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Scientific
Publications - Work Done by Microbiology Reader Bioscreen C
Letters in Applied Microbiology, 2000, Jun,
30(6), 468-472
A comparison of the Bioscreen method and microscopy for the
determination of lag times of individual cells of Listeria monocytogenes
Wu Y, Griffiths MW, McKellar RC
ABSTRACT
Lag phase durations (tLag) of individual Listeria monocytogenes
cells were analysed using the NightOwl Molecular Imaging System, and results
were compared with mean individual cell lag times (tL) obtained from the
detection time (td) method using Bioscreen. With Bioscreen, an average tL of
6.39+/-0.89 h was obtained from five separate experiments. With the NightOwl
method, an average tLag of 2.73+/-0.06 h was obtained from three experiments
consisting of eight total replicates. Lag values from the NightOwl and Bioscreen
are related by the equation: tLag = tL + DT, where DT is the doubling time. The
equivalent tLag mean value for the Bioscreen method was 7.11+/-0.84 h.
Individual lag times measured by both methods were normally distributed (r2 for
Bioscreen and NightOwl ranged from 0.951 to 0.999 and from 0.884 to 0.982,
respectively). The results suggest that the NightOwl method can provide accurate
estimates of individual cell lag times, which will facilitate the development of
combined discrete continuous models for bacterial growth.
INTRODUCTION
Predictive microbiology, the use of mathematical models to
describe the growth and death of foodborne micro-organisms, has been an area of
considerable activity over the last decade. It is based upon the premise that
the responses of populations of micro-organisms to environmental factors are
reproducible, and that it is possible, from past observations, to predict the
responses of micro-organisms by considering environments in terms of
identifiable dominating constraints. Proponents claim that predictive
microbiology offers many benefits to the practice of food microbiology, and
there is growing international interest in its use ( Ross and McMeekin 1994).
Kinetic models are perhaps the most useful, since they can be
used to predict changes in microbial numbers with time, even if one (or more) of
the controlling factors affecting growth is changing e.g. during a chilled
distribution chain ( McClure et al. 1993). Many kinetic models have been
developed by fitting growth curves of viable count data obtained from cultures
grown in liquid media ( Buchanan and Phillips 1990; Wijtzes et al. 1993). Viable
count is a traditional, sensitive method for estimating the microbial growth
curve, but it is time-consuming and labour-intensive ( McClure et al. 1993;
Dalgaard et al. 1994). The amount of data required to generate reliable models
has led some researchers to use simple, and often indirect, methods of data
collection, such as turbidimetry in laboratory media, rather than the viable
count method. Determining bacterial growth rates in broth systems using
turbidimetric methods provides a rapid and inexpensive means of modelling;
however, the use of indirect methods for growth curve generation may result in
generation times different from those determined by using viable counts (
Baranyi et al. 1993; Dalgaard et al. 1994).
Turbidimetric methods such as the Bioscreen method have been used to generate
kinetic data for modelling by fitting non-linear regression functions to optical
density (OD) data ( McClure et al. 1993; Stephens et al. 1997).
Determinations of the specific growth rate (µ) and population lag phase duration
( ) are often
difficult with this approach. Thus attempts have been made to use detection
times (t d) ( Cuppers and Smelt 1993). The t d
for a turbidimetric instrument can be defined as the time required for an
initial measurable increase in OD. When t d values are plotted
against the corresponding inoculum size µ can be calculated as the negative
reciprocal of the slope of the regression line ( Cuppers and Smelt 1993). The
mean individual cell lag times (t L) can be calculated
subsequently as the difference between the predicted t d based
on the µ, and the observed t d. This approach has been used
recently to determine t L for L. monocytogenes (
McKellar 1998; McKellar and Knight 2000).
With the combination of microscopy and imaging, direct observation of single
cell growth has become possible, providing a direct observation of the growth
kinetics of individual bacterial cells. Lag phase can be obtained by determining
when the first doubling occurs, and growth rate can be estimated by counting the
doubling time of the cells. Few studies have attempted to derive kinetic models
using microscopy techniques. The behaviour of individual cells in foods is
poorly understood. In spoilage situations this may not be important; however,
some food-borne pathogens can cause illness from only a few cells ( Mackey and
Gibson 1997). Thus, determining the lag time of single cells would provide
valuable information for risk analysis. Therefore, the purpose of this study was
to determine the lag phase of individual cells using microscopy, and to compare
the distribution of individual cell lag times with that obtained using the
Bioscreen method.
