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Correlating Single Cell Motility With Population
Growth Dynamics for Flagellated Bacteria
Sucheta Arora, Vidya Bhat, Aditya Mittal
Department of Biochemical Engineering & Biotechnology,
Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India;
telephone: 91 11 26591052; fax: 91 11 26582282; e-mail: [email protected]
Received 20 September 2006; accepted 29 January 2007
Published online 1 February 2007 in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/bit.21372
ABSTRACT: Many bacteria used for biotechnological applications are naturally motile. Their ‘‘bio-nanopropeller’’
driven movement allows searching for better environments
in a process called chemotaxis. Since bacteria are extremely
small in size compared to the bulk fluid volumes in bioreactors, single cell motility is not considered to influence
bioreactor operations. However, with increasing interest in
localized fluid flow inside reactors, it is important to ask
whether individual motility characteristics of bacteria are
important in bioreactor operations. The first step in this
direction is to try to correlate single cell measurements with
population data of motile bacteria in a bioreactor. Thus, we
observed the motility behavior of individual bacterial cells,
using video microscopy with 33 ms time resolution, as a
function of population growth dynamics of batch cultures in
shake flasks. While observing the motility behavior of the
most intensively studied bacteria, Escherichia coli, we find
that overall bacterial motility decreases with progression of
the growth curve. Remarkably, this is due to a decrease in a
specific motility behavior called ‘‘running’’. Our results not
only have direct implications on biofilm formations, but also
provide a new direction in bioprocess design research highlighting the role of individual bacterial cell motility as an
important parameter.
Biotechnol. Bioeng. 2007;97: 1644–1649.
ß 2007 Wiley Periodicals, Inc.
KEYWORDS: motility; growth curve; nanopropeller;
chemotaxis; bioprocess design; video microscopy
Introduction
Industrial production of several important metabolites is
achieved through growth of bacterial cultures in bioreactors
(Glazer and Nikaido, 1995). Growth of bacteria is dependent
on several factors including environmental parameters
apart from the nutritional status of the culture medium.
Bioreactors are designed and operated to ensure homogeneity in the system with the aim of maintaining identical
growth conditions for each cell (Shuler and Kargi, 2002).
Although the operational conditions are aimed at completely homogenizing the bulk fluid in the bioreactors,
formation of localized fluid flow patterns is inevitable
(Ranade, 2002).
Many bacteria that are used for production of biotechnological products are naturally motile. For example,
Escherichia coli, through the course of evolution, has
developed an ability to move in search of favorable
environments by using its own natural motor–propeller
assembly—the flagellar motor system (Berg, 2003; Kojima
and Blair, 2004). Bacterial cell movement is enabled by
flagellar proteins on their surfaces that act as nanopropellers
to direct the bacteria towards better living environments in a
process called chemotaxis (Wadhams and Armitage, 2004).
This nanopropeller system is embedded in the bacterial cell
membrane and driven by ion gradients across the cell
membrane (Gabel and Berg, 2003; Sowa et al., 2005).
Since the bacteria are extremely small in size, compared to
the bulk fluid volumes handled in bioreactors, motility of
individual bacterial cells is not considered to influence
bioreactor operations. However, with increasing interest in
localized fluid flow inside reactors, along with the bulk flow,
it is important to ask the question whether individual
motility characteristics of bacteria are significant in
bioreactor operations. Motion of a submerged body in a
fluid depends on the ability of its propeller to overcome
mainly two forces: the inertial and the viscous forces. For
macroscopic bodies the inertial forces dominate. However,
for microscopic bodies inertial forces are negligible and the
viscous forces are the primary constraints in their motility
(Behkam and Sitti, 2005). In principle, bacterial motility
might appear to be non-relevant; for large-scale bioreactors,
however, its relevance (or lack of) in bioreactor operations is
not proven either way. Thus, it is important to ask whether
bacterial motility is important in bioprocesses. It might be
especially important in operational conditions of bioreac-
Correspondence to: A. Mittal
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ß 2007 Wiley Periodicals, Inc.
tors which deal with high viscosity media and low agitation
rates to avoid shear stress on cells.
Bacterial motility is also an extremely important
parameter biofilm formation (Wood et al., 2006) and
would certainly be relevant in microfluidic devices. While
chemotactic motility has been intensely studied from
microbiological (Berg, 2003; Kojima and Blair, 2004) and
biophysical (Behkam and Sitti, 2005; Berg and Brown, 1972;
Brown and Berg, 1974; DiLuzio et al., 2005; Lauga et al.,
2006) perspectives in the last three decades, correlating
single cell motility with population studies (in bioreactor
systems) of motile bacteria is still an unexplored territory
(but see Staropoli and Alon, 2000). In this work we hope to
fill this gap by providing the first experimental evidence
on the possible variations in motion characteristics of
individual bacterial cells as a function of the growth cycle of
the motile bacteria in shake flask bioreactors.
