Quantitative SRS Imaging of Cancer Metabolism at Single Cell Level

项目总结： 尽管细胞新陈代谢改变正在成为癌症的一个既定标志，但仍有一个关键的需要 在单个活细胞水平上对代谢物进行定量的新工具。特别是，由于缺乏具体的标签， 对于代谢物，对高分辨率成像工具的需求尚未得到满足，这些工具能够绘制代谢物和 小分子(脂肪酸、碳水化合物、氨基酸)在癌症发病机制中发挥重要作用。 在通过IMAT计划提供的R21赠款的支持下，我们的团队通过开发一个 多重受激拉曼散射(SRS)显微镜，使代谢物振动成像成为可能 活的肿瘤细胞和完整的活检以每光谱5微秒的速度进行。此R33应用程序旨在 通过(一)技术简化推动高光谱受激拉曼成像技术更上一层楼 和验证，(Ii)开发稳健的高光谱图像分割框架，以及(Iii)整合 与商业自发拉曼显微镜的SRS模式，非专家广泛使用。我们有 为拟议的开发项目组建了一个跨学科团队。三个具体目标是：(1)发展 一种易于操作、高灵敏度的逐行高光谱SRS显微镜，并验证其能力 单细胞水平的肿瘤代谢成像。(2)建立切分的特征分析框架 基于非参数贝叶斯模型的高光谱遥感影像。(3)集成和验证 自发拉曼显微镜上的受激拉曼成像模式。通过填写建议的 在开发和验证活动中，我们将生成一个非常新颖的光谱成像系统 广泛适用于细胞和组织中化学成分的分析，用于发现驱动的研究和标记- 基于精确诊断。

Project summary: Although altered cell metabolism is becoming an established hallmark of cancer, there remains a crucial need of new tools for quantitation of metabolites at single living cell level. In particular, due to lack of specific labels for metabolites, there is an unmet need for high-resolution imaging tools capable of mapping metabolites and small molecules (fatty acids, carbohydrates, amino acids) that play essential roles in pathogenesis of cancer. Supported by a R21 grant through the IMAT program, our team partially addressed this need via developing a multiplex stimulated Raman scattering (SRS) microscope, which enabled vibrational imaging of metabolites in live tumor cells and intact biopsies at the speed of 5 microseconds per spectrum. This R33 application aims to push the hyperspectral stimulated Raman imaging technology to the next level through (i) technical simplification and validation, (ii) developing a robust hyperspectral image segmentation framework, and (iii) integrating the SRS modality with a commercial spontaneous Raman microscope towards broad use by non-experts. We have assembled an inter-disciplinary team for the proposed development. The three specific aims are: (1) Developing an easy-to-operate, highly sensitive line-by-line hyperspectral SRS microscope and validate its capacity for cancer metabolic imaging at single cell level. (2) Establishing a feature analysis framework for segmentation of hyperspectral SRS images using the non-parametric Bayesian model. (3) Integrating and validating the stimulated Raman imaging modality on a spontaneous Raman microscope. By completing the proposed development and validation activities, we will have generated a highly novel spectroscopic imaging system broadly applicable to analysis of chemical contents of cells and tissues for discovery-driven research and marker- based precision diagnosis.