High-Throughput mapping of the regulatory DNA
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
SUBMITTER: Nisha Rajagopal
PROVIDER: E-GEOD-76318 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with increased risk for colorectal cancer (CRC). A molecular understanding of the functional consequences of this genetic variation is complicated because most GWAS SNPs are located in non-coding regions. After identifying H3K27Ac peaks in HCT116 colon cancer cells that harbor SNPs associated with an increased risk for CRC, we used CRISPR/Cas9 nuclease to delete 2 CRC risk-associated H3K27Ac peaks (E7 and E24), a peak that is not associated with CRC risk (18qE) and a region that doesn?t have H3K27Ac peak (18qNE) from HCT116 cells and analyzed effects on the transcriptome and epigenome. We also deleted E7 region from HEK 293 cells and analyzed effects on the transcriptome of HEK 293. We also confirmed the physical interaction between enhancers of our interest and their putative target genes. Analysis of RNA-seq data and ChIP-seq between control clones and enhancer deleted clones in HCT116 cell. For Control, gRNA empty vectorplasmid was transfected with Cas9-GFP. For Deletion, gRNAs that have enhancer target sequences were transfected along with Cas9-GFP. Cells with high GFP expression were identified using fluorescence-activated cell sorting. Sorted cells were plated into individual wells of a 24 well plate and then re-plated as single cells in 10cm dishes and subsequently expanded for further analyses. Samples used in this study were clonal populations. Interaction profiling of enhancers of interest with putative target genes using 4C-seq
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Project description:Background: The CRISPR/Cas9 toolbox has recently been expanded to include approaches for modulating gene expression. To successfully build on this work, and apply it for answering biological questions, it is important to establish it in a broad range of circumstances. Genome-scale CRISPR interference (CRISPRi) has been used in human cells lines, however the rules for designing effective guide RNAs (gRNAs) in different organisms are not well known. We sought to determine rules that determine gRNA effectiveness at transcriptional repression in Saccharomyces cerevisiae. Results: We created an inducible single plasmid CRISPRi system for gene repression in yeast, and used it to analyze fitness effects of gRNAs under 18 small molecule treatments. Our approach correctly identified previously-described chemical-genetic interactions, as well as a new mechanism of suppressing fluconazole toxicity by repression of the ERG25 gene. Assessment of multiple target loci across treatments allowed us to determine generalizable features associated with gRNA efficacy. Guides that target regions with low nucleosome occupancy and high chromatin accessibility were clearly more effective. We also found the best region to target gRNAs was between the transcription start site (TSS) and 200bp upstream of the TSS. Finally, unlike nuclease-proficient Cas9 in human cells, point mutations were tolerated equally well by truncated (18 nt specificity sequence) and full length (20 nt) gRNAs, however, 18 nt gRNAs were generally less potent than full length gRNAs. Conclusions: Our results establish a powerful functional genomics screening method, provide rules for designing effective gRNAs for gene repression, and show that 18 nt and 20 nt gRNAs exhibit similar tolerance to mismatches in the target sequence. These findings will enable effective library design and genome-wide screening in many genetic backgrounds.
2016-01-18 | GSE71490 | GEO
Project description:Background: The CRISPR/Cas9 toolbox has recently been expanded to include approaches for modulating gene expression. To successfully build on this work, and apply it for answering biological questions, it is important to establish it in a broad range of circumstances. Genome-scale CRISPR interference (CRISPRi) has been used in human cells lines, however the rules for designing effective guide RNAs (gRNAs) in different organisms are not well known. We sought to determine rules that determine gRNA effectiveness at transcriptional repression in Saccharomyces cerevisiae. Results: We created an inducible single plasmid CRISPRi system for gene repression in yeast, and used it to analyze fitness effects of gRNAs under 18 small molecule treatments. Our approach correctly identified previously-described chemical-genetic interactions, as well as a new mechanism of suppressing fluconazole toxicity by repression of the ERG25 gene. Assessment of multiple target loci across treatments allowed us to determine generalizable features associated with gRNA efficacy. Guides that target regions with low nucleosome occupancy and high chromatin accessibility were clearly more effective. We also found the best region to target gRNAs was between the transcription start site (TSS) and 200bp upstream of the TSS. Finally, unlike nuclease-proficient Cas9 in human cells, point mutations were tolerated equally well by truncated (18 nt specificity sequence) and full length (20 nt) gRNAs, however, 18 nt gRNAs were generally less potent than full length gRNAs. Conclusions: Our results establish a powerful functional genomics screening method, provide rules for designing effective gRNAs for gene repression, and show that 18 nt and 20 nt gRNAs exhibit similar tolerance to mismatches in the target sequence. These findings will enable effective library design and genome-wide screening in many genetic backgrounds. An expression construct was created for inducible CRISPRi in yeast. Key features include ORFs expressing dCas9-Mxi1 and the tetracycline repressor (TetR), as well as a tetracycline inducible gRNA locus containing the RPR1 promoter with a TetO site, a NotI site for cloning new gRNA specificity sequences, and the constant part of the gRNA. When yeast containing this plasmid are grown in the absence of anhydrotetracycline (ATc) TetR binds the gRNA promoter and prevents PolIII from binding and transcribing the gRNA. This in turn prevents dCas9-Mxi1 from binding the target site. In the presence of ATc, TetR dissociates and gRNA is expressed, allowing dCas9-Mxi1 to bind its target locus, and repress gene expression. gRNA libraries were cloned into this construct and transformed into yeast to create pools. Experiments were conducted in which yeast pools were grown in inducing (+ATc) and non-inducing conditions (-ATc) in the presence of different drugs. After multiple generations of growth in these conditions, yeast plasmids were minipreped and the gRNA locus was PCRed and sequenced via MiSeq. Counts of each gRNA were compared in different conditions.
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Project description:Recent technological developments in single-cell RNA-seq CRISPR screens enable high-throughput investigation of the genome. Through transduction of a gRNA library to a cell population followed by transcriptomic profiling by scRNA-seq, it is possible to characterize the effects of thousands of genomic perturbations on global gene expression. A major source of noise in scRNA-seq CRISPR screens are ambient gRNAs, which are contaminating gRNAs that likely originate from other cells. If not properly filtered, ambient gRNAs can result in an excess of false positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNA noise in single-cell CRISPR screens. We use these datasets to develop and train CLEANSER, a mixture model that identifies and filters ambient gRNA noise. This model takes advantage of the bimodal distribution between native and ambient gRNAs and includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy.
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