Department of Materials Science and Technology, Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan.
Department of Pathophysiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
Sci Rep. 2023 Nov 23;13(1):20555. doi: 10.1038/s41598-023-47565-z.
Fatty acids play various physiological roles owing to their diverse structural characteristics, such as hydrocarbon chain length (HCL) and degree of saturation (DS). Although the distribution of fatty acids in biological tissues is associated with lipid metabolism, in situ imaging tools are still lacking for HCL and DS. Here, we introduce a framework of near-infrared (1000-1400 nm) hyperspectral label-free imaging with machine learning analysis of the fatty acid HCL and DS distribution in the liver at each pixel, in addition to the previously reported total lipid content. The training data of 16 typical fatty acids were obtained by gas chromatography from liver samples of mice fed with various diets. A two-dimensional mapping of these two parameters was successfully performed. Furthermore, the HCL/DS plot exhibited characteristic clustering among the different diet groups. Visualization of fatty acid distribution would provide insights for revealing the pathophysiological conditions of liver diseases and metabolism.
脂肪酸因其结构特征的多样性而发挥各种生理作用，例如烃链长度（HCL）和饱和度（DS）。尽管生物组织中脂肪酸的分布与脂质代谢有关，但仍缺乏用于 HCL 和 DS 的原位成像工具。在这里，我们引入了一种近红外（1000-1400nm）高光谱无标记成像框架，并结合机器学习分析，对肝脏中每个像素的脂肪酸 HCL 和 DS 分布进行分析，此外还报告了之前报道的总脂质含量。通过气相色谱从喂食不同饮食的小鼠的肝样本中获得了 16 种典型脂肪酸的训练数据。成功地进行了这两个参数的二维映射。此外，HCL/DS 图在不同饮食组之间表现出特征聚类。脂肪酸分布的可视化将为揭示肝脏疾病和代谢的病理生理条件提供深入了解。
