Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Pre-T cell receptor self-MHC sampling restricts thymocyte dedifferentiation

Abstract

Programming T cells to distinguish self from non-self is a vital, multi-step process that occurs in the thymus1,2,3,4. Signalling through the pre-T cell receptor (preTCR), a CD3-associated heterodimer comprising an invariant pTα chain and a clone-specific β chain, is a critical early checkpoint in thymocyte development within the αβ T cell lineage5,6. PreTCRs arrayed on CD4CD8 double-negative thymocytes ligate peptides bound to major histocompatibility complex molecules (pMHC) on thymic stroma, similar to αβ T cell receptors that appear on CD4+CD8+ double-positive thymocytes, but via a different molecular docking strategy7,8,9,10. Here we show the consequences of these distinct interactions for thymocyte progression using synchronized fetal thymic progenitor cultures that differ in the presence or absence of pMHC on support stroma, and single-cell transcriptomes at key thymocyte developmental transitions. Although major histocompatibility complex (MHC)-negative stroma fosters αβ T cell differentiation, the absence of preTCR–pMHC interactions leads to deviant thymocyte transcriptional programming associated with dedifferentiation. Highly proliferative double-negative and double-positive thymocyte subsets emerge, with antecedent characteristics of T cell lymphoblastic and myeloid malignancies. Compensatory upregulation of diverse MHC class Ib proteins in B2m/H2-Ab1 MHC-knockout mice partially safeguards in vivo thymocyte progression, although disseminated double-positive thymic tumours may develop with ageing. Thus, as well as promoting β chain repertoire broadening for subsequent αβ T cell receptor utilization, preTCR–pMHC interactions limit cellular plasticity to facilitate normal thymocyte differentiation and proliferation that, if absent, introduce developmental vulnerabilities.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Developmental trajectories for thymocyte-like development on MHC+ or MHC supporting stroma.
Fig. 2: Uncoupling of the transcriptome and TCR β chain repertoire from phenotype in thymocyte-like cells developing on MHC stroma.
Fig. 3: Single-cell transcriptomics of the MHC DN4 unusual cluster reveal complex proliferative and lineage abnormalities.
Fig. 4: Single-cell transcriptomics of the MHC DP abnormal cluster reveal dedifferentiation and reprogramming to include a myeloid programme.

Similar content being viewed by others

Data availability

All sequence files have been deposited at NCBI Gene Expression Omnibus (GEO) under accession GSE186049.  Source data are provided with this paper.

References

  1. Hosokawa, H. & Rothenberg, E. V. How transcription factors drive choice of the T cell fate. Nat. Rev. Immunol. 21, 162–176 (2021).

    Article  CAS  Google Scholar 

  2. Koch, U. et al. Delta-like 4 is the essential, nonredundant ligand for Notch1 during thymic T cell lineage commitment. J. Exp. Med. 205, 2515–2523 (2008).

    Article  CAS  Google Scholar 

  3. Rodewald, H. R., Ogawa, M., Haller, C., Waskow, C. & DiSanto, J. P. Pro-thymocyte expansion by c-kit and the common cytokine receptor γ chain is essential for repertoire formation. Immunity 6, 265–272 (1997).

    Article  CAS  Google Scholar 

  4. Shortman, K., Egerton, M., Spangrude, G. J. & Scollay, R. The generation and fate of thymocytes. Semin. Immunol. 2, 3–12 (1990).

    CAS  Google Scholar 

  5. Kreslavsky, T. et al. β-Selection-induced proliferation is required for αβ T cell differentiation. Immunity 37, 840–853 (2012).

    Article  CAS  Google Scholar 

  6. von Boehmer, H. The thymus in immunity and in malignancy. Cancer Immunol. Res. 2, 592–597 (2014).

    Article  Google Scholar 

  7. Das, D. K. et al. Pre-T cell receptors (Pre-TCRs) leverage Vβ complementarity determining regions (CDRs) and hydrophobic patch in mechanosensing thymic self-ligands. J. Biol. Chem. 291, 25292–25305 (2016).

