Search bioRxiv⌕ Search

Biology subjects

Poupart, V.

Publications and source records attributed to Poupart, V..

2 recordsLinked to original sources

The spatiotemporal distribution of LIN-5/NuMA regulates spindle orientation and tissue organization in the C. elegans germ line

Mitotic spindle orientation sets the cell division plane and is thus critical for maintaining tissue organization. The C. elegans gonad is tube-shaped, with germ cells forming a circumferential monolayer around a shared inner core of cytoplasm called the rachis. Each germ cell is connected to the rachis via a stable cytoplasmic bridge, polarizing germ cells along their rachis-basal axis. How this tissue organization is maintained during development is unclear, as germ cells lack the canonical cell-cell junctions that, in other tissue types, ensure proper spindle orientation. Here we use live-cell imaging of C. elegans germ cells, both in situ and in gonad explants, to show that the microtubule force generator dynein and its conserved regulator LIN-5/NuMA regulate spindle orientation in C. elegans germ cells and are required for germline tissue organization. We uncover a cyclic, polarized pattern of LIN-5/NuMA cortical localization that predicts centriole/centrosome positioning throughout the cell cycle, providing a means to align spindle orientation with the tissue plane. This work reveals a new mechanism by which oriented cell division can be achieved to maintain tissue organization during animal development.

cell biology↗

CentTracker: a trainable, machine learning-based tool for large-scale analyses of C. elegans germline stem cell mitosis

Investigating the complex interactions between stem cells and their native environment requires an efficient means to image them in situ. Caenorhabditis elegans germline stem cells (GSCs) are distinctly accessible for intravital imaging; however, long-term image acquisition and analysis of dividing GSCs can be technically challenging. Here we present a systematic investigation into the technical factors impacting GSC physiology during live imaging and provide an optimized method for monitoring GSC mitosis under minimally disruptive conditions. We describe CentTracker, an automated and generalizable image analysis tool that uses machine learning to pair mitotic centrosomes and which can extract a variety of mitotic parameters rapidly from large-scale datasets. We employ CentTracker to assess a range of mitotic features in GSCs and show that subpopulations with distinct mitotic profiles are unlikely to exist within the stem cell pool. We further find evidence for spatial clustering of GSC mitoses within the germline tissue and for biases in mitotic spindle orientation relative to the germlines distal-proximal axis, and thus the niche. The technical and analytical tools provided herein pave the way for large-scale screening studies of multiple mitotic processes in GSCs dividing in situ, in an intact tissue, in a living animal, under seemingly physiological conditions.

cell biology↗