3D full-field quantification of cell-induced large deformations in fibrillar biomaterials by combining non-rigid image registration with label-free second harmonic generation.

To advance our current understanding of cell-matrix mechanics and its importance for biomaterials development, advanced three-dimensional (3D) measurement techniques are necessary. Cell-induced deformations of the surrounding matrix are commonly derived from the displacement of embedded fiducial markers, as part of traction force microscopy (TFM) procedures. However, these fluorescent markers may alter the mechanical properties of the matrix or can be taken up by the embedded cells, and therefore influence cellular behavior and fate. In addition, the currently developed methods for calculating cell-induced deformations are generally limited to relatively small deformations, with displacement magnitudes and strains typically of the order of a few microns and less than 10% respectively. Yet, large, complex deformation fields can be expected from cells exerting tractions in fibrillar biomaterials, like collagen. To circumvent these hurdles, we present a technique for the 3D full-field quantification of large cell-generated deformations in collagen, without the need of fiducial markers. We applied non-rigid, Free Form Deformation (FFD)-based image registration to compute full-field displacements induced by MRC-5 human lung fibroblasts in a collagen type I hydrogel by solely relying on second harmonic generation (SHG) from the collagen fibrils. By executing comparative experiments, we show that comparable displacement fields can be derived from both fibrils and fluorescent beads. SHG-based fibril imaging can circumvent all described disadvantages of using fiducial markers. This approach allows measuring 3D full-field deformations under large displacement (of the order of 10 μm) and strain regimes (up to 40%). As such, it holds great promise for the study of large cell-induced deformations as an inherent component of cell-biomaterial interactions and cell-mediated biomaterial remodeling.

[1]  Ning Wang,et al.  Spatial and temporal traction response in human airway smooth muscle cells. , 2002, American journal of physiology. Cell physiology.

[2]  E. Bar-Kochba,et al.  A Fast Iterative Digital Volume Correlation Algorithm for Large Deformations , 2015 .

[3]  Kenneth M. Yamada,et al.  Taking Cell-Matrix Adhesions to the Third Dimension , 2001, Science.

[4]  David J. Mooney,et al.  Harnessing Traction-Mediated Manipulation of the Cell-Matrix Interface to Control Stem Cell Fate , 2010, Nature materials.

[5]  Michael L. Thomas,et al.  Minimization of Childhood Maltreatment Is Common and Consequential: Results from a Large, Multinational Sample Using the Childhood Trauma Questionnaire , 2016, PloS one.

[6]  Miss A.O. Penney.  (b) , 1974, The New Yale Book of Quotations.

[7]  Paola Velardi,et al.  Can Twitter Be a Source of Information on Allergy? Correlation of Pollen Counts with Tweets Reporting Symptoms of Allergic Rhinoconjunctivitis and Names of Antihistamine Drugs , 2015, PloS one.

[8]  K. Jacobson,et al.  Imaging the traction stresses exerted by locomoting cells with the elastic substratum method. , 1996, Biophysical journal.

[9]  Marcel Ameloot,et al.  Label-free mapping of microstructural organisation in self-aligning cellular collagen hydrogels using image correlation spectroscopy. , 2016, Acta biomaterialia.

[10]  Donald E Ingber,et al.  Mechanical control of tissue and organ development , 2010, Development.

[11]  Clare M Waterman,et al.  High-resolution traction force microscopy. , 2014, Methods in cell biology.

[12]  Shouren Ge,et al.  Cell adaptation to a physiologically relevant ECM mimic with different viscoelastic properties. , 2007, Biomaterials.

[13]  Carlos Ortiz-de-Solorzano,et al.  Free Form Deformation–Based Image Registration Improves Accuracy of Traction Force Microscopy , 2015, PloS one.

[14]  Ben Fabry,et al.  3D Traction Forces in Cancer Cell Invasion , 2012, PloS one.

[15]  Wesley R. Legant,et al.  Measurement of mechanical tractions exerted by cells in three-dimensional matrices , 2010, Nature Methods.

[16]  K. Jacobson,et al.  Traction forces generated by locomoting keratocytes , 1994, The Journal of cell biology.

[17]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[18]  J. Wang,et al.  The principles and biological applications of cell traction force microscopy , 2010 .

[19]  Dietmar W. Hutmacher,et al.  Bioengineered 3D platform to explore cell-ECM interactions and drug resistance of epithelial ovarian cancer cells. , 2010, Biomaterials.

[20]  Shawn P. Carey,et al.  Biophysical control of invasive tumor cell behavior by extracellular matrix microarchitecture. , 2012, Biomaterials.

[21]  F. Pampaloni,et al.  The third dimension bridges the gap between cell culture and live tissue , 2007, Nature Reviews Molecular Cell Biology.

[22]  K. Jacobson,et al.  Traction forces in locomoting cells. , 1995, Cell motility and the cytoskeleton.

[23]  Matthias P Lutolf,et al.  The effect of matrix characteristics on fibroblast proliferation in 3D gels. , 2010, Biomaterials.

[24]  Damien Garcia,et al.  Robust smoothing of gridded data in one and higher dimensions with missing values , 2010, Comput. Stat. Data Anal..

[25]  A Muñoz-Barrutia,et al.  Validation tool for traction force microscopy , 2015, Computer methods in biomechanics and biomedical engineering.

[26]  Paul W Wiseman.  Image correlation spectroscopy: mapping correlations in space, time, and reciprocal space. , 2013, Methods in enzymology.

[27]  Matthias P. Lutolf,et al.  Designing materials to direct stem-cell fate , 2009, Nature.

[28]  Bo Sun,et al.  Three-Dimensional Reflectance Traction Microscopy , 2016, PloS one.

[29]  Santiago Costantino,et al.  Spatiotemporal image correlation spectroscopy (STICS) theory, verification, and application to protein velocity mapping in living CHO cells. , 2005, Biophysical journal.

[30]  Max A. Viergever,et al.  Adaptive Stochastic Gradient Descent Optimisation for Image Registration , 2009, International Journal of Computer Vision.

[31]  Christian Eggeling,et al.  Super-Resolved Traction Force Microscopy (STFM) , 2016, Nano letters.

[32]  Gaudenz Danuser,et al.  Traction microscopy to identify force modulation in sub-resolution adhesions , 2015, Nature Methods.

[33]  S. Thrun,et al.  Substrate Elasticity Regulates Skeletal Muscle Stem Cell Self-Renewal in Culture , 2010, Science.

[34]  Christian Franck,et al.  Mean deformation metrics for quantifying 3D cell–matrix interactions without requiring information about matrix material properties , 2016, Proceedings of the National Academy of Sciences.

[35]  Suliana Manley,et al.  Correction of a Depth-Dependent Lateral Distortion in 3D Super-Resolution Imaging , 2015, PloS one.

[36]  Ben Fabry,et al.  Three-dimensional force microscopy of cells in biopolymer networks , 2015, Nature Methods.

[37]  Francesco S. Pavone,et al.  Second Harmonic Generation Imaging , 2016 .