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SATB1 is a key regulator of quiescence in stem-like CD8+ T cells

Nature Immunology volume 26, pages 1737–1751 (2025)Cite this article

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Abstract

Stem-like progenitor CD8+ T (TPRO) cells sustain cytotoxic immunity during chronic infection and cancer through quiescence, multipotency and self-renewal, hallmarks shared with memory T cells. However, how these properties are maintained under persistent antigen stimulation remains unclear. Here we identify the genomic organizer SATB1 as selectively enriched in both TPRO and memory CD8+ T cells. Given its role in promoting quiescence in hematopoietic stem cells, we hypothesized that SATB1 supports CD8+ T cell stemness. Using CD8+ T cell-specific CRISPR deletion of the Satb1 gene, we show that SATB1 is essential for maintaining TPRO cells during chronic lymphocytic choriomeningitis virus infection and for memory CD8+ T cell formation during acute infection. Multi-omic profiling revealed that SATB1 regulates the chromatin accessibility, transcriptional activity and genome architecture of stemness-associated genes including Tcf7, Bach2 and Myb. These findings reveal a critical role for SATB1 in preserving the transcriptional and epigenetic programs that sustain the stem-like state of antigen-specific CD8+ T cells.

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Fig. 1: SATB1 is specifically enriched in progenitor CD8+ T cells during chronic viral infection.
Fig. 2: SATB1 deficiency promotes late-stage proliferation of CD8+ T cells during chronic viral infection.
Fig. 3: SATB1 deficiency disrupts the maintenance of the TPRO subset during the chronic phase of viral infection.
Fig. 4: SATB1 deficiency promotes terminal differentiation during the chronic phase of viral infection.
Fig. 5: SATB1 is required for memory CD8+ T cell formation during acute viral infection.
Fig. 6: SATB1 regulates the epigenetic landscape of the TPRO subset.
Fig. 7: Single-cell multi-omics reveals that SATB1 maintains the quiescent state of TPRO cells by regulating stemness-associated genes in a subset-specific manner.

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Data availability

The CD8+ T cell subset-specific ATAC-seq and CUT&Tag-seq (H3K27ac and H3K27ac) data were extracted from GSE149752 and GSE149810, respectively. Data from the ChIP–seq for SATB1 were extracted from GSE191146 (ref. 43). Data to create the module score for the three subsets of CD8+ T cells were extracted from GSE129139. The sequencing data generated in this study have been uploaded to the Gene Expression Omnibus with the following accession numbers: bulk RNA data (GSE275974); bulk ATAC data (GSE275969); single-cell Multiome data (GSE276087); Hi-C data (GSE302478). Source data are provided with this paper.

Code availability

This article does not report any original code. All functionality used to analyze the sequencing data is publicly available via the software packages described in Methods.

References

  1. Kaech, S. M. & Cui, W. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat. Rev. Immunol. 12, 749–761 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Obar, J. J. & Lefrançois, L. Memory CD8+ T cell differentiation. Ann. N. Y. Acad. Sci. 1183, 251–266 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Badovinac, V. P., Porter, B. B. & Harty, J. T. CD8+ T cell contraction is controlled by early inflammation. Nat. Immunol. 5, 809–817 (2004).

    CAS  PubMed  Google Scholar 

  4. Joshi, N. S. et al. Inflammation directs memory precursor and short-lived effector CD8+ T cell fates via the graded expression of T-bet transcription factor. Immunity 27, 281–295 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Sarkar, S. et al. Functional and genomic profiling of effector CD8 T cell subsets with distinct memory fates. J. Exp. Med. 205, 625–640 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Chen, Y. et al. BATF regulates progenitor to cytolytic effector CD8+ T cell transition during chronic viral infection. Nat. Immunol. 22, 996–1007 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Daniel, B. et al. Divergent clonal differentiation trajectories of T cell exhaustion. Nat. Immunol. 23, 1614–1627 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Giles, J. R. et al. Shared and distinct biological circuits in effector, memory and exhausted CD8+ T cells revealed by temporal single-cell transcriptomics and epigenetics. Nat. Immunol. 23, 1600–1613 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Utzschneider, D. T. et al. T cell factor 1-expressing memory-like CD8+ T cells sustain the immune response to chronic viral infections. Immunity 45, 415–427 (2016).

