A microfluidic setup for quantifying single-cell transcription regulatory dynamics

Bacteria are exposed to fluctuations in their environment and can respond to such changes by regulating gene expression, often at the level of transcription. Since gene expression is an inherently stochastic process, identical cells within a single environment display heterogeneous expression levels. To understand how the stochastic processes in gene expression affect the dynamics of single-cell gene regulation it is necessary to observe gene expression in single cells in changing environments. Recently developed microfluidic devices combined with quantitative fluorescence time-lapse microscopy allow lineages of single cells to be followed over long timescales and to measure their growth and gene expression phenotypes simultaneously. However these devices are missing the environmental control needed to study gene regulation. Therefore we set out to find a way to combine the longterm observation of single cells with precise environmental control in a single microfluidic chip. As a basis we chose a device called the Mother Machine in which single files of cells are growing in small dead end growth channels. These growth channels are connected to a main channel with a constant flow of medium for nutrient diffusion into the growth channels. The cells at the dead end of the growth channels are trapped and when dividing push their progeny into the main channel where they are removed by the flow. Therefore the trapped cell can be monitored essentially for its whole lifetime, while its progeny can only be observed for a short timeframe before they leave the growth channel. By combining the Mother Machine design with a specialized dual input junction and mixing serpentines for environmental control we developed a device that offers new prospects in studying gene regulation. Together with the device we developed an easy to use software solution to analyze data from Mother Machine like devices together with our collaboration partners. This integrated experimental and computational setup will be an important tool to understand the genetic basis for differences in single-cell expression distributions, and to understand how natural selection has shaped single-cell gene regulation. As a first example we show how single cells differ in the regulation of the expression of the lac operon when exposed to alternating changes in the available carbon source switching between glucose and lactose every 4h.