Materials and Methods
E. coli strain JM109 was used for this study. The strain
JM109 was chosen since it has been reported to be a fairly
motile strain of E. coli compared to other strains (Wood
et al., 2006). Initial studies were also carried out with E. coli
strain DH5-a. However, the motility was not quantifiable
for most experiments (less than 1%). The lack of
quantifiable motility is in agreement with the results
obtained by Wood et al. (2006). Nutrient broth of analytical
grade was obtained from HiMedia Laboratories Pvt. Ltd
(Mumbai, India).
For inoculum preparation, bacterial cells were grown to
exponential phase in 50 mL conical flasks, until the turbidity
reached 1 at 600 nm (approximately 4 h). These cultures
were then inoculated into 250 mL of final culture volumes to
be aerobically grown in 500 mL shake flasks. Specifically, 250
mL of freshly prepared Nutrient Broth media (HiMedia
Laboratories Pvt. Ltd., Mumbai, India) was inoculated with
10% of the inoculum culture in a 500 mL conical flask and
grown at 378C, 200 rpm in an incubator-shaker (ORBITEK
AID Electronics, Chennai, India) for 9 h. Samples for
analysis were collected using pipettes under aseptic
conditions every half an hour, throughout the growth
period i.e., 9 h. The culture flask was restored in its original
conditions as soon as possible.
Thirty-three millisecond (ms) resolution video microscopy to record bacterial motility was done as described
earlier (Gupta et al., 2006; Sharma et al., 2007). Briefly, for
each sample, the collected sample was transferred to a glass
slide immediately (within a few seconds), covered with a
cover slip and digital videos of bacterial motion were recorded under the 100 oil immersion objective of Motic B1
upright microscope (MOTIC Microscopes) equipped with a
digital camera. The camera is capable of acquiring images at
the rate of 30 frames per second (i.e., 33 ms time resolution)
using the software Motic Images Plus 2.0 ML. Note that our
motility recordings were done within 30–60 s of the time
when the sample was extracted from the culture flask to
eliminate the possibility of any change in motility of bacteria
due to change in its surrounding environment (from
shaking inside a flask to that on a glass slide). It must be
noted that the microscope stage temperature was not
maintained at the same temperature as that of the shake
flasks (378C). However, there was no large change in
environment upon going from 378C to ambient (microscope stage), which was at 27–288C at all times. Whatever
small changes were experienced during the transfer of the
culture, were expected to be normalized over all the
experiments.
Simultaneously, an aliquot of each sample was stored at
48C for optical density measurement at 600 nm, to get
the growth kinetics, using a Manual Simultaneous Spectrophotometer (SPEKOL-1200 Analytik Jena AG, Jena,
Germany), which was carried out at the end of the
experiment. The experiments were done in triplicates; with
each video recording 20–200 bacterial cells simultaneously
(less number of cells was essentially seen only in the very
initial phases of growth due to very low number of cells). In
one of the experiments the optical density of samples was
measured immediately when the samples were taken out
from the flask. This was done to check if there was any
variation in the results due to storage of samples. Growth
curve shown in Figure 1 confirmed that the absorbance was
independent of whether it was measured for fresh samples or
it was measured after a few hours of storage at 48C. Videos
were then analyzed for motility data of individual cells by
extracting the frames (images) with 33 ms resolution.
Figure 1. Growth curve for batch culture of bacteria. Growth is expressed in
absorbance of the sample at 600 nm and shown by solid circles representing
mean SD of three independent experiments. The smooth curve is drawn to visually
guide through different stages of the growth curve, approximately demarcated by
arrows into lag phase, log phase; and stationary phase.
Arora et al.: Single Cell Motility in Bioreactor Operations
Biotechnology and Bioengineering. DOI 10.1002/bit
1645
Figure 2.
Motility characteristics of single bacterial cells studied via 33 ms resolution video microscopy. If (A) is considered to be an image taken at time t, (B) shows the same
field at time ¼ t þ 33 ms, and (C) shows the same field at time ¼ t þ 66 ms. The bacterial cell shown in the rectangle exhibits running motion relative to a fixed point in the field of view
marked by the arrow and the encircled bacterial cell shows tumbling motion.
Results
Experiments were carried out with E. coli strains DH5-a and
JM109. However, the motility was not quantifiable for
experiments done with the former strain (less than 1%). The
lack of quantifiable motility for the E. coli strain DH5-a is in
agreement with the results obtained by Wood et al. (2006).
