Automated Confocal Laser Scanning Microscopy and

APPLIED AND ENVIRONMENTAL MICROBIOLOGY,0099-2240/98/$04.0010

Nov. 1998, p. 4115–4127 Vol. 64, No. 11

Copyright © 1998, American Society for Microbiology. All Rights Reserved.

Automated Confocal Laser Scanning Microscopy andSemiautomated Image Processing for Analysis of Biofilms

MARTIN KUEHN,1 MARTINA HAUSNER,1 HANS-JOACHIM BUNGARTZ,2 MICHAEL WAGNER,3

PETER A. WILDERER,1 AND STEFAN WUERTZ1*

Institute of Water Quality Control and Waste Management, Technical University of Munich, D-85748 Garching,1

and Department of Computer Science2 and Department of Microbiology,3

Technical University of Munich, D-80290 Munich, Germany

Received 16 March 1998/Accepted 23 July 1998

The purpose of this study was to develop and apply a quantitative optical method suitable for routinemeasurements of biofilm structures under in situ conditions. A computer program was designed to performautomated investigations of biofilms by using image acquisition and image analysis techniques. To obtain arepresentative profile of a growing biofilm, a nondestructive procedure was created to study and quantifyundisturbed microbial populations within the physical environment of a glass flow cell. Key components of thecomputer-controlled processing described in this paper are the on-line collection of confocal two-dimensional(2D) cross-sectional images from a preset 3D domain of interest followed by the off-line analysis of these 2Dimages. With the quantitative extraction of information contained in each image, a three-dimensional recon-struction of the principal biological events can be achieved. The program is convenient to handle and wasgenerated to determine biovolumes and thus facilitate the examination of dynamic processes within biofilms.In the present study, Pseudomonas fluorescens or a green fluorescent protein-expressing Escherichia coli strain,EC12, was inoculated into glass flow cells and the respective monoculture biofilms were analyzed in threedimensions. In this paper we describe a method for the routine measurements of biofilms by using automatedimage acquisition and semiautomated image analysis.

Biofilms are formed by colonies of microorganisms embed-ded in a matrix of extracellular polymeric substances (EPS),and they accumulate rapidly wherever surfaces immersed inwater offer favorable physiological conditions (11, 13). Theyare controlled by the growth kinetics of cell clusters influencedby diffusion and mass transport processes and are subject tothe hydrodynamics of aqueous environments. To study thedevelopment and architecture of undisturbed biofilms in flowcells, nondestructive procedures including microscopic tech-niques and image analysis (5, 8, 9, 15, 16, 30, 36, 44), spectro-chemical methods such as Fourier transform-infrared spectros-copy (50, 54), or electrochemical and piezoelectric approaches(47) have been the focal point of basic research interests.Conventional concepts concerning the internal structure ofbiofilms assume a rather homogeneous layer of cells. This viewhas been questioned since observations of intact and undis-turbed biofilms by confocal laser scanning microscopy (CLSM)have revealed heterogeneous spatial structures consisting ofclusters of bacteria as well as voids and channels (16, 31, 33, 36,40). For the geometric description or modeling of such porousmedia, several simplifying and statistical approaches such ascapillary models (60), the hydraulic radius theory (49), orpacked beds (55) have been developed. Recently, the conceptof fractals (37, 38) has been applied to the description of thegeometric structure of biofilms (23). The density of the bio-films under investigation increased in the direction of the flow,which led to a characteristic increase in the fractal nature. Thebiofilms became more dense and more compact with age, sug-

gesting that existing pores and channels inside the biofilmdecrease over time (3, 23).

Channels within biofilms obviously allow flow. By the use offluorescent beads, it was demonstrated that even layers close tothe substratum were accessible to particulate material intro-duced into the bulk phase above the biofilm (53). Using fluo-rescent dextrans, Lawrence et al. (33) showed that solublesubstrates could penetrate biofilms through pores and chan-nels. The calculated diffusion rates were always lower than thecorresponding rates in water, and the decrease was dependenton the molecular weight of the dextrans used. Investigatinglocal diffusion coefficients in a heterogeneous biofilm, de Beeret al. (18) demonstrated that low-molecular-weight compoundssuch 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 im-peded in cell clusters but not in voids. These and similar ob-servations give rise to different biofilm models. van Loosdrechtet al. (57) proposed that a biofilm is influenced by substrateavailability as well as detachment forces. Wimpenny and Cola-santi (63, 64) suggested a unifying hypothesis for the structureof microbial biofilms based on cellular automaton modelswhich indicated that biofilm structure was determined mainlyby substrate concentration. van Loosdrecht et al. (58) respond-ed by suggesting that biofilm structure is determined by a bal-ance between substrate gradient and the shear rate at the bio-film surface. The same authors paraphrased Wimpenny’s theoryby stating that “biofilm structure was largely determined by thesubstrate concentration gradient at the biofilm-liquid inter-face.” Little is known, however, about the relevance of flowwithin the biofilm and the resulting convective transport (62).Bulk flow velocity influences mass transport (17) and the flowin biofilms. Based on oxygen concentration gradients, it wasreported that convective transport did not play a role in termsof mass transport until a minimum flow velocity is reached, i.e.,

* Corresponding author. Mailing address: Institute of Water QualityControl and Waste Management, Technical University of Munich, AmCoulombwall, D-85748 Garching, Germany. Phone: 49 (89) 2891 3708.Fax: 49 (89) 2891 3718. E-mail: [email protected].

