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With more than 2 million predicted enhancers interacting in unique genetic and epigenetic contexts, genome-wide functional assessment of these regulatory elements has lagged behind identification. To overcome this issue, a study in Molecular Cell reports the development of Mosaic-seq — short for mosaic single-cell analysis by indexed CRISPR sequencing — which analyses at the single-cell level changes in the transcriptome mediated via targeted enhancer repression.

The strategy uses a catalytically inactive Cas9 (dCas9) fused to KRAB, a strong repressor of enhancer function, and a barcoded single guide RNA (sgRNA) library to generate a mosaic of cells with targeted epigenetic alterations resulting in differing gene expression. Phenotypic characterization was carried out by single-cell RNA sequencing (RNA-seq), and the single-cell transcriptome profiles were correlated with the enhancers targeted by the barcoded sgRNAs.

To validate their approach, the group targeted the β-globin locus in K562 cells. Recruitment of dCas9−KRAB to the enhancer HS2 led to repression of gene expression of its downstream targets HBG1, HBG2 and HBE1 to a similar degree to that achieved if the promoters of these genes were targeted directly.

To ensure high quality at large scales, the team generated gold-standard data sets using a TNFα stimulation system in K562 cells using both single-cell and bulk-cell RNA-seq approaches. By examining the distribution of gene expression changes, Mosaic-seq could be used for 'virtual fluorescence-activated cell sorting', with the ability to use as few as 300 cells to detect differential expression of low and moderately expressed genes.

After validation, the researchers applied Mosaic-seq to the functional interrogation of 71 constituent enhancers from 15 super-enhancers, asking whether each constituent enhancer contributes equally or whether a subset contributes more to gene regulation. With a focus on DNase I-hypersensitive sites in the β-globin locus control region, the team were able to confirm previous results that the enhancer HS2 is the major contributor to gene expression. Moving to super-enhancers flanking the gene PIM1, the team could identify those DNase I-hypersensitive regions that contribute to most gene expression.

Using the Mosaic-seq data set, the researchers were able to investigate general features of enhancers. Looking at epigenetic features of active enhancers in more than 500 chromatin immunoprecipitation followed by sequencing (ChIP–seq) data sets, the team noted that the histone acetyltransferase p300 and RNA polymerase II, along with the sequence-specific transcription factors TAL1 and GATA-2, were enriched at KRAB-responsive enhancers. Moreover, the authors built a statistical model (based on the single-cell penetrance and gene expression contribution of an enhancer) that reflects endogenous enhancer usage.

Finally, Xie et al. wondered what role might be played by constituent components of super-enhancers that have little influence on gene expression. To explore this question, combinations of sgRNAs were used to target multiple constituents of super-enhancers simultaneously. The repression of multiple individual components was shown to be significantly greater than that of individual constituents, suggestive of a compensatory relationship between weak enhancers.

Whereas techniques exist that merge single-cell RNA sequencing with genome editing, such as Perturb-seq and CRISP-seq, the analysis of enhancers is hampered by the small number of target genes for each enhancer. Although Xie et al. acknowledge the limitations of their system — such as whether KRAB is a universal repressor — Mosaic-seq enables genome-wide functional analysis of enhancers and super-enhancers with an unbiased, scalable and combinatorial single-cell approach.