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 6

Prediction of cell TER from 8 AMD iPSC-RPE cell lines derived from 3 separate donors.

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Prediction of cell TER from 8 AMD iPSC-RPE cell lines derived from 3 sep...
(A) Representative QBAM image and SEM image of day-75 iPSC-RPE from 1 clone from each donor. (B) Mean absorbance as assessed by QBAM imaging versus TER for n = 8 clones (3 replicate measures) across the last 5 days of cell maturation; blue dotted line represents linear best fit for these cells. (C) RF prediction of iPSC-RPE function across 3 clones that the algorithm had not seen previously. The black dashed line represents a perfect prediction. A 95% CI is shown as the blue region. (D) Heatmap of the important cell image features for predicting iPSC-RPE TER across 18 different clone combinations sorted by mean feature importance across all clone combinations. The top 10 cell image features of 315 analyzed are shown. Red indicates most important features, while blue represents least important features. Scale bars: 100 μm (A, top panels); 5 μm (A, bottom panels). Color calibration bar is shown in milli-absorbance units. See also Supplemental Figure 7 and Supplemental Table 4.