Biological research is relying on increasingly complex genetic systems and circuits to perform sophisticated operations in living cells. Performing these operations often requires simultaneous delivery of many genes, and optimizing the stoichiometry of these genes can yield drastic improvements in performance. However, sufficiently sampling the large design space of gene expression stoichiometries in mammalian cells using current methods is cumbersome, complex, or expensive. We present a ‘poly-transfection’ method as a simple yet high-throughput alternative that enables comprehensive evaluation of genetic systems in a single, readily-prepared transfection sample. Each cell in a poly-transfection represents an independent measurement at a distinct gene expression stoichiometry, fully leveraging the single-cell nature of transfection experiments. We first benchmark poly-transfection against co-transfection, showing that titration curves for commonly-used regulators agree between the two methods. We then use poly-transfections to efficiently generate new insights, for example in CRISPRa and synthetic miRNA systems. Finally, we use poly-transfection to rapidly engineer a difficult-to-optimize miRNA-based cell classifier for discriminating cancerous cells. One-pot evaluation enabled by poly-transfection accelerates and simplifies the design of genetic systems, providing a new high-information strategy for interrogating biology.

MicroRNAs (miRNAs) regulate a majority of protein-coding genes, affecting nearly all biological pathways. However, the quantitative dimensions of miRNA-based regulation are not fully understood. In particular, the implications of miRNA target site location, composition rules for multiple target sites, and cooperativity limits for genes regulated by many miRNAs have not been quantitatively characterized. We explore these aspects of miRNA biology at a quantitative single-cell level using a library of 620 miRNA sensors and reporters that are regulated by many miRNA target sites at different positions. Interestingly, we find that miRNA target site sets within the same untranslated region exhibit combined miRNA activity described by an antagonistic relationship while those in separate untranslated regions show synergy. The resulting antagonistic/synergistic computational model enables the high-fidelity prediction of miRNA sensor activity for sensors containing many miRNA targets. These findings may help to accelerate the development of sophisticated sensors for clinical and research applications.

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