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METHODS
published: 21 April 2022
doi: 10.3389/fmicb.2022.836808
Edited by:
Iain G. Duggin,
University of Technology Sydney,
Australia
Reviewed by:
Alex Bisson,
Brandeis University, United States
Georgia Squyres,
California Institute of Technology,
United States
*Correspondence:
Jay L. Nadeau
nadeau@pdx.edu
Specialty section:
This article was submitted to
Extreme Microbiology,
a section of the journal
Frontiers in Microbiology
Received: 16 December 2021
Accepted: 10 March 2022
Published: 21 April 2022
Citation:
Dubay MM, Johnston N,
Wronkiewicz M, Lee J,
Lindensmith CA and Nadeau JL
(2022) Quantification of Motility in
Bacillus subtilis at Temperatures Up to
84◦C Using a Submersible Volumetric
Microscope and Automated Tracking.
Front. Microbiol. 13:836808.
doi: 10.3389/fmicb.2022.836808
Quantification of Motility in Bacillus
subtilis at Temperatures Up to 84◦C
Using a Submersible Volumetric
Microscope and Automated Tracking
Megan M. Dubay1, Nikki Johnston1, Mark Wronkiewicz2, Jake Lee2,
Christian A. Lindensmith2and Jay L. Nadeau1*
1Department of Physics, Portland State University, Portland, OR, United States, 2Jet Propulsion Laboratory, California
Institute of Technology, Pasadena, CA, United States
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 subtilis up to 66◦C. As temperatures rose, most cells became
immobilized on the surface, but a fraction of cells remained highly motile at distances
of >100 µm above the surface. Suspended non-motile cells showed Brownian motion
that scaled consistently with temperature and viscosity. A novel open-source automated
tracking package was used to obtain 2D tracks of motile cells and quantify motility
parameters, showing that swimming speed increased with temperature until ∼40◦C,
then plateaued. These findings are consistent with the observed heterogeneity of
B. subtilis populations, and represent the highest reported temperature for swimming
in this species. This technique is a simple, low-cost method for quantifying motility at
high temperatures and could be useful for investigation of many different cell types,
including thermophilic archaea.
Keywords: Bacillus subtilis, bacterial motility, temperature effects, heat shock, holographic microscopy,
thermophile, tracking
INTRODUCTION
The effects of elevated temperatures on bacterial motility have not been fully explored. There
are both physical and physiological effects of temperature on flagellar swimming at low Reynolds
number. Both viscosity and temperature contribute to the Stokes-Einstein equation for the diffusion
coefficient, D
D=kBT
6πηr(1)
where kBis Boltzmann’s constant, Tis the absolute temperature, ηis the dynamic viscosity of the
medium, and ris the radius of the diffusing particle. Water shows a dramatic decrease in dynamic
viscosity with temperature, described by the equation
η(T)=AeB/(T−C),(2)
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where A = 2.414 ×10−5Pa•s, B = 247.8 K, and C = 140 K. This
results in values ranging from 1.002 mPa s at 20◦C to 0.315 mPa
s at 90◦C. As a result, there is a significant change in the rate of
Brownian motion of cells at elevated temperatures: as a simple
example, 0.30 µm2/s at 33◦C for a 1 µm diameter cell, and 0.86
µm2/s for the same cell at 91◦C.
The effects of viscosity on active swimming are less clear. The
drag force is proportional to ηand thus it would be expected
that swimming speeds would increase with decreased viscosity,
but instead a surprising decrease in bacterial swimming speeds
with decreased viscosity is seen in polymer solutions. Several
papers have suggested that this is due to microstructure of the
polymer (Magariyama and Kudo, 2002;Zottl and Yeomans,
2019). In ordinary aqueous solution, a roughly linear increase
in swimming speed with temperatures up to 50◦C has been
reported for Escherichia coli (Maeda et al., 1976) and for multiple
other strains representing polar, bipolar, and peritrichous
flagellar arrangements (Schneider and Doetsch, 1977). One study
reported a linear increase in E. coli swimming speed with
temperature up to 40◦C in medium supplemented with L-serine;
in the absence of supplementation, speeds increased only up to
30◦C and decreased thereafter (Demir and Salman, 2012). This
linear relationship is related to increased flagellar rotation rates
at high temperatures as well as altered viscosities both inside and
outside the cell and has been modeled semi-empirically using
a large number of available motility datasets. Speed is generally
assumed to be directly proportional to flagellar rotation rate;
though this is not true in all datasets, a positive correlation is
always present (Humphries, 2013).
As a simple model, the force on a swimming cell may be
approximated as the sum of the flagellar force Fand the velocity-
dependent drag force Dv,
F=ma =F−Dv,(3)
where D is the drag coefficient given in Eq. (1). Although models
predict a viscosity dependence, recent studies have found that F is
independent of viscosity (Armstrong et al., 2020). This equation
of motion yields a terminal velocity of
v∞=F
D∝F
η
,(4)
where the terminal velocity should be observed during a long run
once the cell is no longer accelerating. The time-averaged velocity
over a long run should thus approximate v∞.
