Abstract
Granulomas are complex cellular structures composed predominantly of macrophages and lymphocytes that function to contain and kill invading pathogens. Here, we investigated the single-cell phenotypes associated with antimicrobial responses in human leprosy granulomas by applying single-cell and spatial sequencing to leprosy biopsy specimens. We focused on reversal reactions (RRs), a dynamic process whereby some patients with disseminated lepromatous leprosy (L-lep) transition toward self-limiting tuberculoid leprosy (T-lep), mounting effective antimicrobial responses. We identified a set of genes encoding proteins involved in antimicrobial responses that are differentially expressed in RR versus L-lep lesions and regulated by interferon-γ and interleukin-1β. By integrating the spatial coordinates of the key cell types and antimicrobial gene expression in RR and T-lep lesions, we constructed a map revealing the organized architecture of granulomas depicting compositional and functional layers by which macrophages, T cells, keratinocytes and fibroblasts can each contribute to the antimicrobial response.
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Code availability
The code used to analyze the data in this study is available at GitHub https://github.com/mafeiyang/leprosy_amg_network.
References
Gordon, S. Alternative activation of macrophages. Nat. Rev. Immunol. 3, 23–35 (2003).
Ridley, D. S. & Jopling, W. H. Classification of leprosy according to immunity. A five-group system. Int. J. Lepr. Other Mycobact. Dis. 34, 255–273 (1966).
Modlin, R. L., Hofman, F. M., Taylor, C. R. & Rea, T. H. T lymphocyte subsets in the skin lesions of patients with leprosy. J. Am. Acad. Dermatol. 8, 182–189 (1983).
Stenger, S. et al. Differential effects of cytolytic T cell subsets on intracellular infection. Science 276, 1684–1687 (1997).
Stenger, S. et al. An antimicrobial activity of cytolytic T cells mediated by granulysin. Science 282, 121–125 (1998).
Ochoa, M. T. et al. T-cell release of granulysin contributes to host defense in leprosy. Nat. Med. 7, 174–179 (2001).
Montoya, D. et al. Divergence of macrophage phagocytic and antimicrobial programs in leprosy. Cell Host Microbe 6, 343–353 (2009).
Cooper, C. L. et al. Analysis of naturally occurring delayed-type hypersensitivity reactions in leprosy by in situ hybridization. J. Exp. Med. 169, 1565–1581 (1989).
Yamamura, M. et al. Defining protective responses to pathogens: cytokine profiles in leprosy lesions. Science 254, 277–279 (1991).
Yamamura, M. et al. Cytokine patterns of immunologically mediated tissue damage. J. Immunol. 149, 1470–1475 (1992).
Teles, R. M. B. et al. Type I interferon suppresses type II interferon-triggered human anti-mycobacterial responses. Science 339, 1448–1453 (2013).
Liu, P. T. et al. Toll-like receptor triggering of a vitamin D-mediated human antimicrobial response. Science 311, 1770–1773 (2006).
Fabri, M. et al. Vitamin D is required for IFN-γ-mediated antimicrobial activity of human macrophages. Sci. Transl. Med. 3, 104ra102 (2011).
Ochoa, M. T. et al. Role of granulysin in immunity to leprosy. Lepr. Rev. 71, S115 (2000).
Dang, A. T. et al. IL-26 contributes to host defense against intracellular bacteria. J. Clin. Invest. 129, 1926–1939 (2019).
Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
Balin, S. J. et al. Human antimicrobial cytotoxic T lymphocytes, defined by NK receptors and antimicrobial proteins, kill intracellular bacteria. Sci. Immunol. 3, eaat7668 (2018).
Busch, M. et al. Lipoarabinomannan-responsive polycytotoxic T cells are associated with protection in human tuberculosis. Am. J. Respir. Crit. Care Med. 194, 345–355 (2016).
Cochain, C. et al. Single-cell RNA-seq reveals the transcriptional landscape and heterogeneity of aortic macrophages in murine atherosclerosis. Circ. Res. 122, 1661–1674 (2018).
Jaitin, D. A. et al. Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner. Cell 178, 686–698.e14 (2019).
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).
Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765.e17 (2017).
Xue, D., Tabib, T., Morse, C. & Lafyatis, R. Transcriptome landscape of myeloid cells in human skin reveals diversity, rare populations and putative DC progenitors. J. Dermatol. Sci. 97, 41–49 (2020).
Wang, E. C. E., Dai, Z., Ferrante, A. W., Drake, C. G. & Christiano, A. M. A subset of TREM2+ dermal macrophages secretes oncostatin M to maintain hair follicle stem cell quiescence and inhibit hair growth. Cell Stem Cell 24, 654–669.e6 (2019).
Xiong, X. et al. Landscape of intercellular crosstalk in healthy and NASH liver revealed by single-cell secretome gene analysis. Mol. Cell 75, 644–660.e5 (2019).
