Previous Article | Next Article 
Applied and Environmental Microbiology, October 1999, p. 4475-4483, Vol. 65, No. 10
0099-2240/99/$04.00+0
Copyright © 1999, American Society for Microbiology. All rights reserved.
Significance of Size and Nucleic Acid Content
Heterogeneity as Measured by Flow Cytometry in Natural Planktonic
Bacteria
Josep M.
Gasol,1,*
Ulla Li
Zweifel,2
Francesc
Peters,1
Jed A.
Fuhrman,3 and
Åke
Hagström2
Departament de Biologia Marina i
Oceanografia, Institut de Ciències del Mar, CSIC, E-08039
Barcelona, Catalunya, Spain1; Department
of Marine Sciences, Kalmar University, Kalmar,
Sweden2; and Department of Biological
Sciences, University of Southern California, Los Angeles, California
90089-03713
Received 6 April 1999/Accepted 27 July 1999
 |
ABSTRACT |
Total bacterial abundances estimated with different epifluorescence
microscopy methods (4',6-diamidino-2-phenylindole [DAPI], SYBR Green,
and Live/Dead) and with flow cytometry (Syto13) showed good
correspondence throughout two microcosm experiments with coastal
Mediterranean water. In the Syto13-stained samples we could
differentiate bacteria with apparent high DNA (HDNA) content and
bacteria with apparent low DNA (LDNA) content. HDNA bacteria, "live" bacteria (determined as such with the Molecular Probes Live/Dead BacLight bacterial viability kit), and
nucleoid-containing bacteria (NuCC) comprised similar fractions of the
total bacterial community. Similarly, LDNA bacteria and "dead"
bacteria (determined with the kit) comprised a similar fraction of the
total bacterial community in one of the experiments. The rates of
change of each type of bacteria during the microcosm experiments were
also positively correlated between methods. In various experiments
where predator pressure on bacteria had been reduced, we detected
growth of the HDNA bacteria without concomitant growth of the LDNA
bacteria, such that the percentage contribution of HDNA bacteria to
total bacterial numbers (%HDNA) increased. This indicates that the
HDNA bacteria are the dynamic members of the bacterial assemblage. Given how quickly and easily the numbers of HDNA and LDNA bacteria can
be obtained, and given the similarity to the numbers of "live" cells and NuCC, the %HDNA is suggested as a reference value for the
percentage of actively growing bacteria in marine planktonic environments.
| |
INTRODUCTION |
Bacteria have a very important role
in planktonic marine microbial food webs (e.g., see reference
1). They comprise an important share of plankton
biomass (9, 11), and their activity has a large impact on
ecosystem metabolism and function (e.g., see reference
7). Thus, the accurate determination of bacterial abundance, biomass, and activity is essential for our understanding of
pelagic oceanography.
Epifluorescence microscopy of acridine orange- or
4',6-diamidino-2-phenylindole (DAPI)-stained bacteria has been the
standard method of determining bacterial abundance in plankton samples for several decades (23, 43, 54). These methods are among the few in aquatic microbial ecology where agreement had been reached
in the interpretation of data obtained. However, Zweifel and
Hagström (55) presented evidence that a large fraction of what were then being counted as bacteria in DAPI-stained samples were in fact particles without a genome: dead cells, or ghosts. At
about the same time, Heissenberger et al. (17) showed
electron microscopic evidence that a large percentage of bacteria in
marine samples had damaged intracellular integrity. These two studies gave renewed importance to a question that had been put forward before:
are bacterial abundances overestimated due to the inclusion in the
counts of nonbacterial particles and dead cells?
It is known that a fraction of the bacteria in the ocean are not
actively growing. Different methods have been used to try to estimate
the relative contribution of actively growing and nongrowing bacteria
to the global bacterial pools: microautoradiography (33),
formazan salts reduction (46, 49), rRNA probes (22, 53), or other fluorescent dyes (e.g., see reference
46). But consensus on what each of the above-listed
methods measures has not yet been reached. Part of the problem in
understanding the meaning of the concepts "active" and
"inactive" when applied to bacteria arises from the fact that each
method partitions bacteria in a different and often not comparable way:
"active" and "live" are often used as analagous terms, and
"inactive" and "dead" are also often confounded. For example,
it is not known to what extent the inactive bacteria detected by some
of these methods include only bacterial ghosts or cell fragments, as
implicitly stated by some authors (17, 52, 55), or also
include cells in a state of very low or null respiration, as suggested
by Choi et al. (4). Similarly, active bacteria can be those
with intact membranes (17), those with detectable
respiratory activity (46, 49), those with compacted DNA
(55) resistant to enzymatic attack (52), or those
with enough rRNA to bind significant amounts of rRNA probes
(22). Intercalibration between the methods is needed.
In the past few years, flow cytometry has become a viable technique for
counting natural planktonic bacteria. Earlier, researchers used large,
expensive, and complicated flow cytometers with UV lasers in
conjunction with the DNA stains Hoechst 33342 and DAPI (34,
45). However, the availability of relatively simple and portable
flow cytometers with lasers emitting in the blue zone of the spectrum,
and of DNA stains that could be excited by these lasers (5, 26,
28, 30, 31), simplified the protocols and allowed an avalanche of
papers from many researchers using flow cytometry to count bacteria
(e.g., see reference 13). One of the observations of
the first researchers using this combination of bench-top flow
cytometers and blue-excitable DNA stains was that at least two groups
of bacteria could be differentiated based on cell-specific DNA staining
(28, 31). Li et al. (28) called the
high-DNA-content group the type II bacteria and the low-DNA-content group the type I bacteria. Jellett et al. (19) suggested the usage of an active cell index (ACI) to refer to the percent
contribution of type II bacteria to the bacterial community. In spite
of the field evidence presented by these authors, whether the ACI
numbers were comparable to other estimates of active bacterial numbers was left unexplored.
In this paper we compare several different methods of counting
planktonic bacteria, both by epifluorescence microscopy and by flow
cytometry. We further describe how the two bacterial groups based on
DNA staining are related to the numbers of "live" (determined as
such with the Live/Dead BacLight bacterial viability kit
from Molecular Probes) and nucleoid-containing NuCC cells on the one side and to the number of "dead" cells on the other side. We
conclude that the type II bacteria, renamed high-DNA (HDNA) bacteria,
are the dynamic members of the bacterial community and that the ACI (renamed %HDNA) is a valid estimate of the proportion of actively metabolizing bacteria in natural plankton communities, as good as, if
not better than, other methods currently in use.
| |
MATERIALS AND METHODS |
Experiments.
Data from experiments 1 and 2 (Exp#1 and Exp#2,
respectively) were used to compare the different methods of defining
bacterial subgroups. Exp#3 and Exp#4 were designed to test for the
effect of predators on the %HDNA bacteria. Exp#5 was used to check the growth of low-DNA (LDNA) and HDNA bacteria in the absence of predators.
Comparison between methods to define bacterial subgroups was done in
microcosm experiments (15-liter containers) with northwestern Mediterranean coastal water obtained between 1.5 and 2 miles offshore from the Masnou harbor in September 1997. After initial treatment (see
below), the microcosms were incubated in an environmental chamber at
20°C and under a 12 h-12 h light-dark cycle (225 microeinsteins m
2 s1). In Exp#1, water was separated into
different size fractions and this treatment was combined with a
turbulence treatment. In Exp#2, a combination of nutrients was added to
the treatments (see reference 41 for details). The
experiments were run for 5 days, and samples were taken every morning
and every afternoon. While all the samples were counted by flow
cytometry, half of the samples were counted with SYBR Green
epifluorescence, and only at 0, 36, and 84 h were samples counted
with the other protocols.
