INTRODUCTION

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Biofilms are formed by colonies of microorganisms embedded in a matrix of extracellular polymeric substances (EPS), and they accumulate rapidly wherever surfaces immersed in water offer favorable physiological conditions (11, 13). They are controlled by the growth kinetics of cell clusters influenced by diffusion and mass transport processes and are subject to the hydrodynamics of aqueous environments. To study the development and architecture of undisturbed biofilms in flow cells, nondestructive procedures including microscopic techniques and image analysis (5, 8, 9, 15, 16, 30, 36, 44), spectrochemical methods such as Fourier transform-infrared spectroscopy (50, 54), or electrochemical and piezoelectric approaches (47) have been the focal point of basic research interests. Conventional concepts concerning the internal structure of biofilms assume a rather homogeneous layer of cells. This view has been questioned since observations of intact and undisturbed biofilms by confocal laser scanning microscopy (CLSM) have revealed heterogeneous spatial structures consisting of clusters of bacteria as well as voids and channels (16, 31, 33, 36, 40). For the geometric description or modeling of such porous media, several simplifying and statistical approaches such as capillary models (60), the hydraulic radius theory (49), or packed beds (55) have been developed. Recently, the concept of fractals (37, 38) has been applied to the description of the geometric structure of biofilms (23). The density of the biofilms under investigation increased in the direction of the flow, which led to a characteristic increase in the fractal nature. The biofilms became more dense and more compact with age, suggesting that existing pores and channels inside the biofilm decrease over time (3, 23).

Channels within biofilms obviously allow flow. By the use of fluorescent beads, it was demonstrated that even layers close to the substratum were accessible to particulate material introduced into the bulk phase above the biofilm (53). Using fluorescent dextrans, Lawrence et al. (33) showed that soluble substrates could penetrate biofilms through pores and channels. The calculated diffusion rates were always lower than the corresponding rates in water, and the decrease was dependent on the molecular weight of the dextrans used. Investigating local diffusion coefficients in a heterogeneous biofilm, de Beer et al. (18) demonstrated that low-molecular-weight compounds such as fluorescein had the same diffusivity in cell clusters, interstitial voids, and sterile medium. The diffusivity of higher-molecular-weight substances such as phycoerythrin was impeded in cell clusters but not in voids. These and similar observations give rise to different biofilm models. van Loosdrecht et al. (57) proposed that a biofilm is influenced by substrate availability as well as detachment forces. Wimpenny and Colasanti (63, 64) suggested a unifying hypothesis for the structure of microbial biofilms based on cellular automaton models which indicated that biofilm structure was determined mainly by substrate concentration. van Loosdrecht et al. (58) responded by suggesting that biofilm structure is determined by a balance between substrate gradient and the shear rate at the biofilm surface. The same authors paraphrased Wimpenny's theory by stating that "biofilm structure was largely determined by the substrate concentration gradient at the biofilm-liquid interface." Little is known, however, about the relevance of flow within the biofilm and the resulting convective transport (62). Bulk flow velocity influences mass transport (17) and the flow in biofilms. Based on oxygen concentration gradients, it was reported that convective transport did not play a role in terms of mass transport until a minimum flow velocity is reached, i.e., when the mass transfer boundary layer follows the heterogeneity of the biofilm surface (17). To model these transport phenomena, a three-dimensional (3D) approach is required.

