Development of a toolbox for identifying and quantifying ... · identifying and quantifying membrane biofouling ... quantifying membrane biofouling in drinking water ... analysis

Development of a toolbox for identifying and quantifying membrane biofouling in drinking water treatment Protocol report.

Techneau WP 3.3.3 June 2007

© 2006 TECHNEAU TECHNEAU is an Integrated Project Funded by the European Commission under the Sixth Framework Programme, Sustainable Development, Global Change and Ecosystems Thematic Priority Area (contractnumber 018320). All rights reserved. No part of this book may be reproduced, stored in a database or retrieval system, or published, in any form or in any way, electronically, mechanically, by print, photoprint, microfilm or any other means without prior written permission from the publisher

Development of a toolbox for identifying and quantifying membrane biofouling in drinking water treatment Protocol report

Techneau WP3.3.3 June 2007

This report is: PU= Public

Colofon

Title Development of a toolbox for identifying and quantifying of membrane biofouling in drinking water treatment. Protocol report. Author(s) Liv Fiksdal and Astrid Bjørkøy Quality Assurance F. Hammes Deliverable number D 3.3.4.

TECHNEAU NTNU © TECHNEAU - 1 - June 2007

Summary

Microbial cells attach to almost any surface submerged in an aquatic environment. The immobilized cells grow, reproduce and produce extracellular polymers, which together can form a biofilm. The unwanted deposition and growth of biofilms is referred to as biofouling. Biofouling is a large problem in environmental membrane separation processes for several reasons, i.e. it leads to higher operating pressures, frequent need for chemical cleanings, membrane deterioration, and compromised water quality. Biofouling is a function of membrane characteristics , process operating conditions (e.g. hydrodynamic factors, backwashing, cleaning) and bulk water properties (chemical and microbiological). Biofouling mitigation and control is a necessity for sustainable operation of membrane processes in drinking water treatment. Both reduction of fouling development and membrane cleaning are central elements. Understanding of biofouling behaviour and responses will enable choice of optimal operating conditions, e.g. flux rates, cleaning protocols, backwashing strategies. In this report the development of a Toolbox for characterisation and quantification of membrane biofoulant components in drinking water treatment, is described. The toolbox methods are based on 1) confocal laser scanning microscopy (CLSM) and image analysis for identification and quantification of biofouling components (i.e. cells, extracellular polysaccharides) on membranes, in particular on curved membrane surfaces, and 2) methods for detecting biological activity, i.e. enzyme activity. A test unit has been constructed. Procedures for sampling, staining of biofouling components, CLSM and enzyme activity measurements have been established. Image analysis software for CLSM analysis on curved surfaces has been developed. In the report, the introduction (chapter 1) describes different techniques for characterising biofilms/biofoulants, and the objective of the work. The test unit, the monitoring systems and examples of water qualities in the test unit chamber are presented in chapter 2. The target enzyme activities and substrates for biofoulant activity measurement are presented in chapter 3, and protocol development for enzyme activity measurement in chapter 4. A large number of fluorescent stains can be used for staining of biofilm components. The stains and staining procedures for cells and polysaccharides we have used are described in chapter 5. A brief description of the principle of CLSM (chapter 6) and a more detailed description of the image acquisition of CLSM (chapter 7) are presented next. This is followed by an introduction to image analysis, a description of some available software packages, and a detailed description of CMem, the software we have developed for quantitative image


analysis of biofoulants on curved membranes (chapter 8). Examples of fluorostaining and confocal imaging results, leading to selection of stains and staining conditions for the protocol, are presented in chapter 9. Examples of use of CMem are also included. Finally, chapter 10 provides the conclusions.


Contents

1 Introduction 6 1.1 Biofouling 6 1.2 Characterisation and monitoring of membrane biofouling 7 1.2.1 Biofilm characterisation techniques 7 1.2.2 Prediction of biofilm/biofouling production 9 1.2.3 On-line methods 9 1.2.4 Early warning systems 9 1.3 Objectives of the present project 10

2 Membrane biofouling test system 11 2.1 Introduction 11 2.2 Bioufouling test unit 11 2.3 Membrane characteristics 12 2.4 Cleaning of membranes 13 2.5 Water flow monitoring 13 2.6 Trans membrane pressure (TMP) measurement and results 13 2.7 Water quality monitoring 15

3 Selection of target enzyme activity 18 3.1 Introduction 18 3.2 Selection of target enzymes 18 3.3 Selection of enzyme substrate 19 3.3.1 Esterase substrate 19 3.3.2 Peptidase substrate 20

4 Enzyme activity assay of membrane biofoulant: Protocol development21 4.1 Collection of membrane samples 21 4.2 Biofoulant removal by ultrasonic treatment 21 4.3 Enzyme activity assays 22 4.3.1 Membranes with biofoulant in PBS 22 4.3.2 Suspension of biofoulant in PBS 22 4.4 Substrate concentration 22 4.5 Temperature 23

5 Staining of biofouling components 26 5.1 Introduction 26 5.2 Cell staining 26 5.3 Polysaccaride staining 27


5.3.1 Stain types, specificity and concentrations used 27 5.3.2 Cell bound and not cell bound polysaccharides 29 5.3.3 Multiple staining 29 5.4 Lipid staining 29 5.5 Protein staining 29

6 THE CLSM MICROSCOPE 31 6.1 Principle of confocal imaging 31 6.2 The confocal laser scanning microscope – LSM 510 META (Zeiss) 33 6.3 The LSM 510 Software 34

7 CLSM microscopy 35 7.1 Membrane sample support for CLSM microscopy 35 7.2 Image acquisition 36

8 Image analysis methods for CLSM 45 8.1 Introduction to use of image analysis software 45 8.2 Image analysis of biofilms 47 8.2.1 Software 47 8.2.2 Biofilm structure parameters 48 8.2.3 ISA reference surface 49 8.3 CMem 50 8.3.1 The reference surface 51 8.3.2 Processing of the biofilm stack 55 8.3.3 Calculating biofilm parameters 56

9 Fluoro-staining and confocal imaging of membrane biofoulant – examples and protocol development 59

9.1 Operational cycle 1 59 9.1.1 3 days old biofoulant 59 9.1.2 7 days old biofoulant 60 9.1.3 10 days old biofoulant 61 9.2 Operational cycle 2 62 9.2.1 3 days old biofoulant 63 9.2.2 6 days old biofoulant 63 9.2.3 9days old biofoulant 64 9.3 Operational cycle 3 64 9.4 Quantification of biofoulant components using CMem 66 9.4.1 Effect of sample tilting on biomass thickness calculation 66 9.4.2 Quantitative image analysis 66 9.5 Summary 68 9.5.1 Staining 68 9.5.2 Image acquisition 68

10 Conclusions 70


11 References 71

12 Appendix 75 12.1 Procedure for membrane module preparation 75 12.2 Water quality and transmembrane pressure data logging 75 12.3 LabVIEW operation. 76 12.4 Biofoulant sampling for CLSM 79 12.5 Specification of LSM 510 META confocal laser microscope 80 12.6 Description of membranes/bundles used in operational cycles. 81


1 Introduction

1.1 Biofouling Microbial cells attach to almost any surface submerged in an aquatic environment. The immobilized cells grow, reproduce and produce extracellular polymers, which together can form a biofilm (Figure 1). Biofilms form in nearly every environment that provides a surface, nutrients and water. The unwanted deposition and growth of biofilms is referred to as biofouling.