    CAS  PubMed  Google Scholar 

  11. Hashimoto, M. et al. CD8 T cell exhaustion in chronic infection and cancer: opportunities for interventions. Annu. Rev. Med. 69, 301–318 (2018).

    CAS  PubMed  Google Scholar 

  12. Gebhardt, T., Park, S. L. & Parish, I. A. Stem-like exhausted and memory CD8+ T cells in cancer. Nat. Rev. Cancer 23, 780–798 (2023).

    CAS  PubMed  Google Scholar 

  13. Dähling, S. et al. Type 1 conventional dendritic cells maintain and guide the differentiation of precursors of exhausted T cells in distinct cellular niches. Immunity 55, 656–670 (2022).

    PubMed  Google Scholar 

  14. Hanna, B. S. et al. Interleukin-10 receptor signaling promotes the maintenance of a PD-1int TCF-1+ CD8+ T cell population that sustains anti-tumor immunity. Immunity 54, 2825–2841 (2021).

    CAS  PubMed  Google Scholar 

  15. Hu, Y. et al. TGF-β regulates the stem-like state of PD-1+ TCF-1+ virus-specific CD8 T cells during chronic infection. J. Exp. Med. 219, e20211574 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Yao, C. et al. BACH2 enforces the transcriptional and epigenetic programs of stem-like CD8+ T cells. Nat. Immunol. 22, 370–380 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Tsui, C. et al. MYB orchestrates T cell exhaustion and response to checkpoint inhibition. Nature 609, 354–360 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Roychoudhuri, R. et al. BACH2 regulates CD8+ T cell differentiation by controlling access of AP-1 factors to enhancers. Nat. Immunol. 17, 851–860 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Gautam, S. et al. The transcription factor c-Myb regulates CD8+ T cell stemness and antitumor immunity. Nat. Immunol. 20, 337–349 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Will, B. et al. Satb1 regulates the self-renewal of hematopoietic stem cells by promoting quiescence and repressing differentiation commitment. Nat. Immunol. 14, 437–445 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Satoh, Y. et al. The Satb1 protein directs hematopoietic stem cell differentiation toward lymphoid lineages. Immunity 38, 1105–1115 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Feng, D. et al. Chromatin organizer SATB1 controls the cell identity of CD4+ CD8+ double-positive thymocytes by regulating the activity of super-enhancers. Nat. Commun. 13, 5554 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Hao, B. et al. An anti-silencer- and SATB1-dependent chromatin hub regulates Rag1 and Rag2 gene expression during thymocyte development. J. Exp. Med. 212, 809–824 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Cai, S., Lee, C. C. & Kohwi-Shigematsu, T. SATB1 packages densely looped, transcriptionally active chromatin for coordinated expression of cytokine genes. Nat. Genet. 38, 1278–1288 (2006).

    CAS  PubMed  Google Scholar 

  25. Yasuda, K. et al. Satb1 regulates the effector program of encephalitogenic tissue Th17 cells in chronic inflammation. Nat. Commun. 10, 549 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Kitagawa, Y. et al. Guidance of regulatory T cell development by Satb1-dependent super-enhancer establishment. Nat. Immunol. 18, 173–183 (2017).

    CAS  PubMed  Google Scholar 

  27. Stephen, T. L. et al. SATB1 expression governs epigenetic repression of PD-1 in tumor-reactive T cells. Immunity 46, 51–64 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Zander, R. et al. CD4+ T cell help is required for the formation of a cytolytic CD8+ T cell subset that protects against chronic infection and cancer. Immunity 51, 1028–1042 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Hudson, W. H. et al. Proliferating transitory T cells with an effector-like transcriptional signature emerge from PD-1+ stem-like CD8+ T cells during chronic infection. Immunity 51, 1043–1058 e1044 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Chen, Z. et al. TCF-1-centered transcriptional network drives an effector versus exhausted CD8 T cell-fate decision. Immunity 51, 840–855 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Nomura, A. et al. Identification of a novel enhancer essential for Satb1 expression in TH2 cells and activated ILC2s. Life Sci. Alliance 6, e202301897 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Nussing, S. et al. Efficient CRISPR/Cas9 gene editing in uncultured naive mouse T cells for in vivo studies. J. Immunol. 204, 2308–2315 (2020).

