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Imaging Architecture of Granulomas Induced by Mycobacterium tuberculosis Infections with Single-Molecule FISH

View ORCID ProfileRanjeet Kumar, View ORCID ProfileAfsal Kolloli, View ORCID ProfileSelvakumar Subbian, View ORCID ProfileDeepak Kaushal, View ORCID ProfileLanbo Shi, View ORCID ProfileSanjay Tyagi
doi: https://doi.org/10.1101/2023.02.02.526702
Ranjeet Kumar
*Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, New Jersey,
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Afsal Kolloli
*Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, New Jersey,
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Selvakumar Subbian
*Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, New Jersey,
†Department of Medicine, New Jersey Medical School, Rutgers University, Newark, New Jersey
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Deepak Kaushal
‡Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, Texas
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Lanbo Shi
*Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, New Jersey,
†Department of Medicine, New Jersey Medical School, Rutgers University, Newark, New Jersey
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Sanjay Tyagi
*Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, New Jersey,
†Department of Medicine, New Jersey Medical School, Rutgers University, Newark, New Jersey
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  • For correspondence: sanjay.tyagi@rutgers.edu
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Abstract

Granulomas are an important hallmark of Mycobacterium tuberculosis (Mtb) infection. They are organized and dynamic structures created by an assembly of immune cells around the sites of infection in the lungs to locally restrict the bacterial growth and the host’s inflammatory responses. The cellular architecture of granulomas is traditionally studied by immunofluorescence labeling of phenotypic surface markers. However, very few antibodies are available for model animals used in tuberculosis research, such as non-human primates and rabbits; secreted immunological markers such as cytokines cannot be imaged in situ using antibodies; and traditional phenotypic surface markers do not provide sufficient resolution for the detection of many subtypes and differentiation states of immune cells. Using single-molecule fluorescent in situ hybridization (smFISH) and its derivatives, amplified smFISH (ampFISH) and iterative smFISH, we developed a platform for imaging mRNAs encoding immune markers in rabbit and macaque tuberculosis granulomas. Multiplexed imaging for several mRNA and protein markers was followed by quantitative measurement of expression of these markers in single cells in situ. A quantitative analysis of combinatorial expressions of these markers allowed us to classify the cells into several subtypes and chart their distributions within granulomas. For one mRNA target, HIF-1α, we were able to image its mRNA and protein in the same cells, demonstrating the specificity of probes. This method paves the way for defining granular differentiation states and cell subtypes from transcriptomic data, identifying key mRNA markers for these cell subtypes, and then locating the cells in the spatial context of granulomas.

Competing Interest Statement

Rutgers University receives royalties from the sale of prelabeled sm-FISH probes by LGC Biosearch Technologies, which markets them as Stellaris probes. A portion of these proceeds is distributed to S.T.'s laboratory for research support and to him personally. These proceeds do not influence the conclusions of this research.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 03, 2023.
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Imaging Architecture of Granulomas Induced by Mycobacterium tuberculosis Infections with Single-Molecule FISH
Ranjeet Kumar, Afsal Kolloli, Selvakumar Subbian, Deepak Kaushal, Lanbo Shi, Sanjay Tyagi
bioRxiv 2023.02.02.526702; doi: https://doi.org/10.1101/2023.02.02.526702
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Imaging Architecture of Granulomas Induced by Mycobacterium tuberculosis Infections with Single-Molecule FISH
Ranjeet Kumar, Afsal Kolloli, Selvakumar Subbian, Deepak Kaushal, Lanbo Shi, Sanjay Tyagi
bioRxiv 2023.02.02.526702; doi: https://doi.org/10.1101/2023.02.02.526702

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