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Molecular mechanisms of lineage decisions in metabolite-specific T cells

Nature Immunology volume 20, pages 1244–1255 (2019)Cite this article

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Abstract

Mucosal-associated invariant T cells (MAIT cells) recognize the microbial metabolite 5-(2-oxopropylideneamino)-6-d-ribitylaminouracil (5-OP-RU) presented by the MHC class Ib molecule, MR1. MAIT cells acquire effector functions during thymic development, but the mechanisms involved are unclear. Here we used single-cell RNA-sequencing to characterize the developmental path of 5-OP-RU-specific thymocytes. In addition to the known MAIT1 and MAIT17 effector subsets selected on bone-marrow-derived hematopoietic cells, we identified 5-OP-RU-specific thymocytes that were selected on thymic epithelial cells and differentiated into CD44− naive T cells. MAIT cell positive selection required signaling through the adapter, SAP, that controlled the expression of the transcription factor, ZBTB16. Pseudotemporal ordering of single cells revealed transcriptional trajectories of 5-OP-RU-specific thymocytes selected on either thymic epithelial cells or hematopoietic cells. The resulting model illustrates T cell lineage decisions.

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Fig. 1: ScRNA-seq identifies distinct MAIT subsets in the thymus.
Fig. 2: ScRNA-seq identifies naive MR1:5-OP-RU+ cells.
Fig. 3: Distinct cells select naive or effector MR1:5-OP-RU+ T cells.
Fig. 4: SAP controls positive selection of MAIT cells.
Fig. 5: Modeling MAIT cell development on positive selection.
Fig. 6: Network inference identifies regulators of MAIT cell ontogeny.
Fig. 7: SAP signaling enhances EGR2 expression and induces ZBTB16.

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Data availability

Microarray data, sequencing data, raw and processed expression matrices produced by the different pipelines have been deposited at the European Bioinformatics Institute (EMBL-EBI) under the accession numbers E-MTAB-7702 (microarray) and E-MTAB-7704 (single-cell RNA-seq). The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

We thank the ICGex NGS platform of the Institut Curie (S. Lameiras, S. Baulande, M. Bohec) for technical help with single-cell RNA-seq experiments. ICGex is supported by the grants ANR10EQPX03 (Equipex) and ANR10INBS0908 (France Génomique Consortium) from the Agence Nationale de la Recherche (‘Investissements d’Avenir’ program), by the Canceropole Ile-de-France and by the SiRICCurie program. For the affymetrix data, we thank A. Vieillefon, A. Rapinat and D. Gentien from the Genomics Platform of the Translational Research Department at Institut Curie. We thank V. Dangles-Marie, M. Garcia, C. Pauchard, the mouse facility technicians and the flow cytometry core at Institut Curie. We thank N. Manel for reviewing the manuscript. F.L. was supported by a Marie-Skłodowska Curie individual fellowship (706353) from the European Commission. This work was supported by the Institut National de la Santé et de la Recherche Médicale, Institut Curie, Agence Nationale de la Recherche (Blanc (MAIT, NEOMAIT and DIABMAIT) and Labex DCBIOL). O.L.’s group was supported by the ‘Equipe labellisée de la Ligue Contre le Cancer’.

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Author notes

  1. These authors contributed equally: François Legoux, Jules Gilet.

Authors and Affiliations

  1. INSERM U932, PSL University, Institut Curie, Paris, France

    François Legoux, Jules Gilet, Emanuele Procopio & Olivier Lantz

  2. Production de Protéines Recombinantes, Centre de Recherche en Cancérologie et Immunologie Nantes-Angers, INSERM-1232, Nantes, France

    Klara Echasserieau & Karine Bernardeau

  3. Laboratoire d’immunologie Clinique, Institut Curie, Paris, France

    Olivier Lantz

  4. Centre d’investigation Clinique en Biothérapie, Gustave-Roussy Institut Curie, Paris, France

    Olivier Lantz

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Contributions

F.L. designed and performed experiments, analyzed data and wrote the paper. J.G. designed and performed the bioinformatic analysis, analyzed data and wrote the paper. E.P. performed experiments. K.E. and K.B. provided key reagents. O.L. supervised the project and wrote the paper.

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Correspondence to François Legoux or Olivier Lantz.

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The authors declare no competing interests.

Additional information

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

Integrated supplementary information

Supplementary Figure 1 Single-cell RNA-seq identifies distinct immature and mature MAIT cell subsets in the thymus.

(a) Schematic representation of the experimental design used for generation of scRNA-seq data from tetramer+ thymocytes. (b) Gating strategy used throughout the study for the identification of tetramer+ cells. (c) Heatmap showing expression of the DE genes between different thymocyte subsets (log2, microarray), defining the T cell signatures used in Fig. 1. (d) Density histogram showing the numbers of UMIs detected per cell in 2 independent scRNA-seq analyses of WT tetramer+ thymocytes. Dashed blue lines represent the threshold used to define droplets containing true cells. Replicate wt02 has a higher fraction of droplets with low-transcriptional content, presumably cellular debris (arrow). (f) Scatter plots showing quality control metrics in the 2 independent scRNA-seq experiments. The violin represents the probability density at each value. Each dot represents one cell (wt01 = 3,319 cells, wt02 = 2,827 cells). (f) Scatter plot showing the number of cells per gene in the 2 independent scRNA-seq experiments. (g) Left: PCA, tSNE and UMAP representation of cell clusters identified in the 2 independent scRNA-seq experiments. Right: Expression levels of Cd24a, Rorc and Tbx21 on a UMAP embedding of both independent experiments. (h) UMAP visualization of the mutual nearest neighbor (MNN) correction across both scRNA-seq datasets.

Supplementary Figure 2 scRNA-seq identifies a small subset of naïve MR1:5-OP-RU-specific T cells in thymus and spleen.