None of these models take into account the upper limits
of possible motility of different strains resulting from protein
denaturation or general organism stress. Microorganisms
show complex heat shock responses, and the expression and
maintenance of flagella can be affected by genes related to heat
stress. Motility is a complex phenotype under tight regulation
in all microorganisms that express it. Bacillus subtilis is a model
organism for which regulation of motility genes (Mukherjee and
Kearns, 2014) and heat shock responses (Schumann, 2003) have
been well studied. There is a strict dependence upon FlgN for
motility in this species (Cairns et al., 2014). Flagellar synthesis
is affected by a number of the genes involved in the heat shock
response. As temperature increases, proteins denature, and
protein degradation systems clear the damaged proteins. The
ClpCP complex in Bacillus subtilis degrades stress-damaged
proteins as well as taking part in regulatory degradation. It also
influences motility by both direct and indirect mechanisms. The
absence of Clp proteases results in defective motility, likely due to
accumulation of Spx, which suppresses flagellar gene expression.
Cells under stress do not necessarily lose motility immediately
via this mechanism, since existing flagella are not affected, rather
the production of new flagella (Moliere et al., 2016).
The heat shock responses of B. subtilis allow it to grow
to at least 53◦C (Warth, 1978). Much has been written about
the mechanisms of thermotolerance in this species. The sigma
factor σBprovides non-specific stress resistance, which includes
thermotolerance. Its activation induces the expression of ∼200
target genes (Hecker et al., 1996;Nannapaneni et al., 2012;
Young et al., 2013). Cells do not need to be exposed to heat
stress in order to gain thermotolerance via this mechanism. In
addition, there are specific heat-shock responses that turn on
under conditions of mild heat stress, leading to protection from
otherwise lethal temperatures. At least three classes of genes are
heat-shock inducible.
Because of the complexity of both the heat shock response and
regulation of motility genes, it is difficult to predict the effects
of high temperature on swimming motility. Most studies focus
on growth rather than persistence of swimming. The persistence
of the motility phenotype under conditions of heat stress has
been little explored, largely due to technical difficulties of imaging
and image analysis as temperatures rise. One study reported that
convection currents made tracking difficult above 40◦C (Riekeles
et al., 2021). In addition, most heated stages can only achieve
temperatures of 55–60◦C. We hypothesized here that some
fraction of B. subtilis cells would be capable of active motility
at temperatures above the maximum growth temperature, with
significant variations consistent with the highly heterogeneous
responses of this species to stress (Kearns and Losick, 2005;Lopez
et al., 2009;Syvertsson et al., 2021).
A few studies have reported microscopic imaging systems
able to reach temperatures up to the boiling point of water,
but none fit our precise goals. A 1995 study of Thermotoga
maritima used a capillary between two Peltier elements (Gluch
et al., 1995). A more recent study reported an environmental
chamber for microbial imaging that allows for pressure and
temperature control (Nishiyama and Arai, 2017). For studying
hyperthermophilic archaea, a “Sulfoscope” with a heated cap and
stage was recently described where temperatures of 75◦C were
stably maintained; temperatures up to 90◦C were achieved but
led to significant evaporation (Pulschen et al., 2020). This design
is appropriate for fluorescence microscopy and is especially
valuable for imaging immobilized cells, a technique which was
described in detail in the study using a semi-soft Gelrite pad.
Air objectives were used. Another recent study attained higher
resolution using oil-immersion objectives heated to 65◦C, with
the entire chamber heated to 75◦C. Imaging was by differential
interference contrast (DIC) (Charles-Orszag et al., 2021).
While high resolution is sometimes desirable, our goal
was to create a simple, inexpensive system for imaging at
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high temperatures with a particular focus on tracking of
microorganisms in a large volume of view. Thus, the goal was
to maximize the depth of field with sufficient resolution to
distinguish individual cells, but not subcellular structure. We
use a custom holographic microscope entirely submerged in a
heated water bath in order to examine motility at temperatures
between 28 and 85◦C, with capability of temperatures up to at
least 95◦C. The materials for the heated bath are inexpensive
(<$200 USD in 2022) and a complete parts list is included for
both the microscope and bath, with a total cost of <$7000 USD
for the complete system. Because the system was designed for
field use and no compound objective lenses are used, there are
minimal effects of temperature on the instrument, even up to
the boiling point.
With this system, cultures of B. subtilis were either heated
slowly over a period of 4 h or exposed rapidly by submersion into
a pre-heated bath. Individual cells were tracked, and velocities,
accelerations, turn frequency, and correlation functions were all
quantified using a custom open-source autonomous program.
The program successfully controlled for thermal currents,
classifying tracks into “motile” vs. “non-motile” based upon a
defined algorithm. All active tracks were manually confirmed
to correspond to cells. Active swimming was observed up to
66◦C in some fraction of the cells; increasing numbers of
immobile cells collected on the sample chamber surface as
temperatures increased. Cells up to 90◦C remained normal in
morphology, without signs of sporulation. Dead cells showed
little movement until temperatures >60◦C, at which point
convection currents became appreciable; however, these were
readily distinguished from active motility by inspection as well
as automated tracking tools. These techniques will be of interest
to anyone exploring bacterial behavior at temperatures up to the
boiling point of water.
