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GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis

Nature Medicine volume 25, pages 1290–1300 (2019)Cite this article

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Abstract

Cytokine dysregulation is a central driver of chronic inflammatory diseases such as multiple sclerosis (MS). Here, we sought to determine the characteristic cellular and cytokine polarization profile in patients with relapsing–remitting multiple sclerosis (RRMS) by high-dimensional single-cell mass cytometry (CyTOF). Using a combination of neural network-based representation learning algorithms, we identified an expanded T helper cell subset in patients with MS, characterized by the expression of granulocyte–macrophage colony-stimulating factor and the C-X-C chemokine receptor type 4. This cellular signature, which includes expression of very late antigen 4 in peripheral blood, was also enriched in the central nervous system of patients with relapsing–remitting multiple sclerosis. In independent validation cohorts, we confirmed that this cell population is increased in patients with MS compared with other inflammatory and non-inflammatory conditions. Lastly, we also found the population to be reduced under effective disease-modifying therapy, suggesting that the identified T cell profile represents a specific therapeutic target in MS.

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Fig. 1: Automated data analysis of cytokine-producing immune cells identifies a dysregulation of GM-CSF in MS.
Fig. 2: GM-CSF-competent TH cells are increased in MS.
Fig. 3: Characterization of the CellCNN-identified TH signature reveals the co-expression of GM-CSF and CXCR4.
Fig. 4: The signature TH population is enriched in MS.
Fig. 5: DMF immunomodulation reduces the identified TH signature in patients with MS.
Fig. 6: GM-CSF and CXCR4 signature cells are enriched in the CSF of patients with MS.

Data availability

Mass cytometry and flow cytometry data analyzed in the manuscript (Figs. 1–6) are available in a public repository at http://flowrepository.org/experiments/2166/. Patient-related data not included in the manuscript may be subject to patient confidentiality.

Code availability

The R-based custom workflow and source codes are available at https://github.com/GalliES/MS_manuscript.

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Acknowledgements

This work was supported by grants from the Swiss National Science Foundation (310030_170320, 316030_150768 and CRSII5_183478; all to B.B.), European Union FP7 projects NeuroKine (to B.B.) and the Swiss Multiple Sclerosis Society (to B.B.). F.J.H. received a Van Riemsdijk PhD fellowship. Lymph node cryosections for establishing histological staining conditions were provided by the tissue bank of the Institute of Pathology and Molecular Pathology of the University Hospital Zurich.

Author information

Author notes

  1. Felix J. Hartmann

    Present address: Department of Dermatology, Hollings Cancer Center, Medical University of South Carolina, Charleston, SC, USA

  2. Carsten Krieg

    Present address: Department of Microbiology and Immunology, Hollings Cancer Center, Medical University of South Carolina, Charleston, SC, USA

  3. These authors contributed equally: Edoardo Galli, Felix J. Hartmann.

Authors and Affiliations

  1. Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland

    Edoardo Galli, Felix J. Hartmann, Bettina Schreiner, Florian Ingelfinger, Dunja Mrdjen, Carsten Krieg & Burkhard Becher

