bioRxiv Science⌕ Search

Biology subjects

Angonezi, A.

Publications and source records attributed to Angonezi, A..

2 recordsLinked to original sources

Nuclear lamina-associated domain biogenesis is regulated by nuclear pore density during embryogenesis and mediates UV protection

Lamina-associated domains (LADs) are critical for genome organization and function, but their formation during development is not well understood. Here, we use DamID and image analysis to reveal the dynamics of LAD biogenesis during C. elegans embryogenesis. At early stages, DNA at the lamina is transcriptionally active and lacks lamina-associated heterochromatin. This state depends on abundant nuclear pores, which prevent heterochromatin accumulation at the nuclear periphery. As development proceeds, pore numbers decline, enabling heterochromatin to access the lamina. Reducing nuclear-pore components induces precocious accumulation of heterochromatin to the lamina. Functionally, we find that heterochromatic LADs confer protection against ultraviolet (UV) radiation. Older embryos are resistant to UV light and concentrate DNA damage at the nuclear periphery, whereas early embryos are UV sensitive and accumulate damage throughout nuclei when unshielded by their mothers in utero. These findings identify embryonic dissipation of nuclear pores as a key step in heterochromatic LAD assembly, allowing older embryos to withstand exposure to UV irradiation.

developmental biology↗

Oriented Cell Dataset: efficient imagery analyses using angular representation

In this work, we propose a new public dataset for cell detection in bright-field microscopy images annotated with Oriented Bounding Boxes (OBBs), named Oriented Cell Dataset (OCD). We show that OBBs provide a more accurate shape representation compared to standard Horizontal Bounding Boxes (HBBs), with slight overhead of one extra click in the annotation process. Our dataset also contains a subset of images with five independent expert annotations, which allows inter-annotation analysis to determine if the results produced by algorithms are within the expected variability of human experts. We investigated how to automate cell biology microscopy images by training seven popular OBB detectors in the proposed dataset, and focused our analyses on two main problems in cancer biology: cell confluence and polarity determination, the latter not possible through HBB representation. All models achieved statistically similar results to the biological applications compared to human annotation, enabling the automation of cell biology and cancer cell biology microscopy image analysis. Our code and dataset are available at https://github.com/LucasKirsten/Deep-Cell-Tracking-EBB.

bioinformatics↗