RESUMEN
The ongoing corona virus disease 2019 (COVID-19) pandemic has led to an urgent demand for appropriate models depicting host-pathogen interactions and disease severity-dependent immune responses. Amongst various animal models, hamster species are particularly valuable as they are permissive to develop a moderate (Mesocricetus auratus) or severe (Phodopus roborovskii) disease course following infection. Here, we use single-cell ribonucleic acid sequencing of white blood cells to dissect cell-specific changes in moderate and severe disease courses of hamsters infected with severe acute respiratory syndrome coronavirus 2. To determine universal and species-specific transcriptional responses, the generated datasets were integrated with two publicly available datasets of human COVID-19 patients (Schulte-Schrepping et al. 2020 and Su et al. 2020) featuring all disease severities. Datasets were integrated using the R package Harmony and the Python package scGen enabling the prediction of disease states through different species using an autoencoder neural network architecture. Specifically, application of a low dimensional latent space embedding allows capturing most relevant transcriptome data structures, identifying shift vectors from healthy to diseased cells as well as interspecies differences. Preliminary results show that interspecies integration of hamster and human data is achievable, and major cell types were identified throughout the datasets. Training of a neuronal network on human blood monocytes enables the prediction of transcriptomic disease severity specific patterns, paving the way for extended analyses involving several cell types and species. In addition to in-depth analysis of COVID-19 signatures in blood of hamsters and humans, successfully established workflows could subsequently be used to study the pathology of extensive lung diseases, shedding light on cellular mechanisms in the transition from healthy to diseased cellular states.
RESUMEN
Severe COVID-19 is linked to both dysfunctional immune response and unrestrained immunopathology, and it remains unclear whether T cells contribute to disease pathology. Here, we combined single-cell transcriptomics and single-cell proteomics with mechanistic studies to assess pathogenic T cell functions and inducing signals. We identified highly activated CD16(+) T cells with increased cytotoxic functions in severe COVID-19. CD16 expression enabled immune-complex-mediated, T cell receptor-independent degranulation and cytotoxicity not found in other diseases. CD16(+) T cells from COVID-19 patients promoted microvascular endothelial cell injury and release of neutrophil and monocyte chemoattractants. CD16(+) T cell clones persisted beyond acute disease maintaining their cytotoxic phenotype. Increased generation of C3a in severe COVID-19 induced activated CD16(+) cytotoxic T cells. Proportions of activated CD16(+) T cells and plasma levels of complement proteins upstream of C3a were associated with fatal outcome of COVID-19, supporting a pathological role of exacerbated cytotoxicity and complement activation in COVID-19.
RESUMEN
Background: Acute kidney injury (AKI) is frequently observed in critically ill patients and is associated with a poor prognosis. AKI has recently moved into the focus of interest during the SARS-CoV-2 pandemic as high rates of AKI have been reported in severe COVID-19. We aimed to delineate cell type-specific molecular phenotypes associated with human AKI, including COVID-associated AKI. Methods: We analyzed human kidney tissues using histology and single-nuclei RNA sequencing. Samples included kidney biopsies obtained within 2 hours post mortem from patients who succumbed to critical illness with and without evidence of AKI. Samples also included tumor-adjacent normal kidney tissues obtained during surgeries. AKI cases included patients with severe courses of COVID-19 (COVID AKI) and patients with other types of critical illness associated with systemic inflammation (Non-COVID AKI). Postmortem kidney tissues obtained 30 min, 1 hour and 2 hours after death from a brain-dead patient without AKI were analyzed to assess the impact of post-mortem effects. Results: Single-nuclei sequencing from kidney tissues yielded data of high transcriptional depth, which allowed transcriptome-based identification and de-novo spatial reconstruction of kidney cells. Principal component and differential gene expression analyses indicated that the presence of clinically confirmed AKI was the primary driver of global kidney transcriptomes and that different molecular subtypes of AKI existed. In contrast, the sampling time post-mortem and the presence of COVID-19 had minor effects. Subclustering analyses of different kidney cell types identified subclasses of cells representing injured kidney tubular cells, which were marked by distinct biomarker expression and expression signatures signifying intrinsic responses to inflammation, an induction of epithelial-to-mesenchymal transition, and an upregulation of hitheto unrecognized novel receptor-ligand pairs. Conclusions: We provide the first cell type-specific molecular atlas of human AKI, revealing unanticipated disease subtypes and cell type-specific injury patterns.