Automated three-dimensional single cell phenotyping of spindle dynamics, cell shape, and volume

TL;DR

This paper introduces a computational approach for 3D cell phenotyping that tracks features and measures cell shape, volume, and spindle dynamics, enabling detailed analysis of cell division processes in living cells.

Contribution

The authors developed a hybrid 3D imaging and tracking method that accurately measures cell and spindle metrics, advancing automated analysis of cell division.

Findings

Genetic deletion of kinesin-5 affects cell size and spindle length correlation.

The method distinguishes pre-anaphase and anaphase spindle populations.

Spindle length becomes uncorrelated with cell size in certain mutants.

Abstract

We present feature finding and tracking algorithms in 3D in living cells, and demonstrate their utility to measure metrics important in cell biological processes. We developed a computational imaging hybrid approach that combines automated three-dimensional tracking of point-like features with surface determination from which cell (or nuclear) volume, shape, and planes of interest can be extracted. After validation, we applied the technique to real space context-rich dynamics of the mitotic spindle, and cell volume and its relationship to spindle length, in dividing living cells. These methods are additionally useful for automated segregation of pre-anaphase and anaphase spindle populations in budding yeast. We found that genetic deletion of the yeast kinesin-5 mitotic motor cin8 leads to large mother and daughter cells that were indistinguishable based on size, and that in those cells the spindle length becomes uncorrelated with cell size. The technique can be used to visualize and quantify tracked feature coordinates relative to cell bounding surface landmarks, such as the plane of cell division, and will enable for advancing studies of non-equilibrium processes in living cells.