Exp#3 and Exp#4 were also done with northwestern Mediterranean coastal
water in microcosms and with samples from January 1997
and March 1997, respectively. The containers were 1-liter borosilicate
glass beakers
and were incubated at 14°C. In Exp#3, water was
filtered through a
150-µm Nitex net or, additionally, through
a 0.8-µm-pore-diameter
MF-Millipore cellulose ester filter. Details
are reported in the paper
by Peters et al. (
40). In Exp#4 the
water was filtered
through either a 1-µm- or a 20-µm-pore-diameter
polycarbonate
filter in order to vary the relative amount of flagellates
in the
samples.
Exp#5 was performed in the Ría de Vigo estuary in the Atlantic
coast of Spain during the cruise Incocéano-1 on board
R/V Cornide de Saavedra in May 1997. Water from a midestuary station
(3B4, at 42°9.05'N, 8°55.11'W) was filtered through polycarbonate
0.8-µm-pore-diameter filters, placed in 2-liter Nalgene bottles,
and
left to grow in the dark and at the in situ temperature in
a so-called
dilution-growth experiment. We estimated bacterial
activity as the rate
of radioactive leucine incorporation by bacteria,
using the method
described in Kirchman (
25) but in Eppendorf
vials as
suggested by Smith and Azam (
48). We added 40 nM leucine
to
quadruplicate vials plus two trichloroacetic acid-killed controls.
Further details can be obtained from the study by Gasol and Morán
(
12).
Flow cytometry.
Samples (1.2 ml) were immediately fixed with
freshly prepared 1% paraformaldehyde plus 0.05% glutaraldehyde (final
concentrations), incubated for 10 min at room temperature, and then
stored frozen in liquid nitrogen. The samples were later thawed,
stained with a dilution of dimethyl sulfoxide-Syto13 (Molecular Probes)
(10:1) at 2.5 µM, left for ~10 min in the dark to complete the
staining, and run through a flow cytometer. We used a Becton Dickinson
FACScalibur bench cytometer with a laser emitting at 488 nm. Samples
were run at low speed (approximately 18 µl min1), and
data were acquired in log mode until around 10,000 events had been
acquired. We were careful to maintain the rate of particle passage
below 300 events per second by diluting the sample, if necessary, with
sheath fluid (MilliQ water in this case). We added (10 µl per sample)
a solution of yellow-green 0.92-µm Polysciences latex beads
(106 beads ml1) as an internal standard. The
bead solution was counted daily, and the counts were cross-calibrated
with those concentrations obtained from the rate of particle passage
and the time of sample passage. This method is based on that published
by del Giorgio et al. (5). Bacteria were detected by their
signatures in a plot of 90° side light scatter (SSC) versus green
fluorescence (FL1). In a plot of FL1 versus red fluorescence (FL3) we
could differentiate photosynthetic prokaryotes from nonphotosynthetic prokaryotes. We separated HDNA bacteria from LDNA bacteria in the SSC
versus FL1 plot. Cytometric noise (that is, particles which cannot be
assigned to any population but that appear close to the position of the
populations) at times interferes with the determination of LDNA
bacteria. This noise can be either electronic or due to the fixative
not being fresh. After delimiting the bacterial populations in the SSC
versus FL1 plot, we separated cells from noise in the FL1 versus FL3
plot. In such a plot, the bacterial cells, both HDNA and LDNA, remain
in a diagonal line while the beads are placed in a parallel diagonal
and noise particles are placed in yet another one (see reference
13 for more details).
Epifluorescence microscopy.
Samples for determination of
bacterial abundance with DAPI (10 to 15 ml) were stained with the dye
(final concentration, 5 µg ml1) for 5 min and then were
filtered through 0.2-µm-pore-diameter black polycarbonate filters
(43). Filters were then mounted on microscope slides with
nonfluorescent oil (R. P. Cargille Lab., Inc.) and stored frozen
until counted. Filters were counted by epifluorescence microscopy with
a Nikon Labophot microscope. About 200 to 400 bacteria per sample were
counted. For heterotrophic nanoflagellates (HNF), slide preparation was
identical except for filtering larger volumes (up to 50 ml) and using
0.8-µm-pore-diameter polycarbonate filters. Between 100 and 200 cells
per filter were counted.
SYBR Green I (Molecular Probes) as supplied by the manufacturer was
diluted with filtered (pore diameter, 0.02 µm) deionized
water
(1:10), and 2.5 µl was added to a drop of filtered (pore
diameter,
0.02 µm), sterile deionized water on the bottom of a
petri dish. The
sample (1 to 5 ml) was filtered onto a 0.02-µm-pore-diameter
aluminum
oxide filter, and the filter was placed side up on the
stain drop left
in the petri dish. After 15 min of staining in
the dark, the filter was
removed, dried, and mounted with 50%
glycerol and 50%
phosphate-buffered saline with 0.1% phenylene-diamine.
Details are
provided by Noble and Fuhrman (
39).
Live/Dead.
We used the Molecular Probes Live/Dead
BacLight viability kit (16), which is formed by
Syto9 as a viability marker and propidium iodine (PI) as a
membrane-compromised cell marker. Both stains are added simultaneously
to the sample and left 10 to 15 min for staining. Thereafter, we
filtered the sample onto a 0.2-µm-pore-diameter polycarbonate filter
and rinsed the filter with 2 ml of isopropanol. The isopropanol rinse,
in our experience, does not reduce the total number of cells but does
distinguish the cells as clearly red or green fluorescent cells, as
opposed to leaving a large number of cells that exhibit a continuum of
in-between colors. Cells with damaged membranes will be penetrated by
both the live stain (Syto9) and the dead stain (PI), though the dead
stain supposedly binds more strongly to DNA. The isopropanol wash
possibly removes the less tightly bound stain in situations of
competition for binding sites.
The number of nucleoid-containing bacteria (NuCC) was quantified
according to the method of Zweifel and Hagström (
55).
Samples were killed with sodium azide (final concentration, 0.5
M),
diluted with filtered (pore diameter, 0.2 µm) MilliQ water,
and then
incubated for 2 h with DAPI (2 µg per ml of sample) and
Triton
X-100 (0.1 [vol/vol]). They were then collected onto the
polycarbonate filters. Ten milliliters of 2-propanol for destaining
of
the DAPI was then filtered, and the filter was air dried and
mounted.
These samples were viewed with a Zeiss Axioplan epifluorescence
microscope at a magnification of ×1,250 with filter set 450-490
FT 510 LP 520. The number of fields (typically more than 20) was
adjusted to
maintain a standard error of the enumeration of <5%.
Data comparisons.
Simultaneous determinations of cell
abundance were scaled to the Syto13 total count (TC) as percentages,
because the Syto13 numbers were always available. The rates of change
in cell abundance were computed as follows:
where
C0 is the cell concentration at the
beginning of the time interval considered,
Ct is
the concentration at the end of
that time interval, and
t is
the length of time (in days) of the
interval considered. The rates of
cell change are dimensionally
equivalent to specific growth
rates.
| |
RESULTS |
Total counts.
Throughout Exp#1 and Exp#2, the bacterial TC
varied one order of magnitude, from between 3.5 × 105
and 4.3 × 105 ml1 to between 3.7 × 106 and 4.4 × 106 ml1.
The detailed analyses of these variations is not the subject of this
paper. Because not all samples had been counted by all the methods, the
comparisons between the TC obtained with each method are uneven. On
average, the different methods of estimating the TC produced very
similar results, with all the log-log slopes between methods being not
significantly different from 1 (Table 1).
