Target Health Blog

Artificial Intelligence for Quality Control of Stem Cell-Derived Tissues

November 18, 2019

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Stem Cell Therapies
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Cells of the retinal pigment epithelium (RPE) nourish the light-sensing photoreceptors in the eye and are among the first to die from geographic atrophy, commonly known as “dry“ AMD. Photoreceptors die without the RPE, resulting in vision loss and a leading cause of blindness.

According to an article published in the Journal of Clinical Investigation (14 November 2019), artificial intelligence (AI) has been used to evaluate stem cell-derived “patches“ of RPE tissue for implanting into the eyes of patients with age-related macular degeneration (AMD). The proof-of-principle study helps pave the way for AI-based quality control of therapeutic cells and tissues. According to the authors, this AI-based method of validating stem cell-derived tissues is a significant improvement over conventional assays, which are low-yield, expensive, and require a trained user.

The authors are working on a technique for making RPE replacement patches from AMD patients' own cells. To do this, patient blood cells are coaxed in the lab to become induced pluripotent stem cells (IPSCs), which can become any type of cell in the body. The IPS cells are then seeded onto a biodegradable scaffold where they are induced to differentiate into mature RPE. The scaffold-RPE “patch“ is implanted in the back of the eye, behind the retina, to rescue photoreceptors and preserve vision. Results were successful in an animal model, and a clinical trial is planned.

The authors' AI-based validation method employed deep neural networks, an AI technique that performs mathematical computations aimed at detecting patterns in unlabeled and unstructured data. The algorithm operated on images of the RPE obtained using quantitative bright-field absorbance microscopy. The networks were trained to identify visual indications of RPE maturation that correlated with positive RPE function. Those single-cell visual characteristics were then fed into traditional machine-learning algorithms, which in turn helped the computers learn to detect discrete cell features crucial to the prediction of RPE tissue function. The method was validated using stem cell-derived RPE from a healthy donor. Its effectiveness was then tested by comparing iPSC-RPE derived from healthy donors with iPSC-RPE from donors with oculocutaneous albinism disorder and with clinical-grade stem cell-derived RPE from donors with AMD.

In particular, the AI-based image analysis method accurately detected known markers of RPE maturity and function: transepithelial resistance, a measure of the junctions between neighboring RPE; and secretion of endothelial growth factors. The method also can match a particular iPSC-RPE tissue sample to other samples from the same donor, which helps confirm the identity of tissues during clinical-grade manufacturing. According to the authors, multiple AI-methods and advanced hardware allowed for the analysis of terabytes and terabytes of imaging data for each individual patient, and do it more accurately and much faster than in the past. The authors added that that this work demonstrates how a garden variety microscope, if used carefully, can make a precise, reproducible measurement of tissue quality.

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