MATERIALS AND METHODS
Microscope and Temperature Control
The microscope used in this study was a common-path
off-axis digital holographic microscope (DHM) as described
previously (Wallace et al., 2015) (U.S. Patent US20160131882A1)
(Figure 1A). Briefly, a single-mode laser (520 nm, Thorlabs,
Newton, NJ) was collimated and passed through separate
reference and sample channels held in a single plane to prevent
mis-alignment. The objective lenses were simple achromats
(numerical aperture 0.31, part number 47–689-INK, Edmund
Optics, Barrington, NJ), yielding an effective magnification of
∼20x and XY spatial resolution of ∼1.0 µm. Since focusing is
performed numerically with DHM, the sample stage was fixed in
position with the in-focus plane Z= 0 at approximately the center
of the sample chamber. The camera was a Prosilica 2460GT
(Allied Vision, purchased from Edmund Optics) monochrome
camera with a 5 MPixel, 3.45 µm pixel pitch format and 15
frames/s maximum frame rate, with acquisition windowed to
2,048 ×2,048 px. A higher frame rate (23.7 fps) can be achieved
by substituting the newer Prosilica 2560GT without further
modification of the system. Both recommended cameras use
global shutter detector readout; rolling shutter readout cameras
are not recommended unless careful consideration is taken in
avoiding distortion of fringes during readout.
To control the temperature of the sample chamber, the
entire microscope was placed into a stainless steel cooking
pot (36 quart, Bayou Classic, sold through amazon.com) and
protected by a 5-gallon, 4 mil thickness plastic bag (Ziploc
brand or autoclave bag). The laser was kept out of the bath
and coupled to the microscope through its single-mode fiber
output. The camera was kept above the water level of the bath
by the microscope tube. The pot was filled with water to a
level above the top of the sample chamber, and the temperature
of the water was gradually increased at a constant rate of
4.3◦C/min using a sous vide circulator (Monoprice Model#:
121594, 800 W, 4 gallon capacity) (Figures 1B,C). The water
temperature indicated on the circulator was confirmed using
a laboratory thermometer (Fisherbrand). In order to raise the
temperature above ∼80◦C, it was necessary to insulate the pot
with metallized bubble wrap (Figure 1C). A thermocouple (Gain
Express K-Type, sold through amazon.com) was taped both
above and below the chamber, in direct contact with the chamber,
in an independent set of experiments to determine how the
sample temperature corresponded to the water temperature; a
nearly constant offset of 5◦C was seen between the chamber
and the water temperature, and this correction was applied to
all reported chamber temperatures (Figure 1D). Supplementary
Figures 1,2show the steps involved in setup, and Supplementary
Datasheet 1 provides a parts list for the entire instrument with
purchasing links. The costliest elements are the laser and camera.
Preliminary thermal testing of elements was performed by
submerging individual parts of the setup into water in Ziploc
bags and heating the water using the sous vide controller to its
maximum temperature (just below boiling). In the case of the
objective lens holder, because of its small size and low cost, it
was placed into boiling water on the stovetop both with and
without the lenses installed. The upper temperature limit of the
microscope is set by the plastic used in the 3D-printed lens holder
and the bonding of the sample chamber; substitutions of these
components with higher temperature materials would enable
higher temperature investigations. None of the components was
tested above 100◦C in this study.
Samples and Chambers
Bacillus subtilis type strain (ATCC 6051) was obtained from the
American Type Culture Collection, Manassas, VA. Stocks were
maintained at −80◦C and periodically streaked onto lysogeny
broth (LB, Thermo Fisher Scientific, Pittsburgh, PA)-agar plates.
16–24 h before each experiment, single colonies were picked
from plates, seeded into liquid LB, and incubated at 30◦C
with shaking to mid-log phase (OD ∼0.6 as measured on a
Clariostar plate reader). After incubation, samples were diluted
1:1,000–1:100 into motility medium (10 mM phosphate buffer
pH 7.4, 10 mM NaCl, 0.1 mM EDTA) and moved to custom
sample chambers at 20 ±2◦C. To control for any possible
non-biological motion at high temperatures, an additional set
of experiments was performed using heat-killed B. subtilis cells
at the same concentration. The cells were exposed to boiling
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FIGURE 1 | Temperature-controlled microscope setup. (A) Schematic of off-axis DHM used in these experiments. (B) The microscope was protected by a
heavy-duty plastic bag and submerged in heated water to control the sample temperature. (C) Photograph of the complete setup showing the insulating materials
necessary to attain the highest temperatures. The microscope is submerged into the pot with the sample chamber located several cm below the water surface. The
insulation shown is required for achieving water and sample temperatures above ∼70◦C. (D) Correspondence between the sous vide measured water temperature
(verified with a laboratory thermometer) and thermocouple measurements of the sample chamber at top and bottom, along with the average between top and
bottom. The slope of the temperature change was consistent between the bath and the chamber, with a nearly constant offset of 5◦C. The lines are linear fits with
slopes indicated.
water for 30 min, then pelleted and washed twice with motility
medium before imaging.
The sample chambers (product of Aline, Rancho Dominguez,
CA) are composed of two channels, one for the sample and one
as a reference channel which is filled with 0.9% saline solution
or sterile growth medium (Figure 2). These 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
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FIGURE 2 | Sample chamber arrangement. (A) Schematic of the 1 mm deep chamber, showing the sample (S) and reference (R) channels that permit the off-axis
DHM geometry. The volume of view of each snapshot is 365 µm×365 µm×1 mm in x, y, and z. (B) The chambers were loaded using a 1 mL syringe and placed
onto the microscope stage at a fixed focus before immersion.
surface influences on motility (Li et al., 2008;Giacche et al., 2010;
Li et al., 2011). The sample was placed into the microscope
before immersion and left throughout the duration of the heating.
Replicate experiments (2–4) were performed on different days
with independent B. subtilis cultures in order to confirm the
reproducibility of the results. Cell density in the chambers varied
from ∼105to 106cells/mL.
Acquisition and Reconstruction of
Holograms
Data were acquired using a custom, open-source software
package, DHMx.1Recordings were between 30 and 60 s long,
with a maximum frame rate of 15 frames per second (fps).
