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

Filippos Tourlomousis,Dilhan M Kalyon,Hongjun Wang,Andreas Mershin,Robert C Chang,Chao Jia,Thrasyvoulos Karydis

doi:10.1038/s41378-019-0055-4

Abstract

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.

Highlights

Cells sense physical aspects of their local microenvironment and respond by acquiring specific phenotypes over time that are tightly related to their function, indicating that an intimate link exists between cell shape and function[1,2,3]
Trial and error methods are utilized for the determination of suitable operating conditions which are systematically varied until the electrostatic stresses acting at the polymer solution–air interface can overcome the surface tension and the elasticity of the polymer, leading to the formation of a stable Taylor cone and the generation of fibers with targeted diameters (Fig. 1a, b)
Discussion the modulation of cellular phenotype with biochemical regulatory factors is well-known, structural and mechanical inputs from the extracellular matrix (ECM) have been identified as key regulators of measurable cell phenotypic attributes

Summary

Introduction

Cells sense physical aspects of their local microenvironment and respond by acquiring specific phenotypes over time that are tightly related to their function, indicating that an intimate link exists between cell shape and function[1,2,3]. The principle of controlling cell function through cell shape manipulation has led to the development of engineered culture models made from natural or synthetic. The non-reproducible nature of these systems due to the local substrate remodeling associated with cell migration renders them nonideal as culture models for cellular mechanosensing studies[19]. One possible method involves the fabrication of functionalized non-woven gel electrospun fiber meshes followed by in situ cross-linking for stiffness control[20]. There is a need for 3D culture models with well-defined cellular-relevant geometrical feature sizes that can decouple stiffness from the architecture of the substrate as well as provide tight control over the porous architecture at the single cell level

Methods

Results

Conclusion