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 3

Prediction of healthy-2 iPSC-RPE function from QBAM images.

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Prediction of healthy-2 iPSC-RPE function from QBAM images. (A) Plot of ...
(A) Plot of the mean absorbance from 12 images collected in each well over time. Shaded region represents 95% SEM. (B) Representative QBAM images of live iPSC-RPE prior to treatment (week 1, top row) and after 8 weeks of maturation (bottom row) in the presence of a maturation promoter (aphidicolin), a maturation inhibitor (HPI4), or neither (control). Color calibration bar shows units in mAU. (C) Fluorescent labeling of a control sample from healthy-2 iPSC-RPE after 8 weeks of culture, where blue shows cell nuclei (DAPI), green shows cell borders (ZO-1), and red shows an RPE-specific maturation marker (RPE65). Scale bars: 100 μm (B); 50 μm (C). (D) Evaluation of iPSC-RPE TEP in response to an ATP challenge. ***P < 0.005. Whiskers represent 4 times the inner quartile range, and boxes show 25% and 75% quantiles. (E) Plot of TER over time for every replicate starting at week 3. Shaded region represents 95% SEM. (F) Plot of TER against mean image absorbance (R2 = 0.19, blue dashed line shows linear regression). (G) Plot of TER predictions from a DNN (DNN-F) against the measured TER (R2 = 0.97, black line represents a perfect prediction from the DNN). See also Supplemental Figure 3 for additional functional testing, and Supplemental Figure 4 for DNN-F prediction of VEGF secretion. n = 12 replicate wells per treatment and 12 images per replicate for all graphs. Linear mixed effect models controlling for repeated measures from a single well over time and for multiple images being taken per well were performed for A, D, and E.