    CAS  PubMed  Google Scholar 

  33. Snell, L. M. et al. CD8+ T cell priming in established chronic viral infection preferentially directs differentiation of memory-like cells for sustained immunity. Immunity 49, 678–694 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Zehn, D., Thimme, R., Lugli, E., de Almeida, G. P. & Oxenius, A. ‘Stem-like’ precursors are the fount to sustain persistent CD8+ T cell responses. Nat. Immunol. 23, 836–847 (2022).

    CAS  PubMed  Google Scholar 

  35. Chu, T. et al. Precursors of exhausted T cells are pre-emptively formed in acute infection. Nature 640, 782–792 (2025).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Gago da Graça, C. et al. Stem-like memory and precursors of exhausted T cells share a common progenitor defined by ID3 expression. Sci. Immunol. 10, eadn1945 (2025).

    PubMed  Google Scholar 

  37. Gallimore, A. et al. Induction and exhaustion of lymphocytic choriomeningitis virus-specific cytotoxic T lymphocytes visualized using soluble tetrameric major histocompatibility complex class I–peptide complexes. J. Exp. Med. 187, 1383–1393 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Leiden, J. M. et al. A novel Ets-related transcription factor, Elf-1, binds to human immunodeficiency virus type 2 regulatory elements that are required for inducible trans activation in T cells. J. Virol. 66, 5890–5897 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Suico, M. A., Shuto, T. & Kai, H. Roles and regulations of the ETS transcription factor ELF4/MEF. J. Mol. Cell Biol. 9, 168–177 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Zelenka, T. et al. The 3D enhancer network of the developing T cell genome is shaped by SATB1. Nat. Commun. 13, 6954 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Zelenka, T. & Spilianakis, C. SATB1-mediated chromatin landscape in T cells. Nucleus 11, 117–131 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Russ, B. E. et al. Active maintenance of CD8+ T cell naivety through regulation of global genome architecture. Cell Rep. 42, 113301 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Kohwi, Y. et al. Genome organization by SATB1 binding to base-unpairing regions (BURs) provides a scaffold for SATB1-regulated gene expression. eLife 14, RP105915 (2025).

    Google Scholar 

  44. Daniels, M. A., Luera, D. & Teixeiro, E. NFκB signaling in T cell memory. Front. Immunol. 14, 1129191 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Teixeiro, E. et al. Different T cell receptor signals determine CD8+ memory versus effector development. Science 323, 502–505 (2009).

    CAS  PubMed  Google Scholar 

  46. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhou, X. et al. Differentiation and persistence of memory CD8+ T cells depend on T cell factor 1. Immunity 33, 229–240 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Skowronska-Krawczyk, D. et al. Required enhancer–matrin-3 network interactions for a homeodomain transcription program. Nature 514, 257–261 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Pavan Kumar, P. et al. Phosphorylation of SATB1, a global gene regulator, acts as a molecular switch regulating its transcriptional activity in vivo. Mol. Cell 22, 231–243 (2006).

    CAS  PubMed  Google Scholar 

  50. Purbey, P. K. et al. Acetylation-dependent interaction of SATB1 and CtBP1 mediates transcriptional repression by SATB1. Mol. Cell. Biol. 29, 1321–1337 (2009).

    CAS  PubMed  Google Scholar 

  51. Kaech, S. M. et al. Selective expression of the interleukin 7 receptor identifies effector CD8 T cells that give rise to long-lived memory cells. Nat. Immunol. 4, 1191–1198 (2003).

    CAS  PubMed  Google Scholar 

  52. Gray, S. M., Amezquita, R. A., Guan, T., Kleinstein, S. H. & Kaech, S. M. Polycomb repressive complex 2-mediated chromatin repression guides effector CD8+ T cell terminal differentiation and loss of multipotency. Immunity 46, 596–608 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Pace, L. et al. The epigenetic control of stemness in CD8+ T cell fate commitment. Science 359, 177–186 (2018).

