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.

This dataset contains a tarball with six files:

• A Seurat object with 5' single-cell gene expression data for all cells in the dataset
• A Seurat object with B cells only, containing 5' single-cell gene expression data and VDJ data in the metadata
• A Seurat object with T cells only, containing 5' single-cell gene expression data and VDJ data in the metadata
• Separate .csv files with the metadata alone for each of the three datasets

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:

• batch_id: the batch
• source: whether the sample is from CSF or PBMC
• processing_site: whether the sample was processed in Munich or Cambridge
• Category: the diagnostic group (MS, Other Inflammatory Neurological Disease, Other Inflammatory Neurological Disease - Infection, and Non-inflammatory Neurological Disease)
• Sex
• OCB: whether the patient had CSF oligoclonal bands
• fully_anonymous_pseudoid: donor ID
• ann_celltypist_lowres: automated cell type assigment at low res
• ann_celltypist_highres: automated cell type assigment at high res

VDJ datasets (B and T cells) contain many additional metadata columns with information on the VDJ and VJ transcripts expressed by each cell.

Necessary datasets to run the Immcantation 10x Tutorial. Below is the description of the files in the data set.

BCR_data_sample1.tsv: data corresponding to the first sample (sample 1) of the two samples analyzed in the 10x tutorial. This is the sample used to show the Change-O steps.

filtered_contig_annotations.csv: filtered contig annotations file for sample 1, output of cellranger vdj.

filtered_contig.fasta: sequence fasta file for sample 1, output of cellranger vdj.

BCR_data.tsv: AIRR rearrangement file containing the data for both samples 1 and 2 used in the 10x tutorial.

BCR.data_08112023.rds: R dataframe object containing the single-cell BCR sequencing data for both samples 1 and 2 used in the 10x tutorial.

GEX.data_08112023.rds: Seurat object containing the single-cell gene expression data used in the 10x tutorial.

The dataset includes spatially-resolved and single-cell antigen receptor, as well as gene expression, data from two different HER2+ breast cancer patients. The tumor piece obtained during surgery from each patient was divided into several regions and tissue sections were used for spatial transcriptomics (Visium, 10x genomics). As indicated, some tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. In parallel, tissue pieces from the same tumor were dissociated for single-cell gene expression analysis (10x genomics GEX, VDJ, and feature barcoding/Hash Tag Oligonucleotide). The deposited data is in the form of fastq files. All processed data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are publicly available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Cell Ranger, Seurat, Space Ranger, and STutility pipelines.

The dataset includes spatially-resolved gene expression and antigen receptor data from two Tonsil samples (1 and 2). Tissue sections from the tonsil samples were used for spatial transcriptomics (Visium, 10x genomics). Tonsil 2 tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. Nearby or adjacent tissue sections (from Tonsil2) were also analyzed by a bulk antigen receptor sequencing approach (amplicon sequencing), by a method also newly developed by us in the same publication (Bulk SS3 VDJ). For Visium, the data were anonymized (all SNPs removed) using Bamboozle (Ziegenhain and Sandberg, Nature Communications 2021). The deposited data is in the form of fastq files. All remaining data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Seurat, Space Ranger, and STutility pipelines.

Single-cell RNA (scRNA) and single-cell TCR (scTCR)-sequencing data data for patients number 11, 12 and 14 from the article " Identification of clinically relevant T-cell receptors for personlized T-cell therapy".

Each compressed file contains two subfolders. One is for the scRNA-seq (GEX-sequencing) and the other for the scTCR-seq (VDJ-sequencing).

In the original article, scRNA-seq and scTCR-seq were aligned to the GRCh38 reference genome using cellranger count (10X Genomics, version 3.0.1) and vdj (10X Genomics, version 3.1.0) respectively. The subsequent data processing was performed using Seurat library (V.4.3.0) on R Statistical Software (V.4.0.3).

This repository contains the Seurat objects used to produce the results in: Rade, M., Grieb, N., Weiss, R. et al. Single-cell multiomic dissection of response and resistance to chimeric antigen receptor T cells against BCMA in relapsed multiple myeloma. Nat Cancer 5, 1318–1333 (2024). https://doi.org/10.1038/s43018-024-00763-8

“seurat_harmony_tcell_pub..Rds” is a subset (only T cells) of “seurat_harmony_pub.Rds”. For the T-cell object, we have added added the VDJ sequences and genes from TCR-Seq to the meta.data slot. This was done with the R package scRepertoire.