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Generation of functionally distinct T-cell populations by altering the viscoelasticity of their extracellular matrix

Abstract

The efficacy of adoptive T-cell therapies largely depends on the generation of T-cell populations that provide rapid effector function and long-term protective immunity. Yet it is becoming clearer that the phenotypes and functions of T cells are inherently linked to their localization in tissues. Here we show that functionally distinct T-cell populations can be generated from T cells that received the same stimulation by altering the viscoelasticity of their surrounding extracellular matrix (ECM). By using a model ECM based on a norbornene-modified collagen type I whose viscoelasticity can be adjusted independently from its bulk stiffness by varying the degree of covalent crosslinking via a bioorthogonal click reaction with tetrazine moieties, we show that ECM viscoelasticity regulates T-cell phenotype and function via the activator-protein-1 signalling pathway, a critical regulator of T-cell activation and fate. Our observations are consistent with the tissue-dependent gene-expression profiles of T cells isolated from mechanically distinct tissues from patients with cancer or fibrosis, and suggest that matrix viscoelasticity could be leveraged when generating T-cell products for therapeutic applications.

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Fig. 1: Tissue microenvironment influences T-cell phenotype.
Fig. 2: Synthesizing collagen-based ECM mimetic with tunable mechanical properties.
Fig. 3: Extracellular-matrix viscoelasticity modulates T-cell transcriptomic profile.
Fig. 4: T-cell populations generated from ECM with different viscoelasticities have different patterns of AP-1 protein expression that can be abrogated by inhibitors.
Fig. 5: ECM viscoelasticity does not select for specific T-cell clones.
Fig. 6: Previous matrix condition influences the phenotype of serially passaged T cells.
Fig. 7: Tuning ECM viscoelasticity results in the generation of functionally distinct T cells.

Data availability

The data supporting the results in this study are available within the paper and its supplementary information. Single-cell-sequencing data are available via the NCBI Gene Expression Omnibus repository (GSE230631, subseries GSE230628, GSE230629). The following RNA-sequencing datasets used to validate the in vitro findings were assessed from publicly available GEO repositories: NSCLC (GSE99254), liver cancer (GSE98638), colorectal cancer (GSE108989), breast cancer (GSE114727), liver fibrosis (GSE136103), IPF (GSE135893, GSE159354) and bulk-RNA-sequencing dataset (GSE126117). All data acquired during the study are available from the corresponding author on reasonable request. Source data for the figures are provided with this paper.

References

  1. Reddy, S. T. The patterns of T-cell target recognition. Nature 547, 36–38 (2017).

    Article  CAS  PubMed  Google Scholar 

  2. Chaplin, D. D. Overview of the immune response. J. Allergy Clin. Immunol. 125, S3–S23 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Adu-Berchie, K. & Mooney, D. J. Biomaterials as local niches for immunomodulation. Acc. Chem. Res. 53, 1749–1760 (2020).

    Article  CAS  PubMed  Google Scholar 

  4. Met, Ö., Jensen, K. M., Chamberlain, C. A., Donia, M. & Svane, I. M. Principles of adoptive T cell therapy in cancer. Semin. Immunopathol. 41, 49–58 (2019).

    Article  PubMed  Google Scholar 

  5. June, C. H., Riddell, S. R. & Schumacher, T. N. Adoptive cellular therapy: a race to the finish line. Sci. Transl. Med. 7, 280ps7 (2015).

    Article  PubMed  Google Scholar 

  6. Pampusch, M. S. et al. Rapid transduction and expansion of transduced T cells with maintenance of central memory populations. Mol. Ther. Methods Clin. Dev. 16, 1–10 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Kalos, M. et al. T cells with chimeric antigen receptors have potent antitumor effects and can establish memory in patients with advanced leukemia. Sci. Transl. Med. 3, 95ra73 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Redeker, A. & Arens, R. Improving adoptive T cell therapy: the particular role of T cell costimulation, cytokines, and post-transfer vaccination. Front. Immunol. 7, 345 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Levine, B. L., Miskin, J., Wonnacott, K. & Keir, C. Global manufacturing of CAR T cell therapy. Mol. Ther. Methods Clin. Dev. 4, 92–101 (2017).

