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:

Chromatin reprogramming and bone regeneration in vitro and in vivo via the microtopography-induced constriction of cell nuclei

Nature Biomedical Engineering volume 7, pages 1514–1529 (2023)Cite this article

Subjects

Abstract

Topographical cues on cells can, through contact guidance, alter cellular plasticity and accelerate the regeneration of cultured tissue. Here we show how changes in the nuclear and cellular morphologies of human mesenchymal stromal cells induced by micropillar patterns via contact guidance influence the conformation of the cells’ chromatin and their osteogenic differentiation in vitro and in vivo. The micropillars impacted nuclear architecture, lamin A/C multimerization and 3D chromatin conformation, and the ensuing transcriptional reprogramming enhanced the cells’ responsiveness to osteogenic differentiation factors and decreased their plasticity and off-target differentiation. In mice with critical-size cranial defects, implants with micropillar patterns inducing nuclear constriction altered the cells’ chromatin conformation and enhanced bone regeneration without the need for exogenous signalling molecules. Our findings suggest that medical device topographies could be designed to facilitate bone regeneration via chromatin reprogramming.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Schematic illustration of the influence of contact-guidance-induced nuclear deformation on bone regeneration.
Fig. 2: Micropillars modulate nuclear morphology by remodelling cytoskeleton components.
Fig. 3: Micropillar-induced nuclear deformation is associated with alterations in nuclear structural components.
Fig. 4: A decrease in chromatin-packing scaling in deformed nuclei enhances the responsiveness of hMSCs to osteogenic differentiation.
Fig. 5: Chromatin accessibility and gene expression alter the hMSC phenotype when cultured on micropillars.
Fig. 6: Micropillar-induced nuclear deformation promotes bone regeneration in vivo.
Fig. 7: Micropillars facilitate osteogenesis via chromatin reprogramming in vivo.

Similar content being viewed by others

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, yet they are available from the corresponding authors on reasonable request. All the sequencing data are available from the Gene Expression Omnibus (GEO) under the accession code GSE224265. Source data are provided with this paper.

Code availability

The custom codes used in this study are available from GitHub at https://github.com/BME2021/LineageSpecificResponsiveness/blob/main/LineageSpecificResponsiveness.ipynb.

References

  1. Dahl, K. N., Ribeiro, A. J. S. & Lammerding, J. Nuclear shape, mechanics, and mechanotransduction. Circ. Res. 102, 1307–1318 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Alisafaei, F., Jokhun, D. S., Shivashankar, G. V. & Shenoy, V. B. Regulation of nuclear architecture, mechanics, and nucleocytoplasmic shuttling of epigenetic factors by cell geometric constraints. Proc. Natl Acad. Sci. USA 116, 13200–13209 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Swift, J. & Discher, D. E. The nuclear lamina is mechano-responsive to ECM elasticity in mature tissue. J. Cell Sci. 127, 3005–3015 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Uhler, C. & Shivashankar, G. V. Nuclear mechanopathology and cancer diagnosis. Trends Cancer 4, 320–331 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Makhija, E., Jokhun, D. S. & Shivashankar, G. V. Nuclear deformability and telomere dynamics are regulated by cell geometric constraints. Proc. Natl Acad. Sci. USA 113, E32–E40 (2016).

    Article  CAS  PubMed  Google Scholar 

  6. Denais, C. M. et al. Nuclear envelope rupture and repair during cancer cell migration. Science 352, 353–358 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Roman, W. et al. Myofibril contraction and crosslinking drive nuclear movement to the periphery of skeletal muscle. Nat. Cell Biol. 19, 1189–1201 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lin, C. et al. Matrix promote mesenchymal stromal cell migration with improved deformation via nuclear stiffness decrease. Biomaterials 217, 119300 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Pajerowski, J. D., Dahl, K. N., Zhong, F. L., Sammak, P. J. & Discher, D. E. Physical plasticity of the nucleus in stem cell differentiation. Proc. Natl Acad. Sci. USA 104, 15619–15624 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Davidson, P. M., Özçelik, H., Hasirci, V., Reiter, G. & Anselme, K. Microstructured surfaces cause severe but non-detrimental deformation of the cell nucleus. Adv. Mater. 21, 3586–3590 (2009).