After initial attachment of microbial cells to the membrane, the cells can divide and produce a matrix that holds them together. The matrix is composed of excreted polymeric compounds called EPS (extracellular polymeric substances). The EPS provides a rather porous structure to the biofilm and contains polysaccharides, proteins, lipids, nucleic acids and inorganic compounds. The biofouling structure and composition change over time, dependent on fluid dynamics, the nutrient and oxygen supply. Biofouling is more complicated than other membrane fouling types, i.e. non microbial colloidal and particulate fouling, which can be controlled by effective pre-treatment. Microorganisms can reproduce on the membrane even if their numbers in the water is reduced by pre-treatment. Different microorganisms may adhere differently to surfaces, depending on growth phase and nutrient conditions, water chemistry and the physicochemical nature of the membrane (AWWA 2005). Depending on the membrane surface characteristics and operational conditions, membrane biofouling will occur at different rates because of differences in physicochemical membrane surface characteristics, bulk water

Figure 1 Biofilm formation: the irreversible attachment of planktonic bacteria to a surface, growth and excretion of extracellular components to form a mixed population of bacteria, fungi and algae (from The Center for Biofilm Engineering, Montana State University, US)


chemistries and microbiology, and hydrodynamic factors. Hydrodynamic factors are critical in bacterial adhesion because the forces imparted by crossflow and permeation velocities largely determine the rate of transport of microorganisms to the membrane surface (AWWA 2005). Biofouling is a large problem in environmental membrane separation processes for several reasons, i.e. it leads to higher operating pressures, frequent need for chemical cleanings, membrane deterioration, and compromised water quality (Flemming 2002). Characterisation and quantification of biofoulant components at different operating conditions can provide knowledge to be used for selection of alternative membrane unit designs and optimal operating conditions. Controlling biofouling is a challenge due to the difficulty of cleaning biofouling in e.g. spiral wound and hollow fibre membrane elements. It is assumed that cleaning can be more efficient when biofouling is in an early stage of colonisation. Assessment of normalised pressure drop (NPD) to determine biofouling is not specific or sensitive (Kappelhof et al. 2003). Understanding biofouling behaviour and responses is necessary to choose optimal operating conditions for biofouling mitigation and control.

1.2 Characterisation and monitoring of membrane biofouling Characterisation and monitoring of membrane biofouling is needed for several reasons: 1) to understand biofouling mechanisms, 2) to allow preventive actions, i.e. anti-fouling and cleaning strategies to be implemented, 3) design of alternative membrane systems (e.g. membrane material, module design/configuration, operating conditions), 4) determining effects of changes in raw water quality.

1.2.1 Biofilm characterisation techniques Examples of biofilm characterisation parameters are listed in Table 1. They are used to analyse the biofilm after removal from a support, or directly on a support, e.g. on membranes that are removed from a test system. Biofilm removed from supports. Conventional biofilm monitoring techniques rely on removal of material from representative sites or on analysis of test surfaces which have been exposed. The procedure is time consuming and, depending on the parameters to be measured, requires skilled laboratory personnel. However, such measurements can provide information on the role of different types of components (e.g. cells, EPS) in biofilm formation. Direct analysis of biofilm on supports. Microscopy is frequently used for direct analysis of biofilms on supports. Biofilms of more than 3- 4 um thickness usually cannot be handled with common light microscopes because material above and below the focal plane will scatter light and interfere with the direct measurement. Such biofilms can be investigated without destructing the biofilm by e.g. Confocal Laser Scanning Microscopy (CLSM). The CLSM allows optical sectioning of the


biofilms and with image analysis a three-dimensional reconstruction of the undisturbed sample is possible. Characterisation and quantification of biofilm components by CLSM are described by several authors (e.g. Neu et al. 2001; Keevil, 2003; Beyenal et al. 2004a; Staudt et al. 2004; Palmer et al. 2006). Fewer reports are available on development and application of this technique for membrane biofouling in water treatment processes, and to our best knowledge very few or none concerning biofouling of hollow fibre membranes. The available image analysis software for thickness measurement and localisation of biofilm components is referring the detected pixels to a horizontal plane. This software is therefore not applicable for quantification of biofouling components related to curved surfaces, e.g. hollow fibre membranes.

Biofilm removed from supports e.g. sacrificial membranes, by mechanical force (scraping) or ultrasonic treatment Culturable bacteria (plate count, MPN) Active bacteria (staining with e.g. CTC, TTC, INT, SFDA, CFDA, Rhodamine 123; Live/dead kits; viable cells: nalidixic acid method) Biomass -Total biomass: Direct total cell count (staining with e.g. AO, DAPI) -Active biomass: ATP Biofilm activity - e.g. hydrolytic enzyme activity Microbial community analysis - Molecular biological techniques (PCR, FISH) Chemical analysis -TOC -EPS (extracellular polymer substances, e.g. soluble and particle bound polysaccharide and protein) -ICP-MS for measurement of abiotic components Direct analysis of biofilm on supports, e.g. on sacrificial membranes Microscopy (light microscopy, epifluorescence microscopy, scanning electron microscopy, confocal laser scanning microscopy, atomic force microscopy) with or without staining of biofoulant components with: - Nucleic acid-specific dyes (e.g.DAPI, AO, Hoechst stains, SYTO 9, SYTOX green, SYBR green, propidium iodide) -EPS specific dyes, e.g. lectins labeled with a fluorochrom -Vital dyes (e.g. CTC, TTC, INT, SFDA, CFDA, Rhodamine 123, Live/Dead kits; nalidixic acid/fluorochrom)

Table 1 Examples of biofilm characterisation parameters


1.2.2 Prediction of biofilm/biofouling production Prediction of biofilm production in a specific water quality can be done by analysing e.g. Assimilable Organic Carbon (AOC) and the Biofilm Formation Rate (BFR) (van der Kooij et al. 1982; van der Kooij et al. 1995; Kappelhof et al. 2003; Vrouwenvelder and Van der Koij 2003; Hammes et al. 2005). BFR is based on ATP analysis of the biofilm in a biofilm monitor and implies extraction and measurement of ATP from biomass on sacrificial supports. The methods can be used for elucidation and control of the processes that are responsible for the fouling problems. However, they will not indicate site (AOC, BFR) and extent (AOC) of biofilm/biofouling.

1.2.3 On-line methods There is a demand for direct, on-line, in situ, continuous, non-destructive real-time information about biofilms in technical systems. Examples of physical or physico-chemical on-line methods have been described and grouped according to the levels of information they provide: (i) systems which detect increase and decrease of material accumulating on a surface but cannot differentiate between biomass and other components of a deposit, (ii) systems which provide biological information and distinguish between biotic and abiotic material, and (iii) systems which provide detailed chemical information. Most of the approaches have not exceeded successful proof of operation at laboratory level (Flemming 2003). None of the methods are (specially) designed for membrane biofouling monitoring.

1.2.4 Early warning systems An early warning system for biofouling monitoring based on oxygen consumption by microorganisms in the biofoulant, has been reported (Kappelhof et al. 2003). The oxygen consumption during operation of the membranes was too small to be detected in-line. In order to increase the oxygen consumption the measurements were performed after circulation of feed water over the membrane unit for a fixed period (e.g.1 hour) or by applying a fixed period of stand still of the membrane unit (e.g. 1 hour). The method has the advantages to be specific for active biomass, applicable in situ, non-destructive and more sensitive than pressure drop measurement (Kappelhof et al. 2003; Vrouvenwelder and van der Kooij 2003; Vrouvenwelder et al. 2003). Normally one may expect that aerobic conditions prevail in membrane biofoulants exposed to drinking water. However, if non-homogeneous accumulation of biofoulant on membranes occurs, this can provide local, oxygen deficient micro-environments, and allow for presence of other than strict aerobic bacteria. The method described above does not take into account the potential presence of non-aerobic microorganisms and their contribution to membrane biofouling. Enzyme activity measurements are used for detection of biofilm activity/active biomass (Schaule et al. 2000) and are in general rapid methods (i.e.< 8h). Enzymes located in the cytoplasma (intracellular) can require destructive treatment with permeabilisers before measurements can be done. For in-situ or at-line monitoring of membrane biofouling, (exo)enzymes in a location not requiring pre-permeabilisation would be preferred as target


enzymes. Exo-enzymes can be defined as enzymes located at a (short) distance from cells, in contact with the cell-surface or in the periplasmic space. Exo-hydrolases hydrolyse organic compounds (e.g polysaccharides, proteins, peptides or lipids) and heterotrophic bacteria in aquatic environments are excellent producers of these hydrolytic enzymes (Chrost 1991). The activity of such exoenzymes can therefore be used as a measure of total active biomass. A hydrolase (protease) assay has recently been applied for measuring active biomass in-situ, on iron oxide deposited on glass beads (Butterfield et al. 2002). The assay time was 10 min and a potential application of the method for entire reactors was suggested. It is reasonable to assume that an important fraction of dissolved organic matter in surface water consists of polymers. These polymers can induce the synthesis of extracellular enzymes by heterotrophic bacteria. The enzymes are involved in degradation of the polymers to produce assimilable organic carbon (AOC) to be used by the microbial community. When membrane filtration is used as a final step in a treatment process chain (e.g. OBM: oxidation, biofiltration and membrane filtration), a fraction of the organic polymers will be degraded to smaller molecules. To what extent this will affect the hydrolase activity of a biofilm on the membrane needs to be investigated. The measurement of hydrolase activity can potentially be used as an early warning method of membrane biofouling

