A 3-D Printed Hardware Platform for Quantitatively Characterizing the Performance Features of Diverse Optogenetic Transcriptional Regulatory Systems in E. coli and S. Cerevisae

There has recently been rapid development of optogenetic tools for optically regulating gene expression in microbes. These tools exhibit diverse spectral responses, light sensitivities, photoconversion and dark reversion rates, and chromophore requirements, which significantly impact their suitability for different experiments. Literature data on these performance characteristics is limited as different studies have performed varied measurements, resulting in only partial assessments. Furthermore, data are of variable quality and are reported in different arbitrary units. Altogether, this makes direct comparisons of the performance of these tools very difficult.

Here, we address this problem by developing a framework to quantitatively assess these and other performance metrics using a custom hardware device termed the LED Plate Apparatus (LPA). The LPA is a 3D-printed device incorporating a programmable array of through-hole LEDs which holds a 24-well microplate, providing an isolated light environment for each well. The two LEDs per well are reconfigurable, enabling them to be easily swapped for LEDs of any desired wavelength. The LPA can be programmed with complex light sequences using the Light Program Interface (LPI), a custom webtool that produces files which are loaded onto the LPA using a SD card. We are using the LPA and LPI to perform characterization experiments on many previously published but incompletely-characterized light-inducible systems, such as pDusk and BphS/MrkH in E. coli and PhyB/PIF6 and Cry/CIB in S. cerevisae. Our data include the action spectra (both activating and deactivating), steady-state light dose response, and step-function response dynamics. Using these characterization datasets, we are producing datasheets on the performance metrics of the current generation of E. coli and S. cerevisae optogenetic tools.

Furthermore, we are using these data to produce predictive mathematical models of the light-to-gene-expression signaling of each system that will enable a) easy selection of appropriate fluorescent protein reporters to combine each tool with and b) the design of multi-LED light input signals which can generate desired gene expression signals in vivo, even when programming multiple systems simultaneously. Our approach should be readily compatible with optogenetic proteolysis systems and mammalian cells. This work dramatically lowers the “barrier to entry” for scientists and engineers wishing to perform optogenetic experiments in their lab by opening up access to an easy-to-use standardized suite of hardware, software, and wetware tools.