  2. Department of Neurology, University Hospital Zurich, Zurich, Switzerland

    Bettina Schreiner

  3. Institute for Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland

    Eirini Arvaniti & Manfred Claassen

  4. Department of Biomedicine, University Hospital Basel, Basel, Switzerland

    Martin Diebold, Nicholas Sanderson & Tobias Derfuss

  5. Institut für Neuropathologie, Klinik für Neurologie, Universitätsmedizin Göttingen, Gottingen, Germany

    Franziska van der Meer & Christine Stadelmann

  6. Neuroimmunology Unit, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

    Faiez Al Nimer, Mohsen Khademi, Fredrik Piehl & Tomas Olsson

  7. Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA

    Faiez Al Nimer

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Contributions

E.G. designed and performed all of the cytometry experiments, and analyzed the data of the validation cohort, DMF cohort and CSF samples. E.G. and F.J.H. designed and performed the cytometry experiments and analyzed the data of the discovery cohort and wrote the manuscript. B.S. and F.I. equally contribuited to the manuscript. B.S. and C.S. performed all of the histological analysis. F.I. performed the cytometry experiments in the DMF cohort. D.M. and C.K. helped with performing the experiments. E.A. and M.C. performed the CellCNN analysis. T.D., N.S., M.D., C.S., F.v.d.M., M.K., F.A.N., F.P. and T.O. selected and characterized the patient cohorts. B.B. supervised and funded the study and wrote the manuscript.

Corresponding author

Correspondence to Burkhard Becher.

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Competing interests

T.O. has received unrestricted MS research grants, in addition to lecture and or advisory board honoraria, from Biogen, Novartis, Sanofi, Merck and Roche. M.D. received speaker honoraria from Biogen Switzerland, which were used exclusively for research purposes. F.P. has received research grants from Biogen, Genzyme, Merck and Novartis, and fees for serving as Data Monitoring Committee Chair in clinical trials with Parexel. T.D. received financial compensation for participation in advisory boards, steering committees and data safety monitoring boards, and for consultation for Novartis Pharmaceuticals, Merck, Biogen, Celgene, GeNeuro, Mitsubishi Tanabe Pharma, MedDay, Roche and Sanofi Genzyme. T.D. also received research support from Novartis, Biogen, the National Swiss Science Foundation, the European Union and the Swiss MS Society.

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Peer review information: Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Single-cell t-SNE profiling of immune cells.

PBMCs from all sample groups were restimulated with phorbol 12-myristate 13-acetate (PMA)/ionomycin and analyzed by mass cytometry. The t-SNE algorithm (30,000 cells, equally selected from healthy donors (HDs; n = 29), NINDCs (n = 31) and patients with MS (n = 31) and from all samples) was used to depict different populations therein. a, The expression of each indicated marker is overlaid. b, FlowSOM-based immune cell populations are overlaid as a color dimension.

Source data

Extended Data Fig. 2 FlowSOM-guided clustering of peripheral blood immune cell lineages.

a, PBMCs from HDs (n = 29), NINDCs (n = 31) and patients with MS (n = 31) were restimulated with PMA/ionomycin and analyzed by mass cytometry. Heat maps of FlowSOM-identified initial nodes and their mean surface marker expression levels are shown, together with their lineage assignment (color coded). b, Biaxial plots showing the expression of the main lineage markers of FlowSOM-based populations (colored). The total samples from HDs (n = 29), NINDCs (n = 31) and patients with MS (n = 31) are shown in gray. c, Data as in a were manually gated to define the same populations. Samples from three independent runs were analyzed. d, Correlation of frequencies for the immune populations (color coded), as defined by FlowSOM and manual gating. Each dot represents the frequency of a leukocyte population of one donor (n = 91). The P value was calculated using linear regression. e, Frequencies of immune cell lineages in peripheral leukocytes of NINDCs (n = 31), patients with RRMS during remission (n = 18) or during relapse (n = 12), patients with secondary progressive MS (SPMS) (n = 5) or PPMS (n = 3), and HDs (n = 29). f, Frequencies of cytokine+ cells within PBMCs of NINDCs (n = 31), patients with RRMS during remission (n = 18) or during relapse (n = 12), patients with SPMS (n = 5) or PPMS (n = 3), and HDs (n = 29). Box plots depict the IQR, with a horizontal line representing the median. Whiskers extend to the farthest data point within a maximum of 1.5× the IQR. Points represent individuals.

