Single cell sequencing data of PBMC and CSF from a cohort of Multiple Sclerosis patients and other neurological disease controls

<p><span>Neuroinflammation is often characterised by immune cell infiltrates in the cerebrospinal fluid (CSF). Here we apply single-cell RNA sequencing to explore the functional characteristics of these cells in patients with various inflammatory, infectious and non-inflammatory neurological disorders. We show that CSF is distinct from the peripheral blood in terms of both cellular composition and gene expression. We report that the cellular and transcriptional landscape of CSF is altered in neuroinflammation, but is strikingly similar across different neuroinflammatory disorders. We find clonal expansion of CSF B and T cells in all disorders but most pronounced in inflammatory diseases, and we functionally characterise the transcriptional features of these cells. Finally, we explore the genetic control of gene expression in CSF lymphocytes. Our results highlight the common features of immune cells in the CSF compartment across diverse neurological diseases and may help to identify new targets for drug development or repurposing in Multiple Sclerosis. </span></p> <p><span>This dataset contains a tarball with six files:</span></p> <ul> <li><span>A Seurat object with 5' single-cell gene expression data for all cells in the dataset</span></li> <li><span>A Seurat object with B cells only, containing 5' single-cell gene expression data and VDJ data in the metadata</span></li> <li><span>A Seurat object with T cells only, containing 5' single-cell gene expression data and VDJ data in the metadata</span></li> <li><span>Separate .csv files with the metadata alone for each of the three datasets</span></li> </ul> <p><span>These data have undergone very light quality control and contain only the raw, non-normalised RNA counts in the RNA assay (adjusted only for ambient RNA contamination). Details of QC steps used in the paper are given in the github. Please note that these data were generated across two sites and across multiple batches, and so any analysis should account for this potential source of technical variability. Metadata include the following key columns:</span></p> <ul> <li><span>batch_id: the batch </span></li> <li><span>source: whether the sample is from CSF or PBMC</span></li> <li><span>processing_site: whether the sample was processed in Munich or Cambridge</span></li> <li><span>Category: the diagnostic group (MS, Other Inflammatory Neurological Disease, Other Inflammatory Neurological Disease - Infection, and Non-inflammatory Neurological Disease)</span></li> <li><span>Sex</span></li> <li><span>OCB: whether the patient had CSF oligoclonal bands&nbsp;</span></li> <li><span>fully_anonymous_pseudoid: donor ID</span></li> <li><span>ann_celltypist_lowres: automated cell type assigment at low res&nbsp;</span></li> <li><span>ann_celltypist_highres: automated cell type assigment at high res</span></li> </ul> <p><span>VDJ datasets (B and T cells) contain many additional metadata columns with information on the VDJ and VJ transcripts expressed by each cell.&nbsp;</span></p>