    Article  CAS  Google Scholar 

  8. Li, X. et al. Pre-T cell receptors topologically sample self-ligands during thymocyte β-selection. Science 371, 181–185 (2021).

    Article  ADS  Google Scholar 

  9. Mallis, R. J., Arthanari, H., Lang, M. J., Reinherz, E. L. & Wagner, G. NMR-directed design of pre-TCRβ and pMHC molecules implies a distinct geometry for pre-TCR relative to αβTCR recognition of pMHC. J. Biol. Chem. 293, 754–766 (2018).

    Article  CAS  Google Scholar 

  10. Mallis, R. J. et al. Pre-TCR ligand binding impacts thymocyte development before αβTCR expression. Proc. Natl Acad. Sci. USA 112, 8373–8378 (2015).

    Article  ADS  CAS  Google Scholar 

  11. Davis, M. M. & Bjorkman, P. J. T-cell antigen receptor genes and T-cell recognition. Nature 334, 395–402 (1988).

    Article  ADS  CAS  Google Scholar 

  12. Rudolph, M. G., Stanfield, R. L. & Wilson, I. A. How TCRs bind MHCs, peptides, and coreceptors. Annu. Rev. Immunol. 24, 419–466 (2006).

    Article  CAS  Google Scholar 

  13. Wang, J. H. & Reinherz, E. L. The structural basis of αβ T-lineage immune recognition: TCR docking topologies, mechanotransduction, and co-receptor function. Immunol. Rev. 250, 102–119 (2012).

    Article  Google Scholar 

  14. Saint-Ruf, C. et al. Analysis and expression of a cloned pre-T cell receptor gene. Science 266, 1208–1212 (1994).

    Article  ADS  CAS  Google Scholar 

  15. Xiong, J., Armato, M. A. & Yankee, T. M. Immature single-positive CD8+ thymocytes represent the transition from Notch-dependent to Notch-independent T-cell development. Int. Immunol. 23, 55–64 (2011).

    Article  CAS  Google Scholar 

  16. Petrie, H. T. et al. Multiple rearrangements in T cell receptor α chain genes maximize the production of useful thymocytes. J. Exp. Med. 178, 615–622 (1993).

    Article  CAS  Google Scholar 

  17. Shinkai, Y. et al. Restoration of T cell development in RAG-2-deficient mice by functional TCR transgenes. Science 259, 822–825 (1993).

    Article  ADS  CAS  Google Scholar 

  18. Wilson, A., Held, W. & MacDonald, H. R. Two waves of recombinase gene expression in developing thymocytes. J. Exp. Med. 179, 1355–1360 (1994).

    Article  CAS  Google Scholar 

  19. Klein, L., Kyewski, B., Allen, P. M. & Hogquist, K. A. Positive and negative selection of the T cell repertoire: what thymocytes see (and don’t see). Nat. Rev. Immunol. 14, 377–391 (2014).

    Article  CAS  Google Scholar 

  20. Fehling, H. J., Krotkova, A., Saint-Ruf, C. & von Boehmer, H. Crucial role of the pre-T-cell receptor α gene in development of αβ but not γδ T cells. Nature 375, 795–798 (1995).

    Article  ADS  CAS  Google Scholar 

  21. Grusby, M. J. et al. Mice lacking major histocompatibility complex class I and class II molecules. Proc. Natl Acad. Sci. USA 90, 3913–3917 (1993).

    Article  ADS  CAS  Google Scholar 

  22. Irving, B. A., Alt, F. W. & Killeen, N. Thymocyte development in the absence of pre-T cell receptor extracellular immunoglobulin domains. Science 280, 905–908 (1998).

    Article  ADS  CAS  Google Scholar 

  23. Koller, B. H., Marrack, P., Kappler, J. W. & Smithies, O. Normal development of mice deficient in β2M, MHC class I proteins, and CD8+ T cells. Science 248, 1227–1230 (1990).

    Article  ADS  CAS  Google Scholar 

  24. Mizsei, R. et al. A general chemical crosslinking strategy for structural analyses of weakly interacting proteins applied to preTCR–pMHC complexes. J. Biol. Chem. 296, 100255 (2021).