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when the mass transfer boundary layer follows the heteroge-neity of the biofilm surface (17). To model these transportphenomena, a three-dimensional (3D) approach is required.

The direct observation of microbial populations and biolog-ical activity is necessary to provide exact information on clusterand population dynamics, metabolic processes, resistance toantimicrobial agents, or predation within an organized func-tional biofilm structure. It is of paramount interest to acquirenumerical information about biofilm morphology. This is doneby digital image processing. Its use in microbiology, especiallyin combination with CLSM, has been extensively summarized(10, 11, 22). Principally, it can be emphasized that digital imageprocessing is time-consuming and tedious. Previous attemptshave been made to detect bacterial cells semiautomatically inaquatic samples (4, 21, 52, 59) or automatically in soil smears(5, 6). However, these methods are based on delineating indi-vidual cells by applying algorithms for automatic edge detec-tion of bacteria (52, 60) and counting the number of pixels percell to determine cell volumes (6). An automated image anal-ysis technique for quantification of growth-related parametersin surface-growing bacterial cells was developed by Moller etal. (41) based on object recognition with the Cellstat program.However, the system is not suitable for the analysis of multi-layer biofilms. The approach taken in the present study was toscan through a biofilm and collect quantitative informationabout the individual components such as microbial species orEPS based on specific fluorescence signals. Striving for auto-mation, CLSM streamlined by the application of digital imageprocessing software, currently “off the shelf,” offers a possiblemeans of improving biofilm examination. Depicting a preset3D domain, we developed a procedure which operates auto-matically in the CLSM mode and is capable of storing series ofsequential images. The on-line collection of confocal 2D cross-sectional images is followed off-line by semiautomated imageanalysis. We deliberately designed this part to be semiauto-mated to allow the researcher to define basic image analysisparameters like the threshold settings and mathematical filtersused. Once these values have been set, image analysis proceedsautomatically.

To test our method, we investigated two different pure-culture biofilms. Pseudomonas fluorescens is a common envi-ronmental isolate and is known to readily colonize surfaces(31, 34). In addition, we chose to investigate biofilm formationby a green fluorescent protein (GFP)-expressing Escherichiacoli strain. GFP from the bioluminescent jellyfish Aequoreavictoria (43) has been gaining increasing importance as a re-porter protein for the visualization of gene expression (7, 14,27, 56) and protein subcellular localization (61). However, itsexpression in biofilms has seldom been evaluated. The use ofoligonucleotide probes for the identification, localization, andquantification of microorganisms in biofilms is also on the rise(24, 39, 42). Taking into account the percentage of the areacovered by microorganisms in relation to a lens-dependentreference area, microbial distribution may be determined foreach confocal optical section. Finally, biovolumes can be ob-tained by a numerical integration algorithm. Due to reflected-and dissipated-light phenomena caused by the flow cell sur-face, some images of P. fluorescens biofilms could not be useddirectly for evaluation. However, a numerical approximationmethod incorporated into the commercial image analysis soft-ware package enabled the quantitative evaluation of biofilmgrowth directly on the surface by extrapolation with sufficientaccuracy.

MATERIALS AND METHODS

Microscopy and image generation. A series of images in the z direction (zseries) were digitized in selected optical planes with a CLSM 410 confocal laserscanning microscope coupled to an AXIOVERT 135M inverse microscope (bothinstruments from C. Zeiss, Jena, Germany). The system used a motorized com-puter-assisted device to control the vertical positioning during optical sectioningof the biofilm. Image scanning was carried out with the 488- and 543-nm laserlines. Images were obtained with a 1003/1.3 NA Plan-Neofluar oil immersionlens.

Images could be generated either interactively by calling appropriate com-mand line scripts by using the dialog and menu facilities of the CLSM software(Zeiss) or by applying user-specified macro sequences. We used the technique ofautomated microscope image acquisition in situ by applying macro routines.After manually setting the calibration value corresponding to lens magnificationand the contrast and gain levels for the microscope, we programmed the systemto acquire images automatically without any operator intervention. The imageswere saved on an external 1-GByte hard disc or on a streamer before beinganalyzed.