Source data

Extended Data Fig. 3 Age analysis of patients with MS and control groups.

a, Box plots depict the age of HDs (n = 29), NINDCs (n = 31) and patients with MS (n = 39). b, Age distribution among HDs (n = 29), NINDCs (n = 31) and patients with MS (n = 39). c, Correlation between frequencies of cytokine-producing PBMCs and age. Regression curves with confidence intervals are depicted for HDs (n = 29), NINDCs (n = 31) and patients with MS (n = 39). Box plots depict the IQR, with a white horizontal line representing the median. Whiskers extend to the farthest data point within a maximum of 1.5× the IQR. P values are based on two-tailed Mann–Whitney–Wilcoxon tests between groups. Controlling for multiple comparisons was accomplished via the Benjamini–Hochberg approach. Every point represents one individual.

Source data

Extended Data Fig. 4 Leukocyte and cytokine production characterization in subgroups of patients with MS.

a, Exemplary GM-CSF production by total leukocytes with (right) or without (left) previous PMA/ionomycin stimulation (three independent experiments). b, Frequencies of immune cell lineages within GM-CSF+ cells in NINDCs (n = 21), patients with RRMS during remission (n = 17) or during relapse (n = 10), patients with SPMS (n = 3) or PPMS (n = 2), and HDs (n = 24). c, Frequency of GM-CSF-positive cells in major immune lineages in NINDCs (n = 31), patients with RRMS during remission (n = 18) or during relapse (n = 12), patients with SPMS (n = 5) or PPMS (n = 3), and HDs (n = 29). d, Frequencies of FlowSOM-based TH memory subpopulations in total TH cells in NINDCs (n = 31), patients with RRMS during remission (n = 18) or during relapse (n = 12), patients with SPMS (n = 5) or PPMS (n = 3), and HDs (n = 29). e, Frequencies of GM-CSF+ TH cells in NINDCs (n = 23), patients with RRMS during remission (n = 18) or during relapse (n = 11), patients with SPMS (n = 4) or PPMS (n = 3), and HDs (n = 27). f, Co-production of other cytokines by GM-CSF+ TH cells in NINDCs (n = 23), patients with RRMS during remission (n = 18) or during relapse (n = 11), patients with SPMS (n = 4) or PPMS (n = 3), and HDs (n = 27). g, Frequencies of GM-CSF production by cytokine+ TH cells in NINDCs (n = 13), patients with RRMS during remission (n = 14) or during relapse (n = 8), patients with SPMS (n = 4) or PPMS (n = 2), and HDs (n = 22). h, FlowSOM was used to identify total TH cell subsets based on their cytokine production profile (k = 17; elbow criterion). Clusters were manually annotated based on this profile. The mean expressions of surface and cytokine markers by the respective TH cell subsets are shown. i, Frequencies of FlowSOM-defined GM-CSF+ TH cell subsets in NINDCs (n = 31), patients with RRMS during remission (n = 19) or during relapse (n = 12), patients with SPMS (n = 5) or PPMS (n = 3), and HDs (n = 29). j, The t-SNE algorithm (30,000 cytokine-expressing TH cells, equally selected from different clinical groups and from all samples) was used to depict different populations therein. FlowSOM-based TH subsets (left) and the expression of each indicated marker (right) are overlaid. Representation plots from randomly selected cells from three independent experiments are shown. Box plots depict the IQR, with a white horizontal line representing the median. Whiskers extend to the farthest data point within a maximum of 1.5× the IQR. Every point represents one individual.

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Extended Data Fig. 5 GM-CSF-producing CD8+ T cells display cytokine production profiles largely analogous to CD4+ T cells.