MATERIALS AND METHODS
Listeria monocytogenes Scott A (human clinical isolate) was obtained
from the culture collection of the Food Research Program (Guelph, Ont. Canada).
API Listeria spp. identification strip (BioMérieux Canada Inc., St
Laurent, PQ, Canada) was used to confirm the identity of the culture. The
culture was grown for 24 h at 30 °C in tryptic soy broth (TSB; Difco
Laboratories, Detroit, MI, USA). Stock cultures were prepared in TSB plus 15%
glycerol (BDH Inc., Toronto, Ont., Canada) and 0·3 ml subsamples were frozen in
cryovials at 
25 °C.
The contents of one cryovial were transferred to 10 ml of TSB, and incubated
for 24 h at 30 °C in a shaking water bath (Model 3100, New Brunswick Scientific,
Edison, NJ, USA) at 1500 rev min
1. The
culture (0·1 ml) was transferred to 10 ml fresh TSB and incubated at 30 °C for
24 h when the cell density was approximately 108 cfu ml
1. For
the Bioscreen, the resulting culture was used as the inoculum for experiments.
For microscopy, the resulting culture was diluted 1 : 10 in TSB, and the diluted
suspension (approximately 107 cfu ml
1) was
used to prepare the slide.
Bioscreen technique
Growth experiments. Serial twofold dilutions of
the inoculum were made using fresh TSB to obtain a range of dilutions
representing approximately 0-105 cfu ml
1. From
each of the twofold dilutions, 350 µl was transferred to wells (40 wells per
dilution) of a Bioscreen plate (Labsystems, Helsinki, Finland). The filled
plates were placed in the Bioscreen for analysis. Measurements were taken using
a wide band filter, with preshaking at medium intensity for 10 s prior to OD
reading, at an incubation temperature of 30 °C. Measurements were taken every
4 min for 25 h. Results were reported as t d (h) and defined
as the time required for the Bioscreen to record a 0·05 increase in OD from the
initial value.
Viable cells were enumerated for each twofold TSB dilution by spread plating
appropriate serial dilutions (0·1 ml) in duplicate onto tryptic soy agar plates
(TSA, Difco Laboratories). The plates were incubated at 30 °C for 48 h and
counts were determined using a Quebec Counter (American Optical Co., Model 15,
Buffalo, NY, USA).
Modelling. Calculation of t L
(mean individual cell lag times) was performed as described previously (
McKellar 1998; McKellar and Knight 2000). Briefly, plots of t d
(detection time) obtained from serial dilutions of the original inoculum against
ln cfu ml
1
were used to calculate µ from the slope using the equation:
The calculated µ was then used in the heterogeneous population model (
McKellar 1997) to predict the time required to detect growth from a defined
number of cells. It was assumed that the dilution giving the largest t
d was equal to 1 cfu well
1 (or ln
cfu well 1 = 0).
Simulated values for t d underestimated the actual t
d by an amount equivalent to t L. Replicate values
of t L and the standard deviation (S.D.L) were
calculated from five independent 40-well trials by subtracting the simulated
value for t d from the experimental t d
values.
For each of the Bioscreen experiments, frequency distributions of individual
t L values from replicate wells were calculated using the
non-linear regression function of Prism Version 3·0 (GraphPad Software for
Intuitive Science, San Diego, CA, USA). Bin widths were set at 0·7 h intervals,
and the number of wells corresponding to each bin was determined. A normal
distribution was then fitted to the resulting frequency distribution using
Prism.
Microscopic technique
Slide preparation. Glass double cavity slides (75  25 mm,
18 mm diameter cavities, 1·75 mm thickness) and coverslips (22 cm2)
(VWR Canlab, Mississauga, Ont., Canada) were autoclaved before use.
Two hundred and fifty microlitres of molten TSA were pipetted into each
cavity of the slide. The agar in each cavity was covered with a coverslip, and
pressure was applied to obtain a flat, smooth surface. The slide was left for
5 min to allow the agar to solidify. The coverslip was removed, 2 µl of sample
suspension was pipetted onto the surface of the agar, and another coverslip was
applied. The slide was placed in a sealed Petri dish and incubated at 30 °C.
Image capture and analysis. Images were acquired
and evaluated using the NightOwl LB 981 Molecular Imaging System (EG & G
Berthold, Bad Wildbad, Germany). Microscopic images were obtained using the CCD
camera of the NightOwl system mounted on a microscope (Olympus BH2, Capsen Ltd,
Markham, Ont., Canada). The growth of the target cells in two or three different
fields of view for each slide was visualized using phase-contrast microscopy.
