3D convolutional neural networks predict cellular metabolic pathway use from fluorescence lifetime decay data.

IF 6.6 3区 医学 Q1 ENGINEERING, BIOMEDICAL
APL Bioengineering Pub Date : 2024-02-27 eCollection Date: 2024-03-01 DOI:10.1063/5.0188476
Linghao Hu, Daniela De Hoyos, Yuanjiu Lei, A Phillip West, Alex J Walsh
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Abstract

Fluorescence lifetime imaging of the co-enzyme reduced nicotinamide adenine dinucleotide (NADH) offers a label-free approach for detecting cellular metabolic perturbations. However, the relationships between variations in NADH lifetime and metabolic pathway changes are complex, preventing robust interpretation of NADH lifetime data relative to metabolic phenotypes. Here, a three-dimensional convolutional neural network (3D CNN) trained at the cell level with 3D NAD(P)H lifetime decay images (two spatial dimensions and one time dimension) was developed to identify metabolic pathway usage by cancer cells. NADH fluorescence lifetime images of MCF7 breast cancer cells with three isolated metabolic pathways, glycolysis, oxidative phosphorylation, and glutaminolysis were obtained by a multiphoton fluorescence lifetime microscope and then segmented into individual cells as the input data for the classification models. The 3D CNN models achieved over 90% accuracy in identifying cancer cells reliant on glycolysis, oxidative phosphorylation, or glutaminolysis. Furthermore, the model trained with human breast cancer cell data successfully predicted the differences in metabolic phenotypes of macrophages from control and POLG-mutated mice. These results suggest that the integration of autofluorescence lifetime imaging with 3D CNNs enables intracellular spatial patterns of NADH intensity and temporal dynamics of the lifetime decay to discriminate multiple metabolic phenotypes. Furthermore, the use of 3D CNNs to identify metabolic phenotypes from NADH fluorescence lifetime decay images eliminates the need for time- and expertise-demanding exponential decay fitting procedures. In summary, metabolic-prediction CNNs will enable live-cell and in vivo metabolic measurements with single-cell resolution, filling a current gap in metabolic measurement technologies.

三维卷积神经网络从荧光寿命衰减数据预测细胞代谢途径的使用。
共酶还原型烟酰胺腺嘌呤二核苷酸(NADH)的荧光寿命成像为检测细胞代谢扰动提供了一种无标记方法。然而,NADH 寿命变化与代谢途径变化之间的关系非常复杂,因此无法对 NADH 寿命数据与代谢表型之间的关系做出可靠的解释。在此,我们利用三维 NAD(P)H 寿命衰减图像(两个空间维度和一个时间维度)开发了一种在细胞水平上训练有素的三维卷积神经网络(3D CNN),用于识别癌细胞使用的代谢途径。通过多光子荧光寿命显微镜获得了MCF7乳腺癌细胞的NADH荧光寿命图像,其中包含糖酵解、氧化磷酸化和谷氨酰胺酵解三种独立的代谢途径,然后将图像分割成单个细胞,作为分类模型的输入数据。三维 CNN 模型在识别依赖糖酵解、氧化磷酸化或谷氨酰胺酵解的癌细胞方面达到了 90% 以上的准确率。此外,用人类乳腺癌细胞数据训练的模型成功预测了对照组和 POLG 突变小鼠巨噬细胞代谢表型的差异。这些结果表明,将自发荧光寿命成像与三维 CNNs 相结合,可以通过 NADH 强度的细胞内空间模式和寿命衰减的时间动态来区分多种代谢表型。此外,使用三维 CNN 从 NADH 荧光寿命衰减图像识别代谢表型,无需时间和专业知识的指数衰减拟合程序。总之,代谢预测 CNN 将实现单细胞分辨率的活细胞和体内代谢测量,填补目前代谢测量技术的空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
APL Bioengineering
APL Bioengineering ENGINEERING, BIOMEDICAL-
CiteScore
9.30
自引率
6.70%
发文量
39
审稿时长
19 weeks
期刊介绍: APL Bioengineering is devoted to research at the intersection of biology, physics, and engineering. The journal publishes high-impact manuscripts specific to the understanding and advancement of physics and engineering of biological systems. APL Bioengineering is the new home for the bioengineering and biomedical research communities. APL Bioengineering publishes original research articles, reviews, and perspectives. Topical coverage includes: -Biofabrication and Bioprinting -Biomedical Materials, Sensors, and Imaging -Engineered Living Systems -Cell and Tissue Engineering -Regenerative Medicine -Molecular, Cell, and Tissue Biomechanics -Systems Biology and Computational Biology
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