Prednisone Reprograms the Transcriptional Immune Cell Landscape in CNS Autoimmune Disease

Single-Cell Transcriptomes and Immune Repertoires Reveal the Cell State and Molecular Changes in Pemphigus Vulgaris

The etiology and pathogenesis of pemphigus vulgaris (PV) entail intricate interactions between immune cells and epithelial cells. However, the specific subtypes of immune cells involved in PV, along with their respective roles, remain elusive. Likewise, the precise functions and mechanisms by which glucocorticoids affect cell types within the disease context require further elucidation. To address these knowledge gaps, we performed 5' single-cell RNA sequencing, combined with V(D)J enrichment on buccal mucosal lesions and peripheral blood samples from treatment-naive patients with PV, in conjunction with post-treatment peripheral blood samples obtained after oral prednisone treatment. Our findings suggest that the IL-1α signaling pathway, myeloid APCs, inflammatory CD8+ resident memory T cells, and dysfunctional CD4+ regulatory T cells are involved in the pathogenesis of PV. Part of these findings were validated by immunohistochemical assays and multiplex immunofluorescence assays. Furthermore, our results highlight the significant impact of prednisone treatment on monocytes and mucosal-associated invariant T cells while revealing a limited effect on CD4+ regulatory T cells. Additionally, we present the CDR3 amino acid sequence of BCR related to PV disease and investigate the characteristics of TCR/BCR clonotypes. In conclusion, our study provides a comprehensive understanding of PV, particularly focusing on the mucosal-dominant type, and sheds light on the effects of glucocorticoids within the PV context. These insights hold promise for the development of new therapeutic strategies in this autoimmune disorder.

mRNA Lipid Nanoparticles for Ex Vivo Engineering of Immunosuppressive T Cells for Autoimmunity Therapies

Cell-based therapies for autoimmune diseases have gained significant traction, with several approaches centered around the regulatory T (Treg) cell─a well-known immunosuppressive cell characterized by its expression of the transcription factor Foxp3. Unfortunately, due to low numbers of Treg cells available in circulation, harvesting and culturing Treg cells remains a challenge. It has been reported that engineering Foxp3 expression in CD4+ T cells can result in a Treg-like phenotype; however, current methods result in the inefficient engineering of these cells. Here, we develop an ionizable lipid nanoparticle (LNP) platform to effectively deliver Foxp3 mRNA to CD4+ T cells. We successfully engineer CD4+ T cells into Foxp3-T (FP3T) cells that transiently exhibit an immunosuppressive phenotype and functionally suppress the proliferation of effector T cells. These results demonstrate the promise of an LNP platform for engineering immunosuppressive T cells with potential applications in autoimmunity therapies.

Single-cell analysis of immune cells on gingiva-derived mesenchymal stem cells in experimental autoimmune uveitis

Gingiva-derived mesenchymal stem cells (GMSCs) have shown astonishing efficacy in the treatment of various autoimmune diseases. However, the mechanisms underlying these immunosuppressive properties remain poorly understood. Here, we generated a lymph node single-cell transcriptomic atlas of GMSC-treated experimental autoimmune uveitis mice. GMSC exerted profound rescue effects on T cells, B cells, dendritic cells, and monocytes. GMSCs rescued the proportion of T helper 17 (Th17) cells and increased the proportion of regulatory T cells. In addition to globally altered transcriptional factors (Fosb and Jund), we observed cell type-dependent gene regulation (e.g., Il17a and Rac1 in Th17 cells), highlighting the GMSCs' cell type-dependent immunomodulatory capacity. GMSCs strongly influenced the phenotypes of Th17 cells, suppressing the formation of the highly inflammatory CCR6-CCR2+ phenotype and enhancing the production of interleukin (IL) -10 in the CCR6+CCR2+ phenotype. Integration of the glucocorticoid-treated transcriptome suggests a more specific immunosuppressive effect of GMSCs on lymphocytes.

Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes

Heterogeneity and limited comprehension of chronic autoimmune disease pathophysiology cause accurate diagnosis a challenging process. With the increasing resources of single-cell sequencing data, a reasonable way could be found to address this issue. In our study, with the use of large-scale public single-cell RNA sequencing (scRNA-seq) data, analysis of dataset integration (3.1 × 10⁵ PBMCs from fifteen SLE patients and eight healthy donors) and cellular cross talking (3.8 × 10⁵ PBMCs from twenty-eight SLE patients and eight healthy donors) were performed to identify the most crucial information characterizing SLE. Our findings revealed that the interactions among the PBMC subpopulations of SLE patients may be weakened under the inflammatory microenvironment, which could result in abnormal emergences or variations in signaling patterns within PBMCs. In particular, the alterations of B cells and monocytes may be the most significant findings. Utilizing this powerful information, an efficient mathematical model of unbiased random forest machine learning was established to distinguish SLE patients from healthy donors via not only scRNA-seq data but also bulk RNA-seq data. Surprisingly, our mathematical model could also accurately identify patients with rheumatoid arthritis and multiple sclerosis, not just SLE, via bulk RNA-seq data (derived from 688 samples). Since the variations in PBMCs should predate the clinical manifestations of these diseases, our machine learning model may be feasible to develop into an efficient tool for accurate diagnosis of chronic autoimmune diseases.