Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease.

Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.

Targeting T cell plasticity in kidney and gut inflammation by pooled single-cell CRISPR screening.

Pro-inflammatory CD4+ T cells are major drivers of autoimmune diseases, yet therapies modulating T cell phenotypes to promote an anti-inflammatory state are lacking. Here, we identify T helper 17 (TH17) cell plasticity in the kidneys of patients with antineutrophil cytoplasmic antibody-associated glomerulonephritis on the basis of single-cell (sc) T cell receptor analysis and scRNA velocity. To uncover molecules driving T cell polarization and plasticity, we established an in vivo pooled scCRISPR droplet sequencing (iCROP-seq) screen and applied it to mouse models of glomerulonephritis and colitis. CRISPR-based gene targeting in TH17 cells could be ranked according to the resulting transcriptional perturbations, and polarization biases into T helper 1 (TH1) and regulatory T cells could be quantified. Furthermore, we show that iCROP-seq can facilitate the identification of therapeutic targets by efficient functional stratification of genes and pathways in a disease- and tissue-specific manner. These findings uncover TH17 to TH1 cell plasticity in the human kidney in the context of renal autoimmunity.

Tr1 Cells Emerge and Suppress Effector Th17 Cells in Glomerulonephritis.

T regulatory type 1 (Tr1) cells, which are defined by their regulatory function, lack of Foxp3, and high expression of IL-10, CD49b, and LAG-3, are known to be able to suppress Th1 and Th17 in the intestine. Th1 and Th17 cells are also the main drivers of crescentic glomerulonephritis (GN), the most severe form of renal autoimmune disease. However, whether Tr1 cells emerge in renal inflammation and, moreover, whether they exhibit regulatory function during GN have not been thoroughly investigated yet. To address these questions, we used a mouse model of experimental crescentic GN and double Foxp3mRFP IL-10eGFP reporter mice. We found that Foxp3neg IL-10-producing CD4+ T cells infiltrate the kidneys during GN progression. Using single-cell RNA sequencing, we could show that these cells express the core transcriptional factors characteristic of Tr1 cells. In line with this, Tr1 cells showed a strong suppressive activity ex vivo and were protective in experimental crescentic GN in vivo. Finally, we could also identify Tr1 cells in the kidneys of patients with antineutrophil cytoplasmic autoantibody-associated GN and define their transcriptional profile. Tr1 cells are currently used in several immune-mediated inflammatory diseases, such as T-cell therapy. Thus, our study provides proof of concept for Tr1 cell-based therapies in experimental GN.

Th17 cell plasticity towards a T-bet-dependent Th1 phenotype is required for bacterial control in Staphylococcus aureus infection.

Staphylococcus aureus is frequently detected in patients with sepsis and thus represents a major health burden worldwide. CD4+ T helper cells are involved in the immune response to S. aureus by supporting antibody production and phagocytosis. In particular, Th1 and Th17 cells secreting IFN-γ and IL-17A, are involved in the control of systemic S. aureus infections in humans and mice. To investigate the role of T cells in severe S. aureus infections, we established a mouse sepsis model in which the kidney was identified to be the organ with the highest bacterial load and abundance of Th17 cells. In this model, IL-17A but not IFN-γ was required for bacterial control. Using Il17aCre × R26YFP mice we could show that Th17 fate cells produce Th17 and Th1 cytokines, indicating a high degree of Th17 cell plasticity. Single cell RNA-sequencing of renal Th17 fate cells uncovered their heterogeneity and identified a cluster with a Th1 expression profile within the Th17 cell population, which was absent in mice with T-bet/Tbx21-deficiency in Th17 cells (Il17aCre x R26eYFP x Tbx21-flox). Blocking Th17 to Th1 transdifferentiation in Th17 fate cells in these mice resulted in increased S. aureus tissue loads. In summary, we highlight the impact of Th17 cells in controlling systemic S. aureus infections and show that T-bet expression by Th17 cells is required for bacterial clearance. While targeting the Th17 cell immune response is an important therapeutic option in autoimmunity, silencing Th17 cells might have detrimental effects in bacterial infections.

Clonal expansion and activation of tissue-resident memory-like Th17 cells expressing GM-CSF in the lungs of severe COVID-19 patients.

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.

IL-17 Receptor C Signaling Controls CD4+ TH17 Immune Responses and Tissue Injury in Immune-Mediated Kidney Diseases.

BACKGROUND: IL-17A-producing CD4+ T helper (TH17) cells play a critical role in autoimmune and chronic inflammatory diseases, such as crescentic GN. The proinflammatory effects of IL-17 are mediated by the activation of the IL-17RA/IL-17RC complex. Although the expression of these receptors on epithelial and endothelial cells is well characterized, the IL-17 receptor expression pattern and function on hematopoietic cells, e.g., CD4+ T cell subsets, remains to be elucidated. METHODS: Crescentic GN (nephrotoxic nephritis) was induced in IL-17A, IFNγ, and Foxp3 triple-reporter mice for sorting of renal CD4+ T cell subsets and subsequent single-cell RNA sequencing. Moreover, we generated TH17 cell-specific IL-17RA and IL-17RC gene-deficient mice and studied the functional role of IL-17 signaling in TH17 cells in crescentic GN, imiquimod-induced psoriasis, and in the CD4+CD45RBhigh T cell transfer colitis model. RESULTS: We identified a specific expression of the IL-17 receptor A/C complex on CD4+ TH17 cells. Single-cell RNA sequencing of TH17 cells revealed the activation of the IL-17 receptor signaling pathway in experimental crescentic GN. Disruption of the IL-17RC signaling pathway in CD4+ T cells and, most importantly, specifically in CD4+ TH17 cells, potentiates the IL-17 cytokine response and results in an accelerated course of experimental crescentic GN. Comparable results were observed in experimental models of psoriasis and colitis. CONCLUSIONS: Our findings indicate that IL-17 receptor C signaling has a previously unrecognized function in the regulation of CD4+ TH17 cells and in the control of organ-specific autoimmunity and might provide new insights into the development of more efficient anti-TH17 treatment strategies.

Pathogen-induced tissue-resident memory TH17 (TRM17) cells amplify autoimmune kidney disease.

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.

Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks.

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.