A method for quantitative analysis of biofilm thickness variability
by Ricardo Murga
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in
Environmental Engineering
Montana State University
© Copyright by Ricardo Murga (1994)
Abstract:
This thesis presents work done on developing an experimental method for measurement of biofilm
thickness. This method involves embedding the biofilm, cross-sectioning it, and applying image
analysis and mathematical techniques to reconstruct biofilm thickness profiles in order to characterize
biofilm thickness variation. Quantitative data as well as qualitative observations obtained from
Pseudomonas aeruginosa, Klebsiella pneumoniae and binary population biofilms revealed that the three
biofilm systems are distinct and heterogeneous in their structure. Statistical analysis of biofilm
thickness data showed that biofilm species composition influences structural characteristics. P.
aeruginosa, K. pneumoniae and binary population biofilms appeared to have different mean
thicknesses, different roughness values, and different spatial, distribution of biomass, voids and water
channels. P. aeruginosa biofilms are relatively uniform and lack voids or water channels. They have an
average thickness of 30 μn, and their roughness coefficient, Ra*, ranges between 0.1 and 0.2. Different
sizes and lengths of water channels were observed in K. pneumoniae biofilms, but the most distinctive
characteristic was large bare areas along the substratum. K. pneumoniae biofilms are patchy and their
thickness varies drastically, having a mean thickness that ranged between 8 μn to almost 300 μm. The
roughness coefficient for K. pneumoniae biofilms was the highest of the three biofilms systems,
ranging between 0.8 to 1.3. The binary population biofilm was also full of water channels and voids.
The mean thickness ranged between 300 and 600 μm, but its roughness coefficient only ranged
between 0.2 and 0.3. The different ranges for the roughness coefficient values suggested that
differences in biofilm thickness variation could be used as a distinctive characteristic for categorizing
biofilms. A METHOD FOR QUANTITATIVE ANALYSIS OF BIOFILM
THICKNESS VARIABILITY
by
Ricardo Murga
A thesis submitted in partial fulfillment
of the requirements for the degree
of
Master of Science
in
Environmental Engineering
MONTANA STATE UNIVERSITY
Bozeman, Montana
April 1994
-tl2 /)2
m<)44
APPROVAL
of a thesis submitted by
Ricardo Murga
This thesis has been read by each member of the committee and has
been found to be satisfactory regarding content, English usage, format,
citations, bibliographic style, and consistency, and is ready for submission to
the College of Graduate Studies.
Date
Chairperson, Graduate Committee
Approved for the Major Department
S '?
Date
Head, Major Department '
Approved for the College of Graduate Studies
Date
Graduate Dean
STATEMENT OF PERMISSION TO USE
In presenting this thesis in partial fulfillment of the requirements for a
master’s degree at Montana State University, I agree that the Library shall make
it available to borrowers under rules of the Library.
If I have indicated my intention to copyright this thesis by including a
copyright notice page, copying is allowable only for scholarly purposes,
consistent with "fair use" as prescribed in the U.S. Copyright Law. Requests for
permission for extended quotation from or reproduction of this thesis in whole
or in parts may be granted only by the copyright holder.
Date
Yf ^
f ^ c/
ACKNOWLEDGEMENTS
First I would like to express my gratitude to my academic and research
advisor Dr. Phil Stewart. He has provided the support, guidance, and
encouragement which have enabled me to accomplish this goal. I also thank
my committee members, Dr. Warren Jones for his counsel and assistance
during my first semester at MSU, and Dr. Zbigniew Lewandowski for his
assistance.
I would like to acknowledge Gayle Callis for the many hours of work and
for her advice on further developing biofilm cryoembedding. I would also like to
acknowledge Don Daly for his advice and help on the use of statistical tools.
The staff and fellow graduate students at the Center for Biofilms
Engineering have been very helpful to me, and have made my stay at the ERC
very pleasant. I would like to especially thank for their help Gary, Dirk, Brian,
Diane, Peg, Jamie, Paul, and Marty. Many thanks also for their help and
friendship to Jason, Xiaoming, Philip, Sam, Mary, Nick, and specially Rohini.
I would like to acknowledge with love and gratitude the support and
encouragement of my wife, Jennifer.
My special thanks to Bill Characklis for the existence of the Center, for
his great example and vision.
My research activities have been sponsored by the National Science
Foundation and the Associates of the Center; my thanks to them.
Most important of all I thank my God for His grace and love for me.
V
TABLE OF CONTENTS
Page
LIST OF TABLES ...............................
............................................................. .. . vii
LIST OF FIGURES .........................................................................
ABSTRACT
................................................................................................................. xi
INTRODUCTION
^ ............................................................................................
1
The Problem ..................................................................................................
1
Goal and Objectives . .................................................................................
3
LITERATURE REVIEW ..........................................................................................
, Evidence of Biofilm Structural H e te ro g e n e ity........................................
4
4
METHOD DEVELOPMENT .........................................................................
10
MATERIALS, EXPERIMENTAL SYSTEMS AND M E T H O D S ..............................
11
Bacterial C h a ra cte ristics.............................................................................. 11
Reactor Design ............................................................................................ 11
Reactor S ta rt-u p ............................................................................................ 16
S a m p lin g ........................................................................................................ 16
Biofilm Cryoembedding .......................................................................
18
C ryo se ctio n in g .............................................................................................. 19
Fixation and Staining of Biofilm Cross-sections ...................................... 21
Image A n a ly s is ............................................................................................ 22
Statistical M e th o d s ...................
23
R ESU LTS.................................................................................................................
27
D IS C U S S IO N ............................................................................................................ 64
Biofilm Thickness M easurem ents........................................................ .. . 64
Statistical Analysis ....................................................................................... 66
,
Biofilm H e te ro g e n e ity ................................................................................ 68
SUMMARY AND CONCLUSIONS .......................................................................
74
RECOMMENDATIONS FOR FUTURE RESEARCH . .........................................
77
REFERENCES ..........................................................................................................
79
vi
APPEN D IC ES.................................
86
APPENDIX A ....................................
87
Particle Sizing Using Image Analysis T e c h n iq u e s..................... 87
Vernier Measurements ..............................
92
Coulter Counter M easurem ents......... ...................................... 95
APPENDIX B .............................................................................................. . 10 1
Optical Measurements ...........................................................
101
Biofilm E m b e d d in g .................
103
APPENDIX C . ■ : ........................................................
109
S-PLUS Program ...............................................................................109
Vll
LIST OF TABLES
Table
Page
1.
References for evidence of heterogeneity ......................................... 5
2.
Medium composition
......................................................................... 15
3.
Phosphate buffer co m p o sitio n ........................................................... 15
4.
Coulter Counter aperture tube data ........................ : ................
. 99
Vlll
LIST OF FIGURES
Figure
Page
1.
Annular reactor schematic
...............
.1 2
2.
Cryoembedding s c h e m a tic ...............................................
20
3.
Photomicrographs of the substratum after biofilm remov . . . . . 32
4.
Photomicrographs of P. aeruginosa biofilm cross-section
5.
Photomicrographs of K. pneumoniae biofilm cross-section . . . 34
6.
Photomicrographs of binary population biofilm cross-section . . 35
7.
P. aeruginosa biofilm profiles from three different random
locations on the slide (a, b and c ) ..................................................36
8.
K. pneumoniae biofilm profiles from three different random
......... 33
locations on the slide (a, b and c ) ................................................. 37
9.
Binary population biofilm profiles from three different random
locations on the slide (a, b and c ) ..................................................38
10.
P. aeruginosa biofilm thickness distributions from profiles on
figures 7a through 7 c ...................................... ............................ 39
11.
K. aeruginosa biofilm thickness distributions from profiles on
figures 8a through 8 c ......................................................................... 40
i
12. .
Binary population biofilm thickness distributions from profiles
on figures 9a through 9c ................................................................41
13.
Variogram curves from P. aeruginosa biofilm profiles on figures
7a through 7c . . %........................................................................... 42
14.
Variogram curves from K. pneumoniae biofilm profiles on
figures 8a through 8 c .......................................... ............ ............43
15.
Variogram curves from binary population biofilm profiles on
figures 9a through 9 c ......................................................................... 44
16.
Values of Ra* (roughness coefficient) for P. aeruginosa, K.
pneumoniae, and binary population b io film s ................................45
ix
17.
2-day old binary population biofilm profiles from four different
random locations on the s lid e .........................................................46
18.
4-day old binary population biofilm profiles from four different
random location's on the s lid e ................................................. ..
47
19.
6-day old binary population biofilm profiles from three different
random locations on the s lid e ...................................................... ,4 8
20.
8-day old binary population biofilm profiles from four different
random locations on the s lid e ...................................................... .4 9
21.
Binary population biofilm thickness distributions from profiles on
figures 17a through 17d ....................................................
50
22.
Binary population biofilm thickness distributions from profiles on
figures 18a through 1 8 d ..................... ......................... ..................51
23.
Binary population biofilm thickness distributions from profiles on
figures 19a through 1 9 c .................................................................... 52
24..
Binary population biofilm thickness distributions from profiles on
figures 20a through 2 0 c .................................................................... 53
25.
Variogram curves from profiles on figures 17a through 17d . . . 54
26.
Variogram curves from profiles on figures 18a through 18d . . . 55
27.
Variogram curves from profiles on figures 19a through 19c . . . 56
28.
Variogram curves from profiles on figures 20a through 20c . . . 57
29.
Ra* values from biofilm profiles on figures 17a through 20c . . . 58
30.
Mean biofilm thickness from biofilm profiles on figures 17a
through 2 0 c ......................................................................................... 59
31.
Biofilm density for binary population biofilm 2, 4, 6, 8 days old . 60
32.
Individual cells and multicellular aggregates observed in the
effluent of a P. aeruginosa biofilm reactor . ................................. 61
33.
Multicellular aggregates observed in the effluent of a K.
............................................................. 62
pneumoniae biofilm reactor
X
34.
35.
Individual cells and multicellular aggregates observed in the
effluent of a binary population biofilm reactor .............................63
Relationship between Ra* and variogram distance for P.
.............. 71
aeruginosa, K. pneumoniae and binary biofilms .
. 36.
Photomicrographs of undefined mixed population biofilm crosssection ...................................................................................................72
37.
Photomicrographs of binary population biofilm cross-section
grown with continuous supply of CaCO3 and k a o lin ...................73
38.
Particle area distribution of detached P. aeruginosa multicellular
aggregates from days 1 through 4 ..................... ......................... 89
39.
Particle area distribution of detached P. aeruginosa multicellular
aggregates collected on hours 1 through 4 of day 5 .............. . 90
40.
Particle area distribution of detached P. aeruginosa multicellular
aggregates collected on hours 5 through 8 of day 5 .................91
41.
Polar P lanim e ter...................................................................
42.
Image Analysis and planimeterresultscomparison ........................ 96
43.
Planimeter resultsfrom measuring square and circular shapes
44.
Method for measuring wet biofilm thickness with an optical
microscope .....................................................................................102
45.
P. aeruginosa and K. pneumoniae biofilm thickness profiles . 106
46.