The direct observation of microbial populations and biological activity is necessary to provide exact information on cluster and population dynamics, metabolic processes, resistance to antimicrobial agents, or predation within an organized functional biofilm structure. It is of paramount interest to acquire numerical information about biofilm morphology. This is done by digital image processing. Its use in microbiology, especially in combination with CLSM, has been extensively summarized (10, 11, 22). Principally, it can be emphasized that digital image processing is time-consuming and tedious. Previous attempts have been made to detect bacterial cells semiautomatically in aquatic samples (4, 21, 52, 59) or automatically in soil smears (5, 6). However, these methods are based on delineating individual cells by applying algorithms for automatic edge detection of bacteria (52, 60) and counting the number of pixels per cell to determine cell volumes (6). An automated image analysis technique for quantification of growth-related parameters in surface-growing bacterial cells was developed by Moller et al. (41) based on object recognition with the Cellstat program. However, the system is not suitable for the analysis of multilayer biofilms. The approach taken in the present study was to scan through a biofilm and collect quantitative information about the individual components such as microbial species or EPS based on specific fluorescence signals. Striving for automation, CLSM streamlined by the application of digital image processing software, currently "off the shelf," offers a possible means of improving biofilm examination. Depicting a preset 3D domain, we developed a procedure which operates automatically in the CLSM mode and is capable of storing series of sequential images. The on-line collection of confocal 2D cross-sectional images is followed off-line by semiautomated image analysis. We deliberately designed this part to be semiautomated to allow the researcher to define basic image analysis parameters like the threshold settings and mathematical filters used. Once these values have been set, image analysis proceeds automatically.

To test our method, we investigated two different pure-culture biofilms. Pseudomonas fluorescens is a common environmental isolate and is known to readily colonize surfaces (31, 34). In addition, we chose to investigate biofilm formation by a green fluorescent protein (GFP)-expressing Escherichia coli strain. GFP from the bioluminescent jellyfish Aequorea victoria (43) has been gaining increasing importance as a reporter protein for the visualization of gene expression (7, 14, 27, 56) and protein subcellular localization (61). However, its expression in biofilms has seldom been evaluated. The use of oligonucleotide probes for the identification, localization, and quantification of microorganisms in biofilms is also on the rise (24, 39, 42). Taking into account the percentage of the area covered by microorganisms in relation to a lens-dependent reference area, microbial distribution may be determined for each confocal optical section. Finally, biovolumes can be obtained by a numerical integration algorithm. Due to reflected- and dissipated-light phenomena caused by the flow cell surface, some images of P. fluorescens biofilms could not be used directly for evaluation. However, a numerical approximation method incorporated into the commercial image analysis software package enabled the quantitative evaluation of biofilm growth directly on the surface by extrapolation with sufficient accuracy.

	MATERIALS AND METHODS

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Microscopy and image generation. A series of images in the z direction (z series) were digitized in selected optical planes with a CLSM 410 confocal laser scanning microscope coupled to an AXIOVERT 135M inverse microscope (both instruments from C. Zeiss, Jena, Germany). The system used a motorized computer-assisted device to control the vertical positioning during optical sectioning of the biofilm. Image scanning was carried out with the 488- and 543-nm laser lines. Images were obtained with a 100×/1.3 NA Plan-Neofluar oil immersion lens.

Automated image acquisition. As reported by Engelhardt and Knebel (20), sagittal (xz) sectioning produces axial aberrations, which skew the results if they are not taken into account. Depending on the resolution of the objective lens, the margin of the relative error may be as high as 75% (28). To cope with this problem, only the xy register for horizontal sectioning was used for image generation during CLSM. As shown in Fig. 1 the stacks are composed of horizontal image sections separated by vertical step intervals z. The pixel resolution in correspondence with the lens magnification defined the reference length. Each package consisted of le images, and each image stack contained ke image packages. The desired number of images in the x and y direction (ie and je, respectively) determines the total number, ne, of stacks. Variable distances, dx and dy, between the image stacks can be chosen independently. Based on a user-specified macro procedure, the image generation was automated.

View larger version (21K): [in this window] [in a new window]   FIG. 1.   Stepwise acquisition of confocal images by means of a user-defined processing routine (see the text for details). i, j, k, l = loop indices; n = image stack count index; ie = number of image stacks in direction x; je = number of image stacks in direction y; ke = number of image packages P(k) within image stack S(n); le = number of horizontal image sections I(l) within image package P(k); and ne = total number of image stacks S(n) within the area of interest.

