Thymocytes trigger self-antigen-controlling pathways in immature medullary thymic epithelial stages

  1. Noella Lopes
  2. Nicolas Boucherit
  3. Jérémy C Santamaria
  4. Nathan Provin
  5. Jonathan Charaix
  6. Pierre Ferrier
  7. Matthieu Giraud
  8. Magali Irla

  • Curated by eLife

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    Evaluation Summary:

    This manuscript will be of interest to readers in the field of immunology and especially in the induction of immune tolerance in the thymus. The work uses several mouse models to substantially broaden the current understanding of MHCII/TCR -mediated cell-cell crosstalk in the thymus and suggests a novel mechanism that contributes to the generation of functional and self-tolerant T-cells.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

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Abstract

Interactions of developing T cells with Aire + medullary thymic epithelial cells expressing high levels of MHCII molecules (mTEC hi ) are critical for the induction of central tolerance in the thymus. In turn, thymocytes regulate the cellularity of Aire + mTEC hi . However, it remains unknown whether thymocytes control the precursors of Aire + mTEC hi that are contained in mTEC lo cells or other mTEC lo subsets that have recently been delineated by single-cell transcriptomic analyses. Here, using three distinct transgenic mouse models, in which antigen presentation between mTECs and CD4 + thymocytes is perturbed, we show by high-throughput RNA-seq that self-reactive CD4 + thymocytes induce key transcriptional regulators in mTEC lo and control the composition of mTEC lo subsets, including Aire + mTEC hi precursors, post-Aire and tuft-like mTECs. Furthermore, these interactions upregulate the expression of tissue-restricted self-antigens, cytokines, chemokines, and adhesion molecules important for T-cell development. This gene activation program induced in mTEC lo is combined with a global increase of the active H3K4me3 histone mark. Finally, we demonstrate that these self-reactive interactions between CD4 + thymocytes and mTECs critically prevent multiorgan autoimmunity. Our genome-wide study thus reveals that self-reactive CD4 + thymocytes control multiple unsuspected facets from immature stages of mTECs, which determines their heterogeneity.

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  1. Version published to 10.7554/elife.69982 on eLife
  2. eLife

    Author Response

    Reviewer #1 (Public Review):

    Lopes et al present an exploration of the functional interactions between developing CD4+ T cells and mTEC in the thymus. The study is interesting both because the precise differentiation stages for mTEC development is currently in flux with substantial recent discoveries, and because the manner in which developing T cells influence this development is also currently in question. The finding that CD4 cells induce a transcriptional reprogramming in mTEClo cells to induce cells suitable for orchestrating T cell development is therefore novel and interesting. The identification of a propensity to multi-organ immunity adds further to the impact of the work.

    We thank Reviewer 1 for highlighting that this study is novel and interesting, especially because the impact of thymocytes on the development of specific mTEC subsets remains elusive.

    A weakness of the work is that, given the complexity of the two-way interaction between CD4 cells and mTEC, some of the experimental interventions are somewhat blunt, leading to conclusions that are not well supported by the results. For instance, the differences in NFkB signalling described in Fig 1 are measured on total mTEClo cells from 'deltaCD4 mice. Given that the premise of the study is heterogeneity in mTEClo cells, it seems important to address whether these differences relate to the differences in representation of the different mTEClo populations (which might exhibit different NfkB signalling) before inferring a direct effect on signalling. Similarly, since the knockout was directed to the mTEC, it is not clear that the phenotype relates to CD4 deficiency. Thus, the phenotype might well be influenced by subtle changes in mTEC composition rather than direct effects on signalling.