1.3 Objectives of the present project The purpose of the present project is to develop methods for specific characterisation and quantification of membrane biofoulant components and activity, in drinking water treatment, in order to understand and predict the behaviour of the membrane filtration reactors when exposed to different conditions. CLSM techniques and image analysis will be used for identification and quantification of biofouling components (i.e. cells, extracellular polysaccharides) on membranes, particularly on curved membrane surfaces applicable to tubular/hollow fibre systems. Investigation of the use of rapid methods based on hydrolase enzyme activity assays for measuring biofoulant activity will be done. The methods can potentially be used for non-destructive early warning in-situ/at-line monitoring of enzyme activity as an indication of biofouling.


2 Membrane biofouling test system

2.1 Introduction Membrane biofouling can be investigated in by-pass systems using reactors with exchangeable membrane samples, at appropriate transmembrane pressure conditions. We have constructed a membrane filtration test-unit described below. In the project period that is reported here, the test-unit has received water from an on-going ozonation-bio-filtration (OBM) pilot plant for removal of natural organic matter (NOM) in drinking water (Figure 2). Development of the OBM processes is part of TECHNEAU WA2.

2.2 Bioufouling test unit The bioufouling test unit (Figure 3) consists of 18 modules (bundles of membranes) with 6-8 membranes in each. The 18 modules are immobilized in a frame, which is positioned vertically in an acrylic glass chamber, in a water volume of ca 10 L. The procedure for membrane module preparation is described in the Appendix (12.1).

Figure 2 Example of an OBM process (ozonation, biofiltration, membrane filtration) including the Biofouling test unit.

Acid/base

NOM concentrate

F

Ozonizer FPilot scale

MembraneFiltration

NOM solution

Tap water

Biofilter

Biofouling test unit

Ozonizationcolumns

Hollow fiber membranes

Acid/base

NOM concentrate

F

Ozonizer FPilot scale

MembraneFiltration

NOM solution

Tap water

Biofilter

Biofouling test unit

Ozonizationcolumns

Hollow fiber membranes


2.3 Membrane characteristics Micro-filtration (MF) membranes with Polyether sulfone (PES) as basic polymer (MicroPES 0.3/2 , Membrana GmbH) have been used in the first 18 month project period (Figure 4). Microfiltration membranes typically remove particles with size > 0.1 μm.

The PES membranes are hydrophilic with a sponge-like structure (inter pore connection). Membrane characterisation parameters are listed in Table 2.

Figure 3 The biofouling test unit and water quality monitoring system (sensors and data logging)

Figure 4 Chemical structure of PES

0 1 2 3 4 5 6 7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

FP-1000

Bioufouling Test Unit

feed

waste

Pressure transducer

Pump

rotameter

Sensors:

Data to computer

Filtered water

0 2

Sensor 1

3


Nominal pore size 0.2 μm Wall thickness 100 μm Inner/outer diameter 300/500 μm Total membrane area (outside)

an*2πr*l a n = number of membranes, r = radius outside, l= membrane length

2.4 Cleaning of membranes Before a new operational run of the test unit was started, bundles of membranes that had been mounted in the unit in the previous run, and not been used, were soaked in a 0.125% sodium hypochlorite solution for 15 h, and then in citric acid (10 g/L) for 5 h.

2.5 Water flow monitoring A Masterflex pump from Cole-Parmer (System Model 7553-77, variable speed 6-600 rpm, size 16 tubing) is used to pump water through the membranes (outside to inside) in the test unit. Typical flux values are 20-60 L/m2, h. The flux is monitored by use of a rotameter (Bailey-Fischer&Porter: Model 10A6131, FA1C1X00A).

2.6 Trans membrane pressure (TMP) measurement and results The biofouling on the membranes in the test unit (Figure 3) is recorded indirectly by measuring the transmembrane pressure (TMP). The sensor measuring TMP is described in 2.7 (Table 3). TMP as function of time during two operation cycles (10.10.06 - 31.10.06 and 24.11.06– 09.01.07) is shown in Figure 5.

Table 2 PES Membrane characteristics


TMP as function of biofouling

time (days) after start of filtration

0 10 20 30 40 50

TMP

(bar

)

1,0

1,1

1,2

1,3

1,4

time (d) vs TMP (23.11.) time (d) vs TMP (10.10.)

TMP as function of time during the operational cycle 17.04. 07-15.05.07 is shown in Figure 6. All membranes were new, and the water flux was set to 62 l/m2h (TMP 1.05 bar). The pressure was quite stable in the beginning, but after 10 days, the rate d(TMP)/d(time) increased significantly. In previous operational cycles (Figure 5), the TMP onset started immediately. In these cycles, both new and used, cleaned membranes were mounted in the test unit. Traces of biofoulant present on the cleaned membranes could have facilitated the initial attachment of bacteria to the membranes and the early onset of TMP.

Figure 5 Trans membrane pressure (TMP) increase, due to biofouling on the membranes during operation (10.10.06 – 31.10.06 and 24.11.06 – 9.1.07)


TMP as function of biofouling

time (days) after start of filtration

0 5 10 15 20 25 30

TMP

(bar

)

0,9

1,0

1,1

1,2

1,3

1,4

1,5

Tem

p (C

)

12

14

16

18

20

22

24

26

28

30

time (d) vs TMP (17.04.07) time (d) vs Temp (C)

2.7 Water quality monitoring Temperature, conductivity and pH of the water flowing through the chamber (Q ca 50 L/h) (Figure 3) are monitored by different sensors (Table 3). A sensor measuring the trans membrane pressure (TMP) (see 2.6) is connected to the same data logging system. All sensors are connected to the terminals of a Field Point I/O module FP-AI-110 (National Instruments) (see Appendix 12.2). The software LabVIEW (National Instruments) is used to acquire, display and store sensor measurements (see Appendix 12.3). Data logging of conductivity and temperature were obtained, however, logging of pH has not functioned satisfactorily. The pH sensor has been replaced, but the problem has not yet been solved. Average logging data of conductivity and temperature during the operational period 24.11.06-09.01.07 is shown in Table 4.

Figure 6 Trans membrane pressure (TMP) increase, due to biofouling on the membranes during operation (17.04.07 – 15.05.07). Temperature variations during the period are also plotted.


Parameter Type/Model, Supplier

Convertera, Supplier

Channel (FP-AI-110)

Measuring range

Temperature Pt 100 SE Prominent

Pt 100 V1, Prominent 0 0-100 °C

TMP MBS 3000, Danfoss - 1 0-2.5 bar

pH PHER 112 SE Prominent

PH V1, Prominient 2 3-14

Conductivity LF 1 FE, Prominent

DMTa, Prominent 3 0.01 – 20

mS/cm a all signals are converted to 4-20 mA.