Images of each area were captured at time intervals of 0, 1, 1·5, 2, 2·25, 2·5,
2·75, 3, 3·25 and 3·5 h. After each observation, the slide was replaced in a
sealed Petri dish and incubated at 30 °C. The number of newly divided cells was
counted and these cells were not counted again in later captured images.
Three experiments were conducted; in experiment 1, two replicate areas were
viewed per slide; in experiments 2 and 3, three replicate areas were viewed.
Modelling. For each microscopy experiment, the
number of cells that had newly divided during each 0·25 h time interval was
determined and used as the frequency distribution. A normal distribution was
then fitted to the resulting frequency distribution using Prism.
RESULTS
The average t L from the Bioscreen was 6·39 ± 0·89 h (
Table 1). All the data fit a normal distribution well; r 2
values ranged from 0·951 to 0·999 ( Fig. 1). Individual values for S.D. from
each experiment ranged from 0·567 to 0·991.
With the microscopic method, eight replicates were obtained from three
experiments. The total cell number in each field of view was highly variable
(from 24 to 166 cells) among replicates because cells were not evenly
distributed on the agar ( Table 2). The average lag phase duration observed
microscopically (t Lag) was 2·73 ± 0·06 h. Non-linear
regression analysis indicated that all the replicates were normally distributed;
r 2 values ranged from 0·884 to 0·984 ( Fig. 1). From the
images it was observed that not all cells divided within the time period of the
experiment although the number of such cells was less than 5% of the total cell
population.
The t L derived using the Bioscreen method assumes that
individual cells start growing at maximal growth rate immediately after
adaptation ( McKellar 1998; McKellar and Knight 2000). The time at which the
first cell was observed microscopically to be dividing (t Lag)
includes the adaptation period (t L) and the time required for
the cell to double (DT). Thus t Lag is related to t
L by equation 2.
DT was calculated from µ using equation 3:
Calculations for t Lag from the Bioscreen data are given in
Table 1.
DISCUSSION
The results of the present study show that mean individual cell lag times
obtained either by the Bioscreen method or by microscopy follow normal
distributions ( Fig. 1). There have been few studies published on the
distribution of bacterial cell lag times. Baranyi (1998) and Baranyi and Pin
(1999) proposed that lag times were exponentially distributed. However, other
important physiological parameters, such as growth rate, were found to be
normally distributed ( Kelley and Rahn 1932; Kubitschek 1966; Rubinow 1980). In
previous studies employing the Bioscreen, it was assumed that t L
values were normally distributed, and, by using this assumption, the decreased
variability in t L between replicate wells with > 1 cell well
1
initial count was correctly predicted ( McKellar 1998; McKellar and Knight
2000). These same studies showed that the assumption of exponential
distributions for t L led to predictions which did not agree
with experimental findings ( McKellar 1998; McKellar and Knight 2000). Further
work is required to more completely classify the distribution of cell
physiological properties.
The lag times determined by the Bioscreen method in this study were slightly
greater than were observed previously ( McKellar 1998; McKellar and Knight
2000); however, the S.D. values are comparable. The t L values
obtained by the Bioscreen method were also longer than those found
microscopically. The reason for this difference is unknown. However, Baranyi
et al. (1993) and Dalgaard et al. (1994) have suggested that the use
of indirect methods for growth curve generation might result in generation times
different from those determined using viable counts. There may also be
differences between growth of micro-organisms in solid media on slides and in
liquid media due to O2 availability and other factors. Further
research is needed to resolve this issue.
Using microscopy to determine the lag, the zero time point was counted when
the first image was captured and there was a slight delay between this first
capturing time and the actual inoculation time. This would result in the actual
t Lag being slightly longer than the calculated t
Lag. Since this slight time difference was about 5 min in each replicate
and was constant for each experiment, it was ignored as a system error.
From the microscopic images, it was observed that some cells did not divide
within the time period of the experiment. These cells can be considered dead,
metabolically inactive or injured. This is one advantage of the microscopic
method since dead, injured or living cells can be distinguished based on their
ability to divide. Thus, cells under stress can be directly observed and their
recovery can be studied in vivo. Furthermore, data on the kinetics of
growth at other points in the growth cycle can be obtained.
FIGURES
Fig. 1 Examples of
normal distribution curves from microscopy. ( )
replicate 2 of experiment 2 (r 2 = 0·984...
Table 1 Results from
Bioscreen
Table 2 Results from
microscopy
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