Dataset Information

Visualization of synaptic inhibition with an optogenetic sensor developed by cell-free protein engineering automation.

ABSTRACT: We describe an engineered fluorescent optogenetic sensor, SuperClomeleon, that robustly detects inhibitory synaptic activity in single, cultured mouse neurons by reporting intracellular chloride changes produced by exogenous GABA or inhibitory synaptic activity. Using a cell-free protein engineering automation methodology that bypasses gene cloning, we iteratively constructed, produced, and assayed hundreds of mutations in binding-site residues to identify improvements in Clomeleon, a first-generation, suboptimal sensor. Structural analysis revealed that these improvements involve halide contacts and distant side chain rearrangements. The development of optogenetic sensors that respond to neural activity enables cellular tracking of neural activity using optical, rather than electrophysiological, signals. Construction of such sensors using in vitro protein engineering establishes a powerful approach for developing new probes for brain imaging.

SUBMITTER: Grimley JS

PROVIDER: S-EPMC3792465 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Visualization of synaptic inhibition with an optogenetic sensor developed by cell-free protein engineering automation.

Grimley Joshua S JS Li Li L Wang Weina W Wen Lei L Beese Lorena S LS Hellinga Homme W HW Augustine George J GJ

The Journal of neuroscience : the official journal of the Society for Neuroscience 20131001 41

We describe an engineered fluorescent optogenetic sensor, SuperClomeleon, that robustly detects inhibitory synaptic activity in single, cultured mouse neurons by reporting intracellular chloride changes produced by exogenous GABA or inhibitory synaptic activity. Using a cell-free protein engineering automation methodology that bypasses gene cloning, we iteratively constructed, produced, and assayed hundreds of mutations in binding-site residues to identify improvements in Clomeleon, a first-gene ...[more]

PMID: 24107961

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Project description:BackgroundMicroorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste.ResultsIn this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies-an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale.ConclusionsFixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.

Dataset Information

Visualization of synaptic inhibition with an optogenetic sensor developed by cell-free protein engineering automation.

Publications

Visualization of synaptic inhibition with an optogenetic sensor developed by cell-free protein engineering automation.

Similar Datasets

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