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 (T H 17) 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 T H 17 cells could be ranked according to the resulting transcriptional perturbations, and polarization biases into T helper 1 (T H 1) 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 T H 17 to T H 1 cell plasticity in the human kidney in the context of renal autoimmunity.
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.
Abstract 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.
Manuscript:Th17 cell plasticity towards a T-bet-dependent Th1 phenotype is required for bacterial control in Staphylococcus aureus infection Bartsch et al. 2022 Mouse: C57BL/6 IL17ACrexRosa26eYFPOrgan: Kidney Cells: FACS-sorted CD45+CD3+CD4+eYFP+Single Cell Dataset Cellranger Pipeline (version 3.1.0) Output:1. matrix.mtx.gz: Count Matrix in Matrix Market format with columns as cells and rows as genes2. barcodes.tsv.gz: Barcodes File in tab-separated values file format, corresponds to the column-names of 1.3. features.tsv.gz: Feature File in tab-separated values file format, corresponds to the row-names of 1.4. web_summary.html: html-file, statistics and metadata from the cellranger pipeline run
ABSTRACT Single-cell mRNA sequencing (scRNA-seq) allows deep molecular and cellular profiling of immunological processes. Longitudinal scRNA-seq datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. Here, we derived longitudinal changes in the abundance of six colonic cell types during inflammatory bowel disease (IBD) from scRNA-seq data of a mouse model of colitis using ODE-based models. We then predicted the immune dynamics of a different mouse colitis protocol and confirmed these scRNA-seq-based predictions with our previously published single-cell-based flow cytometry data. We further 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 model simulations by predicting genes indicative of treatment response in human IBD patients. The predictive power of IBD deterministic modelling from scRNA-seq data highlights its potential to advance our understanding of immune dynamics in health and disease.
Significance Statement CD4 + IL-17A–producing CD4 + T helper (T H 17) cells play a unique role in autoimmune and chronic inflammatory diseases of the kidney, skin, and gut. Their proinflammatory functions are mediated through the release of IL-17A and -F, which activate the IL-17 receptor A (IL-17RA) and IL-17RC signaling pathways in epithelial and endothelial cells. We report that the IL-17RA/IL-17RC complex is highly expressed in CD4 + T H 17 cells. Disruption of the IL-17R signaling pathway in these cells potentiates T H 17 cell pathogenicity and accelerates experimental crescentic GN. Comparable results were observed in experimental models of psoriasis and colitis. These findings indicate that IL-17 receptor signaling controls the T H 17 response via the IL-17RA/IL-17RC complex through a self-inhibitory loop in immune-mediated diseases and might provide new insights into the development of more efficient anti-T H 17 treatment strategies. Background IL-17A–producing CD4 + T helper (T H 17) 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, IFN γ , and Foxp3 triple-reporter mice for sorting of renal CD4 + T cell subsets and subsequent single-cell RNA sequencing. Moreover, we generated T H 17 cell–specific IL-17RA and IL-17RC gene–deficient mice and studied the functional role of IL-17 signaling in T H 17 cells in crescentic GN, imiquimod-induced psoriasis, and in the CD4 + CD45RB high T cell transfer colitis model. Results We identified a specific expression of the IL-17 receptor A/C complex on CD4 + T H 17 cells. Single-cell RNA sequencing of T H 17 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 + T H 17 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 + T H 17 cells and in the control of organ-specific autoimmunity and might provide new insights into the development of more efficient anti-T H 17 treatment strategies.
Abstract 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 (cscGANs) for the realistic generation of single cell RNA-seq data. cscGANs learn non-linear gene-gene dependencies from complex, multi cell type samples and use 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. cscGANs outperform 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.