Digital image processing and analysis. Digital image processing and analysis (Fig. 2) were performed with a QUANTIMET 570 computer system (Leica, Cambridge, United Kingdom). The morphological processor enhanced images, discarded unwanted details, and accelerated grey-scale processing. Computerized image analysis comprises a sequence of operations dependent on the specimen being studied. Before the automated image processing on the QUANTIMET was activated, pixel calibration and image setup were defined relative to the calibration value used for image generation on the CLSM system. In this dialog, the size and position of the "area of interest" for measurements were specified manually. The QUANTIMET was programmed to perform a sequence of automated operations such as image acquisition, grey image analysis, detection, measurements, volume integration, and data recording and display. Image acquisition involved loading a series of CLSM images from an external hard disc into the QUANTIMET memory. Morphological transforms on grey images were carried out by applying the mathematical filters WSharpen in conjunction with WTopHat or the Median filter. Detection involves setting thresholds and allows us to score binary images identified as 0 or 1. Field measurement sequences were made within the measurement frame by using binary images. In this dialog, the area covered by microbial growth was measured for each confocal plane relative to the thickness of the biofilm. The measuring parameter was defined as the area fraction, i.e., the ratio of detected pixels in the image to the area of the measurement frame. The calculation of biovolumes was guided by a numerical integration method by following the trapezoidal rule. Data recording and display encompassed procedures such as saving the measured parameters in an ASCII file for the off-line design of Microsoft EXCEL diagrams for documentation or optional on-line screening of data tables, histograms, or grey profile plots on the monitor for a quick examination. The QUANTIMET image analyzer was operated from a graphical user interface with an interactive image analysis software package. The command line scripts of QUIN (QUANTIMET under Windows) allowed a stepwise interactive protocol. The syntax of this interpreter-based language is similar to that of popular PC languages such as QBASIC. Repeatable measurement routines were rapidly created with the built-in image-processing macro software QUIPS (QUANTIMET Image Processing System). The programming codes are available from the authors on request.

View larger version (40K): [in this window] [in a new window]   FIG. 2.   Flowchart of the macro routine for image analysis with the Leica QUANTIMET computer. The software was programmed to perform a sequence of automated operations including image acquisition, grey image analysis, detection, measurements, approximation, biovolume integration, and data recording and display. k, l = loop indices; n = image stack count index; ke = number of image packages P(k) within image stack S(n); le = number of horizontal image sections I(l) within image package P(k); ne = total number of image stacks S(n) within area of interest.

Flow cell system. To cultivate cells under defined conditions, an aerated and stirred reservoir can be used either as a fermentor or as a nutrient vessel depending on the mode of operation (Fig. 3). For microscopic observations and measurements, the reservoir was connected to a glass flow cell with silicone tubing. The flow cell was integrated into the suction branch of the hydraulic loop midway between the displacement pump and the reservoir. Oxygen and pH probes were used to monitor conditions in the bulk fluid inside the tubing. A pressure-independent displacement pump (Netsch, Waldkraiburg, Germany) was used to ensure a constant and continuous flow. The pump was equipped with a rotating screw spindle. To keep pulsation effects on the volume flow small, the pump was operated at higher rotations per minute than needed. The volume flow could then be adjusted to the desired flow rate by positioning a thrush-valve downstream. The increasing hydraulic pressure between the pump and thrush-valve was alleviated by means of a valve incorporated into a hydraulic feedback loop encompassing the displacement pump. By controlling the rate of pumping in connection with appropriate thrush-valve and hydraulic pressure abatement settings, a fluid velocity essentially free of oscillations could be attained.

View larger version (26K): [in this window] [in a new window]   FIG. 3.   Schematic diagram of the experimental setup. The main components are the nutrient reservoir and the flow cell under a Zeiss confocal laser scanning microscope coupled to a Leica QUANTIMET image analysis computer.

