High-Content Screening and Analysis of Stem Cell-Derived Neural Interfaces Using a Combinatorial Nanotechnology and Machine Learning Approach
Open Access
- 14 September 2022
- journal article
- research article
- Published by American Association for the Advancement of Science (AAAS) in Research
- Vol. 2022, 9784273
- https://doi.org/10.34133/2022/9784273
Abstract
A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-based therapies. Nevertheless, high-throughput investigation of the cell-type-specific biophysical cues associated with stem cell-derived neural interfaces continues to be a significant obstacle to overcome. To this end, we developed a combinatorial nanoarray-based method for high-throughput investigation of neural interface micro-/nanostructures (physical cues comprising geometrical, topographical, and mechanical aspects) and the effects of these complex physical cues on stem cell fate decisions. Furthermore, by applying a machine learning (ML)-based analytical approach to a large number of stem cell-derived neural interfaces, we comprehensively mapped stem cell adhesion, differentiation, and proliferation, which allowed for the cell-type-specific design of biomaterials for neural interfacing, including both adult and human-induced pluripotent stem cells (hiPSCs) with varying genetic backgrounds. In short, we successfully demonstrated how an innovative combinatorial nanoarray and ML-based platform technology can aid with the rational design of stem cell-derived neural interfaces, potentially facilitating precision, and personalized tissue engineering applications.Keywords
Funding Information
- National Institutes of Health (5T32EB005583, 3R01DC016612-01S1, R01 c1R01DC016612)
- SAS-Grossman Innovation Prize (5R01DC016612-02S1)
- New Jersey Commission on Spinal Cord Research (CSCR22ERG023, CSCR17IRG010)
- National Science Foundation (CBET-1803517)
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