Biology subjects

Tollervey, F.

Publications and source records attributed to Tollervey, F..

2 recordsLinked to original sources

bioRxiv2024

Native molecular architectures of centrosomes in C. elegans embryos

Centrosomes organize microtubules that are essential for mitotic divisions in animal cells. They consist of centrioles surrounded by Pericentriolar Material (PCM). Questions related to mechanisms of centriole assembly, PCM organization, and microtubule formation remain unanswered, in part due to limited availability of molecular-resolution structural analyses in situ. Here, we use cryo-electron tomography to visualize centrosomes across the cell cycle in cells isolated from C. elegans embryos. We describe a pseudo-timeline of centriole assembly and identify distinct structural features including a cartwheel in daughter centrioles, and incomplete microtubule doublets surrounded by a star-shaped density in mother centrioles. We find that centriole and PCM microtubules differ in protofilament number (13 versus 11) indicating distinct nucleation mechanisms. This difference could be explained by atypical {gamma}-tubulin ring complexes with 11-fold symmetry identified at the minus ends of short PCM microtubules. We further characterize a porous and disordered network that forms the interconnected PCM. Thus, our work builds a three-dimensional structural atlas that helps explain how centrosomes assemble, grow, and achieve function.

cell biology↗

bioRxiv2022

Convolutional networks for supervised mining of molecular patterns within cellular context

Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (Deep Picker in Context), an open-source deep-learning framework for supervised structure segmentation and macromolecular complex localization in cellular cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles and cytosol. By comparing our method to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally-distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, by applying pre-trained networks to a HeLa cell dataset, we demonstrate that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate exploitation by the community.

bioinformatics↗