Abstract
The use of microfluidics in live cell imaging allows the acquisition of dense time-series from individual cells that can be perturbed through computer-controlled changes of growth medium. Systems and synthetic biologists frequently perform gene expression studies that require changes in growth conditions to characterize the stability of switches, the transfer function of a genetic device, or the oscillations of gene networks. It is rarely possible to know a priori at what times the various changes should be made, and the success of the experiment is unknown until all of the image processing is completed well after the completion of the experiment. This results in wasted time and resources, due to the need to repeat the experiment to fine-tune the imaging parameters. To overcome this limitation, we have developed an adaptive imaging platform called GenoSIGHT that processes images as they are recorded, and uses the resulting data to make real-time adjustments to experimental conditions. We have validated this closed-loop control of the experiment using galactose-inducible expression of the yellow fluorescent protein Venus in Saccharomyces cerevisiae. We show that adaptive imaging improves the reproducibility of gene expression data resulting in more accurate estimates of gene network parameters while increasing productivity ten-fold.
Highlights
Quantitative time-lapse microscopy, or imaging cytometry, has become a tool of choice to characterize the dynamics of gene networks in individual cells [1,2,3], because it allows the study of cell-to-cell heterogeneity of the network rather than just the average behavior [4]
Synthetic biologists have turned to imaging cytometry to study engineered genetic clocks [17,18], and improved coupling between various genetic circuits based on overloaded protein degradation machinery [19]
We present GenoSIGHT, the first imaging system relying on a closed-loop control algorithm to adapt the collection of a series of time-lapse images to optimize the measurement of gene expression data in individual cells
Summary
Quantitative time-lapse microscopy, or imaging cytometry, has become a tool of choice to characterize the dynamics of gene networks in individual cells [1,2,3], because it allows the study of cell-to-cell heterogeneity (noise) of the network rather than just the average behavior [4]. Imaging of live yeast cells was instrumental to observe and understand the impact of molecular noise on the timing of cell division [5], the coherence [6,7] and irreversibility [8] of the start transition. It was used to validate a mathematical model of the cell cycle regulatory network [9] and to measure the periodic expression of proteins involved in the control of cell division [10]. Synthetic biologists have turned to imaging cytometry to study engineered genetic clocks [17,18], and improved coupling between various genetic circuits based on overloaded protein degradation machinery [19]
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