    Article  CAS  PubMed  Google Scholar 

  10. Hunder, N. N. et al. Treatment of metastatic melanoma with autologous CD4+ T cells against NY-ESO-1. N. Engl. J. Med. 358, 2698–2703 (2008).

  11. Cheung, A. S., Zhang, D. K. Y., Koshy, S. T. & Mooney, D. J. Scaffolds that mimic antigen-presenting cells enable ex vivo expansion of primary T cells. Nat. Biotechnol. 36, 160–169 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. McGarrity, G. J. et al. Patient monitoring and follow-up in lentiviral clinical trials. J. Gene Med. 15, 78–82 (2013).

    Article  CAS  PubMed  Google Scholar 

  13. Huls, M. H. et al. Clinical application of sleeping beauty and artificial antigen presenting cells to genetically modify T cells from peripheral and umbilical cord blood. J. Vis. Exp. https://doi.org/10.3791/50070 (2013).

  14. Beatty, G. L. et al. Mesothelin-specific chimeric antigen receptor mRNA-engineered T cells induce anti-tumor activity in solid malignancies. Cancer Immunol. Res. 2, 112–120 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Gett, A. V., Sallusto, F., Lanzavecchia, A. & Geginat, J. T cell fitness determined by signal strength. Nat. Immunol. 4, 355–360 (2003).

    Article  CAS  PubMed  Google Scholar 

  16. Masopust, D., Vezys, V., Wherry, E. J., Barber, D. L. & Ahmed, R. Cutting edge: gut microenvironment promotes differentiation of a unique memory CD8 T cell population. J. Immunol. 176, 2079–2083 (2006).

    Article  CAS  PubMed  Google Scholar 

  17. Schenkel, J. M. & Masopust, D. Tissue-resident memory T cells. Immunity 41, 886–897 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Szabo, P. A. et al. Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease. Nat. Commun. 10, 4706 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wells, R. G. Tissue mechanics and fibrosis. Biochim. Biophys. Acta 1832, 884–890 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Chaudhuri, O., Cooper-White, J., Janmey, P. A., Mooney, D. J. & Shenoy, V. B. Effects of extracellular matrix viscoelasticity on cellular behaviour. Nature 584, 535–546 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Boraschi-Diaz, I., Wang, J., Mort, J. S. & Komarova, S. V. Collagen type I as a ligand for receptor-mediated signaling. Front. Phys. 5, 12 (2017).

    Article  Google Scholar 

  22. Dynabeads magnetic separation technology. Thermo Fisher Scientific https://www.thermofisher.com/us/en/home/brands/product-brand/dynal.html (2023).

  23. Guo, X. et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat. Med. 24, 978–985 (2018).

    Article  CAS  PubMed  Google Scholar 

  24. Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356.e16 (2017).

    Article  CAS  PubMed  Google Scholar 

  25. Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Secomb, T. W. & Pries, A. R. Blood viscosity in microvessels: experiment and theory. C. R. Phys. 14, 470–478 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Liu, C., Pei, H. & Tan, F. Matrix stiffness and colorectal cancer. Onco Targets Ther. 13, 2747–2755 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Huang, J. et al. Extracellular matrix and its therapeutic potential for cancer treatment. Signal Transduct. Target Ther. 6, 153 (2021).

    Google Scholar 

  29. Oliveira, B. L., Guo, Z. & Bernardes, G. J. L. Inverse electron demand Diels–Alder reactions in chemical biology. Chem. Soc. Rev. 46, 4895–4950 (2017).