Recordings were obtained every ∼5◦C until reaching a maximum
sample temperature of 84◦C. Additional tests were performed
which subjected the bacteria to rapid increases in temperature.
Samples were loaded into chambers at 20◦C and submerged
into pre-heated water at varying temperatures. For these “heat
shock” experiments, recordings were obtained immediately after
submersion as well as 2 and 10 min afterward. The 10 min
datasets were chosen for analysis.
The holograms for each recording were either median
subtracted (as we described previously Bedrossian et al., 2020)
or frame-to-frame subtracted then reconstructed in amplitude
using Fiji (ImageJ) (RRID:SCR_002285) (Schindelin et al., 2012).
Reconstructions were performed using the angular spectrum
method (Mann et al., 2005) implemented in a custom plug-in
described in detail elsewhere (Cohoe et al., 2019) and available
from our update site.2The choice of frame-to-frame subtraction
was necessary at temperatures >50◦C in order to eliminate
noise due to non-stationary cells on the chamber surface. The
z thickness chosen for reconstruction ranged from 400 to 800
µm and varied somewhat among datasets depending upon the
location of active cells. The resulting stacks were maximum
projected in Z using Fiji to create a 2D time series of all of the
cells of interest.
1https://github.com/dhm-org/dhm_suite
2https://github.com/sudgy/
High Resolution Light and Fluorescence
Microscopy
In order to carefully evaluate cell morphology and possible
presence of spores, cells were examined on an Olympus IX-
71 inverted microscope with a 100x, NA = 1.4 oil immersion
objective using either brightfield illumination or fluorescence
illumination with a Hg lamp and a 450/50 nm excitation filter
and 520 nm longpass (Chroma Technology, Bellows Falls, VT).
Fraction Motile
Non-motile cells were difficult to count from images, especially
at higher temperatures as cells began to cluster, so the fraction
of motile cells was estimated as the number of motile cells per
volume of view averaged over the length of the recording, divided
by the total cell concentration (as measured by original OD
divided by dilution factor; the relationship between OD and
cell concentration was established using a hemocytometer). The
instantaneous volume of view is 0.365 ×0.365 ×1 mm3, or
0.13 µL, corresponding to ∼100 cells/frame at 106cells/mL. The
number of motile cells per frame was calculated using frame-
to-frame subtracted projections so that non-motile cells did not
interfere with the analysis. Fiji Analyze Particles3was used to
count the number of cells per frame.
Tracking and Statistics
Bacteria were tracked using a custom software package,
Holographic Examination for Life-like Motility (HELM),4which
was developed to autonomously detect, track, and characterize
motile cells. HELM identifies pixel changes in sequential DHM
images, tracks clusters of change as particle movement, and
classifies particles as motile or non-motile based on their
movement patterns. The pixel changes are computed by simple
background subtraction of the median video image. Clusters
of pixel changes are identified using DBSCAN. Tracks are
then generated using the Linear Assignment Problem (LAP)
tracker method (Jaqaman et al., 2008). With the spatiotemporal
3https://imagej.net/imaging/particle-analysis
4https://github.com/JPLMLIA/OWLS-Autonomy
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points, HELM computes approximately two dozen metrics that
form a feature vector for each track. These include features
like mean speed, mean turn angle, total track displacement,
etc. A random forest classifier (Breiman, 2001) is then used
to classify each track as motile or non-motile. The classifier
was trained using manually labeled tracks from both prepared
laboratory and field-acquired ocean water samples, both with
and without fluid flow in the sample chamber. HELM can be
used on raw, unreconstructed holograms or on 2D projections of
reconstructed holograms. Analysis presented here was performed
on projections of reconstructed holograms as described in section
“Motility Analysis.”
HELM also generates multiple contextual products to support
assessment of a DHM recording. One of these, the Motion
History Image (MHI), summarizes a full video in one image by
color mapping each pixel to the time index of largest intensity
change. The MHI image allows a rapid understanding of how
many particles were present in the recording as well as the
presence and characteristics of potential motile organisms.
Diffusion coefficients were measured using NanoTrackJ
(Wagner et al., 2014). A video of at least 30 s containing at least 10
trackable particles was analyzed using the Maxima and Gaussian
Fit center estimator and the covariance diffusion coefficient
estimator. Parameters used were: minimum estimated particle
size, 20 pixels; minimum number of steps per track, 5; pixel size,
178 nm; and frame rate 15 frames/s.
Statistical analysis of HELM outputs and graphing were
performed using Prism 9 (GraphPad Software, San Diego,
CA). Statistical relevance was estimated using the ANOVA
package in Prism after ensuring that distributions were Gaussian.
The correlation matrix was computed using the Multivariable
Analysis package in Prism.
RESULTS
Microscope Function and Maintenance
at High Temperatures
We tested all elements of the system for their ability to withstand
temperatures up to 100◦C. The DHM optics are robust, as
they contain no compound objectives, adhesives, or other heat-
sensitive elements. The 3D printed lens holder and objectives
withstood boiling up to 30 min. The most sensitive element
of the setup was the fiber optic cable. Under preliminary tests,
the cable housing degraded, allowing light to escape, when the
cable was heated to 90◦C. This could be prevented by shielding
the cable from the heat using an aluminum mini-box as shown
in Supplementary Figure 1. On occasion, condensation would
form on the sample chamber, objective lens, or collimating lens.
