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Machine learning metrology of cell confinement three-dimensional biomaterial substrates

Machine learning metrology of cell confinement in melt electrowritten three-dimensional biomaterial substrates

Tuning cell shape by altering the biophysical properties of biomaterial substrates on which cells operate would provide a potential shape-driven pathway to control cell phenotype. However, there is an unexplored dimensional scale window of three-dimensional (3D) substrates with precisely tunable porous microarchitectures and geometrical feature sizes at the cell’s operating length scales (10–100 μm). This paper demonstrates the fabrication of such high-fidelity fibrous substrates using a melt electrowriting (MEW) technique. This advanced manufacturing approach is biologically qualified with a metrology framework that models and classifies cell confinement states under various substrate dimensionalities and architectures. Using fibroblasts as a model cell system, the mechanosensing response of adherent cells is investigated as a function of variable substrate dimensionality (2D vs. 3D) and porous microarchitecture (randomly oriented, “non-woven” vs. precision-stacked, “woven”). Single-cell confinement states are modeled using confocal fluorescence microscopy in conjunction with an automated single-cell bioimage data analysis workflow that extracts quantitative metrics of the whole cell and sub-cellular focal adhesion protein features measured. The extracted multidimensional dataset is employed to train a machine learning algorithm to classify cell shape phenotypes. The results show that cells assume distinct confinement states that are enforced by the prescribed substrate dimensionalities and porous microarchitectures with the woven MEW substrates promoting the highest cell shape homogeneity compared to non-woven fibrous substrates. The technology platform established here constitutes a significant step towards the development of integrated additive manufacturing—metrology platforms for a wide range of applications including fundamental mechanobiology studies and 3D bioprinting of tissue constructs to yield specific biological designs qualified at the single-cell level.1

a Solution electrospinning (SES) vs. melt electrospinning (MES). The main differentiating feature between the two processes is the extent of the jet instabilities that arise from the electrostatic forces acting at the polymer jet-air interface. For MES, the chaotic jet regime is limited close to the grounded collector plate due to the high viscosity and dielectric properties of the pure polymer melt. b Direct melt electrowriting (MEW) and its operating principle. (i) 3D conical fiber structures are obtained by the layered deposition of fibers in circular patterns due to jet instabilities close to the stationary collector plate. (ii) The jet instabilities can be eliminated by moving the grounded collector plate at prescribed translational stage speeds. (iii) Micrograph depicting various fiber topographies that are obtained by tuning the translational stage speed (UT [SI: mm/s)]. Coiling fiber structures are obtained for the lowest UT setting. Coiling frequency of these fiber structures can be gradually eliminated by gradually increasing UT to achieve aligned fibers at the critical UT setting
Electrohydrodynamics (EHD)-based fabrication methods employed in this study.

a 2D non-woven fibrous mesh fabricated with solution electrospinning (SES) and a prescribed spinning time set of 1 min. The sample is designated as SES-1 min. b 2D non-woven fibrous mesh fabricated with SES and a prescribed spinning time set of 3 min. The sample is designated as SES-3 min. c 3D woven fibrous mesh with “0–90°” pore microarchitecture fabricated with direct melt electrowriting (MEW). The sample is designated MEW|0–90°. d 3D woven fibrous mesh with “0–45°” pore microarchitecture. The sample is designated as MEW|0–45°
Fibrous mesh morphologies employed in this study.
  1. Tourlomousis, F., Jia, C., Karydis, T. et al. Machine learning metrology of cell confinement in melt electrowritten three-dimensional biomaterial substrates. Microsyst Nanoeng5, 15 (2019).

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