    Article  CAS  Google Scholar 

  25. Xiao, S. Y., Li, Y. & Chen, W. F. Kinetics of thymocyte developmental process in fetal and neonatal mice. Cell Res. 13, 265–273 (2003).

    Article  Google Scholar 

  26. Mingueneau, M. et al. The transcriptional landscape of αβ T cell differentiation. Nat. Immunol. 14, 619–632 (2013).

    Article  CAS  Google Scholar 

  27. Allman, D. et al. Separation of Notch1 promoted lineage commitment and expansion/transformation in developing T cells. J. Exp. Med. 194, 99–106 (2001).

    Article  CAS  Google Scholar 

  28. Forman, J. & Fischer Lindahl, K. Listing, location, binding motifs, and expression of nonclassical class i and related genes and molecules. Curr. Protoc. Immunol. 49, A.1M.1–A.1M.13 (2002).

    Google Scholar 

  29. Fujita, T., Yuno, M., Okuzaki, D., Ohki, R. & Fujii, H. Identification of non-coding RNAs associated with telomeres using a combination of enChIP and RNA sequencing. PLoS ONE 10, e0123387 (2015).

    Article  Google Scholar 

  30. Lin, Y. W. & Aplan, P. D. Gene expression profiling of precursor T-cell lymphoblastic leukemia/lymphoma identifies oncogenic pathways that are potential therapeutic targets. Leukemia 21, 1276–1284 (2007).

    Article  CAS  Google Scholar 

  31. Li, R. & Guan, M. X. Human mitochondrial leucyl-tRNA synthetase corrects mitochondrial dysfunctions due to the tRNALeu(UUR) A3243G mutation, associated with mitochondrial encephalomyopathy, lactic acidosis, and stroke-like symptoms and diabetes. Mol. Cell. Biol. 30, 2147–2154 (2010).

    Article  CAS  Google Scholar 

  32. Coustan-Smith, E. et al. Early T-cell precursor leukaemia: a subtype of very high-risk acute lymphoblastic leukaemia. Lancet Oncol. 10, 147–156 (2009).

    Article  CAS  Google Scholar 

  33. Vadillo, E., Dorantes-Acosta, E., Pelayo, R. & Schnoor, M. T cell acute lymphoblastic leukemia (T-ALL): New insights into the cellular origins and infiltration mechanisms common and unique among hematologic malignancies. Blood Rev. 32, 36–51 (2018).

    Article  CAS  Google Scholar 

  34. Dai, Y.-T. et al. Transcriptome-wide subtyping of pediatric and adult T cell acute lymphoblastic leukemia in an international study of 707 cases. Proc. Natl Acad. Sci. USA 119, e2120787119 (2022).

    Article  Google Scholar 

  35. Pellicci, D. G., Koay, H. F. & Berzins, S. P. Thymic development of unconventional T cells: how NKT cells, MAIT cells and γδ T cells emerge. Nat. Rev. Immunol. 20, 756–770 (2020).

    Article  CAS  Google Scholar 

  36. Thoms, J. A. I. et al. Disruption of a GATA2, TAL1, ERG regulatory circuit promotes erythroid transition in healthy and leukemic stem cells. Blood 138, 1441–1455 (2021).

    Article  CAS  Google Scholar 

  37. Ng, S. W. et al. A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature 540, 433–437 (2016).

    Article  ADS  CAS  Google Scholar 

  38. Mandal, M. et al. The BCL2A1 gene as a pre-T cell receptor-induced regulator of thymocyte survival. J. Exp. Med. 201, 603–614 (2005).

    Article  CAS  Google Scholar 

  39. Koyasu, S. et al. Pre-TCR signaling components trigger transcriptional activation of a rearranged TCR α gene locus and silencing of the pre-TCR α locus: implications for intrathymic differentiation. Int. Immunol. 9, 1475–1480 (1997).

    Article  CAS  Google Scholar 

  40. Amson, R. et al. The human protooncogene product p33pim is expressed during fetal hematopoiesis and in diverse leukemias. Proc. Natl Acad. Sci. USA 86, 8857–8861 (1989).

