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Biasuz, K.

Publications and source records attributed to Biasuz, K..

2 recordsLinked to original sources

Automated cell naming reveals reproducible and variable features of ascidian embryogenesis

Ascidians develop with highly reproducible cell lineages, making them ideal models for quantitative comparisons of morphogenesis between individuals and species. Yet, identifying corresponding cells across embryos has so far relied on slow, manual annotation following the Conklin nomenclature, which limits scalability and consistency. We present an automated framework that assigns cell identities in three-dimensional time-lapse reconstructions of ascidian embryos by transferring names from a reference set of manually curated embryos. The process operates in two steps: an initiation phase, which globally aligns early embryos to establish initial correspondences, and a propagation phase, which propagates names through time and cell divisions by comparing the pattern of contacts each cell forms with its neighbors. Applied to eight wild-type Phallusia mammillata and one Ascidiella aspersa embryo, the pipeline assigns consistent names up to stages containing about 700 cells, corrects inconsistencies in earlier datasets, and extends Conklins rules to internal tissues beyond gastrulation. Using this unified reference, we quantify natural variability in division timing and orientation, confirming the global robustness and revealing local variability of ascidian morphogenesis. The same framework also demonstrates its use to quantitatively phenotype experimentally perturbed embryos, such as those with inhibited ERK signaling. This work provides both a validated collection of coherently named ascidian embryos and open-source tools for automated cell identification and phenotypic comparison, establishing a foundation for systematic, quantitative, and evolutionary analyses of animal development.

developmental biology↗

MorphoNet 2.0: An innovative approach for qualitative assessment and segmentation curation of large-scale 3D time-lapse imaging datasets

Thanks to recent promising advances in AI, automated segmentation of imaging datasets has made significant strides. However, the evaluation and curation of 3D and 3D+t datasets remain extremely challenging and highly resource-intensive. We present MorphoNet 2.0, a major conceptual and technical evolution designed to facilitate the segmentation, self-evaluation, and correction of 3D images. The application is accessible to non-programming biologists through user-friendly graphical interfaces and works on all major operating systems. We showcase its power in enhancing segmentation accuracy and boosting interpretability across five previously published segmented datasets. This new approach is crucial for producing ground-truth datasets of discovery-level scientific quality, critical for training and benchmarking advanced AI-driven segmentation tools, as well as for competitive challenges.

bioinformatics↗