Label-free spatially maintained measurements of metabolic phenotypes in cells.
Label-free spatially maintained measurements of metabolic phenotypes in cells.
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DOI：
10.3389/fbioe.2023.1293268
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发表时间：
2023
影响因子：
5.7
通讯作者：
中科院分区：
文献类型：
--
作者：
细胞水平的代谢重编程导致许多疾病，包括癌症，但很少有分析能够测量活样品中单个细胞的代谢途径使用。在这里，自体荧光寿命成像与单细胞分割和机器学习模型相结合，以预测癌细胞的代谢途径使用。MCF 7乳腺癌细胞和HepG 2肝癌细胞的代谢活性通过使细胞在具有特定底物和代谢抑制剂的培养基中生长来控制。利用多光子荧光寿命显微镜获取了两种内源性代谢辅酶还原型烟酰胺腺嘌呤二核苷酸（NADH）和氧化型黄素腺嘌呤二核苷酸（FAD）的荧光寿命图像，并在细胞水平上进行了分析。利用糖酵解、氧化磷酸化和氨解，观察到细胞中NADH和FAD寿命组分的定量变化。使用自体荧光特征训练的传统机器学习模型将细胞分类为依赖于糖酵解或氧化代谢，准确率为90%-92%。此外，通过调整卷积神经网络来预测来自自体荧光寿命图像的癌细胞代谢扰动，与通过提取特征训练的传统模型相比，性能得到了改善，准确率达到95%。此外，用癌细胞的寿命特征训练的模型可以转移到T细胞的自体荧光寿命图像，预测80%的活化T细胞是糖酵解的，97%的静止T细胞是氧化的。总之，自体荧光寿命成像结合机器学习模型可以在细胞水平上检测活样本的糖酵解和氧化代谢之间的代谢扰动，为研究细胞代谢和代谢异质性提供了无标记技术。
Metabolic reprogramming at a cellular level contributes to many diseases including cancer, yet few assays are capable of measuring metabolic pathway usage by individual cells within living samples. Here, autofluorescence lifetime imaging is combined with single-cell segmentation and machine-learning models to predict the metabolic pathway usage of cancer cells. The metabolic activities of MCF7 breast cancer cells and HepG2 liver cancer cells were controlled by growing the cells in culture media with specific substrates and metabolic inhibitors. Fluorescence lifetime images of two endogenous metabolic coenzymes, reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD), were acquired by a multi-photon fluorescence lifetime microscope and analyzed at the cellular level. Quantitative changes of NADH and FAD lifetime components were observed for cells using glycolysis, oxidative phosphorylation, and glutaminolysis. Conventional machine learning models trained with the autofluorescence features classified cells as dependent on glycolytic or oxidative metabolism with 90%–92% accuracy. Furthermore, adapting convolutional neural networks to predict cancer cell metabolic perturbations from the autofluorescence lifetime images provided improved performance, 95% accuracy, over traditional models trained via extracted features. Additionally, the model trained with the lifetime features of cancer cells could be transferred to autofluorescence lifetime images of T cells, with a prediction that 80% of activated T cells were glycolytic, and 97% of quiescent T cells were oxidative. In summary, autofluorescence lifetime imaging combined with machine learning models can detect metabolic perturbations between glycolysis and oxidative metabolism of living samples at a cellular level, providing a label-free technology to study cellular metabolism and metabolic heterogeneity.
影响因子：
24
作者：
Fayad ZA;Swirski FK;Calcagno C;Robbins CS;Mulder W;Kovacic JC
通讯作者：
Kovacic JC
DOI：
10.1083/jcb.51.1.123
发表时间：
1971-10
期刊：
The Journal of cell biology
影响因子：
--
作者：
Hackenbrock CR;Rehn TG;Weinbach EC;Lemasters JJ
通讯作者：
Lemasters JJ