Numerical learning of deep features from drug-exposed cell images to calculate IC50 without staining.
Numerical learning of deep features from drug-exposed cell images to calculate IC50 without staining.
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DOI：
10.1038/s41598-022-10643-9
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发表时间：
2022-04-22
影响因子：
4.6
通讯作者：
Kim, Eunjoo
中科院分区：
文献类型：
--
作者：
Cho, Kookrae;Choi, Eun-Sook;Kim, Jung-Hee;Son, Jong-Wuk;Kim, Eunjoo
为了便于快速确定由药物抑制作用引起的细胞活力，将数字深度学习算法用于由光学显微镜捕获的未标记细胞培养图像作为输入。在这项研究中，培养A549，HEK 293和NCI-H1975细胞，每种细胞都具有不同的分子形状和对阿霉素（DOX）的药物反应性水平。这些细胞在暴露于各种浓度的DOX后的显微图像使用比色细胞增殖测定用细胞活力的测量值训练。使用增强的图像数据构建研究细胞的卷积神经网络（CNN）模型;将使用CNN模型预测的细胞活力与通过比色测定测量的细胞活力进行比较。对于三种细胞类型，测定的细胞活力与预测的细胞活力之间的线性关系系数（r2）确定为0.94-0.95。此外，测定和预测的IC 50值无统计学差异。当使用用不同细胞类型训练的同种异体模型估计药物反应性时，相关系数降低至0.004085-0.8643。我们的模型可以应用于无标记细胞进行快速和大规模的测试，同时最大限度地减少成本和劳动力，例如药物反应性的高通量筛选。
To facilitate rapid determination of cellular viability caused by the inhibitory effect of drugs, numerical deep learning algorithms was used for unlabeled cell culture images captured by a light microscope as input. In this study, A549, HEK293, and NCI-H1975 cells were cultured, each of which have different molecular shapes and levels of drug responsiveness to doxorubicin (DOX). The microscopic images of these cells following exposure to various concentrations of DOX were trained with the measured value of cell viability using a colorimetric cell proliferation assay. Convolutional neural network (CNN) models for the study cells were constructed using augmented image data; the predicted cell viability using CNN models was compared to the cell viability measured by colorimetric assay. The linear relationship coefficient (r2) between measured and predicted cell viability was determined as 0.94–0.95 for the three cell types. In addition, the measured and predicted IC50 values were not statistically different. When drug responsiveness was estimated using allogenic models that were trained with a different cell type, the correlation coefficient decreased to 0.004085–0.8643. Our models could be applied to label-free cells to conduct rapid and large-scale tests while minimizing cost and labor, such as high-throughput screening for drug responsiveness.
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影响因子：
2.7
作者：
Collins, Tony J.
通讯作者：
Collins, Tony J.
影响因子：
8.1
作者：
Shorten, Connor;Khoshgoftaar, Taghi M.
通讯作者：
Khoshgoftaar, Taghi M.
影响因子：
6
作者：
Liu, Qian;Junker, Anna;Hu, Pingzhao
通讯作者：
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Alexnet和Googlenet在细胞生长抑制IC50预测中的性能比较。
影响因子：
5.6
作者：
Lee Y;Nam S
通讯作者：
Nam S
DOI：
10.1016/j.compmedimag.2020.101813
发表时间：
2021-01-01
影响因子：
5.7
作者：
Rahman, Samiur;Azam, Bakht;Khan, Raja Jalees ul Hussen
通讯作者：
Khan, Raja Jalees ul Hussen