Global analysis of enhancer targets: Bulk RNA-Seq validation
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE129825 | GEO | 2019/10/31
REPOSITORIES: GEO
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Project description:Single-cell perturbation assays such as Mosaic-seq enable highly multiplexed functional assessment of enhancers in their endogenous genomic context. By introducing a few computational and experimental improvements, we expanded the Mosaic-seq analysis to capture the secondary gene targets of enhancers. Our analysis of >500 putative enhancers in K562 cells demonstrates that many secondary hits are shared among enhancers targeting different transcriptional factors, which reveals an interwoven enhancer-driven gene regulatory network. Together, our data underscore the flexibility of manipulating gene transcription by modifying enhancer activity.
2019-10-31 | GSE129826 | GEO
Project description:Transcriptional enhancers orchestrate cell-type specific gene expression programs critical to eukaryotic development, physiology, and disease. However, despite the large number of enhancers now identified, only a small number have been functionally assessed. Here, we develop MOsaic Single-cell Analysis by Indexed CRISPR Sequencing (Mosaic-seq), a method that measures one direct phenotype of enhancer repression: change of the transcriptome, at the single cell level. Using dCas9-KRAB to suppress enhancer function, we first implement a multiplexed system to allow the simultaneous measurement of the transcriptome and detection of sgRNAs by single cell RNA sequencing. We validate this approach by targeting the HS2 enhancer in the well-studied beta-globin locus. Next, through computational simulation, we demonstrate strategies to robustly detect changes in gene expression in these single cell measurements. Finally, we use Mosaic-seq to target 71 hypersensitive regions belonging to 15 super-enhancers in K562 cells by utilizing a lentiviral library containing 241 unique-barcoded sgRNAs. Our results demonstrate that Mosaic-seq is a reliable approach to study enhancer function in single cells in a high-throughput manner.
2017-04-13 | GSE81412 | GEO
Project description:Transcriptional enhancers orchestrate cell-type specific gene expression programs critical to eukaryotic development, physiology, and disease. However, despite the large number of enhancers now identified, only a small number have been functionally assessed. Here, we develop MOsaic Single-cell Analysis by Indexed CRISPR Sequencing (Mosaic-seq), a method that measures one direct phenotype of enhancer repression: change of the transcriptome, at the single cell level. Using dCas9-KRAB to suppress enhancer function, we first implement a multiplexed system to allow the simultaneous measurement of the transcriptome and detection of sgRNAs by single cell RNA sequencing. We validate this approach by targeting the HS2 enhancer in the well-studied beta-globin locus. Next, through computational simulation, we demonstrate strategies to robustly detect changes in gene expression in these single cell measurements. Finally, we use Mosaic-seq to target 71 hypersensitive regions belonging to 15 super-enhancers in K562 cells by utilizing a lentiviral library containing 241 unique-barcoded sgRNAs. Our results demonstrate that Mosaic-seq is a reliable approach to study enhancer function in single cells in a high-throughput manner.
2017-04-13 | GSE96827 | GEO
Project description:Transcriptional enhancers orchestrate cell-type specific gene expression programs critical to eukaryotic development, physiology, and disease. However, despite the large number of enhancers now identified, only a small number have been functionally assessed. Here, we develop MOsaic Single-cell Analysis by Indexed CRISPR Sequencing (Mosaic-seq), a method that measures one direct phenotype of enhancer repression: change of the transcriptome, at the single cell level. Using dCas9-KRAB to suppress enhancer function, we first implement a multiplexed system to allow the simultaneous measurement of the transcriptome and detection of sgRNAs by single cell RNA sequencing. We validate this approach by targeting the HS2 enhancer in the well-studied beta-globin locus. Next, through computational simulation, we demonstrate strategies to robustly detect changes in gene expression in these single cell measurements. Finally, we use Mosaic-seq to target 71 hypersensitive regions belonging to 15 super-enhancers in K562 cells by utilizing a lentiviral library containing 241 unique-barcoded sgRNAs. Our results demonstrate that Mosaic-seq is a reliable approach to study enhancer function in single cells in a high-throughput manner.
2017-04-13 | GSE81750 | GEO
Project description:Transcriptional enhancers orchestrate cell-type specific gene expression programs critical to eukaryotic development, physiology, and disease. However, despite the large number of enhancers now identified, only a small number have been functionally assessed. Here, we develop MOsaic Single-cell Analysis by Indexed CRISPR Sequencing (Mosaic-seq), a method that measures one direct phenotype of enhancer repression: change of the transcriptome, at the single cell level. Using dCas9-KRAB to suppress enhancer function, we first implement a multiplexed system to allow the simultaneous measurement of the transcriptome and detection of sgRNAs by single cell RNA sequencing. We validate this approach by targeting the HS2 enhancer in the well-studied beta-globin locus. Next, through computational simulation, we demonstrate strategies to robustly detect changes in gene expression in these single cell measurements. Finally, we use Mosaic-seq to target 71 hypersensitive regions belonging to 15 super-enhancers in K562 cells by utilizing a lentiviral library containing 241 unique-barcoded sgRNAs. Our results demonstrate that Mosaic-seq is a reliable approach to study enhancer function in single cells in a high-throughput manner.