View larger version (29K): [in this window] [in a new window]   FIG. 4.   Detailed view of the glass flow cell. Channel length (Lc) = 46 mm; channel width (Wc) = 8 mm; channel height (Hc) = 2.7 mm; channel cross-sectional area (Ac) = 21.6 mm2.

Flow cell hydrodynamics and domains of measurement. Taking into account the flow rate, Q_c, the mean bulk fluid velocity, v_m, in the cell can be formulated as

(1)

where Q_c is the flow rate in the flow cell, A_c is the cross-sectional area of the cell, and v_m is the mean bulk velocity of the fluid.

View larger version (39K): [in this window] [in a new window]   FIG. 5.   Graphical outline of the flow cell, indicating qualitatively the velocity profile of the fluid flow and the geometrical position of the domain of measurement within the cell. The dimensions given refer to the domain of measurement used on P. fluorescens biofilms. Domain of measurement: length in direction x, 639 µm; length in direction y, 3,067 µm; length in direction z, 20 µm.

Experimental conditions for the measurements of bacterial autofluorescence. An overnight culture of P. fluorescens cultivated in 0.1× Merck standard I broth was inoculated into an aerobic fermentor. The fermentor was connected to the flow cell with silicone tubing. The hydraulic loop was open, and the bacterial suspension was circulated through the conduit system at a constant flow rate, Q_c, of 3.0 liters h¹ by a pressure-independent pump while maintaining a temperature of 20°C within the fermentor. Merck standard I broth was added at 5.0 ml min¹. The stirrer device was operated at 150 rpm. The individual components of the complete system were either autoclaved or sterilized with 0.1× acetyl hydroperoxide and rinsed with autoclaved distilled water before use. Before inoculation, a blank solution of autoclaved nutrients consisting of 0.1× Merck standard I broth was passed through the experimental system to prime the tubes and to purge air from the system.

1

Experimental conditions for the investigation of a biofilm formed by a GFP-expressing E. coli strain. The pGFP cDNA vector (Clontech, Palo Alto, Calif.) was introduced into E. coli DH5 by transformation. The resulting transformants manifested ampicillin resistance (20 µg ml¹) and fluoresced brightly when illuminated with UV light (365 nm with a transilluminator). One transformant colony was purified by substreaking and was termed strain EC12. An overnight culture of E. coli EC12, grown in Luria broth-ampicillin (20 µg ml¹) medium was inoculated into the flow cell. The culture was allowed to reside in the flow cell for 2 h after inoculation to facilitate attachment of the cells to the walls of the flow cell. In this experiment, the setup was also operated as an open system. Luria broth (0.1×) was pumped through the flow cell at a rate of 15 ml h¹. This very slow flow minimized medium expenditure and allowed observable E. coli EC12 biofilm development over a 7-day incubation period. GFP fluorescence was observed with a 488-nm Ar laser. Light of the desired wavelengths was collected by using a 510- to 525-nm bandpass emission filter. Data collection and image analyses were carried out as described above.

Hybridization of E. coli EC12 biofilm cells. On day 7, the biofilm was hybridized directly in the flow cell with the probe EUB338, specific for the domain Bacteria (2). The probe was labeled with the isothiocyanate derivative CY3 (MWG-Biotech, Ebersberg, Germany). Before hybridization, the biofilm was fixed with a 4% paraformaldehyde solution (2 h at room temperature). The paraformaldehyde solution was washed out with phosphate-buffered saline (NaCl, 8 g liter¹; KCL, 0.2 g liter¹; Na₂HPO₄, 1.44 g liter¹; NaH₂PO₄, 0.2 g liter¹ [pH 7.0]). A dehydration step was not included, to keep the 3D biofilm structure intact. Based on the dimensions of the flow cell (46 by 8 by 2.7 mm), the volume of the cell was calculated to be 993.6 mm³. To hybridize cells on both the top and bottom coverslips, 1 ml of a hybridization solution (30% formamide, 0.9 M NaCl, 20 mM Tris-HCl [pH 7.2], 100 µl of EUB338 probe [30 ng µl¹]) was introduced into the flow cell. Hybridization was carried out at 46°C for 2 h. The hybridization solution was washed out with a wash solution (112 mM NaCl, 20 mM Tris-HCl [pH 7.2], 5 mM EDTA), and the biofilm was incubated with fresh wash solution for 30 min at 48°C. Finally, the wash solution was replaced with phosphate-buffered saline and the labeled biofilm was observed by CLSM. The CY3-conjugated probe was visualized with the 543-nm line of the HeNe laser, using a 570-nm longpass emission filter. Data collection and image analysis were carried out as described above.