    CAS  PubMed  Google Scholar 

  54. Chaurio, R. A. et al. TGF-β-mediated silencing of genomic organizer SATB1 promotes Tfh cell differentiation and formation of intra-tumoral tertiary lymphoid structures. Immunity 55, 115–128 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Zander, R. et al. Tfh-cell-derived interleukin 21 sustains effector CD8+ T cell responses during chronic viral infection. Immunity 55, 475–493 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Kumar, P. P. et al. Functional interaction between PML and SATB1 regulates chromatin-loop architecture and transcription of the MHC class I locus. Nat. Cell Biol. 9, 45–56 (2007).

    PubMed  Google Scholar 

  57. Dickinson, L. A., Joh, T., Kohwi, Y. & Kohwi-Shigematsu, T. A tissue-specific MAR/SAR DNA-binding protein with unusual binding site recognition. Cell 70, 631–645 (1992).

    CAS  PubMed  Google Scholar 

  58. Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    CAS  PubMed  Google Scholar 

  60. Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).

    CAS  PubMed  Google Scholar 

  61. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  63. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Wherry, E. J. et al. Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670–684 (2007).

    CAS  PubMed  Google Scholar 

  69. Doering, T. A. et al. Network analysis reveals centrally connected genes and pathways involved in CD8+ T cell exhaustion versus memory. Immunity 37, 1130–1144 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Ji, Y. et al. Repression of the DNA-binding inhibitor Id3 by Blimp-1 limits the formation of memory CD8+ T cells. Nat. Immunol. 12, 1230–1237 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Gil, M. P. et al. Regulating type 1 IFN effects in CD8 T cells during viral infections: changing STAT4 and STAT1 expression for function. Blood 120, 3718–3728 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Fu, W. et al. A multiply redundant genetic switch ‘locks in’ the transcriptional signature of regulatory T cells. Nat. Immunol. 13, 972–980 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Li, D. et al. WashU Epigenome Browser update 2022. Nucleic Acids Res. 50, W774–W781 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Castro-Mondragon, J. A. et al. JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 50, D165–D173 (2022).

    CAS  PubMed  Google Scholar 

  78. Ren, G. et al. CTCF-mediated enhancer-promoter interaction is a critical regulator of cell-to-cell variation of gene expression. Mol. Cell 67, 1049–1058 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).

    PubMed  PubMed Central  Google Scholar 

  80. Open2C et al. Cooltools: enabling high-resolution Hi-C analysis in Python. PLoS Comput. Biol. 20, e1012067 (2024).

  81. Ramírez, F. et al. High-resolution TADs reveal DNA sequences underlying genome organization in flies. Nat. Commun. 9, 189 (2018).

    PubMed  PubMed Central  Google Scholar 

  82. Wolff, J. et al. Galaxy HiCExplorer: a web server for reproducible Hi-C data analysis, quality control and visualization. Nucleic Acids Res. 46, W11–W16 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Wolff, J. et al. Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization. Nucleic Acids Res. 48, W177–W184 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Ge, S. X., Jung, D. & Yao, R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 36, 2628–2629 (2020).

    CAS  PubMed  Google Scholar 

  85. Flyamer, I. M., Illingworth, R. S. & Bickmore, W. A. Coolpup.py: versatile pile-up analysis of Hi-C data. Bioinformatics 36, 2980–2985 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Lopez-Delisle, L. et al. pyGenomeTracks: reproducible plots for multivariate genomic datasets. Bioinformatics 37, 422–423 (2021).

