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

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

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 1

Methodology for image acquisition and image analysis.

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Methodology for image acquisition and image analysis. (A) An overview of...
(A) An overview of QBAM imaging. The method converts pixel values in bright-field images to absorbance values. (B) Methods of analyzing QBAM images of human iPSC-RPE to predict function, identity and developmental outliers. Three DNNs were constructed for this study: (a) Predicts function (TER and VEGF-ratio) from QBAM images (DNN-F) using entire FOVs from microscopes, (b) identifies whether QBAM images from different clones came from the same donor (DNN-I), (c) segments individual iPSC-RPE cells in absorbance images (DNN-S). QBAM images of iPSC-RPE that were segmented with DNN-S also had individual cell image features extracted with the WIPP, and these features were used to predict cell function, cell identity, and whether cells were developmental outliers. More information on QBAM imaging is presented in Supplemental Figure 1, and the DNN architectures are presented in Supplemental Figure 2. Features extracted by WIPP are listed in Supplemental Table 1.