Multiplexed imaging of human tuberculosis granulomas uncovers immunoregulatory features conserved across tissue and blood

Abstract

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that is distinctly characterized by granuloma formation within infected tissues. Granulomas are dynamic and organized immune cell aggregates that limit dissemination, but can also hinder bacterial clearance. Consequently, outcome in TB is influenced by how granuloma structure and composition shift the balance between these two functions. To date, our understanding of what factors drive granuloma function in humans is limited. With this in mind, we used Multiplexed Ion Beam Imaging by Time-of-Flight (MIBI-TOF) to profile 37 proteins in tissues from thirteen patients with active TB disease from the U.S. and South Africa. With this dataset, we constructed a comprehensive tissue atlas where the lineage, functional state, and spatial distribution of 19 unique cell subsets were mapped onto eight phenotypically-distinct granuloma microenvironments. This work revealed an immunosuppressed microenvironment specific to TB granulomas with spatially coordinated co-expression of IDO1 and PD-L1 by myeloid cells and proliferating regulatory T cells. Interestingly, this microenvironment lacked markers consistent with T-cell activation, supporting a myeloid-mediated mechanism of immune suppression. We observed similar trends in gene expression of immunoregulatory proteins in a confirmatory transcriptomic analysis of peripheral blood collected from over 1500 individuals with latent or active TB infection and healthy controls across 29 cohorts spanning 14 countries. Notably, PD-L1 gene expression was found to correlate with TB progression and treatment response, supporting its potential use as a blood-based biomarker. Taken together, this study serves as a framework for leveraging independent cohorts and complementary methodologies to understand how local and systemic immune responses are linked in human health and disease.

Multimodal profiling of lung granulomas reveals cellular correlates of tuberculosis control

“…Subcluster 11 was relatively abundant (2.4% of granuloma cells, Table S4C) and was characterized by expression of transcripts associated with cellular proliferation (MKI67, STMN1, and TOP2A) ( Figure 3C-D, Table S8B), consistent with published data that T cell proliferation occurs within NHP and human granulomas (Gideon et al, 2015;McCaffrey et al, 2020;Ohtani, 2013;Phuah et al, 2016;Phuah et al, 2012;Wong et al, 2018). Subcluster 12, representing 0.6% of granuloma cells, is characterized by enrichment of genes associated with nuclear speckles and splicing factors such as PNISR and SRRM2 ( Figure 3C), the latter of which has been associated with alternate splicing in Parkinson disease (Shehadeh et al, 2010) and has a critical role in organization of 3D genome (Hu et al, 2019).…”

Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma

“…The workflow outlined in Figure 1 enabled high-dimensional, subcellular imaging of dozens of proteins that recapitulated the tissue architecture observed in H&E ( Figure 2A). Multiplexed imaging data were processed with a low-level pipeline prior to singlecell segmentation ( Figure 2B, Figure S2B) (Keren et al, 2018;McCaffrey et al, 2020;Moen et al, 2019;Valen et al, 2016), which identified on average ~924 cells in each FOV (sd = 317). To determine cell location with respect to canonical histological features, we demarcated duct, stroma, and myoepithelial regions of each image based on combinatorial marker expression ( Figure 2B bottom-right).…”

“…Antibodies were conjugated to isotopic metal reporters as described previously (Keren et al, 2018;McCaffrey et al, 2020). Following conjugation antibodies were diluted in Candor PBS Antibody Stabilization solution (Candor Bioscience).…”

“…A spatial enrichment approached was used as previously described (Keren et al, 2018(Keren et al, , 2019McCaffrey et al, 2020) to identify patterns of protein enrichment or exclusion across all protein pairs. HH3 was excluded from the analysis.…”

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

“…This diversity of data allows models trained on TissueNet to handle data from many different experimental setups and biological questions. The images included in TissueNet were acquired from the published and unpublished works of labs who routinely perform tissue imaging [44][45][46][47][48][49][50][51] . Thus, while this first release of TissueNet encompasses the tissue types most commonly analyzed by the community, we expect that subsequent versions of TissueNet will be expanded to include less-studied organs.…”