Most noticeably, the slopes of the relationship between the Syto13 TC
and the DAPI TC and between the SYBR Green TC and the Live/Dead TC were
between 0.94 and 1.09. Some individual values were sometimes different,
a fact that is reflected in relatively low correlation coefficients
(Table 1). The results achieved by the Live/Dead TC differed most from
those of the other methods. These relationships had lower correlation
coefficients and slopes further away from 1.
Flow cytometric heterogeneity.
When stained samples are run
through a flow cytometer, it is usually possible to separate bacteria
from cytometric noise both in a plot of 90° SSC versus green
fluorescence (normally FL1) and in a plot of FL3 versus FL1 (Fig.
1). In this last plot and with the
exception of extremely stratified oceanic waters, prochlorophytes and
cyanobacteria can be separated from chemotrophic bacteria (13). As shown by Fig. 1, on the basis of their SSC, FL1,
and FL3 signals, it is also possible to differentiate two groups of bacteria: we have called HDNA bacteria those that have high FL1 and FL3
values and LDNA bacteria those with slightly lower SSC values and much
lower fluorescence values.
View larger version (50K):
[in this window]
[in a new window]
|
FIG. 1.
Different ways of presenting the heterogeneity in the
bacterial population as it can be detected in a flow cytometric run of
a Syto13-stained sample. The dot plots correspond to the 10,000 events
acquired. The lower plots present the same data in a density plot where
the gray shading is proportional to the number of points in each area.
The HDNA bacteria and the LDNA bacteria form discrete clusters, as do
the reference 0.92-µm yellow-green beads. A second peak,
corresponding to the doublets of the beads, is also visible. In the
upper part of the graph we included three-dimensional plots of the same
data, where the two groups of bacteria and the beads can also be
differentiated. Note that, in the case of the SSC versus FL1 plot, we
have changed the angle of view.
|
|
HDNA bacteria.
In Exp#1 and Exp#2, the numbers of HDNA
bacteria varied by 14-fold and 21-fold, respectively, while the TCs
varied by 8-fold and 10-fold, respectively (Table
2). The numbers of HDNA bacteria were
well correlated with those of "live" bacteria (Pearson's correlation coefficient R = 0.52, n = 57, P < 0.0005) but not with the numbers of NuCC. However, the percentage
of the bacterial population classified as HDNA, NuCC, or live was very
similar in both experiments (Fig. 2),
averaging 60%.
View larger version (37K):
[in this window]
[in a new window]
|
FIG. 2.
Relationships between the different counts and types of
bacteria. Each individual value was scaled to the TC obtained with
Syto13 and flow cytometry (as those were the values that existed for
all sampling times), and all values for each experiment were averaged.
Error bars represent standard errors of the means. (A) Exp#1; (B)
Exp#2. FC, flow cytometry.
|
|
HDNA bacteria and "live" bacteria probably include the same cells;
this is evident from a plot in which we compared the rates
of variation
of HDNA bacteria throughout the different experiments
with those of the
"live" cells and those of NuCC (Fig.
3). Because
samples for "live" and
NuCC had only been collected at 0, 36,
and 84 h, the plot presents
the rates of variation of these types
of cells between 0 and 36 h
and between 36 and 84 h. The rates
of change of NuCC, HDNA cells,
and "live" cells were highly correlated
(
P was <0.0005
for all) (Table
3).
View larger version (22K):
[in this window]
[in a new window]
|
FIG. 3.
Relationships between the rates of change
(day1) of HDNA bacteria, NuCC, and "live" bacteria,
calculated between those sampling times for which there was data for
the three types. Data of Exp#1 and Exp#2 were placed together.
Statistical data for these graphs are presented in Table 3.
|
|
LDNA bacteria.
In Exp#1 and Exp#2, the numbers of LDNA
bacteria varied by 3-fold and 6-fold, respectively, while the TCs
varied by 8-fold and 10-fold, respectively (Table 2). The numbers of
LDNA bacteria were not significantly correlated with those of
"dead" bacteria, but the rates of change of these two groups of
bacteria were slightly correlated (P = 0.06) (Table 3).
In Exp#1 the average percentage of the TC classified as LDNA was very
close to the average percentage of the count classified as "dead"
cells (Fig. 2), with values around 37%. However, in Exp#2, while the
percentage of the count classified as "dead" cells averaged
18%, the average percentage of the TC classified as LDNA
bacteria was 41% (Fig. 2).
Predators and bacterial community composition.
The absence of
predators, or the uncoupling of bacterial predators and larger
predators in bottle experiments or in microcosms, immediately resulted
in the growth of HDNA bacteria without a concomitant growth of LDNA
bacteria (Fig. 4 to
6)
to the extent that the %HDNA bacteria approached >95% in 1 or 2 days. In Exp#3, and after a few days of incubation, HNF developed in
the treatment that had not been filtered through 0.8-µm-diameter
pores and produced a decline in the total number of bacteria (not
shown), with a dramatic decline in the %HDNA bacteria (Fig. 4).
View larger version (20K):
[in this window]
[in a new window]
|
FIG. 4.
Change in the %HDNA ( ) in microcosm Exp#3, where
water had been either filtered through 0.8-µm-diameter pores (upper
panel) or through 150-µm-diameter pores (lower panel). Results are
averages and standard errors of two replicates. After three days, we
counted the number of HNF ( ) in one of the replicates. Flagellates
did not grow in the <0.8-µm-pore-diameter filtrates.
|
|
View larger version (25K):
[in this window]
[in a new window]
|
FIG. 5.
The upper panel shows the change in the number of HDNA
bacteria and LDNA bacteria in samples of microcosm Exp#4, where water
had been filtered either through 1-µm pores (closed symbols) or
through 20-µm pores (open symbols). Results are averages and standard
errors of two replicated microcosms. The lower panel shows the change
in the number of HNF in the same experiment.
|
|
View larger version (24K):
[in this window]
[in a new window]
|
FIG. 6.
The upper panel shows the change in the numbers of HDNA
(), LDNA ( ), and total bacteria () in a dilution-growth
experiment performed in the Ría de Vigo estuary (Exp#5). The
lower panel shows the change in the %HDNA () and the rate of
radioactive leucine uptake by the whole community () in the same
experiment. inc., incorporation.
|
|
We designed Exp#4 to discern whether HNF development was associated
with a decrease in HDNA bacteria, with an increase in
LDNA bacteria, or
with both. The different treatments (filtration
through 1-µm pores
and filtration through 20-µm pores) left many
HNF in the water, but
these organisms grew only in the <20-µm-pore-diameter
filter
treatment (Fig.
5). HNF growth was associated with a slight
decrease in
the numbers of LDNA bacteria and with no net growth
of HDNA bacteria.
However, in the treatment where HNF did not
grow (pore diameter, <1
µm), a 2.5-fold increase in HDNA bacteria
occurred parallel to the
stability in the numbers of LDNA bacteria
(Fig.
5). HNF affected HDNA
bacteria in the treatment where they
developed (pore diameter, <20
µm).
Bacterial growth.
The HDNA bacteria were the dynamic
components of the bacterial community. In dilution-growth experiments
like the one whose results are presented in Fig. 6, where predators are
absent or unable to compensate for bacterial growth, a slight increase
in both HDNA and LDNA bacteria in the first 10 h was followed by a
decrease and stabilization of the numbers of LDNA bacteria and a
several-fold net increase of HDNA bacteria (Fig. 6). A large increase
in the rate of leucine uptake after 10 h was followed by HDNA
bacteria growth and an increase of the %HDNA to up to 90% of the
community (Fig. 6).
| |
DISCUSSION |
Total bacterial counts.