(a) Histogram showing Trbv gene segment expression in scRNA-seq data. (b) Flow cytometry showing CD4 and CD8α expression in tetramer+CD24-CD62L+CD44- thymocytes from WT and Mr1−/− B6-MAITCast mice following tetramer-based magnetic enrichment. Representative of 2 independent experiments.

Supplementary Figure 3 SAP controls effector differentiation of MAIT cells.

(a) Left: tetramer staining and TCR-β expression in splenocytes from the indicated B6-MAITCast strains following tetramer-based enrichment. Representative of 2 independent experiments. Right: Frequency of tetramer+ splenocytes from WT (n=5), Sh2d1ay/− (n=7) and Mr1−/− (n=5) mice before magnetic enrichment. *p<0.01 (two-tailed Mann-Whitney tests). (b). TCR-β mfi in tetramer+ thymocytes relative to tetramer−CD24− SP8 thymocytes from the same sample. Data from WT (n=6), Sh2d1ay/− (n=5) and Mr1−/− (n=6) mice are shown. *p<0.01, **p<0.001 (two-tailed t tests) (c) CD4 and CD8α expression in CD24−tetramer+ thymocytes from the indicated strains. Representative of 2 independent experiments. (d). MR1 expression on thymocytes from the indicated mice after in vitro incubation with synthetic 5-OP-RU. Representative of two independent experiments. (e) Percentage of TRBV13+ cells in the indicated tetramer+ populations from WT (n=6) or Sh2d1ay/− (n=3) mice, pooled from two independent experiments. (f) Left: Zbtb16-eGFP reporter expression in tetramer+CD24−CD44− thymocytes from the indicated mice. Representative of 2 independent experiments. Right: Zbtb16-eGFP mfi in the indicated tetramer+ thymocytes subsets relative to tetramer−CD24− SP8 thymocytes from the same sample (n=3, mean with SEM is indicated). (g) Left: intranuclear Ki67 expression in CD24−CD44−tetramer+ thymocytes from the indicated mice. Representative of 2 independent experiments. Right: Percentage of Ki67+tetramer+ thymocytes in the indicated subsets in WT (n=9), Sh2d1ay/− (n=3) and Mr1−/− (n=3) mice (mean with SEM is indicated). (h). Left: tetramer staining and CD44 expression in lung T cells of the indicated mice. Representative of 3 independent experiments. Right: Frequency of tetramer+ cells in lungs of WT (n=8), Sh2d1ay/− (n=5) and Mr1−/− (n=5) mice. *p<0.01 (two-tailed Mann-Whitney tests).

Supplementary Figure 4 T cell development modeling reveals major transcriptional changes upon positive selection by hematopoietic cells.

(a) Zbtb16-eGFP reporter expression in tetramer+CD24−CD44+ thymocytes expressing CD138 (MAIT17) or CD122 (MAIT1). Representative of 5 independent experiments. (b) Partition-based Approximate Graph Abstraction based on scRNA-seq data from WT tetramer+ thymocytes. Size of the nodes (dots) reflects the number of cells per cluster, thickness of the edges (lines) is proportional to the transition probability between cell types (minimum threshold=0.1). (c) Diffusion map based on scRNA-seq data from merged WT and Sh2d1ay/− tetramer+ thymocytes. (d) Raw counts of spliced and unspliced variants, intron retention, and signature of spliceosome inhibitors (see Supplementary Table 1a) visualized on a UMAP embedding. (e) Inference of transcriptional dynamics with velocyto. Vectors of RNA velocities (arrows) are projected on a UMAP embedding (left) or diffusion map embedding (right). Intron retention is defined as the level of unspliced transcripts over all forms of mRNA. Spliceosome inhibitor signature is derived from the list provided in Supplementary Table 1a-h.

Supplementary Figure 5 Network analysis identifies Egr2 as a key gene during early differentiation of MAIT cells.

(a) Gene regulatory networks inferred from WT and Sh2d1ay/− samples (representation layout: Kuwai-Kamai algorithm). (b) Top: Global inferred network showing gene communities identified by a greedy optimization of modularity. Bottom: Representation of the global network provided as supplementary material (cytoscape file), blue and orange edges represent expression linkages identified in WT and Sh2d1ay/− samples, respectively (representation layouts: Kuwai-Kamai algorithm). (c) Number of genes present in each community identified in (b). (d) List of the top 25 genes per community identified in (b), sorted by decreased normalized betweenness centrality. (e) Functional enrichment test of gene communities identified in (b) (see Methods). (f) Graph showing the start of expression, inferred from the pseudotime defined in Fig. 5, of the top 25 (by decreased betweenness centrality) TF genes from the communities identified in (b).

Supplementary Figure 6 Graphical summary of the mechanisms of lineage decisions in MR1:5-OP-RU-specific T cells.

Left: generation of naive metabolite-specific T cells upon positive selection by MR1 on thymic epithelial cells. Right: differentiation of effector MAIT cells selected by MR1 on hematopoietic cells. The color code refers to the clusters defined in Fig. 1b and Fig. 7c.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Supplementary Table 2 and Supplementary Note

Reporting Summary

Supplementary Table 1a

Lists of gene signatures.

Supplementary Table 1b

Differentially expressed genes per cluster.

Supplementary Table 1c

Gene communities identified by network inference.

Supplementary Table 1d

Functional enrichment tests per gene community.

Cytoscape

Gene regulatory networks inferred from WT and Sh2d1ay/− samples

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Legoux, F., Gilet, J., Procopio, E. et al. Molecular mechanisms of lineage decisions in metabolite-specific T cells. Nat Immunol 20, 1244–1255 (2019). https://doi.org/10.1038/s41590-019-0465-3

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