Deep Single-Cell, Proteogenomic Insights from Sars-Cov-2 Infected Lung Tissues

ABSTRACT

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019 has spread globally, causing a pandemic of respiratory illness designated coronavirus disease 2019 (COVID-19) and is likely to lead to complexities in treating thoracic malignancies. Patients with lung cancer are at an increased risk of becoming infected with the SARS-CoV-2 virus and experience higher morbidity and mortality than the general population. However, little is known about the host tissue and cellular responses associated with SARS-CoV-2 infection, symptoms, and disease severity. Methods Here, we use the Nanostring GeoMX Digital Spatial Profiler (DSP) and CoxMX Spatial Molecular Imager (SMI) technology to determine tissue signatures, and spatially resolved quantitative single-cell proteogenomic changes driven by SARS-CoV-2 infection. This dual approach was used to generate an in-depth picture of the pumonary transcriptional and proteomic landscape of COVID-19, pandemic H1N1 and uninfected control patients.1 Rapid autopsy COVID-19 lung samples were collected across two independent cohorts of patients, and tissue microarrays (TMAs) were prepared. For GeoMx, n=10 COVID-19, n=10 pH1N1 and n=5 normal control tissues were compared. For CosMx, n=19 COVID-19 cores in technical replicates, and n=20 normal control tissues were compared. Tissue-based gene signatures were subsequently tested in the peripheral samples from COVID-19 patients. Results SARS-CoV-2 viral presence was confirmed by RNAscope and integrated to inform region of interest and cell types involved in infection. Analysis of the Nanostring GeoMx data revealed tissue signatures associated with SARS-CoV-2 infection, including Type 1 IFN, blood coagulation, hypoxia and angiogenesis. Analysis of the Nanostring CosMx data enabled single cell typing and mapping of tissue-specific signatures to cellular compartments of interest (e.g. macrophages, fibroblasts) and investigation of complex cell population heterogeneity and interactions. All these while preserving spatial context and highlighted differential cell type distribution in the lungs of COVID-19 patients compared to non-infected controls. Our tissue-based Type 1 IFN signatures, when tested in the blood, were found to be predictive of disease severity in COVID-19 patients when measured within the first few days of symptom onset. Conclusions Here, we've used innovative, cutting-edge spatial transcriptomics approaches to delineate tissue signatures and cellular profiles unique to COVID-19 and common across acute respiratory distress syndrome. These data will aid in understanding the proteogenomic landscape of SARS-CoV-2 infected lung tissues and form new knowledge for the impact on thoracic malignancies, and treatments such as immunotherapy. Moreover, the study demonstrates how tissue-based findings can be rapidly developed into signatures tested in noninvasive samples.