PT  - JOURNAL ARTICLE
AU  - Matthias Kaiser
AU  - Florian Jug
AU  - Olin Silander
AU  - Siddharth Deshpande
AU  - Thomas Pfohl
AU  - Thomas Julou
AU  - Gene Myers
AU  - Erik van Nimwegen
TI  - Tracking single-cell gene regulation in dynamically controlled environments using an integrated microfluidic and computational setup
AID  - 10.1101/076224
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 076224
4099  - http://biorxiv.org/content/early/2016/09/20/076224.short
4100  - http://biorxiv.org/content/early/2016/09/20/076224.full
AB  - Bacteria adapt to changes in their environment by regulating gene expression, often at the level of transcription. However, since the molecular processes underlying gene regulation are subject to thermodynamic and other stochastic fluctuations, gene expression is inherently noisy, and identical cells in a homogeneous environment can display highly heterogeneous expression levels. To study how stochasticity affects gene regulation at the single-cell level, it is crucial to be able to directly follow gene expression dynamics in single cells under changing environmental conditions. Recently developed microfluidic devices, used in combination with quantitative fluorescence time-lapse microscopy, represent a highly promising experimental approach, allowing tracking of lineages of single cells over long time-scales while simultaneously measuring their growth and gene expression. However, current devices do not allow controlled dynamical changes to the environmental conditions which are needed to study gene regulation. In addition, automated analysis of the imaging data from such devices is still highly challenging and no standard software is currently available. To address these challenges, we here present an integrated experimental and computational setup featuring, on the one hand, a new dual-input microfluidic chip which allows mixing and switching between two growth media and, on the other hand, a novel image analysis software which jointly optimizes segmentation and tracking of the cells and allows interactive user-guided fine-tuning of its results. To demonstrate the power of our approach, we study the lac operon regulation in E. coli cells grown in an environment that switches between glucose and lactose, and quantify stochastic lag times and memory at the single cell level.