15 research outputs found
Genome Engineering of the 2,3-Butanediol Biosynthetic Pathway for Tight Regulation in Cyanobacteria
Genetic Engineering of Cyanobacteria: Design, Implementation, and Characterization of Recombinant Synechocystis sp. PCC 6803
Photothermal Genetic Engineering
Optical methods for manipulation of cellular function have enabled deconstruction of genetic and neural circuits in vitro and in vivo. Plasmonic gold nanomaterials provide an alternative platform for external optical manipulation of genetic circuits. The tunable absorption of gold nanoparticles in the infrared spectral region and straightforward surface functionalization has led to applications in intracellular delivery and photorelease of short RNAs, recently enabling bidirectional photothermal modulation of specific genes via RNA interference (RNAi). We discuss recent advances in optical gene circuit engineering and plasmonic nanomaterials, as well as future research opportunities and challenges in photothermal gene manipulation
Synthetic Biology Toolbox for Controlling Gene Expression in the Cyanobacterium Synechococcus
Pooled CRISPR screens with imaging on microraft arrays reveals stress granule-regulatory factors
Genetic screens using pooled CRISPR-based approaches are scalable and inexpensive, but restricted to standard readouts, including survival, proliferation and sortable markers. However, many biologically relevant cell states involve cellular and subcellular changes that are only accessible by microscopic visualization, and are currently impossible to screen with pooled methods. Here we combine pooled CRISPR-Cas9 screening with microraft array technology and high-content imaging to screen image-based phenotypes (CRaft-ID; CRISPR-based microRaft followed by guide RNA identification). By isolating microrafts that contain genetic clones harboring individual guide RNAs (gRNA), we identify RNA-binding proteins (RBPs) that influence the formation of stress granules, the punctate protein-RNA assemblies that form during stress. To automate hit identification, we developed a machine-learning model trained on nuclear morphology to remove unhealthy cells or imaging artifacts. In doing so, we identified and validated previously uncharacterized RBPs that modulate stress granule abundance, highlighting the applicability of our approach to facilitate image-based pooled CRISPR screens
