A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS)
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 28, 2019
- Source ID
- 10.1038/s41467-019-10912-8
Entities
People
- Adina Binder-nissim
- Casper Enghuus
- Doron Stupp
- Erez Pery
- Eva Maria Novoa Pardo
- Karen Weisinger
- Lior Nissim
- Manolis Kellis
- Melissa Humphrey
- Ming-ru Wu
- Ron Weiss
- Samuel D Rabkin
- Sebastian R. Palacios
- Timothy K. Lu
- Yuval Tabach
- Zhizhuo Zhang
Organizations
- United States Department of Defense