Undefined mixed population biofilm thickness p r o file .............. 108
93
97
Xl
ABSTRACT
This thesis presents work done on developing an experimental method
for measurement of biofilm thickness. This method involves embedding the
biofilm, cross-sectioning it, and applying image analysis and mathematical
techniques to reconstruct biofilm thickness profiles in order to characterize
biofilm thickness variation. Quantitative data as well as qualitative observations
obtained from Pseudomonas aeruginosa, Klebsiella pneumoniae and binary
population biofilms revealed that the three biofilm systems are distinct and
heterogeneous in their structure. Statistical analysis of biofilm thickness data
showed that biofilm species composition influences structural characteristics. P.
aeruginosa, K. pneumoniae and binary population biofilms appeared to have
different mean thicknesses, different roughness values, and different spatial,
distribution of biomass, voids and water channels. P. aeruginosa biofilms are
relatively uniform and lack voids or water channels. They have an average
thickness of 30 urn, and their roughness coefficient, Ra*, ranges between 0.1
and 0.2. Different sizes and lengths of water channels were observed in K.
pneumoniae biofilms, but the most distinctive characteristic was large bare
areas along the substratum. K. pneumoniae biofilms are patchy and their
thickness varies drastically, having a mean thickness that ranged between 8 urn
to almost 300 p,m. The roughness coefficient for K. pneumoniae biofilms was
the highest of the three biofilms systems, ranging between 0.8 to 1.3. The
binary population biofilm was also full of water channels and voids. The mean
thickness ranged between 300 and 600 p,m, but its roughness coefficient only
ranged between 0.2 and 0.3. The different ranges for the roughness coefficient
values suggested that differences in biofilm thickness variation could be used
as a distinctive characteristic for categorizing biofilms.
1
INTRODUCTION
The Problem
. Microbial cells attach firmly to almost any surface submerged in an
aquatic environment. The immobilized cells grow, reproduce, and produce
extracellular polymers which help them form a complex matrix that provides
structure to the biomass accumulation termed a biofilm (Characklis and
Marshall, 1990). The adhesion to surfaces provides considerable advantages
for bacteria within the biofilm. The biofilm matrix provides protection from
antimicrobial agents, as well as facilitating the exchange of nutrients and
genetic material due to close proximity of cells. While bacteria benefit from their
close association with the surface, the physical presence of the biofilm
compromises the function of the surface, reducing the efficiency of equipment
in industry. This damage to the surface is referred to as biofouling. Some of
the problems stemming from biofouling are summarized in the following
paragraph.
Microbially induced corrosion (MIC) is initiated when clusters of bacteria
form a patchy biofilm. These clusters establish microscale chemical gradients
around the surface and thereby create both anodic and cathodic sites (Lee and
Characklis, 1993., Geesey and Bremer, 1992., Videla and Characklis, 1992.,
Bremer and Geesey, 1991., Characklis et al. 1991, Geesey, 1990., Jelley et al.,
1989., Characklis, 1989). Increase of fluid-frictional resistance in industrial
2
pipes results from the increase in biofilm thickness and roughness (Characklis,
Zelver and Turkhia, 1981., Characklis and Turkhia, 1981., Picologlou et al.,
1980., Kirkpatrick et al. 1980., Characklis, Zelver and Picologlou, 1978).
Increase of heat transfer resistance results from the accretion of biomass and
inorganic sediments (Turakhia and Characklis, 1984., Mussalli and Characklis,
1984., Characklis et al. 1984, 1983., Zelver et al., 1982., Characklis Nimmons
and Picologlou, 1981., Characklis, Zelver and Turakhia, 1981). Bacterial
colonization of a variety of medical implants and devices results in persistent
infections (Costerton, 1982., Marrie et al., 1982., Marrie and Costerton, 1983.,
Marrie and Costerton, 1984). Detachment of microbial aggregates into the bulk
liquid degrades products in the paper manufacturing and water distribution
processes (Nix, et al. 1992., Block, 1992., Jung, et al. 1990., McFeters1 1990.,
van der Wende, et al. 1989., LeChevaIIier, et al. 1988., Camper, et al. 1986).
Biofilm development and activity is the result of several physical,
chemical and biological rate processes. Such processes include transport of
cells to the substratum, adsorption of cells onto the substratum, growth and
other metabolic processes within the biofilm, and detachment of portions of the
biofilm. These processes are influenced by the biofilm structure and the
properties of the environment around the biofilm. Surface roughness, for
example, significantly influences transport rates and microbial attachment for
several reasons including the following: 1) increased convective mass transport
near the surface, 2) shelter from shear forces for small particles, and 3)
3
increased surface area for attachment (Characklis1 W.G., 1981). Surface
roughness is one of the many aspects that reflect the structural heterogeneity
of the biofilm matrix. This structural heterogeneity should therefore be studied
and quantitatively described in order to better understand, predict and solve the
problems caused by biofouling. Obtaining quantitative information on surface
roughness and structural heterogeneity remains a challenge to biofilm
researchers.
Goal and Objectives
The long term goal of this research is to understand how biofilm
structural heterogeneity impacts biofilm function. The broad objective of this
thesis was to develop experimental methods and apply mathematical
techniques to characterize biofilm thickness variation. The specific objectives
were: 1) to develop an experimental method suitable for measurement of biofilm
thickness profiles, 2) to construct t-dimensional thickness profiles of steady
state pure culture biofilms of P. aeruginosa, K. pneumoniae, and the binary
population combination, 3) to calculate a roughness index (Ra*) and to perform
a spatial correlation analysis for each profile, 4) to obtain photomicrographs of
particles in the effluent of pure culture and binary population biofilm reactors,
5) to document temporal changes in biofilm thickness characteristics during
development of binary population biofilms by obtaining biofilm thickness
profiles, Ra* values, and spatial correlation analyses in time series.
4
LITERATURE REVIEW
• Evidence of Biofilm Structural Heterogeneity
. Recent publications provide evidence of structural heterogeneity in
biofilms. Table 1 shows a few examples of the types of evidence described in
publications from the last decade alone. Several techniques have been used to
qualitatively describe biofilm structure including light microscopy, atomic force
microscopy (AFM), scanning electron microscopy (SEM), transmission electron
microscopy (TEM), confocal scanning laser microscopy (CSLM), and Fourier
transmission infrared spectroscopy (FTIR). The systems in which these
observations were made range from laboratory reactors to medical and
industrial systems.
Many factors may contribute to the structural heterogeneity of biofilms
such as the uneven distribution of microbial species, the presence of different
shapes of bacteria buried in amorphous extracellular polymers that constitute
the biofilm matrix, nitrogen bubble formation in some cases, shear and
hydrodynamic forces, deposits of sediments, and encrustations on pipe walls.
All these contribute to different forms of structural heterogeneity in biofilms due
to the disruption of the microbial matrix and biomass detachment (sometimes
leaving entire bare areas), helping to the formation of patchy biofilms. Most
heterogeneous biofilms have water channels, pores, microtowers, ridges, and
large variations in biofilm thickness and surface roughness.
Table 1.
References for evidence of heterogeneity.
Ref
Microorganism
Allen et al, 1980.
1
Mixed population.
Wat. dist. system
Electron micrographs revealed a
hard but porous surface.
Bremer et al, 1992.
7
CCI#8 bacterium.
Copper coupons
submerged in flasks
with culture medium.
Atomic Force Microscopy (AFM)
showed biofilm "several cells thick
in places, distributed
heterogeneously over coupon
surface with considerable
variation in surface contour.
Bryers, 1991.
9
P. putida.
Annular reactor.
Observed sloughing events due
to nitrogen bubble formation that
lead to disruption of the microbial
matrix.
Bryers and
Characklis, 1981.
10
Undefined mixed
population from
sewage.
Tubular reactor
system running as
CSTR
Microscopically, biofilms
appeared as discrete fibers
randomly distributed, 1.5 to 1500
nm long.
Capdeville et al,
1990.
12
Heterotrophes.
Annular reactor
SEM and photographs showed
observations of thickness profile
and top view of rough,
filamentous and patchy surface.
Christensen et al,
1988.
24
Denitrifying biofilm
from wastewater.
Annular reactor
Pictures show heterogeneity in
biofilm accumulation due to
nitrogen bubble formation.
Author
Reactor Design
Evidence
Table 1 (cont.)
Eighmy et al, 1983.
29
Gujer, 1987.
34
Hamoda and
Abd-el-bary, 1987.
35
Harremoes et al,
1980.
37
Howell and
Atkinson, 1976.
38
Undefined biofilm
from wastewater.
Inert substratum and
supporting structure
suitable for
submersion in wastestream, bacterial
adhesion and
subsequent
examination by TEM
and SEM.
SEM and TEM micrographs
showed filamentous non-smooth
surface.
A model of
segregation of
biomass in biofilms.
Describes a biofilm as an
inhomogeneous microbial matrix
due to uneven distributions of
microbial species.
Mixed culture from
activated sludge.
Aerated submerged
fixed-film (ASFF)
bioreactor.
Microscopic observation of
spongy biofilms with thickness of
up to 2.3 mm.
Denitrifying biofilm.
Annular reactor.
Extreme heterogeneity in biofilms
due to nitrogen bubbles
formation that detached big
particles of biofilm leaving large
bare sections of the wall visible.
Trickling filter biofilm
model.
Computer output showed film
thickness profiles to have a
varied irregular pattern.
Table 1 (cont.)
Huang et al, 1985.
39
Undefined mixed
population from
primary effluent
from sewage
treatment plant.
Annular reactor.
Microscopic observations
showed biofilm texture that was
loose and porous.
Kugaprasatham et
al, 1992.
44
Nitrifying biofilm.
Cylindrical PVC
reactors.
Photographic observations of
patchy and filamentous biofilm
where surface was not smooth.
Lawrence, J.R. et al,
1991.
45
P. fluorescens,
P.aeruginosa,
Vibrio
parahaemolyticus
Gyratory shaker.
Highly hydrated biofilms, open
structures 73 to 98% EPS and
space. Also, significant
channeling and porosity of the
biofilm were observed.
LeChevaIIier et al,
1987.
46
Mixed population,
bacteria, fungi,
protozoa, yeasts,
etc.
Distribution systems
SEM showed biomass attached
to pipe walls in a heterogenous
pattern determined by shearforces,deposits of sediment,
encrustations and tubercles on
pipe wall surface.
Mack et al, 1975.
49
Mixed population,
bacteria and algae.
Trickling filters.
SEM showed rough uneven
surfaces.
Nickel and
Costerton, 1992.
56
Klebsiella species
and Enterococcus
faecalis bacteriuria.
Foley catheter.
SEM depicted thick
heterogenous multispecies
biofilm.
Table 1 (cont.)
Picologlou et al,
1980.
59
Undefined mixed
population from
wastewater
inoculum.
Tubular fouling
reactor (TFR).
Fluid friction measurements
indicate hydrodynamic
roughness.
Robinson et al,
1984.
60
Methanogens.
Anaerobic fixed-bed
reactor.
SEM showed volcano-like
structures opening to the
interior - 500 ^m in diameter overall surface was rough and
uneven.
Rogers and Keevil,
1992.
61
Inoculum from
outbreak of
Legionnaires’
disease.
Coupons of glass or
polybutylene pipe
sections suspended
in two-stage
chemostat model of
a water distribution
system.
Basal biofilm layer was
approximately 5 ^m with taller
stacks exceeding 100 jum in
height.
Siebel and
Characklis, 1991.
63
P. aeruginosa,
Annular reactor.
Nomarsky microscopic pictures
showed P. biofilms to be
somewhat smooth, K. biofilms to
be clusters with bare spots in
between, and the binary biofilm
to be patchy with microtowers.
Rotating biological
contactor.
Filamentous and porous
structured biofilm.
K. pneumoniae
and binary of P.
and K.
Siegrist and Gujer,
1985.
64
Heterotrophic
biofilm.
Table 1 (cont.)
Stewart, 1992.
66
Stewart et al, 1993.
67
P. aeruginosa
Mathematical model
of biofilm
detachment.
Model incorporates the discrete
nature of the detachment process
and therefore the variations in
biofilm thickness, patchiness and
roughness.
Annular reactor.
Optical method and image
analysis showed evidence of
heterogeneity in both surface
roughness and size of the
detached biomass in the effluent.
Anaerobic fixed-film
reactors (AF, AFB
and UAS B).
SEM showed dense and thin
films with rough uneven surfaces,
channels and ridges.
biofilms.
Switzenbaum and
Eimstad, 1987.
68
Methanogens.
