Functional genomic approaches to elucidate the role of enhancers during development

Advanced Review

Genevieve E. Ryan, Emma K. Farley

Published Online: Dec 05 2019

DOI: 10.1002/wsbm.1467

Full article on Wiley Online Library: HTML PDF

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Abstract Successful development depends on the precise tissue‐specific regulation of genes by enhancers, genetic elements that act as switches to control when and where genes are expressed. Because enhancers are critical for development, and the majority of disease‐associated mutations reside within enhancers, it is essential to understand which sequences within enhancers are important for function. Advances in sequencing technology have enabled the rapid generation of genomic data that predict putative active enhancers, but functionally validating these sequences at scale remains a fundamental challenge. Herein, we discuss the power of genome‐wide strategies used to identify candidate enhancers, and also highlight limitations and misconceptions that have arisen from these data. We discuss the use of massively parallel reporter assays to test enhancers for function at scale. We also review recent advances in our ability to study gene regulation during development, including CRISPR‐based tools to manipulate genomes and single‐cell transcriptomics to finely map gene expression. Finally, we look ahead to a synthesis of complementary genomic approaches that will advance our understanding of enhancer function during development. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Developmental Biology > Developmental Processes in Health and Disease Laboratory Methods and Technologies > Genetic/Genomic Methods

In vivo massively parallel reporter assays to study enhancer function. (a) Enhancer‐FACS‐seq identifies active, tissue‐specific enhancers in whole Drosophila embryos. Two flies are crossed, the first carrying a candidate enhancer sequence driving GFP expression, and the second expressing a cell surface marker in a tissue‐specific manner. Embryos resulting from this cross are dissociated and FACS sorted for the tissue‐specific cell surface marker and GFP. Genomic DNA can then be extracted from sorted cells to identify enhancers that are active in the specific tissue. (b) Synthetic enhancer library‐sequencing (SEL‐seq) allows millions of enhancer variants to be tested for function in whole Ciona embryos. Synthetic enhancer variants are attached to a minimal promoter, GFP coding sequence, and a unique transcribable barcode. The enhancer library is electroporated into hundreds of thousands of fertilized Ciona eggs, which are then allowed to develop until the desired developmental stage. Barcode‐tagged mRNA can then be isolated and sequenced to identify active enhancer variants, providing insight into which sequences within an enhancer are important for function. Figure created with BioRender

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In vitro massively parallel reporter assays (MPRAs) to study enhancer function. MPRAs utilize advances in oligonucleotide synthesis and sequencing technology to test large libraries of candidate enhancer sequences for function. (a) MPRA libraries typically contain candidate enhancer sequences upstream of a minimal promoter and reporter gene. A transcribable barcode unique to each candidate enhancer is placed in the 3′ untranslated region of the reporter gene, allowing active enhancers to be identified by sequencing barcode‐tagged mRNAs. (b) Self‐transcribing active regulatory region sequencing (STARR‐seq) is a variation of the MPRA that exploits the characteristic that enhancers can function independently of their relative positions. In STARR‐seq, candidate enhancer sequences are placed downstream of a minimal promoter and reporter gene, allowing active enhancers to transcribe themselves. Figure created with BioRender

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Genome‐wide methods to identify putative enhancer–promoter interactions. Distal enhancers (shown in pink) may be brought within close proximity to the promoters they regulate (shown in black) through the formation of chromatin loops. Looping is facilitated by protein–protein interactions of transcription factors that assemble at promoters and enhancers. The basic principles of methods to detect chromatin interactions are shown. Both (a) chromatin conformation capture (3C)‐based methods, including Hi‐C, and (b) chromatin interaction analysis with paired‐end tag sequencing (ChIA‐PET) preserve and detect chromatin interactions through crosslinking, fragmentation, and proximity ligation followed by high‐throughput sequencing. ChIA‐PET includes a chromatin immunoprecipitation step, often using antibodies targeting RNA pol II (shown), to enrich for complexes containing promoters. (c) Genome architecture mapping captures the distance between genomic loci by cryosectioning and laser microdissection (which allow spatial information to be preserved) followed by DNA sequencing

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Genome‐wide methods to identify putative enhancers. The basic principle of each experimental approach and the corresponding data readout is shown for each method. (a) Chromatin immunoprecipitation followed by high‐throughput sequencing (ChIP‐seq) uses antibodies targeting a specific TF to determine the location of its binding sites genome‐wide. The broad peaks generated by ChIP‐seq cannot determine precise footprints of TF binding, but this can be achieved by adding a 5′ to 3′ exonuclease digestion step to the protocol (ChIP‐exo). (b) Nucleosomes flanking active enhancers often carry stereotypical histone modifications (e.g., H3K4me1 and H3K27ac) that can be detected with specific antibodies by ChIP‐seq. (c) Active enhancers and other cis‐regulatory elements are found within open chromatin that is depleted of nucleosomes. Accessible chromatin can be detected by DNase I digestion followed by high‐throughput sequencing (DNase‐seq). (d) Accessible chromatin can also be detected by assay for transposase‐accessible chromatin using sequencing (ATAC‐seq), where Tn5 transposase simultaneously fragments and tags accessible DNA prior to sequencing

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CRISPR‐based methods to identify and study candidate enhancers. (a) Any genomic region containing a protospacer‐adjacent motif (PAM) can be targeted by CRISPR/Cas9. (b) The workflow for high‐throughput CRISPR screens includes building a library of sgRNAs, cloning the library into lentiviral vectors, infecting cells that stably express Cas9 or one of its variant forms, and phenotyping cells. The sgRNA(s) carried by cells of a given phenotype can easily be identified by sequencing, and can give insight into candidate cis‐regulatory regions that affect phenotype. CRISPR screens can utilize (c) one sgRNA to mutate a particular locus, or (d) two sgRNAs to delete a genomic region. In both cases, DNA cut by Cas9 will be repaired by the error‐prone non‐homologous end joining (NHEJ) pathway, creating indels. Variant forms of Cas9 that are used in high‐throughput screens include (e) CRISPRa to activate transcription and (f) CRISPRi to repress transcription. Both CRISPRa and CRISPRi utilize catalytically dead Cas9 fused to an effector protein and impact gene expression through chromatin remodeling. Figure created with BioRender

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scRNA methodologies to build atlases of animal development. Single cell RNA‐sequencing (scRNA‐seq) methods used to generate atlases of animal development are illustrated using Drosophila as a model system. Staged embryos are collected and dissociated before being subjected to (a) droplet‐based scRNA‐seq or (b) single‐cell combinatorial indexing RNA‐seq (sciRNA‐seq). Droplet‐based scRNA‐seq methods utilize beads bearing primers that contain two unique barcodes, one that is cell‐specific and one that is transcript‐specific (Unique Molecular Identifier). The primers also contain a PCR handle for subsequent amplification. For sciRNA‐seq, cells are distributed to multiwell plates where they receive a well‐specific barcode during reverse transcription. Cells are then pooled and distributed to new multiwell plates where they receive a second well‐specific barcode during amplification. A third level of indexing can increase the number of cells processed in a single experiment (not shown). (c) Single‐cell transcriptomic data is represented as a t‐distributed stochastic neighbor embedding (t‐SNE) or uniform manifold approximation and projection (UMAP) plot, which cluster cells based on the similarity of their transcriptomes. (d) Cell type‐specific markers and in situ hybridization data can be used to map single‐cell transcriptomes back to a virtual embryo

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