Design of approach for modular media blending
We sought to develop an approach capable of identifying important,
beneficial modifications for media tailored to a given phenotype of
interest. We reasoned that key requirements for such an approach would
be that it is fast and automatable, with minimal dependence on complex
analytical assay development. Such features would enable routine
application to any measurable phenotype of interest. In general, media
blending allows both speed and low analytical complexity. We aimed to
retain these features while minimizing the labor and constraints on
compositions imposed by linear combinations of fully formed and unique
media. We reasoned that diverse and flexible blends of media could be
created by defining simple concentrated stock solutions as basic modules
to combine further. These modules would comprise individual components
or common subsets of components with compatible solubilities (e.g. YNB).
If media components could be formulated in concentrated stock solutions
that could be stored stably over time, then the components could be
routinely and interchangeably combined and diluted to the desired final
concentrations. This approach would yield a broadly applicable modular
strategy for media blending amenable to conventional liquid handling
automation.
To test the feasibility of this approach, we first assessed whether many
common media components could be formulated in concentrated stable
aqueous stock solutions. Using the CHO medium eRDF as a reference, we
estimated the solubility of each component of this medium, using data
from AqSolDB as well as other online sources (Combs, 2012; FSA Panel on
Additives and Products or Substances used in Animal Feed (FEEDAP), 2011;
Ritacco et al., 2018; Schnellbaecher, Binder, Bellmaine, & Zimmer,
2019; Sorkun, Khetan, & Er, 2019; Yamamoto & Ishihara, n.d.). We
compared the estimated solubility of each media component to its
concentration in eRDF and found that, individually, most media
components are soluble at levels >10x higher than their
eRDF concentration (Figure 1A ). The existence of a wide range
of commercially available concentrated supplements further supports this
result: >50x concentrated solutions of amino acids,
vitamins, lipids, and trace metal supplements are common and
commercially available.
Next, we used the product information of commercially available
supplements, literature sources, and inspection to estimate the
percentage of eRDF media components that could be stored in stable
solutions for >6 months. We estimated that over 75% of
eRDF components met this criterion (Figure 1B ). To address
stability challenges caused by less stable components, we reasoned that
less stable components or supplements, such as vitamins, could be
prepared, aliquoted, and stored frozen for long-term storage
(Schnellbaecher et al., 2019); these aliquots could then be thawed and
used within a defined period to mitigate component instability and
enable their integration into our modular blending strategy. Together,
these solubility and stability data suggested that a modular approach to
media development could be defined in this way to accommodate a range of
new formulations easily.
We next automated the process for constructing media, using the
Opentrons OT-2. We chose this liquid handler due to its low cost,
reliability, and compatibility with simple formats for data input, such
as Excel spreadsheets. We then created an open-source Python package,
named Openblend, which simplified the media construction process by
handling routine experimental design and execution steps (Figure 1C).
Openblend creates an experimental design spreadsheet, specifying the
number of 24 well plates, the desired media composition of each well,
and stock solution names and concentrations. The script then checks the
feasibility of the experimental design, ensuring that the total volume
of each well will not exceed the target volume and avoiding the addition
of sub-microliter stock solution volumes. If the design passes this
assessment, the script then outputs a new spreadsheet containing the
setup for the OT-2 deck and required volumes of stock solutions,
providing a user with instructions on how to setup the OT-2 liquid
handler. We found that our typical time to execute this script, setup
the OT-2 and initiate plate building was ~15 minutes,
and the time for the automated steps was about two hours.
Finally, we defined a modular approach for optimization to effectively
leverage the Openblend tool (Figure 1D ). Beginning from an
initial basal medium, improved media are constructed through successive
rounds of optimization. In each round, a library of media components and
supplements are screened to identify beneficial additives. These
additives are then screened in combination and over a range of
concentrations to further optimize the performance of the medium. Each
modular addition and optimization of additives can be guided simply by
measurements of the phenotype of interest (e.g. biomass accumulation).
This greedy approach to multi-dimensional optimization could continue
iteratively until the resulting media met desired specifications, all
available media components were explored, or no additional gains in
performance realized.