-10
METHOD DEVELOPMENT
The objective of this thesis came about from the desire to quantify one of
the qualities that describe the structure of biofilms. Prior to pursuing a method
for measuring biofilm thickness variability, a research goal was to develop a
method for measuring the size of the biomass particles that detach from
biofilms. Three methods were pursued, none of which were successful.
Appendix A explains these methods.
Developing a method for measuring biofilm thickness also required much
effort. Two different methods were pursued before trying the cryoembedding of
biofilm, which worked the best. The first two methods are explained in
Appendix B.
/
11
MATERIALS, EXPERIMENTAL SYSTEMS AND METHODS
Bacterial Characteristics
Pseudomonas aeruginosa and Klebsiella pneumoniae were used to
grow mono and binary population biofilms. Pseudomonas aeruginosa is a
Gram-negative rod shaped (approximately 0.5 to 1.0 p.m by 1.5 to 4 urn)
aerobic chemoorganotroph. It is motile by means of a single polar flagellum.
(Brock, 1974). Pseudomonas aeruginosa is an obligate aerobe, although in
some cases nitrate (NO3) can be used as an alternate electron acceptor. The
optimum temperature for growth is 37°C, although growth occurs from below
4°C to 43°C.
Klebsiella pneumoniae is a Gram-negative straight rod shaped (0.3 - 1. 0
urn by 1.0 - 6.0 p,m) non-motile chemoorganotroph. This organism is a
facultative anaerobe, having both respiratory and fermentative metabolic
pathways. Its optimum temperature for growth is 35°C, although growth can
occur from 10°C to 44°C (Bergey’s, 1984).
Reactor Design
A continuous flow annular reactor (Figure 1) was used to grow biofilm.
The reactor design has several characteristics which make it versatile for bidfilm
research (Peyton, 1992). It consists of two polycarbonate cylinders, a
stationary outer cylinder and a co-axial rotating inner cylinder. The inner
12
Buffer solution
Outer Drum
Removable
Slide (12)
Recirculation
I Tube (4)
Rotating
Inner
Cylinder
Outlet
Figure 1.
Annular reactor schematic.
13
cylinder has four vertical draft tubes positioned at angles so that the rotation of
the inner cylinder pumps the fluid through the tubes providing vertical mixing.
The reactor can thus be considered to be well-mixed (Characklis & Marshall, .
1990). The inside wall of the outer cylinder, with an area of 676 x 10"4 m2 upon
which biofilm can grow, has vertical grooves that allow twelve removable slides
for sampling of biofilm. The slides, each with an area of 33 x 10"4 m2, can be
removed through the top of the reactor through rubber-stoppered holes. Fluid
shear stress in the reactor is dependent on the rotational speed of the inner
cylinder. The outer cylinder undergoes a uniform shear stress since the gap
between cylinders is constant throughout the height of the reactor (Drury,
1992). Because the shear stress is constant and the reactor is completely
mixed, it is commonly assumed that the biofilm growing on the walls of the
outer cylinder is relatively uniform, so that a biofilm specimen collected from any
of the twelve slides is a representative sample. Also, because the annular
reactor is completely mixed, the effluent from the annular reactor is a
representative sample of the bulk fluid.
Three separate streams were added to the annular reactor, producing a
cumulative flow rate of 19.2 x 10"4 m3 h"1 (32 ml/min), and a hydraulic retention
time of 0.34 h. At this retention time, suspended bacteria are quickly washed
away from the reactor and their activity can be neglected. The three streams
were substrate (6 x 10"5 m3 h"1, or 1 ml min"1), phosphate buffer (6 x 10"5 m3 h"1,
or 1 ml min"1), and dilution water (0.0018 m3 h"1, or 30 ml min"1). Tables 3 and 4
14
show the composition of substrate and phosphate buffer, respectively. Flow
rates were measured with in-line flow meters (Gilmont Instruments Co., Nos. 11
and 12). Peristaltic pumps (Masterflex #7553-30, Cole-Farmer Instruments Co.,
with pump heads 7021 -20 and 7518-00) were used to supply the substrate,
buffer solution, and dilution water.
Dilution water was RO water (Culligan Automatic Water Conditioner,
Culligan USA Division) filtered using an ultrapure water system (NANOpure
Mod. No. D4741, Barnstead/Thermolyne). The solution water was aerated to
ensure oxygen saturation before it was supplied to the annular reactor.
Sterilization of the dilution water was obtained by filtering through two 0.2 ^m
capsule filters (Gelman Sciences, Inc., #12122) in series.
Nutrient (not including glucose) and phosphate buffer solutions were
prepared in 10 L containers and autoclaved at 12i °C for 6 hours. Glucose was
added to the container by injecting 10 ml of concentrated glucose solution
through a septum. The glucose solution was filter-sterilized through a 0.2 ^m
filter (Sterile Acrodisk, Gelman Sciences, Product No. 4129) at the time of
addition. All experiments were conducted at room temperature.
15
TABLE 2
SUBSTRATE (in 10 L carboy)
Glucose
6.4516 g (add after autoclave)
KNO3
4.352 g
MgSO4
0.3225 g
CaCO3
0.3225 g
Trace minerals:
(in stock, add 10 ml)
(HOCOCH2)3N
0.0645 g
(NH4)6Mo7O24- 4H20
0.000451 g
ZnSO4- TH2O
0.0485 g
MnSO4- H2O
0.0036TT g
CuSO4- SH2O
0.000903 g
NaB4O7- IOH2O
0.000451 g
Co(NO3)2- H2O
0.000T41 g
FeSO4- TH2O
0.05129 g
Flow rate = 1 ml/min
TABLE 3
BUFFER (in 10 L carboy)
KH2PO4
65.6 g
Na2HPO4
136.32 g
Flow rate = 1 ml/min
16
Reactor Start-up
The reactor system, Masterflex 6411-1.5 tubing (Cole-Parmer Instrument
Co.), and SS slides (316L) were washed with Micro-soap solution (Cole-Parmer
Instrument Co., model 8790-10).
The reactor assembly (including the twelve slides, connecting tubing,
flowmeters, flow breaks, and dilution water filters) was autoclaved for 25 min at
121 °C. All open tube ends were covered with aluminum foil.
Connections
were made to the substrate and phosphate buffer containers through sterile
glass tubing. The reactor was filled with substrate, buffer solution, and dilution
water in the right proportion using the peristaltic pumps. Rotation of the inner
cylinder was initiated and the reactor ran abioticly for a couple of hours, after
which time all pumps were stopped. One ml frozen stock culture (10s cells/ml)
of each bacterial species was allowed to melt from -70°C to room temperature.
The reactor was then inoculated with the thawed bacterial stock by injecting it
through a septum and allowing it to grow in batch, mode for 24 hours. After
this incubation period, influent flows were started again.
Sampling
Effluent for microscopic evidence of detached particles.
Eighteen ml of the reactor effluent were collected and fixed with 2 ml of
2% formaldehyde. 0.6 ml of the fixed effluent was stained with 0.6 ml of
acridine orange (AO) for 7 min, and then filtered through a 0.2 p.m Nuclepore
17
polycarbonate filtration membrane (Costar Corporation). The membrane was
placed on a microscope slide in order to be photographed using a 35mm
camera (Olympus C-35AD-2) mounted on the microscope (Olympus BH-2).
Effluent plated for viability and contamination.
Every day approximately 10 ml of the reactor effluent were collected and
homogenized for 1 min using a tissumizer (Tekmar Co., Typ. SDT 1810, and
TR-10 power controller). Several dilutions of the homogenized effluent were
plated daily for viable count. The drop plate method (Miles and Misra, 1938)
and an electronic digital pipette (EDP2, RAINI Instrument Co. Inc.) were used.
Biofilm sampled for determination of total bacterial number.
The slides were removed from.the annular reactor and the biofilm
scraped into 100 ml of phosphate buffer solution and homogenized for 5 min.
f
9 ml of the homogenized cells were fixed with 1 ml of 2% formaldehyde. A
Nuclepore polycarbonate membrane (pore size of 0.2 p,m and 25mm diameter)
was placed on a cell free glass Millipore filter apparatus, and rinsed with cell
free water. 0.05 ml of the sample was added along with 0.1 ml of AC and
allowed to. stain for 10 min. The dye solution was removed with vacuum (about
13KPA) and the filter rinsed again with about 1 ml of water. The filter was
placed on the top of a drop of oil (R.P. Cargille Laboratories Inc., non drying
immersion oil type FF) on a microscope slide and covered with another drop
of oil and a glass cover slip. The total number of bacteria on the filter was
calculated using a microscope and the microscope eye-piece grid. For better
18
statistical results 10 or more grid fields were observed each time for a total of
400 or more cells counted.
Biofilm Crvoembeddinq
Cryoembedding is a technique adapted from tissue histopathology.
Using a commercial tissue embedding medium, cryoembedding preserves the
physical structure of the biofilm (Yu, P. et al).
Cryoembedding the biofilm requires ideal instant freezing upon addition
of the embedding agent (Tissue-Tek OCT [optimum cutting temperature]
compound, Miles Inc.). Nearly instant freezing could only be achieved by
placing the biofilm sample on dry ice (CO2)1 and allowing conduction through
the slide to reach the biofilm and freeze it. Initial attempts were unsuccessful.
The slide was removed from the reactor and placed on top of the dry ice. The
biofilm was immediately covered with a thick layer of the embedding medium
and allowed to freeze. The biofilm was separated from the substratum by
gently bending the slide to remove the frozen biofilm. The frozen specimen
was then turned over and a thick layer of embedding agent was added to
embed the side that was previously attached to the substratum, letting it
overflow on every edge. Everything was done carefully at room temperature,
but when the samples were cryosectioned the biomass shattered. After many
experimental runs and wasted biofilm samples, the biofilms continued, to
shatter. The only possible reason was that ice crystals were being formed
during the freezing and when the blade tried to cut the sections the ice crystals
19
shattered. Pouring the embedding agent first and then placing the slide on the
dry ice was also tried and. the same results were obtained. If indeed crystals
were being formed during the embedding procedure, there were two possible
sources: it could be that the. embedding agent was not infiltrating the biofilm
(OCT was not designed for infiltration) and the water voids and water channels
were still full of water; or perhaps when the biofilm was removed from the
substratum and a second layer of the embedding agent was added,
condensation was occurring resulting in ice crystals entrapped between the two
layers of embedding agent. Both possibilities were solved by letting the
embedding agent sit for 5 to 10 minutes allowing it to displace as much water
as possible from channels and voids before freezing.
The embedded biofilm
was then placed on dry ice and allowed to rapidly freeze until the whole
specimen turned opaque white. After 5 more min the specimen (still attached
to the slide) was wrapped in aluminum foil and stored at -70°C. See Figure 2
for cryoembedding steps.
Crvosectioninq
Frozen sections were cut with a cryostat (Reichert-Jung Cryocut 1800,
Leica) operated at -19°C. The frozen specimen from storage at -70°C was
placed back on dry ice inside the cryostat to ensure a very low temperature
and then separated from the substratum (slide) by gently bending the slide to
remove the frozen biofilm. The frozen specimen was then turned over and
I
H
A slide was removed from the Rototorque, and the biofilm
immediately covered with a thick layer of a cryoembedding
agent (Tissue-Tek OCT [optimum cutting temperature]
compound, Miles Inc.). The embedding agent was allowed to
infiltrate the biofilm for 10 min. The embedded biofilm was
then placed on dry ice and allowed to rapidly freeze until the
whole specimen turned opaque white. The frozen specimen
was then placed inside the cryostat for the rest of the
procedure. The specimen was turned over, removed from the
dry ice and allowed to equilibrate to -18°C for 10 min. A thick
layer of embedding agent was added to embed the side that
was previously attached to the substratum, letting it overflow
on every edge. The sample was transferred back to dry ice
(while still inside the cryostat) to rapidly freeze until opaque
white. The specimen was then removed from the dry ice, and
allowed to again reach thermal equilibrium inside the cryostat.
