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基于生物打印肾球体的深度学习辅助肾毒性测试

Deep Learning-Assisted Nephrotoxicity Testing with Bioprinted Renal Spheroids.

作者信息

Tröndle Kevin, Miotto Guilherme, Rizzo Ludovica, Pichler Roman, Koch Fritz, Koltay Peter, Zengerle Roland, Lienkamp Soeren S, Kartmann Sabrina, Zimmermann Stefan

机构信息

University of Freiburg, IMTEK - Department of Microsystems Engineering, Freiburg, 79110, Germany.

Hahn-Schickard, Freiburg, 79110, Germany.

出版信息

Int J Bioprint. 2022 Jan 19;8(2):528. doi: 10.18063/ijb.v8i2.528. eCollection 2022.

Abstract

We used arrays of bioprinted renal epithelial cell spheroids for toxicity testing with cisplatin. The concentration-dependent cell death rate was determined using a lactate dehydrogenase assay. Bioprinted spheroids showed enhanced sensitivity to the treatment in comparison to monolayers of the same cell type. The measured dose-response curves revealed an inhibitory concentration of the spheroids of = 9 ± 3 μM in contrast to the monolayers with = 17 ± 2 μM. Fluorescent labeling of a nephrotoxicity biomarker, kidney injury molecule 1 indicated an accumulation of the molecule in the central lumen of the spheroids. Finally, we tested an approach for an automatic readout of toxicity based on microscopic images with deep learning. Therefore, we created a dataset comprising images of single spheroids, with corresponding labels of the determined cell death rates for training. The algorithm was able to distinguish between three classes of no, mild, and severe treatment effects with a balanced accuracy of 78.7%.

摘要

我们使用生物打印的肾上皮细胞球体阵列进行顺铂毒性测试。使用乳酸脱氢酶测定法确定浓度依赖性细胞死亡率。与相同细胞类型的单层细胞相比,生物打印的球体对该处理表现出更高的敏感性。测得的剂量反应曲线显示,球体的抑制浓度为 = 9 ± 3 μM,而单层细胞的抑制浓度为 = 17 ± 2 μM。肾毒性生物标志物肾损伤分子1的荧光标记表明该分子在球体的中央腔中积累。最后,我们测试了一种基于深度学习的显微图像自动读出毒性的方法。因此,我们创建了一个数据集,该数据集包含单个球体的图像以及用于训练的确定细胞死亡率的相应标签。该算法能够区分无、轻度和重度治疗效果这三类,平衡准确率为78.7%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ba8/9186384/f1aaa734a927/IJB-8-2-528-g001.jpg

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