Function-based identification of mammalian enhancers using site-specific integration

Abstract

The accurate and comprehensive identification of functional regulatory sequences in mammalian genomes remains a major challenge. Here we describe Site-specific Integration FACS-sequencing (SIF-seq), an unbiased, medium-throughput functional assay for the discovery of distant-acting enhancers. Pluripotent cell reporter assays, targeted single-copy genomic integration, and flow cytometry are coupled with high-throughput DNA sequencing to enable parallel screening of large numbers of DNA sequences. We demonstrate the utility of this method by functionally interrogating >500 kb of mouse and human sequence for enhancer activity and identifying embryonic stem (ES) cell enhancers at pluripotency loci including NANOG. We also demonstrate the effectiveness of the approach in differentiated cell populations through the identification of cardiac enhancers from cardiomyocytes and neuronal enhancers from neural progenitors. SIF-seq is a powerful and flexible method for the de novo functional identification of mammalian enhancers in a potentially wide variety of cell types.

Massively parallel cis-regulatory analysis in the mammalian central nervous system

“…Our capture-and-clone approach provides a strategy for assaying candidate CREs with lengths of a desired range. Moreover, the capture approach can be used in conjunction with any existing MPRA-like approach, including those that already rely on DNA fragmentation (Dickel et al 2014;Murtha et al 2014). For example, STARR-seq (Arnold et al 2013) has been used to assess long DNA fragments obtained by whole-genome shotgun cloning of the Drosophila genome.…”

“…When averaged over the entire population of assayed cells, the cell-type-specific activity of the DHS may be obscured. For tissues with highly heterogeneous cell populations such as the cerebral cortex, it should be possible to target specific subpopulations by combining AAV-mediated CREseq with fluorescence-activated cell sorting (FACS) of defined cell types (Okaty et al 2011;Gisselbrecht et al 2013;Dickel et al 2014). Second, the minimal promoter used in this study contains a possible weak CRX site, whose affinity is predicted to be ∼10% that of the CRX consensus motif (Chen and Zack 1996;Lee et al 2010).…”

Regulatory network control of blood stem cells

“…Shown at the top are representative examples of microarray expression profiles, 73 ChIP-Seq TF binding maps, 74 and single cell expression analysis, 47 which can all be used to infer regulatory hierarchies. The bottom part of the figure illustrates the importance of follow-up functional experiments such as perturbation and high-throughput transcriptional assays 39,40 to validate inferred regulatory connections. 56 offer exciting perspectives, which will be enhanced further once protocols are developed that permit quantification of TF protein and target gene mRNA levels in the same single cell.…”

Linear models enable powerful differential activity analysis in massively parallel reporter assays

“…Characterization studies examine thousands of different putative regulatory elements that have a wide variety of sequence features and try to correlate these sequence features with measured activity levels (Grossman et al, 2017;Guo et al, 2017;Safra et al, 2017;Levo et al, 2017;Maricque, Dougherty, and Cohen, 2017;Groff et al, 2016;Ernst et al, 2016;White, Kwasnieski, et al, 2016;Ferreira et al, 2016;Fiore and Cohen, 2016;Farley et al, 2015;Kamps-Hughes et al, 2015;Dickel et al, 2014;Kwasnieski, Fiore, et al, 2014;Mogno, Kwasnieski, and Cohen, 2013;Gisselbrecht et al, 2013;White, Myers, et al, 2013;Smith et al, 2013). Typical statistical analyses use regression to study the impact of multiple features simultaneously.…”