  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.