Therefore, all the results are reported for the E. coli strain
JM109 only.
Figure 1 shows the bacterial growth curve obtained by us.
It also represents the growth curve of a typical batch culture
of bacteria in a reactor (Bailey and Ollis, 1986). The various
stages of culture growth in a bioreactor are shown in the
figure as the lag phase, the log phase and the stationary
phase. Note that that our experiments were carried out up to
the onset of stationary phase only and Figure 1 does not
include the growth curve of bacteria in the death phase. The
above is due to the facts that (a) we assume that the motility
behavior of the bacterial cells at the onset of the stationary
phase represents the behavior for the remaining stationary
phase and (b) death phase is not relevant for the purposes of
this study.
We also measured the number of viable bacterial cells at a
few points, by the CFU (colony forming units) assay to ensure
the basic features of the batch culture growth curve. The CFU
numbers correlated well with the absorbance data shown in
Figure 1 (not shown), thus confirming that up to the time of
our measurements, we did not have a significant number of
dead cells contributing to our batch culture growth kinetics
data. Note that absorbance at 600 nm, as shown in Figure 1, is
a standard measure representing estimates for cell numbers in
E. coli batch cultures (Bailey and Ollis, 1986; Olfosson et al.,
2003), and for the rest of this work, we will use this absorbance
to compare motility characteristics.
Individual bacterial cells could be essentially classified
into four categories: non-motile, runners (that swam in
linear trajectories for several decades of microns), tumblers
(that tumbled or rotated at a somewhat fixed position) and
tumbling-runners (that displayed short runs with tumbling
behavior). Figure 2 shows three successive frames of a video
Figure 3.
Percentage of bacteria that are motile in a batch culture as a function of growth in a batch culture w.r.t. time (A), and, absorbance at 600 nm (B). The percentage
was calculated by counting the total bacterial cells in the field of view and then finding the number of motile bacteria amongst them. The smooth curve in (A) and the smooth line in
(B) are shown to visually guide through the negative correlation (see text for details). Results are expressed as mean SD of three independent triplicate experiments.
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DOI 10.1002/bit
Figure 4. Percentage of runners as a function of growth in a batch culture w.r.t. time (A), and, absorbance at 600 nm (B). The percentage was calculated by counting the total
motile bacterial cells in the field of view and then finding the number of runners amongst them. The smooth curve in (A) and the smooth line in (B) are shown to visually guide through
the negative correlation (see text for details). Results are expressed as mean SD of three independent triplicate experiments in (A) and only the mean is shown in (B).
recording for a field in a sample. Relative to a fixed point in
the field of view (marked by an arrow), the bacterial cell
enclosed in a square is clearly seen to be a runner in the
upward direction from A to C. In contrast, the encircled
bacterium is a tumbler.
Having recorded motility of bacteria at each point of the
growth curve shown in Figure 1, we decided to investigate
the motility characteristics of bacteria as a function of
the growth curve. Figure 3 shows that the percentage of
motile bacteria (percentage of bacteria showing any
Figure 5. Percentage of tumblers (A and B) and tumbling-runners (C and D) as a function of growth in a batch culture w.r.t. time (A and C), and, absorbance at 600 nm (B and
D). The percentage was calculated by counting the total motile bacterial cells in the field of view and then finding the number of tumblers and tumbling-runners amongst them. The
smooth line in (B) and (D) are shown to visually guide through a lack of correlation (see text for details). Results are expressed as mean SD of three independent triplicate
experiments in (A and C) and only the mean is shown in (B and D).
Arora et al.: Single Cell Motility in Bioreactor Operations
Biotechnology and Bioengineering. DOI 10.1002/bit
1647
motility), tend to decrease with both time (Fig. 3A) and
increasing absorbance (Fig. 3B).
These results were statistically corroborated by the fact
that fairly strong negative correlation coefficients were
obtained between absorbance and percentage of motile
bacteria (i.e. for data shown in Fig. 3B) in the growth curve.
While the overall average percent of bacteria that were
motile (i.e. averages of triplicates) showed a correlation
coefficient (CC) value of 0.58 against absorbance, the
average CC, when considering individual experiments, was
found to be 0.42 0.18. Note that investigating the latter
was important, since experiment to experiment variations
result in large standard deviations as seen in Figure 3B.
Was there a specific kind of motile bacteria that were
decreasing as growth progresses in a growth curve, or all
kinds of individual bacterial motion decrease with time and
cell numbers in a batch culture? To answer this question we
first plotted percentage of runners out of the total motile
bacteria (and not total bacteria in the field of view as in
Fig. 3), against time and absorbance as shown in Figure 4.