    Article  CAS  Google Scholar 

  11. Hanson, L. et al. Vertical nanopillars for in situ probing of nuclear mechanics in adherent cells. Nat. Nanotechnol. 10, 554–562 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Pan, Z. et al. Control of cell nucleus shapes via micropillar patterns. Biomaterials 33, 1730–1735 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Liu, X. et al. Subcellular cell geometry on micropillars regulates stem cell differentiation. Biomaterials 111, 27–39 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Carthew, J. et al. Precision surface microtopography regulates cell fate via changes to actomyosin contractility and nuclear architecture. Adv. Sci. 8, 2003186 (2021).

    Article  CAS  Google Scholar 

  15. Hasturk, O., Ermis, M., Demirci, U., Hasirci, N. & Hasirci, V. Square prism micropillars on poly(methyl methacrylate) surfaces modulate the morphology and differentiation of human dental pulp mesenchymal stem cells. Colloids Surf. B 178, 44–55 (2019).

    Article  CAS  Google Scholar 

  16. Stowers, R. S. et al. Matrix stiffness induces a tumorigenic phenotype in mammary epithelium through changes in chromatin accessibility. Nat. Biomed. Eng. 3, 1009–1019 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Virk, R. K. A. et al. Disordered chromatin packing regulates phenotypic plasticity. Sci. Adv. 6, eaax6232 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Li, Yue et al. Analysis of three-dimensional chromatin packing domains by chromatin scanning transmission electron microscopy (ChromSTEM). Sci. Rep. 12, 12198 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Badique, F. et al. Directing nuclear deformation on micropillared surfaces by substrate geometry and cytoskeleton organization. Biomaterials 34, 2991–3001 (2013).

    Article  CAS  PubMed  Google Scholar 

  20. Dupont, S. et al. Role of YAP/TAZ in mechanotransduction. Nature 474, 179–183 (2011).

    Article  CAS  PubMed  Google Scholar 

  21. Amar, K., Wei, F., Chen, J. & Wang, N. Effects of forces on chromatin. APL Bioeng. 5, 041503–041503 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Briand, N. & Collas, P. Lamina-associated domains: peripheral matters and internal affairs. Genome Biol. 21, 85 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Buchwalter, A., Kaneshiro, J. M. & Hetzer, M. W. Coaching from the sidelines: the nuclear periphery in genome regulation. Nat. Rev. Genet. 20, 39–50 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ihalainen, T. O. et al. Differential basal-to-apical accessibility of lamin A/C epitopes in the nuclear lamina regulated by changes in cytoskeletal tension. Nat. Mater. 14, 1252–1261 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kim, D.-H. & Wirtz, D. Cytoskeletal tension induces the polarized architecture of the nucleus. Biomaterials 48, 161–172 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kim, J.-K. et al. Nuclear lamin A/C harnesses the perinuclear apical actin cables to protect nuclear morphology. Nat. Commun. 8, 2123 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kalverda, B., Röling, M. D. & Fornerod, M. Chromatin organization in relation to the nuclear periphery. FEBS Lett. 582, 2017–2022 (2008).

    Article  CAS  PubMed  Google Scholar 

  28. Maeshima, K., Tamura, S. & Shimamoto, Y. Chromatin as a nuclear spring. Biophys. Physicobiol. 15, 189–195 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Amiad-Pavlov, D. et al. Live imaging of chromatin distribution reveals novel principles of nuclear architecture and chromatin compartmentalization. Sci. Adv. 7, eabf6251 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Almassalha, L. M. et al. Label-free imaging of the native, living cellular nanoarchitecture using partial-wave spectroscopic microscopy. Proc. Natl Acad. Sci. USA 113, E6372–e6381 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Eid, A. et al. Characterizing chromatin packing scaling in whole nuclei using interferometric microscopy. Opt. Lett. 45, 4810–4813 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Almassalha, L. M. et al. Macrogenomic engineering via modulation of the scaling of chromatin packing density. Nat. Biomed. Eng. 1, 902–913 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Bajpai, G., Amiad Pavlov, D., Lorber, D., Volk, T. & Safran, S. Mesoscale phase separation of chromatin in the nucleus. eLife 10, e63976 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Killaars, A. R., Walker, C. J. & Anseth, K. S. Nuclear mechanosensing controls MSC osteogenic potential through HDAC epigenetic remodeling. Proc. Natl Acad. Sci. USA 117, 21258–21266 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yourek, G., Hussain, M. A. & Mao, J. J. Cytoskeletal changes of mesenchymal stem cells during differentiation. ASAIO J. 53, 219–228 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Downing, T. L. et al. Biophysical regulation of epigenetic state and cell reprogramming. Nat. Mater. 12, 1154–1162 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hasturk, O., Ermis, M., Demirci, U., Hasirci, N. & Hasirci, V. Square prism micropillars improve osteogenicity of poly(methyl methacrylate) surfaces. J. Mater. Sci. Mater. Med. 29, 53 (2018).