Esaulova, E. et al. The immune landscape in tuberculosis reveals populations linked to disease and latency. Cell Host Microbe 29, 165–178.e8 (2021).
Tabib, T., Morse, C., Wang, T., Chen, W. & Lafyatis, R. SFRP2/DPP4 and FMO1/LSP1 define major fibroblast populations in human skin. J. Invest. Dermatol. 138, 802–810 (2018).
He, H. et al. Single-cell transcriptome analysis of human skin identifies novel fibroblast subpopulation and enrichment of immune subsets in atopic dermatitis. J. Allergy Clin. Immunol. 145, 1615–1628 (2020).
Chen, Y., Rabson, A. B. & Gorski, D. H. MEOX2 regulates nuclear factor-κB activity in vascular endothelial cells through interactions with p65 and IκBβ. Cardiovasc. Res. 87, 723–731 (2010).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Wang, G., Li, X. & Wang, Z. APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res. 44, D1087–D1093 (2016).
Solé-Boldo, L. et al. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun. Biol. 3, 188 (2020).
Meller, S. et al. TH17 cells promote microbial killing and innate immune sensing of DNA via interleukin 26. Nat. Immunol. 16, 970–979 (2015).
Iyer, A. M. et al. Leprosy-specific B-cells within cellular infiltrates in active leprosy lesions. Hum. Pathol. 38, 1065–1073 (2007).
Ochoa, M. T. et al. A role for interleukin-5 in promoting increased immunoglobulin M at the site of disease in leprosy. Immunology 131, 405–414 (2010).
Montoya, D. J. et al. Dual RNA-seq of human leprosy lesions identifies bacterial determinants linked to host immune response. Cell Rep. 26, 3574–3585.e3 (2019).
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience 9, giaa151 (2020).
Teles, R. M. B. et al. Identification of a systemic interferon-γ inducible antimicrobial gene signature in leprosy patients undergoing reversal reaction. PLoS Negl. Trop. Dis. 13, e0007764 (2019).
Andrade, P. R. et al. The cell fate regulator NUPR1 is induced by Mycobacterium leprae via type I interferon in human leprosy. PLoS Negl. Trop. Dis. 13, e0007589 (2019).
Wang, H. et al. Cellular, molecular, and immunological characteristics of Langhans multinucleated giant cells programmed by IL-15. J. Invest. Dermatol. 140, 1824–1836.e7 (2020).
Waddell, S. J. et al. Dissecting interferon-induced transcriptional programs in human peripheral blood cells. PLoS ONE 5, e9753 (2010).
Inkeles, M. S. et al. Cell-type deconvolution with immune pathways identifies gene networks of host defense and immunopathology in leprosy. JCI Insight 1, e88843 (2016).
Martinez, A. N. et al. Molecular determination of Mycobacterium leprae viability by use of real-time PCR. J. Clin. Microbiol. 47, 2124–2130 (2009).
Acknowledgements
This work was supported in part by National Institutes of Health (NIH) grant nos. AI-22553, AR-073252, AR074302 and AR-40312 to R.L.M.; no. NIH-P30 AR075043 to J.E.G.; the Searle Scholars Program, the Beckman Young Investigator Program, a Sloan Fellowship in Chemistry, NIH grant no. 5U24AI118672 and the Bill and Melinda Gates Foundation to A.K.S.
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F.M., T.K.H., B.B., A.K.S., B.R.B., M.P. and R.L.M. conceptualized the study. F.M., T.K.H., R.M.B.T., P.R.A., B.J.A.S., O.P., L.C.T., T.D., M.H.W., M.P. and R.L.M. devised the methodology. F.M., T.K.H., R.M.B.T., P.R.A., B.J.A.S., T.D., M.T.O. and E.N.S. carried out the investigation. F.M., T.K.H., B.B., R.M.B.T., P.R.A., B.J.A.S., T.D. and M.H.W. carried out the formal analysis. F.M., T.K.H., R.M.B.T., P.R.A., B.J.A.S., M.H.W., A.O., M.L.I.-A., E.K., B.B., A.K.S., B.R.B., J.E.G., M.P. and R.L.M. wrote the manuscript. M.P. and R.L.M. supervised the study.
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A.K.S. reports compensation for consulting and/or scientific advisory board membership from Merck, Honeycomb Biotechnologies, Cellarity, Cogen Therapeutics, Orche Bio and Dahlia Biosciences. The other authors declare no competing interests.
