Machine Learning Improves the Precision and Robustness of High-Content Screens

Abstract

Imaging-based high-content screens often rely on single cell-based evaluation of phenotypes in large data sets of microscopic images. Traditionally, these screens are analyzed by extracting a few image-related parameters and use their ratios (linear single or multiparametric separation) to classify the cells into various phenotypic classes. In this study, the authors show how machine learning–based classification of individual cells outperforms those classical ratio-based techniques. Using fluorescent intensity and morphological and texture features, they evaluated how the performance of data analysis increases with increasing feature numbers. Their findings are based on a case study involving an siRNA screen monitoring nucleoplasmic and nucleolar accumulation of a fluorescently tagged reporter protein. For the analysis, they developed a complete analysis workflow incorporating image segmentation, feature extraction, cell classification, hit detection, and visualization of the results. For the classification task, the authors have established a new graphical framework, the Advanced Cell Classifier, which provides a very accurate high-content screen analysis with minimal user interaction, offering access to a variety of advanced machine learning methods.