    Article  CAS  PubMed  Google Scholar 

  38. Gjorevski, N. et al. Tissue geometry drives deterministic organoid patterning. Science 375, eaaw9021 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wang, Y., Kibbe, M. R. & Ameer, G. A. Photo-crosslinked biodegradable elastomers for controlled nitric oxide delivery. Biomater. Sci. 1, 625–632 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ware, H. O. T. et al. High-speed on-demand 3D printed bioresorbable vascular scaffolds. Mater. Today Chem. 7, 25–34 (2018).

    Article  CAS  Google Scholar 

  41. Gladstein, S. et al. Multimodal interference-based imaging of nanoscale structure and macromolecular motion uncovers UV induced cellular paroxysm. Nat. Commun. 10, 1652 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Heo, S.-J. et al. Aberrant chromatin reorganization in cells from diseased fibrous connective tissue in response to altered chemomechanical cues. Nat. Biomed. Eng. 7, 177–191 (2023).

    Article  CAS  PubMed  Google Scholar 

  43. Damodaran, K. et al. Compressive force induces reversible chromatin condensation and cell geometry–dependent transcriptional response. Mol. Biol. Cell 29, 3039–3051 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Seelbinder, B. et al. Nuclear deformation guides chromatin reorganization in cardiac development and disease. Nat. Biomed. Eng. 5, 1500–1516 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Uhler, C. & Shivashankar, G. V. Regulation of genome organization and gene expression by nuclear mechanotransduction. Nat. Rev. Mol. Cell Biol. 18, 717–727 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. van Lith, R., Wang, X. & Ameer, G. Biodegradable elastomers with antioxidant and retinoid-like properties. ACS Biomater. Sci. Eng. 2, 268–277 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Wang, X. et al. Influence of cell size on cellular uptake of gold nanoparticles. Biomater. Sci. 4, 970–978 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Ngo, J. T. et al. Click-EM for imaging metabolically tagged nonprotein biomolecules. Nat. Chem. Biol. 12, 459–465 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cherkezyan, L. et al. Nanoscale changes in chromatin organization represent the initial steps of tumorigenesis: a transmission electron microscopy study. BMC Cancer 14, 189 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  51. 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 

  52. 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 

  53. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Lee, S., Cook, D. & Lawrence, M. Plyranges: a grammar of genomic data transformation. Genome Biol. 20, 4 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Dumanian, Z. P. et al. Repair of critical sized cranial defects with BMP9-transduced calvarial cells delivered in a thermoresponsive scaffold. PLoS ONE 12, e0172327–e0172327 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Goedhart, J. SuperPlotsOfData—a web app for the transparent display and quantitative comparison of continuous data from different conditions. Mol. Biol. Cell 32, 470–474 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Science Foundation (NSF) Emerging Frontiers in Research and Innovation (EFRI) (no. 1830968 to G.A.A.), the National Cancer Institute (NCI) (no. R00CA188293, no. R01CA248770 and no.U54CA193419 to P.N.), National Institutes of Health (NIH) grants U54CA268084 and R01CA228272, NSF grant EFMA-1830961 (to V.B.) and philanthropic support from K. Hudson and R. Goldman, S. Brice and J. Esteve, M. E. Holliday and I. Schneider, the Christina Carinato Charitable Foundation, and D. Sachs. This work was performed as a collaboration between the Center for Advanced Regenerative Engineering (CARE) and the Center for Physical Genomics and Engineering (CPGE) at Northwestern University. This work made use of the EPIC facility, the NUFAB facility, and the BioCryo facility of Northwestern University’s NUANCE Center, which has received support from the SHyNE Resource (NSF ECCS-2025633), the International Institute for Nanotechnology (IIN) and Northwestern’s MRSEC programme (NSF DMR-1720139). This work also made use of the Northwestern University NUSeq Core and the Biological Imaging Facility (BIF). We also thank S. Blythe (Molecular Biosciences, Northwestern University) for his guidance in ATAC-seq data analysis.