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Peer review information Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
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Extended data
Extended Data Fig. 1 IFN-α/β and IFN-γ signature on CTL subtypes and co-expression of IFNG, GZMB, PRF1 and GNLY.
a, UMAP plot for 2,290 T cells colored by patient identities. b, Individual UMAP for the T cells from each patient. c, Enrichment analysis on differentially expressed genes (adjusted p value < 0.05) between TC1 (RR CTL) and TC2 (L-lep CTL) using IFN-α/β and IFN-γ specific genes identified in human PBMC. Dotted lines indicate (left) no enrichment or (right) the hypergeometric test p value of 0.05 (log p value = 1.3). d, UMAP plots showing co-expression of IFNG, GMZB, PRF1 and GNLY in RR CTL and amCTL. e, T cell subtype comparisons between scRNA-seq datasets from human leprosy skin biopsy specimens and from lung samples of a non-human primate model of tuberculosis. The color scale represents the z scores of the gene expression signature scores.
Extended Data Fig. 2 Dendritic cell subpopulations and comparison of macrophage sub-clusters.
a, UMAP plot for 991 myeloid cells colored by patient identities. b, Individual UMAP for the myeloid cells from each patient. c, UMAP plots showing CD1C and LAMP3 expression in ML0. Only few co-expression events were observed, indicating distinct dendritic cell subpopulations. d, Heatmap showing top differentially expressed genes between ML2 and ML4. ML3 expressed both ML2 and ML4 specific genes. e, UMAP plots showing TREM2 expression, APOE expression and TREM2 module score. The color scale represents the expression level of the genes or the level of the module score. f, Myeloid cell subtype comparisons between scRNA-seq data sets from human leprosy skin biopsy specimens and from lung samples of a non-human primate model of tuberculosis. The color scale represents the z scores of the gene expression signature scores.
Extended Data Fig. 3 Identification of keratinocyte subtypes.
a, UMAP plot for 3,748 keratinocytes colored by subtypes. b, UMAP plot for 3,748 keratinocytes colored by clinical forms. c, Heatmap showing marker genes for each keratinocyte subtype. The representative genes are labelled. d, Abundance composition across all samples for each keratinocyte subtype. e, UMAP plot for 1,010 fibroblasts colored by subtypes. f, Abundance composition across all samples for each fibroblast subtype. g, Heatmap showing marker genes for each fibroblast subtype. The representative genes are labelled. h, UMAP plot for 1,219 endothelial cells colored by subtypes. i, Abundance composition across all samples for each endothelial cell subtype. j, Dot plot showing 10 marker genes for each endothelial subtype. The color scale represents the scaled expression of the gene. The size of the dot represents the percentage of cells expressing the gene.
Extended Data Fig. 4 Pseudotime construction in macrophages and keratinocytes.
a, Pseudo-temporal trajectory colored by pseudotime (top) and by sub-cluster identity (bottom) for macrophage sub-cluster 2, 3 and 4. b, Heatmap showing six expression patterns along the macrophage pseudotime trajectory as depicted on the x axis. Representative genes regulated by IL1B and IFNG are labelled. c, Pseudo-temporal trajectory colored by pseudotime (top) and by sub-cluster identity (bottom) for keratinocyte sub-cluster 1, 2 and 3. d, Heatmap showing six expression patterns along the keratinocyte pseudotime trajectory as depicted on the x axis. Representative genes regulated by IL1B and IFNG are labelled.
Extended Data Fig. 5 Representative antimicrobial genes expressed by the major cell types.
a, Bar plot showing the expression of the antimicrobial genes in each RR subtype. The height of the bar represents the z score of the gene in each subtype. The dots represent the gene’s expression level in individual cells. b, Heat map showing z scores of antimicrobial genes in RR cell types. The red boxes indicate distinct sets of antimicrobial genes highly expressed in endothelial cells, fibroblasts, keratinocytes, myeloid cells and T cells.
Extended Data Fig. 6 Cell type composition and clustering of the RR6 and T-lep1 spatial-seq sample.
a, Scatter pie plot showing the cell type composition of the RR6 spatial-seq sample. Each spot is represented as a pie chart showing the relative proportion of the cell types. b, Heatmap showing the average cell type prediction score for each cluster of the RR6 spatial-seq sample. c, H & E staining of the T-lep1 biopsy used for spatial sequencing. Scale bar: 0.5 mm. d, Scatter pie plot showing the cell type composition of the T-lep1 spatial-seq sample. Each spot is represented as a pie chart showing the relative proportion of the cell types. e, Spatial plot for 1,154 spots colored by clusters, the coordinates of the spot correspond to the location in the T-lep1 sample tissue. f, Heatmap showing the average cell type prediction score for each cluster of the T-lep1 spatial-seq sample.