    Article  CAS  PubMed  Google Scholar 

  30. Vining, K. H. & Mooney, D. J. Mechanical forces direct stem cell behaviour in development and regeneration. Nat. Rev. Mol. Cell Biol. 18, 728–742 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Butcher, D. T., Alliston, T. & Weaver, V. M. A tense situation: forcing tumour progression. Nat. Rev. Cancer 9, 108–122 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Levental, K. R. et al. Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell 139, 891–906 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kass, L., Erler, J. T., Dembo, M. & Weaver, V. M. Mammary epithelial cell: influence of extracellular matrix composition and organization during development and tumorigenesis. Int. J. Biochem. Cell Biol. 39, 1987–1994 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cox, T. R. & Erler, J. T. Remodeling and homeostasis of the extracellular matrix: implications for fibrotic diseases and cancer. Dis. Model. Mech. 4, 165–178 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Staunton, J. R. et al. Mechanical properties of the tumor stromal microenvironment probed in vitro and ex vivo by in situ-calibrated optical trap-based active microrheology. Cell. Mol. Bioeng. 9, 398–417 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Kotliar, D. et al. Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-seq. eLife 8, e43803 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bediaga, N. G. et al. Multi-level remodelling of chromatin underlying activation of human T cells. Sci. Rep. 11, 528 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308.e36 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Weiskirchen, R., Weiskirchen, S. & Tacke, F. Organ and tissue fibrosis: molecular signals, cellular mechanisms and translational implications. Mol. Aspects Med. 65, 2–15 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Dobie, R. et al. Single-cell transcriptomics uncovers zonation of function in the mesenchyme during liver fibrosis. Cell Rep. 29, 1832–1847.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Habermann, A. C. et al. Single-cell RNA sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis. Sci. Adv. 6, eaba1972 (2020).

  42. DePianto, D. J. et al. Molecular mapping of interstitial lung disease reveals a phenotypically distinct senescent basal epithelial cell population. JCI Insight 6, e143626 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lynn, R. C. et al. c-Jun overexpression in CAR T cells induces exhaustion resistance. Nature 576, 293–300 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kurachi, M. et al. The transcription factor BATF operates as an essential differentiation checkpoint in early effector CD8+ T cells. Nat. Immunol. 15, 373–383 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Quigley, M. et al. Transcriptional analysis of HIV-specific CD8+ T cells shows that PD-1 inhibits T cell function by upregulating BATF. Nat. Med. 16, 1147–1151 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Seo, H. et al. BATF and IRF4 cooperate to counter exhaustion in tumor-infiltrating CAR T cells. Nat. Immunol. 22, 983–995 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Koizumi, S. et al. JunB regulates homeostasis and suppressive functions of effector regulatory T cells. Nat. Commun. 9, 5344 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Schütte, J. et al. jun-B inhibits and c-fos stimulates the transforming and trans-activating activities of c-jun. Cell 59, 987–997 (1989).

  49. Chiu, R., Angel, P. & Karin, M. Jun-B differs in its biological properties from, and is a negative regulator of, c-Jun. Cell 59, 979–986 (1989).

    Article  CAS  PubMed  Google Scholar 

  50. Atsaves, V., Leventaki, V., Rassidakis, G. Z. & Claret, F. X. AP-1 transcription factors as regulators of immune responses in cancer. Cancers 11, 1037 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Bennett, B. L. et al. SP600125, an anthrapyrazolone inhibitor of Jun N-terminal kinase. Proc. Natl Acad. Sci. 98, 13681–13686 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kullmann, A. et al. CD137 for isolation and expansion of Ag-specific T cells using Dynabeads®.

  53. Ji, Y. et al. Identification of the genomic insertion site of Pmel-1 TCR α and β transgenes by next-generation sequencing. PLoS ONE 9, e96650 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Overwijk, W. W. et al. Tumor regression and autoimmunity after reversal of a functionally tolerant state of self-reactive CD8+ T cells. J. Exp. Med. 198, 569–580 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. T Cell TransActTM, human. Miltenyi Biotec https://www.miltenyibiotec.com/US-en/products/t-cell-transact-human.html#130-111-160 (2023).

  56. Meng, K. P., Majedi, F. S., Thauland, T. J. & Butte, M. J. Mechanosensing through YAP controls T cell activation and metabolism. J. Exp. Med. 217, e20200053 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Judokusumo, E., Tabdanov, E., Kumari, S., Dustin, M. L. & Kam, L. C. Mechanosensing in T lymphocyte activation. Biophys. J. 102, L5–L7 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. O’Connor, R. S. et al. Substrate rigidity regulates human T cell activation and proliferation. J. Immunol. 189, 1330–1339 (2012).