    CAS  PubMed  Google Scholar 

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Acknowledgements

This work is supported by National Institutes of Health grant nos. AI176611 and AI148403 (W.C.). S.L., R.B. and A.B. are members of the Medical Scientist Training Program at the Medical College of Wisconsin, which is partially supported by a training grant from the National Institute of General Medical Sciences (no. T32-GM080202). We thank the NIH Tetramer Core Facility (NIH Contract 75N93020D00005 and RRID:SCR_026557) for providing LCMV tetramers. We thank the Robert H. Lurie Comprehensive Cancer Center of Northwestern University for the use of the Flow Cytometry Core Facility. The Robert H. Lurie Comprehensive Cancer Center is supported in part by a National Cancer Institute Cancer Center Support Grant no. P30 CA060553. We also thank the Mary Jane McMillen Crowe Foundation for the support of the multispectral flow cytometer. Finally, we sincerely thank S. Lin for performing the foundational Hi-C data analysis that enabled the subsequent in-depth analyses and interpretation presented in this article.

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Authors and Affiliations

  1. Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Siying Lin, Hongshen Niu, Yuqi Zhang, Kexin Gai, Ryan Brown, Ashley Brown, Jian Shen, Ziyang Xu & Weiguo Cui

  2. Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, USA

    Siying Lin, Ryan Brown, Ashley Brown & Anthony E. Zamora

  3. Center for Human Immunobiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Siying Lin, Hongshen Niu, Yuqi Zhang, Kexin Gai, Ryan Brown, Jian Shen & Weiguo Cui

  4. Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Ziyang Xu

  5. Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA

    Ravi K. Shah, Jessica L. Schmeling, Marlenny Vargas-Cortes & Anthony E. Zamora

  6. Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA

    Terumi Kohwi-Shigematsu

  7. Department of Medicine, Hematology and Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Jie Fan & Bin Zhang

  8. Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL, USA

    Weiguo Cui

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  1. Siying Lin
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Contributions

S.L. and W.C. designed and performed the experiments and analyzed the data. H.N., Y.Z. and A.B. helped perform the flow cytometry and FACS experiments. J.S., K.G., J.L.S., M.V.-C., R.K.S. and A.E.Z. helped with the sequencing experiments. R.B. and Z.X. helped with the sequencing data analysis and visualization. T.K.-S. helped with the SATB1 ChIP–seq analysis and provided helpful insights. J.F. and B.Z. provided insights and helped with the tumor experiments. S.L. and W.C. wrote the paper. W.C. supervised the study.

Corresponding author

Correspondence to Weiguo Cui.

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Nature Immunology thanks Charalampos Spilianakis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: L. A. Dempsey, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 The TPRO subset shows enriched SATB1 expression.

a, Flow plot illustrating CX3CR1 expression stratified by SATB1 levels. b, Representative western blots and summary graph of total SATB1 normalized by GAPDH levels in progenitor, effector, and exhausted CD8+ T cell subsets (n = 2). c, Genomic tracks of mapped ATAC-seq (GSE149752), H3K27ac CUT&TAG, H3K27me3 CUT&TAG (GSE149810) for the 3 subsets of CD8+ T cells at the Satb1 locus and nearby Gm20098 locus, displayed on WashU Epigenome Browser. The transcription start site is marked by TSS, with red bars denoting the promoter and enhancer sites.

Source data

Extended Data Fig. 2 SATB1 deficiency promotes CD8+ T cell proliferation in various tissues in chronic viral infection.

a-b, Knock-out efficacy of SATB1 in LCMV Cl13 infection; representative flow plots (a) and western Blot (b) of SATB1 comparing sgCtrl (control; orange) and sgSatb1 (SATB1-deficient; blue) P14 cells. c-d, Summary of liver P14 frequency (c) and numbers (d) at various timepoints p.i. (n = 5). e-f, Summary of lung P14 frequency (e) and numbers (f) at various timepoints p.i. (n = 5). g, Experimental design of CRISPR-RNP in single-transfer experiment: CD45.1+ P14 CD8+ cells were transduced with sgCtrl (control; orange cell) or sgSatb1 (SATB1-deficient; blue cell) and then adoptively single-transferred into separate CD45.2+ C57BL/6 recipient mice, which were subsequently infected with LCMV Cl13. h, Flow plot and summary showing the frequency of splenic sgCtrl and sgSatb1P14 cells in single-transfer experiment on day 21 p.i. (n = 5). i, Representative flow plots and summary displaying the frequencies of sgCtrl and sgSatb1 P14 cells in lymph nodes on day 8 p.i. j, Representative flow plots and summary data of SATB1 protein expression in Ly108+ precursor and Tim3+ early effector cells on day 8 p.i. (n = 5). Data in a-j are representative of 2-3 independent experiments, and data points with bars in c-f are means ± s.d. All data points (n) represent individual mice as biological replicates. Exact P-values are shown in each graph; ns = not significant; two-sided multiple pair t- test with Holm-Šídák correction was used in c-f, and i; two-sided unpaired t-test was used in h; two-sided paired t-test was used in j.

