 Tracking bacteria at high density with FAST, the Feature-Assisted Segmenter/TrackerOliver J Meacock and William M Durham PLOS Computational Biology, 2023, vol. 19, issue 10, 1-26 Abstract: Most bacteria live attached to surfaces in densely-packed communities. While new experimental and imaging techniques are beginning to provide a window on the complex processes that play out in these communities, resolving the behaviour of individual cells through time and space remains a major challenge. Although a number of different software solutions have been developed to track microorganisms, these typically require users either to tune a large number of parameters or to groundtruth a large volume of imaging data to train a deep learning model—both manual processes which can be very time consuming for novel experiments. To overcome these limitations, we have developed FAST, the Feature-Assisted Segmenter/Tracker, which uses unsupervised machine learning to optimise tracking while maintaining ease of use. Our approach, rooted in information theory, largely eliminates the need for users to iteratively adjust parameters manually and make qualitative assessments of the resulting cell trajectories. Instead, FAST measures multiple distinguishing ‘features’ for each cell and then autonomously quantifies the amount of unique information each feature provides. We then use these measurements to determine how data from different features should be combined to minimize tracking errors. Comparing our algorithm with a naïve approach that uses cell position alone revealed that FAST produced 4 to 10 fold fewer tracking errors. The modular design of FAST combines our novel tracking method with tools for segmentation, extensive data visualisation, lineage assignment, and manual track correction. It is also highly extensible, allowing users to extract custom information from images and seamlessly integrate it into downstream analyses. FAST therefore enables high-throughput, data-rich analyses with minimal user input. It has been released for use either in Matlab or as a compiled stand-alone application, and is available at https://bit.ly/3vovDHn, along with extensive tutorials and detailed documentation.Author summary: Much of what we know about bacterial behaviour comes from tracking solitary cells through space and time. For example, one can unpick the mechanisms that drive chemotaxis by quantifying the movement of individual microbes as they respond to a nutrient source. However, in infections, industrial processes and the environment, bacteria usually live in tightly-packed communities where they display unique behaviours not observed in solitary cells, such as the activation of contact-dependent weapons to kill their neighbours. Tracking individuals in these dense assemblages is technically challenging because it is difficult to follow cells using only their position in each frame. Here we present a new software tool called FAST which combines machine learning and information theory to optimise cell tracking. Our approach uses a range of different cell characteristics such as shape and fluorescent intensity to better distinguish individuals over time, reducing errors up to 10-fold compared to traditional approaches. In addition, FAST estimates how tracking accuracy changes over time, alerting users to potential problems such as out of focus frames. By tracking cells in a diverse range of conditions with minimal user input, FAST provides a new quantitative platform to study how bacteria have adapted to live in groups. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1011524 DOI: 10.1371/journal.pcbi.1011524 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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