Conductivity (μS/cm) Temperature (ºC) 268 ± 25 15.88 ± 1.00 Examples of inlet water quality measured by non-sensor techniques in October/November 2006 and March/May 2007 are shown in Table 5 and Table 6. The data logging of temperature and conductivity (Table 4) in the test unit was started shortly after the non-sensor measurements shown in Table 5, and the results (Table 4 and Table 5) can therefore not be compared.

Date: 31-10 3-11 7-11 Average Conductivity (μs/cm2) 287 290 286 288 Temperature (oC) 14,7 14,3 14,1 14,4 pH 7,20 7,33 7,21 7,25 Turbidity (NTU) 0,375 0,487 0,318 0,393 Color (mg Pt/L) 9,54 16,51 9,69 11,91 UV254 0,076 0,081 0,069 0,075 DOC (mg C/L) 4,73 5,26 5,02 5,00 Total P (mg/L)filtered /nonfiltered 0,020/0,168 0,006/0,158 0,028/0,274 0,018/0,200

Total N (mg/L)filtered /nonfiltered 0,343/0,360 0,325/0,385 0,363/0,377 0,344/0,374

NO2 (mg/L)filtered <0.05 <0.05 <0.05 NOX (mg/L) filtered 0,2987 0,2873 0,2873 0,2911

Table 3 Sensor types and characteristics

Table 4 Average temperature and conductivity (logged 24.11.06 – 09.01.07)

Table 5 Water quality of inlet water (after biofiltration) (31.10, 03.11, 0 7.11, 2006)


In the period 15.03.07.-15.5.07 (Table 6), parameters were analyzed daily, except alkalinity (measured weekly) and heterotrophic bacteria (HPC) (one measurement). A relatively high HPC number, i.e. 106 colony forming units/mL (Table 6) was observed in water from the biological filter entering the membrane biofouling test unit. In the next project period both HPC and total cell number will be monitored.

Raw water After ozoation After biofiltration Temperature (oC) 12.2 14.2 14.4 Conductivity (μs/cm) 395 402 406 pH 6.4 6.58 6.65 Turbidity (NTU) 0.29 0.23 0.20 Color (mg Pt/L) 49 13 11 UV254 0.27 0.12 0.10 DOC (mg C/L) 6.73 5.49 4.98 Alkalinity (mmol/L) 0.52 0.43 0.42 HPC (plate count/mL) 103 negligible 106

Table 6 Water quality of water in OBM process 15.03.07.-15.5.07 (average values) (measurements done as part of Techneau WP2.2 by NTNU).


3 Selection of target enzyme activity

3.1 Introduction The metabolic activity of a biofilm determines the rates of substrate conversion and biofilm growth. Metabolic activity parameters e.g. ATP, oxygen uptake and enzyme activity, can therefore provide a (indirect) measure of active biomass. ATP measurement is based on the luciferine-luciferase assay of sacrificial samples, measuring light production after exposing the microorganisms to a nucleotide releasing reagent. Oxygen uptake can be used for measuring aerobic microbial activity, and the sensitivity is determined by the oxygen measurement technique (e.g. oxygen electrode, chemical method). Enzyme activity measurement represents an alternative method, which can be very sensitive depending on detection technique (see 3.3). In this project we are aiming at developing sensitive enzyme assays for measuring biomass activity on the membrane. The assays can potentially be used as an early warning system of membrane biofouling. Pre-permeabilisation of cells is not required when non-intracellular enzymes are used as target enzymes. Such enzymes are therefore selected for monitoring of biofoulant activity in this project. Three terms are commonly used for the enzymes involved in the transformation and degradation of polymeric substrates outside the cell membrane. They are: ecto-enzymes, extracellular enzymes and exoenzymes (Chróst 1991). In this report exo-enzymes will be used as a common term for enzymes that are acting close to/in contact with the cell surface, or in the periplasmic space outside the cell membrane. The majority (>95%) organic matter in aquatic environments is composed of polymeric, high-molecular-weight compounds (Chróst 1991). In order for polymeric organic substances to be available for bacterial metabolism they have to be transformed to smaller molecules, involving e.g. exo-enzyme catalysed hydrolysis.

3.2 Selection of target enzymes Hydrolytic enzymes, i.e. hydrolases, catalyse the hydrolytic cleavage of e.g. C-O (in esters and glycosides) and C-N (proteins and peptides) bonds. Hydrolases are divided into groups according to their ability to act on the different bonds, e.g. esterases acting on ester bonds, and peptidases acting on peptide bonds (NCIUBM 2006).

Enzyme activities of esterases for general metabolic activities, β-glucosidase for carbohydrate metabolism and alanine-amino-peptidase for protein metabolism were measured to physiologically characterise biofilms in surface water and drinking water treatment steps (Emtiazi et al. 2004). In all biofilm


samples, activity of alanine-amino-peptidase was much lower than of esterase, and glucosidase activity was very low, indicating a high general cell and protein but a low carbohydrate metabolism. We have selected to analyse esterase and peptidase activity for monitoring of membrane biofoulant activity.

3.3 Selection of enzyme substrate Spectroscopic detection of fluorescent emission is magnitudes more sensitive than measurement of UV or visible light transmission. Useful substrates will produce fluorophores that emit at significantly different wave lengths than the preceding fluorogen contained in the substrates. Depending on the acidity of the fluorophore, high pH values may be needed to achieve sensitivity. This may require that an alkaline buffer is added before fluorescence is measured which compromise use in continuous or live cell assays (Haugland 2006).

3.3.1 Esterase substrate Fluorescein diacetate (FDA) that forms fluorescein, was on of the first probes to be used as a fluorescent indicator of cell viability based on intracellular esterase activity. Another substrate for intracellular nonspecific esterases, Carboxyfluorescein Diacetate (CFDA) forms carboxyfluorescein. As compared with fluorescein, carboxyfluorescein contains extra negative charges and is therefore better retained in cells than FDA. CFDA is moderately permeant to most cell membranes (Haugland 2006) and permeabilisation of Gram-negative bacterial suspensions is done to increase permeability of the cell membranes for the esterase substrates (Diaper and Edwards 1994). Permeabilisation should be avoided when esterase activity measurement are performed as in-situ measurements of biofilms or membrane biofoulants. Methylumbelliferyl hepatonate (MUH) (Figure 7) has previously been used as substrate in enzyme assays analysing the exo-lipase/esterase activity of suspended heterotrophic bacteria in environmental waters (e.g. Hoppe 1983). MUH MU

The fluorophore MU (Figure 7) is strongly dependent on pH and is not fully fluorescent unless pH of the assay medium is raised above 10. However, esterase activity of natural waters, with low concentration of culturable bacteria, i.e. 103 colony forming units/mL, can be detected also at an assay pH 8 (Tryland and Fiksdal 1998). A modification of this technique (i.e. sample preparation) is used for monitoring biofoulant esterase activity in the project.

Figure 7 Cleavage of 4-methylumbelliferyl hepatnoate (MUH) into fluorescent 4-methylumbelliferone (MU)

H3C(H2C)5OH ++ H2O

H3C(H2C)5OH ++ H2O


3.3.2 Peptidase substrate The measurement of exoproteolytic activity using L-leucine-beta-naphtylamide (LLBN) (Figure 8) as substrate in enzyme assays to assess biomass in bacterial suspensions (Billen 1991, Laurent and Servais 1995) and in biofilms in situ (Butterfield et al. 2002) has been reported. A modification of this method (i.e. sample preparation) is used for monitoring biofoulant protease activity with LLBN as substrate. LLBN NA

Figure 8 Cleavage of L-leucine-beta-nephtylamide (LLBN) into fluorescing 2-Naphtylamine (NA)

OH +

+ H2O


4 Enzyme activity assay of membrane biofoulant: Protocol development

Two procedures for enzyme activity measurement have been used: • Membranes with biofoulants in phosphor buffered saline (PBS)

solution (see 4.3.1) • Suspension of biofoulant in PBS (see 4.3.2)

The first procedure allows analysis of intact biofoulant on the membrane, but is labour consuming. The number of samples that can be analysed in one experimental run is limited. This procedure was used for measuring the effect of temperature on enzyme activity. The second procedure requires removal of the biofoulant into suspension, but allows the use of plate counting techniques. Higher numbers of samples can be analysed in one experimental run. This procedure was used for measuring the effect of substrate concentration on enzyme activity. It will be the preferred procedure in the next project period.

