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Spatial transcriptomic characterization of COVID-19 pneumonitis identifies immune circuits related to tissue injury
Amy R. Cross, … , Stephen N. Sansom, Fadi Issa
Amy R. Cross, … , Stephen N. Sansom, Fadi Issa
Published December 6, 2022
Citation Information: JCI Insight. 2023;8(2):e157837. https://doi.org/10.1172/jci.insight.157837.
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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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Usage data is cumulative from December 2022 through January 2023.

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