The different methods used to count
total bacteria provided reasonably close counts (Table 1). This is
especially true given that the different methods were analyzed by
different operators and that between-operator variability is recognized
as one of the main sources of error in counting planktonic bacteria
(24, 36). Interuser variability in counting DAPI-stained
samples usually ranges between 5 and 20% depending on the level of
complexity of the sample (e.g., see reference 23).
While the experiments presented here were not designed to test for good
correlation of results between methods, the data nevertheless clustered
around the 1:1 line and log-log slopes were very close to 1 (Table 1). Our correlations are similar to those presented by other researchers that have compared flow cytometry and epifluorescence TCs (5, 26,
28, 31).
The relationship between the Syto13 TCs and SYBR Green TCs was the
closest of all studied. While the DAPI counts could slightly
underestimate true concentration (~2 to 6% of the bacteria can
cross
the polycarbonate 0.2-µm-pore-diameter filter in these waters
[
12]), the SYBR Green-stained samples were counted on
0.02-µm-pore-diameter
filters, and it could be assumed that no
bacteria were left out
of the counts. Flow cytometry would also include
all bacteria,
because the method is so sensitive that even viruses can
be seen
and counted (
32). The Live/Dead method provided
systematically
lower counts and worse correlations than all other
methods considered.
The protocol for the double staining, which
includes displacement
of one stain by the other, could affect
reliability (
16). Even
with that in mind, however, the
correlations between the Live/Dead
TCs and those of the other methods
were
significant.
In a comparison of different nucleic acid dyes, Lebaron et al.
(
26) recommended Syto9 (the live component of the Live/Dead
kit) or SYBR Green II as the best stain for counting bacteria.
Syto13
generated slightly lower fluorescence yields than these
two stains,
especially in seawater, but is unlikely that this
could affect TC
determinations in routine work. The fact that
our Syto13 TCs correlate
well with those of other methods validates
the use of this stain for
normal work, without dismissing the
potential of other stains. Given
the recognized interuser variability
in counting total bacteria, we
consider our Syto13 flow cytometric
method a viable alternative for
obtaining TCs of marine planktonic
bacteria.
Relationship between bacterial size and fluorescence
intensity.
Syto13 is assumed to stain both DNA and RNA with a
similar quantum yield (16). However, Guindulain et al.
(14) found it to stain mainly DNA in natural marine samples,
a result that had also been observed for other stains, such as TO-PRO
and TO-TO (28). This is probably due to differences in the
ability of the stains to bind to the two nucleic acids in salt water
conditions, or to the relatively more protected structure of rRNA with
respect to DNA, rather than being due to the cell-specific amounts of RNA in natural marine bacteria (which tend to have similar amounts of
RNA and DNA [27]).
Evidence has accumulated to permit the association of high bacterial
fluorescence with high DNA content and large bacterial
size and of low
fluorescence with low DNA content and small bacterial
size. (i)
Filtration through filters of varying nominal pore sizes
alters the
composition of the bacterial community in terms of
%HDNA, strongly
suggesting that there is a correspondence between
bacterial average
size and fluorescence intensity (
12). (ii)
This
correspondence has also been established for bacteria stained
with
other DNA stains, suggesting a direct relationship between
fluorescence
and bacterial size through the relationship between
fluorescence and
DNA content and that between DNA content and
size (
50).
(iii) The average fluorescence of the bacterial population,
as
normalized to that of the beads, is well correlated
(
r2 = 0.66) with bacterial size (range
analyzed, 0.028 to 0.072 µm
3) determined empirically in a
range of planktonic environments
by image analysis of DAPI-stained
samples (
44).
To further explore the relationship between size and relative
fluorescence, we performed a small experiment in which we centrifuged
a
bacterial community for 5 min and measured the relative concentration
of each group of bacteria (Table
4). HDNA
bacteria sedimented
faster than LDNA bacteria. When the sample was
resuspended, concentrations
of LDNA and HDNA cells similar to the
original ones were recovered,
providing an indication of the different
relative densities of
the two types of cells, which, even if not
definitive by itself,
provides evidence that supports the results cited
above. In a
similar experiment NuCC were found to sediment faster than
the
ghost cells (
55). Thus, the HDNA bacteria are larger and
denser
cells and the LDNA bacteria are smaller and less dense cells.
View this table:
[in this window]
[in a new window]
|
TABLE 4.
Concentrations of total, HDNA, and LDNA bacteria in, and
%HDNA values for, the supernatant of a plankton
marine samplea
|
|
Two groups of marine planktonic bacteria.
Sieracki and Viles
(47) detected the presence of a very abundant type of small
and dim particles stained both with DAPI and with acridine orange in a
detailed image analysis study of bacteria in the North Atlantic. Those
small and dim particles that were stained with DAPI were 45 to 85% of
the DAPI TC, and in dot plots of fluorescence versus size, they were
clustered separately from the high fluorescence, large size cells.
These authors speculated that the low fluorescence group of bacteria could be either large viruses, microbacteria, or even DAPI-stained free
DNA. A similar low DAPI fluorescence group of cells appeared in the
flow cytometry analysis of Kaneohe Bay bacteria presented by Monger and
Landry (34). Since the publication of that paper, the two
groups of bacteria have been observed in TO-TO- and TO-PRO-stained marine bacteria (28), in DAPI-stained freshwater bacteria
(3), and in SYBR Green-stained marine bacteria
(31).
Large (0.4 µm) viruses exist in the ocean (
2) and could
form a portion of the low fluorescence particles, but it is certainly
unlikely that they would be such a large percentage of the total
bacterial count. Bacteria with sizes around 0.2 µm
(ultramicrobacteria
[
29]) but completely active could
also make up part of the low
fluorescence particles. However, if the
LDNA particles include
active bacteria, why should there be a clear
separation between
both (high and low fluorescence, HDNA and LDNA)
bacterial groups?
And why did such LDNA bacteria not show any growth in
the dilution-growth
experiments (Fig.
6)? While bacterial
subpopulations other than
our LDNA and HDNA groups have been described
in some studies (
13,
18,
28,
31), only these two groups are
consistently found
in all
samples.
Even though the bacterial subgroups have been observed by many authors,
few studies have been conducted to characterize the
significance of
each of the groups. Li et al. (
28) found that
their group II
bacteria (HDNA) counts were better correlated with
chlorophyll
a than their group I (LDNA) bacteria and that the
fluorescence difference between the two groups was positively
related
to chlorophyll. Also, in a dilution-growth experiment,
they showed that
HDNA bacteria grew three times as fast as LDNA
bacteria. In a follow-up
paper, Jellett et al. (
19) compared
the %HDNA (their ACI)
to tritiated substrate uptake rates and
found patterns of %HDNA and
substrate uptake that were similar.
They also determined that the HDNA
cells had on average five times
more DNA per cell than the LDNA cells.
The work of Li and colleagues,
which were the only studies that tried
to unveil the meaning of
the different subpopulations, suggested that
the %HDNA values
had potential for being a useful index of bacterial
growth.
Interestingly, Jellett et al. (
19) considered the LDNA
bacteria to be inactive cells rather than bacterial ghosts on the
basis
of a slow growth pattern in the experiments reported and
on the basis
of varying concentrations in different sites. Button
et al.
(
3) considered the dim particles (LDNA) that appeared
in
Lake Zürich not to be ghosts because they had normal light
scatter signals. That LDNA bacteria have 90° scatter signals which
are similar to those of the HDNA bacteria is also apparent from
Fig.
1.
However, the flow cytometric measurement of light scatter
for particles
below 0.5 µm is problematic, cannot be considered
precise, and by no
means can be related to bacterial size in natural
planktonic bacteria
(
51).
We directly compared the values of HDNA bacteria with those of
"live" bacteria and with those of NuCC and obtained a striking
correspondence between average values (Fig.
2) and between directly
estimated rates of change through time (Fig.