    Article  PubMed  Google Scholar 

  59. Liu, B., Chen, W., Evavold, B. D. & Zhu, C. Accumulation of dynamic catch bonds between TCR and agonist peptide-MHC triggers T cell signaling. Cell 157, 357–368 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kim, S. T. et al. TCR mechanobiology: torques and tunable structures linked to early T cell signaling. Front. Immunol. 3, 76 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Wang, J.-H. T cell receptors, mechanosensors, catch bonds and immunotherapy. Prog. Biophys. Mol. Biol. 153, 23–27 (2020).

    Article  CAS  PubMed  Google Scholar 

  62. Das, D. K. et al. Force-dependent transition in the T-cell receptor β-subunit allosterically regulates peptide discrimination and pMHC bond lifetime. Proc. Natl Acad. Sci. USA 112, 1517–1522 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Lambert, L. H. et al. Improving T cell expansion with a soft touch. Nano Lett. 17, 821–826 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Hickey, J. W. et al. Engineering an artificial T-cell stimulating matrix for immunotherapy. Adv. Mater. 31, e1807359 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Majedi, F. S. et al. T-cell activation is modulated by the 3D mechanical microenvironment. Biomaterials 252, 120058 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kuczek, D. E. et al. Collagen density regulates the activity of tumor-infiltrating T cells. J. Immunother. Cancer 7, 68 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Park, S. L., Gebhardt, T. & Mackay, L. K. Tissue-resident memory T cells in cancer immunosurveillance. Trends Immunol. 40, 735–747 (2019).

    Article  CAS  PubMed  Google Scholar 

  68. Beura, L. K. et al. Intravital mucosal imaging of CD8+ resident memory T cells shows tissue-autonomous recall responses that amplify secondary memory. Nat. Immunol. 19, 173–182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Wernig, G. et al. Unifying mechanism for different fibrotic diseases. Proc. Natl Acad. Sci. USA 114, 4757–4762 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Cui, L. et al. Activation of JUN in fibroblasts promotes pro-fibrotic programme and modulates protective immunity. Nat. Commun. 11, 2795 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Schulien, I. et al. The transcription factor c-Jun/AP-1 promotes liver fibrosis during non-alcoholic steatohepatitis by regulating Osteopontin expression. Cell Death Differ. 26, 1688–1699 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Man, K. et al. Transcription factor IRF4 promotes CD8+ T cell exhaustion and limits the development of memory-like T cells during chronic infection. Immunity 47, 1129–1141.e5 (2017).

  73. Papavassiliou, A. G. & Musti, A. M. The multifaceted output of c-Jun biological activity: focus at the junction of CD8 T cell activation and exhaustion. Cells 9, 2470 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Kolde, R. pheatmap: pretty heatmaps. R package v.1.0.12 (2019); https://CRAN.R-project.org/package=pheatmap

  75. Wickham, H. in ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  76. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Lukas P. M. Kremer. ggpointdensity: a cross between a 2D ensity plot and a scatter plot. R package v.0.1.0 (2019); https://CRAN.R-project.org/package=ggpointdensity

  78. Xie, Z. et al. Gene set knowledge discovery with enrichr. Curr. Protoc. 1, e90 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Biological research—pathway studio. Elsevier https://www.elsevier.com/solutions/pathway-studio-biological-research (2023).

  80. Krupa, S. et al. The NCI-nature pathway interaction database: a cell signaling resource. Nat. Prec. https://doi.org/10.1038/npre.2007.1311.1 (2007).

  81. Gao, C. H. ggVennDiagram: a ‘ggplot2’ implement of venn diagram. R package v.1.2.2 (2022); https://CRAN.R-project.org/package=ggVennDiagram

  82. Signorell, A. et al. DescTools: tools for descriptive statistics. R package v.0.99.44 (2021); https://CRAN.R-project.org/package=DescTools