O
CRYOSECTlONlNG
Figure 2.
Cryoembedding schematic.
21
removed from the dry ice, but kept in the cryostat for 10 min to allow thermal
equilibration. A thick layer of embedding agent was added to embed the side
that was previously attached to the substratum, letting it overflow on every
edge. The sample was transferred back to dry ice to rapidly freeze until
opaque white. The specimen was then removed from the dry ice, and allowed
to again reach thermal equilibrium inside the cryostat. A blade at room
temperature was used to bisect small pieces of cryoembedded biofilm, 1 to 2
cm long. A small amount of embedding agent was poured onto a precooled
(-19°C) specimen chuck and the small specimen pieces pressed onto the
em bedded medium. When the embedding agent solidified, the specimen
sample was fixed on the chuck and ready for sectioning. The specimen was ,
oriented perpendicular to a disposable microtome blade (#815, Reichert-Jung,
i.
Leica). Each 5 \im thick frozen cross section was collected on a glass slide
(Superfrost Plus, Fisher Scientific) for fixation and staining.
Fixation and Staining of Biofilm Cross-sections
Sections on glass slides were dried overnight, fixed with 95% ethanol for
5 minutes, and air dried for 30 minutes. Sections were then stained in Gill Il
hematoxylin for 6 minutes and rinsed gently with running tap water for 2
minutes. Sections were blued in Scott’s tap water substitute for 30 seconds,
rinsed with running tap water for 2 minutes, dehydrated through 70% ETOH
(1 change) for 1 min, 95% ETOH (2 changes) 1 minute each, 100 % ETOH
/
22
(2 changes) 1 minute each, xylene (2 changes), and finally coverslipped with
permanent mounting media.
Image Analysis
American Innovision Videometric 150
The American Innovision Videometric 150 image analysis software
(American Innovision Inc.) was used to capture and store photomicrographs of
the biofilm cross-sections, using a American Innovision Inc. Color CCD, 1.5 LUX
camera (Unit# 150) camera mounted onto a Olympus BH-2 microscope with
transmitted lig&t. First the objective settings and camera zoom settings had to
be calibrated using an Olympus Object Micrometer (Scientific Instrument Co.)
and color video monitor (Mod. No. PVM-1342Q, Sony Corporation).
Once captured, the images were converted to ".HF" files and imported
into another image analysis software, Mark (Harkin and Shope, 1993), for the
measurement of biofilm thickness. For details see American. Innovision
Videometric 15.0 user’s manual.
The Mark Image Analysis Software
The images retrieved into the Mark software were analyzed for biofilm
thickness. Thickness was measured in the direction of the flow. Therefore,
profiles were, reconstructed showing the x-axis as distance along the
substratum in the direction of the flow.
A description of the software calibration and procedure for analyzing
23
images follows. When Mark is initiated, a small window will appear with the
only option "File." Enter "File" and another window will open with different
options. Using the mouse, select the correct directory under "Directories,"
under "Files" select the correct filename, make sure the right filename shows in
the "Selection" window, and enter "OK." A bigger window will now appear
showing the image you retrieved. On the menu option "Enhance," select
"Spread" (for a sharper black and white image). Under the menu option
"Registration," select "Calibrate" and a window for the calibration method will
appear. Select "Enter pixels per unit length," and enter "OK." The Calibrations
units window will appear, select "Micron," and enter "OK." A request for the '
number of pixels per micron will appear. For the example of 10 Fix = 9.77, the
number to be entered should be 104-9.77, or 1.0235415, followed by "Enter." A
window for setting the grid spacing will appear, select "2" microns using the
mouse, then enter "Done." Another window will appear requesting the name
desired for the file that will contain the thickness measurements every 2 urn and
the corresponding cumulative distance along the substratum. To terminate
select "Close" under the main menu option "File."
Statistical Methods
The Mark Image Analysis System generated files which contained
thickness of the biofilm versus the distance along the substratum. Thickness
was measured every 2 ^m. These files were imported into the statistics
24
software S-PLUS (Statistical Sciences, Inc.) which was used to generate
thickness profiles, Ra*, and variogram curves for statistical analyses of the data.
A hardcopy of the code used for the calculations can be found in Appendix C.
The first statistical analysis performed on the thickness data was to
calculate a coefficient of surface roughness or thickness variability, Ra* . , This
coefficient was suggested by the common and well accepted measure of
surface roughness Ra, which measures the mean absolute deviation. Ra is
defined as
A a -/V-/Ifl
Z-Hf I
=1
where Lft is each of the thickness measurements, overlined Lf is the mean
thickness, and N is the number of thickness measurements. However, Ra was
used to analyze biofilm thickness data and was found to be strongly correlated
to the mean thickness. Therefore, in order to standardize Ra for different mean
thickness values, Ra* was derived as the ratio of Ra to mean thickness. Ra*
was defined as
\ L f r L fl
Tf
Ra* is an index of how much the thickness measurements deviate from the
mean thickness.
The next statistical analysis performed on these thickness data was the
spatial correlation analysis based on a variogram. The variogram is a measure
25
of the correlation (association) as a function of distance. If there is a strong
association between two variables, then knowing one can do a great deal to
predict the other. But when there is a weak association, information about one
variable does not help much in estimating the other. Correlation is measured
from a cloud of data points which are half the squared difference between two
data points a set distance “d" apart along the substratum. The varipgram
function, denoted by the greek letter gamma, is a curve fit through the data
points in the correlation cloud using non parametric regression. The variogram
can also be estimated as
y^
= ~ 2 N ^ iMd,rd ( Lfr uj f
where [Lfi , Lfj) are all pairs of thickness measurements separated a distance
-
'd," and N is the number of pairs. The spatial correlation between two data
points is always a value between 0 and 1, expressed as p(d) (0 <; p(d) <; 1),
and it is an index of linear association between two data points a certain
distance "d" apart. A correlation value of zero means no linear association
between the two data points, while a correlation value of 1 represents the best
linear association. Thus, conceptually, the variogram analysis can be
expressed as
y ( d ) = a2(1 - p ( d ) )
where a2 is the total variation. As p(d) ranges between 1 and zero, y(d) varies
between zero and a2. This range of values corresponds to the part of the
26
variogram curve that indicates best to no linear association between two
thickness measurements. Therefore, the information provided by the variogram
analysis can be obtained by finding the location where the slope of the
variogram curve reaches zero (y(d) = a 2), and reading the corresponding value
of distance. This is the distance at which two thickness measurements become
uncorrelated. In other words, this is the minimum distance required between
two thickness measurements in order for them to have no linear association.
More information on the variogram curve and coefficient of variation can
be found in Isaaks et al (1989) and Freedman et al (1980).
27
RESULTS
An experimental method has been developed for measurement of biofilm
thickness. This method involves embedding the biofilm, cross-sectioning it, and
applying image analysis and mathematical techniques to recreate biofilm
thickness profiles in order to characterize biofilm thickness variations. The
procedure for cryoembedding and cryosectioning the biofilms is explained in
j
detail in the "Materials, Experimental Systems and Methods" section of this
thesis. A schematic of the embedding procedure is found in Figure 2.
Photomicrographs of the slide were obtained (Figures 3a and 3b) in order to
ensure that the biofilm was being removed thoroughly and not leaving layers of
cells attached to the substratum. Figures 4 through 6 are photomicrographs of
mature biofilm cross sections obtained from P. aeruginosa biofilm, K.
pneumonia biofilm, and binary population biofilms respectively.
Thickness profile plots were created by using image analysis systems to
measure thickness every 2 p.m along the substratum for distances of a few
thousand micrometers. These profiles, however, do not represent the structure
of the biofilm. Thickness measurements do not distinguish between the dense
biofilm matrix and water channels or voids. Each thickness measurement
represents the distance between the substratum and the outermost cell,
skipping over voids and channels.
Three thickness profiles from a pure P.
aeruginosa mature biofilm are shown in Figures 7a, 7b and 7c.
The mean
28
thickness varied between 28 and 31 pm for these biofilms. Also apparent is a
localized thickness variation where the maximum and minimum values of
X
thickness never differed by more than 40 pm. Three different profiles from a
pure K. pneumoniae mature biofilm are shown in Figures 8a, 8b and 8c. These
profiles show the diverse structure and thickness variation of pure K.
pneumoniae biofilms. The mean thickness ranged from 8 to 260 pm.
Large
areas of bare substratum can be observed which help classify pure K.
pneumoniae biofilms as patchy. Three different profiles from a binary
population mature biofilm are shown in Figures 9a, 9b and 9c. Binary
population biofilms are distinct from the pure culture biofilms in that the rnean
thickness varied between 205 and 640 pm. All profiles were obtained from
three random locations on the slide.
The thickness measurements shown on the profiles can be presented
also as cumulative percentage plots. These plots show the percentage of
thickness measurements that were equal to or less than the corresponding
thickness shown in the x-axis. Figures 10a through 12c show the respective
cumulative percentage plots of thickness for profiles on Figures 7a through 9c.
One of the statistical analysis performed on these thickness data was the
spatial correlation analysis based on a variogram. The variogram is a measure
of the correlation (association) as a function of distance, See statistical
methods section of Materials and Methods for an explanation of this analysis.
Figures 13a through 15c show the variograms corresponding to biofilm profiles
29
on Figures 7a through 9c. Notice that the correlation distances varied
considerably even within the same species, see Figure 35. P. aeruginosa
biofilms thickness measurements are independent at distances that range
between 100 to 380 ^m. The thickness measurements for K. pneumoniae
biofilms are independent at a distance that varies between 500 and 4400 p.m.
And similarly, thickness measurements for binary population biofilms are
independent at distances varying between 760 and 4800 jxm.
Figure 16 shows the values of Ra* for the biofilm profiles on Figures Ta
through 9c.
Notice that Ra* values fall in three distinct ranges which
correspond to the three distinct biofilms analyzed; 0.10 to. 0.17 for P.
aeruginosa biofilms, 0.83 to 1.33 for K. pneumoniae biofilms, and 0.21 to 0.30
for binary population biofilms. Both the variogram and Ra* have proven to be
useful statistical tools for describing the very distinct thickness profiles of the
three different biofilm systems.
The next set of results (Figures 1Ta through 31) describe the changes in
thickness variability for a binary population biofilm during the first eight days of
its development. For this particular experiment, the biofilm was grown with the
trace minerals listed on Table 3 and glucose as the only source of nutrients,
KNO3, MgSO4, and CaCO3 were not added to the substrate. Figures 17a 17b,
1Tc and 17d show profiles of the biofilm on day 2 from four random locations
on the slide. These profiles show the initial patchy colonization of the
substratum, with mean thickness values ranging from 2 to 4.3 p,m. Figure 18
30
(a, b, c, and d) shows profiles from day 4. These profiles show a fully grown
biofilm with a very rough surface. Mean thickness values varied between 28
and 161 ^m. Figure 19 (a, b, and c) shows profiles from day 6, very similar to
those from day 4, having mean thickness values varying between 97 and 117
urn. Finally, Figure 20 (a, b, and c) shows profiles from day 8, where surface
roughness and thickness has diminished from those of previous days, having
mean thickness values between 23 and 40 ^m. Another way of visualizing the
surface roughness is through cumulative percentage plots. Figures 21 (a, b, c,
d), 31 (a, b, c, d), 32 (a, b, c), and 33 (a, b, c) correspond to the thickness
distribution of profiles on Figures 17a through 20c. Figures 25 (a, b, c, d), 35
(a, b, c, d), 36 (a, b, c), and 37 (a, b, c) correspond to Figures 17a through
29c, and they show their variogram curves. These curves are very helpful in
showing the distance along the substratum at which two thickness
measurements become uncorrelated. Thickness measurements of the biofilms
on day 2 are independent at a distance that varied between 190 and 450 ^m.