	RESULTS

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Biofilm analysis immediately on the substratum surface. The images depicting bacterial aggregations of P. fluorescens adhering to the glass surface (substratum) of the flow cell at position z = 0 µm were occasionally obscured by diffuse light reflections (Fig. 6). Consequently, these images could not be used for image analysis. For adhesion studies, where the growth of biofilms immediately adjacent to the substratum is of interest, it is essential to include this cell material in the overall biofilm characterization. For P. fluorescens, it is known that the highest density of cells is found near the substratum (29, 32). Therefore, a numerical approximation procedure was integrated into the analysis software for optional use. The main problem was to find a best-curve-fitting algorithm which estimated the cells directly on the substratum with an acceptable degree of accuracy. Usually, measured data show a certain statistical scattering. To extract the essential underlying functional correlation, linear regression models allow the construction of polynomials of a certain degree that fit optimally to the measured data points in a least-squares sense. If the measured data are statistically relevant (and if there exists a functional relationship that describes reality with sufficient accuracy), we can then use the polynomial for interpolation, i.e., for calculation of values between points of measurement, and for extrapolation, i.e., for approximation of values in regions where a measurement is not possible or reliable. The main advantage of least-squares-based methods is their computational simplicity. They are linear methods and generally do not require any iteration on the data. Their main disadvantage is that they may lead to biased estimates in the presence of measurement noise. However, the experimental data did not show any effect of such measurement noise. In our case, an approach of polynomial degree 6 turned out to be sufficient:

(2)

If necessary, the coefficients a_i must be determined for the region near z = 0 µm for each image stack separately (Fig. 1). The numerical algorithm is based on MATHEMATICA routines and is stated elsewhere (66). A typical test sample is given in Fig. 7. The open symbols feature measured data points and are used for the calculation of the coefficients a_i. The figure presents the area of microbial colonization plotted against z-scanning positions for one stack typical of P. fluorescens colonization within the flow cell. For this stack, the curve-fitting method leads to the following coefficients of the polynomial: a₀ = 25.6375, a₁ = 12.2715, a₂ = 2.76465, a₃ = 0.330758, a₄ = 0.0212805, a₅ = 0.000691, and a₆ = 8.85432 × 10⁶.

View larger version (99K): [in this window] [in a new window]   FIG. 6.   Optical sectioning produced by CLSM, illustrating the horizontal colonization of P. fluorescens at different flow cell depths. In some cases, images were affected by light reflections at the position closest to the substratum.

View larger version (17K): [in this window] [in a new window]   FIG. 7.   Typical diagram indicating P. fluorescens biofilm colonization near the substratum. The numerical approximation procedure was a least-squares fit of measured data points to a polynomial function (see the text for details).