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

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. Computer-ized image analysis comprises a sequence of operations dependent on the spec-imen being studied. Before the automated image processing on the QUANTI-MET was activated, pixel calibration and image setup were defined relative tothe calibration value used for image generation on the CLSM system. In thisdialog, the size and position of the “area of interest” for measurements werespecified manually. The QUANTIMET was programmed to perform a sequenceof automated operations such as image acquisition, grey image analysis, detec-tion, measurements, volume integration, and data recording and display. Imageacquisition involved loading a series of CLSM images from an external hard discinto the QUANTIMET memory. Morphological transforms on grey images werecarried out by applying the mathematical filters WSharpen in conjunction withWTopHat or the Median filter. Detection involves setting thresholds and allowsus to score binary images identified as 0 or 1. Field measurement sequences weremade within the measurement frame by using binary images. In this dialog, thearea covered by microbial growth was measured for each confocal plane relativeto the thickness of the biofilm. The measuring parameter was defined as the areafraction, i.e., the ratio of detected pixels in the image to the area of the mea-surement frame. The calculation of biovolumes was guided by a numericalintegration method by following the trapezoidal rule. Data recording and displayencompassed procedures such as saving the measured parameters in an ASCIIfile for the off-line design of Microsoft EXCEL diagrams for documentation oroptional on-line screening of data tables, histograms, or grey profile plots on themonitor for a quick examination. The QUANTIMET image analyzer was oper-ated from a graphical user interface with an interactive image analysis softwarepackage. The command line scripts of QUIN (QUANTIMET under Windows)allowed a stepwise interactive protocol. The syntax of this interpreter-basedlanguage is similar to that of popular PC languages such as QBASIC. Repeatablemeasurement routines were rapidly created with the built-in image-processingmacro software QUIPS (QUANTIMET Image Processing System). The pro-gramming codes are available from the authors on request.

Flow cell system. To cultivate cells under defined conditions, an aerated andstirred reservoir can be used either as a fermentor or as a nutrient vesseldepending on the mode of operation (Fig. 3). For microscopic observations andmeasurements, the reservoir was connected to a glass flow cell with siliconetubing. The flow cell was integrated into the suction branch of the hydraulic loopmidway between the displacement pump and the reservoir. Oxygen and pHprobes were used to monitor conditions in the bulk fluid inside the tubing. Apressure-independent displacement pump (Netsch, Waldkraiburg, Germany)was used to ensure a constant and continuous flow. The pump was equipped witha rotating screw spindle. To keep pulsation effects on the volume flow small, thepump was operated at higher rotations per minute than needed. The volume flowcould then be adjusted to the desired flow rate by positioning a thrush-valvedownstream. The increasing hydraulic pressure between the pump and thrush-valve was alleviated by means of a valve incorporated into a hydraulic feedbackloop encompassing the displacement pump. By controlling the rate of pumping

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in connection with appropriate thrush-valve and hydraulic pressure abatementsettings, a fluid velocity essentially free of oscillations could be attained.

The test setup worked as an open loop. The glass flow cell depicted in Fig. 4was 46 mm long, 8 mm wide, and 2.7 mm high and consisted of two coverslips,each 0.2 mm thick, glued with silicon sealant to a stainless steel frame. The areaof the canal cross section, Ac, measured 21.6 mm2.

Flow cell hydrodynamics and domains of measurement. Taking into accountthe flow rate, Qc, the mean bulk fluid velocity, vm, in the cell can be formulatedas

vm 5 Qc/Ac (1)

where Qc is the flow rate in the flow cell, Ac is the cross-sectional area of the cell,and vm is the mean bulk velocity of the fluid.

To study microbial colonization patterns in continuous- and laminar-flowenvironments, images were generated within specific flow cell regions (Fig. 5).For experiments based on autofluorescence produced by P. fluorescens, thedomain of measurements had the following geometry: length in the x direction,639 mm; length in the y direction, 3,067 mm; and length in the z direction, 20 mm.This resulted in a three-dimensional analysis of a 3.92 3 107-mm3 box with a basicarea of 1.96 3 106 mm2. For the treatment of the 3.92 3 107-mm3 box describingthe P. fluorescens culture, 1,920 images were captured. The procedure was car-ried out fully automatically by using a macro routine running on the CLSMcomputer and required 60 min to scan the probe and control the mechanicalmovement of the microscopic stage. To build up a single horizontal image sectionof 512 by 512 pixels, the scanning time was 1 s. The domain of interest was splitinto four stacks for the x direction and 24 stacks for the y direction, with 20horizontal image sections per stack (Fig. 1 and 5). To obtain statistically repre-sentative results for a P. fluorescens culture, Korber et al. (25) proposed the useof an analysis area exceeding 105 mm2 for each vertical step within the box ofmeasurement. Comments about this task may be found in Discussion. Whenobtaining images from the E. coli EC12 biofilm, a domain of 511.2 mm (x) by511.2 mm (y) by 5 mm (z) was scanned. The scanned domain of interest consistedof 96 images corresponding to four stacks in the x direction and four stacks in they direction with six images per stack. At each depth, 2.6 3 105 mm2, encompass-ing a total volume of 1.3 3 106 mm3, was scanned. By choosing a time of 1 s foreach scan, the total computer time of the CLSM device, including control andmechanical movement of the microscope stage, was about 15 min. For bothexperiments, the starting points of the measurements were positioned near theflow cell wall.

