bioRxivImmunologyPreprint
Systematic Analysis of Immune Changes after B-cell Depletion in Autoimmune Multiple Sclerosis
J. Wei, J. Moon, Y. Yasumizu, L. Zhang, K. Radassi, N. Buitrago-Pocasangre, M. E. Deerhake, N. Strauli, A. Chan, A. Herman, R. Pedotti, C. Raposo, I. Yim, J. Pappalardo, E. E. Longbrake, T. S. Sumida, P.-P. Axisa, D. A. Hafler
Abstract
Multiple sclerosis (MS) is a complex genetically mediated autoimmune disease of the central nervous system where anti-CD20-mediated B cell depletion is remarkably effective in the treatment of early disease. While previous studies investigated the effect of B cell depletion on select immune cell subsets using flow cytometry-based methods, the therapeutic impact on patient immune landscape is unknown. In this study, we explored how a therapy-driven "in vivo perturbation" modulates the diverse immune landscape by measuring transcriptomic granularity with single-cell RNA sequencing (scRNAseq). We demonstrate that B cell depletion leads to cell type-specific changes in the abundance and function of CSF macrophages and peripheral blood monocytes. Specifically, a CSF-specific macrophage population with an anti-inflammatory transcriptomic signature and peripheral CD16+ monocytes increased in frequency post-B cell depletion. This was accompanied by increases in TNF messenger RNA and protein in monocytes post-B cell depletion, consistent with the finding that anti-TNF treatment exacerbates autoimmune activity in MS. In parallel, B cell depletion induced changes in peripheral CD4+ T cell populations, including increases in the frequency of TIGIT+ regulatory T cells and marked decreases in the frequency of myelin peptide loaded-tetramer binding CD4+ T cells. Collectively, this study provides an exhaustive transcriptomic map of immunological changes, revealing different mechanisms of action contributing to the high efficacy in B cell depletion treatment of MS.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- approach: deep learning
- approach: statistical model
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