4.1 Collection of membrane samples Membrane samples were collected from the test unit as described in section 12.4 and transferred into PBS.

4.2 Biofoulant removal by ultrasonic treatment For optimising substrate concentration and investigation of the effect of assay temperature, the biofoulant had to be transferred into a suspension. Hollow fibre membrane samples (ca. 2 cm) were removed from the test cell and introduced into a sterile glass tube containing 10 mL sterile phosphate buffered saline (PBS) (0.01 M, pH 7.2). The glass tubes were placed in ice and sonicated (ELMA Transsonic, 35kHz). Aliquots of the suspension were withdrawn from the glass tube after 1, 1.5, 2, 2.5 and 3 min sonication, respectively, followed by vortex mixing. The heterotrophic plate count (HPC) of the sonicated samples was then determined on R2A agar (7days, 20 oC). Initial results of the effect of sonication time are shown in Figure 9.

0,0E+00

5,0E+06

1,0E+07

1,5E+07

2,0E+07

2,5E+07

0 30 60 90 120 150 180 210sonication time (sec)

HPC

(cfu

/mL)

Figure 9 Effect of sonication time on removal of biofoulant from hollow fibre membrane (PES) exposed 28 days in drinking water


4.3 Enzyme activity assays

4.3.1 Membranes with biofoulant in PBS A membrane sample was transferred into a 200 mL flask containing 10 mL phosphate buffered saline (PBS) (0.01 M, pH 8.0) and supplemented with 20 μL of 4-methylumbelliferyl heptanoate (MUH) (Sigma Chemical Co.) solution (0.01 g MUH per mL acetone) or 200 μL of L-leucine-beta-naphtylamide (LLBN) (Sigma Chemical Co.) solution (0.01 g LLBN per mL H2O) to a final concentration of 0.01 mg MUH per mL PBS (see 4.5) or 0.2 mg LLBN per mL PBS (see 4.5), respectively. The flasks were incubated in a shaking water bath at different temperatures (see 4.5) and the fluorescence intensities of sample aliquots were measured every 5 min for 25 min with a Perkin Elmer 3000 fluorescence spectrometer with excitation at 365 nm and emission at 440 nm (MUHase activity) or with excitation at 340 nm and emission at 410 nm (LLBNase activity). Enzymatic activity, determined by a least-squares linear regression, was calculated by determining the amount of 4-methylumbelliferone (MU) or 2-beta-naphtylamine (NA) released per minute per litre. Activity per cm2 membrane surface was calculated as μmol of MU or NA released per minute, per cm2. Two replicates were analysed. A calibration of the fluorescence response to the amount of MU or NA was performed by preparing standards with known concentrations of MU and NA (Sigma), respectively, in PBS (0.01 M, pH 8.0) and determining the fluorescence response for each known concentration.

4.3.2 Suspension of biofoulant in PBS Membrane biofoulant was transferred into PBS by ultrasonic treatment (3 min) (see 4.2) and 250 μL suspension was transferred into wells of a 144 well plate. MUH or LLBN substrate solutions were added to each well to varying final substrate concentrations (see 4.4). Fluorescence reading was performed at 21oC for 10 -20 min by a plate reader (PerkinElmer Victor 1420 Multilabel counter). Enzymatic activity, determined by a least-squares linear regression, was calculated by determining the amount of 4-methylumbelliferone (MU) or 2-beta-naphtylamine (NA) released per minute per litre. Activity per cm2 membrane surface was calculated as μmol of MU or NA released per minute, per cm2. 6-12 replicate wells were analysed. A calibration of the fluorescence response to the amount of MU or NA was performed by preparing standards with known concentrations of MU and NA (Sigma), respectively, in PBS (0.01 M, pH 8.0) and determining the fluorescence response for each known concentration.

4.4 Substrate concentration Optimisation of substrate concentration was done to determine optimum concentrations for the biofoulant enzyme activity assays. Biofoulant suspension was exposed to different substrate concentrations, and maximum


values for enzyme activities were obtained for substrate concentrations ≥ 0.02 mg MUH/L (Figure 10) and ≥ 0.2 mg LLBN/mL (Figure 11).

0,0000

0,0005

0,0010

0,0015

0,0020

0,0025

0 0,01 0,02 0,03 0,04 0,05 0,06

Substrate concentration (mg MU/mL)

umol

MU

min

-1, c

m-2

27 days

Similar results for the effect of LLBN concentration have been reported previously for sea water samples (Somville and Billen 1983). Concentrations of 0.02 mg MUH /mL (final solution) and 0.2 mg LLBN/mL (final solution) will be used for the protocol in the next project period.

4.5 Temperature

Enzymes have a temperature range in which a maximal rate of reaction is achieved. In this project an investigation of the effect of the assay temperature was done for two reasons: 1) improving sensitivity of laboratory assays and 2) to evaluate temperature effects during potential at-line enzyme activity monitoring at ambient temperature.

Hollow fibre samples with biofoulant were collected from the same bundle in the test unit (Figure 3) and transferred to flasks with PBS (4.1). Flasks were incubated at three different temperatures, 10, 15, and 22 oC. After addition of

Figure 10 Effect of substrate concentration on biofoulant MUHase activity. Samples were collected 27 days after start of membrane filtration.

Figure 11 Effect of substrate concentration on biofoulant LLBNase activity. Samples were collected 5 and 7 days after start of membrane filtration.

0,0000

0,0005

0,0010

0,0015

0,0020

0,0025

0,0 0,2 0,4 0,6 0,8 1,0 1,2Substrate concentration (mg LLBN/mL)

umol

NA

min

-1,c

m-2

5 days

7 days


substrate to final concentrations of 0,01 mg MUH/mL or 0,2 mg LLBN/mL, the enzyme activity assay was performed as described in section 4.3.1. A linear increase of enzyme activities was observed when the temperature increased, examples are shown in Figure 12 (MUHase) and Figure 13 (LLBNase). An assay temperature of 22oC will be used for the final protocol of laboratory assays. The temperature effect on MUHase activity was investigated in two separate experiments using membrane biofoulants of age 116 d and 117 d, respectively. The observed activity increases were 1* 10-4 μmol MU min-1, cm-

2 oC-1 (116 d) and 0.6 * 10-4 μmol MU min-1, cm-2 oC-1 (117 d), respectively. The temperature effect on LLBNase activity was investigated in three separate experiments using membrane biofoulants of age 104d, 109d and 124 d, respectively. The observed activity increases were 0.9 * 10-4 μmol NA min-1, cm-2 oC-1 (116 d), 0.3 * 10-4 μmol NA min-1, cm-2 oC-1 (109d) and 0.2* 10-4 μmol NA min-1, cm-2 oC-1 (124 d). Not only biofoulant age, but also location of the membrane samples was different in each series of temperature experiments. Further investigations are necessary to confirm the specific temperature coefficients for the enzyme activities, taking into account both biofoulant age and location of hollow fibre membranes in the test unit.

0,0000

0,0005

0,0010

0,0015

0,0020

0,0025

0,0030

0,0035

0 5 10 15 20 25

Temperature (oC)

umol

MU

min

-1, c

m-2

Figure 12 Effect of temperature on biofoulant MUHase activity (biofoulant age 116d)


0,0000

0,0005

0,0010

0,0015

0,0020

0,0025

0,0030

0,0035

0 5 10 15 20 25

Temperature (oC)um

ol N

A m

in-1

, cm

-2

Figure 13 Effect of temperature on LLBNase activity (biofoulant age 104d)


5 Staining of biofouling components

5.1 Introduction Characterization of the biofilm components by microbiological and chemical disruptive methods after removal from supports (see Table 1) provide knowledge of the quantity of e.g. cells, polysaccharide and protein of the biofoulant, but not information about the physical properties (e.g. porosity, location of different components) of the intact biofilm. Such information can be important for understanding the effect of different process configurations and operating conditions on development of biofouling of membranes, and water production. A nondisruptive method for studying a biofilm on a sacrificial support, is fluorescence microscopy. After incubation with specific fluorescent dyes, the spatial distribution of the different components and the overall structure or morphology of the biofilm can be studied in a confocal scanning laser microscope (CLSM). Quantfication of the components and computations of parameters characterizing biofilm structure (e.g. biofilm thickness, EPS composition and structure) is possible using appropriate image analysis programs.