CD8+ T cells were subdivided into naive, effector, effector memory and central memory cells based on FlowSOM-defined clusters. a, Left, mean expression levels of the indicated surface markers in the respective subpopulation. Right, frequencies of these subpopulations in total CD8+ T cells in NINDCs (n = 31), patients with RRMS during remission (n = 18) or during relapse (n = 12), patients with SPMS (n = 5) or PPMS (n = 3), and HDs (n = 29). b, Frequency of these subpopulations in GM-CSF+ CD8+ T cells in NINDCs (n = 16), patients with RRMS during remission (n = 16) or during relapse (n = 10), patients with SPMS (n = 3) or PPMS (n = 1), and HDs (n = 21). c, Frequency of cytokine+ in GM-CSF+ CD8+ T cells. d, Production of GM-CSF by CD8+ T cells positive for the indicated cytokine cells in NINDCs (n = 11), patients with RRMS during remission (n = 5) or during relapse (n = 3), patients with SPMS (n = 2) or PPMS (n = 1), and HDs (n = 14). e, FlowSOM was used to identify GM-CSF+ CD8+ T cell subsets based on their cytokine production profile (k = 10; elbow criterion). Clusters were manually annotated based on this production profile. Top, mean expression of surface and cytokine markers by the respective subsets. Bottom, relative fractions (left) and absolute frequencies (right) of FlowSOM-defined GM-CSF+ CD8+ T cell subsets in NINDCs (n = 11), patients with RRMS during remission (n = 12) or during relapse (n = 6), patients with SPMS (n = 1) or PPMS (n = 1), and HDs (n = 14). f, Categorical t-SNE analysis, with heat maps depicting mean expression levels in each bin. Box plots depict the IQR, with a white horizontal line representing the median. Whiskers extend to the farthest data point within a maximum of 1.5× the IQR. Every point represents one individual.

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Extended Data Fig. 6 Co-production profiles of GM-CSF-expressing NK and B cells.

a, Total NK cells were selected, and the expression level of all relevant surface markers was correlated (Pearson’s r) with GM-CSF expression on a single-cell level. The heat map depicts Spearman correlation coefficients. b, Frequencies (left) and an example (right) of cytokine co-expression by GM-CSF+ NK cells in NINDCs (n = 31), patients with RRMS during remission (n = 19) or during relapse (n = 12), patients with SPMS (n = 5) or PPMS (n = 3), and HDs (n = 29). c, B cells were selected, and the expression level of all relevant surface markers was correlated (Pearson’s r) with GM-CSF expression on a single-cell level. d, Frequencies (left) and an example (right) of cytokine expression by GM-CSF+ B cells in patients, as in b. Box plots depict the IQR, with a white horizontal line representing the median. Whiskers extend to the farthest data point within a maximum of 1.5× the IQR. Every point represents one individual.

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Extended Data Fig. 7 Clinical correlations in the validation cohort.

a, Age distribution among patients with RRMS (n = 12), HCs (n = 15), NINDCs (n = 14), IDCs (n = 9) and patients with CIS (n = 8). b, Box plots depict the age in patients with RRMS (n = 12), HCs (n = 15), NINDCs (n = 14), IDCs (n = 9) and patients with CIS (n = 8). c, Correlation between frequencies of the CellCNN-defined immune signature and age in patients with RRMS (n = 12), HCs (n = 15), NINDCs (n = 14), IDCs (n = 9) and patients with CIS (n = 8). Regression curves with confidence intervals are depicted for each group. d, Correlation of the frequency of the signature population in TH cells and age among patients with RRMS (n = 12), HCs (n = 15), NINDCs (n = 14), IDCs (n = 9) and patients with CIS (n = 8). Each symbol identifies an individual patient among patients with CIS (n = 8), or with RRMS in remission (n = 8) or relapsing (n = 4). The regression lines with confidence intervals are based on the frequency of the signature population among the other control groups. e, Correlation between frequencies of the CellCNN-defined immune signature and clinical parameters in patients with RRMS in remission (n = 8) or relapsing (n = 3) and patients with CIS (n = 8). Regression curves with confidence intervals are depicted for each parameter. Box plots depict the IQR, with a white horizontal line representing the median. Whiskers extend to the farthest data point within a maximum of 1.5× the IQR. P values are based on two-tailed Mann–Whitney–Wilcoxon tests between groups. The linear correlation equation was calculated on the pool of all analysed samples. Every point represents one individual.

