A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS)

Ming-Ru Wu, Lior Nissim, Doron Stupp, Erez Pery, Adina Binder-Nissim, Karen Weisinger, Casper Enghuus, Sebastian R. Palacios, Melissa Humphrey, Zhizhuo Zhang, Eva Maria Novoa, Manolis Kellis, Ron Weiss, Samuel D. Rabkin, Yuval Tabach () and Timothy K. Lu ()
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Ming-Ru Wu: Massachusetts Institute of Technology
Lior Nissim: The Hebrew University of Jerusalem
Doron Stupp: The Hebrew University of Jerusalem
Erez Pery: Massachusetts Institute of Technology
Adina Binder-Nissim: Massachusetts Institute of Technology
Karen Weisinger: Massachusetts Institute of Technology
Casper Enghuus: Massachusetts Institute of Technology
Sebastian R. Palacios: Massachusetts Institute of Technology
Melissa Humphrey: Massachusetts General Hospital
Zhizhuo Zhang: Massachusetts Institute of Technology
Eva Maria Novoa: Massachusetts Institute of Technology
Manolis Kellis: Massachusetts Institute of Technology
Ron Weiss: Massachusetts Institute of Technology
Samuel D. Rabkin: Massachusetts General Hospital
Yuval Tabach: The Hebrew University of Jerusalem
Timothy K. Lu: Massachusetts Institute of Technology

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-10912-8

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