Cell cycle-dependent palmitoylation of protocadherin 7 by ZDHHC5 promotes successful cytokinesis.

Cell division requires dramatic reorganization of the cell cortex, which is primarily driven by the actomyosin network. We previously reported that protocadherin 7 (PCDH7) gets enriched at the cell surface during mitosis, which is required to build up the full mitotic rounding pressure. Here, we report that PCDH7 interacts with and is palmitoylated by the palmitoyltransferase, ZDHHC5. PCDH7 and ZDHHC5 colocalize at the mitotic cell surface and translocate to the cleavage furrow during cytokinesis. The localization of PCDH7 depends on the palmitoylation activity of ZDHHC5. Silencing PCDH7 increases the percentage of multinucleated cells and the duration of mitosis. Loss of PCDH7 expression correlates with reduced levels of active RhoA and phospho-myosin at the cleavage furrow. This work uncovers a palmitoylation-dependent translocation mechanism for PCDH7, which contributes to the reorganization of the cortical cytoskeleton during cell division.

Proteomics in Cell Division.

Cell division requires a coordinated action of the cell cycle machinery, cytoskeletal elements, chromosomes, and membranes. Cell division studies have greatly benefitted from the mass spectrometry (MS)-based proteomic approaches for probing the biochemistry of highly dynamic complexes and their coordination with each other as a cell progresses into division. In this review, the authors first summarize a wide-range of proteomic studies that focus on the identification of sub-cellular components/protein complexes of the cell division machinery including kinetochores, mitotic spindle, midzone, and centrosomes. The authors also highlight MS-based large-scale analyses of the cellular components that are largely understudied during cell division such as the cell surface and lipids. Then, the authors focus on posttranslational modification analyses, especially phosphorylation and the resulting crosstalk with other modifications as a cell undergoes cell division. Combining proteomic approaches that probe the biochemistry of cell division components with functional genomic assays will lead to breakthroughs toward a systems-level understanding of cell division.

Adaptive tracking algorithm for trajectory analysis of cells and layer-by-layer assessment of motility dynamics.

Tracking biological objects such as cells or subcellular components imaged with time-lapse microscopy enables us to understand the molecular principles about the dynamics of cell behaviors. However, automatic object detection, segmentation and extracting trajectories remain as a rate-limiting step due to intrinsic challenges of video processing. This paper presents an adaptive tracking algorithm (Adtari) that automatically finds the optimum search radius and cell linkages to determine trajectories in consecutive frames. A critical assumption in most tracking studies is that displacement remains unchanged throughout the movie and cells in a few frames are usually analyzed to determine its magnitude. Tracking errors and inaccurate association of cells may occur if the user does not correctly evaluate the value or prior knowledge is not present on cell movement. The key novelty of our method is that minimum intercellular distance and maximum displacement of cells between frames are dynamically computed and used to determine the threshold distance. Since the space between cells is highly variable in a given frame, our software recursively alters the magnitude to determine all plausible matches in the trajectory analysis. Our method therefore eliminates a major preprocessing step where a constant distance was used to determine the neighbor cells in tracking methods. Cells having multiple overlaps and splitting events were further evaluated by using the shape attributes including perimeter, area, ellipticity and distance. The features were applied to determine the closest matches by minimizing the difference in their magnitudes. Finally, reporting section of our software were used to generate instant maps by overlaying cell features and trajectories. Adtari was validated by using videos with variable signal-to-noise, contrast ratio and cell density. We compared the adaptive tracking with constant distance and other methods to evaluate performance and its efficiency. Our algorithm yields reduced mismatch ratio, increased ratio of whole cell track, higher frame tracking efficiency and allows layer-by-layer assessment of motility to characterize single-cells. Adaptive tracking provides a reliable, accurate, time efficient and user-friendly open source software that is well suited for analysis of 2D fluorescence microscopy video datasets.