  83. 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).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Cryo-SEM was performed at the Center for Nanoscale Systems (CNS) at Harvard. Single-cell RNA-seq was performed at the Bauer Core Facility at Harvard University. We thank W.-H. Jung, Y. Binenbaum and N. Jeffreys for their scientific inputs; the staff at the Wyss Institute for Biologically Inspired Engineering at Harvard University for providing the support needed to perform the required experiments, including T. Ferrante, M. Perez, G. Cuneo, E. Zigon and M. Carr; A. Sharpe for scientific input during the early stages of the project; and E. Weber and E. Sotillo from C. Mackall’s lab for generously sharing their protocol for c-Jun co-immunoprecipitation. K.A.-B. and Y.L. acknowledge funding from the National Institutes of Health (R01 CA276459-01), the Food and Drug Administration (R01FD006589) and the Wellcome Leap HOPE Program. A.G. discloses support from the Medical Scientist Training Program grant (T32 GM007753) from the National Institute of General Medical Sciences at Harvard Medical School. B.A.N. discloses support from postdoctoral fellowships from the NIH (NIBIB, T32EB016652; NIDDK, F32DK134115). The contents are those of the authors and do not necessarily represent the official views of, or an endorsement by, the funding agencies.

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Authors and Affiliations

Authors

Contributions

K.A.-B., Y.L. and D.J.M. conceptualized and designed the study. K.A.-B., Y.L., D.K.Y.Z., B.A.N. and A.G. performed experiments and analysed data. K.A.-B., Y.L., J.M.B., K.H.V. and B.R.F. planned and performed material characterization. K.A.-B., Y.L. and D.J.M. wrote the paper. All authors read and contributed to editing the paper.

Corresponding author

Correspondence to David J. Mooney.

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Competing interests

K.A.-B., Y.L. and D.J.M. have applied through Harvard University for patents on this technology. Not related to this research, D.J.M. has the following interests: Novartis, sponsored research; Agnovos, consulting; Lyell, equity; Attivare, equity; IVIVA Medical, consulting; J&J, consulting. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Material synthesis and characterization.

a. Collagen is modified with norbornene using NHS and gelled at 37 °C with T cells. A low MW divalent crosslinker containing two methyltetrazines is diffused into gels at room temperature until equilibrated with the surrounding solution, to achieve even distribution within the gel. Subsequently increasing the temperature allows covalent crosslinks to form between norbornene and methyltetrazine functionalities. b. Comparison of Young’s moduli for soft and stiff gels to those of published different soft tissues. Data represents minimum, maximum, and mean. c. Stress relaxation profiles of fast relaxing and slow relaxing, soft (left) and stiff (right) collagen matrices. Values are normalized to initial modulus after deformation is applied. d. SHG images for 2 mg/ml fast relaxing and slow relaxing collagen gels. eg. Quantification of fiber length (e), waviness (f) and angle between fibers (g) for fast and slow relaxing matrices. Data are mean ± s.e.m. for (e) n = 20, (f) n = 10, and (g) n = 20 biological samples. P-values were determined by performing two-tailed unpaired t-test. hi. Storage and loss moduli determined using nanoindentation on collagen matrices. Data are mean ± s.e.m. P-values were determined by performing two-tailed unpaired t-test for n = 4 to n = 10 indents.

Source data

Extended Data Fig. 2 ECM viscoelasticity modulates T-cell phenotype in complex ECM.

a. Stress relaxation half time (left) and loss angle (right) measurements of fast relaxing and slow relaxing collagen-matrigel IPNs (Col-Mat IPN). Data are mean ± s.e.m. P-values were determined by performing two-tailed unpaired t-test for n = 3 biological replicates. b. Umap plots of phenotyped T cells after embedding in fast and slow relaxing Col-Mat IPNs. Data for b shows pooled samples for n = 3 biological replicates. c, d. K-means clustering was performed on pooled T cells from all experimental conditions. c. Heat map plot showing characteristic markers for each cluster. Heat map is z-scored by column. Legend for colour scale is shown. d. Heat map plot showing the frequencies of cells per condition for each cluster. Heat map is z-scored by row. Legend for colour scale is shown. e. PCA plot showing relative similarities between the different conditions.

