A Multiplexed Imaging Workflow for Studying the Single Cell Structure of Tuberculosis Granulomas in Non-Human Primates


Identification: Delmastro-Alea


Description

A Multiplexed Imaging Workflow for Studying the Single Cell Structure of Tuberculosis Granulomas in Non-Human Primates

Alea Delmastro1, Erin McCaffrey1, Joshua Mattila2, Noah Greenwald1, Marc Bosse1, and Michael Angelo1

1Stanford Dept. of Pathology; 2Dept. of Infectious Diseases and Microbiology, U. of Pittsburgh

An estimated one-third of the world’s population is infected with tuberculosis (TB). TB infection drives formation of organized immune cell aggregates known as granulomas. It is thought that immune responses in granulomas drive disparate infection outcomes, but histological correlates of protective immunity in TB are not understood. The inability to routinely sample human granulomas and fundamental differences between human and mouse TB presentation have hampered research in this area. Alternatively, macaques are experimentally-tractable non-human primates that recapitulate human TB. Here, we describe an experimental workflow for multiplexed imaging of macaque granulomas. Tissue pathobiology is typically studied with immunohistochemical (IHC) approaches; however, these approaches are limited to analysis of 1-3 proteins at once, preventing acquisition of high-dimensional single cell and spatial information. Multiplexed ion beam imaging (MIBI) overcomes this limitation by simultaneously imaging over 40 antibodies in formalin-fixed paraffin-embedded tissues. Our pipeline combines MIBI, deep-learning based segmentation, and single cell analyses to elucidate the composition and structure of granulomas and describe factors that differentiate granulomas within and between individuals. We validated >90 reagents via chromogenic IHC for cross-reactivity in macaque tissues with >60 targets optimized for MIBI. We validated our MIBI workflow and obtained proof-of-concept preliminary results in rhesus macaque splenic granulomas. We applied the FlowSOM clustering algorithm to identify cell lineages and quantify expression of key functional markers, such as IDO1, granzymeB, and Ki67, across populations. In the future, we will apply this workflow to a cohort of cynomolgus macaque lung granulomas across various stages of TB progression. We will assess the single cell structure of granulomas as it relates to bacterial load, infection time, and the inflammatory profile of granulomas. The results from this study will enable deep characterization of the heterogeneous immune response to TB and may identify immune pathways that could be critical to targeted therapy.

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