Author information

Author notes

  1. Panagiotis Ntziachristos

    Present address: Department of Biomolecular Medicine, Ghent University, Ghent, Belgium

  2. These authors contributed equally: Xinlong Wang, Vasundhara Agrawal.

Authors and Affiliations

  1. Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA

    Xinlong Wang, Vasundhara Agrawal, Cody L. Dunton, Yugang Liu, Ranya K. A. Virk, Lucas Carter, Emily M. Pujadas, Yue Li, Surbhi Jain, Nancy Rivera-Bolanos, Jane Frederick, Chongwen Duan, Bin Jiang, Hariharan Subramanian, Vadim Backman & Guillermo A. Ameer

  2. Center for Advanced Regenerative Engineering, Northwestern University, Evanston, IL, USA

    Xinlong Wang, Yugang Liu, Nancy Rivera-Bolanos, Chongwen Duan, Tong Chuan He, Russell R. Reid, Bin Jiang, Vadim Backman & Guillermo A. Ameer

  3. Center for Physical Genomics and Engineering, Northwestern University, Evanston, IL, USA

    Vasundhara Agrawal, Cody L. Dunton, Ranya K. A. Virk, Lucas Carter, Emily M. Pujadas, Yue Li, Surbhi Jain, Jane Frederick, Hariharan Subramanian, Vadim Backman & Guillermo A. Ameer

  4. Quantitative Data Science Core, Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Priyam A. Patel

  5. Molecular Oncology Laboratory, Department of Orthopedic Surgery and Rehabilitation Medicine, The University of Chicago Medical Center, Chicago, IL, USA

    Hao Wang, Na Ni & Tong Chuan He

  6. Department of Radiology, The University of Chicago, Chicago, IL, USA

    Hsiu-Ming Tsai

  7. Department of Materials Sciences and Engineering, Northwestern University, Evanston, IL, USA

    Eric Roth & Reiner Bleher

  8. Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Panagiotis Ntziachristos, Bin Jiang & Guillermo A. Ameer

  9. Department of Biochemistry and Molecular Genetics, Northwestern University, Chicago, IL, USA

    Panagiotis Ntziachristos

  10. Simpson Querrey Center for Epigenetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Panagiotis Ntziachristos

  11. Laboratory of Craniofacial Biology and Development, Section of Plastic and Reconstructive Surgery, Department of Surgery, The University of Chicago Medical Center, Chicago, IL, USA

    Russell R. Reid

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

    Vadim Backman

  13. Chemistry of Life Process Institute, Northwestern University, Chicago, IL, USA

    Guillermo A. Ameer

  14. International Institute for Nanotechnology, Northwestern University, Evanston, IL, USA

    Guillermo A. Ameer

  15. Simpson Querrey Institute for Bionanotechnology, Northwestern University, Chicago, IL, USA

    Guillermo A. Ameer

Authors
  1. Xinlong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vasundhara Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cody L. Dunton
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yugang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ranya K. A. Virk
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Priyam A. Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lucas Carter
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Emily M. Pujadas
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yue Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Surbhi Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Na Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Hsiu-Ming Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Nancy Rivera-Bolanos
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jane Frederick
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Eric Roth
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Reiner Bleher
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Chongwen Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Panagiotis Ntziachristos
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Tong Chuan He
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Russell R. Reid
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Bin Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Hariharan Subramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Vadim Backman
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Guillermo A. Ameer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

X.W., V.A., V.B. and G.A.A. designed the experiments. X.W. and V.A. performed most experiments and analysed the data. C.L.D. performed the imaging experiments on tissue samples and DN-KASH hMSCs. R.K.A.V. helped with gene set enrichment analysis and transcriptional response analysis, P.A.P. did the RNA-seq differential gene expression analysis, and J.F. assisted with the RNA-seq analysis. L.C. performed the ATAC-seq analysis. E.M.P. performed the WB and protein quantification analysis. Y.Li helped with the EM data collection and analysis. S.J. helped with transcriptional data interpretation and analysis. E.R. and R.B. helped with ChromTEM sample preparation. X.W., Y.Liu, H.W., N.N., H.-M.T., T.C.H., R.R.R. and G.A.A. designed and performed in vivo animal work. N.R.-B. and C.D. helped with cell culture and sample preparation. X.W., V.A., B.J., P.N., H.S., V.B. and G.A.A. wrote the manuscript. All the authors discussed the results and reviewed the manuscript.

