Autologous cell replacement: a noninvasive AI approach to clinical release testing
Budd A. Tucker, Robert F. Mullins, and Edwin M. Stone
Institute for Vision Research, Department of Ophthalmology and Visual Science, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USA.
Address correspondence to: Budd A. Tucker, 375 Newton Road, Iowa City, Iowa 52242, USA. Phone: 319.353.4488; Email: budd-tucker@uiowa.edu.
Find articles by Tucker, B. in: JCI | PubMed | Google Scholar
Institute for Vision Research, Department of Ophthalmology and Visual Science, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USA.
Address correspondence to: Budd A. Tucker, 375 Newton Road, Iowa City, Iowa 52242, USA. Phone: 319.353.4488; Email: budd-tucker@uiowa.edu.
Find articles by Mullins, R. in: JCI | PubMed | Google Scholar
Institute for Vision Research, Department of Ophthalmology and Visual Science, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USA.
Address correspondence to: Budd A. Tucker, 375 Newton Road, Iowa City, Iowa 52242, USA. Phone: 319.353.4488; Email: budd-tucker@uiowa.edu.
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Stone, E.
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First published January 21, 2020 - More info
J Clin Invest. 2020;130(2):608–611. https://doi.org/10.1172/JCI133821.
© 2020 American Society for Clinical Investigation
The advent of human induced pluripotent stem cells (iPSCs) provided a means for avoiding ethical concerns associated with the use of cells isolated from human embryos. The number of labs now using iPSCs to generate photoreceptor, retinal pigmented epithelial (RPE), and—more recently—choroidal endothelial cells has grown exponentially. However, for autologous cell replacement to be effective, manufacturing strategies will need to change. Many tasks carried out by hand will need simplifying and automating. In this issue of the JCI, Schaub and colleagues combined quantitative bright-field microscopy and artificial intelligence (deep neural networks and traditional machine learning) to noninvasively monitor iPSC-derived graft maturation, predict donor cell identity, and evaluate graft function prior to transplantation. This approach allowed the authors to preemptively identify and remove abnormal grafts. Notably, the method is (a) transferable, (b) cost and time effective, (c) high throughput, and (d) useful for primary product validation.
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Copyright © 2020 American Society for Clinical Investigation
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