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.
Bibliographical noteFunding Information:
We thank the Swanson Biotechnology Center at Koch Institute for assisting with NGS. TKL is supported by the Department of Defense (W81XWH-16-1-0565, W81XWH-18-1-0513), the Defense Advanced Research Projects Agency, MIT Portugal Program, and the Koch Institute Bridge Project. TKL and YT are supported by the United States-Israel Binational Science Foundation (#2017189) and YT is supported by the Israel Cancer Association (#0394837). SDR is supported in part by the NIH (R01 CA160762). MW is supported by the Department of Defense (W81XWH-16-1-0452). EMN was supported by an HFSP long-term post-doctoral fellowship (LT000307/2013-L).
© 2019, The Author(s).