2017-04-13 | GSE81848 | GEO
Project description:Transcriptional enhancers orchestrate cell-type specific gene expression programs critical to eukaryotic development, physiology, and disease. However, despite the large number of enhancers now identified, only a small number have been functionally assessed. Here, we develop MOsaic Single-cell Analysis by Indexed CRISPR Sequencing (Mosaic-seq), a method that measures one direct phenotype of enhancer repression: change of the transcriptome, at the single cell level. Using dCas9-KRAB to suppress enhancer function, we first implement a multiplexed system to allow the simultaneous measurement of the transcriptome and detection of sgRNAs by single cell RNA sequencing. We validate this approach by targeting the HS2 enhancer in the well-studied beta-globin locus. Next, through computational simulation, we demonstrate strategies to robustly detect changes in gene expression in these single cell measurements. Finally, we use Mosaic-seq to target 71 hypersensitive regions belonging to 15 super-enhancers in K562 cells by utilizing a lentiviral library containing 241 unique-barcoded sgRNAs. Our results demonstrate that Mosaic-seq is a reliable approach to study enhancer function in single cells in a high-throughput manner.
2017-04-13 | GSE81749 | GEO
Project description:Transcriptional enhancers orchestrate cell-type specific gene expression programs critical to eukaryotic development, physiology, and disease. However, despite the large number of enhancers now identified, only a small number have been functionally assessed. Here, we develop MOsaic Single-cell Analysis by Indexed CRISPR Sequencing (Mosaic-seq), a method that measures one direct phenotype of enhancer repression: change of the transcriptome, at the single cell level. Using dCas9-KRAB to suppress enhancer function, we first implement a multiplexed system to allow the simultaneous measurement of the transcriptome and detection of sgRNAs by single cell RNA sequencing. We validate this approach by targeting the HS2 enhancer in the well-studied beta-globin locus. Next, through computational simulation, we demonstrate strategies to robustly detect changes in gene expression in these single cell measurements. Finally, we use Mosaic-seq to target 71 hypersensitive regions belonging to 15 super-enhancers in K562 cells by utilizing a lentiviral library containing 241 unique-barcoded sgRNAs. Our results demonstrate that Mosaic-seq is a reliable approach to study enhancer function in single cells in a high-throughput manner.
2017-04-13 | GSE81748 | GEO
Project description:Transcriptional enhancers orchestrate cell-type specific gene expression programs critical to eukaryotic development, physiology, and disease. However, despite the large number of enhancers now identified, only a small number have been functionally assessed. Here, we develop MOsaic Single-cell Analysis by Indexed CRISPR Sequencing (Mosaic-seq), a method that measures one direct phenotype of enhancer repression: change of the transcriptome, at the single cell level. Using dCas9-KRAB to suppress enhancer function, we first implement a multiplexed system to allow the simultaneous measurement of the transcriptome and detection of sgRNAs by single cell RNA sequencing. We validate this approach by targeting the HS2 enhancer in the well-studied beta-globin locus. Next, through computational simulation, we demonstrate strategies to robustly detect changes in gene expression in these single cell measurements. Finally, we use Mosaic-seq to target 71 hypersensitive regions belonging to 15 super-enhancers in K562 cells by utilizing a lentiviral library containing 241 unique-barcoded sgRNAs. Our results demonstrate that Mosaic-seq is a reliable approach to study enhancer function in single cells in a high-throughput manner.
2017-04-13 | GSE81747 | GEO
Project description:Enhancers play key roles in gene regulation. However, comprehensive enhancer discovery is challenging because most enhancers, especially those affected in complex diseases, have weak effects on gene expression. Through gene regulatory network modeling, we identified that dynamic cell state transitions, a critical missing component in prevalent enhancer discovery strategies, can be utilized to improve the cells’ sensitivity to enhancer perturbation. Guided by the modeling results, we performed a mid-transition CRISPRi-based enhancer screen utilizing human embryonic stem cell definitive endoderm differentiation as a dynamic transition system. The screen discovered a comprehensive set of enhancers (4 to 9 per locus) for each of the core lineage-specifying transcription factors (TFs), including many enhancers with weak to moderate effects. Integrating the screening results with enhancer activity measurements (ATAC-seq, H3K27ac ChIP-seq) and three-dimensional enhancer-promoter interaction information (CTCF looping, Hi-C), we were able to develop a CTCF loop-constrained Interaction Activity (CIA) model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Together, our dynamic network-guided enhancer screen and the CIA enhancer prediction model provide generalizable strategies for sensitive and more comprehensive enhancer discovery in both normal and pathological cell state transitions.
2023-06-17 | PXD043070 | Pride
Project description:The information about when and where each gene is to be expressed is mainly encoded in the DNA sequence of enhancers, sequence elements that comprise binding sites (motifs) for different transcription factors (TFs). Most of the research on enhancer sequences has been focused on TF motif presence, while the enhancer syntax, i.e. the flexibility of important motif positions and how the sequence context modulates the activity of TF motifs, remain poorly understood. Here, we explore the rules of enhancer syntax by a two-pronged approach in Drosophila melanogaster S2 cells: we (1) replace important motifs by an exhaustive set of all possible 65,536 eight-nucleotide-long random sequences and (2) paste eight important TF motif types into 763 motif positions within 496 enhancers. These complementary strategies reveal that enhancers display constrained sequence flexibility and the context-specific modulation of motif function. Important motifs can be functionally replaced by hundreds of sequences constituting several distinct motif types, but only a fraction of all possible sequences and motif types restore enhancer activity. Moreover, TF motifs contribute with different intrinsic strengths that are strongly modulated by the enhancer sequence context (the flanking sequence, presence and diversity of other motif types, and distance between motifs), such that not all motif types can work in all positions. Constrained sequence flexibility and the context-specific modulation of motif function are also hallmarks of human enhancers and TF motifs, as we demonstrate experimentally. Overall, these two general principles of enhancer sequences are important to understand and predict enhancer function during development, evolution and in disease.
2022-12-15 | GSE211654 | GEO