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.
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.
This paper describes research to develop an efficient system that provides a binary decision as to the presence of speech in a short (one to three second) time sample of an acoustic signal. A method which is efficient and reliably detects human speech in the presence of structured noise (such as wind, music, traffic sounds, etc.) is described.first algorithm detects the presence of speech by testing for concave and / or convex formant shapes. The second algorithm is a statistical pattern classifier utilizing radial basis function (RBF) networks with mel-cepstra feature vectors. Classification errors are not consistent across these two different methods. As a consequence, we plan to reduce our error rate by fusion of these methods.Two separate algorithms were developed. The
High-content screening studies of mitotic checkpoints are important for identifying cancer targets and developing novel cancerspecific 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. (Journal of Biomolecular Screening 2007:490-496)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.