Furthermore, secondary analyses of datasets that contain only a few measurements per spindle may not be very informative. Although these studies are information-rich resources, gaining access to raw data may be problematic, making meta-analysis and data reuse difficult. ![]() Despite the clear biological and biomedical significance of the spindle, quantitative descriptions of spindle morphology are limited to a small number of studies in model organisms including yeast ( Vizeacoumar et al., 2010), Caenorhabditis elegans ( Farhadifar et al., 2015), and humans ( Neumann et al., 2010 Sironi et al., 2011 Young et al., 2014). Proper spindle function is essential to maintain ploidy during cell division, and factors that influence spindle assembly and morphology are frequently altered in cancer ( Gordon et al., 2012). Spindle morphology varies across a wide range of cellular environments ( Helmke and Heald, 2014 Mitchison et al., 2015). The spindle is a complex and dynamic macromolecular machine that functions to accurately distribute chromosomes during eukaryotic cell division. ![]() The approaches outlined can be applied by other researchers studying spindle morphology and adapted with minimal modification to other experimental systems. Our analyses provide new insight into the diversity of spindle types and suggest areas for future study. We also apply our pipeline to analyze nuclear morphology in human cell culture, showing the general utility of the segmentation approach. Extract mixing experiments reveal that some spindle features titrate, while others undergo switch-like transitions, and multivariate analysis shows the pleiotropic morphological effects of modulating the levels of TPX2, a key spindle assembly factor. Our analysis of different spindle types illustrates how kinetochore microtubules amplify spindle microtubule density. Here, we present a large dataset of Xenopus extract spindle images together with an analysis pipeline designed to assess spindle morphology across a range of experimental conditions. However, few cell biologists possess the tools necessary to analyze them. Imaging datasets are rich in quantitative information.
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