Source data

Extended Data Fig. 3 SATB1 is essential for maintaining TPRO quiescence during chronic infection.

a-b, Representative flow plots and summary displaying gMFI of T-bet (a) and EOMES (b) in splenic sgCtrl (orange) and sgSatb1 (blue) P14 cells on day 21 p.i. with LCMV Cl13 (n = 5). c, Representative flow plots and summary of CD8+ T cells subset distribution in lymph nodes, showing TPRO (Ly108+ CX3CR1−), TEFF (Ly108− CX3CR1+), and TEXH (Ly108− CX3CR1−) sgCtrl (orange) and sgSatb1(blue) P14 cells on day 21 p.i. (n = 9). d, Representative flow plot and summary of TPRO1 (Ly108+ CD62L+) and TPRO2 (Ly108+ CD62L−) frequencies of sgCtrl and sgSatb1 P14 cells in lymph nodes on day 21 p.i. (n = 9). e, PCA plot of RNA-seq data for sgCtrl and sgSatb1 P14 cells on day 21 p.i. with LCMV Cl13 (n = 5). f, Heatmaps of 1767 DEGs between sgCtrl (838 genes) and sgSatb1 (929 genes) P14 cells (adjusted p value < 0.05); selected DEGs are indicated. g, GSEA of hematopoietic cell lineage commitment from KEGG (Pathway ID: mmu04640) between sgCtrl and sgSatb1 P14 cells. h-m, Flow and summary plots corresponding to Fig. 3j. Representative flow plot and summary of Ki67 expression (h), Annexin-V (i), granzyme B (j), TCF1 (k), LEF1 (l), and CD62L (m), and in sgCtrl and sgSat1 splenic P14 cells on day 14 post-transfer (n = 5). All data points (n) represent individual mice as biological replicates. Exact P-values are shown in each graph. Data in a-d and h-m are representative of 2-3 independent experiments. Two-sided paired t-test is used in a-b, h-j; two-sided multiple pair t-test with Holm-Šídák correction was used in c-d; ns = not significant.

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Extended Data Fig. 4 SATB1 limits terminal differentiation in the chronic phase of infection.

a-d, Representative flow plots and summary showing gMFI of CD101 (a), LAG3 (b), TIM-3 (c), and TOX (d) in sgCtrl (control; orange) and sgSatb1 (SATB1-deficient; blue) splenic P14 cells on day 21 p.i. with LCMV Cl13 (n = 5). e, Heatmaps of DEGs representing inhibitory molecules between sgCtrl and sgSatb1 (adjusted p value < 0.05; n = 5). f-j, Flow and summary plots corresponding to Fig. 4h (n = 5). f, Summary of splenic P14 frequencies in sgCtrl (orange) and sgSatb1 (blue) as shown Fig. 4h. g, Representative flow plots and summary of Ki67+ percentages in P14 cells. h, Summary of absolute cell number in TPRO, TEFF, and TEXH subsets. i-j, Representative flow plots and summary LEF1+ percentages (i), and CD62L+ percentages (j) in P14 cells (n = 5). k, Summary data of viral titer in the sera from experimental mice receiving sgCtrl or sgSatb1 P14 cells (n = 5; bar and interval represent means ± s.d.). All data points (n) represent individual mice as biological replicates. Exact P-values are shown in each graph. Data in a-d and f-j are representative of 2-3 independent experiments; two-sided paired t-test was used in a-g and i-j; two-sided multiple paired t-test with Holm-Šídák correction was used in h; unpaired t-test with Welch’s correction was used in k; ns = not significant.

