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Human Whole Genome SAM CRISPRa Pooled Lentiviral Library Kit Zeo

ITEM#: 3042-HSAMZEO1KT

MFR#: HSAMZEO-1KT

The transcriptional activator VP64 fused to nuclease-dead SpCas9 (dCas9) forms a ribonucleoprotein complex (RNP) with the CRISPR guide RNA (gRNA). The stem- and tetra-loop sequences in the gRNA scaffold have been modified into minimal hairpin RNA apt

The transcriptional activator VP64 fused to nuclease-dead SpCas9 (dCas9) forms a ribonucleoprotein complex (RNP) with the CRISPR guide RNA (gRNA). The stem- and tetra-loop sequences in the gRNA scaffold have been modified into minimal hairpin RNA aptamers, which selectively bind dimerized MS2 bacteriophage coat proteins. MS2 coat protein is fused to the p65 subunit of NF-kappaB (NFkappaB) and the activation domain of human heat-shock factor 1 (HSF1). The guide RNA contains two aptamers, each capable of binding two MS2 coactivator proteins, effectively recruiting four co-activators for every CRISPR targeting activator complex. The dCas9-VP64,MS2-p65-HSF1 helper constructs and gRNA are delivered as three separate plasmids/Lentiviral particles. In contrast to arrayed gRNA libraries containing each clone in a separate plate well, pooled gRNA libraries contain thousands of individual gRNAs in a single tube enabling efficient screening of the whole human genome (16,000+ genes) at the bench-top without robotics or specialized equipment. The SAM CRISPR library is provided in 3 sub-pools. Each sub-pool contains approximately 23,500 gRNAs, with 3 gRNAs per RefSeqID. The whole genome library contains approximately 70,000 gRNAs, in total, targeting 19,000 unique gene symbols (including alternative isoforms encompassing 23,500 RefSeqIDs). 100 Non-targeting (i.e. negative) control gRNAs are included within each pool for use as a baseline in the statistical characterization of changes in gRNA frequency as measured by deep sequencing. Negative control gRNAs share minimal homology with the target genome and should not undergo significant changes in representation throughout control cell treatments. Due to the nature of CRISPR activator screening experiments, positive controls must be determined empirically, taking into account the specific phenotype to be explored in the screen. Together, these controls create many options for upfront experimental design and downstream data analysis.