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Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy
Nicholas J. Schaub, … , Peter Bajcsy, Kapil Bharti
Nicholas J. Schaub, … , Peter Bajcsy, Kapil Bharti
Published November 12, 2019
Citation Information: J Clin Invest. 2020;130(2):1010-1023. https://doi.org/10.1172/JCI131187.
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Research Article Ophthalmology

Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy

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Abstract

Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and noninvasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to noninvasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell–derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine-learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity. These results demonstrate noninvasive cell therapy characterization can be achieved with QBAM and machine learning.

Authors

Nicholas J. Schaub, Nathan A. Hotaling, Petre Manescu, Sarala Padi, Qin Wan, Ruchi Sharma, Aman George, Joe Chalfoun, Mylene Simon, Mohamed Ouladi, Carl G. Simon Jr., Peter Bajcsy, Kapil Bharti

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Figure 7

Identification of iPSC-RPE monolayer developmental outliers and donor identity using only QBAM images and either DNNs or TML algorithms.

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Identification of iPSC-RPE monolayer developmental outliers and donor id...
(A) Principle component analysis of the first principle component versus the second principle component of cell image features from QBAM images of all clones. (B) Hierarchical cluster of all clones to show similarity of clones from 3 donors; red boxes indicate the 3 least related groups from the total population. (C) Heatmap of all 18 clone combinations of all cell image features important for the L-SVM to classifying cell identity. The top 10 cell image features are shown (of 315); red indicates the features most important to predicted cell identity, while blue indicates the features least important. n = 8 clones with 3 replicate measures

Copyright © 2021 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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