Login / Signup

Bioinspired 3D microprinted cell scaffolds: Integration of graph theory to recapitulate complex network wiring in lymph nodes.

Matthew H W ChinBarry ReidVeronika LachinaSophie E ActonMarc-Olivier Coppens
Published in: Biotechnology journal (2023)
Physical networks are ubiquitous in nature, but many of them possess a complex organisational structure that is difficult to recapitulate in artificial systems. This is especially the case in biomedical and tissue engineering, where the microstructural details of 3D cell scaffolds are important. Studies of biological networks - such as fibroblastic reticular cell (FRC) networks - have revealed the crucial role of network topology in a range of biological functions. However, cell scaffolds are rarely analysed, or designed, using graph theory. To understand how networks affect adhered cells, 3D culture platforms capturing the complex topological properties of biologically relevant networks would be needed. In this work, we took inspiration from the small-world organisation (high clustering and low path length) of FRC networks to design cell scaffolds. An algorithmic toolset was created to generate the networks and process them to improve their 3D printability. We employed tools from graph theory to show that the networks were small-world (omega factor, ω = -0.10 ± 0.02; small-world propensity, SWP = 0.74 ± 0.01). 3D microprinting was employed to physicalise networks as scaffolds, which supported the survival of FRCs. This work, therefore, represents a bioinspired, graph theory-driven approach to control the networks of microscale cell niches. This article is protected by copyright. All rights reserved.
Keyphrases
  • single cell
  • tissue engineering
  • cell therapy
  • lymph node
  • multiple sclerosis
  • mental health
  • machine learning
  • rna seq
  • physical activity
  • cell death
  • convolutional neural network
  • deep learning