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

Nature Biomedical Engineering volume 7, pages 1374–1391 (2023)Cite this article

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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.

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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.

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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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Author notes

  1. These authors contributed equally: Kwasi Adu-Berchie, Yutong Liu.

Authors and Affiliations

  1. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Kwasi Adu-Berchie, Yutong Liu, David K. Y. Zhang, Benjamin R. Freedman, Joshua M. Brockman, Kyle H. Vining, Bryan A. Nerger & David J. Mooney

  2. The Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Kwasi Adu-Berchie, Yutong Liu, David K. Y. Zhang, Benjamin R. Freedman, Joshua M. Brockman, Kyle H. Vining, Bryan A. Nerger & David J. Mooney

  3. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Kyle H. Vining

  4. Department of Preventative and Restorative Sciences, School of Dental Medicine, and Department of Materials Science and Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Kyle H. Vining

  5. Harvard Medical School, Boston, MA, USA

    Andrea Garmilla

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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. e–g. 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. h–i. 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. a–e. 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.

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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.

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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.

a–e. 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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