Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Cytometry A. 2011 Mar;79(3):227-32. doi: 10.1002/cyto.a.21029. Epub 2011 Feb 9.
The wound healing assay is a commonly used technique to measure cell motility and migration. Traditional methods of performing the wound healing assay suffer from low throughput and a lack of quantitative data analysis. We have developed a new method to perform a high-throughput wound healing assay that produces quantitative data using the LEAP™ instrument. The LEAP™ instrument is used to create reproducible wounds in each well of a 96-well plate by laser ablation. The LEAP™ then records bright field images of each well at several time points. A custom texture segmentation algorithm is used to determine the wound area of each well at each time point. This texture segmentation analysis can provide faster and more accurate image analysis than traditional methods. Experimental results show that reproducible wounds are created by laser ablation with a wound area that varies by less than 10%. This method was tested by confirming that neuregulin-2β increases the rate of wound healing by MCF7 cells in a dose dependent manner. This automated wound healing assay has greatly improved the speed and accuracy, making it a suitable high-throughput method for drug screening.
伤口愈合分析是一种常用于测量细胞迁移能力的技术。传统的伤口愈合分析方法存在通量低和缺乏定量数据分析的问题。我们开发了一种新的方法来进行高通量伤口愈合分析,该方法使用 LEAP™仪器产生定量数据。LEAP™仪器通过激光烧蚀在 96 孔板的每个孔中产生可重复的伤口。然后,LEAP™在几个时间点记录每个孔的明场图像。使用自定义纹理分割算法来确定每个孔在每个时间点的伤口面积。与传统方法相比,这种纹理分割分析可以提供更快、更准确的图像分析。实验结果表明,激光烧蚀可以产生可重复的伤口,其伤口面积的变化小于 10%。该方法通过确认神经调节蛋白-2β以剂量依赖的方式增加 MCF7 细胞的伤口愈合率得到了验证。这种自动化的伤口愈合分析大大提高了速度和准确性,使其成为一种适合药物筛选的高通量方法。