Live-dead assay on unlabeled cells using phase imaging with computational specificity.
Live-dead assay on unlabeled cells using phase imaging with computational specificity.
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DOI：
10.1038/s41467-022-28214-x
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发表时间：
2022-02-07
影响因子：
16.6
通讯作者：
Popescu G
中科院分区：
文献类型：
--
作者：
Hu C;He S;Lee YJ;He Y;Kong EM;Li H;Anastasio MA;Popescu G
现有的评估细胞活力的方法包括用化学试剂对细胞进行染色。然而，外源性染色的步骤使得这些方法不适合快速，非破坏性和长期的研究。在这里，我们使用具有计算特异性的相位成像对未标记细胞进行瞬时活力评估。该概念利用深度学习技术来计算与无标记定量相位成像测量的标本相关的活力标记。在不同的活细胞培养中证明，所提出的方法在识别活细胞和死细胞方面的准确率约为95%。标记和未标记群体的细胞干质量和细胞核面积的演变表明，化学试剂降低了活力。这里介绍的非破坏性方法可能会有广泛的应用，从监测生物制药的生产到评估癌症治疗的有效性。表征细胞活力的常用方法包括用化学试剂对细胞进行染色。在这里，作者报告了一种不需要标记的细胞活力评估方法；它使用定量相位成像与深度学习相结合。
Existing approaches to evaluate cell viability involve cell staining with chemical reagents. However, the step of exogenous staining makes these methods undesirable for rapid, nondestructive, and long-term investigation. Here, we present an instantaneous viability assessment of unlabeled cells using phase imaging with computation specificity. This concept utilizes deep learning techniques to compute viability markers associated with the specimen measured by label-free quantitative phase imaging. Demonstrated on different live cell cultures, the proposed method reports approximately 95% accuracy in identifying live and dead cells. The evolution of the cell dry mass and nucleus area for the labeled and unlabeled populations reveal that the chemical reagents decrease viability. The nondestructive approach presented here may find a broad range of applications, from monitoring the production of biopharmaceuticals to assessing the effectiveness of cancer treatments. Common methods for characterising cell viability involve cell staining with chemical reagents. Here the authors report a method for cell viability assessment that does not require labelling; this uses quantitative phase imaging combined with deep learning.
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影响因子：
19.4
作者：
Chen, Xi;Kandel, Mikhail E.;Popescu, Gabriel
通讯作者：
Popescu, Gabriel
影响因子：
35
作者：
Kim, Taewoo;Zhou, Renjie;Popescu, Gabriel
通讯作者：
Popescu, Gabriel
影响因子：
5.8
作者：
Duprez, Linde;Wirawan, Ellen;Vandenabeele, Peter
通讯作者：
Vandenabeele, Peter
影响因子：
13.6
作者：
Kim BS;Kim MK;Cho Y;Hamed EE;Gillette MU;Cha H;Miljkovic N;Aakalu VK;Kang K;Son KN;Schachtschneider KM;Schook LB;Hu C;Popescu G;Park Y;Ballance WC;Yu S;Im SG;Lee J;Lee CH;Kong H
通讯作者：
Kong H
影响因子：
3.4
作者：
Eldridge, Will J.;Steelman, Zachary A.;Wax, Adam
通讯作者：
Wax, Adam