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Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data., Author summary Time-lapse microscopy can generate large image datasets which track single-cell properties like gene expression or growth rate over time. Deep learning tools are very useful for analyzing these data and can identify the location of cells and track their position. In this work, we introduce a new version of our Deep Learning for Time-lapse Analysis (DeLTA) software, which includes the ability to robustly segment and track bacteria that are growing in two dimensions, such as on agarose pads or within microfluidic environments. This capability is essential for experiments where spatial and positional effects are important, such as conditions with microbial co-cultures, cell-to-cell interactions, or spatial patterning. The software also tracks pole age and can be used to analyze replicative aging. These new features join other improvements, such as the ability to work directly with many common microscopy file formats. DeLTA 2.0 can reliably track hundreds of cells with low error rates, making it an ideal tool for high throughput analysis of microscopy data.

We introduce a tractable stochastic spatial Moran model to explain experimentally-observed patterns of rod-shaped bacteria growing in rectangular microfluidic traps. Our model shows that spatial patterns can arise as a result of a tug-of-war between boundary effects and modulations of growth rate due to cell-cell interactions. Cells alignparallelto the long side of the trap when boundary effects dominate. However, when the magnitude of cell-cell interactions exceeds a critical value, cells align orthogonally to the trap’s long side. Our model is analytically tractable, and completely solvable under a mean-field approximation. This allows us to elucidate the mechanisms that govern the formation of population-level patterns. The model can be easily extended to examine various types of interactions that can shape the collective behavior in bacterial populations.