5.2 Cell staining A large number of dyes can be used to stain different intra- and extracellular components (www.invitrogen.com). We have used the nucleic acid stains shown in Table 7 (concentrations and staining times used are shown chapter 9: Table 16, Table 17 and Table 18). Acridine orange (MW 301.82) is useful for cell cycle studies, because it emits green fluorescence when bound to dsDNA (by intercalation) and red fluorescence when bound to ddDNA or RNA (by electrostatic attraction). Acridine Orange is not suitable for multiple staining. Hoechst 33342 (MW 615.99) is very sensitive to DNA conformation and chromatin state in cells, and is useful for counterstaining, apoptosis and cellcycle studies. The dye binds to all nucleic acids (by intercalation), but has preference for AT-rich regions of dsDNA. Propidium iodide (MW 668.4) is excluded from intact cell membranes and is therefore a dead cell stain. Solid PI can be dissolved in deionized water. The stain we have used is one of the components of the LIVE/DEAD BacLight Bacterial Viability Kit L7012. The other component of this kit is SYTO 9 (MW ~400) which stains both live and dead bacteria. According to the protocol of the Bacterial Viability Kit, a 1:1 mixture of the two components is added to the bacteria. The proportions can be adjusted for optimal discrimination. Syto 9 will then penetrate all bacteria, whereas Propidium iodide penetrates bacteria with damaged


membranes. Bacteria with intact membranes stain fluorescent green and those with damaged membranes stain both green and red (yellow). Syto 59 (MW ~550) is a red fluorescent cell-permeant dye.

Dye name Cat# Ex/Em1

(nm) Applications

Conditions for staining2

Acridine orange (AO)

A1301 A3568

500/526 (DNA) 460/650 (RNA

Permeant3 RNA/DNA discrimination measurements

max 0.1 mg/ml 20 minutes

Hoeschst 33342 H1399 350/461

Permeant3 dsDNA selective AT-selective Chromosome and nuclear counterstain

0.1 – 12 μg/ml, pH 7.4, 10-30 minutes

Propidium iodide (PI)

L-7012 535/617 490/6354

Impermeant3 Dead cell stain Chromosome and nuclear counterstain

30 μM 15 minutes

SYTO 9 L-7012

485/498 (DNA) 486/501 (RNA)

Binds to DNA and RNA 50 nM – 20 μM 1-30 minutes

SYTO 59 S-11341 622/645 Binds to DNA and RNA 50 nM – 20 μM 1-30 minutes

1: Ex = excitation maxima and Em = emission maxima 2: recommendations for bacteria 3: permeant or impermeant to live cells 4: as a component of the LIVE/DEAD BacLight Bacterial Viability Kit L7012

5.3 Polysaccaride staining

5.3.1 Stain types, specificity and concentrations used A variety of different polysaccharides are located on the surface of the bacteria, inside the cells and in the extracellular matrix. Sugar molecules are also found attached to proteins and lipids (glycoproteins and glycolipids). Lectins are oligosaccharide-binding proteins found in plants and animals that may cause cell agglutination and precipitation of glycoconjugates. In animals, lectins are involved in regulation of cell adhesion, control of protein levels in blood, glycoprotein synthesis and immune system responses. In biofilm studies, lectins are used to stain specific oligosaccharides (see Table 8). Both the type of flourochrome attached to the lectin and the presence of

Table 7 Properties of some nucleic acid stains (from Haugland 2006)


carbohydrates in the biofilm that inhibit or enhance binding will affect the specificity of a lectin (Neu et al. 2001). Typically, concentrations of lectins in staining solutions for biofilms range from 5 μg/ml to 0.1 mg/ml or 10 -100 μg/cm2 membrane (e.g. Staudt et al. 2004, Neu et al. 2001). Increasing the lectin concentration will increase the flourescence intensity (Neu et al. 2001). Information about the lectins we have investigated is shown in Table 9 and Table 10.

Specificity Lectin (species) Abbrev. Glucose

Mannose Canavalia ensiformis (Concanavalin A or Jack Bean)

Con A

Galactose Arachis hypogaea (Peanut) PNA Fucose Aleuria Aurantia,

Ulex Europaeus (Gorse) AAL UEA I

N-Acetyl-Galactosamin

Glycine max (Soybean) Helix pomatia (Roman snail) Dolichos Biflorus (Horse Gram) Ricinus Communis (Castor bean)

SBA HPA DBA RCA120

N-Acetyl-Glucosamine

Griffonia simplicifolia, Triticum Vulgaris (Wheat Germ Agglutinin) Solanum Tuberosum (Potato)

GS II/GSL II WGA STL/PL

Sialic Acid Triticum Vulgaris (Wheat Germ Agglutinin) WGA Comlex Structures Phaseolus vulgaris (Red kidney bean) PHA-L

Lectin MW

(kDa) Cat# (I1) Ex/Em (nm) Cat#(V2) Ex/Em (nm)

Con A 52 or 1043 C 860 555/580

PNA 110 L 32460 L 21409

495/519 650/668

UEA 1 63 SBA 120 L 11272 495/519 DBA 120 WGA 38 W 11261 495/519

Rhodamine Lectin Kit 1 RLK-2200

550/575

1: supplier: Invitrogen/Molecular Probes 2: supplier: Vector Laboratories 3: above pH 7: 104 kDa (from dimer to tetramer form)

Table 8 Specificities of some lectins typically used in biofilm studies((www.sigmaaldrich.com, www.invitrogen.com )

Table 9 Lectins from Invitrogen and Vector Laboratories


Lectin Stock solution1 Working concentrations2

ConA 2 mg/ml in 0.1 M sodium bicarbonate pH 8.3

50-200 μg/ml (immunohisto-chemical applications)

PNA 1 mg/ml in deionized water

SBA 2 mg/ml in deionized water 1-10 mg/ml (staining of glycoproteins in gels)

WGA 1 mg/ml in PBS pH 7.4 1-10 μg/ml

Lectins from Vector

2 mg/ml in10 mM HEPES, 0.15 M NaCl, 0.1 mM Ca2+, 0.08 % sodium azide, pH 7.5

2- 50 μg/ml

1: aliquots stored at - 20 ºC, lectins from Vector stored at - 4 ºC. 2: recommended by the supplier

5.3.2 Cell bound and not cell bound polysaccharides Some of the polysaccharides secreted by the bacteria are tightly bound to the cell membrane. Also, some lectins are able to enter the cells. Hence, stained cells and lectins may appear co-localized in the confocal microscope. Lectins bound to polysaccharides that are not densely packed and not bound to the cells, appear as diffuse clouds. Such clouds are therefore EPS. The cell wall of gram-positive bacteria is a network of crosslinked peptidoglycans. The peptidoglycan polymer is composed of an alternating sequence of N-acetylglucosamine and N-acetyl-muraminic acid. Lectins with specificity for these sugars could therefore bind to the cell wall. The cell wall of gram-negative bacteria consists of a phospholipid bilayer, lipoproteins and polysaccharides facing outwards. Lectins may bind to these polysaccharides.

5.3.3 Multiple staining The biofilm can be stained with conjugated lectins having different absorbance and emission maxima. The lasers and the filter settings of the confocal microscope must be considered before selecting the fluorochromes to be used. For multiple staining, successive staining is often used. The order of stain addition can influence the results (Neu et al. 2001).