Experimental conditions for the measurements of bacterial autofluorescence.An overnight culture of P. fluorescens cultivated in 0.13 Merck standard I brothwas inoculated into an aerobic fermentor. The fermentor was connected to theflow cell with silicone tubing. The hydraulic loop was open, and the bacterialsuspension was circulated through the conduit system at a constant flow rate, Qc,

of 3.0 liters h21 by a pressure-independent pump while maintaining a tempera-ture of 20°C within the fermentor. Merck standard I broth was added at 5.0 mlmin21. The stirrer device was operated at 150 rpm. The individual componentsof the complete system were either autoclaved or sterilized with 0.13 acetylhydroperoxide and rinsed with autoclaved distilled water before use. Beforeinoculation, a blank solution of autoclaved nutrients consisting of 0.13 Merckstandard I broth was passed through the experimental system to prime the tubesand to purge air from the system.

The mean flow velocity, vm, in the observation cell was 3.5 cm s21, correspond-ing to a Reynolds number Re ' 140. Therefore, adhesion and growth of bacteriaoccurred under laminar-flow conditions and were influenced by the frictionalforces of the fluid. During the course of the experiment, the temperature and pHof the bulk fluid were continuously monitored. By applying a laser beam at awavelength of 488 nm, cells could be visualized directly in the flow cell based ontheir autofluorescence. The attachment of cells was monitored microscopically ata magnification of 3100. After 13 h, cells had already settled along the cell walls.After 14 h, cell attachment in the biofilm was subjected to CLSM image acqui-sition in situ. All the images generated during the experiment underwent auto-mated image analysis and data evaluation on the QUANTIMET computersystem.

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 DH5a by transformation. The resulting transformantsmanifested ampicillin resistance (20 mg ml21) and fluoresced brightly whenilluminated with UV light (365 nm with a transilluminator). One transformantcolony was purified by substreaking and was termed strain EC12. An overnightculture of E. coli EC12, grown in Luria broth-ampicillin (20 mg ml21) mediumwas inoculated into the flow cell. The culture was allowed to reside in the flowcell for 2 h after inoculation to facilitate attachment of the cells to the walls ofthe flow cell. In this experiment, the setup was also operated as an open system.Luria broth (0.13) was pumped through the flow cell at a rate of 15 ml h21. Thisvery slow flow minimized medium expenditure and allowed observable E. coliEC12 biofilm development over a 7-day incubation period. GFP fluorescencewas observed with a 488-nm Ar laser. Light of the desired wavelengths wascollected by using a 510- to 525-nm bandpass emission filter. Data collection andimage analyses were carried out as described above.

Hybridization of E. coli EC12 biofilm cells. On day 7, the biofilm was hybrid-ized directly in the flow cell with the probe EUB338, specific for the domainBacteria (2). The probe was labeled with the isothiocyanate derivative CY3(MWG-Biotech, Ebersberg, Germany). Before hybridization, the biofilm wasfixed with a 4% paraformaldehyde solution (2 h at room temperature). Theparaformaldehyde solution was washed out with phosphate-buffered saline(NaCl, 8 g liter21; KCL, 0.2 g liter21; Na2HPO4, 1.44 g liter21; NaH2PO4, 0.2 gliter21 [pH 7.0]). A dehydration step was not included, to keep the 3D biofilm

FIG. 1. Stepwise acquisition of confocal images by means of a user-defined processing routine (see the text for details). i, j, k, l 5 loop indices; n 5 image stackcount index; ie 5 number of image stacks in direction x; je 5 number of image stacks in direction y; ke 5 number of image packages P(k) within image stack S(n);le 5 number of horizontal image sections I(l) within image package P(k); and ne 5 total number of image stacks S(n) within the area of interest.

VOL. 64, 1998 QUANTITATIVE ANALYSIS OF BIOFILMS 4117

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structure intact. Based on the dimensions of the flow cell (46 by 8 by 2.7 mm), thevolume of the cell was calculated to be 993.6 mm3. To hybridize cells on both thetop and bottom coverslips, 1 ml of a hybridization solution (30% formamide, 0.9M NaCl, 20 mM Tris-HCl [pH 7.2], 100 ml of EUB338 probe [30 ng ml21]) wasintroduced into the flow cell. Hybridization was carried out at 46°C for 2 h. Thehybridization solution was washed out with a wash solution (112 mM NaCl, 20mM Tris-HCl [pH 7.2], 5 mM EDTA), and the biofilm was incubated with freshwash solution for 30 min at 48°C. Finally, the wash solution was replaced withphosphate-buffered saline and the labeled biofilm was observed by CLSM. TheCY3-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 werecarried out as described above.

