4.8 Article

4D-Printed Transformable Tube Array for High-Throughput 3D Cell Culture and Histology

Journal

ADVANCED MATERIALS
Volume 32, Issue 40, Pages -

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202004285

Keywords

3D cell culture; 4D printing; high-throughput; content screening; parallel histology; shape-memory polymers

Funding

  1. National Cancer Institute (NCI), National Institutes of Health (NIH) [HHSN261200800001E]
  2. National Science Foundation (NSF) [CBET-1804591]
  3. Rutgers Cancer Institute of New Jersey Shared Resources
  4. NCI-CCSG [P30CA072720]
  5. NCI [5R01CA226746]
  6. NSF [CHE-1429062, CMMI-1751520]
  7. NIH R01 [1R01DC016612, 3R01DC016612-01S1]

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3D cell cultures are rapidly emerging as a promising tool to model various human physiologies and pathologies by closely recapitulating key characteristics and functions of in vivo microenvironment. While high-throughput 3D culture is readily available using multi-well plates, assessing the internal microstructure of 3D cell cultures still remains extremely slow because of the manual, laborious, and time-consuming histological procedures. Here, a 4D-printed transformable tube array (TTA) using a shape-memory polymer that enables massively parallel histological analysis of 3D cultures is presented. The interconnected TTA can be programmed to be expanded by 3.6 times of its printed dimension to match the size of a multi-well plate, with the ability to restore its original dimension for transferring all cultures to a histology cassette in order. Being compatible with microtome sectioning, the TTA allows for parallel histology processing for the entire samples cultured in a multi-well plate. The test result with human neural progenitor cell spheroids suggests a remarkable reduction in histology processing time by an order of magnitude. High-throughput analysis of 3D cultures enabled by this TTA has great potential to further accelerate innovations in various 3D culture applications such as high-throughput/content screening, drug discovery, disease modeling, and personalized medicine.

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