Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy
Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy
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DOI：
10.1172/jci131187
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发表时间：
2020-02-03
影响因子：
15.9
通讯作者：
Bharti, Kapil
中科院分区：
文献类型：
--
作者：
Schaub, Nicholas J.;Hotaling, Nathan A.;Bharti, Kapil
临床前和临床阶段细胞疗法数量的增加促使需要可靠和非侵入性的测定来验证临床生物制造中的移植功能。我们开发了一种强大的表征方法，由定量明场吸光度显微镜（QBAM）和深度神经网络（DNN）组成，以非侵入性地预测组织功能和细胞供体身份。使用临床级诱导多能干细胞衍生的视网膜色素上皮细胞（iPSC-RPE）验证该方法。iPSC-RPE的QBAM图像用于训练DNN，其预测iPSC-RPE单层跨上皮阻力，预测极化血管内皮生长因子（VEGF）分泌，并将iPSC-RPE单层与干细胞供体匹配。DNN预测补充了传统的机器学习算法，这些算法识别了用于预测组织功能和iPSC供体身份的单细胞的形状和纹理特征。这些结果表明，可以使用QBAM和机器学习实现非侵入性细胞治疗表征。
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.