Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER [CRISPR]
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PROVIDER: PRJNA1136903 | ENA |
REPOSITORIES: ENA
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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.
2024-10-08 | GSE272454 | GEO
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.
2024-10-08 | GSE272457 | GEO
Project description:Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER [scRNA-seq]
| PRJNA1137951 | ENA
Project description:We combined CRISPR genome editing with single-cell RNA sequencing to assess complex phenotypes in pooled cellular screens. Our method for CRISPR droplet sequencing (CROP-seq) comprises four key components: a gRNA vector that makes individual gRNAs detectable in single-cell transcriptomes, a high-throughput assay for single-cell RNA-seq, a computational pipeline for assigning single-cell transcriptomes to gRNAs, and a bioinformatic method for analyzing and interpreting gRNA-induced transcriptional profiles. CROP-seq allowed us to link gRNA expression to the associated transcriptome responses in thousands of single cells using a straightforward and broadly applicable screening workflow. Additional information are available from the CROP-seq website http://crop-seq.computational-epigenetics.org
2017-01-18 | GSE92872 | 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.
2016-01-18 | E-GEOD-71490 | biostudies-arrayexpress
Project description:In this study, we used a surrogate lentivirus library to capture CRISPR editing outcome in HEK cells. The dataset include quantification of indel frequencies for SpCas9 gRNAs in 12,000 surrogate sites. After filtering low quality sites, the high quality SpCas9 gRNA activities from a total of 10592 sites have been used to develop an improved deep learning-based prediction model CRISPRon (https://rth.dk/resources/crispr/.).
2021-05-03 | GSE173708 | GEO
Project description:CRISPR-based gene perturbation enables unbiased investigations of single and combinatorial genotype-to-phenotype associations. In light of efforts to map combinatorial gene dependencies at scale, choosing an efficient and robust CRISPR-associated (Cas) nuclease is of utmost importance. Even though SpCas9 and AsCas12a are widely used for single, combinatorial, and orthogonal screenings, side-by-side comparisons remain sparse. Here, we systematically compared combinatorial SpCas9, AsCas12a, and CHyMErA in hTERT-immortalized retinal pigment epithelial cells and extracted performance-critical parameters for combinatorial and orthogonal CRISPR screens. Our analyses identified SpCas9 to be superior to enhanced and optimized AsCas12a, with CHyMErA being largely inactive in the tested conditions. Since AsCas12a contains RNA processing activity, we used arrayed dual-gRNAs to improve AsCas12a and CHyMErA applications. While this negatively influenced the effect size of combinatorial AsCas12a applications, it enhanced the performance of CHyMErA. This improved performance, however, was limited to AsCas12a dual-gRNAs, as SpCas9 gRNAs remained largely inactive. To avoid the use of hybrid gRNAs for orthogonal applications, we engineered the multiplex SpCas9-enAsCas12a system (multiSPAS) that avoids RNA processing for efficient orthogonal gene editing.
2023-05-10 | GSE215175 | GEO
Project description:Novel single cell RNA-seq analysis combined with CRISPR screens enables the high- throughput characterization of transcriptional changes caused by genetic perturbations. Dedicated software to annotate CRISPR guide RNA (gRNA) libraries and associate them with single cell transcriptomes are however lacking. Here, we generated a CRISPR droplet sequencing dataset. We demonstrate that the current default tool fails to detect mutant gRNAs. We therefore developed GiRAFR, a pysam-based software tool to characterize intact and mutant gRNAs. We show that mutant gRNAs are dysfunctional, and failure to detect and annotate them leads to an inflated estimate of the number of untransformed cells as well as an underestimated multiplet frequency. These findings are mirrored in publicly available datasets, where we find that up to 34 % of cells are transduced with a mutant gRNA. Applying GiRAFR hence stands to improve the annotation and quality of single cell CRISPR screens.
2023-09-29 | GSE216040 | GEO
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