Coupling cell growth to protein secretion enables pooled screening at single-cell resolution in bioreactors
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Abstract
Preserving the genotype-phenotype linkage is critical in high-throughput screens exploring large parameter spaces. Screening pooled libraries for recombinant protein secretion is therefore particularly difficult, since protein diffusion into the medium disrupts this linkage. Existing methods impose significant restrictions on the environment of the screened cells. Here, we present AutoGrowth, a growth-based screening strategy linking growth rate to secretion via a single-cell biosensor. We demonstrated the proportional modulation of growth rate by secretion and validated the approach on five model proteins. As proof of concept, we screened a signal peptide library in bioreactors for improved secretion of a single-chain variable fragment by coupling AutoGrowth with deep sequencing. Additionally, through spike-in experiments, we showed that AutoGrowth can identify favorable variants diluted at ratios as low as one in hundreds of millions in three days. Our growth-based strategy provides unprecedented throughput potential and ensures selection of genetic variants presenting advantageous secretion and fitness capabilities.
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Great job on the visualizations, especially in Figure 1. It's clear what the genetic components are, where they localize, and how they affect readouts and functions. I especially appreciate that each set of components appears in miniature next to the methods for that set's readout. I noticed MFα1/MFα2 appears in the figures but not explicitly in the text. Either removing the label form the figure or explaining it further in the text would be better. For the paracrine strain activations, I'd like to see a bit more evidence, especially since it might help debug any activation seen by other when growing strains in different conditions. Activation was measured at 10 h post-induction. What does it look like at higher ODs or with a longer induction? Overall, I found this paper and its claims to be well-evidenced, the applications exciting, …
Great job on the visualizations, especially in Figure 1. It's clear what the genetic components are, where they localize, and how they affect readouts and functions. I especially appreciate that each set of components appears in miniature next to the methods for that set's readout. I noticed MFα1/MFα2 appears in the figures but not explicitly in the text. Either removing the label form the figure or explaining it further in the text would be better. For the paracrine strain activations, I'd like to see a bit more evidence, especially since it might help debug any activation seen by other when growing strains in different conditions. Activation was measured at 10 h post-induction. What does it look like at higher ODs or with a longer induction? Overall, I found this paper and its claims to be well-evidenced, the applications exciting, and I think it was written very clearly.
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