The mechanism underlying cytokinesis, the final step in cell division, remains one of the major unsolved questions in basic cell biology. Thanks to advances in functional genomics and proteomics, we are now able to assemble a "parts list" of proteins involved in cytokinesis. In this review, we discuss how to relate this parts list to biological mechanism. For easier analysis, we split cytokinesis into discrete steps: cleavage plane specification, rearrangement of microtubule structures, contractile ring assembly, ring ingression, and completion. We report on the advances that have been made to understand these steps and how they can be integrated into a global understanding of cytokinesis. We also discuss the extent to which classic questions have been answered and identify major outstanding questions.

Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection. Now, automated image analysis can effectively score many phenotypes. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. First, automated cytological profiling extracts hundreds of numerical descriptors for every cell in every image. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.high-content screening ͉ high-throughput image analysis ͉ phenotype T he history of biology has been dramatically shaped by classic visual screens in model organisms, including Drosophila melanogaster (1-3), Saccharomyces cerevisiae (4), Caenorhabditis elegans (5), and the zebrafish Danio rerio (6, 7). In each case, biological pathways were discovered because researchers were intrigued by groups of peculiar-looking mutants and identified the genes underlying their phenotypes. Because researchers have favored the extensive study of relatively few genes (8), classic, wide-net approaches like screening are as relevant as ever to probe known biological pathways and discover new ones. Modern technology now enables large-scale experiments in cultured cells to identify human genes that underlie biological processes via RNAi. Automation also allows the screening of chemical libraries to identify perturbants useful as research tools or drugs.Despite these advances, scoring cells in images for rare and unusual morphologies has, in general, remained a significant bottleneck (9-12). Cell image analysis allows accurate identification and measurement of cells' features, enabling automated analysis of certain phenotypes that were previously intractable (13-26). However, many interesting phenotypes require the assessment of several measured features of cells. Machine learning methods that select and combine multiple features for automated cell classification have been used to score many phenotypes (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). These methods require the provision of example cells that do and do not display the morphology of interest (i.e., positive and negative cells). Finding posi...

SummaryAlthough massive membrane rearrangements occur during cell division, little is known about specific roles that lipids might play in this process. We report that the lipidome changes with the cell cycle. LC-MS-based lipid profiling shows that 11 lipids with specific chemical structures accumulate in dividing cells. Using AFM, we demonstrate differences in the mechanical properties of live dividing cells and their isolated lipids relative to nondividing cells. In parallel, systematic RNAi knockdown of lipid biosynthetic enzymes identified enzymes required for division, which highly correlated with lipids accumulated in dividing cells. We show that cells specifically regulate the localization of lipids to midbodies, membrane-based structures where cleavage occurs. We conclude that cells actively regulate and modulate their lipid composition and localization during division, with both signaling and structural roles likely. This work has broader implications for the active and sustained participation of lipids in basic biology.