Determination of biovolumes. Once all the images have been analyzed (see Materials and Methods), the biovolumes of the individual stacks are calculated by numerical integration over all packages of a stack (Fig. 2). Such a summation of the local volumes enclosed in each package within the respective stack (Fig. 1 and 2) allows the assessment of the accumulated biovolume within the region of interest. Thus, our aim is the integral

(3)

where V denotes a given stack, f(x,y,z) denotes the local density of bacterial accumulation, and denotes the bacterial accumulation on a horizontal sectional area as a function of z. In our special situation, the numerical integration in the x and y directions is done automatically by the image analysis process, resulting in a table of discrete values of F(z). Therefore, of course, the integral in the z direction must also be approximated numerically, and this must be done explicitly. The most widespread algorithms for the numerical integration of a function F over a finite interval [za, ze] are weighted sums of values F(z_m) of F in a finite number of nodal points (or optical sections) z_m  [za, ze]. Due to the setting indicated in Fig. 1 (ke packages of le images), we obtain

(4)

with (usually) nonnegative weights c_m and .

m  me

(5)

z_m+1  z_m = z

(6)

n = (i  

  i  ie

  j  je

(7)

Bacterial colonization measured by autofluorescence. Figure 6 shows a P. fluorescens colonization event at different cell depths for a representative y section after 14 h of flow cell operation. Images in the zones near the glass surface (substratum) of the flow cell were quantified by the numerical approximation method as described above.

View larger version (68K): [in this window] [in a new window]   FIG. 8.   3D representation of a P. fluorescens biofilm in the flow cell. The biofilm was analyzed after a 14-h test run. The results characterize the early stage of colonization. During the experiment, the flow rate setting was 3 liters h1.

View larger version (91K): [in this window] [in a new window]   FIG. 9.   Calculated biofilm volume stacks in the flow cell. At each xy position, the areas of microbial colonization portrayed by 20 horizontal CLSM sections were integrated by the numerical procedure outlined in the text. During the experiment, the flow rate setting was 3 liters h1. The biofilm was analyzed after a 14-h test run. The results represent the early stage of colonization of an axenic cell culture of P. fluorescens.

E. coli EC12 biofilm. Figure 10 illustrates the E. coli EC12 biofilm on day 7 as determined by GFP fluorescence and by fluorescent in situ hybridization with the EUB338 oligonucleotide probe at position x = 447.3 µm. As depicted in Fig. 10, the cellular distribution shows that the biofilm was thickest at 1 to 2 µm from the attachment surface. Hence, colonization varied nonlinearly with depth, in contrast to the colonization of P. fluorescens (Fig. 9). Extrapolation by polynomial approximation to account for bacterial cells at z = 0 µm was not necessary since no diffuse light reflections were observed. The E. coli biofilm consisted primarily of cell clusters attached by a few cells to the glass surface (data not shown). GFP fluorescence was also observed in the deeper layers of the biofilm. Signals from in situ hybridization of the biofilm suggested the same colonization pattern (Fig. 10). At each xy position, the results for six horizontal CLSM sections were integrated by the numerical procedure discussed above. The volumes were calculated as described above and are displayed in Fig. 11. There was no clear trend in terms of the distribution of cell material across the flow channel as measured by biovolumes based on GFP fluorescence. This may be because the biofilm was older (7 days) and the data represent a mature biofilm, whereas the P. fluorescens biofilm represented the initial colonization stages, which are highly influenced by the flow regimen in the channel.

View larger version (39K): [in this window] [in a new window]   FIG. 10.   E. coli EC12 biofilm on day 7, shown as colonization percentage at position x = 447.3 µm. Both GFP fluorescence and signals from in situ hybridization with the EUB338 probe are depicted. Note the similar distribution patterns of GFP and CY3-EUB338 fluorescence.

View larger version (53K): [in this window] [in a new window]   FIG. 11.   Calculated E. coli EC12 biofilm volume stacks at selected positions in the flow cell. At each xy position, the areas of microbial colonization portrayed by six horizontal CLSM sections were integrated by using the numerical procedure outlined in the text. During the experiment, the flow rate setting was 15 ml h1. The biofilm was analyzed after a 7-day test run.

