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Classification of T-cell activation via autofluorescence lifetime imaging.
Nature Biomedical Engineering ( IF 28.1 ) Pub Date : 2020-07-27 , DOI: 10.1038/s41551-020-0592-z
Alex J Walsh 1, 2 , Katherine P Mueller 3, 4 , Kelsey Tweed 1, 4 , Isabel Jones 1 , Christine M Walsh 1, 5 , Nicole J Piscopo 3, 4 , Natalie M Niemi 1, 6 , David J Pagliarini 1, 6, 7 , Krishanu Saha 3, 4 , Melissa C Skala 1, 4
Affiliation  

The function of a T cell depends on its subtype and activation state. Here, we show that imaging of the autofluorescence lifetime signals of quiescent and activated T cells can be used to classify the cells. T cells isolated from human peripheral blood and activated in culture using tetrameric antibodies against the surface ligands CD2, CD3 and CD28 showed specific activation-state-dependent patterns of autofluorescence lifetime. Logistic regression models and random forest models classified T cells according to activation state with 97–99% accuracy, and according to activation state (quiescent or activated) and subtype (CD3+CD8+ or CD3+CD4+) with 97% accuracy. Autofluorescence lifetime imaging can be used to non-destructively determine T-cell function.



中文翻译:

通过自体荧光寿命成像对 T 细胞活化进行分类。

T 细胞的功能取决于其亚型和激活状态。在这里,我们展示了静止和活化 T 细胞的自发荧光寿命信号的成像可用于对细胞进行分类。从人外周血中分离并使用针对表面配体 CD2、CD3 和 CD28 的四聚体抗体在培养中激活的 T 细胞显示出特定的激活状态依赖性模式的自发荧光寿命。逻辑回归模型和随机森林模型根据激活状态对 T 细胞进行分类,准确率为 97-99%,并根据激活状态(静止或激活)和亚型(CD3 + CD8 +或 CD3 + CD4 +) 的准确率为 97%。自发荧光寿命成像可用于非破坏性地确定 T 细胞功能。

更新日期:2020-07-27
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