Single cell analysis of M. tuberculosis phenotype and macrophage lineages in the infected lung.
Single cell analysis of M. tuberculosis phenotype and macrophage lineages in the infected lung.
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DOI：
10.1084/jem.20210615
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发表时间：
2021-09-06
期刊：
影响因子：
--
通讯作者：
Russell DG
中科院分区：
文献类型：
--
作者：
Pisu D;Huang L;Narang V;Theriault M;Lê-Bury G;Lee B;Lakudzala AE;Mzinza DT;Mhango DV;Mitini-Nkhoma SC;Jambo KC;Singhal A;Mwandumba HC;Russell DG
通过scRNA-seq和ATAC-seq，Pisu等人表征了限制或促进M.结核病增长。人类气道中存在类似的巨噬细胞群体，细胞显示出表观遗传印记的证据。在这项研究中，我们详细介绍了一种新的方法，该方法将细菌健身荧光报告菌株与scRNA-seq相结合，同时获得每个感染细胞的宿主转录组，表面标记表达和细菌表型。这种方法有利于解剖M的功能异质性。肺结核感染的肺泡巨噬细胞（AM）和间质巨噬细胞（IM）。除了具有异质性细菌表型的三种不同的IM群体外，我们还鉴定了与应激细菌相关的促炎AM群。最后，我们表明，肺中的主要巨噬细胞群体在对感染的反应中受到表观遗传学的限制，而种间比较表明，大多数AM亚群在小鼠和人类之间是保守的。这种概念性方法很容易转移到其他传染病病原体，有可能增加对不同宿主细胞群在感染过程中所起作用的理解。
Through scRNA-seq and ATAC-seq, Pisu et al. characterize specific lung macrophage subsets that either restrict or promote M. tuberculosis growth. Comparable macrophage populations are present in the human airways, and cells show evidence of epigenetic imprinting. In this study, we detail a novel approach that combines bacterial fitness fluorescent reporter strains with scRNA-seq to simultaneously acquire the host transcriptome, surface marker expression, and bacterial phenotype for each infected cell. This approach facilitates the dissection of the functional heterogeneity of M. tuberculosis–infected alveolar (AMs) and interstitial macrophages (IMs) in vivo. We identify clusters of pro-inflammatory AMs associated with stressed bacteria, in addition to three different populations of IMs with heterogeneous bacterial phenotypes. Finally, we show that the main macrophage populations in the lung are epigenetically constrained in their response to infection, while inter-species comparison reveals that most AMs subsets are conserved between mice and humans. This conceptual approach is readily transferable to other infectious disease agents with the potential for an increased understanding of the roles that different host cell populations play during the course of an infection.
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影响因子：
64.5
作者：
Dixon SJ;Lemberg KM;Lamprecht MR;Skouta R;Zaitsev EM;Gleason CE;Patel DN;Bauer AJ;Cantley AM;Yang WS;Morrison B 3rd;Stockwell BR
通讯作者：
Stockwell BR
DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
16.6
作者：
Angelidis, Ilias;Simon, Lukas M.;Schiller, Herbert B.
通讯作者：
Schiller, Herbert B.
影响因子：
32.4
作者：
Evren, Elza;Ringqvist, Emma;Willinger, Tim
通讯作者：
Willinger, Tim
影响因子：
11.4
作者：
Gatbonton-Schwager, Tonibelle N.;Sadhukhan, Sushabhan;Tochtrop, Gregory P.
通讯作者：
Tochtrop, Gregory P.