Abstract
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.
Highlights
The automation of hardware and software for microscopy has resulted in researchers’ ability to generate massive datasets containing images of cells over time
Time-lapse microscopy can generate large image datasets which track single-cell properties like gene expression or growth rate over time
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
Summary
The automation of hardware and software for microscopy has resulted in researchers’ ability to generate massive datasets containing images of cells over time. Recent studies have used closed-loop microscopy and optogenetic platforms to control gene expression in single cells in real time [2,3,4] These improvements in microscopy have motivated the need for automated image analysis, as traditional approaches that require manual error correction cannot keep pace with the size of these new datasets or the rate at which they can be acquired. Segmentation and tracking have historically required intensive user input as well as custom image processing code or experimental modifications such as the use of dedicated fluorophores [1,5,6,7,8] These requirements limit throughput and can introduce burdensome experimental constraints
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