3). That the relationships
are relatively strong is important because the range of the data
was
not large and, as commented above, each method bears a certain
degree
of inexactitude in the determinations that adds up when
two methods are
compared.
It is now recognized that a certain percentage of all bacteria in
planktonic environments are not actively growing (
35).
To
have better estimates of the rates of activity and growth of
individual
bacteria in the sea, bulk parameters should be scaled
to the number of
bacteria that are alive or maintain cellular
activity (
8,
46). It is equally important that the absolute
number of bacteria
that rRNA or DNA probes are expected to recover
be known to determine
whether there are bacteria that are not
recognized by the probes
(
42). A wide range of methods are available
for the
determination of this number of active (or actively growing)
bacteria:
microautoradiography (
33), the direct viable count
(
20), formazan salts (
46,
49), universal rRNA
probes (
22,
53), etc. However, the methods differ in what
they are actually
measuring and no consensus has been reached yet on
whether the
results are at all comparable. In that sense, our results
include
the determination of the %HDNA as a valid method for the fast
determination of the number of active, actively growing, and/or
live
bacteria. HDNA bacteria are live bacteria, but our data do
not allow us
to conclude whether they are live and active or live
and
inactive.
We also compared the abundance of "dead" (PI-stained) and LDNA
bacteria and found some evidence of correspondence between
those
numbers (Fig.
2 and Table
3). At least three types of particles
could
be included in the LDNA bacteria pool: inactive cells, recently
dead
bacteria, and fragments of cells that still have pieces of
DNA able to
bind the stains. The last two of these three groups
of bacteria could
properly be called bacterial ghosts. Direct
evidence for the presence
of dead cells in seawater has been provided
by Heissenberger et al.
(
17), who observed free-living cells
by transmission
electron microscopy and found that only 34% of
the cells had
well-preserved internal structures while 42% of
the cells exhibited
cellular damage and 24% lacked any internal
structure.
Inactive cells can be assumed to have lower DNA content than growing
cells due to their nonreplicative state. Jellett et al.
(
19)
calculated that HDNA bacteria had on average five times
more DNA per
cell than LDNA bacteria, and this would seem to indicate
that dead
cells must be a significant component of the LDNA fraction.
A fast
growing cell can have two to three times more DNA than
an inactive cell
due to multiple replication forks. Additionally,
inactive cells may
lose extra DNA, such as plasmids or copies
of the same gene (e.g., see
reference
38). LDNA cells did not
present any growth
when predators had been removed in Exp#4 and
Exp#5 (Fig.
5 and
6).
Thus, it seems likely that dead cells (i.e.,
recently dead cells or
cell fragments with DNA remains) are a
large part of the LDNA fraction.
Because there should be a continuum
between cell fragments with no DNA
and recently dead cells with
still large amounts of DNA, the separation
of LDNA bacteria from
noise in a SSC versus FL1 plot could at times be
problematic (Fig.
1).
HDNA and LDNA bacteria in nature.
In the experiments reported,
the ratio of HDNA bacteria to total bacterial abundance (HDNA plus
LDNA) ranged from <40 to >80%, with an average of ~55%. We have
further unpublished data on %HDNA in field samples that range from
<15% (in deep central Atlantic samples) to 95% (in a eutrophic
reservoir). Li et al. (28) in the Mediterranean and in the
North Atlantic (10 to 90%) and Jellett et al. (19) in
Bedford Basin (19 to 80%) found similar ranges. These %HDNA values
compare well to those of active bacteria obtained by different methods,
values that tend to cluster at around ~50% when autoradiography and
rRNA probes are used (e.g., see reference 22) or at
~25% when formazan salts are used (46). The values are
relatively larger than the NuCC values published to date, which tend to
be below 30 to 40% (4, 15, 55), with a few exceptions
approaching (10, 22, 42) or surpassing (52) 50%.
What drives the variability in the relative amount of active, live,
and/or HDNA bacteria and inactive, dead, and/or LDNA bacteria?
Our
results provide evidence that when predators are absent the
%HDNA
tends to approach a value close to the maximum (Fig.
4 to
6). The
relaxation in predator control allows the growth of HDNA
bacteria to
the point that they dominate the community. The development
of
predators reverses the situation (Fig.
4), probably due to
their
preferential predation of HDNA bacteria. It has been shown
that
predators prefer larger-sized (
21) and active (
6)
bacteria,
consistent with our interpretation that HDNA bacteria are
large
and active cells. Grazing on bacteria by flagellates
(
37) or
viral activity could be responsible for the
production of LDNA
bacteria if they are composed mainly of dead and
damaged cells.
However, if LDNA bacteria include both of these types of
cells
and inactive or starved cells, then nutrient availability could
be the ultimate factor responsible for the variability of LDNA
bacterial
numbers.
Conclusion.
We have presented evidence of a good relationship
between the numbers of "live" bacteria as measured with the
Molecular Probes Live/Dead kit, of NuCC, and of HDNA cells as
determined by flow cytometry of Syto13-stained plankton samples. We
also show that HDNA bacteria are the dynamic members of the bacterial
community, those that respond immediately to changes in predation
pressure and nutrient availability. We endorse the use of the %HDNA as an index of the amount of active or live bacterial cells in plankton that can be obtained in less than a minute if a flow cytometer is available.
We envision a representation of bacteria in plankton in which the total
DAPI count, which has been and still is for most researchers
the true
number of bacteria, is composed of different particles.
Ordered from
less active to more active, (i) some are not even
bacteria (large
viruses and cell fragments, or ghosts); (ii) some
are dead cells, with
no potential for growth but intact with regard
to shape; (iii) some are
inactive because the proper conditions
for their development are not
present; (iv) some are growing at
a very low rate; and (v) some are
large, growing at a fast pace.
To study the composition of the
bacterial community we have many
tools that will correctly identify the
fifth group as highly active
and live and the first and second groups
as inactive and dead.
The problem arises with the third and fourth
groups, which probably
are the most abundant ones, for which each
method indicates different
quantities and whose cells are assigned to
an active or an inactive
pool depending on the method, the protocol,
and even the researcher
involved, thus generating the lack of consensus
for a universal
method to determine these numbers. In that framework,
we suggest
that the determinations of HDNA and LDNA bacteria and of the
%HDNA
values are a fast and simple alternative, as good as, or even
better than, most of the other currently used
techniques.
| |
ACKNOWLEDGMENTS |
We thank Cèlia Marrasé, who brought us all together
for the TURMED experiments, and we thank all the other researchers
that participated in the workshop. Data of the Incocéano cruise
was collected with the help of Carlos Pedrós-Alió, who also
supported this work in many other ways. R. Massana and M. Schauer
contributed to the estimation of the DAPI count variability in our lab.
We also appreciate the usual help of S. Canut and the comments and encouragement of P. A. del Giorgio, C. Marrassé, and R. Massana.
This work was supported by two EU grants,
MAS3-CT95-0016 (MEDEA) and MAR95-1901-C03-03 (MIDAS).
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Departament de
Biologia Marina i Oceanografia, Institut de Ciències del Mar,
CSIC, Passeig Joan de Borbó s/n, E-08039 Barcelona, Catalunya,
Spain. Phone: (3493) 2216416. Fax: (3493) 2217340. E-mail:
pepgasol{at}icm.csic.es.
| |
REFERENCES |
| 1.
|
Azam, F.,
T. Fenchel,
J. G. Field,
J. S. Gray,
L.-A. Meyer-Reil, and F. Thingstad.
1983.
The ecological role of water-column microbes in the sea.
Mar. Ecol. Prog. Ser.
10:257-263.
|
| 2.
|
Bratbak, G.,
O. H. Haslund,
M. Heldal,
A. Næs, and T. Røeggen.
1992.