    Article  ADS  CAS  Google Scholar 

  41. Reinherz, E. L., Kung, P. C., Goldstein, G., Levey, R. H. & Schlossman, S. F. Discrete stages of human intrathymic differentiation: analysis of normal thymocytes and leukemic lymphoblasts of T-cell lineage. Proc. Natl Acad. Sci. USA 77, 1588–1592 (1980).

    Article  ADS  CAS  Google Scholar 

  42. Van Vlierberghe, P. & Ferrando, A. The molecular basis of T cell acute lymphoblastic leukemia. J. Clin. Invest. 122, 3398–3406 (2012).

    Article  Google Scholar 

  43. Girardi, T., Vicente, C., Cools, J. & De Keersmaecker, K. The genetics and molecular biology of T-ALL. Blood 129, 1113–1123 (2017).

    Article  CAS  Google Scholar 

  44. Zhang, J. et al. The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature 481, 157–163 (2012).

    Article  ADS  CAS  Google Scholar 

  45. Condorelli, G. L. et al. T-cell-directed TAL-1 expression induces T-cell malignancies in transgenic mice. Cancer Res. 56, 5113–5119 (1996).

    CAS  Google Scholar 

  46. Kelliher, M. A., Seldin, D. C. & Leder, P. Tal-1 induces T cell acute lymphoblastic leukemia accelerated by casein kinase IIα. EMBO J. 15, 5160–5166 (1996).

    Article  CAS  Google Scholar 

  47. De Keersmaecker, K. et al. The TLX1 oncogene drives aneuploidy in T cell transformation. Nat. Med. 16, 1321–1327 (2010).

    Article  Google Scholar 

  48. Rakowski, L. A., Lehotzky, E. A. & Chiang, M. Y. Transient responses to NOTCH and TLX1/HOX11 inhibition in T-cell acute lymphoblastic leukemia/lymphoma. PLoS ONE 6, e16761 (2011).

    Article  ADS  CAS  Google Scholar 

  49. Martins, V. C. et al. Cell competition is a tumour suppressor mechanism in the thymus. Nature 509, 465–470 (2014).

    Article  ADS  CAS  Google Scholar 

  50. Paiva, R. A. et al. Self-renewal of double-negative 3 early thymocytes enables thymus autonomy but compromises the β-selection checkpoint. Cell Rep. 35, 108967 (2021).

    Article  CAS  Google Scholar 

  51. Khan, M., Siddiqi, R. & Naqvi, K. An update on classification, genetics, and clinical approach to mixed phenotype acute leukemia (MPAL). Ann. Hematol. 97, 945–953 (2018).

    Article  CAS  Google Scholar 

  52. Kai, T. & Spradling, A. Differentiating germ cells can revert into functional stem cells in Drosophila melanogaster ovaries. Nature 428, 564–569 (2004).

    Article  ADS  CAS  Google Scholar 

  53. Cobaleda, C., Jochum, W. & Busslinger, M. Conversion of mature B cells into T cells by dedifferentiation to uncommitted progenitors. Nature 449, 473–477 (2007).

    Article  ADS  CAS  Google Scholar 

  54. Laiosa, C. V., Stadtfeld, M., Xie, H., de Andres-Aguayo, L. & Graf, T. Reprogramming of committed T cell progenitors to macrophages and dendritic cells by C/EBPα and PU.1 transcription factors. Immunity 25, 731–744 (2006).

    Article  CAS  Google Scholar 

  55. Riddell, J. et al. Reprogramming committed murine blood cells to induced hematopoietic stem cells with defined factors. Cell 157, 549–564 (2014).

    Article  CAS  Google Scholar 

  56. Jacobs, H. et al. Oncogenic potential of a pre-T cell receptor lacking the TCRβ variable domain. Oncogene 12, 2089–2099 (1996).

    CAS  Google Scholar 

  57. Charnley, M., Ludford-Menting, M., Pham, K. & Russell, S. M. A new role for Notch in the control of polarity and asymmetric cell division of developing T cells. J. Cell Sci. 133, jcs235358 (2019).

