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 2

Assessment of QBAM reproducibility, accuracy, and robustness.

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Assessment of QBAM reproducibility, accuracy, and robustness. (A) ND fil...
(A) ND filters were analyzed with a spectrometer and compared with absorbance values from QBAM images. n = 3 replicates per point; error bars = 3 SD (smaller than size of data point). (B) Three different ND filters were imaged on 3 different microscopes using different color filters to determine the comparability of absorbance values between different configurations (e.g., filters, cameras, etc.). n = 3 replicates per point[ error bars = 3 SD (smaller than size of data point). (C) iPSC-RPE from 2 healthy patients were imaged over time with QBAM (n = 12 wells per donor) to observe changes in pigmentation as iPSC-RPE mature. Each data point represents the mean of 12 images captured from 1 well. Shaded region represents 95% SEM. (D) iPSC-RPE from patients with OCA were imaged to determine whether QBAM was able to recapitulate clinical presentation (OCA patients have iPSC-RPE with low pigment). Each data point represents 1 FOV of each sample. Whiskers represent 3 times the inner quartile range; boxes show 25% and 75% quantiles. n = 9 replicates for severe; n = 10 replicates for moderate; and n = 8 replicates for mild. A linear mixed effect model controlling for multiple images being taken per well was performed for albino cells.