On day 4 the distance varied between 490 and 1600 ^m. On day 6 this
distance varied between 850 and 3900 jim , and on day 8 it varied between 320
and 2000 p.m. Figure 29 shows the Ra* values for the profiles from days 2, 4,
6, and 8. Ra* values varied for each day, ranging between 1.12 and 1.4 on day
2, between 0.25 and 0.99 on day 4, between 0.23 and 0.93 on day 6, and
between 0.76 and 0.98 on day 8. Figure 30 shows how the mean thickness
from those profiles varied with time: Here we see that the values of thickness
31
were very consistent for days 2 and 8, but during the time at which surface
roughness was maximum, they were hard to predict. Figure 31 shows the
biofilm density, expressed as number of cells per meter cubed for days 2, 4, 6
and 8. Biofilm density varied between 3.6x1015 and 1.35x1017 cells/m3. For each
type of biofilm, photomicrographs of the cellular aggregates found in the
effluent of the reactor were obtained. Figures 32, 33 and 34 are
photomicrographs of typical aggregates of cells collected from the effluent of
the reactor. They picture aggregates detached from pure culture
Pseudomonas aeruginosa, Klebsiella pneumoniae, and binary population
biofilms, respectively. All the photographs collectively showed that in P.
aeruginosa biofilm systems the main mechanism of detachment is erosion of
individual cells. They also show that K. pneumoniae biofilm systems undergo
.
detachment in the form of sloughing. Sloughing occurs when large multicellular
aggregates detach from the biofilm surface sometimes leaving behind large
areas of the substratum bare.
32
Figure 3.
Photomicrographs of the substratum after biofilm removal.
Small white dots are retained cells.
33
Figure 4.
Photomicrograph of P. aeruginosa biofilm cross-section.
Figure 5.
Photomicrograph of K. pneumoniae biofilm cross-section.
35
3 5 0 u rn
Figure 6.
Photomicrograph of binary population biofilm cross-section.
36
20 40
0
20 40
b)
0
Thickness (microns)
Distance (microns)
0
1000
2000
3000
4000
Distance (microns)
50
Thickness (microns;
2000
0 20
Thickness (mil
a)
0
Figure 7.
1000
2000
3000
Distance (microns)
P. aeruginosa biofilm profiles a), b) and c) from three
different random locations on the slide.
4000
600
0
10000
15000
150
Distance (microns)
0 50
Thickness (microns)
5000
0
2000
4000
6000
8000
Distance (microns)
c)
20 40
Thickness (microns)
0
o
Thickness (microns)
37
Figure 8.
4000
Distance (microns)
K. pn eum oniae biofilm profiles a), b) and c) from three
different random locations on the slide.
150
0
30i
300
0
Thickness (microns]
2000
Distance (microns)
2000
3000
Distance (microns)
c)
600
Thickness (microns]
a)
0
Thickness (microns)
38
Figure 9.
4000
Distance (microns)
Binary population biofilm profiles a), b) and c) from
three different random locations on the slide.
39
B io f ilm
Figure 10.
T h ic k n e s s ( // m )
P. aeruginosa biofilm thickness distributions a), b) and
c) from profiles on Figure 7a through 7c.
40
c)
C u m u la tiv e P e r c e n ta g e
100
80
60
40
20
0
1000
B io f ilm
Figure 11.
T h ic k n e s s (/^ m )
K. pn eum oniae biofilm thickness distributions a), b)
and c) from profiles on Figure 8a through 8c.
1200
41
10 00
B io f ilm
Figure 12.
T h ic k n e s s ( / / m )
Binary population biofilm thickness distributions a), b)
and c) from profiles on Figure 9a through 9c.
1200
100
a)
0
Gamma(Distance)
42
000
' 2000
3000
20 40
0
Gamma(Distance)
Distance between points (microns)
0
1000
2000
3000
4000
150
O 50
Gamma(Distance)
Distance between points (microns)
c)
Z
0
Figure 13.
1000
2000
3000
Distance between points (microns)
Variogram curves from P. aeruginosa biofilm profiles
on Figure 7a through 7c.
4000
0
600000
a)
5000
10000
15000
Distance between points (microns)
0
3000
b)
2000
4000
6000
250
Distance between points (microns)
c)
O 100
Gamma(Distance)
Gamma(Distance)
Gamma(Distance)
43
0
Figure 14.
2000
4000
6000
Distance between points (microns)
Variogram curves from K. pn eum oniae biofilm profiles
on Figure 8a through 8c.
8000
6000
0
2000
3000
30000
Distance between points (microns)
0
Gamma(Distance)
30
0
1000
2000
3000
4000
Distance between points (microns)
c)
150000
Gamma(Distance)
a)
O
Gamma(Distance)
44
Figure 15.
300
4000
6000
Distance between points (microns)
Variogram curves a), b) and c) from binary population
biofilm profiles on Figure 9a through 9c.
5000
45
1.4
1.2
1.0
R a*
0.8
0.6
0.4
0.2
0.0
0
100
200
300
M e a n B io f ilm
Figure 16.
400
500
T h ic k n e s s
600
(/x m )
Values of Ra* (roughness coefficient) for (■) P.
aeruginosa, (▼) K. pneumoniae, and (•) binary
population biofilms.
700
46
150
1000
o
„--------------
2000
---- / V
A
...
^
Al
3000
4000
5000
Distance (microns)
0
150
b)
0
1000
2000
3000
4000
5000
Distance (microns)
C)
150
Thickness (microns)
—
0
Thickness (microns)
0
.
0
1000
2000
3000
4000
5000
4000
5000
Distance (microns)
d)
150
Thickness (microns)
>x
0
Thickness (i
a)
0
Figure 17.
1000
2000
3000
Distance (microns)
2-day old binary population biofilm profiles a), b), c)
and d) from different random locations on the slide.
150
0
4000
0
150
Distance (microns)
0
1000
2000
3000
4000
5000
4000
5000
6000
Distance (microns)
150
Thickness (microns)
3000
0
Thickness (microns)
DO
1000
2000
3000
Distance (microns)
150
Thickness (microns)
a)
0
Thickness (microns)
47
Figure 18.
2000
3000
4000
Distance (microns)
4-day old binary population biofilm profiles a), b), c)
and d) from different random locations on the slide.
6000
48
CO
C
O
a)
2000
3000
4000
Distance (microns)
CO
DO
3000
Distance (microns)
3000
4000
Distance (microns)
Figure 19.
6-day old binary population biofilm profiles a), b) and
c) from different random locations on the slide.
49
V)
C
O
UD
§
b)
DO
3000
4000
Distance (microns)
UD
4000
Distance (microns)
Figure 20.
8-day old binary population biofilm profiles a), b) and
c) from different random locations on the slide.
50
a)
B io f ilm
Figure 21.
T h ic k n e s s (yum)
Binary population biofilm thickness distributions a), b),
c) and d) from profiles on Figures 17a through 17d.
51
60
-
B io f ilm
Figure 22.
T h ic k n e s s (/^ m )
Binary population biofilm thickness distributions a), b),
c) and d) from profiles on Figures 18a through 18d.
52
B io f ilm
Figure 23.
T h ic k n e s s (/z m )
Binary population biofilm thickness distributions a), b)
and c) from profiles on Figures 19a through 19c.
53
B io f ilm
Figure 24.
T h ic k n e s s (/z m )
Binary population biofilm thickness distributions a), b)
and c) from profiles on Figures 20a through 20c.
500
a)
0 200
Gamma(Distance)
54
I
2000
3000
4000
150
b)
0
Gamma(Distance)
Distance between points (microns)
0
1000
2000
3000
4000
5000
0 20
50
c)
0
1000
2000
3000
4000
5000
Distance between points (microns)
50
d)
O 20
Gamma(Distance)
Gamma(Distance)
Distance between points (microns)
0
Figure 25.
1000
2000
3000
4000
Distance between points (microns)
Variogram curves a), b), c) and d) from profiles on
Figures 17a through 17d.
5000
4000
a)
0
Gamma(Distance)
55
2000
3000
4000
4000
0
2000
3000
4000
Distance between points (microns)
c)
0 2000
Gamma(Distance)
Gamma(Distance)
b)
2000
3000
4000
Distance between points (microns)
d)
O 2000
Gamma(Distance)
Distance between points (microns)
Figure 26.
2000
3000
4000
Distance between points (microns)
Variogram curves a), b), c) and d) from profiles on
Figures 18a through 18d.
6000
6000
a)
o
Gamma(Distance)
56
0
2000
3000
4(
4000
b)
0
Gamma(Distance)
Distance between points (microns)
0
1000
2000
3000
4000
5000
30000
c)
O
Gamma(Distance)
Distance between points (microns)
2000
3000
4000
5000
Distance between points (microns)
Figure 27.
Variogram curves a), b) and c) from profiles on Figures
19a through 19c.
6000
2000
a)
o
Gamma(Distance)
57
2000
3000
4000
4000
b)
0
Gamma(Distance)
Distance between points (microns)
0
1000
2000
3000
4000
5000
c)
O 4000
Gamma(Distance)
Distance between points (microns)
2000
4000
Distance between points (microns)
Figure 28.
Variogram curves a), b) and c) from profiles on Figures
20a through 20c.
6000
58
1.6
1. 4
1.2
Ra*
1.0
0.8
0.6
0. 4
0.2
0.0
0
1
2
3
4
5
6
7
8
9
T im e (days)
Figure 29.
Values of Ra* from biofilm profiles on Figures 17a
through 20c.
10
59
T im e (days)
Figure 30.
Mean biofilm thickness from biofilm profiles on Figures
17a through 20c.
60
\
B
03
p-H
rH
0)
O
03
ti
CU
Q
N -I
O
3
4
5
6
7
T im e (days)
Figure 31.
Biofilm density of binary population biofilm 2, 4, 6 and
8 days old.
61
Figure 32.
Individual cells and multicellular aggregates observed
in the effluent of a P. aeruginosa biofilm reactor.
Figure 33.
Multicellular aggregates observed in the effluent of a K.
pneumoniae biofilm reactor.
63
Figure 34.
Individual cells and multicellular aggregates observed
in the effluent of a binary population biofilm reactor.
&
64
DISCUSSION
This chapter discusses the techniques used for measuring biofilm
thickness, the data obtained from those measurements, the. statistical analysis
of the data and the relevance of the structural heterogeneity observed.
Biofilm Thickness Measurements
Thickness measurements were obtained using two image analysis and
processing softwares. The American Innovision image analysis software was
used to capture the images. In order to obtain good and accurate
measurements the images had to be captured and stored at 200X to 400X
magnifications. Therefore, sequential images had to be collected and later
pasted together in order to measure thickness along crosS-sectidns thousands
of micrometers long. The images were collected very carefully making sure that
they would match perfectly when reconstructing the sequence. The Mark
software was used for automatically measuring thickness. The software
measured thickness from the computer monitor by counting the number of
pixels. The calibration of pixels to
m depended on the magnification at which
the image was collected. For the images analyzed as part of this thesis, the
calibration at which images were stored and measured was 0.7 jxm ^ 1 pixel ^
1.1 p.m, which resulted in an accuracy of thickness measurements very close to
+ / - 1 urn. One other factor that could alter the accuracy of the measurements
was whether the biofilm was being completely removed from the substratum. If
65
one or more layers of cells were left on the surface, then the measurements of
thickness would be off by the thickness of those layers. The photomicrographs
on Figure 3 show that only a few cells remained attached to the substratum
after.the biofilm had been frozen and removed. Those cells could not be easily
removed because they appear to be tightly attached at grain boundaries on
the metal surface. A study by Gillis (1993), showed that the average width and
average depth for such grain boundaries in stainless steel are 1.6 ^m and 0.6
jxm, respectively. These dimensions are comparable to the size of an individual
cell.
