3D-Printed Soft Lithography for Complex Compartmentalized Microfluidic Neural Devices

Research output: Contribution to journalArticle

Abstract

Compartmentalized microfluidic platforms are an invaluable tool in neuroscience research. However, harnessing the full potential of this technology remains hindered by the lack of a simple fabrication approach for the creation of intricate device architectures with high-aspect ratio features. Here, a hybrid additive manufacturing approach is presented for the fabrication of open-well compartmentalized neural devices that provides larger freedom of device design, removes the need for manual postprocessing, and allows an increase in the biocompatibility of the system. Suitability of the method for multimaterial integration allows to tailor the device architecture for the long-term maintenance of healthy human stem-cell derived neurons and astrocytes, spanning at least 40 days. Leveraging fast-prototyping capabilities at both micro and macroscale, a proof-of-principle human in vitro model of the nigrostriatal pathway is created. By presenting a route for novel materials and unique architectures in microfluidic systems, the method provides new possibilities in biological research beyond neuroscience applications.

Details

Authors
  • Janko Kajtez
  • Sebastian Buchmann
  • Shashank Vasudevan
  • Marcella Birtele
  • Stefano Rocchetti
  • Christian Jonathan Pless
  • Arto Heiskanen
  • Roger A. Barker
  • Alberto Martínez-Serrano
  • Malin Parmar
  • Johan Ulrik Lind
  • Jenny Emnéus
Organisations
External organisations
  • Technical University of Denmark
  • Autonomous University of Madrid
  • University of Cambridge
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Other Electrical Engineering, Electronic Engineering, Information Engineering
  • Other Medical Sciences not elsewhere specified

Keywords

  • 3D printing, compartmentalized devices, fast prototyping, human neural stem cells, neurite guidance, nigrostriatal pathway, soft lithography
Original languageEnglish
Article number2001150
JournalAdvanced Science
Volume7
Issue number16
Early online date2020
Publication statusPublished - 2020 Aug 19
Publication categoryResearch
Peer-reviewedYes