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Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

机译:大肠癌细胞表面蛋白分析使用抗体芯片和荧光多路复用

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摘要

The current prognosis and classification of CRC relies on staging systems that integrate histopathologic and clinical findings. However, in the majority of CRC cases, cell dysfunction is the result of numerous mutations that modify protein expression and post-translational modification1.A number of cell surface antigens, including cluster of differentiation (CD) antigens, have been identified as potential prognostic or metastatic biomarkers in CRC. These antigens make ideal biomarkers as their expression often changes with tumour progression or interactions with other cell types, such as tumour-infiltrating lymphocytes (TILs) and tumour-associated macrophages (TAMs).The use of immunohistochemistry (IHC) for cancer sub-classification and prognostication is well established for some tumour types2,3. However, no single ‘marker’ has shown prognostic significance greater than clinico-pathological staging or gained wide acceptance for use in routine pathology reporting of all CRC cases. A more recent approach to prognostic stratification of disease phenotypes relies on surface protein profiles using multiple 'markers'. While expression profiling of tumours using proteomic techniques such as iTRAQ is a powerful tool for the discovery of biomarkers4, it is not optimal for routine use in diagnostic laboratories and cannot distinguish different cell types in a mixed population. In addition, large amounts of tumour tissue are required for the profiling of purified plasma membrane glycoproteins by these methods. In this video we described a simple method for surface proteome profiling of viable cells from disaggregated CRC samples using a DotScan CRC antibody microarray. The 122-antibody microarray consists of a standard 82-antibody region recognizing a range of lineage-specific leukocyte markers, adhesion molecules, receptors and markers of inflammation and immune response5, together with a satellite region for detection of 40 potentially prognostic markers for CRC. Cells are captured only on antibodies for which they express the corresponding antigen. The cell density per dot, determined by optical scanning, reflects the proportion of cells expressing that antigen, the level of expression of the antigen and affinity of the antibody6. For CRC tissue or normal intestinal mucosa, optical scans reflect the immunophenotype of mixed populations of cells. Fluorescence multiplexing can then be used to profile selected sub-populations of cells of interest captured on the array. For example, Alexa 647-anti-epithelial cell adhesion molecule (EpCAM; CD326), is a pan-epithelial differentiation antigen that was used to detect CRC cells and also epithelial cells of normal intestinal mucosa, while Phycoerythrin-anti-CD3, was used to detect infiltrating T-cells7. The DotScan CRC microarray should be the prototype for a diagnostic alternative to the anatomically-based CRC staging system.
机译:CRC的当前预后和分类依赖于将组织病理学和临床发现相结合的分期系统。然而,在大多数CRC病例中,细胞功能障碍是许多突变的结果,这些突变会修饰蛋白质表达和翻译后修饰 1 。许多细胞表面抗原,包括分化簇(CD)抗原,已被确定为CRC中潜在的预后或转移性生物标志物。这些抗原成为理想的生物标志物,因为它们的表达经常随肿瘤进展或与其他细胞类型(例如肿瘤浸润淋巴细胞(TIL)和肿瘤相关巨噬细胞(TAM))的相互作用而改变。免疫组织化学(IHC)在癌症亚分类中的应用对于某些类型的 2,3 肿瘤,预后良好。但是,没有一个“标志物”显示出比临床病理学分期更重要的预后意义,也没有被广泛用于所有CRC病例的常规病理报告中。疾病表型的预后分层的最新方法依赖于使用多个“标志物”的表面蛋白谱。尽管使用蛋白质组学技术(例如iTRAQ)对肿瘤进行表达谱分析是发现生物标志物的强大工具4,但它并不是诊断实验室常规使用的最佳方法,并且无法区分混合人群中的不同细胞类型。另外,通过这些方法对纯化的质膜糖蛋白进行谱分析需要大量肿瘤组织。在此视频中,我们描述了一种使用DotScan CRC抗体微阵列从分解的CRC样品中筛选活细胞表面蛋白的简单方法。 122抗体微阵列由一个标准的82抗体区域组成,该区域可识别多种谱系特异性白细胞标志物,黏附分子,炎症和免疫应答 5 的受体和标志物,以及一个用于检测的卫星区域CRC的40种潜在预后指标。仅在表达相应抗原的抗体上捕获细胞。通过光学扫描确定的每个点的细胞密度反映了表达该抗原的细胞比例,抗原的表达水平和抗体 6 的亲和力。对于CRC组织或正常肠粘膜,光学扫描可反映出混合细胞群的免疫表型。然后可以使用荧光多路复用对阵列上捕获的感兴趣细胞的选定亚群进行分析。例如,Alexa 647抗上皮细胞粘附分子(EpCAM; CD326)是一种泛上皮分化抗原,用于检测正常肠粘膜的CRC细胞以及上皮细胞,而使用藻红蛋白抗CD3检测浸润的T细胞 7 。 DotScan CRC芯片应该是基于解剖学的CRC分期系统的诊断替代品的原型。

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