Heaster Tiffany M, Landman Bennett A, Skala Melissa C
Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.
Morgridge Institute for Research, Madison, WI, United States.
Front Oncol. 2019 Nov 1;9:1144. doi: 10.3389/fonc.2019.01144. eCollection 2019.
Metabolic preferences of tumor cells vary within a single tumor, contributing to tumor heterogeneity, drug resistance, and patient relapse. However, the relationship between tumor treatment response and metabolically distinct tumor cell populations is not well-understood. Here, a quantitative approach was developed to characterize spatial patterns of metabolic heterogeneity in tumor cell populations within xenografts and 3D cultures (i.e., organoids) of head and neck cancer. Label-free images of cell metabolism were acquired using two-photon fluorescence lifetime microscopy of the metabolic co-enzymes NAD(P)H and FAD. Previous studies have shown that NAD(P)H mean fluorescence lifetimes can identify metabolically distinct cells with varying drug response. Thus, density-based clustering of the NAD(P)H mean fluorescence lifetime was used to identify metabolic sub-populations of cells, then assessed in control, cetuximab-, cisplatin-, and combination-treated xenografts 13 days post-treatment and organoids 24 h post-treatment. Proximity analysis of these metabolically distinct cells was designed to quantify differences in spatial patterns between treatment groups and between xenografts and organoids. Multivariate spatial autocorrelation and principal components analyses of all autofluorescence intensity and lifetime variables were developed to further improve separation between cell sub-populations. Spatial principal components analysis and Z-score calculations of autofluorescence and spatial distribution variables also visualized differences between models. This analysis captures spatial distributions of tumor cell sub-populations influenced by treatment conditions and model-specific environments. Overall, this novel spatial analysis could provide new insights into tumor growth, treatment resistance, and more effective drug treatments across a range of microscopic imaging modalities (e.g., immunofluorescence, imaging mass spectrometry).
肿瘤细胞的代谢偏好会在单个肿瘤内部发生变化，这导致了肿瘤异质性、耐药性和患者复发。然而，肿瘤治疗反应与代谢不同的肿瘤细胞群体之间的关系尚未得到充分理解。在此，我们开发了一种定量方法，以表征头颈癌异种移植瘤和3D培养物（即类器官）中肿瘤细胞群体代谢异质性的空间模式。利用代谢辅酶NAD(P)H和FAD的双光子荧光寿命显微镜，获取了细胞代谢的无标记图像。先前的研究表明，NAD(P)H平均荧光寿命可以识别出具有不同药物反应的代谢不同的细胞。因此，基于NAD(P)H平均荧光寿命的密度聚类被用于识别细胞的代谢亚群，然后在治疗后13天的对照、西妥昔单抗、顺铂和联合治疗的异种移植瘤以及治疗后24小时的类器官中进行评估。对这些代谢不同的细胞进行邻近分析，旨在量化治疗组之间以及异种移植瘤和类器官之间空间模式的差异。开发了对所有自发荧光强度和寿命变量的多变量空间自相关和主成分分析，以进一步改善细胞亚群之间的分离。自发荧光和空间分布变量的空间主成分分析和Z分数计算也可视化了模型之间 的差异。该分析捕捉了受治疗条件和模型特定环境影响的肿瘤细胞亚群的空间分布。总体而言，这种新颖的空间分析可以为肿瘤生长、治疗抗性以及一系列微观成像模式（例如免疫荧光、成像质谱）下更有效的药物治疗提供新的见解。
