A ‘poly-transfection’ method for rapid, one-pot characterization and optimization of genetic systems
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gkz623.pdf
Description
Published version
Size
3.61 MB
Format
Adobe PDF
Checksum (MD5)
18d97ef9d8bfe8ec825ee4b96d8507b3
Author(s)
Gam, Jeremy J.
DiAndreth, Breanna
Jones, Ross D.
Huh, Jin
Weiss, Ron
Date Issued
August 2019
Journal
Nucleic acids research
Publisher
Oxford University Press (OUP)
Citation
Gam, Jeremy J., et al., "A ‘poly-transfection’ method for rapid, one-pot characterization and optimization of genetic systems." Nucleic acids research 47, 18 (August 2019): p. e106 doi 10.1093/NAR/GKZ623 ©2019 Author(s)
Version
Final published version
Abstract
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. ©2019
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/NAR/GKZ623