RESULTS

Biofilm analysis immediately on the substratum surface.The images depicting bacterial aggregations of P. fluorescensadhering to the glass surface (substratum) of the flow cell at

position z 5 0 mm were occasionally obscured by diffuse lightreflections (Fig. 6). Consequently, these images could not beused for image analysis. For adhesion studies, where thegrowth of biofilms immediately adjacent to the substratum is ofinterest, it is essential to include this cell material in the overallbiofilm characterization. For P. fluorescens, it is known that thehighest density of cells is found near the substratum (29, 32).Therefore, a numerical approximation procedure was inte-grated into the analysis software for optional use. The mainproblem was to find a best-curve-fitting algorithm which esti-mated the cells directly on the substratum with an acceptabledegree of accuracy. Usually, measured data show a certainstatistical scattering. To extract the essential underlying func-tional correlation, linear regression models allow the construc-tion of polynomials of a certain degree that fit optimally to the

FIG. 2. Flowchart of the macro routine for image analysis with the Leica QUANTIMET computer. The software was programmed to perform a sequence ofautomated operations including image acquisition, grey image analysis, detection, measurements, approximation, biovolume integration, and data recording and display.k, l 5 loop indices; n 5 image stack count index; ke 5 number of image packages P(k) within image stack S(n); le 5 number of horizontal image sections I(l) withinimage package P(k); ne 5 total number of image stacks S(n) within area of interest.


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measured data points in a least-squares sense. If the measureddata are statistically relevant (and if there exists a functionalrelationship that describes reality with sufficient accuracy), wecan then use the polynomial for interpolation, i.e., for calcu-lation of values between points of measurement, and for ex-trapolation, i.e., for approximation of values in regions wherea measurement is not possible or reliable. The main advantageof least-squares-based methods is their computational simplic-ity. They are linear methods and generally do not require anyiteration 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 suchmeasurement noise. In our case, an approach of polynomialdegree 6 turned out to be sufficient:

p~z! 5 Oi50

6

ai 3 zi (2)

If necessary, the coefficients ai must be determined for theregion near z 5 0 mm for each image stack separately (Fig. 1).The numerical algorithm is based on MATHEMATICA rou-tines and is stated elsewhere (66). A typical test sample is givenin Fig. 7. The open symbols feature measured data points andare used for the calculation of the coefficients ai. The figurepresents the area of microbial colonization plotted againstz-scanning positions for one stack typical of P. fluorescens col-onization within the flow cell. For this stack, the curve-fittingmethod leads to the following coefficients of the polynomial: a05 25.6375, a1 5 212.2715, a2 5 2.76465, a3 5 20.330758, a45 0.0212805, a5 5 20.000691, and a6 5 8.85432 3 1026.

Note that, for this stack, the small values of a5 and a6 indi-cate that, at least for the extrapolation in z 5 0 mm, a polyno-mial of degree 4 might have been sufficient. However, forreliable interpolation for larger values of z, the higher coeffi-cients are also important.

Determination of biovolumes. Once all the images havebeen analyzed (see Materials and Methods), the biovolumes ofthe individual stacks are calculated by numerical integrationover all packages of a stack (Fig. 2). Such a summation of thelocal volumes enclosed in each package within the respectivestack (Fig. 1 and 2) allows the assessment of the accumulated

FIG. 3. Schematic diagram of the experimental setup. The main components are the nutrient reservoir and the flow cell under a Zeiss confocal laser scanningmicroscope coupled to a Leica QUANTIMET image analysis computer.

FIG. 4. Detailed view of the glass flow cell. Channel length (Lc) 5 46 mm;channel width (Wc) 5 8 mm; channel height (Hc) 5 2.7 mm; channel cross-sectional area (Ac) 5 21.6 mm2.


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biovolume within the region of interest. Thus, our aim is theintegral

Ev

f~x,y,z!dV 5 Ez

F~z!dz (3)

where V denotes a given stack, f(x,y,z) denotes the local densityof bacterial accumulation, and F~z! 5 E

~x,y!

f~x,y,z!d~x,y! denotes

the bacterial accumulation on a horizontal sectional area as afunction of z. In our special situation, the numerical integrationin the x and y directions is done automatically by the imageanalysis process, resulting in a table of discrete values of F(z).Therefore, of course, the integral in the z direction must alsobe approximated numerically, and this must be done explicitly.The most widespread algorithms for the numerical integrationof a function F over a finite interval [za, ze] are weighted sumsof values F(zm) of F in a finite number of nodal points (oroptical sections) zm e [za, ze]. Due to the setting indicated inFig. 1 (ke packages of le images), we obtain

Eza

ze

F~z!dz < Om51

me

cm 3 F~zm!, me 5 ke 3 le (4)

with (usually) nonnegative weights cm and Om51

me

cm 5 ze2za.

