Department of Pharmaceutical Sciences, University of Michigan College of Pharmacy, Ann Arbor, Michigan, 48109, USA.
Consulting for Statistics, Computing, and Analytics Research (CSCAR) Center, University of Michigan, Ann Arbor, Michigan, 48109, USA.
Pharm Res. 2018 Nov 6;36(1):2. doi: 10.1007/s11095-018-2540-0.
To improve cytometric phenotyping abilities and better understand cell populations with high interindividual variability, a novel Raman-based microanalysis was developed to characterize macrophages on the basis of chemical composition, specifically to measure and characterize intracellular drug distribution and phase separation in relation to endogenous cellular biomolecules.
The microanalysis was developed for the commercially-available WiTec alpha300R confocal Raman microscope. Alveolar macrophages were isolated and incubated in the presence of pharmaceutical compounds nilotinib, chloroquine, or etravirine. A Raman data processing algorithm was specifically developed to acquire the Raman signals emitted from single-cells and calculate the signal contributions from each of the major molecular components present in cell samples.
Our methodology enabled analysis of the most abundant biochemicals present in typical eukaryotic cells and clearly identified "foamy" lipid-laden macrophages throughout cell populations, indicating feasibility for cellular lipid content analysis in the context of different diseases. Single-cell imaging revealed differences in intracellular distribution behavior for each drug; nilotinib underwent phase separation and self-aggregation while chloroquine and etravirine accumulated primarily via lipid partitioning.
This methodology establishes a versatile cytometric analysis of drug cargo loading in macrophages requiring small numbers of cells with foreseeable applications in toxicology, disease pathology, and drug discovery.
为了提高细胞表型分析能力,并更好地理解个体间变异性较大的细胞群体,我们开发了一种基于拉曼的新型微分析方法,基于化学成分对巨噬细胞进行特征分析,特别是用于测量和描述细胞内药物分布以及与内源性细胞生物分子相关的相分离。
该微分析是为市售的 WiTec alpha300R 共焦拉曼显微镜开发的。肺泡巨噬细胞在存在药物化合物尼洛替尼、氯喹或依曲韦林的情况下被分离和孵育。专门开发了一种拉曼数据分析算法,以获取单个细胞发出的拉曼信号,并计算细胞样品中存在的主要分子成分的信号贡献。
我们的方法能够分析典型真核细胞中存在的最丰富的生物化学物质,并在整个细胞群体中清楚地识别出富含脂质的泡沫样巨噬细胞,表明在不同疾病背景下对细胞脂质含量进行分析的可行性。单细胞成像揭示了每种药物在细胞内分布行为上的差异;尼洛替尼发生相分离和自聚集,而氯喹和依曲韦林主要通过脂质分配积累。
该方法建立了一种用于巨噬细胞中药物负荷的多功能细胞计量分析,需要少量细胞,具有毒理学、疾病病理学和药物发现方面的潜在应用。