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Extended Data Fig. 8 Immune profiling of validation cohort.

a, PBMCs from patients with RRMS (n = 12), HCs (n = 15), NINDCs (n = 14), IDCs (n = 9) and patients with CIS (n = 8) were restimulated with PMA/ionomycin and analyzed by mass cytometry. The t-SNE algorithm (20,000 cells randomly selected from all samples) was used to depict different populations therein. FlowSOM-based immune cell populations are overlaid as a color dimension. b, Mean population expression levels of all markers used for t-SNE visualization and FlowSOM clustering. c,d, Sample-specific (c) and frequencies of (d) immune cell lineages in peripheral leukocytes in patients with RRMS (n = 12), HCs (n = 15), NINDCs (n = 14), IDCs (n = 9) and patients with CIS (n = 8). e, Representative plot of cytokine staining in the unstimulated control (upper) and stimulated samples (lower). Cells randomly selected from the experimental run are shown. The positivity threshold was set on the residual staining, as described in the Methods. f–i, Frequencies of cytokine production by TH (f), TC (g), NK (h) and B cells (i) among patients with RRMS (n = 12), HCs (n = 15), NINDCs (n = 14), IDCs (n = 9) and patients with CIS (n = 8). Box plots depict the IQR, with a white horizontal line representing the median. Whiskers extend to the farthest data point within a maximum of 1.5× the IQR. Every point represents one individual.

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Extended Data Fig. 9 SDF1α induces signature cells’ migration towards a chemokine gradient.

a, Representative plots of the gating strategy of immune cell populations (n = 7). b, Frequency of migrating cells when SDF1α was added to the lower, upper or both chambers (n = 7). c, Frequency of migrating cells in the lower chamber towards an SDF1α gradient, calculated as the frequency of population-specific input cells (n = 7). d,e, Representative plots (d) and quantification (e) of different cytokine-producing TH cells, calculated as the frequency of input cells (n = 7). Representative plots of two independent experiments. P values are based on two-tailed Mann–Whitney–Wilcoxon tests between groups. Box plots depict the IQR, with a horizontal line representing the median. Column plots represent means. Whiskers extend to the farthest data point within a maximum of 1.5× the IQR. Every point represents one individual.

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Extended Data Fig. 10 CNS immune features of patients with MS.

a, Quantification of cell viability in paired PBMC–CSF samples (n = 9). b, Frequencies of immune cell lineages in CSF between fresh (n = 3) and cryopreserved CSF samples (n = 9). c, Scaffold reference map of the TH cell compartment, constructed from mass cytometry data. Gray bubbles represent the 100 FlowSOM nodes, and colored landmarks are based on FlowSOM-defined TH cell subsets. d, Expression of CellCNN signature-defining cytokines and chemokine receptors within mapped FlowSOM nodes. e, Immunohistochemistry of MS brain lesions depicting a demyelinated lesion (top left; myelin immunohistochemistry) with KiM1P-positive macrophages/activated microglial cells (top right; scale bar: 200 µm), and CD3-positive perivenular T cell infiltration within the demyelinated lesion (bottom left) as well as in the meninges (bottom right; scale bar: 30 µm). f, Immunofluorescence control for the staining of secondary antibodies (left) and MS brain lesion (right). The experiment was repeated from the brain biopsies of three individual patients with MS as for Fig. 6j. Scale bars: 30 μm. P values are based on two-tailed Mann–Whitney–Wilcoxon tests between groups. r values were calculated from the z-statistic of the Mann–-Whitney–-Wilcoxon test. Box plots depict the IQR, with a white horizontal line representing the median. Whiskers extend to the farthest data point within a maximum of 1.5× the IQR. Every point represents one individual.

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Galli, E., Hartmann, F.J., Schreiner, B. et al. GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis. Nat Med 25, 1290–1300 (2019). https://doi.org/10.1038/s41591-019-0521-4

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