Much is still not understood about how gene regulatory interactions control cell fate decisions in single cells, in part due to the difficulty of directly observing gene regulatory processes in vivo. We introduce here a novel integrated setup consisting of a microfluidic chip and accompanying analysis software that enable long-term quantitative tracking of growth and gene expression in single cells. The dual-input Mother Machine (DIMM) chip enables controlled and continuous variation of external conditions, allowing direct observation of gene regulatory responses to changing conditions in single cells. The Mother Machine Analyzer (MoMA) software achieves unprecedented accuracy in segmenting and tracking cells, and streamlines high-throughput curation with a novel leveraged editing procedure. We demonstrate the power of the method by uncovering several novel features of an iconic gene regulatory program: the induction of Escherichia coli ’s lac operon in response to a switch from glucose to lactose.

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.

We introduce a graphical model for the joint segmentation and tracking of E. coli cells from time lapse videos. In our setup cells are grown in narrow columns (growth channels) in a so-called “Mother Machine” [1]. In these growth channels, cells are vertically aligned, grow and divide over time, and eventually leave the channel at the top. The model is built on a large set of cell segmentation hypotheses for each video frame that we extract from data using a novel parametric max-flow variation. Possible tracking assignments between segments across time, including cell identity mapping, cell division, and cell exit events are enumerated. Each such assignment is represented as a binary decision variable with unary costs based on image and object features of the involved segments. We find a cost-minimal and consistent solution by solving an integer linear program. We introduce a new and important type of constraint that ensures that cells exit the Mother Machine in the correct order. Our method finds a globally optimal tracking solution with an accuracy of > 95% (1.22 times the inter-observer error) and is on average 2 − 11 times faster than the microscope produces the raw data.