Corresponding authors

Correspondence to Vadim Backman or Guillermo A. Ameer.

Ethics declarations

Competing interests

An Invention Disclosure has been filed for the mPOC micropillar scaffold through Northwestern University (X.W., V.A., V.B. and G.A.A.). G.A.A. is the inventor of US Food and Drug Administration-approved citrate-based biomaterials. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Matthew Dalby, Pilnam Kim 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

Extended Data Fig. 1 ChromTEM validates a decrease in chromatin-packing scaling in deformed hMSC nuclei.

A. Spatial autocorrelation function (ACF) of chromatin density in the log-log scale for the whole nucleus, and peripheral chromatin. We obtained the chromatin-packing scaling by performing a linear regression of the ACF in the log-log scale within both 50-200 nm and 80-200 nm for the whole nucleus and within 50-80 nm for the nuclear periphery. B. Chromatin-packing scaling shows a significant difference for whole-cell nuclei and peripheral chromatin in hMSCs cultured on flat (n = 20 cells) and pillar surfaces (n = 12 cells), indicating a drastic change in the chromatin organization. N = 2 experiments. Statistics were compared using Student’s t-test (two-sided). The lengths of the boxes indicate interquartile ranges (IQRs) of the first and third quartiles of samples, the horizontal lines represent the median values of the samples, and the whiskers indicate 1.5 IQR.

Source data

Extended Data Fig. 2 Role of the LINC complex in regulating chromatin-packing scaling in hMSCs cultured on micropillars.

A. Representative images of Nesprin-2 and DAPI staining of mcherry tagged DN-KASH hMSCs cultured in flat and micropillar surfaces. Non-transfected cells on a flat surface were shown as a control. B. Disruption of the link between the nucleus and the cytoskeleton in DN-KASH hMSCs cultured on micropillars has limited effect on average D. hMSCs and DN-KASH hMSCs were cultured on flat (n = 95 cells, and 71 DN-KASH cells) and micropillar surfaces (n = 56 cells, and 30 DN-KASH cells). N = 3 experiments, ****p < 0.0001, n.s.=not significant. Data are presented as the mean and the standard deviation. Statistics were determined using Bonferroni’s method for multiple comparisons.

Source data

Extended Data Fig. 3 Epigenetics profile of hMSCs cultured on micropillars in growth medium.

A. Immunostaining images of histone acetylation including acetylation of H3 at lysine 9 (H3K9ac), 14 (H3K14ac), 18 (H3K18ac), and 27 (H3K27ac), and total histone H3 acetylation (H3Ac) in hMSCs on flat and micropillar surfaces. B. Immunostaining images of active transcription markers include methylation of H3 at lysine 4 (H3K4me2) and 36 (H3K36me2 and H3K36me3), and repressive transcription markers include methylation of H3 at lysine 9 (H3K9me3) and 27 (H3K27me3) on flat and micropillar surfaces. C. Immunostaining images of histone deacetylase 1 (HDAC1) and 2 (HDAC2) in hMSCs on flat and micropillar surfaces. D. Immunostaining images of HDAC 3 in cells on flat and micropillar surfaces. White and yellow arrows indicate staining signals in the nucleus and cytosol, respectively. E. Intensity ratio of nuclear HDAC3 to cytoplasmic HDAC3 fluorescence intensity per area of cells on flat and micropillar surfaces. N = 3 experiments. F. Immunostaining images of EZH2 in cells on flat and micropillar surfaces. White and yellow arrows indicate staining signals in the nucleus and cytosol, respectively. G. Relative change of EZH2 expression compared to total H3 expression in cells. The relative expression level on a flat surface was normalized to be 1 (****p < 0.0001, N = 3 experiments). Data are presented as the mean and the standard deviation. Statistics were compared using Student’s t-test (two-sided).

Source data

Extended Data Fig. 4 Characterization of enriched histone modifications on micropillar surfaces in response to osteogenic induction.