Cross-sections can be stained by various methods. The only criteria is
that the stain should offer a good contrast for microscopy. Fluorescent Stains
are not recommended. Fluorescent stains can sometimes be bright enough to
provide good contrast, but they create a glow around the cells that makes
boundaries harder to define. This is especially true if the images are to be
captured as computer files. The computer software will assign levels of
intensity for the pixels as the image is saved, and when the image is retrieved
the boundary or edge of the stained sample will be even less defined. This
could become a more serious problem if the images are saved as color and
then converted to black & white, for example. If such manipulation of the file is
necessary, the use of a black & white camera is recommended.
Thickness profiles and photomicrographs of biofilm cross-sections
showed that biofilms have distinct structural heterogeneities and surface
66
roughness. P. aeruginosa, K. pneumoniae and binary population biofilms
appeared to have different mean thicknesses, and different spatial distributions
of biomass, voids and water channels. This is not surprising, but it affords
insights that should impact the way that biofilm development and the
processes occurring within biofilm systems are studied, interpreted, and
modeled. P. aeruginosa biofilms, for example, grow as closely packed and
fairly flat biomass matrices that lack the voids and water channels present in
other biofilm systems. Their average thickness of 30 p.m is also very distinct
even from that of a binary population biofilm. K. pneumoniae biofilms, on the
other hand, are locally denser at their surface than near the substratum.
Different sizes and lengths of water channels were observed, but the mostdistinctive characteristic was the large bare areas along the substratum proving
these biofilm systems to be very patchy. Their thickness varies drastically,
having a mean thickness that ranged between 8 ^m to almost 300 ^m. Again,
if a K. pneumoniae biofilm system was to be modeled, the transport and
diffusion rates as well as the mechanisms of detachment should be different
from those of P. aeruginosa biofilm systems. Binary population biofilms are also
distinct from pure cultures, having water channels and voids within their matrix,
and a mean thickness ranging between 300 and 600 p.m.
Statistical Analysis
Extensive thickness measurements were obtained from the method
developed. These data points were used for creating thickness profiles of the
67
biofilms. But in order to interpret the data and get some useful information,
some statistical analysis was needed. A measure of surface roughness or
thickness variation was desired. The development of the parameter Ra* was a
simple and useful way to quantitatively describe such roughness. The problem
is that Ra* results from averaging the difference between every thickness data
point and the mean thickness. Ra* for a given data set will be the same even if
the data points were sorted in some specific order. That is, the location at
which the data point was measured has no affect on Ra*. Therefore, it is
possible that many different profiles of very distinct roughness would result in
the same value of Ra*. That is why a measure of the spatial variability or
correlation of thickness was needed. A well known method for studying
relationships between two variables is through the use of a graph called a
scatter diagram, commonly referred as the scatterplot or correlation cloud.
Often the overall shape of the cloud clearly reveals a relationship between two
variables that can more easily be described by a smooth curve drawn through
the cloud than by a straight line. The scatterplot commonly called a variogram
cloud almost always reveals a nonlinear relationship between gamma and the
variable under study (thickness in this case). This cloud is best described by a
smooth curve, called the variogram curve. There are several methods that can
be used for calculating such a curve, some using parametric and some non
parametric regression. Whatever the method used, it is still a curve, and the
information provided by the variogram curve is subjective. It can, however,
68
provide a very good idea of the distance at which two thickness measurements
become uncorrelated for a particular profile. This information, along with the
roughness coefficient Ra*, can be used to quantitatively categorize mature
biofilms. However, while the values of Ra* fell inside relatively narrow ranges,
the correlation distances given by the variogram curve varied considerably, as
shown in Figure 35. For example, for P. aeruginosa biofilm systems, the
roughness coefficient, Ra*, ranged roughly between 0.1 and 0.2, and the
distance of spatial correlation as read from the variogram curves ranged from
100 to 380 urn. K. pneumoniae biofilms had Ra* values ranging between 0.8 to
1.3, and a distance for spatial correlation ranging between 490 and 4400 \im.
Finally, binary population biofilms had Ra* values ranging between 0.2 to 0.3,
and a distance for spatial correlation ranging between 760 to 4900 jim.
Both of these statistical tools were then applied to a binary population
biofilm at different stages of its development in order to compare the variations
in thickness. The results of this experiment are difficult to interpret.
Biofilm Heterogeneity
Photomicrographs of pure P. aeruginosa, K. pneumoniae and binary
population biofilm cross-sections (Figures 4, 5 and 6 respectively) are good
examples of the differences in structure that can be found when biofilms are
observed microscopically. However, these biofilms are relatively simple
compared to what an undefined mixed population biofilm, might look like an
industrial pipe or a medical device, where abiotic particles such as wood fibers,
69
corrosion products, or mineral sediments might play a role in matrix formation.
The Literature Review section of this thesis provides many other examples of
the variety of evidence that researchers have observed in laboratory
experiments. Figure 36 shows photomicrographs of an undefined mixed
population biofilm cross-section. The biofilm was embedded as described in
the Biofilm Embedding section of the Method Development Chapter of this
thesis. These photomicrographs are a better representation of what an
industrial biofilm looks like, even though the abiotic particles are not present.
There are several features important to notice on this photomicrograph. The
thickness of this biofilm is one order of magnitude larger than the pure culture
biofilms. P. aeruginosa forms biofilms that are considerably flat, and K.
pneumoniae develops biofilms that are mostly flat with several microtowers
sticking out. The undefined mixed population biofilm is the furthest from the
idealized concept of a flat homogeneous biofilm. Among its distinctive
characteristics are the formation of mushroom-like structures that remain firmly
attached to the substratum by a narrow stem of biomass while a large
microcolony or cluster of cells elevates into the bulk fluid.
The next photomicrographs, Figure 37, shows cross sections of a week
old binary population biofilm (P. aeruginosa and K. pneumoniae) that was
grown with continuous supply of 1ml/min of abiotic particles, CaCO3 (50 hng/L),
and a native hydrated aluminum silicate, kaolin (50 mg/L). Notice how the
matrix structure differs from that of the same biofilm when grown without the
70
abiotic particles (see Figure 6). The biofilm with the abiotic particles appears to
be tightly packed, while the number of water channels is increased. Nutrients
and anything else that might be present in the bulk liquid can still be
transported into the depths of the biofilms matrix through the water channels.
From the evidence presented we can speculate that microorganisms forming
biofilm systems are more likely to develop matrices of heterogeneous structure
with tight clusters and water channels for several reasons: the structural
heterogeneity facilitates the transport of nutrients, it increases the surface area
(again, more contact with the nutrients), but it also offers protection to the cells
inside the clusters against antimicrobial agents.
If the species distribution in a biofilm is. heterogeneous, species growth
and decay (Siebel et al., 1991, and Srinivasan et al., 1994) could be
independent of each other. This may explain why domination of one species
over the other in binary population biofilms is not observed in laboratory
experiments even though it is theoretically predicted by existing models.
Photomicrographs of the effluent of the reactor also provided some
qualitative information about the nature of the detachment mechanism.
Detached multicellular aggregates found in the effluent of the reactor suggest
that the mechanism of detachment is dependent of the species composition of
the biofilm.
71
1. 4
▼
▼
1.2
1.0
T
Ra*
0.8
0.6
-
0. 4 -
0.2
0 .0 ------------------'------------------'------------------'------------------'—
0
10 00
2000
V a rio g ra m
Figure 35.
3000
4000
d is ta n c e
Relationship between Ra* value and variogram
distance for (■) P. aeruginosa, (▼) K. pneumoniae, and
(•) binary population biofilms.
5000
72
100 iim
100 |im
Figure 36.
Photomicrograph of undefined mixed population biofilm
cross-section.
73
fF-••*
.
J
t . 4 rr
&
'“ ‘
v:
- - 'A
-<
V f4
?
u r
'H.
>,
%
A
■
%
#
»>
•U . , '
>'F-*
;
>
1
100 ^m
«
<
R H R
-# ^
Wk
r^#'
* 1
^ v v v t is *
s v'
200 ^m
Figure 37.
Photomicrograph of binary population biofilm crosssection, grown with continuous supply of abiotic
particles, CaCO31 and kaolin.
74
SUMMARY AND CONCLUSIONS
The Method
An experimental method has been developed for measuring biofilm
thickness. This method involves embedding the biofilm, cross-sectioning it, and
applying image analysis and mathematical techniques to reconstruct biofilm
thickness profiles in order to characterize biofilm thickness variations. This
method also allows for qualitative observations of structural features of the
biofilm such as water channels and pores. Thickness profiles and
photomicrographs of biofilm cross-sections collectively showed biofilms of
distinct structural characteristics with rough surfaces.
Thickness Profiles
Photomicrographs of.biofilm cross-sections were captured as computer
images and biofilm thickness was measured every two micrometers for the ,
recreation of thickness profiles. The American Innovision Videometric 150
image analysis system (American Innovision, Inc) was used to capture the
images. The Mark Image Analysis System (Developed by Gary Harkih at the
Center for Biofilm Engineering, Montana State University) was used to measure
thickness and generate data files. Date files were transferred to S-PLUS
(Statistical Sciences, Inc), a statistics software, for the reconstruction of
thickness profiles.
Roughness Index
An index of roughness (Ra*) was developed to describe the degree of
75
biofilm roughness as measured from thickness profiles. Ra* values clearly
distinguish between a patchy biofilm and a rough biofilm, and between various
degrees of roughness. A Ra* value close to 1 describes a patchy bidfilm.
Such is the case for K. pneumoniae biofilms, having Ra* values varying
between 0.8 and 1.3. Values less than 1 describe biofilms with various degrees
of roughness. Ra* values for P. aeruginosa biofilms varied between 0.1 and
0.2. Ra* values for a binary population biofilm (both species combined) varied
between 0.2 and 0.3.
Spatial Correlation Analysis
A spatial correlation analysis was performed on the thickness data. A
spatial correlation distance for each data set was obtained through the use of
the variogram function. The variogram function is a curvefit through a
correlation cloud that describes correlation as a function of spatial distance
between the data points. P. aeruginosa biofilms had a correlation distance
varying between 100 and 375 p.m. For K. pneumoniae biofilms the distance
varied between 490 and 4420 ^m. For the binary population biofilm it varied
between 760 and 4840 p,m.
Photomicrographs of Biomass Particles
Samples of reactor effluent were fixed, stained, and filtered for
photomicrographic observations. Particles detached from P. aeruginosa
biofilms were mostly individual cells, which may explain why these biofilms have
a consistent mean thickness. Particles detached from either K. pneumoniae
76
biofilms or the binary combination consisted of aggregates of cells sometimes
one or two hundred micrometers in diameter. This may explain why these
biofilms have rough, surfaces.
Temporal changes in biofilm thickness were also recorded in this thesis
in the form of thickness profiles, Ra* values and correlation distances.
77
RECOMMENDATIONS FOR FUTURE RESEARCH
1.
The quantitative data and qualitative observations of biofilm structural
heterogeneity obtained from this method should be incorporated into
existing mathematical models for a more realistic description of the
transport mechanisms affecting biofilm systems.
2.
The method presented in this thesis should be further applied for the
quantitative and qualitative study of undefined mixed population biofilm
structural heterogeneity. In particular, biofilm samples from industrial
sites should be studied for a better understanding of the effect that
abiotic particles (such as wood fibers or corrosion products) have on the
structural heterogeneity.
3.
Statistical analysis of the data should be further developed. Ra* is
perhaps only the first step towards the development of a better
roughness coefficient. The analysis for linear dependency, variogram
curve, should also be developed further. The variogram curve Can only
provide an approximation based on the correlation cloud, and its
interpretation is subjective.
4.
Antibody probing of the biofilm cross-sections should be pursued for
determining the location and spatial distribution gf the species present.
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79
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continuous culture. Air Wat. Pollut. In t J. 10, 253-276.
