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Brief description not available

Brief description not available

Brief description not available

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To develop a novel imaging-based single cell RNA-sequencing (scRNA-Seq) platform that allows capturing of spatiotemporal information and cellular behavior of the sequenced cells within tissue.

A new strategy for barcoding single living cells using lipid-modified oligonucleotides that can vastly enhance sample multiplexing in droplet microfluidics-based RNA sequencing

Echo-CV is a novel, fully-automated system for analyzing images obtained from an echocardiogram that can be deployed on the web.

A robust learning software platform capable of combining both patient physiologic monitor alarms and data in EMR (e.g., laboratory tests) to more precisely monitor patients.

BACKGROUND: Genetic engineering of bacteria has become a prevalent way of producing chemicals, providing a cost-effective, scalable, and environmentally safe manufacturing process. However, maximizing the final product titer remains a challenge, requiring the optimization of the bacteria’s metabolism and the identification of the optimal protein production rates. Currently, the production rates of these proteins are determined through trial and error, by random mutagenesis, or equivalent random selection methods. As such, the development costs are typically very high.   TECHNOLOGY:   UCSF researchers have developed a novel methodology that allows the user to select the production rate of a given protein from very low to very high rates (1 to 100000), thus eliminating the use of trial and error techniques during the optimization process and enabling extremely high protein expression levels. The method is accessible via interactive web-based software.