    We agree that differences observed in IKKα and p38 signaling in mTEClo described in Figure 1 could be influenced by the composition in mTEClo subsets. Unfortunately, it was technically not possible to detect total and phosphorylated IKKα and p38 proteins in tuft-like mTEC by flow cytometry. Indeed, antibodies directed against these proteins are all produced in rabbit, similarly to the anti-DCLK1 antibody used to identify tuft-like cells, and are all detected through anti-rabbit secondary antibodies. Moreover, there is no valuable markers to date that allow the identification of post-Aire and TAC-TEC cells by flow cytometry. Therefore, we cannot exclude that an altered composition in mTEClo subsets in deltaCD4 mice could influence the level of total and phosphorylated IKKα and p38 proteins. Nevertheless, given that there is no defect in the proportion of CCL21+ cells that represent the majority of mTEClo (cf. new Figure 3D) and that there is only a slight reduction in the proportion of DCLK1+ tuft-like cells in mTEClo (cf. Figure 3E), it is unlikely that the substantial and homogeneous reduction in the levels of Phospho-IKKα and Phospho-p38 observed in deltaCD4 mice (cf. Figure 1A) could only rely on mTEClo subset composition. Overall, these observations argue in favor of defective IKKα and p38 signaling in absence of CD4+ thymocytes. This point is discussed in lines 396-400.

    More generally, the single cell analysis that forms the major part of the manuscript is difficult to interpret given the context - that dynamic changes in the differentiation state of this heterogeneous population of cells is likely to lead to differences in gene expression states, but the 'snapshot' analyses inherent to this single cell analysis does not allow for dissection of cause and effect. For instance, Figs 1- 3 convincingly demonstrate that the mTEC composition is different in the different mice, and that signalling and transcription is different in the mTEClo precursors. Demonstration of a functional connection between these two observations would add substantially to these findings.

    We analyzed single-cell RNA-seq data derived from Wells KL et al. (Elife. 2020) (cf. new Figure 1-figure supplement 4) to determine the respective expression pattern in mTEC subsets (i.e. CCL21+, TAC-TEC, Aire+, Post-Aire and Tuft-like cells) of genes upregulated by self-reactive CD4+ thymocytes. Some of these genes were associated with Post-Aire and Tuft-like cells (cf. new Figures 1I, 2K and 4L) in accordance with the reduced proportions of Post-Aire and DCLK1+ Tuft-like cells observed by flow cytometry among mTEClo cells (cf. Figure 3E, Figure 5H, Figure 3-figure supplement 2A and Figure 5-figure supplement 3A). Importantly, many of these genes were highly expressed by Aire+ mTECs, indicating that self-reactive CD4+ thymocytes enhance the transcriptional activity in mTEClo accompanying the transition to Aire+ mTECs. Accordingly, several of these genes were already expressed in the precursors of Aire+ mTEChi, recently called TAC-TEC (for transit-amplifying TEC). These new results are now discussed in the text.

    Reviewer #2 (Public Review):

    The study by Lopes et al focuses on the role of thymocyte-epithelial cell cross-talk in the thymus and aims to determine the role of thymocyte derived signals in the differentiation of thymic epithelial cells. The study uses three different knockout models in which the thymocyte derived signals are defective and studies the resulting effect on mTEC maturation. The study suggests that these signals indeed play a crucial role in mTEc maturation and proposes a novel mechanism by which the developing T-cells direct the functionality of the thymic stromal compartment.

    The study is mostly well designed and performed and the manuscript well written. Although the conclusions are largely based on substantial scientific evidence few points should be addressed in order to make the message of the study more precise and clearer:

    We thank Reviewer 2 for highlighting the relevance of our study in providing a novel mechanism by which the developing T cells direct the functionality of the thymic stromal compartment.

    1. According to the recent scRNA sequencing studies (reviewed in Kadouri et al 2020), the mTEClow mTECs contain at least two distinct subpopulations: the functionally mature CCL21-producing mTEC I and the immature mTECs giving rise to mTEC II and III. In its current form, however, the manuscript largely ignores the presence of mTEC I cells. The authors should make effort to analyze the changes in this population in the knockout models (by sequencing and/or qPCR) and cover this population also in the introduction and discussion.

    We now analyzed in this revised version the CCL21-producing mTEC I subset by flow cytometry in the three distinct transgenic mouse models used to decipher the impact of CD4+ thymocyte interactions on the mTEC compartment (cf. New Figure 3D, new Figure 5G and new Figure 3-figure supplement 3B). Using single-cell RNA-seq data, we also investigated whether genes upregulated by crosstalk with CD4+ thymocytes are specifically associated or not with CCL21+ mTECs (cf. New Figures 1I, 2K and 4L). Overall, we found that self-reactive CD4+ thymocytes have a moderate or no impact on CCL21+ mTEC in the different mouse models analyzed. The mTEC I subset is now covered in the introduction and these new results are discussed.

