Transcriptomic and clonal characterization of T cells in the human central nervous system

Abstract

T cells provide critical immune surveillance to the central nervous system (CNS), and the cerebrospinal fluid (CSF) is thought to be a main route for their entry. Further characterization of the state of T cells in the CSF in healthy individuals is important for understanding how T cells provide protective immune surveillance without damaging the delicate environment of the CNS and providing tissue-specific context for understanding immune dysfunction in neuroinflammatory disease. Here, we have profiled T cells in the CSF of healthy human donors and have identified signatures related to cytotoxic capacity and tissue adaptation that are further exemplified in clonally expanded CSF T cells. By comparing profiles of clonally expanded T cells obtained from the CSF of patients with multiple sclerosis (MS) and healthy donors, we report that clonally expanded T cells from the CSF of patients with MS have heightened expression of genes related to T cell activation and cytotoxicity.

NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data

“…1 Overview of the input, output, and computational efficiency of NEBULA. a A flowchart of NEBULA including the input data, the major estimation steps in NEBULA, and the analyses that were conducted using NEBULA in the application to the real data 5,27,78 . b The computational time (measured in log 10 (seconds)) of fitting an NBMM for 10,000 genes with respect to CPS by NEBULA, glmer.nb 18 , and glmmTMB 19 .…”

“…5 and the scRNA-seq data set in ref. 27 . We simulated genes of different mean expression by tuning β 0 between −4 and 2 (When the subject-level overdispersion σ 2 is small, expðβ 0 Þ approximately equals the counts per cell (CPC) defined by the total counts of a gene divided by the total number of cells).…”

“…NEBULA accurately identifies cell-type marker genes. To demonstrate that NEBULA is an efficient tool to find marker genes for annotating cell clusters, we next analyzed a recent multi-subject peripheral blood mononuclear cell (PBMC) scRNAseq data set 27 . The PBMC scRNA-seq data contain 69,516 cells from the peripheral blood of 11 subjects.…”

Single-cell biology to decode the immune cellular composition of kidney inflammation

“…In viral or bacterial infections, the antigen-specific naïve B/T cells can divide and expand themselves profoundly (Tu et al 2019 ). B/T cell infiltration and expansion have been linked to multiple autoimmune diseases such as multiple sclerosis (MS) (Pappalardo et al 2020 ; Arneth 2019 ), inflammatory bowel disease (Smillie et al 2019 ; Mizoguchi et al 2017 ), and glomerulonephritis (Krebs et al 2017 ; Schrezenmeier et al 2018 ), while it is still unclear whether auto-antigen triggered B/T cell expansion occurs in immune-medicated kidney disease (Kitching et al 2020 ).…”

Single-cell immune profiling reveals the impact of antiretroviral therapy on HIV-1-induced immune dysfunction, T cell clonal expansion, and HIV-1 persistencein vivo

“…Advancements in single-cell technologies enabled high-dimensional immune profiling to dissect the heterogeneous states of immune cells, high-resolution capture of rare cells, T cell clonality, and identification of upstream drivers of immune dysregulation [32][33][34][35][36][37] . Further, computational techniques including supervised and unsupervised machine learning, network analysis, and statistical methodologies enable confident identification of higher-fidelity predictors of different cellular states from the sparse and highly complex single-cell multi-modal data [38][39][40][41][42][43] .…”