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

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

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
Article number15
JournalMicrosystems and Nanoengineering
Volume5
Issue number1
DOIs
StatePublished - Dec 1 2019

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Biomaterials
Learning systems
Substrates
Confocal microscopy
Fluorescence microscopy
Fibroblasts
Learning algorithms
Adhesion
Tuning
Tissue
Proteins
Fabrication

All Science Journal Classification (ASJC) codes

  • Condensed Matter Physics
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Materials Science (miscellaneous)

Cite this

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title = "Machine learning metrology of cell confinement in melt electrowritten three-dimensional biomaterial substrates",
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.",
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Machine learning metrology of cell confinement in melt electrowritten three-dimensional biomaterial substrates. / Tourlomousis, Filippos; Jia, Chao; Karydis, Thrasyvoulos; Mershin, Andreas; Wang, Hongjun; Kalyon, Dilhan; Chang, Robert.

In: Microsystems and Nanoengineering, Vol. 5, No. 1, 15, 01.12.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Machine learning metrology of cell confinement in melt electrowritten three-dimensional biomaterial substrates

AU - Tourlomousis, Filippos

AU - Jia, Chao

AU - Karydis, Thrasyvoulos

AU - Mershin, Andreas

AU - Wang, Hongjun

AU - Kalyon, Dilhan

AU - Chang, Robert

PY - 2019/12/1

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