Integrating high-content screening and ligand-target prediction to identify mechanism of action.

High-content screening is transforming drug discovery by enabling simultaneous measurement of multiple features of cellular phenotype that are relevant to therapeutic and toxic activities of compounds. High-content screening studies typically generate immense datasets of image-based phenotypic information, and how best to mine relevant phenotypic data is an unsolved challenge. Here, we introduce factor analysis as a data-driven tool for defining cell phenotypes and profiling compound activities. This method allows a large data reduction while retaining relevant information, and the data-derived factors used to quantify phenotype have discernable biological meaning. We used factor analysis of cells stained with fluorescent markers of cell cycle state to profile a compound library and cluster the hits into seven phenotypic categories. We then compared phenotypic profiles, chemical similarity and predicted protein binding activities of active compounds. By integrating these different descriptors of measured and potential biological activity, we can effectively draw mechanism-of-action inferences.

Distinct sequence elements of cyclin B1 promote localization to chromatin, centrosomes, and kinetochores during mitosis.

The mitotic cyclins promote cell division by binding and activating cyclin-dependent kinases (CDKs). Each cyclin has a unique pattern of subcellular localization that plays a vital role in regulating cell division. During mitosis, cyclin B1 is known to localize to centrosomes, microtubules, and chromatin. To determine the mechanisms of cyclin B1 localization in M phase, we imaged full-length and mutant versions of human cyclin B1-enhanced green fluorescent protein in live cells by using spinning disk confocal microscopy. In addition to centrosome, microtubule, and chromatin localization, we found that cyclin B1 also localizes to unattached kinetochores after nuclear envelope breakdown. Kinetochore recruitment of cyclin B1 required the kinetochore proteins Hec1 and Mad2, and it was stimulated by microtubule destabilization. Mutagenesis studies revealed that cyclin B1 is recruited to kinetochores through both CDK1-dependent and -independent mechanisms. In contrast, localization of cyclin B1 to chromatin and centrosomes is independent of CDK1 binding. The N-terminal domain of cyclin B1 is necessary and sufficient for chromatin association, whereas centrosome recruitment relies on sequences within the cyclin box. Our data support a role for cyclin B1 function at unattached kinetochores, and they demonstrate that separable and distinct sequence elements target cyclin B1 to kinetochores, chromatin, and centrosomes during mitosis.

A support vector machine classifier for recognizing mitotic subphases using high-content screening data.

High-content screening studies of mitotic checkpoints are important for identifying cancer targets and developing novel cancer-specific therapies. A crucial step in such a study is to determine the stage of cell cycle. Due to the overwhelming number of cells assayed in a high-content screening experiment and the multiple factors that need to be taken into consideration for accurate determination of mitotic subphases, an automated classifier is necessary. In this article, the authors describe in detail a support vector machine (SVM) classifier that they have implemented to recognize various mitotic subphases. In contrast to previous studies to recognize subcellular patterns, they used only low-resolution cell images and a few parameters that can be calculated inexpensively with off-the-shelf image-processing software. The performance of the SVM was evaluated with a cross-validation method and was shown to be comparable to that of a human expert.