An arrayed CRISPR library for individual, combinatorial and multiplexed gene knockout III
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE97433 | GEO | 2017/07/20
SECONDARY ACCESSION(S): PRJNA381778
REPOSITORIES: GEO
Similar Datasets
Project description:We have combined a machine-learning approach with other strategies to optimize the efficiency of sgRNAs for CRISPR screens and have constructed a genome-wide, sequence-verified, arrayed CRISPR library. This incorporates expression strategies to facilitate multiplexed or combinatorial screening. By conducting parallel loss-of-function screens, we compare our approach to existing sgRNA design and expression strategies.
2017-07-20 | GSE97432 | GEO
Project description:We have combined a machine-learning approach with other strategies to optimize the efficiency of sgRNAs for CRISPR screens and have constructed a genome-wide, sequence-verified, arrayed CRISPR library. This incorporates expression strategies to facilitate multiplexed or combinatorial screening. By conducting parallel loss-of-function screens, we compare our approach to existing sgRNA design and expression strategies.
2017-07-20 | GSE97431 | GEO
Project description:An arrayed CRISPR library for individual, combinatorial and multiplexed gene knockout
| PRJNA381776 | ENA
Project description:Technologies allowing for specific regulation of endogenous genes are valuable for the study of gene functions and have great potential in therapeutics. We created the CRISPR-on system, a two-component transcriptional activator consisting of a nuclease-dead Cas9 (dCas9) protein fused with a transcriptional activation domain and single guide RNAs (sgRNAs) with complementary sequence to gene promoters. We demonstrate that CRISPR-on can efficiently activate exogenous reporter genes in both human and mouse cells in a tunable manner. In addition, we show that robust reporter gene activation in vivo can be achieved by injecting the system components into mouse zygotes. Furthermore we show that CRISPR-on can activate the endogenous IL1RN, SOX2, and OCT4 genes. The most efficient gene activation was achieved by clusters of 3 to 4 sgRNAs binding to the proximal promoters suggesting their synergistic action in gene induction. Significantly, when sgRNAs targeting multiple genes were simultaneously introduced into cells, robust multiplexed endogenous gene activation was achieved. Genome-wide expression profiling demonstrated high specificity of the system. We used microarray to assay the gene expression changes after transfection of dCas9VP160 with the different sgRNAs
2013-08-10 | E-GEOD-49701 | biostudies-arrayexpress
Project description:Single-cell CRISPR screens allow for the exploration of mammalian gene function and genetic regulatory networks, but their utility has been limited in part by their reliance on indirect sgRNA indexing. Here, we present direct capture Perturb-seq, a versatile screening approach in which expressed sgRNAs are sequenced alongside single-cell transcriptomes. Direct capture Perturb-seq enables the detection of multiple distinct sgRNAs expressed from a single vector within individual cells and thus allows pooled single-cell CRISPR screens to be easily paired with combinatorial perturbation libraries. We demonstrate that this approach allows high-throughput investigations of genetic interactions, and we leverage this ability to dissect epistatic interactions between cholesterol biogenesis and DNA repair. We also show that targeting individual genes with multiple sgRNAs per cell improves the efficacy of CRISPR interference and activation, facilitating the use of compact, highly active CRISPR libraries for single-cell screens. Lastly, we show that hybridization-based target enrichment permits sensitive, specific sequencing of informative transcripts from single-cell RNA-seq experiments.
2020-03-03 | GSE146194 | 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:Dysregulation of cytokines in the bone marrow microenvironment promotes acute myeloid leukemia cell growth. Due to the complexity and low throughput of in vivo stem-cell based assays, studying the role of cytokines in the bone marrow niche in a screening setting is challenging. Herein, we developed an ex vivo cytokine screen using 11 arrayed molecular barcodes, allowing for a competitive in vivo readout of leukemia-initiating capacity. With this approach, we assessed the effect of 114 murine cytokines on MLL-AF9 acute myeloid leukemia mouse cells and identified the tumor necrosis factor ligand superfamily member 13 (TNFSF13) as a positive regulator of leukemia-initiating cells. By using Tnfsf13-/- recipient mice, we confirmed that TNFSF13 supports leukemia-initiation also under physiological conditions. TNFSF13 was secreted by normal myeloid cells but not by leukemia mouse cells, suggesting that mature myeloid bone marrow cells support leukemia cells by secreting TNFSF13. TNFSF13 supported leukemia cell proliferation in an NF-κB-dependent manner by binding TNFRSF17 and suppressed apoptosis. Moreover, TNFSF13 supported the growth and survival of several human myeloid leukemia cell lines, demonstrating that our findings translate to human disease. Taken together, using arrayed molecular barcoding, we identified a previously unrecognized role of TNFSF13 as a positive regulator of acute myeloid leukemia-initiating cells. The arrayed barcoded screening methodology is not limited to cytokines and leukemia, but can be extended to other types of ex vivo screens, where a multiplexed in vivo read-out of stem cell functionality is needed.
2018-10-30 | GSE104425 | 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 | 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 | GSE81748 | GEO