Circulating cancer stem cells (CCSCs), a rare circulating tumor cell (CTC) type, recently arose as a useful resource for monitoring and characterizing both cancers and their metastatic derivatives. However, due to the scarcity of CCSCs among hematologic cells in the blood and the complexity of the phenotype confirmation process, CCSC research can be extremely challenging. Hence, we report a nanoparticle-mediated Raman imaging method for CCSC characterization which profiles CCSCs based on their surface marker expression phenotypes. We have developed an integrated combinatorial Raman-Active Nanoprobe (RAN) system combined with a microfluidic chip to successfully process complete blood samples. CCSCs and CTCs were detected (90% efficiency) and classified in accordance with their respective surface marker expression via completely distinct Raman signals of RANs. Selectively isolated CCSCs (93% accuracy) were employed for both in vitro and in vivo tumor phenotyping to identify the tumorigenicity of the CCSCs. We utilized our new method to predict metastasis by screening blood samples from xenograft models, showing that upon CCSC detection, all subjects exhibited liver metastasis. Having highly efficient detection and noninvasive isolation capabilities, we have demonstrated that our RAN-based Raman imaging method will be valuable for predicting cancer metastasis and relapse via CCSC detection. Moreover, the exclusion of peak overlapping in CCSC analysis with our Raman imaging method will allow to expand the RAN families for various cancer types, therefore, increasing therapeutic efficacy by providing detailed molecular features of tumor subtypes.

循环癌症干细胞（CCSC）是一种罕见的循环肿瘤细胞（CTC）类型，最近作为监视和表征癌症及其转移衍生物的有用资源而兴起。但是，由于血液中血液细胞中CCSC的缺乏以及表型确认过程的复杂性，CCSC研究可能极具挑战性。因此，我们报告了一种用于CCSC表征的纳米粒子介导的拉曼成像方法，该方法基于CCSC的表面标志物表达表型来对其进行分析。我们已经开发了集成的组合式拉曼活性纳米探针（RAN）系统和微流控芯片，可以成功处理完整的血液样本。检测出CCSC和CTC（效率为90％），并根据它们各自的表面标记表达通过完全不同的RAN拉曼信号。选择性分离的CCSC（准确度为93％）用于体外和体内肿瘤表型鉴定，以鉴定CCSC的致瘤性。我们利用我们的新方法通过从异种移植模型中筛选血液样本来预测转移，表明在CCSC检测后，所有受试者均表现出肝转移。具有高效的检测和非侵入性隔离功能，我们证明了我们基于RAN的拉曼成像方法将对通过以下途径预测癌症转移和复发具有重要价值：CCSC检测。此外，用我们的拉曼成像方法排除CCSC分析中的峰重叠将允许扩展各种癌症类型的RAN家族，因此，通过提供肿瘤亚型的详细分子特征，提高了治疗效果。