    Article  Google Scholar 

  58. Mohtashami, M. et al. Direct comparison of Dll1- and Dll4-mediated Notch activation levels shows differential lymphomyeloid lineage commitment outcomes. J. Immunol. 185, 867–876 (2010).

    Article  CAS  Google Scholar 

  59. Mamedov, I. Z. et al. Preparing unbiased T-cell receptor and antibody cDNA libraries for the deep next generation sequencing profiling. Front. Immunol. 4, 456 (2013).

    Article  Google Scholar 

  60. Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015).

    Article  CAS  Google Scholar 

  61. Shugay, M. et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS Comput. Biol. 11, e1004503 (2015).

    Article  Google Scholar 

  62. Wang, X., Spandidos, A., Wang, H. & Seed, B. PrimerBank: a PCR primer database for quantitative gene expression analysis, 2012 update. Nucleic Acids Res. 40, D1144–D1149 (2012).

    Article  CAS  Google Scholar 

  63. Ruijter, J. M. et al. Evaluation of qPCR curve analysis methods for reliable biomarker discovery: bias, resolution, precision, and implications. Methods 59, 32–46 (2013).

    Article  CAS  Google Scholar 

  64. Shugay, M. et al. Towards error-free profiling of immune repertoires. Nat. Methods 11, 653–655 (2014).

    Article  CAS  Google Scholar 

  65. Han, F. F. et al. Profiling the pattern of human TRB/IGH-CDR3 repertoire in liver transplantation patients via high-throughput sequencing analysis. Scand. J. Immunol. 92, e12912 (2020).

    Article  CAS  Google Scholar 

  66. Stevant, I. et al. Dissecting cell lineage specification and sex fate determination in gonadal somatic cells using single cell transcriptomics. Cell Rep. 26, 3272–3283.e3 (2019).

    Article  CAS  Google Scholar 

  67. Godfrey, A. K. et al. Quantitative analysis of Y-chromosome gene expression across 36 human tissues. Genome Res. 30, 860–873 (2020).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This research was supported by NIH NIAID grant AI136301. C.M.M. and P.H.L. were supported additionally by the Expect Miracles Foundation and the Robert and Renée Belfer Foundation. We thank J.-h. Wang for scientific discussion and insight, D. A. Barbie and C. P. Paweletz for facilitating the scRNA-seq analyses, the Dana-Farber/Harvard Cancer Center Specialized Histopathology Core (NIH NCI grant P30 CA006516-57) and S. Moskovitz for graphic design of figures.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: J.S.D.-C., A.A., R.J.M., W.H., M.J.L. and E.L.R. Methodology: J.S.D.-C., A.A., R.J.M. and E.L.R. Investigation: J.S.D.-C, A.A., C.M.M., P.H.L. and J.C.A. Writing, original draft: J.S.D.-C. and E.L.R. Writing, review and editing: J.S.D.-C., R.J.M., A.A., W.H., M.J.L. and E.L.R. Funding acquisition: M.J.L., E.L.R. and J.C.A. Supervision: J.S.D.-C. and E.L.R.

Corresponding authors

Correspondence to Jonathan S. Duke-Cohan or Ellis L. Reinherz.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Charles Mullighan, Hans-Reimer Rodewald and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Schematic for FACS isolation of thymocyte subsets (DN3a, DN3b, DN4, DP) for 10X scRNA-Seq and single cell TCR α and β chain clonotype sequencing.

Sorted cells were isolated as DN3a cells (CD25+CD44CD28), DN3b cells (CD25+CD44CD28+), DN4 (CD25CD44CD28+) cells, and DP (CD4+CD8+) cells.

Extended Data Fig. 2 Cluster delineation of DN3a to DPsm cell transitions.

