Piltti Katja M, Haus Daniel L, Do Eileen, Perez Harvey, Anderson A J, Cummings B J
Physical Medicine & Rehabilitation, University of California, Irvine, CA 92696-4540, USA.
Stem Cell Res. 2011 Nov;7(3):256-63. doi: 10.1016/j.scr.2011.05.005. Epub 2011 Jun 7.
Accurate automated cell fate analysis of immunostained human stem cells from 2- and 3-dimensional (2D-3D) images would improve efficiency in the field of stem cell research. Development of an accurate and precise tool that reduces variability and the time needed for human stem cell fate analysis will improve productivity and interpretability of the data across research groups. In this study, we have created protocols for high performance image analysis software Volocity® to classify and quantify cytoplasmic and nuclear cell fate markers from 2D-3D images of human neural stem cells after in vitro differentiation. To enhance 3D image capture efficiency, we optimized the image acquisition settings of an Olympus FV10i® confocal laser scanning microscope to match our quantification protocols and improve cell fate classification. The methods developed in this study will allow for a more time efficient and accurate software based, operator validated, stem cell fate classification and quantification from 2D and 3D images, and yield the highest ≥94.4% correspondence with human recognized objects.
对来自二维和三维（2D - 3D）图像的免疫染色人类干细胞进行准确的自动细胞命运分析，将提高干细胞研究领域的效率。开发一种准确且精确的工具，减少人类干细胞命运分析所需的变异性和时间，将提高各研究团队数据的生产力和可解释性。在本研究中，我们为高性能图像分析软件Volocity®创建了协议，用于对体外分化后的人类神经干细胞的二维和三维图像中的细胞质和细胞核细胞命运标记物进行分类和定量。为提高三维图像捕获效率，我们优化了奥林巴斯FV10i®共聚焦激光扫描显微镜的图像采集设置，以匹配我们的定量协议并改善细胞命运分类。本研究中开发的方法将实现更高效、准确的基于软件且经过操作员验证的二维和三维图像干细胞命运分类与定量，并且与人类识别的对象具有≥94.4%的最高对应率。