Giant marine viruses?
Mar. Ecol. Prog. Ser.
85:201-202.
|
| 3.
|
Button, D. K.,
B. R. Robertson, and F. Jüttner.
1996.
Microflora of a subalpine lake: bacterial populations, size and DNA distributions, and their dependence on phosphate.
FEMS Microbiol. Ecol.
21:87-101.
|
| 4.
|
Choi, J. W.,
E. B. Sherr, and B. F. Sherr.
1996.
Relation between presence-absence of a visible nucleoid and metabolic activity in bacterioplankton cells.
Limnol. Oceanogr.
41:1161-1168.
|
| 5.
|
del Giorgio, P. A.,
D. F. Bird,
Y. T. Prairie, and D. Planas.
1996.
Flow cytometric determination of bacterial abundance in lake plankton with the green nucleic acid stain SYTO 13.
Limnol. Oceanogr.
41:783-789.
|
| 6.
|
del Giorgio, P. A.,
J. M. Gasol,
D. Vaqué,
P. Mura,
S. Agustí, and C. M. Duarte.
1996.
Protistan control of the proportion of metabolically active cells in coastal marine bacterioplankton.
Limnol. Oceanogr.
41:1169-1179.
|
| 7.
|
del Giorgio, P. A.,
J. J. Cole, and A. Cimbleris.
1997.
Respiration rates in bacteria exceed phytoplankton production in unproductive aquatic systems.
Nature
385:148-151.
|
| 8.
|
del Giorgio, P. A.,
Y. T. Prairie, and D. F. Bird.
1997.
Coupling between rates of bacterial production and the abundance of metabolically active bacteria in lakes, enumerated using CTC reduction and flow cytometry.
Microb. Ecol.
34:144-154[Medline].
|
| 9.
|
Fuhrman, J. A.,
T. D. Sleeter,
C. A. Carlson, and L. M. Proctor.
1989.
Dominance of bacterial biomass in the Sargasso Sea and its ecological implications.
Mar. Ecol. Prog. Ser.
57:207-217.
|
| 10.
|
Gasol, J. M.,
M. D. Doval,
J. Pinhassi,
J. I. Calderón-Paz,
N. Guixa-Boixereu,
D. Vaqué, and C. Pedrós-Alió.
1998.
Diel variations in bacterial heterotrophic production in the northwestern Mediterranean Sea.
Mar. Ecol. Prog. Ser.
164:125-133.
|
| 11.
|
Gasol, J. M.,
P. A. del Giorgio, and C. M. Duarte.
1997.
Biomass distribution of marine planktonic communities.
Limnol. Oceanogr.
42:1353-1363.
|
| 12.
|
Gasol, J. M., and X. A. G. Morán.
1999.
Effects of filtration on bacterial activity and picoplankton community structure as assessed by flow cytometry.
Aquat. Microb. Ecol.
16:251-264.
|
| 13.
| Gasol, J. M., and P. A. del Giorgio.
Using flow cytometry for counting natural planktonic bacteria and
understanding the structure of planktonic bacterial communities. Sci.
Mar, in press.
|
| 14.
|
Guindulain, T.,
J. Comas, and J. Vives-Rego.
1997.
Use of nucleic acid dyes SYTO-13, TOTO-1, and YOYO-1 in the study of Escherichia coli and marine prokaryotic populations by flow cytometry.
Appl. Environ. Microbiol.
63:4608-4611[Abstract].
|
| 15.
| Hagström, Å., J. Pinhassi, and U. L. Zweifel. Unpublished results.
|
| 16.
|
Haugland, R. P.
1996.
Handbook of fluorescent probes and research chemicals.
Molecular Probes Inc., Eugene, Oreg.
|
| 17.
|
Heissenberger, A.,
G. G. Leppard, and G. J. Herndl.
1996.
Relationship between the intracellular integrity and the morphology of the capsular envelope in attached and free-living marine bacteria.
Appl. Environ. Microbiol.
62:4521-4528[Abstract].
|
| 18.
|
Jacquet, S.,
J.-F. Lennon,
D. Marie, and D. Vaulot.
1998.
Picoplankton population dynamics in coastal waters of the northwestern Mediterranean Sea.
Limnol. Oceanogr.
43:1916-1931.
|
| 19.
|
Jellett, J. F.,
W. K. W. Li,
P. M. Dickie,
A. Boraie, and P. E. Kepkay.
1996.
Metabolic activity of bacterioplankton communities assessed by flow cytometry and single carbon substrate utilization.
Mar. Ecol. Prog. Ser.
136:213-225.
|
| 20.
|
Joux, F., and P. Lebaron.
1997.
Ecological implications of an improved direct viable count method for aquatic bacteria.
Appl. Environ. Microbiol.
63:3643-3647[Abstract].
|
| 21.
|
Jürgens, K., and H. Güde.
1994.
The potential importance of grazing-resistant bacteria in planktonic systems.
Mar. Ecol. Prog. Ser.
112:169-188.
|
| 22.
|
Karner, M., and J. A. Fuhrman.
1997.
Determination of active marine bacterioplankton: a comparison of universal 16S rRNA probes, autoradiography, and nucleoid staining.
Appl. Environ. Microbiol.
63:1208-1213[Abstract].
|
| 23.
|
Kepner, R. L., Jr., and J. R. Pratt.
1994.
Use of fluorochromes for direct enumeration of total bacteria in environmental samples: past and present.
Microbiol. Rev.
58:603-615[Abstract/Free Full Text].
|
| 24.
|
Kirchman, D. L.
1993.
Statistical analysis of direct counts of microbial abundance, p. 117-119.
In
P. F. Kemp, B. F. Sherr, E. B. Sherr, and J. J. Cole (ed.), Handbook of methods in aquatic microbial ecology. Lewis Publishers, Boca Raton, Fla.
|
| 25.
|
Kirchman, D. L.
1993.
Leucine incorporation as a measure of biomass production by heterotrophic bacteria, p. 509-512.
In
P. F. Kemp, B. F. Sherr, E. B. Sherr, and J. J. Cole (ed.), Handbook of methods in aquatic microbial ecology. Lewis Publishers, Boca Raton, Fla.
|
| 26.
|
Lebaron, P.,
N. Parthuisot, and P. Catala.
1998.
Comparison of blue nucleic acid dyes for flow cytometric enumeration of bacteria in aquatic systems.
Appl. Environ. Microbiol.
64:1725-1730[Abstract/Free Full Text].
|
| 27.
|
Lee, S.-H., and P. F. Kemp.
1994.
Single-cell RNA content of natural marine planktonic bacteria measured by hybridization with multiple 16S rRNA-targeted fluorescent probes.
Limnol. Oceanogr.
39:869-879.
|
| 28.
|
Li, W. K. W.,
J. F. Jellett, and P. M. Dickie.
1995.
DNA distributions in planktonic bacteria stained with TOTO or TO-PRO.
Limnol. Oceanogr.
40:1485-1495.
|
| 29.
|
MacDonell, M. T., and M. A. Hood.
1982.
Isolation and characterization of ultramicrobacteria from a Gulf Coast estuary.
Appl. Environ. Microbiol.
43:566-571[Abstract/Free Full Text].
|
| 30.
|
Marie, D.,
D. Vaulot, and F. Partensky.
1996.
Application of the novel nucleic acid dyes YOYO-1, YO-PRO-1, and PicoGreen for flow cytometric analysis of marine prokaryotes.
Appl. Environ. Microbiol.
62:1649-1655[Abstract].
|
| 31.
|
Marie, D.,
F. Partensky,
S. Jacquet, and D. Vaulot.
1997.
