Department of Orthopaedic Surgery and Rehabilitation, Division of Musculoskeletal Sciences, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania, USA.
J Microsc. 2013 Mar;249(3):206-14. doi: 10.1111/jmi.12010. Epub 2013 Jan 16.
Robotic, high-throughput microscopy is a powerful tool for small molecule screening and classifying cell phenotype, proteomic and genomic data. An important hurdle in the field is the automated classification and visualization of results collected from a data set of tens of thousands of images. We present a method that approaches these problems from the perspective of flow cytometry with supporting open-source code. Image analysis software was created that allowed high-throughput microscopy data to be analysed in a similar manner as flow cytometry. Each cell on an image is considered an object and a series of gates similar to flow cytometry is used to classify and quantify the properties of cells including size and level of fluorescent intensity. This method is released with open-source software and code that demonstrates the method's implementation. Accuracy of the software was determined by measuring the levels of apoptosis in a primary murine myoblast cell line after exposure to staurosporine and comparing these results to flow cytometry.
机器人高通量显微镜是小分子筛选和细胞表型、蛋白质组学和基因组学数据分类的有力工具。该领域的一个重要障碍是对来自数万张图像数据集的结果进行自动分类和可视化。我们提出了一种从流式细胞术的角度来解决这些问题的方法，并提供了支持的开源代码。创建了图像分析软件，允许以类似于流式细胞术的方式分析高通量显微镜数据。图像上的每个细胞都被视为一个对象，并使用类似于流式细胞术的一系列门来对细胞的特性（包括大小和荧光强度水平）进行分类和量化。该方法随附开源软件和代码，展示了该方法的实现。通过测量在接触星形孢菌素后原代鼠成肌细胞系中的细胞凋亡水平，并将这些结果与流式细胞术进行比较，来确定软件的准确性。
