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Single-cell profiling reveals GPCR heterogeneity and functional patterning during neuroinflammation
Denise Tischner, Myriam Grimm, Harmandeep Kaur, Daniel Staudenraus, Jorge Carvalho, Mario Looso, Stefan Günther, Florian Wanke, Sonja Moos, Nelly Siller, Johanna Breuer, Nicholas Schwab, Frauke Zipp, Ari Waisman, Florian C. Kurschus, Stefan Offermanns, Nina Wettschureck
Denise Tischner, Myriam Grimm, Harmandeep Kaur, Daniel Staudenraus, Jorge Carvalho, Mario Looso, Stefan Günther, Florian Wanke, Sonja Moos, Nelly Siller, Johanna Breuer, Nicholas Schwab, Frauke Zipp, Ari Waisman, Florian C. Kurschus, Stefan Offermanns, Nina Wettschureck
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Research Article Immunology

Single-cell profiling reveals GPCR heterogeneity and functional patterning during neuroinflammation

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Abstract

GPCR expression was intensively studied in bulk cDNA of leukocyte populations, but limited data are available with respect to expression in individual cells. Here, we show a microfluidic-based single-cell GPCR expression analysis in primary T cells, myeloid cells, and endothelial cells under naive conditions and during experimental autoimmune encephalomyelitis, the mouse model of multiple sclerosis. We found that neuroinflammation induces characteristic changes in GPCR heterogeneity and patterning, and we identify various functionally relevant subgroups with specific GPCR profiles among spinal cord–infiltrating CD4 T cells, macrophages, microglia, or endothelial cells. Using GPCRs CXCR4, S1P1, and LPHN2 as examples, we show how this information can be used to develop new strategies for the functional modulation of Th17 cells and activated endothelial cells. Taken together, single-cell GPCR expression analysis identifies functionally relevant subpopulations with specific GPCR repertoires and provides a basis for the development of new therapeutic strategies in immune disorders.

Authors

Denise Tischner, Myriam Grimm, Harmandeep Kaur, Daniel Staudenraus, Jorge Carvalho, Mario Looso, Stefan Günther, Florian Wanke, Sonja Moos, Nelly Siller, Johanna Breuer, Nicholas Schwab, Frauke Zipp, Ari Waisman, Florian C. Kurschus, Stefan Offermanns, Nina Wettschureck

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Figure 3

Functional subgroups within spinal cord–infiltrating CD4 T cells (CD4sc).

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Functional subgroups within spinal cord–infiltrating CD4 T cells (CD4sc)...
(A) T-SNE plot showing similarities between individual CD4sc and results of k-means cluster analysis (the closer together cells are plotted, the more similar they are; k-means cluster assignment is indicated by color; 136 cells). (B–E) Genes differentially expressed in cluster 6 (B), cluster 4 (C), cluster 1 (D), or cluster 3 (E) compared with all cells. Only genes with fold enrichment > 1.5 or < 0.7 and P < 0.05 are shown. (F) Heat map of CD4sc cells from cluster 1 (top) and cluster 3 (bottom) (only Rorc-positive cells are shown). (G) Representation of Spearman’s rank coefficients for the correlation of expression between individual genes (width of connecting lines indicates strength of correlation). (H and I) Flow cytometric analysis of TNFα and GM-CSF expression in CCR8-negative and CCR8-positive Th17-CD4sc. (H) Gating strategy for cell sort and cytokine expression analysis in CD4+IL-17A+CCR8– (left) and CD4+IL-17A+CCR8+ T cells. (I) Statistical evaluation (n = 3). (J) Statistical analysis of the percentage of TNFα/GMCSF–double positive cells in CXCR4-negative and CXCR4-positive Th17-CD4sc (n = 4). Expression data are calculated as 2(Lod Ct – sample Ct); LoD Ct was set to 24. Function-defining genes are shown in blue.*P < 0.05; **P < 0.01; ***P < 0.001 (F, unpaired t test; I–J, paired t test).

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