    1. As together with the classical mTEC classification (mTEChigh etc), the new scRNAseq based classification of mTECs (mTEC I etc) is used more and more often, it would be helpful to give in parallel the names of the subpopulations according to both of these classifications, at least when different mTECs are described/introduced in the beginning of the manuscript.

    Thank you for this comment. We now mentioned in the introduction the new scRNAseq based classification of mTECs (i.e. mTEC I-IV; cf. lines 71-74) when mTEC subsets are described. We also used this classification when we have analyzed single-cell RNA-seq data in order to deepen and strengthen our study (cf. new Figure 1-figure supplement 4 and lines 150-155)

    1. In Figures 2 and 4 the authors show data only on selected chemokines, cytokines and adhesion molecules and make a conclusion suggesting that these groups of proteins are down-regulated. To make this conclusion, however, the authors should analyze/show the whole groups i.e. all chemokines, cytokines and adhesion molecules (as they do for TRAs). Alternatively, the authors should be more careful/specific with their conclusions. The same is true for HDAC3 regulated transcriptional regulators and transcription factors.

    Heatmaps described in Figures 1, 2 and 4 show the whole set of chemokines, cytokines and adhesion molecules that was statistically differentially regulated in the mutant mice analyzed. Similarly, we only show HDAC3-regulated transcriptional regulators (cf. Figure 1G, Figure 2I and Figure 4I) and activation factors (cf. Figure 2G and Figure 4H) that were statistically differentially regulated. We clarified this point in the corresponding legends.

    Reviewer #3 (Public Review):

    The authors have performed extensive studies to analyse the role of MHCII/TCR interactions in shaping mTEC differentiation. This has been an important question in the field. There are at least two different messages in the manuscript which are related but make the authors' message less clear; -the main message appears to be that the absence of MHCII/TCR interactions between mTECs and CD4+ alters the mTEClo compartment -a secondary message is that disrupted MHCII/TCR interactions between mTECs and CD4+ thymocytes lead to an altered TCRVβ repertoire (see comment below).

    The authors conclude that their RNAseq data in figures 1 and 4 show that genes are upregulated/downregulated. However, it could also be that their differential gene/cytokine expression is due to the presence of different mTEClo subsets, and the authors show this in figure 3. This would change the conclusion to: CD4+ thymocytes alter mTEClo differentiation states, associated with differential gene expression. This is also the case in figure 2. For instance, Lopez et al state that AIRE expression 4.5-fold higher in mTECdMHCII cells but then they show that there are different percentages of AIRE+ cells (change in the mTEClo subsets in the ko mice).

    To clarify whether differences in gene expression observed by RNAseq could be due to the presence of different mTEClo subsets, we analyzed their respective expression pattern in mTEC (i.e. CCL21+, TAC-TEC, Aire+, Post-Aire and Tuft-like cells) by analyzing single-cell RNA-seq data recently published by Wells KL et al. (Elife. 2020) (cf. new Figure 1-figure supplement 4). Some differentially regulated genes were associated with Post-Aire and Tuft-like cells (cf. new Figure 1I, new Figure 2K and new Figure 4L). These findings are in agreement with the reduced proportions of Post-Aire and DCLK1+ Tuft-like cells observed respectively by histology and flow cytometry (cf. Figure 3D, Figure 5H, Figure 3-figure supplement 2A and Figure 5-figure supplement 3A). Importantly, many of these genes were highly expressed by Aire+ mTECs, indicating that self-reactive CD4+ thymocytes enhance the transcriptional activity in mTEClo accompanying the transition to Aire+ mTECs. Accordingly, several of these genes were already expressed in the precursors of Aire+ mTEChi, recently called TAC-TEC (for transit-amplifying TEC). These new results are now discussed in the text.

    In many instances, mTEClo subsets are shown as percentages but quantifications are presented as total numbers. This is sometimes confusing as percentages of mTEClo cells is often not different between WT, dCD4 and mTECdMHCII mice. Are differences due to lower total levels of thymocytes/ mTECs?

