Cell-to-Cell Heterogeneity in Cortical Tension Specifies Curvature of Contact Surfaces in Caenorhabditis elegans Embryos

In the two-cell stage embryos of Caenorhabditis elegans, the contact surface of the two blastomeres forms a curve that bulges from the AB blastomere to the P1 blastomere. This curve is a consequence of the high intracellular hydrostatic pressure of AB compared with that of P1. However, the higher pressure in AB is intriguing because AB has a larger volume than P1. In soap bubbles, which are a widely used model of cell shape, a larger bubble has lower pressure than a smaller bubble. Here, we reveal that the higher pressure in AB is mediated by its higher cortical tension. The cell fusion experiments confirmed that the curvature of the contact surface is related to the pressure difference between the cells. Chemical and genetic interferences showed that the pressure difference is mediated by actomyosin. Fluorescence imaging indicated that non-muscle myosin is enriched in the AB cortex. The cell killing experiments provided evidence that AB but not P1 is responsible for the pressure difference. Computer simulation clarified that the cell-to-cell heterogeneity of cortical tensions is indispensable for explaining the pressure difference. This study demonstrates that heterogeneity in surface tension results in significant deviations of cell behavior compared to simple soap bubble models, and thus must be taken into consideration in understanding cell shape within embryos

Evaluation of the effectiveness of simple nuclei-segmentation methods on Caenorhabditis elegans embryogenesis images

BACKGROUND: For the analysis of spatio-temporal dynamics, various automated processing methods have been developed for nuclei segmentation. These methods tend to be complex for segmentation of images with crowded nuclei, preventing the simple reapplication of the methods to other problems. Thus, it is useful to evaluate the ability of simple methods to segment images with various degrees of crowded nuclei. RESULTS: Here, we selected six simple methods from various watershed based and local maxima detection based methods that are frequently used for nuclei segmentation, and evaluated their segmentation accuracy for each developmental stage of the Caenorhabditis elegans. We included a 4D noise filter, in addition to 2D and 3D noise filters, as a pre-processing step to evaluate the potential of simple methods as widely as possible. By applying the methods to image data between the 50- to 500-cell developmental stages at 50-cell intervals, the error rate for nuclei detection could be reduced to ≤ 2.1% at every stage until the 350-cell stage. The fractions of total errors throughout the stages could be reduced to ≤ 2.4%. The error rates improved at most of the stages and the total errors improved when a 4D noise filter was used. The methods with the least errors were two watershed-based methods with 4D noise filters. For all the other methods, the error rate and the fraction of errors could be reduced to ≤ 4.2% and ≤ 4.1%, respectively. The minimum error rate for each stage between the 400- to 500-cell stages ranged from 6.0% to 8.4%. However, similarities between the computational and manual segmentations measured by volume overlap and Hausdorff distance were not good. The methods were also applied to Drosophila and zebrafish embryos and found to be effective. CONCLUSIONS: The simple segmentation methods were found to be useful for detecting nuclei until the 350-cell stage, but not very useful after the 400-cell stage. The incorporation of a 4D noise filter to the simple methods could improve their performances. Error types and the temporal biases of errors were dependent on the methods used. Combining multiple simple methods could also give good segmentations

Local cortical pulling-force repression switches centrosomal centration and posterior displacement in C. elegans

Centrosome positioning is actively regulated by forces acting on microtubules radiating from the centrosomes. Two mechanisms, center-directed and polarized cortical pulling, are major contributors to the successive centering and posteriorly displacing migrations of the centrosomes in single-cell–stage Caenorhabditis elegans. In this study, we analyze the spatial distribution of the forces acting on the centrosomes to examine the mechanism that switches centrosomal migration from centering to displacing. We clarify the spatial distribution of the forces using image processing to measure the micrometer-scale movements of the centrosomes. The changes in distribution show that polarized cortical pulling functions during centering migration. The polarized cortical pulling force directed posteriorly is repressed predominantly in the lateral regions during centering migration and is derepressed during posteriorly displacing migration. Computer simulations show that this local repression of cortical pulling force is sufficient for switching between centering and displacing migration. Local regulation of cortical pulling might be a mechanism conserved for the precise temporal regulation of centrosomal dynamic positioning

