Journal article
Autologous cell replacement: a noninvasive AI approach to clinical release testing
The Journal of clinical investigation, Vol.130(2), pp.608-611
02/03/2020
DOI: 10.1172/JCI133821
PMID: 31961338
Abstract
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\nJCI\n, 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.
Details
- Title: Subtitle
- Autologous cell replacement: a noninvasive AI approach to clinical release testing
- Creators
- Budd A Tucker - Institute for Vision Research, Department of Ophthalmology and Visual Science, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USARobert F Mullins - Institute for Vision Research, Department of Ophthalmology and Visual Science, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USAEdwin M Stone - Institute for Vision Research, Department of Ophthalmology and Visual Science, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USA
- Resource Type
- Journal article
- Publication Details
- The Journal of clinical investigation, Vol.130(2), pp.608-611
- Publisher
- American Society for Clinical Investigation
- DOI
- 10.1172/JCI133821
- PMID
- 31961338
- ISSN
- 0021-9738
- eISSN
- 1558-8238
- Language
- English
- Date published
- 02/03/2020
- Academic Unit
- The University of Iowa Institute for Vision Research; Iowa Neuroscience Institute; John and Marcia Carver Nonprofit Genetic Testing Laboratory; Ophthalmology and Visual Sciences
- Record Identifier
- 9984070708702771
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