This could be removed by wiping with lens paper. After multiple
rounds of experiments or if image quality appeared poor, all of
the lenses were removed and cleaned with 100% isopropanol. It
was also important to keep the camera outside of the hot area to
prevent condensation on the window.
Morphology and Brownian Motion
There 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 (kBT)
FIGURE 3 | 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.
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FIGURE 4 | Cell sizes and diffusion coefficients with temperature. (A) Cell area as estimated by microscopy, shown as mean ±standard deviation. The differences in
the means from one temperature to the next was not significant. (B) Measured diffusion coefficients at 3 temperatures (black dots), with measured standard
deviations, compared with predicted values according to Eq. (1) for particle radii of 2.0, 2.5, and 3.0 µm.
FIGURE 5 | Data reconstruction and processing for tracking. (A) A portion of the field of view of a median-subtracted hologram from the 34◦C dataset. The inset
shows part of the image magnified 4x to show the fringes. (B) Amplitude reconstruction of the same field of view in (A) at +50 µm. (C) Maximum Z projection of the
same field of view, representing reconstructions from −200 to +200 µm in steps of 10 µm.
and dynamic viscosity of water (η). The Stokes-Einstein relation
(Eq. 1) gave an excellent fit to the measured data for particle
radius r= 2.1 µm (Figure 4B). This was consistent with the sizes
measured by direct imaging, with a large amount of variability
seen in both measured size and diffusion coefficient due to
the presence of elongated and clustered cells. For temperatures
higher than 44◦C, Brownian motion was impossible to evaluate
because of either thermal currents or large numbers of cells
immobilized on the glass surface.
Motility Analysis
Motility analysis was performed on reconstructions and
projections of holograms. An example hologram is shown in
Figure 5A, and a single-plane amplitude reconstruction in
Figure 5B. Single plane reconstructions provided qualitative
insight into cell morphologies, speeds, and fraction of motile
cells; however, their signal to noise ratio was insufficient for
automated tracking. Maximum projections through 40–80 Z
planes, representing 400–800 µm sample depth (Figure 5C),
permitted automated tracking to create 2D trajectories. Motion
history images (MHIs, as described in “Materials and Methods”)
were used to identify tracks of motile organisms from these
projections (see Supplementary Video 1). Figure 6 shows
time-coded MHIs for temperatures from 28 to 84◦C for a
selected set of experiments. The spacing between points on each
track gives a rough estimate of cell speed. Where thermal drift
was significant, motile cells could be identified by their paths
of travel against the drift current. Only cells moving counter
to the thermal drift were selected for tracking, as they clearly
represented active motility.
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FIGURE 6 | MHI analysis shows changes in motility patterns with increasing temperature and assists in identifying motile tracks. The tracks are time-coded, with the
time index indicating frame number (15 frames/s). The scale bar applies to all panels. (A–F) Full field of view of all identified tracks in selected datasets. (A) 28◦C,
showing a distribution of motile and non-motile cells and distinct swimming patterns. (B) 34◦C, showing increased motility and speed. (C) 44◦C, showing nearly all
cells motile at high speed. (D) 61◦C, showing a reduction in the number of motile cells, but some swimming at high speed. (E) 66◦C, showing a large amount of
thermal drift with a few motile cells. (F) 84◦C, with all motion due to thermal drift. (G–J) Selected examples of swimming types identified in the tracks. Magenta
indicates tracks identified as motile by the software; tracks in cyan were identified as non-motile. (G) Helical swimming. (H) Long straight runs. (I) Circling or
spinning. (J) Run and tumble.
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TABLE 1 | Motility parameters.
Temperature (◦C) Fraction of
cells
motile ±SD
Total # tracks
analyzed
(# replicates)
# fast runs/
tumbles/helices/
circles
Mean ±SD speed
(µm/s) (range)
Mean ±SD
acceleration
(µm/s2)
Mean ±SD
displacement
(µm/s) (range)
28 20 ±1 36 (2) 10/8/10/8 16 ±7 (3–32) 150 ±70 5 ±4 (0.2–16)
34 24 ±2 66 (2) 24/26/8/8 23 ±6 (12–42)* 200 ±60* 9 ±5 (0.7–21)
44 61 ±2* 57 (2) 18/30/5/4 24 ±7 (8–45)* 210 ±90* 10 ±6 (0.1–25)*
61 6 ±2* 16 (2) 10/5/1/0 24 ±7µm/s (14–38)* 230 ±90* 10 ±7 (1.3–27)
66 <1* 35 (2) 18/13/2/0 23 ±6 (12–41)* 220 ±70* 8 ±5 (1.3–26)
51 heat shock Not done 21 (1) 2/2/10/7 28 ±6 (15–47)* 250 ±110* 10 ±7 (2–24)
60 heat shock Not done 24 (1) 2/12/8/2 22 ±6 (13–37)* 160 ±70 8 ±5 (1.1–18)
“Replicates” represent independent experiments done on different days with fresh cultures of B. subtilis. Tracks were characterized according to Figure 6 by visual
inspection and correlation with parameters as in Figure 7. SD, standard deviation. (*), significantly different from value at 28◦C(p<0.01).