5.4 Lipid staining For staining of lipids in the biofoulant matrix, plasma membranes and intracellular membranes, several fluorescent analogs of natural lipids and lipophilic organic dyes are used (www.Invitrogen.com Three potential probes for lipid detection are listed in Table 11. We have used D-3823 (Invitrogen), as a general lipid probe.

5.5 Protein staining Proteins are found in the biofoulant matrix, inside the cells and in the flagellas and fimbriae, which are extracellular bacterial cell structures. In this project, the main focus will be on imaging of biofoulant cells and

Table 10 Lectin storage and conditions for staining


polysaccharides. However, some analysis of biofoulant protein by staining and CLSM will be considered for the last project period.

Lipid probes Cat# 1 Ex/Em (nm)

Stock solution Working concentrations

Fatty acid D-3823 508/514 2 mg/ml (5 mM) in DMSO ~ 250 μM

Phospholipid O-12650 501/526 2 mg/ml in chloroform -

Glycerophosphocholine D-3793 509/513 1 mg/ml in chloroform -

1: supplier: Invitrogen/Molecular Probes

Table 11 Lipid probes


6 THE CLSM MICROSCOPE

6.1 Principle of confocal imaging In conventional fluorescent and darkfield microscopy, objects that are out of focus produce unwanted light that is collected by the objective. Therefore, the depth of field (the depth of the image that appears to be sharply in focus) is greater than the axial resolution. The axial resolution z depends on the wavelength λ of the light source, the numerical aperture NAobj of the objective and the refractive index nobj of the object medium (Eq. 1) z = 2 λ nobj/(NAobj)2 (1) The lateral resolution r depends on λ and NAobj (Eq. 2) r = 0.61 λ /NAobj (2) In addition, the lateral resolution increases as the field of view increases. In confocal imaging, out-of-focus light from points other than the focal point does not enter the detector because it is rejected by the confocal pinhole (Figure 14). The focal plane is moved by moving the stage with a stepping motor in the vertical z-direction (z-motor). The z-interval can be specified by the operator. When a laser is used as light source, the specimen can be point-illuminated. The position of the spot in the horizontal xy-plane is moved by scanning mirrors. The detector, a photomultiplier tube (PMT), collects emitted or reflected photons from the focal plane during scanning and converts them into to electrons. An analog to digital converter (ADC) samples the voltage for some nanoseconds per position in the scanned area. The voltage is turned into a digital number or a gray level. The gray levels in the digitized image should range from 0 to 255. A pixel is an image element. The pixel’s coordinates (x,y) corresponds to a certain position in the scanned area and its gray value contains information about the intensity of light emitted from this position. In other words, information from the scanned area is mapped onto a digitized image that is made up of thousands or millions of pixels, arranged in rows and columns. The number of bits used to represent each pixel’s gray value determines how many colors or shades of gray that can be displayed. For example, in 8-bit color mode, the color monitor uses 8 bits for each pixel, making it possible to display 28 = 256 different colors or shades of gray. On color monitors, each pixel is actually composed of three dots: a red, a blue, and a green one.


The combination of dichroic mirrors and emission filters used defines the fluorescence beam path. The HFT-mirror separates excitation and emission light, the NFT-mirror effect spectral division of the emitted light. The emission filter determines the bandwidth of fluorescent light for the respective channel or PMT: BP = band pass, KP = short pass, LP = long pass. When collecting images of a volume (3D), the focal plane is shifted vertically after each image acquisition, from user defined top to bottom z-positions. The result is a stack of digital images. The operator can adjust image quality, resolution and scanning speed by changing some of the settings as described in the following: The objective lens used determines image quality, such as resolution. According to Eq. 1 and 2, both lateral and axial resolution is improved by choosing an objective with a high numerical aperture. More details in the image are seen if the frame size (number of pixels per line + line per frame) is increased or the scan zoom function is used. Optimum z-interval should be 0.5 x optical slice thickness (z in Eq. 1).

Figure 14 Schematic of laser-scanning confocal microscope (from www.zeiss.de)


The intensity registered or the brightness of the image can be adjusted by the parameters Detector Gain (PMT high voltage), Amplifier Offset (black level setting) and Amplifier Gain (electronic post-amplification). Ideally, the adjustments should result in least number of both undermodulated pixels (gray value = 0) and overmodulated pixels (gray value = 255). Increasing the laser effect or power and increasing photon sampling time per pixel (pixel time) increases the signal. However, photobleaching increases. Averaging recorded signals improves the image quality and reduces noise. The scanning speed (frames/sec) depends on frame size (number of pixels) and pixel time. Both parameters are adjustable. The image becomes brighter if the confocal pinhole is made larger, but this is mainly due to contribution from out-of-focus planes and causes a decrease in the z-resolution. An optimal pinhole is 1 Airy unit. To summarize, the advantages of confocal microscopy over conventional microscopy is higher resolution, better contrast and precise spatial data.

6.2 The confocal laser scanning microscope – LSM 510 META (Zeiss) Three different laser scanning microscopes have been used, all LSM 510 META from Carl Zeiss (Figure 15) (www.zeiss.de), and located at the department of physics, the Laboratory center, and department of cancer research and molecular biology (MTFS), respectively, at NTNU. This microscope is inverted and motorized (see specifications in Appendix 12.5).

Figure 15 LSM 510 Meta from Carl Zeiss


6.3 The LSM 510 Software The LSM 510 software (version 2.5) allows the user to configure and control the laser scanning microscope. Its main menu is shown in Figure 16. All microscope parameters can be stored, ensuring repeatability from sample to sample. The software controls the z-drive of the motorized stage and the laser modules, and synchronizes the z-steps and the image acquisitions. A z-stack of images is generated and stored. The software offers tools for image processing and analysis, interactive and automatic measurements. The measurement data can be stored and exported to spread-sheet programs, such as Excel.

The sample handling, the configuration of the microscope and the image acquisition will be described in the next chapter. It is important to follow the instructions in chapter 7.2, to be able to analyze the images with the software described in chapter 8.

Figure 16 The main window of LSM 510 Expert Mode


7 CLSM microscopy

In CLSM of biofoulants on curved membrane surfaces the images are recorded in stacks at different levels of z (vertical distance from a horisontal reference level). To obtain reliable results along a membrane sample, it is important that samples are exactly aligned parallel to the horizontal plane.

7.1 Membrane sample support for CLSM microscopy The laser scanning microscopes used are inverted, and it was therefore initially decided to mount the membranes in a Lab-Tek Chambered Coverglass from Nunc (4 and 8 chambers) during CLSM inspection. The membranes were covered with filter-sterilized (0.2 μm filter pore size) water from the test unit chamber. It became apparent that two problems had to be solved (Figure 17):

• The biofoulant was squeezed towards the bottom of the chamber • The membrane was tilted due to air bubbles in the lumen

After several unsuccessful attempts to solve the problems, we finally managed to make stable supports for the membrane (Figure 18). Two small rectangular strips (spacers) of transparency film are fixed to the bottom of the chamber with glue (Loctite 3430 A&B). The thickness of the film used is ca. 80 μm. A small droplet of glue is applied. The droplet is squeezed between the bottom of the chamber and the spacer. The stained membrane is fixed on top of the spacers with water resistant tape and the tilting is minimized. Finally, the chamber is filled with filter-sterilized water and positioned on the microscope stage. The working distance of the objective used must be taken into account when choosing transparency film or spacers. The gap must not exceed the working distance.

Figure 17 A – Squeezing of biofilm towards bottom of chamber; B – tilting of membrane due to air bubbles in lumen

membrane

lumen air bubbles A B

squeezing of biofilm


7.2 Image acquisition Flourescent biofilm components, for example cells, are visualized as shown in Figure 19. The stained component is excited with the laser light (wavelength λex). The emitted light (wavelength λem) from the components in the focal plane is detected.