	DISCUSSION

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There is certainly no lack of prominent publications on biofilm studies focusing on technical innovations, especially in the field of CLSM techniques in connection with analytical imaging (6, 10, 11, 35). However, little attention has been paid to the question of how to perform an overall analysis covering extended 3D regions of biofilm structures cultivated under various flow conditions. The procedure should facilitate a quantitative estimate by means of an automated approach without any operator intervention.

We presented a method involving an automated procedure for studying biofilm growth in extended 3D domains. In combining in situ CLSM with off-line image processing and analysis techniques, we sought to establish a user-friendly method that would allow automated quantitative measurements. Designing the routine was a multifaceted approach enclosing elements of biotechnology, signal processing, elementary mathematics, and computer programming. The image analysis processing has been made intentionally semiautomated, since biofilm research is unquestionably a complex task, based upon the degree of judgment and experience of the individual researcher. Hence, before the automated image analysis can begin, pixel calibration and image setup must be defined with respect to the calibration value used for the image generation on the CLSM system. In addition, laser beam, mathematical filter, and threshold settings are not suitable for automation.

To demonstrate the power of this method, we performed biofilm analysis based on both autofluorescence of P. fluorescens and GFP expression including in situ hybridization of E. coli EC12. Analysis of the P. fluorescens biofilm revealed that most of the biomass, expressed as microbial colonization detected by autofluorescence, was concentrated at the attachment surface (z position, 0 µm [Fig. 8]). These observations are in accordance with the results from other investigations (29, 32). P. fluorescens has a tendency to form a tightly packed surface monolayer of cells which serves as a base for the less dense biofilm layers that extend into the bulk aqueous phase. In addition, P. fluorescens, due to its motility, has been shown to colonize surfaces more rapidly than nonmotile microorganisms do. Korber et al. (26) confirmed that cellular motility plays an important role during cell positioning and formation of biofilm microenvironments in studies with motile (Mot⁺) and nonmotile (Mot) P. fluorescens strains. The effect of motility on bacterial colonization of surfaces has been discussed in a review on behavioral strategies of surface-colonizing bacteria (34). In contrast, the E. coli biofilm manifested the highest cell density 1 to 2 µm from the attachment surface, as revealed by GFP fluorescence and in situ hybridization (Fig. 10). Cellular agglomerates were observed, with a few cells attached at the surface facilitating a sessile connection with the bulk of the clusters. Investigating Vibrio parahaemolyticus biofilms, Lawrence et al. (31) observed that the lowest cell density was found at the attachment surface whereas the highest cell density was detected in the outer region of the biofilm. The researchers suggested that the maintenance of a minimal basal layer was important in preventing uncontrolled sloughing of the biofilm. Interestingly, Neu and Lawrence (46) demonstrated in natural-biofilm studies that substantial cell density was also present at some distance from the attachment surface and not in the lowest layers of the biofilm.

Since GFP fluorescence persists even after treatment with formaldehyde, the detection of GFP-expressing cells may be combined with fluorescent in situ hybridization with oligonucleotide probes (19). Our observations confirmed that most cells which exhibited GFP fluorescence could also be hybridized with the EUB338 probe (Fig. 10). Figure 11 shows that the detected GFP fluorescence occupied on the average a slightly greater biovolume than that represented by signals from in situ hybridization. The observed difference in biovolume was not due to a higher relative signal intensity caused by GFP than that caused by hybridization. Likewise, the omission of ethanol in the fixation procedure (see Materials and Methods) did not interfere with hybridization of E. coli cells. This step was avoided, to maintain the spatial structure of the biofilm. Moller et al. (42) reported that the dehydration step caused a biofilm to shrink in the z direction from an average of 365 µm for the fully hydrated biofilm to a thickness of 25 to 70 µm. It should be noted, however, that the application of paraformaldehyde also results in some loss of water. It is possible that after the experiment had run for 7 days, the biofilm cells were no longer as active as, for example, at the beginning of the experiment. Since GFP fluorescence persists in the stationary phase (56), it is likely that cells with a lower ribosomal content were detected by GFP fluorescence but not by the oligonucleotide probe. For example, Kalmbach et al. (24) showed that the respiratory activity and ribosome content of adherent bacterial cells decreased continually during the early stages of development of a drinking-water biofilm. Similarly, Poulsen et al. (48) demonstrated that the doubling time in an established biofilm was significantly longer than in a young biofilm. Amann et al. (1) suggested that nonlogarithmically growing cells of certain bacterial genera were difficult to detect with oligonucleotide probes because of their low cellular rRNA content. To test this assumption in our study, it would have been necessary to run parallel setups and to hybridize the biofilm at the beginning and throughout the experimental period as well. However, this was not feasible due to the limitations imposed by a single-flow cell. Another possibility would be to increase the hybridization time. For example, Moller et al. (42) applied an EUB338 probe to a multispecies biofilm and carried out the hybridization for 16 h at 37°C. In contrast, the signals obtained from a groundwater biofilm hybridized with the EUB338 probe for 2 or 16 h did not differ significantly (45).

