Introduction
The genus Saccharomyces (Saccharomycetales, Ascomycota)
originated ~100 million years ago facilitated by whole
genome duplication and an increased metabolic capacity to degrade sugar
to ethanol . It currently comprises eight species, which diverged 4-20M
years ago . All species are monophyletic with high levels of
sequence collinearity but show vast genetic and ecological diversity .
Recently, the genomic and ecological data available for all eight
species (and their hybrids) have considerably grown , promoting yeast
from a traditional laboratory model system that only included a few
clonal strains to one in which we can answer questions relevant for
ecology and evolution . For instance, we can now resolve the genetic
basis of adaptation to environmental challenges , we can investigate
which ecological factors determine the distribution and structure of
yeast populations in the wild , and measure the level of divergence and
gene flow between them . Our growing knowledge of the phenotypic and
genetic diversity of wild yeasts also allows us to study important
ecological traits comparatively, across species backgrounds, alleviating
limitations resulting from only including a handful of
well-characterized laboratory strains in our analyses. One ecological
trait that stands out as particularly diverse between species are their
temperature preference profiles . Resistance and adaptation to high or
fluctuating temperatures are an increasing research focus for climate
change biology. Temperature profiles are also important for the
development of industrial strains, e.g. for fermentation products and
biofuels . Of the eight species included here, five are considered
cold-tolerant (S. kudriavzevii, S. arboricola, S.
uvarum , S. eubayanus , and the recently discovered S.
jurei) , one species is thermo-tolerant (S. cerevisiae ), and two
species are considered a thermo-generalist (S. paradoxus andS. mikatae ) growing well in a broad range of temperatures .
Developing wild strains into effective systems for research and industry
requires the systematic testing and measuring of fundamental population
phenotypes including their growth rates, kinetics (e.g. the length of
the lag phase), and yield.
Microbial research often applies high-throughput methods to estimate
population growth and fitness in environments of interest, e.g. media
containing different nutritional and stress conditions. A common
technique is to measure the optical density (OD) of microbial cultures
using a spectrophotometer (‘plate reader’). Optical density measures the
turbidity of liquid cultures, which is assumed to be proportional to the
cell number, i.e. the concentration of cells in the sample .
Specifically, OD is the negative log of transmittance, i.e. the fraction
of light detected when passed through a cuvette or micro-titer plate
containing the microbial culture. It is typically measured at a
wavelength of 600nm as this electromagnetic radiation is thought to not
cause cell damage. Calculations follow the Beer-Lambert law , which
states that OD is proportional to the concentration of a solution.
However, this law only applies to cultures with low cell densities
(typically OD600 up to 0.1). At higher densities, the
light gets increasingly scattered between cells, and OD does not
increase as fast as the cell titer. Using spectrophotometry to infer
population fitness has additional limitations affecting the translation
of OD into cell counts. Importantly, the method does not differentiate
between dead and alive cells and the absorption coefficient (ε) can be
affected by cell size (Fukuda 2023). Different methods exist for the
calibration of OD measurements , including the use of silica
microspheres, direct cell counting with microscopy, and colony counting
in serial dilutions on agar plates . But the most efficient,
high-throughput method is flow cytometry, using laser-based detection of
individual cells that allows for accurate cell count estimates
Here, we explore the variation in the relationship between OD measures
from spectrophotometry and cell counts from flow cytometry, across all
eight Saccharomyces yeast species. To also test for variation
within species, we used three strains per species (except for S.
jurei , where only two strains were available) from different geographic
and ecological origins, including isolates from fruit, soil, rotten
wood, and tree bark from Europe, Asia, North and South America, and
Australia (Table S1). We hope our study expands the knowledge base of
important growth parameters of non-cerevisiae strains and
improves the biological interpretation of population fitness data from
wild, non-domesticated yeasts.