The Virginia Bioinformatics Institute at Virginia Tech is awarded a grant to develop Cyto.IQ, an adaptive imaging system specifically designed to characterize the noisy dynamics of gene expression and other molecular interactions in individual live cells. Cyto.IQ analyzes microscopic images on the fly to produce statistical plots and other quantitative indicators capturing important parameters of the cell physiology. The instrument has the capability to optimize the frequency of image acquisition and the total number of images taken using machine learning algorithms. It finds an optimal tradeoff between the cost of an experiment and the information it generates using a priori knowledge of the expected gene expression dynamics and previously acquired data. The control software is able to determine what cells to observe and when to observe them to ensure a fast convergence of statistical estimators while minimizing adverse effects of light exposure, and the overall duration of the experiment. Cyto.IQ is specifically designed to meet the needs of systems biologists, bioengineers, or biophysicists who are developing quantitative models of gene networks. Due to the noise affecting gene expression mechanisms, this rapidly growing community of users needs an instrument to observe the state of many individual cells over time. Current methods used to extract this type of data out of time-series of images collected using standard imaging platforms are inherently inefficient. They represent a major obstacle to the refinement of our understanding of the dynamics of cellular processes. Cyto.IQ increases the productivity of scientists working in this field by reducing the time it takes to perform an experiment and the number of experiments needed to collect suitable data sets. The adaptive control software is open source and available from www.cytoiq.org.
We have developed an automated imaging cytometry platform, GenoSIGHT, to collect time course data on individual cells. The hardware includes an incubation chamber and computer-controllable microfluidics system to enable control of as many environmental variables as possible. The software collects and analyzes phase contrast and fluorescence images to automatically identify suitable fields-of-view, monitor cell count, and track the changes in fluorescence. The images and resulting information are processed in real-time to allow for automatic control of an experiment via feedback through the microfluidics device (Fig 1). These features provide important advantages over traditional instruments used in synthetic biology: (1) decreased labor required for experiments, (2) state-of-the-art control of environmental variables via microfluidics, and (3) collection of information-rich datasets. Automatic real-time processing of images is key to the reduction in experimental labor costs enabled by GenoSIGHT. Experiments that might have taken a week of off-and-on attention to prepare, run, and process can now be condensed to an hour of setup and a few hours of unmonitored execution. Beyond handling the experimental and processing steps simultaneously, real-time processing allows multi-step experiments to be performed without user intervention via microfluidics. For example, GenoSIGHT can add an inducer molecule or change growth media in response to observed changes in fluorescence, growth rate, or cell count. These functions are fully customizable. Further, the system can detect problems in an experiment and alert the experimenter by email or text message, potentially averting wasted hours on a failed experiment. The integration of microfluidics also addresses some of the growing concerns about context dependencies of gene circuits by controlling for environmental variables where possible. In particular, the ability to maintain constant nutrient availability helps to control for growth phase and nutrient-dependent metabolic state.