Detection of nuclei in 4D Nomarski DIC microscope images of early Caenorhabditis elegans embryos using local image entropy and object tracking

BACKGROUND: The ability to detect nuclei in embryos is essential for studying the development of multicellular organisms. A system of automated nuclear detection has already been tested on a set of four-dimensional (4D) Nomarski differential interference contrast (DIC) microscope images of Caenorhabditis elegans embryos. However, the system needed laborious hand-tuning of its parameters every time a new image set was used. It could not detect nuclei in the process of cell division, and could detect nuclei only from the two- to eight-cell stages. RESULTS: We developed a system that automates the detection of nuclei in a set of 4D DIC microscope images of C. elegans embryos. Local image entropy is used to produce regions of the images that have the image texture of the nucleus. From these regions, those that actually detect nuclei are manually selected at the first and last time points of the image set, and an object-tracking algorithm then selects regions that detect nuclei in between the first and last time points. The use of local image entropy makes the system applicable to multiple image sets without the need to change its parameter values. The use of an object-tracking algorithm enables the system to detect nuclei in the process of cell division. The system detected nuclei with high sensitivity and specificity from the one- to 24-cell stages. CONCLUSION: A combination of local image entropy and an object-tracking algorithm enabled highly objective and productive detection of nuclei in a set of 4D DIC microscope images of C. elegans embryos. The system will facilitate genomic and computational analyses of C. elegans embryos

Centromere/kinetochore is assembled through CENP-C oligomerization

Kinetochore is an essential protein complex required for accurate chromosome segregation. The constitutive centromere-associated network (CCAN), a subcomplex of the kinetochore, associates with centromeric chromatin and provides a platform for the kinetochore assembly. The CCAN protein CENP-C is thought to be a central hub for the centromere/kinetochore organization. However, the role of CENP-C in CCAN assembly needs to be elucidated. Here, we demonstrate that both the CCAN-binding domain and the C-terminal region that includes the Cupin domain of CENP-C are necessary and sufficient for chicken CENP-C function. Structural and biochemical analyses reveal self-oligomerization of the Cupin domains of chicken and human CENP-C. We find that the CENP-C Cupin domain oligomerization is vital for CENP-C function, centromeric localization of CCAN, and centromeric chromatin organization. These results suggest that CENP-C facilitates the centromere/kinetochore assembly through its oligomerization.Hara M., Ariyoshi M., Sano T., et al. Centromere/kinetochore is assembled through CENP-C oligomerization. Molecular Cell 83, 2188 (2023); https://doi.org/10.1016/j.molcel.2023.05.023

Biologically constrained optimization based cell membrane segmentation in C. elegans embryos

Abstract Background Recent advances in bioimaging and automated analysis methods have enabled the large-scale systematic analysis of cellular dynamics during the embryonic development of Caenorhabditis elegans. Most of these analyses have focused on cell lineage tracing rather than cell shape dynamics. Cell shape analysis requires cell membrane segmentation, which is challenging because of insufficient resolution and image quality. This problem is currently solved by complicated segmentation methods requiring laborious and time consuming parameter adjustments. Results Our new framework BCOMS (Biologically Constrained Optimization based cell Membrane Segmentation) automates the extraction of the cell shape of C. elegans embryos. Both the segmentation and evaluation processes are automated. To automate the evaluation, we solve an optimization problem under biological constraints. The performance of BCOMS was validated against a manually created ground truth of the 24-cell stage embryo. The average deviation of 25 cell shape features was 5.6%. The deviation was mainly caused by membranes parallel to the focal planes, which either contact the surfaces of adjacent cells or make no contact with other cells. Because segmentation of these membranes was difficult even by manual inspection, the automated segmentation was sufficiently accurate for cell shape analysis. As the number of manually created ground truths is necessarily limited, we compared the segmentation results between two adjacent time points. Across all cells and all cell cycles, the average deviation of the 25 cell shape features was 4.3%, smaller than that between the automated segmentation result and ground truth. Conclusions BCOMS automated the accurate extraction of cell shapes in developing C. elegans embryos. By replacing image processing parameters with easily adjustable biological constraints, BCOMS provides a user-friendly framework. The framework is also applicable to other model organisms. Creating the biological constraints is a critical step requiring collaboration between an experimentalist and a software developer