Source data

Extended Data Fig. 3 The AP-1 pathway is modulated by matrix mechanics.

a, b. AP-1 pathway analysis was performed on enriched cNMF modules from Fig. 3. a. Relative significance of AP-1 related genes in cNMF modules, showing significant AP-1 enrichment in Module 5. b. Violin plots comparing expression levels of Module 5 (AP-1 Module) for T cells with shared TCR clonotypes in tumours, adjacent normal tissues and blood for NSCLC, liver cancer and colorectal cancer. P-values were determined by performing two-tailed one-way Anova. c, d. AP-1 pathway analysis was performed on cNMF modules generated from the indicated fibrosis datasets. c. Relative significance of AP-1 related genes in cNMF modules. d. Violin plots comparing the expression levels of the top AP-1 module in CD8+ T cells derived from healthy and fibrotic tissues for each fibrosis study. e. Violin plots comparing expression levels of our in vitro generated AP-1 Module in CD8+ T cells derived from healthy and fibrotic tissues for the same datasets. P-values were determined by using the Wilcoxon Rank Sum test.

Source data

Extended Data Fig. 4 Phosphorylation states of transcription factors and kinases known to impact or be impacted by AP-1.

a. Heat map plots comparing expression and phosphorylation levels of different transcription factors and kinases for the different gel conditions. Heat maps are z-scored by column. Legend for colour scale is shown. b. PCA plots comparing the relative similarities between the different collagen conditions. c. PCA loadings for PC1 showing the relative significance of features that drive the observations in b. Data shows pooled samples for n = 3 biological replicates.

Source data

Extended Data Fig. 5 T-cell receptor β (TRβ) profile is not skewed by ECM viscoelasticity.

a. Frequency distribution of the different TRβV genes for plate-cultured T cells, T cells embedded in fast-relaxing and slow relaxing matrices. b. Venn diagram showing the distribution of TRβV genes across the different conditions. c. Shannon entropy to estimate TRβV gene diversity. d. Plot comparing the mean expression levels of the Slow-Relaxing Module (SModule) for T cells from the indicated conditions grouped by TRβV genes.

Source data

Extended Data Fig. 6 T-cell phenotypes persist after they are harvested from matrices.

T cells were first cultured on plates or in fast relaxing or slow relaxing collagen matrices, after which they were harvested and subsequently cultured in suspension with or without dynabead restimulation. ae. T cell phenotyping without dynabead restimulation. a. Expansion fold (left) and viability (right) of T cells cultured in suspension after they were harvested from their indicated matrices. Data are mean ± s.e.m. n = 3 biological replicates. b. Umap plots of phenotyped T cells. c-d. K-means clustering was performed on pooled T cells from all experimental conditions. c. Heat map plot showing characteristic markers for each cluster. Heat map is z-scored by column. Legend for colour scale is shown. d. Heat map plot showing the frequencies of cells per condition for each cluster. Heat map is z-scored by row. Legend for colour scale is shown. e. PCA plot showing relative similarities between the different conditions. Data for b-d show pooled samples for n = 3 replicates. f–j. Similar analyses as a-e but for T cells cultured with dynabead restimulation. Data are mean ± s.e.m. n = 3 biological replicates.

Source data

Extended Data Fig. 7 Extended profiling and long-term imprinting of T-cell phenotype.

a. CD4/CD8 ratios of T cells cultured in fast relaxing and slow relaxing, soft and stiff collagen gels for 3 days and 7 days. P-values were determined by performing two-tailed one-way Anova with Tukey post-hoc test. Data shows n = 3 biological replicates and represents mean ± s.e.m. b. CD62L and CD45RA expression profiles for CD4+ and CD8+ T cells cultured in different collagen conditions for 3 days and 7 days. c, d. Heat maps for CD8+ T cells (c) and CD4+ T cells (d) showing relative marker expression levels between the different gel conditions. Heat maps are z-scored by column. Legend for colour scale is shown. e. PCA plots showing relative similarities between the different collagen conditions for CD4+ and CD8+ T cells. f. Imprinting of T cell phenotype: T cells were harvested from collagen gels after 3-day and 7-day gel culture and further cultured in suspension for 7 days. The plot shows pairwise distances between T cells harvested from fast relaxing and slow relaxing collagen gels after suspension culture, showing greater differences between T cells cultured in collagen gels for 7 days. Data shows pooled samples n = 3.