Enumeration and cell cycle analysis of natural populations of marine picoplankton by flow cytometry using the nucleic acid stain SYBR Green I.
Appl. Environ. Microbiol.
63:186-193[Abstract].
|
| 32.
|
Marie, D.,
C. P. D. Brussard,
R. Thyrhaug,
G. Bratbak, and D. Vaulot.
1999.
Enumeration of marine viruses in culture and natural samples by flow cytometry.
Appl. Environ. Microbiol.
65:45-52[Abstract/Free Full Text].
|
| 33.
|
Meyer-Reil, L.-A.
1978.
Autoradiography and epifluorescence microscopy combined for the determination of number and spectrum of actively metabolizing bacteria in natural waters.
Appl. Environ. Microbiol.
36:506-512[Abstract/Free Full Text].
|
| 34.
|
Monger, B. C., and M. R. Landry.
1993.
Flow cytometric analysis of marine bacteria with Hoechst 33342.
Mar. Ecol. Prog. Ser.
59:905-911.
|
| 35.
|
Morita, R. Y.
1997.
Bacteria in oligotrophic environments. Starvation-survival lifestyle.
Chapman & Hall, New York, N.Y.
|
| 36.
|
Nagata, T.,
T. Someya,
T. Konda,
M. Yamamoto,
K. Morikawa,
M. Fukui,
N. Kuroda,
T. Takahashi,
S. Oh,
M. Mori,
S. Araki, and K. Kato.
1989.
Intercalibration of the acridine orange direct count method of aquatic bacteria.
Bull. Jpn. Soc. Microb. Ecol.
4:89.
|
| 37.
|
Nagata, T., and D. L. Kirchman.
1992.
Release of macromolecular organic complexes by heterotrophic marine flagellates.
Mar. Ecol. Prog. Ser.
83:233-240.
|
| 38.
|
Neidhardt, F. C.,
J. L. Ingraham, and M. Schaechter.
1990.
Physiology of the bacterial cell. A molecular approach.
Sinauer Associates, Inc., Sunderland, Mass.
|
| 39.
|
Noble, R. T., and J. A. Fuhrman.
1998.
Use of SYBR Green I for rapid epifluorescence counts of marine viruses and bacteria.
Aquat. Microb. Ecol.
14:113-118.
|
| 40.
|
Peters, F.,
C. Marrasé,
J. M. Gasol,
M. M. Sala, and L. Arin.
1998.
Food-web mediated effects of turbulence on bacterial production and growth.
Mar. Ecol. Prog. Ser.
172:293-303.
|
| 41.
| Peters, F., C. Marrasé, H. Havskum, F. Rassoulzadegan, J. Dolan, M. Alcaraz, and J. M. Gasol.
Unpublished results.
|
| 42.
|
Pinhassi, J.,
U. L. Zweifel, and Å. Hagström.
1997.
Dominant marine bacterioplankton species found among colony-forming bacteria.
Appl. Environ. Microbiol.
63:3359-3366[Abstract].
|
| 43.
|
Porter, K. G., and Y. S. Feig.
1980.
The use of DAPI for identification and enumeration of bacteria and blue-green algae.
Limnol. Oceanogr.
25:943-948.
|
| 44.
| Prairie, Y. T., P. A. del Giorgio, D. F. Bird, and J. M. Gasol. Unpublished results.
|
| 45.
|
Robertson, B. R., and D. K. Button.
1989.
Characterizing aquatic bacteria according to population, cell size, and apparent DNA content by flow cytometry.
Cytometry
10:70-76[Medline].
|
| 46.
|
Sherr, B. F.,
P. A. del Giorgio, and E. B. Sherr.
1999.
Estimating abundance and single-cell characteristics of actively respiring bacteria via the redox dye CTC.
Aquat. Microb. Ecol.
18:117-131.
|
| 47.
|
Sieracki, M. E., and C. L. Viles.
1992.
Distributions and fluorochrome-staining properties of submicrometer particles and bacteria in the North Atlantic.
Deep-Sea Res.
39:1919-1929.
|
| 48.
|
Smith, D. C., and F. Azam.
1992.
A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine.
Mar. Microb. Food Webs
6:107-114.
|
| 49.
|
Tabor, P. S., and R. A. Neihof.
1982.
Improved method for determination of respiring individual microorganisms in natural waters.
Appl. Environ. Microbiol.
43:1249-1255[Abstract/Free Full Text].
|
| 50.
|
Veldhuis, M. J. W.,
T. L. Cucci, and M. E. Sieracki.
1997.
Cellular DNA content of marine phytoplankton using two new fluorochromes: taxonomic and ecological implications.
J. Phycol.
33:527-541.
|
| 51.
|
Vives-Rego, J.,
R. López-Amorós, and J. Comas.
1994.
Flow cytometric narrow-angle light scatter and cell size during starvation of Escherichia coli in artificial sea water.
Lett. Appl. Microbiol.
19:374-376.
|
| 52.
|
Vosjan, J. H., and G. J. van Noort.
1998.
Enumerating nucleoid-visible marine bacterioplankton: bacterial abundance determined after storage of formalin fixed samples agree with isopropanol rinsing method.
Aquat. Microb. Ecol.
14:149-154.
|
| 53.
|
Williams, S. C.,
Y. Hong,
D. C. A. Danavall,
M. H. Howard-Jones,
D. Gibson,
M. E. Frischer, and P. G. Verity.
1998.
Distinguishing between living and nonliving bacteria: evaluation of the vital stain propidium iodine and its combined use with molecular probes in aquatic samples.
J. Microbiol. Methods
32:225-236.
|
| 54.
|
Zimmermann, R., and L.-A. Meyer-Reil.
1974.
A new method for fluorescence staining of bacterial populations on membrane filters.
Kieler Meeresforsch.
30:24-27.
|
| 55.
|
Zweifel, U. L., and Å. Hagström.
1995.
Total counts of marine bacteria include a large fraction of non-nucleoid-containing bacteria (ghosts).
Appl. Environ. Microbiol.
61:2180-2185[Abstract].
|
Applied and Environmental Microbiology, October 1999, p. 4475-4483, Vol. 65, No. 10
0099-2240/99/$04.00+0
Copyright © 1999, American Society for Microbiology. All rights reserved.
This article has been cited by other articles:
-
Cunliffe, M., Whiteley, A. S., Newbold, L., Oliver, A., Schafer, H., Murrell, J. C.
(2009). Comparison of Bacterioneuston and Bacterioplankton Dynamics during a Phytoplankton Bloom in a Fjord Mesocosm. Appl. Environ. Microbiol.
75: 7173-7181
[Abstract]
[Full Text]
-
Bonilla-Findji, O., Herndl, G. J., Gattuso, J.-P., Weinbauer, M. G.
(2009). Viral and Flagellate Control of Prokaryotic Production and Community Structure in Offshore Mediterranean Waters. Appl. Environ. Microbiol.
75: 4801-4812
[Abstract]
[Full Text]
-
Falcioni, T., Papa, S., Gasol, J. M.
(2008). Evaluating the Flow-Cytometric Nucleic Acid Double-Staining Protocol in Realistic Situations of Planktonic Bacterial Death. Appl. Environ. Microbiol.
74: 1767-1779
[Abstract]
[Full Text]
-
Nocker, A., Sossa-Fernandez, P., Burr, M. D., Camper, A. K.
(2007). Use of Propidium Monoazide for Live/Dead Distinction in Microbial Ecology. Appl. Environ. Microbiol.
73: 5111-5117
[Abstract]
[Full Text]
-
Felip, M., Andreatta, S., Sommaruga, R., Straskrabova, V., Catalan, J.
