Multimodal profiling of lung granulomas in macaques reveals cellular correlates of tuberculosis control
Name
1-s2.0-S1074761322001753-main.pdf
Description
Published version
Size
4.8 MB
Format
Adobe PDF
Checksum (MD5)
2cae1a00e898e48d46ddeaffcfd79fd6
Author(s)
Shalek, Alex
Love, John
Berger, Bonnie
Date Issued
2022
Journal
Immunity
Publisher
Elsevier BV
Citation
Shalek, Alex, Love, John and Berger, Bonnie. 2022. "Multimodal profiling of lung granulomas in macaques reveals cellular correlates of tuberculosis control." Immunity, 55 (5).
Version
Final published version
Abstract
Mycobacterium tuberculosis lung infection results in a complex multicellular structure: the granuloma. In some granulomas, immune activity promotes bacterial clearance, but in others, bacteria persist and grow. We identified correlates of bacterial control in cynomolgus macaque lung granulomas by co-registering longitudinal positron emission tomography and computed tomography imaging, single-cell RNA sequencing, and measures of bacterial clearance. Bacterial persistence occurred in granulomas enriched for mast, endothelial, fibroblast, and plasma cells, signaling amongst themselves via type 2 immunity and wound-healing pathways. Granulomas that drove bacterial control were characterized by cellular ecosystems enriched for type 1-type 17, stem-like, and cytotoxic T cells engaged in pro-inflammatory signaling networks involving diverse cell populations. Granulomas that arose later in infection displayed functional characteristics of restrictive granulomas and were more capable of killing Mtb. Our results define the complex multicellular ecosystems underlying (lack of) granuloma resolution and highlight host immune targets that can be leveraged to develop new vaccine and therapeutic strategies for TB.
MIT Department
Koch Institute for Integrative Cancer Research at MIT. Laboratory for Multiscale Regenerative Technologies
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.IMMUNI.2022.04.004
