Hyperinflammation contributes to lung injury and subsequent acute respiratory distress syndrome (ARDS) with high mortality in patients with severe coronavirus disease 2019 (COVID-19). To understand the underlying mechanisms involved in lung pathology, we investigated the role of the lung-specific immune response. We profiled immune cells in bronchoalveolar lavage fluid and blood collected from COVID-19 patients with severe disease and bacterial pneumonia patients not associated with viral infection. By tracking T cell clones across tissues, we identified clonally expanded tissue-resident memory-like Th17 cells (Trm17 cells) in the lungs even after viral clearance. These Trm17 cells were characterized by a a potentially pathogenic cytokine expression profile of IL17A and CSF2 (GM-CSF). Interactome analysis suggests that Trm17 cells can interact with lung macrophages and cytotoxic CD8+ T cells, which have been associated with disease severity and lung damage. High IL-17A and GM-CSF protein levels in the serum of COVID-19 patients were associated with a more severe clinical course. Collectively, our study suggests that pulmonary Trm17 cells are one potential orchestrator of the hyperinflammation in severe COVID-19.

A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here, we propose the use of conditional single-cell generative adversarial neural networks (cscGAN) for the realistic generation of single-cell RNA-seq data. cscGAN learns non-linear gene-gene dependencies from complex, multiple cell type samples and uses this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGAN outperforms existing methods for single-cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types.

Although it is well established that microbial infections predispose to autoimmune diseases, the underlying mechanisms remain poorly understood. After infection, tissue-resident memory T (TRM) cells persist in peripheral organs and provide immune protection against reinfection. However, whether TRM cells participate in responses unrelated to the primary infection, such as autoimmune inflammation, is unknown. By using high-dimensional single-cell analysis, we identified CD4+ TRM cells with a TH17 signature (termed TRM17 cells) in kidneys of patients with ANCA-associated glomerulonephritis. Experimental models demonstrated that renal TRM17 cells were induced by pathogens infecting the kidney, such as Staphylococcus aureus, Candida albicans, and uropathogenic Escherichia coli, and persisted after the clearance of infections. Upon induction of experimental glomerulonephritis, these kidney TRM17 cells rapidly responded to local proinflammatory cytokines by producing IL-17A and thereby exacerbate renal pathology. Thus, our data show that pathogen-induced TRM17 cells have a previously unrecognized function in aggravating autoimmune disease.