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Extended Data Fig. 5 Satb1fl/fl-CD8cre mice phenocopy CRISPR-mediated SATB1-deletion mice.

a, Schematic of the experimental design: Satb1 + /+ CD8Cre (WT) or Satb1fl/fl CD8Cre (KO) mice were infected with LCMV Cl13 and analyzed at day 21 p.i. b, Frequency of GP33-41-specific CD8+ T cells in spleen. c-d, Representative flow cytometry plots of GP33-41 tetramer staining (c) and CD8+ T-cell subset distribution. e, Quantification of TPRO and combined TEFF + TEXH subsets among GP33-41-specific CD8+ T cells. f-i, Representative flow plots and summary of SATB1 (f), TCF-1 (g), PD-1 (h), and CXCR6 (i) expression in WT and KO GP33-41-specific CD8+ T cells. Data represent two independent experiments with n = 5 mice per group. All data points (n) represent individual mice as biological replicates. Bar and interval in graphs represent means ± s.d. Exact P-values are shown in each graph. Statistical significance was determined by two-sided unpaired t-test with Welch’s correction in b, f-i; multiple unpaired t-test with Holm-Šídák correction was used in e.

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Extended Data Fig. 6 SATB1 restrains effector differentiation during acute infection.

a, Knockout efficacy of SATB1 on day 35 p.i. with LCMV Armstrong: representative flow plots and a summary of SATB1 gMFI comparing sgCtrl (control; orange) and sgSatb1 (SATB1-deficient; blue) P14 cells (n = 5). b, Representative flow plots depicting sgCtrl (top panel; orange) and sgSatb1 (bottom panel; blue) KLRG1+ IL-7Rα+ P14 short-lived effector cells (SLECs) and KLRG1− IL-7Rα+ memory precursor effector cells (MPECs) isolated from blood at various post-infection timepoints. c-d, Summary of sgCtrl and sgSatb1 P14 SLECs (c) and MPECs (d) at various post-infection timepoints shown in b (n = 12; data points with interval represents means ± s.d.). e, Representative flow plots displaying the frequencies of sgCtrl (orange) and sgSatb1 (blue) splenic P14 cells on day 7 p.i. with LCMV Armstrong. f, Representative flow plots and summary depicting the frequencies of sgCtrl and sgSatb1 SLECs and MPECs isolated from splenocytes (n = 5). g-i, Representative flow plots and summary showing the percentages of TCF-1+ (g), CXCR3+ (h), and CX3CR1+ P14 cells in sgCtrl and sgSatb1 on day 7 p.i. (n = 5). All data points (n) represent individual mice as biological replicates. Exact P-values are shown in each graph. Data in a-i are representative of 2-3 independent experiments. Statistical significance was determined by two-side paired t-test in a and g-I; multiple paired t-test with Holm-Šídák correction was used in c-e.

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Extended Data Fig. 7 SATB1 is necessary for differentiating TCM but not TRM cells.

a, Summary of absolute number of sgCtrl (control; orange) and sgSatb1 (SATB1-deficient; blue) P14 cells in spleen on day 35 p.i. with LCMV Armstrong (n = 4). b-c, Representative flow plot and summary showing the percentages of CX3CR1+ (b) and KLRG1+ (c) splenic P14 cells in sgCtrl and sgSat1 on day 35 p.i. (n = 5). d, Representative flow plots displaying the frequencies of sgCtrl and sgSatb1 P14 cells in lymph nodes on day 35 p.i. (n = 5). e, Representative flow plots and summary depicting sgCtrl and sgSatb1 CD62L+ central memory (TCM) and CD62L− effector memory (TEM) cells in lymph nodes on day 35 p.i. (n = 5). f, Representative flow plots and summary showing percentage of TCF-1+ TCM cells in lymph nodes of sgCtrl and sgSatb1. (n = 5). g, Representative flow plots displaying the frequencies of sgCtrl and sgSatb1 P14 cells in intestinal intraepithelial lymphocytes (IEL) on day 35 p.i. h, Representative flow plots and summary showing percentages of sgCtrl and sgSatb1 CD69+ CD103+ resident memory (TRM) cells from IEL on day 35 p.i. (n = 5). All data points (n) represent individual mice as biological replicates. Exact P-values are shown in each graph. Data in a-h are representative of 2-3 independent experiments. Statistical significance was determined by two-side paired t-test in a and a-b and h; multiple paired t-test with Holm-Šídák correction was used in e. ns = not significant.