The 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. Motile tracks could still be identified
in single-plane reconstructions (Supplementary Video 5) and
in MHI analysis projections through Z (Figures 6D,E and
Supplementary Video 6). At 84◦C, only thermal currents and
drift were observed, with no active counter-current motility;
the fraction of motile cells was deemed 0% (Figure 6F and
Supplementary Video 7). Qualitative analysis of these tracks on
a cell-by-cell basis revealed that there were 4 basic track types:
(1) helical tracks (Figure 6G); (2) long straight runs (Figure 6H);
(3) circular tracks (spinning) (Figure 6I); and (4) runs with
tumbles and directional changes, which could feature either
straight or helical swimming or some combination of both; any
track with distinct directional changes was classified as run and
tumble (Figure 6J).
Heat-killed cells showed no active motility, and the
MHI images correspondingly showed few features at the
lower temperatures (Supplementary Figure 6). Amplitude
reconstructions of the holograms revealed cells undergoing
slight drift and Brownian motion (Supplementary Video 8
shows 28◦C). At temperatures of 60◦C and higher, substantial
thermal drift and convection became apparent. At the highest
temperatures, multiple drift planes could be observed, but
in all cases motion was clearly due to bulk flow without any
evidence of run and tumble events (Supplementary Figure 6
and Supplementary Video 9 shows 84◦C). HELM’s classification
algorithm was 100% successful at classifying tracks as non-
motile at temperatures <50◦C, and >95% successful at higher
temperatures (Supplementary Video 10 shows classification of
tracks at 84◦C). This classification is not based upon any one
parameter, and correlating its results with physical parameters
will be the subject of a future study. Visual inspection is always
required to correlate tracks with cells and to perceive patterns
suggesting active motility.
Quantitative analysis of tracks was performed by using the
automated tracker combined with the MHI to identify tracks
that represented valid cell trajectories. Tracks that were not
following organisms or which were fewer than 15 frames long
were excluded from analysis. Table 1 gives classification of the
analyzed motile tracks for selected datasets. Parameters extracted
from these tracks are shown in Table 1 and Figure 7. Data
from independent experiments were consistent, so full tracking
was performed using selected tracks from pools of replicates
(see Supplementary Figure 7 for comparisons of replicate
experiments). Plotted in Figure 7 are selected parameters
where significant differences were seen among the different
temperatures or where parameters assisted in classifying tracks.
The full datasets are available in Supplementary Datasheets 2–7.
The mean speed was defined for each trajectory as
v=1
N
N
X
i=i
||−→
xi−−→
xi−1||
1t,(5)
where Nis all of the points in the identified trajectory and 1t
is the (constant) frame rate. There was a significant increase
in mean speed between 28◦C and all of the other elevated
temperatures. There was a statistically significant difference
(p<0.001) for comparison between 28◦C and the other
temperatures, and not significant between any other pairs. The
fastest speeds were seen in the 51◦C heat shock sample (p<0.001
for comparison with 28, 35, and 60◦C heat shock). The values for
the 60◦C heat shock were comparable to those at all other elevated
temperatures (significantly different from 28 to 51◦C heat shock,
all others non-significant). Values are given in Table 1 and means
with standard errors of the mean are plotted in Figure 7A for
both live and killed cells; distributions were Gaussian at each
temperature. The speeds of the killed cells were significantly less
than those of the live cells, even when drift was significant. There
was no significant difference in maximum trajectory speed seen
between any pairs of data sets of live cells (not shown; means
80–100 µm/s for maximum instantaneous speeds).
The mean speed times viscosity, which should give an
approximate measure of flagellar force as given in Eq. (4),
decreased essentially linearly at higher temperatures. This is
consistent with denaturation of the proteins as the temperatures
rise, representing a typical optimum performance curve with the
optimal temperature near 310 K (37◦C) (Figure 7B). Because
heat denaturation is a time-dependent process involving multiple
parameters (Peterson et al., 2007), additional time points at each
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Dubay et al. Bacterial Motility at High Temperature
FIGURE 7 | Selected motility parameters. Definitions of the parameters, values and statistical significance are given in the text. Error bars shown are
mean ±standard error of the mean unless noted. Numbers of analyzed tracks and their classifications are given in Table 1. When error bars do not appear, they are
smaller than the symbols. (A) Mean speed. (B) Mean speed times viscosity of water at that temperature. The lines are linear fits to temperatures above or below 310
K. (C) Total displacement normalized to track length and average speed <v>.(D) Histogram of selected values of D/ <v>T, showing classification into long runs vs.
run-and-tumble traces. (E) Sinuosity (no error bars given as the distributions were not Gaussian and the mean value had little significance). These values are plotted
on a Log_2 scale for ease of visualization of the ranges involved. Circular tracks were identified as those with sinuosity >20. (F) Correlation matrix of the measured
parameters.
temperature would be needed to extract meaningful physical
parameters from this temperature dependence.
As distinguished from mean speed, displacement (µm/s)
looks at the whole trajectory rather than each frame, and is
the norm of the total XY path, D = q(4x)2+(4y)2. For
a straight track, displacement will equal mean speed <v>
multiplied by total time T of the track lifetime; for a circular track,
displacement will be close to 0 regardless of mean speed. Dividing
the displacement by <v>Tgives a dimensionless number that
can be used to classify tracks. Figure 7C shows the average
D/<v>Tvs. temperature, and (Figure 7D) shows a histogram
of displacements for selected measured tracks at multiple
temperatures; values of D/<v>T≥0.55 indicated long runs.
Sinuosity is a measure of movement inefficiency defined as
end-to-end displacement over total path length in µm. For a
straight path, sinuosity will equal 1. For a serpentine or circular
movement pattern, sinuosity will be 1. Values of >20 were
only seen at lower temperatures (Figure 7E). The cell-by-cell
classifications in Table 1 agreed with this analysis, as circular
tracks were not found in the elevated temperatures.