Extended Data Fig. 7 Spatial sequencing for two additional RR samples and subtypes location for the RR6 sample.
a, H & E staining of the RR7 biopsy used for spatial sequencing (top left), scale bar: 0.5 mm. Scatter pie plot showing the cell type composition of the RR7 spatial-seq sample. Each spot is represented as a pie chart showing the relative proportion of the cell types (top right). Spatial plot for 418 spots colored by clusters, the coordinates of the spot correspond to the location in the tissue (bottom left). Heatmap showing the average cell type prediction score for each cluster (bottom right). b, H & E staining of the RR8 biopsy used for spatial sequencing (top left), scale bar: 0.5 mm. Scatter pie plot showing the cell type composition of the RR8 spatial-seq sample. Each spot is represented as a pie chart showing the relative proportion of the cell types (top right). Spatial plot for 214 spots colored by clusters, the coordinates of the spot correspond to the location in the tissue (bottom left). Heatmap showing the average cell type prediction score for each cluster (bottom right). c, Subtype prediction scores for the RR6 spatial-seq sample. The spots in the corresponding cluster were used to plot the subtype scores. For example, the FB0 prediction score was plotted in the spots from cluster 5, which was annotated as fibroblasts.
Extended Data Fig. 8 Spatial distance between the FB0 and FB2 sub-clusters and the epidermis in the RR and T-lep samples.
a, Spatial plots showing the identified epidermis, FB0 and FB2 spots in the RR6 sample (left). Violin plot showing the distance of each FB0 and FB2 spot to the nearest epidermis spot. b, Spatial plots showing the identified epidermis, FB0 and FB2 spots in the T-lep1 sample (left). Violin plot showing the distance of each FB0 and FB2 spot to the nearest epidermis spot. c, Spatial plots showing the identified epidermis, FB0 and FB2 spots in the RR7 sample (left). Violin plot showing the distance of each FB0 and FB2 spot to the nearest epidermis spot. d, Spatial plots showing the identified epidermis, FB0 and FB2 spots in the RR8 sample (left). Violin plot showing the distance of each FB0 and FB2 spot to the nearest epidermis spot. e, Violin plot showing the distance of each FB0 and FB2 spot to the nearest epidermis spot in all three RR and the T-lep spatial-seq samples. The p value (1.32e-14) was calculated from a two-sided Wilcoxon rank sum test (152 FB0 spots vs 120 FB2 spots).
Extended Data Fig. 9 Immunohistochemistry validation of DEFB1, CXCL14 and TAC1 protein expression in the epidermis of three RR and three L-lep biopsy specimens.
a, Immunohistochemistry using monoclonal antibodies to DEFB1. The corresponding isotype controls were negative. Scale bar: 20 μm. Original magnification: x200. b, Immunohistochemistry using monoclonal antibodies to CXCL14. The corresponding isotype controls were negative. Scale bar: 20 μm. Original magnification: x200. c, Immunohistochemistry using monoclonal antibodies to TAC1. The corresponding isotype controls were negative. Scale bar: 20 μm. Original magnification: x200. d, Antimicrobial activity was determined by pretreatment of MDMs from healthy donors with 1 µM of DEFB1 (n = 3), CXCL14 (n = 4) and TAC1 (n = 5) for 30 minutes followed by M. leprae infection (MOI 5:1). Rifampicin (RIF, 10ug/ml) was used as positive control. M. leprae viability was determined by qPCR after 4 days and % antimicrobial activity was calculated by assigning 100% viability to the media control. All data represent the mean ± SEM. Statistical analyses were performed using mixed effects analysis with Dunnet’s multiple comparisons test in the GraphPad Prism 8 software. *p < 0.05, **p < 0.01, ****p < 0.0001.
Extended Data Fig. 10 TREM2 module score comparison in the RR, T-lep and L-lep spatial-seq samples.
a, H & E staining of the two L-lep biopsies used for spatial sequencing. The first tissue slide was folded in the dermis region, thus a replicate slide from the same tissue covering the dermis region was processed in the same spatial-seq run. Scale bar: 0.5 mm. b, Scatter pie plot showing the cell type composition of the two L-lep spatial-seq samples. Each spot is represented as a pie chart showing the relative proportion of the cell types. c, Spatial plots showing the TREM2 module score in the myeloid spots from all spatial-seq samples. The same color scale was applied across the samples. d, Violin plot showing the TREM2 module score in the myeloid spots for individual samples. The p value (<2.2e-16) was calculated from a two-sided Wilcoxon rank sum test (329 RR and T-lep spots vs 760 L-lep spots). e, Heatmap showing the number of neighbors for each pair of cell types in the RR and T-lep spatial-seq samples. The percentage of the numbers were calculated for each row, thus each row sum to 1. f, Heatmap showing the number of neighbors for each pair of cell types in the L-lep spatial-seq samples. The percentage of the numbers were calculated for each row, thus each row sum to 1.
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Ma, F., Hughes, T.K., Teles, R.M.B. et al. The cellular architecture of the antimicrobial response network in human leprosy granulomas. Nat Immunol 22, 839–850 (2021). https://doi.org/10.1038/s41590-021-00956-8
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DOI: https://doi.org/10.1038/s41590-021-00956-8
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