Source data

Extended Data Fig. 8 ECM viscoelasticity modulates the phenotypes of T cells subjected to different modes of prior activation.

a. Umap density plots of phenotyped T cells after stimulation with aCD3/aCD28 dynabeads. Legend for density colour scale is shown. b, c. K-means clustering was performed on pooled T cells from all experimental conditions. b. Heat map plot showing the frequencies of cells per condition for each cluster. Heat map is z-scored by row. Legend for colour scale is shown. c. Heat map plot showing characteristic markers for each cluster. Heat map is z-scored by column. Legend for colour scale is shown. d. PCA plot showing relative similarities between the different conditions. Data for a-d show pooled samples for n = 3 biological replicates. e–h. Similar analyses as a-d but for T cells stimulated with aCD3/aCD28/aCD137 dynabeads.

Extended Data Fig. 9 T cells from different matrix conditions are functionally distinct against different tumour types.

ae. Human anti-CD19 CAR T cells were first activated using different dynabead to T cell ratios, subsequently cultured in fast relaxing, slow relaxing matrices or plate-culture and then co-cultured with Raji tumour cells. a. Umap plots of anti-CD19 CAR T cells phenotyped for their intracellular cytokine profiles after co-culture with Raji tumour cells. b-d. K-means clustering was performed on pooled T cells from all experimental conditions b. Umap plot of T cells overlaid with corresponding K-means clusters. c. Heat map plot showing characteristic markers for each cluster. Heat map is z-scored by row. Legend for colour scale is shown. d. Heat map plot showing the frequencies of cells per condition for each cluster. Heat map is z-scored by column. Legend for colour scale is shown. e. PCA plot showing relative similarities between the different conditions. Data shows pooled samples n = 3. f. Differential levels of anti-CD19 CAR T cell killing of Raji cells after plate-culture or culturing in different matrix conditions. Data are mean ± s.e.m. P-values were calculated by using two-way Anova for n = 3 biological replicates per dynabead stimulation level. g. Mouse Pmel-1 T cells were cultured in fast relaxing, slow relaxing matrices or plate-culture and subsequently co-cultured with B16-F10 melanoma cells. Plot shows differential killing of B16-F10 tumour cells by Pmel-1 T cells. Data are mean ± s.e.m. P-values were determined by performing two-tailed one-way ANOVA for n = 6 biological replicates from 2 independent experiments.

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Extended Data Fig. 10 ECM viscoelasticity modulates the phenotype and function of T cells undergoing chronic stimulation.

a–d. Determination of AP-1 protein expression and interactions after chronic stimulation in matrices. a. Representative flow cytometry histograms showing relative expression of c-Jun for T cells cultured in fast relaxing and slow relaxing, soft and stiff matrices after chronic stimulation. b. Heat map plot showing relative expression of indicated AP-1 proteins for T cells cultured in the different collagen conditions after chronic stimulation. Heat map is z-scored by column. Legend for colour scale is shown. c. c-Jun co-immunoprecipitation was performed to probe for c-Jun binding partners. Western blots compare relative amounts of indicated proteins bound to c-Jun as a function of the different mechanical conditions. d. Comparison of the extent to which the individual AP-1 proteins changed between T cells with and without chronic stimulation for the different gel conditions. Data shows pooled samples n = 2–4. e, f. T cell phenotyping after chronic stimulation in collagen matrices. e. Heat map plot comparing marker expression for the different collagen conditions. Heat map is z-scored by column. Legend for colour scale is shown. f. Plot showing memory and activation/inhibitory signatures for T cells cultured in the different gel conditions receiving chronic stimulation. Data shows pooled samples n = 2–4. g. Differential levels of anti-CD19 CAR T cell killing of Raji cells after chronic stimulation in collagen matrices. Data are mean ± s.e.m. P-values were calculated by using two-tailed one-way Anova for n = 3-4 biological replicates. h. Heat map plot showing production of different cytokines and effector molecules by chronically stimulated anti-CD19 CAR T cells after co-culture with Raji cells. Heat map is z-scored by row. Legend for colour scale is shown. i. Correlation analyses showing how the expression of indicated AP-1 proteins for T cells cultured in the different mechanical conditions correlate with their observed killing of Raji cells after chronic stimulation.

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Adu-Berchie, K., Liu, Y., Zhang, D.K.Y. et al. Generation of functionally distinct T-cell populations by altering the viscoelasticity of their extracellular matrix. Nat. Biomed. Eng 7, 1374–1391 (2023). https://doi.org/10.1038/s41551-023-01052-y

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