63.
Siebel, Maarten A. and William Characklis. 1991. Observations of Binary
Population Biofilms. Biotech, and Bioenq. 37, 778-789.
64.
Siegrist, H. and Gujer, W. 1985. Mass transfer mechanisms in a
heterotrophic biofilm. Water. Res. 19:1369-1378.
65.
Srinivasan, R. 1994. Action of Monochloramine on P. aeruginosa and K.
pneumoniae biofilms. MS Thesis, Montana State University.
66.
Stewart, P.S. 1992. A model of biofilm detachment. Biotech. Bioengr.
' 41:111,117
67.
Stewart, P A , Peyton, B.M., Drury, W. J., and Murga, R. 1993.
Quantitative observations of heterogeneities in Pseudomonas aeruginosa
biofilms. Aool. Eviron. Microbiol. 59:327-329.
68.
Switzenbaum, M.S. and Eimstad, R.B. 1987. Analysis of anaerobic
biofilms. Environ. Technol. Lett. 8:21 -32.
69.
Trulear, Michael G., and William Characklis. 1982, Dynamics of Biofilm
Processes. Journal WPCF. Vol. 54, No, 9. pp 1288-1301.
85
70.
Turakhia, M. and W.G. Characklis. 1984. "Fouling of Heat Exchange
Surfaces: Measurement and Diagnosis," Heat Transfer Engineering.
5:93-101.
71.
vander Wende, E., W.G. Characklis and.D.B. Smith. 1989. Biofilms and
Bacterial Drinking Water Quality. Wat. Res. Vol 23 No. 10, pp 1313-1322.
72.
Videla, H A and W.G. Characklis. 1992. "Biofouling and Micfobially
Influenced Corrosion.” Biodeterioration & Biodegradation. 29:195-212.
73
Yu, F., G. Callis, P. Stewart, I . Griebe and G. McFeters. Cryosectioning
of biofilms for microscopic examination. Biofoulinq. In press.
74.
Zelver, N., W.G. Characklis, J.A. Robinson, F.L Roe,,Z. Dicic, K.R.
Chappie, and A. Ribaudo. 1984. Tube material, fluid velocity, surface
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Tower Institute, Houston, TX.
APPENDICES
87
APPENDIX A
Particle Sizing Using Image Analysis Techniques
The first step in the study of structural heterogeneity of biofilms was to
try to measure the size of the detachment biomass particles. Three techniques
were tried. The first technique attempted to use the American Innovision
Videometric 150 image analysis software (American InnOvision, Inc.) to measure
the area of the particles detached from biofilms as observed in the effluent of
the reactor. Appendix A contains a summary of the user’s manual that
describes the software operation and the system components and set-up.
Experimental Procedure
The reactor set-up is explained in the Materials, Experimental Systems
and Methods of this thesis. A sample of the reactor effluent (9 ml) was fixed
with 1 ml of 2% Formaldehyde. A Nuclepore polycarbonate membrane (pore
size of 0.2 urn and 25mm diameter) was .placed on a cell free glass Millipore
filter apparatus, and rinsed with cell free water. 1.0 ml of the sample was
added along with 1.0 ml of DAPI or Acridine Orange (AO) and allowed to stain
for 10 min. The dye solution was removed with vacuum (about 13KPA) and the
filter rinsed again with about 1 ml of water. The filter was placed on the top of
a drop of oil (R.P. Cargille Laboratories Inc., non drying immersion oil type FF)
on a microscope slide. Photomicrographs of the particles on the filter were
captured and stored as TIF formatted image files using the image analysis. For
88
each filtered sample, 30 to 50 photomicrographs (images or fields from different
locations within the same filter) were captured for the analysis. After capturing
the images, each of them was analyzed as described in the Feature Detection.
section of the manual in Appendix A. The only criterion for selecting features
(cells or groups of cells in the image) is color, as explained in the Color
Thresholds section of the manual. Feature Sizes, Feature Editing, and Feature
Extraction sections of the manual explain how the software was used to select
and shade the features of interest on each image. The Statistics section
explains how the software was used to number and size those selected areas
of the image on the screen. The program creates a statistics output that lists
each feature and its respective size (area in micrometers).
Results
Figures 38, 39 and 40 show the particle area distribution for the effluent
of a reactor with a pure Pseudomonas aeruginosa biofilm. The data on these
figures are presented as cumulative percentage plots. These plots show the
percentage of counted particles that had an area equal or less than the value
shown in the x-axis. Figure 38 shows the particle area distribution of the reactor
effluent for days 1 through 4 of biofilm formation. Figures 39 and 40 show the
' particle area distribution of the effluent for day 5 (considered a steady state
biofilm) sampled every hour for eight hours. Notice that on days 1 through 4,
more than 90% of the particles measured had an area of 1 to 2 urn2, which is
approximately equal to the area of an individual cell (0.5 to 1.0 p,m by 1.5 to 4
Cumulative Num be
E
60
1950
1468
I
10
15
20
25
30
35
Areas in square microns
40
45
50
5
io
is
40
45
50
Cumulative N um ber %
Areas In square microns
E
5
10
Figure 38.
15
20
25
30
35
Area in square microns
40
45
50
60
20
25
30
35
Area in square microns
Particle area distribution of detached P. aeruginosa multicellular aggregates
collected on days 1 through 4. "n" indicated the number of particles filtered.
Cumulative Numbe
1707
Si
3261
10
15
20
30
35
Areas In square microns
40
45
50
5
10
15
20
25
30
35
Area In square microns
40
10
15
20
25
30
35
Area in square microns
40
45
50
5
10
15
20
25
30
35
Area in square microns
40
45
50
Cumulative Number %
5
1855
Figure 39.
Particle area distribution of detached P. aeruginosa multicellular aggregates
collected on hours 1 through 4 of day 5. "n" indicated the number of
particles filtered.
45
50
too
too
6
5
80
Cumulative Numbei
80
Z
70
I
60
50
40
70
60
I
30
O
3282
10
15
20
25
30
35
Area in square m icrons
40
45
50
I
5675
10
15
20
25
30
35
Area in square microns
40
45
50
Cumulative Number %
8
5
Figure 40.
10
15
20
25
30
35
Area in square microns
40
45
50
Particle area distribution of detached P. aeruginosa multicellular aggregates
collected on hours 5 through 8 of day 5. "n" indicated the number of
particles filtered.
92
urn, Brock, 1974). On days 4 and 5 bigger particles were detected in the .
effluent, but in very small percentages. While monitoring a mature biofilm
(assumed to have reached the steady-state) on day five, a sloughing event was
detected, during which the percentage of particles larger than an individual cell
increased dramatically (see Figure 39 for the fourth hour).
Discussion
This technique presents several disadvantages. The color thresholding
of the cells depends on the brightness of their color, but not all the cells are
stained with the same color intensity and the feature extraction process tends
to overshade some of the features and undershade some others. That is, the
area measured is not the actual area. Much time had to be invested in order to
minimize this error. The main disadvantage of this method is that it is very slow
and labor intensive.
Vernier Measurements
The next attempt for measuring the size of detached particles arose from
the desire to confirm the accuracy of the image analysis technique, or at least
to give a measure of its error. This new technique involved the use Of a polar
planimeter. Figure 41 shows a polar planimeter. This method required
photographic enlargements of the bacterial aggregates.
Determination of Areas Using A Polar Planimeter
The planimeter determines a rectangle whose area equals the traced
93
The polar planimeter consists essentially of three parts: the pole arm
"P," a tracer arm "I," and a carriage. The pole arm and the tracer
arm are pivoted together and are free to rotate around a weighted
needle point or pole "p" which holds that end of the pole arm at a
fixed point on the paper. The tracer arm carries a tracer arm T at
one end and the carriage at the other end. The carriage supports
a vertical measuring wheel "E" that turns on a horizontal axle which
is parallel to the tracer arm. "K" is an adjusting screw. "A" is clampscrew for bearings. The carriage on Planimeter with adjustable
tracer arm can be moved on the tracer arm to the required setting
and clamped in the position with clamp-screw L The rim of the
measuring wheel E is in contact with the paper and rolls or slides as
the boundary of the area is traced. Since the axis of the measuring
wheel E is parallel to the tracer arm, the wheel rolls only when the
tracer arm moves parallel to itself.
Figure 41.
Polar planimeter.
94
area. One side of the equivalent rectangle is equal to the net distance rolled by
the wheel. In use, the tracing point is set at some initial point of the boundary
of the area to be measured and the readings of the dial and measuring wheel
scale are noted. The tracing point "d" is carefully guided around the boundary
in a clockwise direction until it has returned to the beginning point and the dial
and wheel scales read again. The difference between initial and final scales
gives a number, which is either the area directly in square inches, o r is
multiplied by the proper conversion factor to obtain the area.
Results
Several enlarged photographs were analyzed with the planimeter for the
measurement of particle areas (vernier units) and the results compared to
image analysis (IA) measurements of the same particles. Over one hundred
particles were measured ranging in sizes of a square micron to a couple of
hundred square microns. Figure 42 shows the areas of these particles as
measured by the IA in square microns versus the same areas measured with
the planimeter in vernier units. The straight line marks the linear regression of
the data.
Discussion
After doing all calculations, it still was not clear if the planimeter
technique would introduce more error than the IA color thresholding technique.
Various sizes of square areas were measured both with the IA system and
planimeter and compared to the areas obtained from several sizes of circular
95
shapes, as shown in Figure 43. The results from measuring square shapes
were very similar. The results from measuring circular shapes showed a small
discrepancy between the techniques
Figure 42 shows that there was a
consistent discrepancy 25% between the two techniques. The use of the
planimeter was also a very slow technique and it required the extra expense of
developing and enlarging photographs. However, neither the IA nor the
planimeter technique were pursued any further because they can only give a
measure of the area the detached particles occupy on the filter. In order to
obtain a volume, two possible assumptions had to be made. The first possible
assumption was that the particles did not collapse when filtered and retained
their original shape for which the particle shape, sphere or cubical, had to be
assumed. Based on the measured area, the third dimension was calculated.
The second possible assumption was that the aggregates of cells collapsed
when filtered and the area observable is that of a flat aggregate of cells with a
certain assumed thickness. These assumptions were not easily tested nor
verified. The difficulty of interpreting area measurements discouraged further
development of these techniques.
Coulter Counter Measurements
The last attempt for studying the structural heterogeneity of the biofilms
was to evaluate the possibility of using a particle counter fpr measurement of
biofilm detachment. The particle counter would measure the size and number
96
CQ
30
<
20
0
20
40
60
P l a n i m e t e r (/xm )
Figure 42.
Image analysis and planimeter results comparison.
00
97
V e rn ie r u n its
Figure 43.
Planimeter results from measuring square (■) and
circular (*) shapes.
98
of particles found in the effluent of the reactor. A comprehensive 320 page
study "Evaluation of Particle Counting as a Measure of Treatment Plant
Performance" was published by AWWA Research Foundation and American
Water Works Association in 1992. This publication thoroughly reports the
available particle counting technologies as well as the performance of
commercial particle counters. Based on this study and the availability of a ZBI
model (Coulter Electronics Inc.), the Coulter technology was pursued.
Theory of Operation
Particles or cells, suspended in an electrolyte, can be sized and counted
by passing them through an orifice (aperture) with a specific path of current
flow for a given length of time. As particles or cells pass through the aperture
and displace an equal volume of electrolyte, the resistance in the path of
current changes. This results in corresponding current and voltage changes.
The quantity (magnitude) of this change is directly proportional to the
volumetric size of the particle or cell. The number of changes within a specific
length of time is proportional to the number of particles or cells within the
suspension.
Discussion
Several limitations kept the Coulter Counter from being useful in this
study. None of the aperture sizes would allow for simultaneous measurement
of individual cells and aggregates of cells (one or two hundred microns in
diameter). See Table 2, or ZBI Coulter Counter user’s manual.