Concerning the choice of the weights cm, the most simpleand popular approach is the trapezoidal sum. The method hasthe advantage of being independent of the number of support-ing data points. Here, the function F(z) is replaced by thepiecewise linear function that interpolates the discrete points[zm, F(zm)], 1 # m # me. This interpolant is now easy tointegrate, since it is just a sum of trapezoids,

Tm 512 @F~zm11! 1 F~zm!# 3 ~zm11 2 zm! (5)

which led to the method’s name. In our case, we have equidis-tant nodal points and thus can note for vertical step intervals ofhorizontal image sections that zm11 2 zm 5 Dz 5 constant.Consequently, the approximate integral Inum for the biovolumeVn enclosed in each stack Sn at a certain xy position (Fig. 2) isgiven by

Vn < Inum 5 F12 3 F~z1! 1 O

m52

me21

F~zm! 112 3 F~zme!G 3 Dz

(6)

Here, n denotes the number of the stack and can be defined asn 5 (i 2 1) 3 je 1 j with loop indices i, 1 # i # ie, and j, 1 #j # je, in the x and y directions, respectively. Note that the errorcaused by approximating the exact integral by the trapezoidalsum is proportional to Dz2 if F is sufficiently smooth.

Finally, all stack volumes summarized over the predefinedarea of interest (Fig. 1 and 5) allow us to assess the total

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 measurementwithin the cell. The dimensions given refer to the domain of measurement used on P. fluorescens biofilms. Domain of measurement: length in direction x, 639 mm; lengthin direction y, 3,067 mm; length in direction z, 20 mm.


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biovolume, Vt, accumulated in the domain of measurement.The description of this process is

Vt 51Vr

3 On51

ne

Vn (7)

where Vr 5 reference volume [ l3, and l r3 5 reference length;

it depends on the magnification of the lens in use.

Bacterial colonization measured by autofluorescence. Fig-ure 6 shows a P. fluorescens colonization event at different celldepths for a representative y section after 14 h of flow celloperation. Images in the zones near the glass surface (substra-tum) of the flow cell were quantified by the numerical approx-imation method as described above.

Figure 8 is a 3D representation of the P. fluorescens culturein the flow cell. The results represent the onset of cell accu-mulation. Featured is the ratio of the area of microbial colo-

FIG. 6. Optical sectioning produced by CLSM, illustrating the horizontal colonization of P. fluorescens at different flow cell depths. In some cases, images wereaffected by light reflections at the position closest to the substratum.


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nization, Am [ F(z), to the microscopic area, Ar 5 lr2, for the x

position at 63.9 mm and all y stations. The reference length lrdepends on the lens magnification and is given by lr 5 127.8mm for the 1003 objective lens. The results, expressed as per-centages, delineate the early stage of cell attachment charac-terized by the boundary layer of the fluid. An exponentialbehavior in the vertical and horizontal directions determinescell growth activity within the whole domain of measurement

controlled by the three-dimensional flow profile of a laminar-flow field. In the initial phase of colonization, cell aggregatesare found where the flow velocity is low within the boundarylayer, i.e., on the glass surface near the flow cell walls.

In Fig. 9, calculated biofilm volume stacks are presented. Ateach xy position, the results for 20 horizontal CLSM sectionswere integrated by means of the numerical procedure dis-cussed above. The volumes Vn occupied by the microbial col-onies are featured as percentages of the total microscopicvolume Vr 5 lr

3, with lr 5 127.8 mm. At position x 5 63.9 mm,the calculated volumes of the microbial colonization data por-trayed in Fig. 8 can be seen as columns over all y stations. Stackvolumes on display in Fig. 9 may be summarized to the totalbiovolume Vt in accordance with equation 7 by using the macroroutine.

E. coli EC12 biofilm. Figure 10 illustrates the E. coli EC12biofilm on day 7 as determined by GFP fluorescence and byfluorescent in situ hybridization with the EUB338 oligonucle-otide probe at position x 5 447.3 mm. As depicted in Fig. 10,the cellular distribution shows that the biofilm was thickest at1 to 2 mm from the attachment surface. Hence, colonizationvaried nonlinearly with depth, in contrast to the colonization ofP. fluorescens (Fig. 9). Extrapolation by polynomial approxi-mation to account for bacterial cells at z 5 0 mm was notnecessary since no diffuse light reflections were observed. TheE. coli biofilm consisted primarily of cell clusters attached by afew cells to the glass surface (data not shown). GFP fluores-cence was also observed in the deeper layers of the biofilm.Signals from in situ hybridization of the biofilm suggested thesame colonization pattern (Fig. 10). At each xy position, theresults for six horizontal CLSM sections were integrated by thenumerical procedure discussed above. The volumes were cal-

FIG. 7. Typical diagram indicating P. fluorescens biofilm colonization nearthe substratum. The numerical approximation procedure was a least-squares fitof measured data points to a polynomial function (see the text for details).

