FIGURE 3 - available via license: Creative Commons Attribution 4.0 International
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| General appearance and size of B. subtilis at normal and elevated temperatures. Shown are phase contrast (top row) and autofluorescence (bottom row); scale bar = 5 µm. (A) 30 • C. (B) 60 • C. (C) 90 • C.
Source publication
We describe a system for high-temperature investigations of bacterial motility using a digital holographic microscope completely submerged in heated water. Temperatures above 90°C could be achieved, with a constant 5°C offset between the sample temperature and the surrounding water bath. Using this system, we observed active motility in Bacillus su...
Contexts in source publication
Context 1
... are composed of two layers of optical quality glass with a middle acrylic layer forming the channels. Homemade chambers using microscope slides, coverslips, and adhesive material such as silicone or Teflon may also be used; for detailed instructions (see Supplementary Figures 3-5). The depth of the chamber is 1.0 mm, which allows for motility far away from the surfaces in order to eliminate surface influences on motility ( Li et al., 2008;Giacche et al., 2010;Li et al., 2011). ...
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... was no significant difference in size or overall morphology of motile cells as the temperature increased (Figures 3, 4A). Non-motile cells showed increased rates of Brownian motion consistent with scaling of diffusion rates with temperature (k B T) and dynamic viscosity of water (η). ...
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... fraction of motile cells (see Table 1 for statistics) increased from ∼20% at 28 • C (Figure 6A and Supplementary Video 2) to 25% at 34 • C (Figure 6B and Supplementary Video 3) to >60% 44 • C (Figure 6C and Supplementary Video 4). However, at 61 and 66 • C, the fraction of motile cells was greatly reduced, to <6% and <1%, respectively. ...
Citations
... We recently presented tracking results for DHM recordings of cultures of Bacillus subtilis swimming at different temperatures, achieved using the LAP tracker (Dubay et al., 2022b). To obtain the tracks, the holograms were reconstructed into Z stacks, then a maximum projection was taken through the Z stack to yield a higher signal-to-noise X,Y time series. ...
Quantitative tracking of rapidly moving micron-scale objects remains an elusive challenge in microscopy due to low signal-to-noise. This paper describes a novel method for tracking micron-sized motile organisms in off-axis Digital Holographic Microscope (DHM) raw holograms and/or reconstructions. We begin by processing the microscopic images with the previously reported Holographic Examination for Life-like Motility (HELM) software, which provides a variety of tracking outputs including motion history images (MHIs). MHIs are stills of videos where the frame-to-frame changes are indicated with color time-coding. This exposes tracks of objects that are difficult to identify in individual frames at a low signal-to-noise ratio. The visible tracks in the MHIs are superior to tracks identified by all tested automated tracking algorithms that start from object identification at the frame level, particularly in low signal-to-noise ratio data, but do not provide quantitative track data. In contrast to other tracking methods, like Kalman filter, where the recording is analyzed frame by frame, MHIs show the whole time span of particle movement at once and eliminate the need to identify objects in individual frames. This feature also enables post-tracking identification of low-SNR objects. We use these tracks, rather than object identification in individual frames, as a basis for quantitative tracking of Bacillus subtilis by first generating MHIs from X, Y, and t stacks (raw holograms or a projection over reconstructed planes), then using a region-tracking algorithm to identify and separate swimming pathways. Subsequently, we identify each object's Z plane of best focus at the corresponding X, Y, and t points, yielding ap full description of the swimming pathways in three spatial dimensions plus time. This approach offers an alternative to object-based tracking for processing large, low signal-to-noise datasets containing highly motile organisms.
Motivation
Many organisms’ survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics.
Results
We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks (GANs) to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen’s ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen’s potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications.
Availability
MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https://github.com/bgmseo/MotGen, for cell tracking and implementation of trained models to generate bacterial motility statistics.
Supplementary information
Supplementary data are available at Bioinformatics online.
