Mass Cytometry Identifies Expansion of T-bet+ B Cells and CD206+ Monocytes in Early Multiple Sclerosis

Laura Couloume, Juliette Ferrant, Simon Le Gallou, Marion Mandon, Rachel Jean, Nadège Bescher, Helene Zephir, Gilles Edan, Eric Thouvenot, Aurelie Ruet, Marc Debouverie, Karin Tarte, Patricia Amé, Mikael Roussel, Laure Michel, Laura Couloume, Juliette Ferrant, Simon Le Gallou, Marion Mandon, Rachel Jean, Nadège Bescher, Helene Zephir, Gilles Edan, Eric Thouvenot, Aurelie Ruet, Marc Debouverie, Karin Tarte, Patricia Amé, Mikael Roussel, Laure Michel

Abstract

Multiple sclerosis (MS) is an immune-driven demyelinating disease of the central nervous system. Immune cell features are particularly promising as predictive biomarkers due to their central role in the pathogenesis but also as drug targets, even if nowadays, they have no impact in clinical practice. Recently, high-resolution approaches, such as mass cytometry (CyTOF), helped to better understand the diversity and functions of the immune system. In this study, we performed an exploratory analysis of blood immune response profiles in healthy controls and MS patients sampled at their first neurological relapse, using two large CyTOF panels including 62 markers exploring myeloid and lymphoid cells. An increased abundance of both a T-bet-expressing B cell subset and a CD206+ classical monocyte subset was detected in the blood of early MS patients. Moreover, T-bet-expressing B cells tended to be enriched in aggressive MS patients. This study provides new insights into understanding the pathophysiology of MS and the identification of immunological biomarkers. Further studies will be required to validate these results and to determine the exact role of the identified clusters in neuroinflammation.

Keywords: B cells; biomarker; immunology; mass cytometry; monocytes; multiple sclerosis.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Couloume, Ferrant, Le Gallou, Mandon, Jean, Bescher, Zephir, Edan, Thouvenot, Ruet, Debouverie, Tarte, Amé, Roussel and Michel.

Figures

Figure 1
Figure 1
Schematic representation of the experimental design of the study. Peripheral blood mononuclear cells (PBMCs) from multiple sclerosis (MS, n=11) patients and healthy controls (HC, n=8) were divided into two equal parts and stained with two antibody panels (Lymphoid panel and Myeloid panel, Supplementary Tables 1, 2) and acquired on the CyTOF instrument. The data were normalized and single intact cells were manually selected. Primary analysis was performed to identify lineage cell subsets and secondary analysis was performed to describe phenotypic alterations per lineage between MS and HC (using t-SNE and CITRUS algorithms). Phenotypic description of the identified clusters was performed.
Figure 2
Figure 2
CITRUS analysis of mass cytometry data from PBMC of MS patients and HC – B, T, NK cell lineages. (A) Visual representation of unsupervised hierarchical clustering of B cells and visualization of the clusters that are part of the significant results (in red). Abundance of the cluster 174494 (of B cells) in MS patients and HC. (B) Expression of CD19, CD27, CD38, T-bet, CXCR3, CCR4, and Ki67 on cells from cluster 174494 as compared to all other (background) B cells. (C) Abundance of cluster 174494 (of B cells, %) in « aggressive MS » patients, « non-aggressive MS » patients and HC. (D) Visual representation of unsupervised hierarchical clustering of T cells and visualization of the cluster that are part of the significant results (in red). Abundance of the cluster 1022457 (of T cells) in MS patients and HC. (E) Expression of CD3, CD4, CD45RA, CD38, CCR7, CD127 measured on cells from cluster 1022457 as compared to all other (background) T cells. (F) Abundance of cluster 1022457 (of T cells, %) in « aggressive MS » patients, « non-aggressive MS » patients and HC. (G) Visual representation of unsupervised hierarchical clustering of NK cells and visualization of the cluster that are part of the significant results (in red). Abundance of the cluster 325121 (of NK cells) in MS patients and HC. (H) Expression of CD16, CD56, CD25, CD38, CXCR3, T-bet, CD161, FoxP3, GATA3, and RORgt measured on cells from cluster 325121 as compared to all other (background) NK cells. (I) Abundance of cluster 325121 (of NK cells, %) in « aggressive MS » patients, « non-aggressive MS » patients and HC.
Figure 3
Figure 3
CITRUS analysis of mass cytometry data from PBMC of MS patients and HC - Myeloid cell lineage. (A) Representative t-SNE maps, of a MS patient and a HC, regarding myeloid cells lineage. The t-SNE maps were generated based on expression levels of all markers of the myeloid panel (Supplementary Table 2). Description of a myeloid subset in a MS patient (circled). Each dot represents one cell and the color spectrum represents individual marker-expression levels (red : high expression; blue : low expression). (B) Visual representation of unsupervised hierarchical clustering of myeloid cells and visualization of the clusters that are part of the significant results (in red). Abundance of the cluster 155313 and 155317 (of myeloid cells) in MS patients and HC. (C) Expression of CD14, CD11b, CD86, CD11c, CD206, CD209, CD64, CCR5, SIRPa, HLA-DR, CD16, PD-L1, S100A9 and CD32 measured on cells from cluster 155313 and 155317 as compared to all other (background) myeloid cells. (D) Abundance of cluster 155313 (of myeloid cells, %), in « aggressive MS » patients, « non aggressive MS » patients and HC.

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