Abstract
Measuring the concentration and viability of yeast cells is an important
and fundamental procedure in scientific research and industrial
fermentation. In consideration of the drawbacks of manual cell counting,
large quantities of yeast cells require methods that provide easy,
objective, and reproducible high-throughput calculations, especially for
samples in complicated backgrounds. To answer this challenge, we
explored and developed an easy-to-use yeast cell counting pipeline that
combined the machine learning-based ilastik tool with the freeware
ImageJ, as well as a conventional photomicroscope. Briefly, learning
from labels provided by the user, ilastik performs
segmentation and classification
automatically in batch processing mode for large numbers of images and
thus discriminates yeast cells from complex backgrounds. The files
processed through ilastik can be recognized by ImageJ, which can set up
customizable parameters based on cell size, perimeter, roundness and so
on. In this work, we programmed an
ImageJ macro, “Yeast Counter”, to compute the numeric results of yeast
cells for automatic batch processing. Taking the yeastCryptococccus deneoformans as an example, we observed that the
customizable software algorithm for yeast counting with ilastik and
ImageJ reduced inter-operator errors significantly and achieved accurate
and objective results in the spotting test, while manual counting with a
haemocytometer exhibited some errors between repeats and required more
time. In summary, a convenient, rapid, reproducible and extremely
low-cost method to count yeast cells is described here that can be
applied to multiple kinds of yeasts in genetics, cell biology and
industrial fermentation.
[Key words]Yeast,
ImageJ, ilastik, haemocytometer,
batch processing