Format

Send to

Choose Destination
Nat Commun. 2019 Jun 28;10(1):2880. doi: 10.1038/s41467-019-10912-8.

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

Author information

1
Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
2
Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel.
3
Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel.
4
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
5
Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
6
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
7
Brain Tumor Research Center, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02144, USA.
8
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
9
Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
10
Center for Genomic Regulation (CRG), 08003, Barcelona, Spain.
11
Department of Neurosurgery (Microbiology & Immunobiology), Harvard Medical School, Boston, MA, 02115, USA.
12
Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel. yuvaltab@ekmd.huji.ac.il.
13
Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. timlu@mit.edu.
14
Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. timlu@mit.edu.
15
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. timlu@mit.edu.
16
Biophysics Program, Harvard University, Boston, MA, 02115, USA. timlu@mit.edu.
17
Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. timlu@mit.edu.

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.

PMID:
31253799
PMCID:
PMC6599391
DOI:
10.1038/s41467-019-10912-8
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center