For each transition, data from the Immune Genome Project (IGP) microarray and RNA-Seq data was used to construct a panel representing genes with the highest fold-change between phenotypically defined stages of thymocyte differentiation. The gene panel was then used to query the MHC+ thymocyte clusters identified by UMAP projection. Combination of library phenotype together with good fit to the interrogating gene panel permitted identification of cluster relationships and developmental trajectories. a. Delineation of early post-β selection checkpoint DN3a/3b thymocytes from pre-β selection checkpoint DN3a thymocytes by differential gene expression. The left-hand heatmap depicts a panel selected by comparison of DN3b thymocyte gene expression from the IGP with DN3a cell expression. The same genes were examined for expression in the clusters defined as DN3a and DN3a/3b in Fig. 1B (right-hand heatmap). The volcano plot depicts the log2-fold increase of expression in the DN3a/3b population over DN3a for the expected normal developmental trajectory (x-axis). Note that for all volcano plots reported here, only the significantly changed transcripts are depicted (Padj < 0.05; y-axis). b. Delineation of late post-β selection checkpoint DN3b/4 thymocytes from early pre-β selection checkpoint DN3a/3b thymocytes by differential gene expression. The heatmap on the far left depicts a panel selected by comparison of DN4 thymocyte gene expression from the IGP with DN3b cell expression (neither DN3a/3b nor DN3b/4 transitional states are explicitly defined in the IGP database). Transcripts in red were predicted from IGP data to be upregulated in the DN3b to DN4 transition but are downregulated for the conditions reported here. c. Delineation of late post-β selection checkpoint DN3b/4 thymocytes from DPbl thymocytes by differential gene expression. The DPbl cluster was extracted from the DP library and delineated from the more mature DPsm population by transcriptome signature as described below. d. Delineation of mature DPsm thymocytes from cycling DPbl thymocytes by differential gene expression. The heatmap on the far left depicts a panel selected by comparison of DPsm thymocyte gene expression from the IGP with DPbl cell. Note that during the DPbl to DPsm transition, significant cell cycling transcripts were downregulated thus significantly upregulated transcripts in the volcano plot represent the DPbl cells.

Extended Data Fig. 3 Delineating the ILC-γ/δ TCR thymocyte cluster and pro-apoptotic cluster from the main α/β TCR lineage pathway.

a. Distinguishing ILC-γ/δ-like cells from DN3b/4 in the DN4 libraries by gene expression. The heatmap on the left shows a manually curated panel of gene transcripts selected by likely high representation in either DN3b/4 or ILC-γ/δ-like cells. Log2 Fold-change (L2FC) and Padj in the DN4 libraries for differential expression between the DN3b/4 clusters and ILC-γ/δ-like clusters are shown in the volcano plot to the right with transcripts associated with ILC development are highlighted in light purple (Id2, Zbtb16, Gata3, Rora). TCR γ and δ transcripts are highlighted in green, and Trbv transcripts highlighted in blue. b. Gene expression profile of the pro-apoptotic cluster. The dominant pro-apoptotic cluster upregulated gene expression changes are similar between all the MHC+ libraries on comparison with the 2 dominant clusters within each of these libraries. All log2-fold changes (L2FC) are relative only to the 3 clusters listed in each heatmap (i.e. local) and not to the average across all clusters in that library.

Extended Data Fig. 4 Development and TCR repertoire analyses for cells growing on MHC+, MHC and scH-2Kb stromal support cells.

a. Total cell recoveries after 9d development from 2,000 seeded HSC (Representative of 6 experiments examining MHC+ (n = 5), MHC (n = 6), and scH-2Kb (n = 3)). For all box plots, the box bounds the 1st to 3rd quartiles; where visible, the dotted line within represents mean, and the solid line represents median. Whiskers above and below (maximum and minimum) are defined as (quartile 3 + 1.5 * interquartile range) and (quartile 1 – 1.5 * interquartile range), respectively. P (= 0.0204) determined by two-tailed t test. b. Apparent thymocyte developmental stage representation as fraction of total cells for cultures represented in panel a. c. Stage-specific analysis of β chain clonotype representation/10,000 cells in d9 MHC+, MHC, and scH-2Kb OP9-DL4 development cultures. Representation of data from replicate experiments of data in Fig. 2c–f. d. TCR β chain clonotype diversity at DN4 on MHC+, MHC, and scH-2Kb stroma. The total number of TCR β chain clonotypes (black) recovered from 104 cells of each DN4 population isolated after growth for 9d on the varying OP9-DL4 stroma is represented by an ellipse of area in direct proportion to unique clonotype count (5 independent experiments). Percentage shared clonotypes of the total for each condition (MHC+ in blue, MHC in pink, and scH-2Kb in green) is depicted. Note that the area of overlap only approximates degree of sharing to maintain consistent orientation of the ellipses for presentation. The overlap of MHC+ and scH-2Kb for experiments 4 and 5 is <1% and too small to represent in this format. Statistics and P calculated from two-tailed t test presented on left.

