Abstract
Cells directly interact with the extracellular matrix (ECM) in their microenvironment; however, the mechanical properties of the networks at this scale are not well defined. This work describes a method to quantify ECM network strain in situ after the application of a known load. Visualization of the ECM in the native 3D organization is facilitated using murine embryos and a novel decellularization method. During embryonic development, the ECM architecture is less dense making it easier to visualize and manipulate. Briefly, embryonic day (E)14.5 forelimbs were harvested and incubated in an acrylamide-based hydrogel mixture to maintain the 3D architecture of the ECM during decellularization. After decellularization, forelimbs were stained for fibrillin-2 and proteoglycans to visualize different networks. Samples were imaged, before and after the application of a static load, using confocal microscopy. A MATLAB-based fast iterative digital volume correlation algorithm was used to quantify network displacement fields by comparing the reference and compressed z-stacks. We observed that the amount of Green-Lagrange strain experienced by different proteins was dependent on whether the sub-region analyzed was located within cartilage or the adjacent connective tissue. The combination of these experimental and computational methods will enable the development of constitutive equations that describe the material behavior of ECM networks. In the future, this information has the potential to improve the fabrication of physiologically relevant scaffolds by establishing mechanical guidelines for microenvironments that support beneficial cell-ECM interactions.
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References
Hynes RO, Naba A (2012) Overview of the matrisome—an inventory of extracellular matrix constituents and functions. CSH Perspect Biol 4(1):a004903. https://doi.org/10.1101/cshperspect.a004903
Kjaer M (2004) Role of extracellular matrix in adaptation of tendon and skeletal muscle to mechanical loading. Physiol Rev 84(2):649–698. https://doi.org/10.1152/physrev.00031.2003
Calve S, Simon HG (2012) Biochemical and mechanical environment cooperatively regulate skeletal muscle regeneration. FASEB J 26(6):2538–2545. https://doi.org/10.1096/fj.11-200162
Hartman CD, Isenberg BC, Chua SG, Wong JY (2016) Vascular smooth muscle cell durotaxis depends on extracellular matrix composition. PNAS 113(40):11190–11195. https://doi.org/10.1073/pnas.1611324113
Rowlands AS, George PA, Cooper-White JJ (2008) Directing osteogenic and myogenic differentiation of MSCs: interplay of stiffness and adhesive ligand presentation. Am J Physiol - Cell 295(4):C1037–C1044. https://doi.org/10.1152/ajpcell.67.2008
Li L, Eyckmans J, Chen CS (2017) Designer biomaterials for mechanobiology. Nat Mater 16:1164. https://doi.org/10.1038/nmat5049
Klotzsch E, Smith ML, Kubow KE, Muntwyler S, Little WC, Beyeler F, Gourdon D, Nelson BJ, Vogel V (2009) Fibronectin forms the most extensible biological fibers displaying switchable force-exposed cryptic binding sites. PNAS 106(43):18267–18272. https://doi.org/10.1073/pnas.0907518106
Li W, Sigley J, Pieters M, Helms Christine C, Nagaswami C, Weisel John W, Guthold M (2016) Fibrin fiber stiffness is strongly affected by fiber diameter, but not by fibrinogen glycation. Biophys J 110(6):1400–1410. https://doi.org/10.1016/j.bpj.2016.02.021
Eriksen T, Wright D, Purslow P, Duance V (2001) Role of Ca2+ for the mechanical properties of fibrillin. Proteins 45(1):90–95
Shen ZL, Kahn H, Ballarini R, Eppell Steven J (2011) Viscoelastic properties of isolated collagen fibrils. Biophys J 100(12):3008–3015. https://doi.org/10.1016/j.bpj.2011.04.052
Richardson DS, Lichtman JW (2015) Clarifying tissue clearing. Cell 162(2):246–257. https://doi.org/10.1016/j.cell.2015.06.067
Acuna A, Drakopoulos MA, Leng Y, Goergen CJ, Calve S (2018) Three-dimensional visualization of extracellular matrix networks during murine development. Dev Biol 435(2):122–129. https://doi.org/10.1016/j.ydbio.2017.12.022
Bar-Kochba E, Toyjanova J, Andrews E, Kim K-S, Franck C (2015) A fast iterative digital volume correlation algorithm for large deformations. Exp Mech 55(1):261–274. https://doi.org/10.1007/s11340-014-9874-2
Tinevez JY, Perry N, Schindelin J, Hoopes GM, Reynolds GD, Laplantine E, Bednarek SY, Shorte SL, Eliceiri KW (2017) TrackMate: An open and extensible platform for single-particle tracking. Methods 115:80–90. https://doi.org/10.1016/j.ymeth.2016.09.016
Midgett DE, Pease ME, Jefferys JL, Patel M, Franck C, Quigley HA, Nguyen TD (2017) The pressure-induced deformation response of the human lamina cribrosa: Analysis of regional variations. Acta Biomater 53:123–139. https://doi.org/10.1016/j.actbio.2016.12.054
Zhang Y, Hamza AB (2006) Vertex-based anisotropic smoothing of 3D mesh data. In: 2006 Canadian Conference on Electrical and Computer Engineering, pp 202–205. doi:https://doi.org/10.1109/CCECE.2006.277433
Yagou H, Ohtake Y, Belyaev AG (2002) Mesh smoothing via mean and median filtering applied to face normals. ieee, geometric modeling and processing. theory and applications. GMP 2002. Proceedings. doi: https://doi.org/10.1109/GMAP.2002.1027503
Zhang Y, Matuszewski BJ, Shark LK, Moore CJ (2008) Medical image segmentation using new hybrid level-set method. In: IEEE International Conference on Biomedical Visualisation. IEEE, London, pp 71–76
Fang Q, Boas DA (2009) Tetrahedral mesh generation from volumetric binary and gray-scale images. Paper presented at the Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro, Boston
Neu CP, Novak T, Gilliland KF, Marshall P, Calve S (2015) Optical clearing in collagen- and proteoglycan-rich osteochondral tissues. Osteoarthr Cart 23(3):405–413. https://doi.org/10.1016/j.joca.2014.11.021
Muhammad MS, Choi TS (2012) Sampling for shape from focus in optical microscopy. IEEE T Pattern Anal 34(3):564–573. https://doi.org/10.1109/TPAMI.2011.144
Lakadamyali M, Babcock H, Bates M, Zhuang X, Lichtman J (2012) 3D multicolor super-resolution imaging offers improved accuracy in neuron tracing. PLoS One 7(1):e30826. https://doi.org/10.1371/journal.pone.0030826
Acknowledgements
This work was supported by the National Institutes of Health [R21 AR069248, R01 AR071359 and DP2 AT009833 to S.C.]. The authors would like to thank Dr. Robert Mecham for providing the fibrillin-2 antibody. We would also like to acknowledge Michael Drakopoulos and Benjamin Sather for their technical assistance and support.
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Acuna, A., Sofronici, S.H., Goergen, C.J. et al. In Situ Measurement of Native Extracellular Matrix Strain. Exp Mech 59, 1307–1321 (2019). https://doi.org/10.1007/s11340-019-00499-y
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DOI: https://doi.org/10.1007/s11340-019-00499-y