    As mentioned in the “Materials and methods”, mTEC subsets were analyzed on CD45- negative enriched cells purified using anti-CD45 magnetic beads by autoMACS. Then, mTEClo were identified in EpCAM+ total TEC as depicted in Figure1-supplement 1. Thus, differences could not be due to lower total levels of thymocytes/mTECs.

    For a better clarity, we now show both the percentages and total numbers of mTEC subsets (cf. new Figure 3A,C-E and Figure 5F-H). These quantifications show that the percentages of mTEC subsets are significantly altered, but to a lesser extent than absolute numbers. This is explained by the fact that transgenic mice with disrupted “crosstalk with CD4+ thymocytes” have reduced numbers of total mTEC (cf. new Figure 3A and 5B).

    In figure 3, the authors show mTEChi cells in dCD4 and mTECdMHCII mice. How do these cells develop?

    In agreement with our previous studies (Irla et al. Immunity 2008 and Plos One 2012), although strongly affected, the development of mTEChi cells is not fully abrogated in dCD4 and mTECdMHCII mice (cf. new Figure 3A). The residual development of these cells in these mice could rely on invariant NKT that also express RANKL. In agreement with this hypothesis, invariant NKT cells have been shown to participate in Aire+ mTEChi differentiation in the post-natal thymus, but to a lesser extent than CD4+ thymocytes (White AJ et al. J Immunol 2014). We clarified this point in the discussion (lines 441-444).

    The authors state that the TCRVb repertoire is altered in autoreactive T cells developing when MHCII/TCR interactions between mTECs and CD4+ thymocytes are abrogated. This is based on percentages of T cells in different TCRVβ families. To show that TCR selection differs, shouldn't the authors sequence the different TCRs and evaluate constraints on TCR-CDR3 segments?

    We agree that the differences observed by flow cytometry in the percentages of TCRVβ usage are insufficient to conclude that the TCRVB repertoire is altered. We modified the text accordingly in the Results section (cf. lines 97, 368) and discussed that future experiments based on TCR sequencing are expected to clarify this issue (cf. lines 482-489).

  3. eLife

    Evaluation Summary:

    This manuscript will be of interest to readers in the field of immunology and especially in the induction of immune tolerance in the thymus. The work uses several mouse models to substantially broaden the current understanding of MHCII/TCR -mediated cell-cell crosstalk in the thymus and suggests a novel mechanism that contributes to the generation of functional and self-tolerant T-cells.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

  4. eLife

    Reviewer #1 (Public Review):

    Lopes et al present an exploration of the functional interactions between developing CD4+ T cells and mTEC in the thymus. The study is interesting both because the precise differentiation stages for mTEC development is currently in flux with substantial recent discoveries, and because the manner in which developing T cells influence this development is also currently in question. The finding that CD4 cells induce a transcriptional reprogramming in mTEClo cells to induce cells suitable for orchestrating T cell development is therefore novel and interesting. The identification of a propensity to multi-organ immunity adds further to the impact of the work.

    The authors are to be commended in applying the analysis to two different mouse models of disrupting CD4 cells ((deltaCD4 and deltaMHCII). However, both are indirect means of removing CD4 cells, and presumably leave open the possibility of additional non-CD4-related disruptions.

    A weakness of the work is that, given the complexity of the two-way interaction between CD4 cells and mTEC, some of the experimental interventions are somewhat blunt, leading to conclusions that are not well supported by the results. For instance, the differences in NFkB signalling described in Fig 1 are measured on total mTEClo cells from 'deltaCD4 mice. Given that the premise of the study is heterogeneity in mTEClo cells, it seems important to address whether these differences relate to the differences in representation of the different mTEClo populations (which might exhibit different NfkB signalling) before inferring a direct effect on signalling. Similarly, since the knockout was directed to the mTEC, it is not clear that the phenotype relates to CD4 deficiency. Thus, the phenotype might well be influenced by subtle changes in mTEC composition rather than direct effects on signalling.