(2007). Suitability of Flow Cytometry for Estimating Bacterial Biovolume in Natural Plankton Samples: Comparison with Microscopy Data. Appl. Environ. Microbiol.
73: 4508-4514
[Abstract]
[Full Text]
-
Berney, M., Hammes, F., Bosshard, F., Weilenmann, H.-U., Egli, T.
(2007). Assessment and Interpretation of Bacterial Viability by Using the LIVE/DEAD BacLight Kit in Combination with Flow Cytometry. Appl. Environ. Microbiol.
73: 3283-3290
[Abstract]
[Full Text]
-
Caro, A., Gros, O., Got, P., De Wit, R., Troussellier, M.
(2007). Characterization of the Population of the Sulfur-Oxidizing Symbiont of Codakia orbicularis (Bivalvia, Lucinidae) by Single-Cell Analyses. Appl. Environ. Microbiol.
73: 2101-2109
[Abstract]
[Full Text]
-
Alonso-Saez, L., Gasol, J. M., Lefort, T., Hofer, J., Sommaruga, R.
(2006). Effect of Natural Sunlight on Bacterial Activity and Differential Sensitivity of Natural Bacterioplankton Groups in Northwestern Mediterranean Coastal Waters. Appl. Environ. Microbiol.
72: 5806-5813
[Abstract]
[Full Text]
-
Ordax, M., Marco-Noales, E., Lopez, M. M., Biosca, E. G.
(2006). Survival Strategy of Erwinia amylovora against Copper: Induction of the Viable-but-Nonculturable State.. Appl. Environ. Microbiol.
72: 3482-3488
[Abstract]
[Full Text]
-
Martens-Habbena, W., Sass, H.
(2006). Sensitive Determination of Microbial Growth by Nucleic Acid Staining in Aqueous Suspension. Appl. Environ. Microbiol.
72: 87-95
[Abstract]
[Full Text]
-
Pinhassi, J., Simo, R., Gonzalez, J. M., Vila, M., Alonso-Saez, L., Kiene, R. P., Moran, M. A., Pedros-Alio, C.
(2005). Dimethylsulfoniopropionate Turnover Is Linked to the Composition and Dynamics of the Bacterioplankton Assemblage during a Microcosm Phytoplankton Bloom. Appl. Environ. Microbiol.
71: 7650-7660
[Abstract]
[Full Text]
-
Longnecker, K., Sherr, B. F., Sherr, E. B.
(2005). Activity and Phylogenetic Diversity of Bacterial Cells with High and Low Nucleic Acid Content and Electron Transport System Activity in an Upwelling Ecosystem. Appl. Environ. Microbiol.
71: 7737-7749
[Abstract]
[Full Text]
-
Nishimura, Y., Kim, C., Nagata, T.
(2005). Vertical and Seasonal Variations of Bacterioplankton Subgroups with Different Nucleic Acid Contents: Possible Regulation by Phosphorus. Appl. Environ. Microbiol.
71: 5828-5836
[Abstract]
[Full Text]
-
Mou, X., Moran, M. A., Stepanauskas, R., Gonzalez, J. M., Hodson, R. E.
(2005). Flow-Cytometric Cell Sorting and Subsequent Molecular Analyses for Culture-Independent Identification of Bacterioplankton Involved in Dimethylsulfoniopropionate Transformations. Appl. Environ. Microbiol.
71: 1405-1416
[Abstract]
[Full Text]
-
Leuko, S., Legat, A., Fendrihan, S., Stan-Lotter, H.
(2004). Evaluation of the LIVE/DEAD BacLight Kit for Detection of Extremophilic Archaea and Visualization of Microorganisms in Environmental Hypersaline Samples. Appl. Environ. Microbiol.
70: 6884-6886
[Abstract]
[Full Text]
-
Sekar, R., Fuchs, B. M., Amann, R., Pernthaler, J.
(2004). Flow Sorting of Marine Bacterioplankton after Fluorescence In Situ Hybridization. Appl. Environ. Microbiol.
70: 6210-6219
[Abstract]
[Full Text]
-
Pernthaler, J., Zollner, E., Warnecke, F., Jurgens, K.
(2004). Bloom of Filamentous Bacteria in a Mesotrophic Lake: Identity and Potential Controlling Mechanism. Appl. Environ. Microbiol.
70: 6272-6281
[Abstract]
[Full Text]
-
Calvo-Diaz, A., Moran, X. A. G., Nogueira, E., Bode, A., Varela, M.
(2004). Picoplankton community structure along the northern Iberian continental margin in late winter-early spring. J PLANKTON RES
26: 1069-1081
[Abstract]
[Full Text]
-
Maalej, S., Denis, M., Dukan, S.
(2004). Temperature and growth-phase effects on Aeromonas hydrophila survival in natural seawater microcosms: role of protein synthesis and nucleic acid content on viable but temporarily nonculturable response. Microbiology
150: 181-187
[Abstract]
[Full Text]
-
Bertilsson, S., Hansson, L.-A., Graneli, W., Philibert, A.
(2003). Size-selective predation on pelagic microorganisms in Arctic freshwaters. J PLANKTON RES
25: 621-632
[Abstract]
[Full Text]
-
Pernthaler, A., Pernthaler, J., Schattenhofer, M., Amann, R.
(2002). Identification of DNA-Synthesizing Bacterial Cells in Coastal North Sea Plankton. Appl. Environ. Microbiol.
68: 5728-5736
[Abstract]
[Full Text]
-
Luna, G. M., Manini, E., Danovaro, R.
(2002). Large Fraction of Dead and Inactive Bacteria in Coastal Marine Sediments: Comparison of Protocols for Determination and Ecological Significance. Appl. Environ. Microbiol.
68: 3509-3513
[Abstract]
[Full Text]
-
Eguchi, M., Ostrowski, M., Fegatella, F., Bowman, J., Nichols, D., Nishino, T., Cavicchioli, R.
(2001). Sphingomonas alaskensis Strain AFO1, an Abundant Oligotrophic Ultramicrobacterium from the North Pacific. Appl. Environ. Microbiol.
67: 4945-4954
[Abstract]
[Full Text]
-
Zubkov, M. V., Fuchs, B. M., Burkill, P. H., Amann, R.
(2001). Comparison of Cellular and Biomass Specific Activities of Dominant Bacterioplankton Groups in Stratified Waters of the Celtic Sea. Appl. Environ. Microbiol.
67: 5210-5218
[Abstract]
[Full Text]
-
Gregori, G., Citterio, S., Ghiani, A., Labra, M., Sgorbati, S., Brown, S., Denis, M.
(2001). Resolution of Viable and Membrane-Compromised Bacteria in Freshwater and Marine Waters Based on Analytical Flow Cytometry and Nucleic Acid Double Staining. Appl. Environ. Microbiol.
67: 4662-4670
[Abstract]
[Full Text]
-
Pernthaler, A., Pernthaler, J., Eilers, H., Amann, R.
(2001). Growth Patterns of Two Marine Isolates: Adaptations to Substrate Patchiness?. Appl. Environ. Microbiol.
67: 4077-4083
[Abstract]
[Full Text]
-
Button, D. K., Robertson, B. R.
(2001). Determination of DNA Content of Aquatic Bacteria by Flow Cytometry. Appl. Environ. Microbiol.
67: 1636-1645
[Abstract]
[Full Text]
-
Lebaron, P., Servais, P., Agogué, H., Courties, C., Joux, F.
(2001). Does the High Nucleic Acid Content of Individual Bacterial Cells Allow Us To Discriminate between Active Cells and Inactive Cells in Aquatic Systems?. Appl. Environ. Microbiol.
67: 1775-1782
[Abstract]
[Full Text]
Top
Abstract
Introduction