A. Immunostaining images and B. western blot images of H3Ac and H3K27me3 in cell nuclei on flat and micropillar surfaces cultured in GM (growth medium) and OM (osteogenic induction medium). Total histone H3 is shown as a control. Osteogenic differentiation induced fold change of C. H3Ac and D. H3K27me3 expression compared to growth control on flat and pillar surfaces (n = 4 independent flat and pillar samples cultured in GM and OM). The samples derive from the same experiment and that blots were processed in parallel. E. Immunostaining images of HDAC3 in cell nuclei on flat and micropillar surfaces cultured in GM and OM. F. Osteogenic differentiation induced fold change that is intensity ratio of nuclear HDAC3 to cytoplasmic HDAC3 fluorescence intensity per area of cells on flat and micropillar surfaces (n = 4 independent flat and pillar samples cultured in GM and OM). G. Immunostaining images and H. western blot images of EZH2 in cell nuclei on flat and micropillar surfaces cultured in GM and OM. GAPDH is shown as a control. I. Osteogenic differentiation induced fold change of EZH2 expression compared to growth control on flat and pillar surfaces (n = 4 independent flat and pillar samples cultured in GM and OM). The samples were derived from the same experiment and the blots were processed in parallel. Data are presented as the mean and the standard deviation. Statistics were compared using Student’s t-test (two-sided).

Source data

Extended Data Fig. 5 Micropillar-induced cytoskeleton deformation modulates histone-modification levels.

A. Immunostaining images of A. H3Ac and C. H3K27me3 in cell nuclei on flat and hybrid micropillar surfaces cultured in growth medium. Nuclear/Cytoplasm intensity quantification of B. H3Ac and D. H3K27me3 expression in hybrid surface compared to growth control on flat and pillar surfaces (****p < 0.0001, n.s.= not significant, n = 175, 122, and 165 cells for H3Ac intensity analysis on flat, pillar and hybrid patterns; n = 245, 146, and 166 cells for H3K27me3 intensity analysis on flat, pillar and hybrid patterns over 3 independent experiments). Data are presented as the mean and the standard deviation. Statistics were compared using one-way analysis of variance (ANOVA) with Tukey’s post-hoc test.

Source data

Extended Data Fig. 6 Inhibition of candidate histone modifications in micropillars.

A. Chromatin conformation in hMSCs treated with GSK126 (EZH2 inhibitor) and RGFP966 (HDAC3 inhibitor) for 24 hours and seeded on flat and micropillar surfaces (n = 160, 186, 149, 135, 110 and 138 cells for flat, flat+GSK, flat+RGFP, pillar, pillar+GSK and pillar+RGFP groups, N = 3 experiments). B. Left: ALP staining images. Right: ALP activity analysis of hMSCs after 7-day osteogenic differentiation induction (****p < 0.0001, n.s.= not significant, n = 3 independent samples). Data are presented as the mean and the standard deviation. Statistics were compared using one-way analysis of variance (ANOVA) with Tukey’s post-hoc test.

Source data

Extended Data Fig. 7 Histological evaluation of flat and micropillar mPOC scaffolds induced cranial defect repair.

A. Gross images of mouse head showing the regenerated tissue with flat and micropillar implants. Black arrows indicate the edge of the defects. B. H&E and C. Masson’s trichrome staining of cranial defects implanted with hMSCs seeded flat and micropillar scaffolds at 6-week post-implantation. Red and green frames indicate the tissue at the edge and central region of the wound. D. IHC staining of OCN, RUNX2, and OPN which are typical osteogenesis markers at 6-week post-implantation. Stronger and thicker stained tissue was observed at the micropillar/tissue interface. E. Negative control (without primary antibody incubation) and negative tissue control (mouse skin tissue) of IHC staining. F. The thickness of regenerated tissue with flat and micropillar implants. n = 5 animals. Data are presented as the mean and the standard deviation. Statistics were compared using Student’s t-test (two-sided).

Source data

Supplementary information

Main Supplementary Information

Supplementary methods, results and discussion, figures, tables and references.

Reporting Summary

Supplementary Data 1

Top-20 processes associated with flat surfaces and micropillars using ATAC-seq and cluster profiler analysis.

Supplementary Data 2

List of genes differentially expressed in induced MSCs on micropillars, compared with those on flat surfaces, with P < 0.05.

Supplementary Data 3

Source data for the supplementary figures.

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

Wang, X., Agrawal, V., Dunton, C.L. et al. Chromatin reprogramming and bone regeneration in vitro and in vivo via the microtopography-induced constriction of cell nuclei. Nat. Biomed. Eng 7, 1514–1529 (2023). https://doi.org/10.1038/s41551-023-01053-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-023-01053-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research