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Spatial transcriptomic characterization of COVID-19 pneumonitis identifies immune circuits related to tissue injury
Amy R. Cross, Carlos E. de Andrea, María Villalba-Esparza, Manuel F. Landecho, Lucia Cerundolo, Praveen Weeratunga, Rachel E. Etherington, Laura Denney, Graham Ogg, Ling-Pei Ho, Ian S.D. Roberts, Joanna Hester, Paul Klenerman, Ignacio Melero, Stephen N. Sansom, Fadi Issa
Amy R. Cross, Carlos E. de Andrea, María Villalba-Esparza, Manuel F. Landecho, Lucia Cerundolo, Praveen Weeratunga, Rachel E. Etherington, Laura Denney, Graham Ogg, Ling-Pei Ho, Ian S.D. Roberts, Joanna Hester, Paul Klenerman, Ignacio Melero, Stephen N. Sansom, Fadi Issa
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Research Article COVID-19 Inflammation

Spatial transcriptomic characterization of COVID-19 pneumonitis identifies immune circuits related to tissue injury

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Abstract

Severe lung damage resulting from COVID-19 involves complex interactions between diverse populations of immune and stromal cells. In this study, we used a spatial transcriptomics approach to delineate the cells, pathways, and genes present across the spectrum of histopathological damage in COVID-19–affected lung tissue. We applied correlation network–based approaches to deconvolve gene expression data from 46 areas of interest covering more than 62,000 cells within well-preserved lung samples from 3 patients. Despite substantial interpatient heterogeneity, we discovered evidence for a common immune-cell signaling circuit in areas of severe tissue that involves crosstalk between cytotoxic lymphocytes and pro-inflammatory macrophages. Expression of IFNG by cytotoxic lymphocytes was associated with induction of chemokines, including CXCL9, CXCL10, and CXCL11, which are known to promote the recruitment of CXCR3+ immune cells. The TNF superfamily members BAFF (TNFSF13B) and TRAIL (TNFSF10) were consistently upregulated in the areas with severe tissue damage. We used published spatial and single-cell SARS-CoV-2 data sets to validate our findings in the lung tissue from additional cohorts of patients with COVID-19. The resulting model of severe COVID-19 immune-mediated tissue pathology may inform future therapeutic strategies.

Authors

Amy R. Cross, Carlos E. de Andrea, María Villalba-Esparza, Manuel F. Landecho, Lucia Cerundolo, Praveen Weeratunga, Rachel E. Etherington, Laura Denney, Graham Ogg, Ling-Pei Ho, Ian S.D. Roberts, Joanna Hester, Paul Klenerman, Ignacio Melero, Stephen N. Sansom, Fadi Issa

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Figure 3

Identification and characterization of gene modules with spatially heterogenous expression in COVID-19 lung tissue.

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Identification and characterization of gene modules with spatially heter...
Application of WGCNA to spatial transcriptomic data (n = 46) identified 17 modules of coexpressed genes (see also Supplemental Figure 3). (A) Correlation between estimated cell-type abundance (as determined by cell deconvolution; see Supplemental Table 6) and WGCNA module eigengene expression (all AOIs; positive Spearman’s correlation values are shown). (B) Selected GO BP, KEGG, and reactome pathways significantly overrepresented in the detected modules (BH adjusted P < 0.05; 1-side Fisher’s exact tests; see also Supplemental Table 5). (C) WGCNA module eigengene expression is shown for each AOI (see also Supplemental Table 4). Sampled AOIs are annotated with patient identity, the severity of damage, adjacent virus antigen presence, and the percentage of CD3+ and CD68+ cells of total nucleated cells. No severity grade was given to 2 AOIs sampling bronchiolar epithelium. Hierarchical clustering of the 46 AOIs by expression of the WGCNA module eigengenes identified 5 spatial groups with distinct patterns of module expression. (D) The severity of tissue damage was correlated to module eigengene expression separately for the AOIs from each patient (Pearson’s correlation). vRNA, viral RNA.

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