School of Molecular & Microbial Biosciences, University of Sydney, NSW 2006, Australia.
J Immunol Methods. 2010 Apr 15;355(1-2):40-51. doi: 10.1016/j.jim.2010.01.015. Epub 2010 Feb 13.
A procedure is described for the disaggregation of colorectal cancers (CRC) and normal intestinal mucosal tissues to produce suspensions of viable single cells, which are then captured on customized antibody microarrays recognising 122 different surface antigens (DotScan CRC microarray). Cell binding patterns recorded by optical scanning of microarrays provide a surface profile of antigens on the cells. Sub-populations of cells bound on the microarray can be profiled by fluorescence multiplexing using monoclonal antibodies tagged with Quantum Dots or other fluorescent dyes. Surface profiles are presented for 6 CRC cell lines (T84, LIM1215, SW480, HT29, CaCo and SW620) and surgical samples from 40 CRC patients. Statistical analysis revealed significant differences between profiles for CRC samples and mucosal controls. Hierarchical clustering of CRC data identified several disease clusters that showed some correlation with clinico-pathological stage as determined by conventional histopathological analysis. Fluorescence multiplexing using Phycoerythrin- or Alexa Fluor 647-conjugated antibodies was more effective than multiplexing with antibodies labelled with Quantum Dots. This relatively simple method yields a large amount of information for each patient sample and, with further application, should provide disease signatures and enable the identification of patients with good or poor prognosis.
介绍了一种从结直肠癌(CRC)和正常肠黏膜组织中分离出可存活的单细胞悬液的方法,然后将其捕获到定制的抗体微阵列上,该微阵列可识别 122 种不同的表面抗原(DotScan CRC 微阵列)。通过对微阵列进行光学扫描记录的细胞结合模式,为细胞表面的抗原提供了一个表面图谱。通过使用标记有量子点或其他荧光染料的单克隆抗体进行荧光多重分析,可以对微阵列上结合的细胞亚群进行分析。为 6 种 CRC 细胞系(T84、LIM1215、SW480、HT29、CaCo 和 SW620)和 40 名 CRC 患者的手术样本提供了表面图谱。统计分析显示,CRC 样本和黏膜对照之间的图谱存在显著差异。CRC 数据的层次聚类确定了几个疾病簇,这些簇与通过传统组织病理学分析确定的临床病理分期有一定的相关性。使用藻红蛋白或 Alexa Fluor 647 标记的抗体进行荧光多重分析比使用量子点标记的抗体进行荧光多重分析更有效。这种相对简单的方法为每个患者样本提供了大量信息,并且随着进一步的应用,应该能够提供疾病特征,并能够识别预后良好或不良的患者。