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 yeast Cryptococccus
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.
Analysis of methods for quantifying yeast cell concentration in complex lignocellulosic fermentation processes