Integrated histopathology, spatial and single cell transcriptomics resolve cellular drivers of early and late alveolar damage in COVID-19 (preprint)

The most common cause of death due to COVID-19 remains respiratory failure. Yet, our understanding of the precise cellular and molecular changes underlying lung alveolar damage is limited. Here, we integrate single cell transcriptomic data of COVID-19 donor lungs with spatial transcriptomic data stratifying histopathological stages of diffuse alveolar damage (DAD). We identify changes in cellular composition across progressive DAD, including waves of molecularly distinct macrophages and depleted epithelial and endothelial populations throughout different types of tissue damage. Predicted markers of pathological states identify immunoregulatory signatures, including IFN-alpha and metallothionein signatures in early DAD, and fibrosis-related collagens in organised DAD. Furthermore, we predict a fibrinolytic shutdown via endothelial upregulation of SERPINE1/PAI-1. Cell-cell interaction analysis revealed macrophage-derived SPP1/osteopontin signalling as a key regulator during early DAD. These results provide the first comprehensive, spatially resolved atlas of DAD stages, highlighting the cellular mechanisms underlying pro-inflammatory and pro-fibrotic pathways across alveolar damage progression.

Down-regulation of MALAT1 is a hallmark of tissue and peripheral proliferative T cells in COVID-19 (preprint)

T cells play key protective but also pathogenic roles in COVID-19. We studied expression of long non-coding RNAs (lncRNAs) in COVID-19 T cell transcriptomes by integrating previously published single-cell RNA sequencing datasets. The long intergenic non-coding RNA MALAT1 was the most highly transcribed lncRNA in T cells, with Th1 cells demonstrating the lowest and CD8+ resident memory cells the highest MALAT1 expression, amongst CD4+ and CD8+ T cells populations, respectively. We then identified gene signatures that covaried with MALAT1 in single T cells. A significantly higher number of transcripts correlated negatively with MALAT1 than those that correlated. Enriched functional annotations of the MALAT1- anti-correlating gene signature included processes associated with T cell activation such as cell division, oxidative phosphorylation and response to cytokine. The MALAT1 anti-correlating gene signature shared by both CD4+ and CD8+ T cells marked dividing T cells in both lung and blood of COVID-19 patients. Focussing on the tissue, we used an independent patient cohort of post-mortem COVID-19 lung samples and demonstrated that MALAT1 suppression was indeed a marker of MKI67+ proliferating CD8+ T cells. Our results reveal MALAT1 suppression and its associated gene signature are a hallmark of human proliferating T cells.

Integrated miRNA/cytokine/chemokine profiling reveals immunopathological step changes associated with COVID-19 severity (preprint)

Circulating microRNAs (miRNAs) are exceptional mechanism-based correlates of disease, yet their potential remains largely untapped in COVID-19. Here, we determined circulating miRNA and cytokine and chemokine (CC) profiles in 171 blood plasma samples from 58 hospitalised COVID-19 patients. Thirty-two miRNAs were differentially expressed in severe cases when compared to moderate and mild cases. These miRNAs and their predicted targets reflected key COVID-19 features including cell death and hypoxia. Compared to mild cases, moderate and severe cases were characterised by a global decrease in circulating miRNA levels. Partial least squares regression using miRNA and CC measurements allowed for discrimination of severe cases with greater accuracy (87%) than using miRNA or CC levels alone. Correlation analysis revealed severity group-specific associations between CC and miRNA levels. Importantly, the miRNAs that correlated with IL6 and CXCL10, two cardinal COVID-19-associated cytokines, were distinct between severity groups, providing a novel qualitative way to stratify patients with similar levels of proinflammatory cytokines but different disease severity. Integration of miRNA and CC levels with clinical parameters revealed severity-specific signatures associated with clinical hallmarks of COVID-19. Our study highlights the existence of severity-specific circulating CC/miRNA networks, providing insight into COVID-19 pathogenesis and a novel approach for monitoring COVID-19 progression.