We observed a strong decline in the number of runners
with the progression of the batch culture growth (Fig. 4).
The statistical strength of our observations was shown by
strong negative CCs between absorbance and percentage of
runners (i.e. for data shown in Fig. 4B) in the growth curve.
While the overall average percent of runners had a very high
CC value of 0.70 against absorbance, the average CC, when
considering individual experiments, was found to be
0.58 0.06. These results certainly indicate that ability
of motile bacteria to ‘‘run’’ sharply decreases with the
growth curve in a batch culture.
Next we plotted percentage of tumblers and tumblingrunners out of the total motile bacteria (as in Fig. 4), against
time and absorbance as shown in Figure 5. We observed that
percentage of tumblers and tumbling-runners are independent of the progression of the batch culture growth (Fig. 5).
Our observations were confirmed by very low CCs between
absorbance and percentage of tumblers and tumblingrunners (i.e. for data shown in Figure 5B and D respectively)
in the growth curve. While the overall average percent of
tumblers and tumbling-runners had CC values of 0.19 and
0.02 respectively against absorbance, the average CC, when
considering individual experiments, was found to be
0.31 0.24 for tumblers and 0.10 0.63 for tumblingrunners. These results certainly indicate that ability of motile
bacteria to exhibit tumbling or combined tumbling-running
behavior does not change with the growth curve in a batch
culture.
Discussion and Conclusions
Bioprocess engineering assumes that the growth curve for a
batch culture represents identical bacterial cells multiplying
inside a bioreactor. Are they identical indeed? We clearly
demonstrate that this is not the case with a physico-chemical
parameter of bacterial motility. The E. coli strain JM109 was
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used in our studies, since this strain has been shown to be
one of the most motile strains (Wood et al., 2006). While
there is no set standard assay for establishing motility of
bacterial strains, it has been studied using either video
microscopy (Berg and Brown, 1972; Diluzio et al., 2005;
Gupta et al., 2006) or measuring the spread of bacterial
‘‘halo’’ on agar plates (Wood et al., 2006). Considering the
fact that the motility of bacteria in fluid would be different
than on an agar plate, we used video microscopy to study
bacterial motility. The bacteria used throughout experimentation should naturally move towards a favorable
nutrient environment (chemotaxis). Therefore, the natural
expectation during a batch culture would be that the overall
bacterial population should include more motile bacteria
with advancing growth due to overall depletion of nutrients
in the culture broth.
We report a finding exactly opposite to the one expected
above, that the percentage of motile bacteria decreases with
increasing population in a batch culture. Remarkably, this
decrease is due to a significant decrease in a specific motility
behavior of bacteria call ‘‘running.’’ This, on the one hand is
in complete contrast to the expected behavior in which one
would expect an increase in motility with progression of the
growth curve due to constant reduction in nutrient levels of
the culture. On the other hand, it could simply be a result of
increasing population of bacterial cells leading to individual
cells impeding the ‘‘running’’ motion for each other. If the
latter was the case, then one would expect it only beyond the
mid-log phase of the batch culture, when the bacterial cell
concentration starts becoming substantial, which is clearly
not the case. Thus, while we do not know at this stage as to
why motility decreases with the progression of the growth
curve, it does raise the possibility that bacterial motility can
be utilized as an important parameter in bioprocess
operations, apart from the normally accepted parameters
of aeration, agitation and other physiological factors. While
cell synchronization on a biochemical level was recognized
to be of utmost importance in bioprocess engineering many
years ago (Bailey and Ollis, 1986), we present the first
evidence that the physical parameter of cell motility may be
equally important. Staropoli and Alon (2000) have reported
for E. coli K-12 strain RP437 that run speeds of bacterial
cells peak in mid-exponential phase and tumbling frequency
increases over the course of growth in a batch culture in 125
mL flasks. However, their elegant experiments were not
designed to investigate motility of fresh cultures, i.e.
substantial time was provided for biological adaptation to
the bacterial samples to undergo chemotaxis driven motility
under various conditions while observing under the
microscope. Thus, their results were quite reasonable in
terms of the expected outcome of the experiments. We, on
the other hand, seem to have stumbled upon counterintuitive results, highlighting the importance of microbial
motility in bioreactor operations, which opens a new line of
thinking that is testable for other strains of motile bacteria.
As of today, motility of microbial cells is not a parameter
considered in bioreactor design and operation. By learning
Biotechnology and Bioengineering, Vol. 97, No. 6, August 15, 2007
DOI 10.1002/bit
from different motility behaviors exhibited by bacterial cells
in different phases of the growth curve, it may be possible to
enhance the bioprocess productivity by incorporating
strategies to normalize chemotaxis driven motility over
the complete growth profile during an operation.
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