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Extended Data Fig. 8 SATB1 regulates the epigenetic program of stemness in TPRO cells.

P14 cells transduced with sgCtrl and sgSatb1 were adoptively transferred into recipient mice, followed by LCMV Cl13 infection. On day 21, p.i., these P14 cells were sorted and analyzed by ATAC-seq. Four replicates were included in each group. a, Representative flow plots showing sgCtrl or sgSatb1 P14 cells sorting panel (a) and post-sort purity (b). c, Spearman distance analysis using all differentially accessible peaks (DAPs) between indicated populations (FDR ≤ 0.01, lfc≥2). Color indicates distances. d-e, Sequencing tracks of SATB1 ChIP seq (GSE191146), ATAC of TPRO subset (GSE149752), and CUT&TAG of TPRO H3K27ac (GSE149810) at indicated (d) Il7r locus and (e) Tcf7 locus. The differentially accessible peaks between sgCtrl and sgSatb1, illustrated in Fig. 6j-k, are highlighted with a red bar.

Extended Data Fig. 9 SATB1 regulates the quiescence of TPRO in a subset-specific manner.

a-b, Dot plots showing expression of cluster markers. Dot size denotes the number of cells with a particular gene expressed, and intensity of dot color indicates the expression level of RNA (a) and gene accessibility (b). c, Module scores of the top 100 differentially expressed genes from previously identified progenitor, effector, and exhausted CD8+ T cells for all subsets in sgCtrl and sgSatb1. d-e, Coverage plots showing ATAC tracks at Myb (d) and Tnfsf8 (e) loci in sgCtrl and sgSatb1 TPRO cluster, with gene expression levels displayed as violin plots on the right side. TSS represents the transcription start site, grey boxes under the track represent putative enhancers, and links between the enhancers and the promoter are associated with promoter-enhancer interaction. Red bars mark examples of differentially accessible enhancers between sgCtrl and sgSatb1 in the TPRO cluster.

Extended Data Fig. 10 SATB1 plays a role in maintaining the chromatin architecture in TPRO-associated genes.

a, Whole-genome Hi-C contact matrices at 100 kb resolution for wild-type (WT left) and Satb1 knockout (Satb1-KO; right) CD8+ T cells. b, Saddle plots ranked by eigenvector percentiles display chromatin compartmentalization in WT (left), KO (middle) conditions, and the log2 ratio of KO/WT compartmentalization (right). c, a Venn diagram displaying differential and shared topologically associating domains (TADs) between WT and KO at 50 kb resolution. d, Pathway enrichment analysis of genes within WT-specific (top panel) and KO-specific TADs (bottom panel), identified using HOMER-based annotation and ShinyGO. e, Loop pileup of TPRO-specific enhancer sites (N = 2,151) anchored at genome regions in WT (right) and KO (left), visualized at 10 kb resolution. f, Loop pileup of enhancer regions associated with T-cell differentiation (N = 2,670) anchored at genome regions in WT (right) and KO (left), visualized at 10 kb resolution. g-h, Hi-C heatmap maps at 10 kb resolution displaying distance-normalized chromatin interactions in WT (top) and KO (bottom) at the Tcf7 (g) and Myb (h) loci, visualized on Pygenome. The green boxes mark chromatin interactions with changes. ATAC-seq tracks of sgCtrl (orange) and sgSatb1 (blue) CD8+ T cells are shown between Hi-C heatmaps.

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Lin, S., Niu, H., Zhang, Y. et al. SATB1 is a key regulator of quiescence in stem-like CD8+ T cells. Nat Immunol 26, 1737–1751 (2025). https://doi.org/10.1038/s41590-025-02257-w

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