, et al. Single-cell immune profiling reveals functional diversity of T cells in tuberculous pleural effusion. J Exp Med, 2022, 219(3): e20211777. doi:10.1084/jem.20211777.
[33]	Yang X, Yan J, Xue Y, et al. Single-cell profiling reveals distinct immune response landscapes in tuberculous pleural effusion and non-TPE. Front Immunol, 2023, 14: 1191357. doi:10.3389/fimmu.2023.1191357.
[34]	Mo S, Shi C, Cai Y, et al. Single-cell transcriptome reveals highly complement activated microglia cells in association with pediatric tuberculous meningitis. Front Immunol, 2024, 15: 1387808. doi:10.3389/fimmu.2024.1387808.
[35]	Agashe VM, Johari AN, Shah M, et al. Diagnosis of osteoarticular tuberculosis: perceptions, protocols, practices, and priorities in the endemic and non-endemic areas of the World—A WAIOT view. Microorganisms, 2020, 8(9): 1312. doi:10.3390/microorganisms8091312.
[36]	Jiang Y, Zhang X, Wang B, et al. Single-cell transcriptomic analysis reveals a decrease in the frequency of macrophage-RGS1^high subsets in patients with osteoarticular tuberculosis. Mol Med, 2024, 30(1): 118. doi:10.1186/s10020-024-00886-9. pmid: 39123125
[37]	Mahmoudi S, García MJ, Drain PK. Current approaches for diagnosis of subclinical pulmonary tuberculosis, clinical implications and future perspectives: a scoping review. Exp Rev Clin Immunol, 2024, 20(7): 715-726. doi:10.1080/1744666X.2024.2326032.
[38]	Kendall EA, Shrestha S, Dowdy DW. The epidemiological importance of subclinical tuberculosis. A critical reappraisal. Am J Respir Crit Care Med, 2021, 203(2): 168-174. doi:10.1164/rccm.202006-2394PP.