Source data

Extended Data Fig. 5 Transcriptome and selected phenotype comparison of MHC+ and MHC OP9-DL4 cells and select gene expression profiles for the DN4 unusual and DPbl abnormal populations.

a. Comparison of MHC+ and MHC OP9-DL4 stromal cells for transcriptome and phenotypic differences. 93.6% of transcripts detected shared by MHC+ and MHC stroma. b. Correlation between cell transcriptomes. Square of two-tailed Pearson correlation coefficient (R2 = 0.958) ideally greater than 0.92 under optimal experimental conditions. c. Differential gene expression is <4% of all transcripts detected. d. Loss of CD1d surface expression in B2m/Tap2 KO MHC OP9-DL4 and confirmation of lack of MHC Class II expression in MHC+ and MHC OP9-DL4. e. Raet expression in MHC+ and MHC OP9-DL4. f. Select transcripts significantly differentially expressed between the MHC DN3b/4 cluster and the DN4 “unusual” cluster. Heatmap depicts log2-fold change (L2FC) of the DN4 “unusual” cluster relative to the DN3b/4 cluster. Actual L2FC values are listed within the heatmap. g. Co-expression of Cd4 transcript with Cd8a and/or Cd8b1 transcripts in an overlay of the MHC libraries focussed on the DN4 unusual, DPbl, and DP abnormal clusters. h. Characteristic myeloid gene transcript expression maps to the MHC DP abnormal cluster. i. Full-length clonotypic TCR β chain transcript expression in 82 of 221 Spi1+ cells (37.1%) in the MHC DP abnormal cluster. j. Mpo-expressing cells in the DP abnormal cluster and the Mpo+Spi1+ subset co-express T lineage Lck and/or Cd3e. k. DP cell yields after 12 d for DN3a and DN4 cells seeded onto MHC+ or MHC- stromal cells. l. Relative expression by qRT-PCR (normalised to Actb = 1000) of Mpo and Spi1 in DP cells developing from DN3a cells seeded 12 d earlier onto MHC+ or MHC stromal cells (Cells pooled from 3 separate cultures; n = 7 qRT-PCR replicates; Mpo: P < 0.00001, Spi1: P = 0.000655). m. Relative expression (normalised to Actb = 1000) of Mpo and Spi1 in DP cells developing from DN4 cells seeded 12 d earlier onto MHC+ or MHC stromal cells (Cells pooled from 3 separate cultures; Mpo: n = 8 qRT-PCR replicates; Spi1: n = 4 qRT-PCR replicates); for l, m: mean ± s.d.; P from two-tailed t-test; representative of 2 independent experiments; Mpo: P = 0.000013, Spi1: P = 0.000233).

Source data

Extended Data Fig. 6 Highly proliferating clonotypic progeny cluster together by transcriptional signature.

a. MHC+ DN4 20 most highly represented clonotypes by cell number. b. MHC DN4 20 most highly represented clonotypes by cell number. The identical MHC+ and MHC DN4 clonotypic cells to those presented in Fig. 3d and Extended Data Table 3a are shown in their mapped positions in the UMAP projection. Each clonotype is represented for each panel in a unique colour with cell number indicated in key. Note that colours are not directly related to those used in Fig. 3d.