Acceleration (µm/s2) is measured as inter-frame differences in
velocity:
a=1
N
N
X
i=i
||−→
vi−−→
vi−1||
4t,(6)
and could be used along with mean speed to classify tracks. Long,
smooth runs showed low values of acceleration, while tracks with
multiple tumble events showed high values. Similarly, the step
angle measures how much a particle turns at each time point.
It gives the angle that a particle deviated from a straight path
per inter-frame interval (in radians). Passively drifting particles
should not very much in their direction from frame to frame,
while highly motile particles that swerve and turn regularly will
show large changes in step angle. Figure 7F shows a correlation
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Dubay et al. Bacterial Motility at High Temperature
FIGURE 8 | Collections of non-motile cells on the chamber surface at 66◦C. (A) Still image; the circle indicates a cluster of cells. (B) Schematic of surface clustered
cells with relative positions of selected motile tracks. (C) Corresponding image of the slide surface with the killed cell dataset at 66◦C.
matrix of the measured parameters, showing that D/<v>T
showed a negative correlation with nearly all measured values.
Autocorrelations of speed, velocity, and turn angle at 1 and 2
s did not vary significantly among datasets (not shown; available
in Supplementary Datasheets 2–7).
Surface Clustering
At higher temperatures (60◦C and above), cells were seen to be
swimming above large patches of surface-adherent bacteria. The
Z projections could not determine how far the highly motile cells
were from the patches, or whether they were interacting. Thus, it
was necessary to use full Z plane reconstructions to identify the
relative position of motile cells vs. collections of non-motile cells
at the chamber bottom. It was seen that the highly motile cells
were moving at focal planes tens to hundreds of microns away
from the chamber bottom (Figures 8A,B and Supplementary
Video 11). While some active motility was seen near the clustered
cells at the surface, it was significantly slower as expected near a
surface (Li et al., 2008, 2011). Similar groups of cells were not seen
with the killed sample (Figure 8C).
DISCUSSION
The novel immersion system reported in this paper allows
for recordings of bacterial motility up to nearly the boiling
point of water. The custom software package that we report
for the first time here aids in tracking of motile organisms as
well as identification of non-motile tracks resulting from drift.
Distinguishing drift from motility was straightforward by visual
inspection, as active motility could occur perpendicular to the
thermal drift. However, the automated algorithm did generate
a large number of false positive tracks, so manual validation
was essential to accurately identify motile organisms. The MHI
images were used to help identify tracks that corresponded to
organisms. Although human intervention is needed, this method
is significantly easier than manual tracking. As HELM was
designed for use on spacecraft, HELM can process a sample in
several minutes on an ordinary laptop computer, and manual
confirmation of motile tracks may be performed in less than an
hour. It is important to note that the current implementation of
the software assumes (and checks for) a constant frame rate, so
it is necessary to ensure that the acquisition camera and software
do not drop frames. Future versions of the software will enable
input of a timestamps file to accommodate varying frame rates.
An approach to extracting tracks directly from the MHI traces is
also in development.
For this work, tracking was only performed on 2D projections
of the 3D tracks. The errors introduced in velocities and turn
angles by this approximation have been reported (Taute et al.,
2015). Given that the axial resolution of the instrument is >2µm,
we decided that in this analysis, the additional computational and
time cost of 3D tracking does not contribute significantly to the
analysis of velocities, as we have shown in a previous analysis
(Acres and Nadeau, 2021). In 2D, helices appear as spirals, with all
of the parameters of the helix readily extractable from the 3D data
(Gurarie et al., 2011). For organisms or instruments that show
higher contrast, HELM could also be used on individual Z planes.
However, with our current implementation, the signal to noise of
B. subtilis is too low for tracking on a single plane, so projections
over multiple planes were used. Since most literature data is
based upon 2D tracks, this also allows for easier comparison
of our measured speeds with those found in other studies. The
speeds we observed, 15–25 µm/s, are consistent with the recent
reports (Turner et al., 2016). However, large variations from
study to study have made general conclusions about parameters
as simple as mean velocity for a given strain largely impossible.
A new database, named BOSO-Micro (Rodrigues et al., 2021), is
aiming to increase standardization of motility experiments and
parameters. Such databases will be important for summarizing
the large amounts of data being generated in the rapidly emerging
field of bacterial tracking.
Using this system and approach, we found that B. subtilis
was capable of active motility up to 66◦C under conditions of
constant heating over a time course of 4 h. Cell size and shape
did not significantly change with temperature. The changes in
speed with temperature were consistent with previous studies up
to 50◦C (Schneider and Doetsch, 1977), with data unavailable
past that point. We observed active motility to 66◦C, but with
a minority of cells being motile at temperatures above ∼45◦C.
The majority of cells were found clustered on the lower surface
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Dubay et al. Bacterial Motility at High Temperature
of the chamber at high temperatures. This is consistent with
cell population heterogeneity in this species (Kearns and Losick,
2005;Lopez et al., 2009;Syvertsson et al., 2021). In addition, some
of the cells in this recording showed a change in the observed
motility type, most commonly a change from run-and-tumble
to long runs. These parameters were indicated by turn angles,
total displacement, and sinuosity in our analysis. Tracks with
high sinuosity, indicating circling, were seen only at the lower
temperatures. Long runs could be readily identified from total
displacement divided by average velocity and time length of
the track. The observed effect may result from the highly non-
linear viscosity of water at high temperatures, but additional
experiments are necessary to attempt to deconvolve viscosity
and temperature.
