A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS)
Name
s41467-019-10912-8.pdf
Description
Published version
Size
1.52 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Pery, Erez
Enghuus, Casper
Palacios, Sebastian R.
Zhang, Zhizhuo
Novoa, Eva Maria
Kellis, Manolis
Weiss, Ron
Lu, Timothy K.
Date Issued
June 2019
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Wu, Ming-Ru et al. “A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS).” Nature Communications 10 (2019): 2880 © 2019 The Author(s)
Version
Final published version
Abstract
Cell state-specific promoters constitute essential tools for basic research and biotechnology because they activate gene expression only under certain biological conditions. Synthetic Promoters with Enhanced Cell-State Specificity (SPECS) can be superior to native ones, but the design of such promoters is challenging and frequently requires gene regulation or transcriptome knowledge that is not readily available. Here, to overcome this challenge, we use a next-generation sequencing approach combined with machine learning to screen a synthetic promoter library with 6107 designs for high-performance SPECS for potentially any cell state. We demonstrate the identification of multiple SPECS that exhibit distinct spatiotemporal activity during the programmed differentiation of induced pluripotent stem cells (iPSCs), as well as SPECS for breast cancer and glioblastoma stem-like cells. We anticipate that this approach could be used to create SPECS for gene therapies that are activated in specific cell states, as well as to study natural transcriptional regulatory networks.
MIT Department
Massachusetts Institute of Technology. Synthetic Biology Center
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S41467-019-10912-8