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 ofcolonization. During the experiment, the flow rate setting was 3 liters h21.


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culated as described above and are displayed in Fig. 11. Therewas no clear trend in terms of the distribution of cell materialacross the flow channel as measured by biovolumes based onGFP fluorescence. This may be because the biofilm was older(7 days) and the data represent a mature biofilm, whereas theP. fluorescens biofilm represented the initial colonizationstages, which are highly influenced by the flow regimen in thechannel.

DISCUSSION

There is certainly no lack of prominent publications on bio-film studies focusing on technical innovations, especially in thefield of CLSM techniques in connection with analytical imag-ing (6, 10, 11, 35). However, little attention has been paid tothe question of how to perform an overall analysis coveringextended 3D regions of biofilm structures cultivated undervarious flow conditions. The procedure should facilitate aquantitative estimate by means of an automated approachwithout any operator intervention.

We presented a method involving an automated procedurefor studying biofilm growth in extended 3D domains. In com-bining in situ CLSM with off-line image processing and analysistechniques, we sought to establish a user-friendly method thatwould allow automated quantitative measurements. Designingthe routine was a multifaceted approach enclosing elements ofbiotechnology, signal processing, elementary mathematics, andcomputer programming. The image analysis processing has

been made intentionally semiautomated, since biofilm re-search is unquestionably a complex task, based upon the de-gree of judgment and experience of the individual researcher.Hence, before the automated image analysis can begin, pixelcalibration and image setup must be defined with respect to thecalibration value used for the image generation on the CLSMsystem. In addition, laser beam, mathematical filter, andthreshold settings are not suitable for automation.

To demonstrate the power of this method, we performedbiofilm analysis based on both autofluorescence of P. fluores-cens and GFP expression including in situ hybridization of E.coli EC12. Analysis of the P. fluorescens biofilm revealed thatmost of the biomass, expressed as microbial colonization de-tected by autofluorescence, was concentrated at the attach-ment surface (z position, 0 mm [Fig. 8]). These observations arein accordance with the results from other investigations (29,32). P. fluorescens has a tendency to form a tightly packedsurface monolayer of cells which serves as a base for the lessdense biofilm layers that extend into the bulk aqueous phase.In addition, P. fluorescens, due to its motility, has been shownto colonize surfaces more rapidly than nonmotile microorgan-isms do. Korber et al. (26) confirmed that cellular motilityplays an important role during cell positioning and formationof biofilm microenvironments in studies with motile (Mot1)and nonmotile (Mot2) P. fluorescens strains. The effect ofmotility on bacterial colonization of surfaces has been dis-cussed in a review on behavioral strategies of surface-coloniz-ing bacteria (34). In contrast, the E. coli biofilm manifested the

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 wereintegrated by the numerical procedure outlined in the text. During the experiment, the flow rate setting was 3 liters h21. 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.


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highest cell density 1 to 2 mm from the attachment surface, asrevealed by GFP fluorescence and in situ hybridization (Fig.10). Cellular agglomerates were observed, with a few cells at-tached at the surface facilitating a sessile connection with thebulk of the clusters. Investigating Vibrio parahaemolyticus bio-films, Lawrence et al. (31) observed that the lowest cell densitywas found at the attachment surface whereas the highest celldensity was detected in the outer region of the biofilm. Theresearchers suggested that the maintenance of a minimal basallayer was important in preventing uncontrolled sloughing ofthe biofilm. Interestingly, Neu and Lawrence (46) demon-strated in natural-biofilm studies that substantial cell densitywas also present at some distance from the attachment surfaceand not in the lowest layers of the biofilm.

Since GFP fluorescence persists even after treatment withformaldehyde, the detection of GFP-expressing cells may becombined with fluorescent in situ hybridization with oligonu-cleotide probes (19). Our observations confirmed that mostcells which exhibited GFP fluorescence could also be hybrid-ized with the EUB338 probe (Fig. 10). Figure 11 shows that thedetected GFP fluorescence occupied on the average a slightlygreater biovolume than that represented by signals from in situhybridization. The observed difference in biovolume was notdue to a higher relative signal intensity caused by GFP thanthat caused by hybridization. Likewise, the omission of ethanolin the fixation procedure (see Materials and Methods) did notinterfere with hybridization of E. coli cells. This step was avoid-ed, to maintain the spatial structure of the biofilm. Moller et al.(42) reported that the dehydration step caused a biofilm toshrink in the z direction from an average of 365 mm for thefully hydrated biofilm to a thickness of 25 to 70 mm. It shouldbe noted, however, that the application of paraformaldehydealso results in some loss of water. It is possible that after theexperiment had run for 7 days, the biofilm cells were no longeras 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 detectedby GFP fluorescence but not by the oligonucleotide probe. Forexample, Kalmbach et al. (24) showed that the respiratoryactivity and ribosome content of adherent bacterial cells de-creased continually during the early stages of development ofa drinking-water biofilm. Similarly, Poulsen et al. (48) demon-strated that the doubling time in an established biofilm wassignificantly longer than in a young biofilm. Amann et al. (1)suggested that nonlogarithmically growing cells of certain bac-terial genera were difficult to detect with oligonucleotideprobes because of their low cellular rRNA content. To test thisassumption in our study, it would have been necessary to runparallel setups and to hybridize the biofilm at the beginningand throughout the experimental period as well. However, thiswas not feasible due to the limitations imposed by a single-flowcell. Another possibility would be to increase the hybridizationtime. For example, Moller et al. (42) applied an EUB338 probeto a multispecies biofilm and carried out the hybridization for16 h at 37°C. In contrast, the signals obtained from a ground-water biofilm hybridized with the EUB338 probe for 2 or 16 hdid not differ significantly (45).