99
TABLE 4
Aperture Tube Data
From Coulter Counter’s users’ manual
Nominal Diameter (in
micrometers)
Nominal Particle Diameter
Range
30 ^m
0.6 to 12.0 pm
50 Jim
1.0 to 20.0 pm
70 urn
1.4 to 28.0 pm
100 pm
2.0 to 40.0 pm
140 pm
2.8 to 54.0 pm
200 pm
4.0 to 80.0 pm
280 pm
5.6 to 108.0 pm
400 pm
8.0 to 160.0 pm
560 pm
11.2 to 216.0 pm
100
Therefore the larger particles in the samples had to be filtered out each
time after using a large aperture tube in order to be analyzed again using a
smaller aperture diameter. Due to the fact that the instrument dilutes the
sample as it analyzes it, it would be impossible to collect the exact same
sample (or its exact volume) after each pass in order to filter it. Also, this
filtration could introduce large amounts of error to the study due to the
possibility of large cell aggregates breaking down into individual cells. Even if
the exact same sample could be collected each time and the filtration
processes was acceptable, the construction of a histogram of the particle size
data represents a statistical challenge and perhaps the introduction of
assumptions and approximations that would only contribute to less accurate
results. Finally, even though the instrument was serviced, it was not able to
generate enough vacuum in order to use the 560 p,m aperture, necessary to
count the larger cellular aggregates. This technique was not pursued further
because of these limitations.
101
APPENDIX B
Optical Measurements
The next attempt for studying the structural heterogeneity of biofilms was
to try to measure biofilm thickness. The first approach was to use a light
microscopic technique that has been perhaps the most commonly employed
method for measuring biofilm thickness.
Experimental Method
This technique was adapted from Sanders (1966) and requires biofilm
growth in a thin polycarbonate slide which forms an integral part of the annular
reactor wall. When a biofilm has grown on the slide, it is withdrawn from the
reactor and placed on a microscope stage. The objective is lowered until the
biofilm surface is in focus and the fine adjustment dial, setting is recorded. The
objective is then lowered further until the plastic growth surface is in focus
(Figure 44). The difference in adjustment settings is then multiplied by the
. calibration coefficient and the thickness obtained. This method has been used
by many researchers (i.e. Bakke and Olson, 1986, Siebel and Characklis, 1991),
although it has only been used to take a few random measurements in order to
obtain ah average number. The goal of this study was to apply this technique
for measuring biofilm thickness along a linear path on fine grid. A motorized
microscope stage (Ludl Electronic Products Ltd.), a stage controller, a
computer, and a software written in C language allowed the biofilm sample to
be repositioned and thickness measurements 5 microns apart were obtained
IOX
objective
lens
Z
Distance traveled
by objective lens
r
I Focus on
102
Focus on
biofilm surface
I
Actual
biofilm
thickness
Biofilm
VVZZZ/
Figure 44.
•
I
I
7T Y T V T rTZ
Method for measuring wet biofilm thickness with an optical microscope
(Trulear 1982).
103.
along a linear path.
Results and Discussion
The positioning stage worked well, but measuring the thickness of the
biofilm presented some difficulties. The biofilm sample had to be immediately
placed under the microscope after being removed from the reactor in order to
keep the biofilm hydrated. Finding the biofilm surface was easy. However, the
area being focused is relatively large and not all of it can be focused at once.
Deciding the exact location of the outermost cell was a subjective task.
Locating substratum was even more difficult and the total time required to
locate both interfaces was long. After a few measurements the biofilm sample
collapsed because the heat of the light had dehydrated the biofilm.
This
technique did not work for this study.
Biofilm Embedding
The second approach to quantitatively measuring biofilm thickness was
to grow the biofilm on a membrane that could be embedded and cut into thin
cross sections for image analysis of biofilm thickness.
Experimental Procedure
MethylceIIuIose or polycarbonate membranes (0.2 p,m) were taped to the
substratum (polycarbonate slides on an annular reactor, or glass slide on a flat
plate reactor) in order to collect a biofilm sample. Autoclavable Scotch brand
polycarbonate tape (SM Co.) was used. The tape was cut-in rectangular
104
shapes 2cm x 4cm with an opening in the middle of about 1cm x 2cm. The
membrane was taped unto the substratum so that most of the membrane was
exposed for the formation of biofilm. After a mature biofilm was obtained, the
membrane was removed and fixed with 2.5% glutaraldehyde for two hours. It
was then transferred to a 25 ml glass vial containing PBS (phosphate buffer
solution) and kept overnight. The biofilm was dehydrated before embedding. It
was rinsed with 70% ETOH for 10 minutes, rinsed with 95% ETOH for 10
minutes (2 changes), rinsed with 100% ETOH for 10 minutes (2 changes), and
rinsed with xylene for 10 minutes (2 changes). Paraffin Tissue Prep Il (Fisher
Co.) and Glycol Methacrylate (GMA), a water soluble plastic .(JB-4, Polysciences
Inc.) were used as embedding materials. The membrane strip was embedded
oriented according to the direction of the flow. 5 ^m cross sections were
obtained using a Reichert AO 820 microtome. The thin cross sections were
then stained as follows:
1.
Rinsed with 1% Periodic acid (FRESH) for 5 minutes
2.
Rinsed with distilled water
3.
SchifFs Reagent (Harleco Brand) for 10 minutes
4.
Rinsed in running tap water for 10 minutes
5.
Gill Il Hematoxylin (Shandon-Lipshaw) for 1 minute
6.
Rinsed in running tap water for 2 minutes
7.
4% Acetic Acid, 10 dips .
8.
Rinsed in running tap water for 10 minutes
105
9.
Scotts tap water (Sigma Co.) for 1 minute
10.
Rinsed in running tap water for 2 minutes
11.
Dehydrated through 95% ETOH, 100% ETOH1 clear in xylene 2 changes
each solution, 30 dips per change
12.
Placed on microscope slide and coversiiped
The American Innovision Image Analysis system was used to measure
thickness by capturing images of the cross sections in the computer. Images
are displayed on a TV monitor. Distances can be calculated using the mouse
to select two points on the image. The mouse is used to move the cursor from
the com puter screen to the TV monitor. The distance between the two points is
calculated and displayed on the screen.
Results and Discussion
None of these embedding procedures were fully satisfactory. Cellulose
membranes dissolved in GMA, but embedded fine in paraffin. However, only
when the biofilm was thick or very dense, did the membrane retain its physical
structure in the thin cross sections. When the biofilm was thin the crosssections curled and wrinkled. Polycarbonate membranes retained their physical
structure better, but wrinkles were always present. These wrinkles were spaced
about 100 ^m apart, but there was no way of determining what length of the
cross section was wrinkled. Figure 45 shows the biofilm thickness profiles of
pure culture P. aeruginosa and pure culture K.pneumoniae biofilms grown on
an annular reactor as measured from a cross section. This biofilm was
K. pneumoniae
. P. aeruginosa
<
v"
Vs
^
800
900
1000
D i s t a n c e (/zm )
Figure 45.
P. aeruginosa and K. pneumoniae biofilm thickness profiles.
1 100
1200
107
em bedded in paraffin. The data gaps correspond to the spaces occupied by
the wrinkles. Figure 46 shows a 3-D profile of biofilm thickness constructed
from the analysis of adjacent cross sections of an undefined mixed population
biofilm grown on a flat plate reactor. A cellulose membrane was used to collect
a very thick biofilm and no wrinkles were observed. This embedding technique
was not pursued further. The data gaps could never be prevented, and
thickness information was left out.
Figure 46.
Undefined mixed population biofilm thickness profile.
109
APPENDIX C
S-PLUS Program
Edit the data files adding column names above each column and
removing blank lines or miscellaneous text.
First record in file should
be a header record containing column names. All remaining records should
be data records with columns separated by blanks.
Check that no padding records have been added to tail of file.
Filename :
R e c . No.
1
2
3
FILENAME.txt
Col. I
Distance
1.727114
3.454228
Col.2
Thickness
33.582
33.582
I . Set up Splus environment. Attach Splus working directories. Open Splus
graphics and help windows. Capture default graphics settings in "defpar".
#
#
#
defpar_par()
to reset default graphics parameters, use
par(defpar)
by the way, a "#" character preceding a line means the line has been
commented out
2. Read the data files into an Splus data frame.
Generate tablular and graphical summaries.
crossection.df_read.table(“FILENAME.dat",header=T)
summary (crossection.df)
par(mfrow=c(3,1),mar=c(7,6,4,2)+.!)
p l o t (crossection.df$Distance,crossection.df$Thickness, type="n",xlab="",
ylab="“, ylim=c(0,300),cex=l.0)
title (xlab="Distance (microns)",ylab="Thickhess (microns)",cex=l.2)
lines(crossection.df$Distance,crossection.df$Thickness)
crossection.df_read.table("FILENAME.dat",header=T)
summary(crossection.df)
3. Estimate standarized mean absolute deviation (Ra*) from various
Plot Ra vs distance skipped.
models.
R a .graph4_function(y,y.hat,...){
R a .skip_Ra.skip.vec2(y,y.hat)
p l o t (Ra.skip$x*2,Ra.skip$y/mean (y),
xlim=c(0,m a x (Ra.skip$x)*2),xlab="", ylab="", pch=".",cex=l.0)
title(xlab="lag (microns)", ylab="Ra*", pch=".",cex=l.2)
R a .median_rep(0,m a x (R a .skip$x))
for (i in I :max(Ra.skip$x)){
R a .median[i ]_median(Ra.skip$y[Ra.skip$x==i])
lines((I:max(Ra.skip$x))*2,R a .median/mean(y),Ity=D
)
#
Ra.graph4 test:
R a .graph4(crossection.df $Thickness,y .hat =mean(crossection.df$Thickness))
4.
Calculate and Plot Variogram Cloud.
Estimate Variogram Function.
110
*
crossection.vario.cloud_vario.de v .fun(crossection.df$Distance,
crossection.df$Thickness)
par(mfrow=c(3,1),mar=c(7,6,4,2)+.I)
# * * * * * * * * * * * * * * * * plot data subset used to compute variogram cloud.
p l o t (crossection.df $Distance[crossection.vario.cloud$index],
crossection.dfSThickness[crossection.vario.cloudSindex],
xlab="", ylab=“”, pch=".", cex=l.0, y Iim=C(0,300))
title( ylab=“Thickness (microns)“,xlab="Distance (microns)",
cex=l.2, pch=".")
#
lines(supsmu(crossection.vario.cloudSx,crossection.vario.cloudSy))
# ****************
plot the variogram cloud.
plot(crossection.vario.cloud,pch=“.", xlab="", ylab="",cex=l.0)
title( xlab="Distance (microns)", ylab="Gamma(Distance)”,cex=1.2))
# * * * * * * * * * * * * * * * * plot a smooth curve through the variogram cloud.
plot(crossection.vario.cloud,pch=".",type="n“,ylim=c ( 0 , 5 0 0 ) ,
cex=l.0 ,xlab="", ylab="")
title( xlab="Distance (microns)", ylab="Gamma(Distance)",cex=1.2)
lines(supsmu(crossection.vario.cloud$x,crossection.vario.cloud$y))
5. Plot the cumulative distribution of Thickness.
p a r (mfrow=c(1,1),mar=c(5,6,4,2)+.I)
plot(sort(crossection.df$Thickness),xlab="", ylab="",
(seq(l,length(crossection.df$Distance))/length(crossection.df$Distance))
*100,type="n", pch=".")
title(xlab="Thickness (microns)", ylab="Cumulative Percentage",cex=l.2,
mar=c(5,6,4,2)+.!)
lines(sort(crossection.df$Thickness),(se q (I ,length(crossection.df$Distance))
/length(crossection.df$Distance))*100)
MONTANA STATE UNIVERSITY LIBRARIES
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