1.
Introduction
Yeast has been widely used as a “cell factory” in industrial
fermentation processes to produce a wide range of valuable products,
including organic acids that are used extensively in manufacturing,
pharmaceutical, cosmetic, food, textile and chemical industries
(Álvarez-Chávez et al., 2012; Chen et al., 2013; de Jong et al., 2014;
Gonzalez-Garcia et al., 2017; Hong and Nielsen, 2012). Compared to
conventional chemical methods for the production of organic acids based
on fossil fuel reserves, microbial production is an attractive approach
due to several advantages including sustainability, less environmental
pollution and cost-effectiveness (Sauer et al., 2008; Steen et al.,
2010). Unlike other hosts that are recalcitrant to genetic manipulation
(Kiatpapan and Murooka, 2002; Zhuge et al., 2013), baker’s yeast
(Saccharomyces cerevisiae ) is an ideal organism to discover new
gene targets for productivity enhancement, because it is a model
eukaryotic organism with high-resolution genomic data. Moreover, the
tolerance of yeast to low pH enables the production of organic acids in
their protonated forms, reducing the costs of downstream recovery and
purification after fermentation. Due to the economic, environmental and
medical importance of organic acid production by yeast, advanced
metabolic engineering and synthetic biology technologies have been
applied to engineer yeast for improved production of different
high-value organic acids, such as lactic acid (Ishida et al., 2005),
succinic acid (Otero et al., 2013), para-hydroxybenzoic acid (Williams
et al., 2015), 3-hydroxypropionic acid (Borodina et al., 2015) and
muconic acid (Curran et al., 2013).
Yeast is also an attractive host for the production of propionic acid
(PA) that is commonly used as a food preservative and a chemical
intermediate, since PA can be formed as a by-product of yeast
fermentation (Eglinton et al., 2002). However, PA is toxic to yeast,
especially at relatively low concentrations, causing an important
problem of tolerance engineering in yeast PA production. Fortunately, Xu
et al. (2019) demonstrated significant improvements in yeast tolerance
to PA using adaptive laboratory evolution (ALE), a powerful tool in the
field of metabolic engineering for the development of superior
industrial microbial strains (Almario et al., 2013; Gonzalez-Ramos et
al., 2016; Kildegaard et al., 2014).
ALE experiments are lab-intensive and time-consuming however, requiring
evaluation of growth kinetics of intermediate populations and numerous
candidate strains to select an ideal strain with improved phenotypes.
The evolution process might be performed over hundreds of generations,
and the traditional growth test based on optical density (OD)
measurements must be conducted over three repetitions for each strain or
population for each population or strain. Moreover, the process lacks
the ability to track the growth of yeast at a single-cell level, and
cannot consider cell size, morphology and viability that may change
during growth. Thus, cell-to-cell variations are obscured and the
ability to screen and select single cells with desired characteristics
(e.g., high growth rate, high tolerance to acids and high secretion of
valuable bio-products) is limited.
In order to address these limitations, an alternative approach is
required to quantitatively track the growth of individual cells within a
population without perturbation and allows parallel, high-throughput
assessment at a single-cell level. Microfluidics can compartmentalize
single cells within monodisperse picolitre-sized droplets in a
cost-effective and high-throughput process, for example, screening of 5
× 107 individual reactions requires only 150 µL of
reagents and seven hours at an estimated cost of only a few dollars, as
demonstrated by Agresti et al. (2010). Over the past decades, droplet
microfluidics has enabled single-cell analysis for a wide range of
applications across biological science, biomedicine and biochemistry
(Agresti et al., 2010; Brouzes et al., 2009; Yu et al., 2018). This is
because (1) the extracellular environments are accurately mimicked
(Hosokawa et al., 2017; Liu et al., 2020); (2) the genotype-phenotype
linkages are established at a single-cell level (Bowman and Alper, 2020;
Fischlechner et al., 2014; Li et al., 2018a, 2018b); (3) the
miniaturized confinement improves the detection limit (Agresti et al.,
2010; Zhu et al., 2012); and (4) massive parallel analysis can be
conducted to probe cellular heterogeneity (Headen et al., 2018; Hindson
et al., 2011; Klein et al., 2015; Ostafe et al., 2014; Zinchenko et al.,
2014).
In this study, we quantitatively tracked the growth of single yeast
cells under varying conditions by using monodisperse microdroplets. In
order to demonstrate the versatility of the microdroplet platform, we
used two species of yeast, Saccharomyces cerevisiae (S.
cerevisiae ) and Pichia pastoris (P. pastoris ), and a
total of four strains, wild-type S. cerevisiae strain
(CEN.PK113-7D), the PA evolved mutant S. cerevisiae strain
(PA-3), GFP-tagged S. cerevisiae strain (CEN.PK2-1C-GFP) and
GFP-tagged P. pastoris strain (CBS7435-GFP). The effects of
organic acids, PA and AA, at different concentrations on the growth of
yeast at the single-cell level were studied, as well as the effect of
K-ions on PA tolerance in yeast. The calculated specific growth rate (μ)
of single yeast grown in microdroplets was effectively identical to that
for cells in bulk cultures at a pH of 3.5, and yeast cells maintained
high viability in microdroplets after 48 hours of culture.