    More generally, the single cell analysis that forms the major part of the manuscript is difficult to interpret given the context - that dynamic changes in the differentiation state of this heterogeneous population of cells is likely to lead to differences in gene expression states, but the 'snapshot' analyses inherent to this single cell analysis does not allow for dissection of cause and effect. For instance, Figs 1- 3 convincingly demonstrate that the mTEC composition is different in the different mice, and that signalling and transcription is different in the mTEClo precursors. Demonstration of a functional connection between these two observations would add substantially to these findings.

  5. eLife

    Reviewer #2 (Public Review):

    The study by Lopes et al focuses on the role of thymocyte-epithelial cell cross-talk in the thymus and aims to determine the role of thymocyte derived signals in the differentiation of thymic epithelial cells. The study uses three different knockout models in which the thymocyte derived signals are defective and studies the resulting effect on mTEC maturation. The study suggests that these signals indeed play a crucial role in mTEc maturation and proposes a novel mechanism by which the developing T-cells direct the functionality of the thymic stromal compartment.
    The study is mostly well designed and performed and the manuscript well written. Although the conclusions are largely based on substantial scientific evidence few points should be addressed in order to make the message of the study more precise and clearer:

    1. According to the recent scRNA sequencing studies (reviewed in Kadouri et al 2020), the mTEClow mTECs contain at least two distinct subpopulations: the functionally mature CCL21-producing mTEC I and the immature mTECs giving rise to mTEC II and III. In its current form, however, the manuscript largely ignores the presence of mTEC I cells. The authors should make effort to analyze the changes in this population in the knockout models (by sequencing and/or qPCR) and cover this population also in the introduction and discussion.

    2. As together with the classical mTEC classification (mTEChigh etc), the new scRNAseq based classification of mTECs (mTEC I etc) is used more and more often, it would be helpful to give in parallel the names of the subpopulations according to both of these classifications, at least when different mTECs are described/introduced in the beginning of the manuscript.

    3. In Figures 2 and 4 the authors show data only on selected chemokines, cytokines and adhesion molecules and make a conclusion suggesting that these groups of proteins are down-regulated. To make this conclusion, however, the authors should analyze/show the whole groups i.e. all chemokines, cytokines and adhesion molecules (as they do for TRAs). Alternatively, the authors should be more careful/specific with their conclusions. The same is true for HDAC3 regulated transcriptional regulators and transcription factors.

  6. eLife

    Reviewer #3 (Public Review):

    The authors have performed extensive studies to analyse the role of MHCII/TCR interactions in shaping mTEC differentiation. This has been an important question in the field. There are at least two different messages in the manuscript which are related but make the authors' message less clear:

    -the main message appears to be that the absence of MHCII/TCR interactions between mTECs and CD4+ alters the mTEClo compartment
    -a secondary message is that disrupted MHCII/TCR interactions between mTECs
    and CD4+ thymocytes lead to an altered TCRVβ repertoire (see comment below)

    The authors conclude that their RNAseq data in figures 1 and 4 show that genes are upregulated/downregulated. However, it could also be that their differential gene/cytokine expression is due to the presence of different mTEClo subsets, and the authors show this in figure 3. This would change the conclusion to: CD4+ thymocytes alter mTEClo differentiation states, associated with differential gene expression. This is also the case in figure 2. For instance, Lopez et al state that AIRE expression 4.5-fold higher in mTECdMHCII cells but then they show that there are different percentages of AIRE+ cells (change in the mTEClo subsets in the ko mice).

    In many instances, mTEClo subsets are shown as percentages but quantifications are presented as total numbers. This is sometimes confusing as percentages of mTEClo cells is often not different between WT, dCD4 and mTECdMHCII mice. Are differences due to lower total levels of thymocytes/ mTECs?

    In figure 3, the authors show mTEChi cells in dCD4 and mTECdMHCII mice. How do these cells develop?

    Th authors state that the TCRVb repertoire is altered in autoreactive T cells developing when MHCII/TCR interactions between mTECs and CD4+ thymocytes are abrogated. This is based on percentages of T cells in different TCRVβ families. To show that TCR selection differs, shouldn't the authors sequence the different TCRs and evaluate constraints on TCR-CDR3 segments?

  7. Version published to 10.1101/2020.11.29.399923v1 on bioRxiv