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.
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.
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.
Project description:We have combined a machine-learning approach with other strategies to optimize knockout efficiency with the CRISPR/Cas9 system. In addition, we have developed a multiplexed sgRNA expression strategy that promotes the functional ablation of single genes and allows for combinatorial targeting. These strategies have been combined to design and construct a genome-wide, sequence-verified, arrayed CRISPR library. This resource allows single-target or combinatorial genetic screens to be carried out at scale in a multiplexed or arrayed format. By conducting parallel loss-of-function screens, we compare our approach to existing sgRNA design and expression strategies.
Project description:Glucose, a primary fuel source under homeostatic conditions, is transported into cells by membrane transporters such as glucose transporter 1 (GLUT1). Due to its essential role in maintaining energy homeostasis, dysregulation of GLUT1 expression and function can adversely affect many physiological processes in the body. This has implications in a wide range of disorders such as Alzheimer's disease (AD) and several types of cancers. However, the regulatory pathways that govern GLUT1 expression, which may be altered in these diseases, are poorly characterized. To gain insight into GLUT1 regulation, we performed an arrayed CRISPR knockout screen using Caco-2 cells as a model cell line. Using an automated high content immunostaining approach to quantify GLUT1 expression, we identified more than 300 genes whose removal led to GLUT1 downregulation. Many of these genes were enriched along signaling pathways associated with G-protein coupled receptors, particularly the rhodopsin-like family. Secondary hit validation confirmed that removal of select genes, or modulation of the activity of a corresponding protein, yielded changes in GLUT1 expression. Overall, this work provides a resource and framework for understanding GLUT1 regulation in health and disease.