Schmitz, R. | Search bioRxiv

bioRxiv2023

T:B cell communication in ectopic lymphoid follicles in CNS autoimmunity

Meningeal ectopic lymphoid follicle-like structures (eLFs) have been described in multiple sclerosis (MS) and its animal model experimental autoimmune encephalomyelitis (EAE), but their role in CNS autoimmunity is unclear. To analyze the cellular phenotypes and interactions within these structures, we employed a Th17 adoptive transfer EAE model featuring formation of large, numerous eLFs. Single-cell transcriptomic analysis revealed that clusters of activated B cells and B1/Marginal Zone-like B cells are overrepresented in the CNS and identified B cells poised for undergoing antigen-driven germinal center (GC) reactions and clonal expansion in the CNS. Furthermore, CNS B cells showed enhanced capacity for antigen presentation and immunological synapse formation compared to peripheral B cells. To directly visualize Th17:B cell cooperation in eLFs, we labeled Th17 cells with a ratiometric calcium sensor, and tracked their interactions with tdTomato-labeled B cells in real-time. Thereby, we demonstrated for the first time that T and B cells form long-lasting antigen-specific contacts in meningeal eLFs that result in reactivation of autoreactive T cells. Consistent with these findings, autoreactive T cells depended on CNS B cells to maintain a pro-inflammatory cytokine profile in the CNS. Collectively, our study reveals that extensive T:B cell cooperation occurs in meningeal eLFs in our model promoting differentiation and clonal expansion of B cells, as well as reactivation of CNS T cells and thereby supporting smoldering inflammatory processes within the CNS compartment. Our results provide valuable insights into the function of eLFs and may provide a direction for future research in MS.

immunology↗

bioRxiv2022

Accessible, interactive and cloud-enabled genomic workflows integrated with the NCI Genomic Data Commons

Cancer data is widely available in repositories such as the National Cancer Institute (NCI) Genomic Data Commons (GDC). These datasets could serve as controls or comparisons in compendium analyses with user data, avoiding the expense and time of generating additional datasets. However, the user must be able to process their new data in the same manner for these comparisons to be useful. This can be non-trivial. Although the executables themselves are usually available in repositories, the GDC pipelines that describe that entire analysis workflow are currently published as text-based standard operating procedures (SOPs). It is difficult to document a computational workflow to the level of detail and accuracy required to reproduce the results. Discrepancies between versions and exclusions of details accumulate as the documentation inevitably lags behind code revisions. We address this problem by converting the SOPs into a downloadable and executable format. Specifically, we converted the GDC DNA sequencing (DNA-Seq) and the GDC mRNA sequencing (mRNA-Seq) SOPs into reproducible, self-installing, containerized, and interactive graphical workflows. These can be applied to reproducibly process user data and to harmonize datasets across repositories. Using our publicly available graphical workflows, we harmonize raw RNA-Seq datasets from the GDC and the Genotype-Tissue Expression (GTEx) project that were originally processed using different methodologies to illustrate the importance of uniform processing of control and treatment data for accurate inference of differentially expressed genes. By disseminating the analytical methodology in a reproducible and easily executed form, we greatly increase the utility of the GDC by enabling researchers to uniformly process custom data and datasets across multiple repositories to enhance data interpretation. Our approach and open-source executable workflows of making the analytical process as readily available as the data can be applied to other data repositories to increase their impact on scientific research.

bioinformatics↗