Vrolijk Hans, Sloos Willem, Mesker Wilma, Franken Patrick, Fodde Riccardo, Morreau Hans, Tanke Hans
Laboratory for Cytochemistry and Cytometry, Leiden University Medical Center, Leiden, The Netherlands.
J Mol Diagn. 2003 Aug;5(3):160-7. doi: 10.1016/S1525-1578(10)60468-0.
At present, limiting factors in the use of tissue microarrays (TMAs) for high-throughput analysis relate to the visual evaluation of the staining patterns of each of the individual cores in the array and to the subsequent input of the results into a database. Such a database is essential to correlate the data with tumor type and outcome, and to evaluate the performance against other markers achieved in separate experiments. So far, these steps are mostly performed by hand, and consequently are time-consuming and potentially prone to bias and errors, respectively. This paper describes the use of a high-resolution flat-bed scanner for digitization of TMAs with a resolution of about 5 x 5 micro m(2). The arrays are acquired, the positions of the tissue cores are automatically determined, and measurement data including the images of the individual cores are archived. The program provides digital zooming of arrays for interactive verification of the results and rapid linkage of individual core images to data sets of other markers derived from the same array. Performance of the system was compared to manual classification for a representative set of arrays containing colorectal tumors stained with different markers.
目前,在使用组织微阵列(TMA)进行高通量分析时,限制因素涉及对阵列中每个单独核心的染色模式进行视觉评估,以及随后将结果输入数据库。这样的数据库对于将数据与肿瘤类型和结果相关联,以及评估与在单独实验中获得的其他标志物相比的性能至关重要。到目前为止,这些步骤大多是手工执行的,因此分别耗时且可能容易出现偏差和错误。本文描述了使用高分辨率平板扫描仪对TMA进行数字化处理,分辨率约为5×5微米²。获取阵列,自动确定组织核心的位置,并存档包括各个核心图像在内的测量数据。该程序提供阵列的数字缩放功能,用于交互式验证结果,并将单个核心图像快速链接到来自同一阵列的其他标志物的数据集。对于一组包含用不同标志物染色的结肠直肠肿瘤的代表性阵列,将该系统的性能与手动分类进行了比较。