Neuro-Oncology Branch, Center for Cancer Research , National Cancer Institute, National Institutes of Health , Bethesda , Maryland 20892 , United States.
Advanced Cytometry Instrumentation Systems, LLC , 19 Elm Street , Buffalo , New York 14203 , United States.
Anal Chem. 2019 Sep 3;91(17):11380-11387. doi: 10.1021/acs.analchem.9b02663. Epub 2019 Aug 19.
Detailed studies of lipids in biological systems, including their role in cellular structure, metabolism, and disease development, comprise an increasingly prominent discipline called lipidomics. However, the conventional lipidomics tools, such as mass spectrometry, cannot investigate lipidomes until they are extracted, and thus they cannot be used for probing the lipid distribution nor for studying in live cells. Furthermore, conventional techniques rely on the lipid extraction from relatively large samples, which averages the data across the cellular populations and masks essential cell-to-cell variations. Further advancement of the discipline of lipidomics critically depends on the capability of high-resolution lipid profiling in live cells and, potentially, in single organelles. Here we report a micro-Raman assay designed for single-organelle lipidomics. We demonstrate how Raman microscopy can be used to measure the local intracellular biochemical composition and lipidome hallmarks-lipid concentration and unsaturation level, cis/trans isomer ratio, sphingolipids and cholesterol levels in live cells-with a sub-micrometer resolution, which is sufficient for profiling of subcellular structures. These lipidome data were generated by a newly developed biomolecular component analysis software, which provides a shared platform for data analysis among different research groups. We outline a robust, reliable, and user-friendly protocol for quantitative analysis of lipid profiles in subcellular structures. This method expands the capabilities of Raman-based lipidomics toward the analysis of single organelles within either live or fixed cells, thus allowing an unprecedented measure of organellar lipid heterogeneity and opening new quantitative ways to study the phenotypic variability in normal and diseased cells.
详细研究生物系统中的脂质,包括它们在细胞结构、代谢和疾病发展中的作用,构成了一个日益重要的学科,称为脂质组学。然而,传统的脂质组学工具,如质谱,不能在提取脂质后研究脂质组,因此不能用于探测脂质分布,也不能用于研究活细胞中的脂质。此外,传统技术依赖于从相对较大的样本中提取脂质,这使得数据在细胞群体中平均化,掩盖了细胞间的重要变化。脂质组学学科的进一步发展取决于在活细胞中进行高分辨率脂质分析的能力,并且可能在单个细胞器中进行。在这里,我们报告了一种用于单个细胞器脂质组学的微拉曼分析方法。我们展示了拉曼显微镜如何用于测量局部细胞内生物化学组成和脂质组学特征-脂质浓度和不饱和度水平、顺/反式异构体比率、鞘脂和胆固醇水平-具有亚微米分辨率,足以对亚细胞结构进行分析。这些脂质组学数据是由新开发的生物分子成分分析软件生成的,该软件为不同研究小组之间的数据分析提供了一个共享平台。我们概述了一种稳健、可靠且用户友好的亚细胞结构中脂质谱定量分析的方法。该方法扩展了基于拉曼的脂质组学分析单个细胞器的能力,从而可以以前所未有的方式测量细胞器中脂质的异质性,并为研究正常和患病细胞中的表型变异性提供新的定量方法。