Project description:HT29-DKO cells were stably transduced with lentiCas9-Blast (Addgene, #52962) and subsequently selected using Blasticidin. Then, 300 million HT29-DKO cells that constitutively express Cas9 were transduced with lentiGuide-Puro from the Brunello library at MOI 0.3. Cells were then selected with puromycin, expanded to 3 billion cells, and then pooled together and cryofrozen in aliquots. One hundred million cells were thawed constituting over 1000× genome coverage worth of mutagenized library. The cells were infecting with PeV-A1 or PeV-A2 at an MOI of 0.1. Virus-resistant colonies were harvested. The uninfected reference used was the unselected starting population. The unselected and selected cells were both processed with QIAamp DNA columns to purify the gDNA. A first round of PCR was used to amplify the guide RNA sequences encoded in the gDNA, followed by a second round of PCR to add the barcodes/adapters for amplicon sequencing. 2% agarose gels and a QIAquick gel extraction kit were used to purify the amplicons. The amplicons were then subjected to next-generation sequencing on a HiSeq instrument lane (Illumina) via Novogene.
Project description:Huh7.5.1 cells were stably transduced with lentiCas9-Blast (Addgene, #52962) and subsequently selected using Blasticidin. Then, Huh7.5.1 cells that constitutively express Cas9 were transduced with lentiGuide-Puro from the druggable genome library at MOI 0.3. Cells were then selected with puromycin, expanded, and then pooled together and cryofrozen in aliquots. Cells were thawed constituting over 1000× genome coverage worth of mutagenized library. The cells were infecting with DENV2_429557 and DENV-2_16681_Hap1-adapted at MOI of 0.1. Virus-resistant colonies were harvested. The uninfected reference used was the unselected starting population. The unselected and selected cells were both processed with QIAamp DNA columns to purify the gDNA. A first round of PCR was used to amplify the guide RNA sequences encoded in the gDNA, followed by a second round of PCR to add the barcodes/adapters for amplicon sequencing. 2% agarose gels and a QIAquick gel extraction kit were used to purify the amplicons. The amplicons were then subjected to next-generation sequencing on a HiSeq instrument lane (Illumina) via Novogene.
Project description:eHAP or U87MG cells were stably transduced with lentiCas9-Blast (Addgene, #52962) and subsequently selected using Blasticidin. Then, 300 million eHAP or U87MG cells that constitutively express Cas9 were transduced with lentiGuide-Puro from the Brunello library at MOI 0.3. Cells were then selected with puromycin, expanded to 3 billion cells, and then pooled together and cryofrozen in aliquots. One hundred million cells were thawed constituting over 1000× genome coverage worth of mutagenized library. Twenty T175 flasks were used for the U87MG-based screen, while twelve T175 flasks were used for the eHAP-based screen. The cells were allowed to recover for 48 hours before infecting with ReoT3D at an MOI of 0.1. Obvious CPE was observed within 72 hours. eHAP-resistant colonies were harvested two weeks later, while U87MG-resistant colonies were harvested six weeks later. The uninfected reference used was the unselected starting population. The unselected and selected cells were both processed with QIAamp DNA columns to purify the gDNA. A first round of PCR was used to amplify the guide RNA sequences encoded in the gDNA, followed by a second round of PCR to add the barcodes/adapters for amplicon sequencing. 2% agarose gels and a QIAquick gel extraction kit were used to purify the amplicons. The amplicons were then subjected to next-generation sequencing on a HiSeq instrument lane (Illumina) via Novogene.
Project description:Amplicon Sequencing of gRNAs from Brunello CRISPR mutagenized U87MG or eHAP cells after live/dead selection with Reovirus Type 3 Dearing Strain.
Project description:Genome-wide cDNA array from HT29,HT29-shROR,HT29-Mock, AGS, AGS-shROR, AGS-Mock cells We used microarrays to detail the global programme of gene expression and identified significantly changed genes after ROR depletion.
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.
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.