1. Introduction
Assessing
cell viability is a convenient and fundamental method to analyse the
effects of various stressors on yeast cells in scientific research and
in any brewing process, in which cell counting-associated technologies,
such as concentration calculations and
spotting
tests, are widely adopted to provide an estimation of viable yeast cells
[1]. These assays can be used to measure the results of yeast
proliferation; to test the growth rate of yeast cells under different
kinds of chemical, physical, or environmental factors; and as an
internal control to achieve consistent fermentations in industry. At
present, commonly used cell counting methods include plate counting
[2], real-time quantitative PCR [3], haemocytometers [4],
automatic cell counting
instruments [5]and flow cytometry counting in biological operation.
The plate counting method is performed by spreading living cells on
solid media to form colony forming units (CFUs), which can be counted
with the naked eye. A corresponding method for automatic colony counting
with ImageJ software has been developed [6]. The advantage of this
method is that non-viable yeast cannot duplicate and form colonies on
plates, but some shortcomings of this method are that the number of
yeast cells only depends on different dilution concentrations and
clumped cells will be registered as one count. Additionally, plate
counting is also time consuming. Real-time quantitative PCR involves the
application of related instruments and the drawing of accurate standard
curves. Of course, this method relies on advanced expensive equipment
and requires the pre-establishment of control genes, high-efficiency
primers or the fluorescent dye PMA [7]. Most cell counters can only
count specific types of cells. Professional yeast counters used in
fermentation engineering are always expensive and may be assisted with
stains that are unfavourable to the operator. In addition, flow
cytometry changes the breakpoint of droplets so that the size of
droplets formed by sheath fluid can wrap a cell [8], but it demands
a more uniform cell size and can be better detected when cells exhibit
certain fluorescence signals. However, the size of yeast cells varies
widely, and a droplet may contain multiple yeast cells. As a
consequence, it is impossible to count yeast cells accurately without
the insertion of a fluorescent protein. Therefore, haemocytometer
counting remains the most commonly used technique to determine the
concentration of a yeast sample because of its ease of use and low cost
[9; 10; 11; 12; 13].
Problems do exist with the haemocytometer method, such as it being time
consuming and inefficient to implement for large-scale analyses. Using a
photomicroscope, some researchers have developed effective software to
automatically count cells on haemocytometer plates to avoid subjective
manual counting and high-throughput statistics. However, some of these
methods are limited to specific types of cells. For example,
CellProfiler [14] can count mammalian and non-mammal cells via a
high-throughput analysis. However, non-mammalian cells are limited to
round cells, fission yeasts and breeding budding yeasts. CellC
[15]can only count labelled cells. CellCounter [16]and OpenCFU
[17] are designed for specialized cells and cannot count high
concentrations of yeast. Moreover, in cases in which a non-homogeneous
liquid is used to test yeast viability or there is background material
in the fermentation process, making yeast cell counting even more
challenging. Therefore, we intended to introduce the use of the free
ilastik and ImageJ software for batch enumeration of yeast cells in
complicated backgrounds.
As
an open source image analysis program that can be run under the
Macintosh, Windows and Linux operating systems, ImageJ has been reported
to be suitable for mammalian cell counting [18]. Considering that
mammalian cells with an irregular morphology generally grow and adhere
to the wall and that their cell size is usually larger than that of
yeast, mammalian cells are much easier to count than yeast cells. Hence,
whether ImageJ can be used for yeast cell counting remains to be
determined. Moreover, the culture media of mammalian cells has fewer
impurities, and the background is easy to separate from cells in
automatic counting. However, depending on the different purposes of
yeast cultures, impurities in yeast media vary tremendously in type and
quantity, which would interfere with the automatic counting of yeast
cells by ImageJ. Currently, ilastik
software can solve this problem
very well. ilastik is easy to operate and can provide end users with
machine learning-based image analysis [19]without substantial
computational expertise; thus, we mainly use the workflow of ilastik to
segment and classify images to maximize the separation of yeast cells
from the background.
Taking the yeast Cryptococccus deneoformans as an example, a new
rapid automated yeast cell counting method using ilastik and ImageJ was
assessed in this study. As an opportunistic pathogenic fungus, the
spotting test is frequently used to study the sensitivity of C.
deneoformans cells to various environmental stresses and drugs, in
which cell counting is the first step. Here, we describe an ImageJ
macro, named “Yeast Counter”, and systematically test its performance
for counting yeast cells. For samples in complicated backgrounds, the
ilastik workflow was able to perform segmentation and classification
with interactively supervised machine learning. Then, the number of
yeast cells was counted using the “Yeast Counter” macro, which can set
up customizable parameters based on cell size, perimeter, roundness and
so on in the batch processing mode. According to the results of the
spotting test, we observed that the customizable software algorithm for
yeast counting reduced inter-operator errors significantly and generated
accurate and objective results, while manual counting with a
haemocytometer exhibited some errors between repeats and required more
time than “Yeast Counter”.