Department of Biochemistry, Stanford Medical School, 279 Campus Drive, Beckman 409, Stanford, CA, USA.
J Microsc. 2010 May;238(2):145-61. doi: 10.1111/j.1365-2818.2009.03337.x.
The recent development of complex chemical and small interfering RNA (siRNA) collections has enabled large-scale cell-based phenotypic screening. High-content and high-throughput imaging are widely used methods to record phenotypic data after chemical and small interfering RNA treatment, and numerous image processing and analysis methods have been used to quantify these phenotypes. Currently, there are no standardized methods for evaluating the effectiveness of new and existing image processing and analysis tools for an arbitrary screening problem. We generated a series of benchmarking images that represent commonly encountered variation in high-throughput screening data and used these image standards to evaluate the robustness of five different image analysis methods to changes in signal-to-noise ratio, focal plane, cell density and phenotype strength. The analysis methods that were most reliable, in the presence of experimental variation, required few cells to accurately distinguish phenotypic changes between control and experimental data sets. We conclude that by applying these simple benchmarking principles an a priori estimate of the image acquisition requirements for phenotypic analysis can be made before initiating an image-based screen. Application of this benchmarking methodology provides a mechanism to significantly reduce data acquisition and analysis burdens and to improve data quality and information content.
最近复杂化学和小干扰 RNA（siRNA）文库的发展使得大规模基于细胞的表型筛选成为可能。高内涵和高通量成像广泛用于记录化学和小干扰 RNA 处理后的表型数据，并且已经使用了许多图像处理和分析方法来定量这些表型。目前，还没有评估新的和现有的图像处理和分析工具对于任意筛选问题的有效性的标准化方法。我们生成了一系列基准图像，这些图像代表了高通量筛选数据中常见的变化，并使用这些图像标准来评估五种不同的图像分析方法在信噪比、焦平面、细胞密度和表型强度变化时的稳健性。在存在实验变异的情况下，最可靠的分析方法仅需要少量细胞即可准确区分对照和实验组数据集中的表型变化。我们得出结论，通过应用这些简单的基准测试原则，可以在启动基于图像的筛选之前，对表型分析的图像采集要求进行先验估计。应用这种基准测试方法可以显著减少数据采集和分析的负担，并提高数据质量和信息量。