After 66◦C, movement in the field was due to thermal drift
alone. The thermocouples used to confirm the temperature of
the chamber showed a slight difference between the top and
bottom glass of the chamber, which could have led to increased
thermal currents, most notably at temperatures past the limit
for autonomous motion. This drift was clearly distinguishable
from active motility on the MHI traces, but attention to correct
classification of tracks as motile or non-motile was essential
during analysis. The algorithm performed well with the killed
samples, identifying nearly all of the tracks as non-motile despite
drift, although it did yield a small number of false positives at
the highest temperature (84◦C). The random forest classifier used
to classify tracks as motile or non-motile relies upon numerous
parameters, no single one of which is sufficient to classify a
track as motile or non-motile. Further analysis will correlate
physical parameters with motility; this may require analysis
beyond the use of means and standard deviations across an
entire track, but close attention to instantaneous parameters.
This analysis will also aid in classifying motile tracks with
regard to run length and tumble frequency. Custom training of
the classification software may be necessary for datasets with
substantially different flow profiles.
The MHI traces are a unique feature of HELM, which was
designed with spaceflight missions in mind, where the detection
of possible signs of life with the least possible processing power is
required. The MHI allows visualization of tracks in low signal-
to-noise recordings where objects cannot easily be identified
by tracking algorithms. These traces may be used as a guide
for selection of complete motile tracks identified by HELM.
However, compared to other software packages such TrackMate,
HELM’s particle identification and tracking is less interactive.
When the algorithms do not perform well, the MHI may be
used as a guide to finding tracks manually or using other
particle detection algorithms in other packages. When HELM’s
detection and tracking works well, tracks are exported in.json
files which may be imported into other packages for stitching or
further analysis.
Reconstruction at individual Z planes at higher temperatures
showed rapidly swimming cells tens to hundreds of µm above
the surface, with large numbers of cells on the surface, some
of which exhibited active motility. The presence of active
motility in mesophilic organisms at temperatures beyond those
at which they can grow is somewhat of a surprising result.
Direct visualization of the motion of individual cells in this
work makes this result unambiguous. The use of killed control
cells eliminated any possibility that the motion was due to
complex thermal currents, and the disappearance of any signs
of active motility above 66◦C further indicates that the “live”
cells became inactive at this point. When reduced viscosity at
these temperatures was considered, there was a temperature-
dependent reduction in flagellar force. This is likely due to protein
denaturation, and longer incubation times at these elevated
temperatures may eventually lead to complete loss of motility.
More detailed experiments with controlled incubation times at
specific temperatures may provide useful models of thermal
stability of motility-related proteins (Daniel and Danson, 2010).
This paper represents the first step toward evaluating the upper
limits of temperature on mesophile motility.
The authors hope that this simple setup will encourage
others to reproduce these experiments and examine other strains
of bacteria and archaea. In contrast to bacteria, especially
test strains such as E. coli and B. subtilis, hyperthemophilic
archaea have not been frequently imaged. The setup we
report here should facilitate studies of thermophilic organisms,
including those such as Pyrococcus furiosus which require
temperatures near the boiling point of water for optimal motility
(Herzog and Wirth, 2012).
The detailed parts list, combined with the open-source
software, should be sufficient to enable duplication of the system
by anyone who wishes to perform these experiments. The only
custom parts are a 3D printed stage and objective lens holder
(plastic) and a custom machined stage (aluminum) to allow
for easy changing of sample chambers without requiring stage
realignment. CAD drawings of these can be provided on request,
and users are encouraged to tailor designs to their own specific
applications. It is important to ensure that these elements are
made from materials that can withstand high temperatures;
any materials chosen should ideally be tested beforehand by
submerging them into hot water at the desired temperatures
before use on the microscope.
DATA AVAILABILITY STATEMENT
The raw holograms for the datasets used here are deposited
in a public depository at Data Dryad, accession https://doi.
org/10.5061/dryad.ns1rn8pv6. Other data are available from the
authors upon request. The software packages used are all open
source and are available at the following sites: DHMx: https:
//github.com/dhm-org/dhm_suite; HELM: https://github.com/
JPLMLIA/OWLS-Autonomy; and Reconstruction Fiji plug-ins:
https://github.com/sudgy/.
AUTHOR CONTRIBUTIONS
MD and NJ: construction and calibration of thermal control
apparatus, growth and maintenance of bacteria, data collection,
data analysis, data archiving, and writing. MW and JL:
development of HELM tracking software, troubleshooting and
debugging of software, and addition of new features upon
request. CL: original concept design and acquisition of funding.
Frontiers in Microbiology | www.frontiersin.org 12 April 2022 | Volume 13 | Article 836808
fmicb-13-836808 April 15, 2022 Time: 9:5 # 13
Dubay et al. Bacterial Motility at High Temperature
JN: acquisition of funding, supervision and planning of
experiments, data analysis, and writing. All authors: editing and
approval of final draft.
FUNDING
The authors acknowledge the support of the National
Science Foundation (Grant No. 1828793). Portions of this
work were supported under a contract from, or performed
at, the Jet Propulsion Laboratory, California Institute of
Technology, under a contract with the National Aeronautics and
Space Administration.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmicb.
2022.836808/full#supplementary-material
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Frontiers in Microbiology | www.frontiersin.org 14 April 2022 | Volume 13 | Article 836808