Korber et al. (25) were the first to investigate large areas ofmicrobial biofilms by using statistical assumptions. It is wellknown that biofilm habitats cannot be considered to have de-terministic structures on a microscopic scale. Consequently, itis hardly feasible to predict precisely the parameters of manyphenomena occurring in such systems. These processes arecalled random processes; therefore, a reliable method is need-ed that predicts the smallest possible domain of interest andalso confirms the “representative areas” within a desired in-terval of confidence. This means that the biofilm unit understudy should be adequately represented, such that the entirerange of data variability of its architecture is encompassed. Thestatistical methods proposed by Korber et al. (25) may in somecases simplify the experimental design, including data analysis,in a straightforward manner. Applying the method of repre-

FIG. 10. E. coli EC12 biofilm on day 7, shown as colonization percentage at position x 5 447.3 mm. Both GFP fluorescence and signals from in situ hybridizationwith the EUB338 probe are depicted. Note the similar distribution patterns of GFP and CY3-EUB338 fluorescence.


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sentative elementary area in connection with the method ofdetermined confidence intervals in practice assumes that thedata in hand are normally distributed or could be normalizedthrough transformation. Categorizing acceptable variabilitylimits for biofilm data is a tedious and excessive process. Thelimit values invariably differ among species, growth inhibitions,or stimulations by environmental conditions. They are alsoinfluenced by morphological alteration, by the motility or re-colonization behavior, and by the arrangement and instrumen-tation of the experimental test setup. Some statistical methodsare distribution hypotheses. In the light of empirical examina-tions, estimation functions based upon hypotheses must bechecked before being used. They may be accepted or rejected.Furthermore, it should be noted that statistical methods havea generalizing character and so some special information hid-den within the biofilm architecture may be lost. Otherwise, itcan be assumed that not all cell distribution patterns within abiofilm 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 statisticalmathematics in parallel for cross-checking the results.

It should be mentioned that the outlined procedure does notrepresent a true 3D approach, even though biovolumes werecalculated, indicating biomass trends. 3D programs are basedon volume units, called voxels (10, 12), and create 3D projec-tions from any viewpoint of the specimen or region of interest.The 3D imaging software currently available on the market isvery expensive, time-consuming in its application, memory in-

tensive, and suitable mainly only for descriptive demonstra-tions of the object. At the moment, no ready-to-use programsare able to elucidate and quantify dynamic processes occurringin real 3D biofilm structures. In the method described here, itis assumed that no gap or wraparound failures are present be-tween adjacent sampling areas, because backlash margins inmaneuvering the scanning stage may be small. To acquire quan-titative data automatically, we applied macro routines writtenby the user. The routines are based on basic 2D computer soft-ware. In evaluating biovolumes, we did not discriminate be-tween cells and extracellular substances. However, suitablegenetic markers, such as GFP or fluorescently labelled rRNA-directed oligonucleotide probes, as well as general nucleic acidstains in conjunction with EPS-specific fluorescent stains orlectins (46), could be used to quantify defined biofilm entities.In the absence of a true estimate of cell numbers in an undis-turbed biofilm, a semiautomated quantification of the relativespatial distribution of cellular and extracellular material pre-sents a promising tool for quantitative biofilm analysis.

ACKNOWLEDGMENTS

We thank P. Hutzler for critically reading the manuscript. We aregrateful to G. Schaule and R. Amann for donating the P. fluorescensand E. coli DH5a strains, respectively.

This research was supported by grant Wu268/1-2 from the GermanResearch Foundation (DFG) to S.W. and by the Research Center forFundamental Studies of Aerobic Biological Wastewater Treatment(SFB411), Munich, Germany.

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 bysix horizontal CLSM sections were integrated by using the numerical procedure outlined in the text. During the experiment, the flow rate setting was 15 ml h21. Thebiofilm was analyzed after a 7-day test run.


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