Korber et al. (25) were the first to investigate large areas of microbial biofilms by using statistical assumptions. It is well known that biofilm habitats cannot be considered to have deterministic structures on a microscopic scale. Consequently, it is hardly feasible to predict precisely the parameters of many phenomena occurring in such systems. These processes are called random processes; therefore, a reliable method is needed that predicts the smallest possible domain of interest and also confirms the "representative areas" within a desired interval of confidence. This means that the biofilm unit under study should be adequately represented, such that the entire range of data variability of its architecture is encompassed. The statistical methods proposed by Korber et al. (25) may in some cases simplify the experimental design, including data analysis, in a straightforward manner. Applying the method of representative elementary area in connection with the method of determined confidence intervals in practice assumes that the data in hand are normally distributed or could be normalized through transformation. Categorizing acceptable variability limits for biofilm data is a tedious and excessive process. The limit values invariably differ among species, growth inhibitions, or stimulations by environmental conditions. They are also influenced by morphological alteration, by the motility or recolonization behavior, and by the arrangement and instrumentation of the experimental test setup. Some statistical methods are distribution hypotheses. In the light of empirical examinations, estimation functions based upon hypotheses must be checked before being used. They may be accepted or rejected. Furthermore, it should be noted that statistical methods have a generalizing character and so some special information hidden within the biofilm architecture may be lost. Otherwise, it can be assumed that not all cell distribution patterns within a biofilm follow the Gaussian probability distribution function. Up to now, we did not incorporate statistics in our concept. Nevertheless, it would be good practice to exercise statistical mathematics in parallel for cross-checking the results.

It should be mentioned that the outlined procedure does not represent a true 3D approach, even though biovolumes were calculated, indicating biomass trends. 3D programs are based on volume units, called voxels (10, 12), and create 3D projections from any viewpoint of the specimen or region of interest. The 3D imaging software currently available on the market is very expensive, time-consuming in its application, memory intensive, and suitable mainly only for descriptive demonstrations of the object. At the moment, no ready-to-use programs are able to elucidate and quantify dynamic processes occurring in real 3D biofilm structures. In the method described here, it is assumed that no gap or wraparound failures are present between adjacent sampling areas, because backlash margins in maneuvering the scanning stage may be small. To acquire quantitative data automatically, we applied macro routines written by the user. The routines are based on basic 2D computer software. In evaluating biovolumes, we did not discriminate between cells and extracellular substances. However, suitable genetic markers, such as GFP or fluorescently labelled rRNA-directed oligonucleotide probes, as well as general nucleic acid stains in conjunction with EPS-specific fluorescent stains or lectins (46), could be used to quantify defined biofilm entities. In the absence of a true estimate of cell numbers in an undisturbed biofilm, a semiautomated quantification of the relative spatial distribution of cellular and extracellular material presents a promising tool for quantitative biofilm analysis.