Single-Cell Transcriptomics of Mtb/HIV Co-Infection.
Single-Cell Transcriptomics of Mtb/HIV Co-Infection.
复制标题
作者：
结核病（TB）和人类免疫缺陷病毒（HIV）合并感染继续构成重大的医疗负担。结核病期间的HIV合并感染使宿主易于重新激活潜伏性结核病感染（LTBI），恶化疾病状况和死亡率。缺乏LTBI再活化的生物标志物和/或免疫相关的转录特征来区分活动性TB与LTBI并预测HIV共感染后的TB再活化。使用下一代基于测序的技术表征单个细胞促进了关于传染病的新生物学发现，包括结核病和艾滋病发病机制。与提供批量评估的更传统的测序技术相比，单细胞RNA测序（scRNA-seq）可以揭示复杂和新的细胞类型，并识别更高分辨率的细胞异质性。本文将总结使用scRNA-seq定义TB和HIV感染免疫图谱的进展，包括宿主-病原体相互作用，HIV发病机制的异质性以及用于建模疾病的动物模型。本次审查还将讨论弥合单一感染与合并感染的疾病结果之间的差距所需的工具。最后，它将详细说明单细胞测序在人类结核病/艾滋病诊断中的转化益处。
Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) co-infection continues to pose a significant healthcare burden. HIV co-infection during TB predisposes the host to the reactivation of latent TB infection (LTBI), worsening disease conditions and mortality. There is a lack of biomarkers of LTBI reactivation and/or immune-related transcriptional signatures to distinguish active TB from LTBI and predict TB reactivation upon HIV co-infection. Characterizing individual cells using next-generation sequencing-based technologies has facilitated novel biological discoveries about infectious diseases, including TB and HIV pathogenesis. Compared to the more conventional sequencing techniques that provide a bulk assessment, single-cell RNA sequencing (scRNA-seq) can reveal complex and new cell types and identify more high-resolution cellular heterogeneity. This review will summarize the progress made in defining the immune atlas of TB and HIV infections using scRNA-seq, including host-pathogen interactions, heterogeneity in HIV pathogenesis, and the animal models employed to model disease. This review will also address the tools needed to bridge the gap between disease outcomes in single infection vs. co-infection. Finally, it will elaborate on the translational benefits of single-cell sequencing in TB/HIV diagnosis in humans.
登录
查看更多内容
DOI：
10.1038/s41579-022-00763-4
发表时间：
2022-12
期刊：
Nature reviews. Microbiology
影响因子：
--
作者：
通讯作者：
--
影响因子：
8.8
作者：
Akter, Sadia;Chauhan, Kuldeep S.;Dunlap, Micah D.;Choreno-Parra, JoseAlberto;Lu, Lan;Esaulova, Ekaterina;Zuniga, Joaquin;Artyomov, Maxim N.;Kaushal, Deepak;Khader, Shabaana A.
通讯作者：
Khader, Shabaana A.
影响因子：
6.7
作者：
Bui, John K.;Halvas, Elias K.;Mellors, John W.
通讯作者：
Mellors, John W.
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
3.8
作者：
Chapman, ALN;Munkanta, M;Lalvani, A
通讯作者：
Lalvani, A