Label-free visualization and characterization of extracellular vesicles in breast cancer
Label-free visualization and characterization of extracellular vesicles in breast cancer
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DOI：
10.1073/pnas.1909243116
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发表时间：
2019-11-26
影响因子：
11.1
通讯作者：
Boppart, Stephen A.
中科院分区：
文献类型：
--
作者：
You, Sixian;Barkalifa, Ronit;Boppart, Stephen A.
尽管广泛的兴趣，细胞外囊泡（EV）的研究仍然在技术上具有挑战性。EV研究中未探索的空白之一是无法根据其代谢特征来表征EV的空间和功能异质性群体。在本文中，我们利用内在的光学代谢和结构的EV对比，并证明在体内/原位表征的EV在各种未处理的（前）临床样品。通过深度神经网络提供的像素级分割掩模，可以在其空间分布的背景下分析单个EV的光学特征。对活的荷瘤动物和新鲜切除的人乳腺组织的定量分析显示，肿瘤内、肿瘤边界附近和血管结构周围富含NAD（P）H的EV。此外，富含NAD（P）H的EV的百分比与人类乳腺癌诊断高度相关，这强调了代谢成像对EV表征的重要作用以及其临床应用的潜力。除了EV特性的表征外，我们还展示了活细胞和动物中EV动力学（摄取，释放和运动）的无标记监测。所提出的方法的原位代谢分析能力以及乳腺癌中富含NAD（P）H的EV亚群增加的发现，有可能增强基础科学中的应用，并增强我们对EV在癌症进展中发挥的活性代谢作用的理解。
Despite extensive interest, extracellular vesicle (EV) research remains technically challenging. One of the unexplored gaps in EV research has been the inability to characterize the spatially and functionally heterogeneous populations of EVs based on their metabolic profile. In this paper, we utilize the intrinsic optical metabolic and structural contrast of EVs and demonstrate in vivo/in situ characterization of EVs in a variety of unprocessed (pre)clinical samples. With a pixel-level segmentation mask provided by the deep neural network, individual EVs can be analyzed in terms of their optical signature in the context of their spatial distribution. Quantitative analysis of living tumor-bearing animals and fresh excised human breast tissue revealed abundance of NAD(P)H-rich EVs within the tumor, near the tumor boundary, and around vessel structures. Furthermore, the percentage of NAD(P)H-rich EVs is highly correlated with human breast cancer diagnosis, which emphasizes the important role of metabolic imaging for EV characterization as well as its potential for clinical applications. In addition to the characterization of EV properties, we also demonstrate label-free monitoring of EV dynamics (uptake, release, and movement) in live cells and animals. The in situ metabolic profiling capacity of the proposed method together with the finding of increasing NAD(P)H-rich EV subpopulations in breast cancer have the potential for empowering applications in basic science and enhancing our understanding of the active metabolic roles that EVs play in cancer progression.