Precise measurement of molecular phenotypes with barcode-based CRISPRi screens
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ABSTRACT:
PROVIDER: PRJNA1118732 | ENA |
REPOSITORIES: ENA
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Project description:Uncovering the genetic basis of molecular processes is essential for understanding a broad range of biological phenomena. CRIPSR-Cas9 approaches enable this discovery through programable and systematic profiling of regulatory genes underlying diverse cellular behaviors. A key challenge for dissecting genetic networks is expanding CRISPR-based screens to interrogate specific phenotypes with high precision. Here, we use a quantitative, sequencing-based CRISPRi platform to enhance the scope and sensitivity of genome-wide screens. We first systematically explore how technical variations distort guide effects and correct for these factors by using RNA reporters and normalizers expressed from closely matched promoters. We then combine this platform with a recombinase-based integration system to interrogate protein and RNA-level phenotypes. We find our approach accurately captures known regulators of protein or RNA quality control with high accuracy and minimal background. These barcode-based CRISPR systems provide a powerful and generalizable platform for dissecting critical cellular regulatory pathways.
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Project description:The sheer complexity of the brain has complicated our ability to understand its cellular mechanisms in health and disease. Genome-wide association studies have uncovered genetic variants associated with specific neurological phenotypes and diseases. In addition, single-cell transcriptomics have provided molecular descriptions of specific brain cell types and the changes they undergo during disease. Although these approaches provide a giant leap forward towards understanding how genetic variation can lead to functional changes in the brain, they do not establish molecular mechanisms. To address this need, we developed a 3D co-culture system termed iAssembloids (induced multi-lineage assembloids) that enables the rapid generation of homogenous neuron-glia spheroids. We characterize these iAssembloids with immunohistochemistry and single-cell transcriptomics and combine them with large-scale CRISPRi-based screens. In our first application, we ask how glial and neuronal cells interact to control neuronal death and survival. Our CRISPRi-based screens identified that GSK3β inhibits the protective NRF2-mediated oxidative stress response in the presence of reactive oxygen species elicited by high neuronal activity, which was not previously found in 2D monoculture neuron screens. We also apply the platform to investigate the role of APOE- 4, a risk variant for Alzheimer’s Disease, in its effect on neuronal survival. We find that APOE- 4 expressing astrocytes may promote more neuronal activity as compared to APOE- 3 expressing astrocytes. This platform expands the toolbox for the unbiased identification of mechanisms of cell-cell interactions in brain health and disease.
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Project description:RNAi screens via pooled short hairpin RNAs (shRNAs) have recently become a powerful tool for the identification of essential genes in mammalian cells. We synthesized DNA microarrays with six overlapping 25 nt long tiling probes complementary to each unique 60 nt molecular barcode sequence associated with every shRNA expression construct.. In this part of the study pooled shRNAs were transduced into MDA-MB-231 breast carcinoma cell lines and their inhibitory effects four weeks post transduction were analyzed via microarray hybridization.
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Project description:CRISPR interference (CRISPRi), the targeting of a catalytically dead Cas protein to block transcription, is the leading technique to silence gene expression in bacteria. However, design rules for CRISPRi remain poorly defined, limiting predictable design for gene interrogation, pathway manipulation, and high-throughput screens. Here we develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing guide depletion in multiple genome-wide essentiality screens, with the surprising discovery that gene-specific features such as transcriptional activity substantially impact prediction of guide activity. Accounting for these features as part of algorithm development allowed us to develop a mixed-effect random forest regression model that provides better estimates of guide efficiency than existing methods, as demonstrated in an independent saturating screen. We further applied methods from explainable AI to extract interpretable design rules from the model, such as sequence preferences in the vicinity of the PAM distinct from those previously described for genome engineering applications. Our approach provides a blueprint for the development of predictive models for CRISPR technologies where only indirect measurements of guide activity are available.
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