Genetically encoded fluorescent biosensors are powerful tools that allow the tracking of chemical events inside
living cells, in real time. Even with a detailed understanding of biochemistry, enzymology, regulatory signaling,
and genetics, there is no substitute for direct empirical information about the dynamics of chemical processes
and signaling in cells. Unlike most biochemical measurements, the biosensors can provide spatial resolution at
the level of single cells or parts of cells, and temporal resolution of seconds (or better). Nevertheless, there are
major gaps in our ability to follow the details of cell signaling or metabolism using biosensors. For many
interesting biochemical processes, we have no biosensors for the key metabolites. And even when a biosensor
exists, it may not have the right sensitivity and specificity required for observing the desired process, or it may
have sensitivity to pH or other environmental parameters that can mislead the experimenters.
Biosensors are constructed by combining a fluorescent protein (like the jellyfish green fluorescent protein, GFP)
with a binding protein for the chemical of interest. But finding the right way to combine the proteins is challenging,
and even with a well-reasoned design, getting a biosensor with a strong, specific, and robust signal requires a
large amount of optimization. This optimization is done by screening targeted random libraries of sensor variants.
Current methods are typically limited to processing hundreds of variants per day, usually with just a single pair
of measurements to guide selection of a variant for further validation.
In the previous grant period, we developed a high-throughput, high-content screening pipeline that can
screen thousands to tens of thousands of variants in a day, selecting “winners” based on detailed dose-response
and selectivity data. Our approach uses microfluidic encapsulation of both DNA and protein for each variant in
a small, semipermeable bead, followed by automated microscope imaging of thousands of beads under a series
of conditions (varying [analyte], other test compounds, pH, etc.). This screen will permit thorough optimization
of sensors and will allow success in otherwise failed sensor projects.
We propose to use the new screening method to optimize some existing sensors (e.g., glucose and ATP:ADP
ratio) and sensor prototypes (e.g., lactate and malonyl-CoA). We will also optimize a new general strategy for
constructing sensors from dimeric transcription factors (a large family of microbial proteins useful for sensing),
and we will exploit the high throughput of the screen in concert with computational methods to change the binding
site specificity of existing sensors to produce sensors for important metabolic target molecules.
In parallel, we will make improvements in the screening pipeline to expand its reach, with the goals of substan-
tially increasing efficiency and throughput, and of recovering genotype information on a large number of pheno-
typed sensor variants. These advances can dramatically improve the development of novel and improved
biosensors, as well as other tools for the study and manipulation of chemical processes in living cells.