Extended Data Fig. 7 Transcriptome comparison of DN and ISP thymocyte subsets from MHC+ and MHC mice.

a. Thymocyte subset cell recoveries from thymi of MHC+ and MHC mice. Mean ± s.d. shown; 3 mice/group; ** p = 0.0169; *** p < 0.0039; **** p < 0.0003 determined by 2-tailed t-test. b. Log2-fold change in expression from global population mean for the MHC knocked out genes (B2m, H2-Ab1), classical and minor MHCI genes, and MHCII genes. Note that for each thymocyte subset there are 3 replicates except for the MHC DN4 cells for which there are duplicates. Asterisks highlight transcripts that are upregulated across all MHC libraries on comparison with MHC+ Q10 (p = 7 × 10−5), H2-T3 (TL) (p = 3 × 10−7), H2-T22 (p = 1 × 10−7) and H2-T-ps (p = 4 × 10−5). P calculated using two-tailed Chi-square test. c. Log2-fold change in expression of all development stage marker genes depicted in Fig. 1a. d. Log2-fold change in TCR Vβ chain segment (Trbv) expression. Mean depicted of triplicates for all libraries except for duplicates for MHC DN4 samples. e. Log2-fold change in Bcl2a1 family transcripts (upper panel), canonical Bcl2 transcripts (middle panel), and Pim1 protooncogene (lower panel). Mean values presented. f. Log2-fold change in TCR Vα chain segment (Trav) expression. Mean depicted of triplicates for all libraries except for duplicates for MHC DN4 samples. g. Display of haematopoietic/immune organs from an MHC dKO mouse with massive thymic growth at 15 months and from age-matched MHC+ control. h. FACS analysis of single cell thymic and splenocyte suspensions stained for CD4 and CD8. Numbers next to gates indicate % of cells in that gate. i. Haematoxylin and eosin staining of representative organs from an age-matched MHC+ wt B6 mouse and an MHC dKO mouse with leukaemic growth. Thymic cortex indicated by ‘c’, and thymic medulla by ‘m’. Cancellous bone indicated by ‘ca’. Arrow indicates leukaemic cell accumulation adjacent and around a hepatic vein. j. Immunohistochemistry of tumour cells in dKO thymus for TdT (immature thymocytes), CD8 (T lineage) and neutrophil elastase (myeloid lineage) and in dKO spleen metastatic focus for the intracellular domain of Notch 1 (NICD1). i, j: For each tissue and condition, the complete section was examined down to the cellular level and the image presented (~1% of each total section) is representative of that complete section. White bar in all images represents 100 μm.

Source data

Extended Data Table 1 Top 20 DPsm TCR β clonotypes developing on scH-2Kb stroma at d9
Extended Data Table 2 Non-classical MHC class I expression in MHC+ and MHC OP9-DL4
Extended Data Table 3 Well-represented clonotypes in MHC+ and MHC libraries

Supplementary information

Reporting Summary

Supplementary File 1.

Gene expression level (UMI/cell), log2fold change (global), and Padj for all clusters in all MHC+ libraries – scRNA-seq. MS Excel file: Supplementary-Information-File1.

Supplementary File 2.

Gene expression level (UMI/cell), log2fold change (global), and Padj for all MHC- clusters – scRNA-seq. MS Excel file: Supplementary-Information-File2.xlsx

Supplementary File 3.

TCR β chain clonotypes for DN3, DN4, DPbl and DPsm thymocytes developing on MHC+, MHC-, and scH-2Kb/VSV8 stroma. MS Excel file: Supplementary-Information-File3.

Supplementary File 4.

Representative FACS separation profiles of developing thymocyte-like subsets in vitro and thymocyte subsets in vivo. PDF file: Supplementary-Information-File4.

Supplementary File 5.

Gene expression for DN3a, DN3b, DN4 and ISP cells isolated from MHC+ and MHC- mice. MS Excel file: Supplementary-Information-File5.

Supplementary File 6.

Gene expression analysis of a chromosome Y transcript panel and expression-matched autosomal panel to address aberrant clonal HSC expansion contributing to the MHC- DN4 unusual and DP abnormal populations. MS Excel file: Supplementary-Information-File6.

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Duke-Cohan, J.S., Akitsu, A., Mallis, R.J. et al. Pre-T cell receptor self-MHC sampling restricts thymocyte dedifferentiation. Nature 613, 565–574 (2023). https://doi.org/10.1038/s41586-022-05555-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-022-05555-7

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing