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. 2023 Jan 20:13:1092028.
doi: 10.3389/fimmu.2022.1092028. eCollection 2022.

Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection

Marte Heimli  1 , Siri Tennebø Flåm  1 , Hanne Sagsveen Hjorthaug  1 , Don Trinh  2 , Michael Frisk  3   4 , Karl-Andreas Dumont  5 , Teodora Ribarska  1 , Xavier Tekpli  1 , Mario Saare  1 , Benedicte Alexandra Lie  1
Affiliations

Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection

Marte Heimli et al. Front Immunol. .

Abstract

To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymu​​s. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ or CD8+ lineage commitment, while markers of different agonist selected T cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff), and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localisation, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.

Keywords: T agonist selection; T cell development; antigen-presenting cells; autoimmunity; human thymus; multi-modal; single-cell RNA sequencing; spatial transcriptomics.

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Conflict of interest statement

TR was employed by the company Exact Sciences Innovation Ltd. The remaining 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.

Figures

Figure 1
Figure 1
Multimodal profiling of human paediatric thymus. (A) Experimental set-up. Tissue was subjected to dissociation (N=5) or snap frozen (N=8) for sectioning. From donors where tissue was to be dissociated, EDTA blood was also collected. CITE-seq was performed for PBMCs (n=5) and thymic cells (n=15) at three separate stages of dissociation: 1. unenriched, 2. APC enriched, and 3. CD45-depleted. Tissue sections were used for spatial transcriptomics. (B) UMAP of 83 847 cells colour-coded for the cell types identified in thymus.
Figure 2
Figure 2
Reanalysis of thymocyte subset. (A) UMAP of thymocyte subset. (B, D) Pseudotime analysis by Monocle3. (C, E) Expression of selected genes in thymocyte subset. The agonist.set.score reflects the mean expression of NR4A1, NR4A3, NFKBID, NFATC1, BCL2L11 and PDCD1. (F) Expression of selected genes in thymocyte subset along pseudotime. Colouring according to annotations in (A).
Figure 3
Figure 3
Reanalysis of DC subset and cell-to-cell interactions predicted by cellPhoneDB. (A) UMAP of DC subset. (B) Expression of selected genes in DC subset. (C) Density plot created by Nebulosa showing expression of selected proteins in the DC subset, adt = antibody derived tag. (D) Heatmap showing the number of predicted interactions between each combination of annotated cell types/states. (E) Expression of selected ligand-receptor pairs in selected pairs of cell types/states. Colour represents the mean of the average expression level of molecule 1 in cell type/state 1, and molecule 2 in cell type/state 2.
Figure 4
Figure 4
Reanalysis of B cell subset. (A) UMAP of B cell subset. (B) Expression of selected genes in B cell subset. (C) Expression of immunoglobulin genes in B cell subset.
Figure 5
Figure 5
Reanalysis of TEC subset and comparison of thymic and peripheral cell populations. (A) UMAP of TEC subset. (B, C) Expression of selected genes in TEC subset. (D) UMAP of PBMCs with predicted annotations. (E) Expression of selected marker genes in PBMCs. (F) Thymic dataset mapped onto the UMAP coordinates of the PBMC dataset. (G) Merged UMAP of PBMC and thymic datasets after mapping of thymic dataset onto the PBMC UMAP coordinates.
Figure 6
Figure 6
Spatial transcriptomics of human paediatric thymus. (A) UMAP of spatial transcriptomics dataset. (B) Expression of selected marker genes in spatial transcriptomics dataset. The agonist.set.score reflects the mean expression of NR4A1, NR4A3, NFKBID, NFATC1, BCL2L11 and PDCD1. (C, E) Representative tissue sections coloured by annotations in (A). (D, F) Spatial deconvolution of representative tissue sections by SPOTlight.
Figure 7
Figure 7
Predicted localisation of selected agonist selected and antigen-presenting cell populations by SPOTlight. (A). Predicted proportion of selected populations among tissue spots. (B–E). Localisation of spots predicted to include selected cell populations on representative tissue sections.
Figure 8
Figure 8
Predicted localisation of selected stromal cell populations by SPOTlight. (A). Predicted proportion of selected populations among tissue spots. (B–E). Localisation of spots predicted to include selected cell populations on representative tissue sections.
Figure 9
Figure 9
Putative model of the cellular milieu for T agonist selection in the thymus.
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Funding was provided by the Norwegian Research Council (project number 274718) and the Norwegian Diabetes Association.