Before starting scanning and image acquisition, the optics must to be configured in the LSM 510 software to match the properties of the flourophore. The first step is to switch on the laser with a lasing wavelength close to the absorption maximum. This is done by activating the Laser button in the main menu and opening the Laser Control Window (Figure 20). The next step is to choose objective in the Microscope Control window (Figure 21). The intensity in the image is a function of NAobj and the magnification M, I = NAobj4/M2, so the best choice is an objective with low M

Figure 18 Support (transparency strips) for membrane in chamber

Figure 19 Excitation and detections of cells in a biofilm, located in the focal plane

Excitation light (laser, λex)

Emitted light (fluorescense, λem)

objective

glue

biofilm

gap: < 100 um

transparency strips

Nunc LabTek chambered coverglass


and high NAobj. However, a high NAobj mean shorter working distance. We use a 40x or 63x objective, water or oil immersion.

Figure 20 Laser Control window in the LSM 510 software

Figure 21 Microscope Control window in the LSM 510 software


The next step is to set up a track for the laser light to the specimen, and for the emitted light to one of the three detectors named Ch2, Ch3 and ChS (the META channel) by activating the Config button in the main menu (Figure 22). The absorption and emission spectra of the fluorophores must be taken into account. An existing track can be applied by activation of the Store/Apply Single Track button. The specimen is now ready for scanning.

Figure 22 Configuration Control-Channel/Multi Track window in the LSM 510 software. The excitation light is focused into the specimen and the emitted light is focused into a pinhole in front of a detector (NFT: secondary beamsplitter, (HFT: main beamsplitter, BP: band pass filter, KP: short pass filter)


In our case, an additional track for scanning of the membranes must be configured. The laser used should not excite one of the fluorophores used. To improve the image quality, a filter that allows some reflected laser light to enter the detector is chosen. The scanning will reveal if some filter adjustments are necessary. Activation of the Scan button in the main menu opens the Scan Control window. The Mode and Frame buttons are selected in the toolbar (Figure 23).

Set image size (512 x 512 pixels), scan speed (1.60 μs/pixel), pixel depth (8 bit) and scan direction (→).

Figure 23 The Scan Control-Mode/Frame window in the LSM 510 software


Image quality is optimized by pressing the Channels button in the Scan Control toolbar (Figure 24), choose Channels in the Channel Setting panel, set pinhole to 1 Airy Units and start fast scanning by pressing the Fast XY button. Adjust Detector Gain (image contrast and brightness) and Amplifier Offset (bleck level). This should be done at the z-position where the emitted signal is most intense. Move the stage up and down manually to find the position.

This procedure is repeated for each channel listed in the Channel Settings panel.

Figure 24 The Scan Control-Channels/Frame window in the LSM 510 software


The scanned area is rotated to align the membrane horizontally in the image. The Rotation slider is moved in the Zoom, Rotation & Offset panel in the Scan Control-Mode window (Figure 23). The Z-setting button in the Scan Control Panel is activated (Figure 25) and while scanning the specimen (use the track for one of the fluorophores and press the Fast XY button), the stage is moved down and the first z-position is selected(Press Mark First/Last button, select Mark First). Then, the stage is moved up and the last z-position is selected (Mark Last). The scanning is then stopped and number of slices in the stack is chosen (Num Slices slider) or dz (Interval slider). The Z Slice button is activated to check the optimum dz (dependent on objective and pinhole), and a dz is chosen as close to this value as possible. If interpolation is necessary (dz > dxy) make sure that dz/dxy = k, where k = 1, 2, 3…. The Reff. Corr slider is moved to select the correct fraction n/n’, where n is the refractive index of water and n’ is the refractive index of the immersion media. Multi tracking is started, i.e. scanning of the stained biofilm and the membrane from first to last z-position. The resulting stacks are stored.


It is important that the z-positions of the last frame in the biofilm stack and the membrane stack coincide. This is best achieved with multi tracking and identical z-settings for all tracks. Figure 26 shows an example of a system configuration for multi tracking of membrane and biofilm signals, recorded 11.05. 2007.

Figure 25 The Scan Control- Z setting/Z stack/Frame window in the LSM 510 software


In this example (Figure 26) the objective used was a water immersion objective, magnification 40x. The number of frames in the stack was 29, and dz = 0.90 μm, dxy = 0.45 μm, dx/dxy = 2. The membrane was excited with the HeNe laser (λex = 543 nm) and the reflected light and some autofluorescence was detected by Channel 3 (band pass filter 535-590 nm in front of the detector). The biofilm was stained with WGA and excited with the Argon laser (λex = 488 nm). Fluorescence was detected by Channel 2 (band pass filter 50-530 nm in front of the detector). The two Z stacks and the settings were stored under the name Mem_WGA.md The merged frames at each z-position are shown in Figure 27. Such stacks of images can be analyzed with the software described in the next chapter. Before the images can be analyzed they have to be converted to tif-format. (Activate the File button in the main menu Figure 16, and then export the files as type .tif). If the file name is “FileName”, the images are stored as FileName0.tif (top of biofilm), FileName1.tif, FileName2.tif , etc.

Figure 26 Setup/configuration of instrument for multi-tracking of biofilm and membrane signals


Figure 27 Multi-tracking of signals from the membrane (green) and the WGA stained polysaccharides (red), all merged frames are shown. 29 images, from top left to bottom right. Numbers show z-position of frames. Upper left corner: top of biofilm (z = 0), lower right corner: last image in stack, z = 25.50 μm)


8 Image analysis methods for CLSM

8.1 Introduction to use of image analysis software The suppliers of microscopes (Olympus, Leica, Zeiss, Nikon) offer image analysis programs for image processing and measurements, and there are a number of other software that can be purchased (Google search on: digital image analysis). The basic features of image processing and measurement are described under, using LSM 510 software from Zeiss (described in chapter 6.3 and 7.2) as an example. These features are also included in the image analysis software that is developed (see 8.3) for imaging of biofoulants on curved surfaces. When activating the Process button in the main menu of LSM 510 (Figure 16), the software offers tools for image filtering, contrast and brightness adjustments and arithmetic operations. Median filtering is typically used to “reduce salt and pepper noise” but simultaneously preserve edges in the image. Each output pixel in the filtered image is the median value of the neighborhood around the corresponding pixel in the input image. The size k of the square neighborhood [k x k] is an input parameter to the function. Increasing k causes a more pronounced smoothing. Other filtering options are lowpass Gaussian filtering and the average filtering. The intensity profile along a line or path can be calculated by activating the Profile button in the menu to the right of the image in Figure 28. The image is slice 25 in the gallery shown in Figure 27 (corresponding microscope configuration is shown in Figure 26). The image histogram can be studied by pressing the Histo button (Figure 28). The histogram can be used to set threshold values. The software can calculate the position of a surface in two different ways, using the Topography module (Figure 28):

• Maximum: for a given x and y position, the z coordinate of the surface is calculated from the slice with the maximum intensity.

• Center: for a given x and y position, the z coordinate of the surface is the center of gravity of all summed intensities.

In addition, it is possible to smooth the surface using median, lowpass Gaussian or average filtering. The XYZ (3D) matrix containing all surface coordinates (x,y,z) can be saved and exported to e.g. Excel. Using the 3D functions (Figure 28), it is possible to perform morphological operations in three dimensions (erode, dilate, open and close) to create regions or objects. Interactive and automatic measurements as well as labelling can be carried out on the regions. These calculations require binary or gray level segmentation (thresholding). The features are listed in Table 12.


Object Features Densitometric Features Volume Features Volume Filled volume Area Filled area Sphere form factor

Min and Max gray value Mean gray value (+ standard deviation)

Number of regions measured Total volume of all regions Total surface area of all regions Min and Max gray values Mean gray value of all regions (+ standard deviation)

An activation of the Topography and the 3D modules of the LSM 510 software require additional licence and is expensive. Furthermore, we require

Figure 28 Intensity profiles along the red line in the image. Dark red line: intensity of WGA signals. Green line: intensity of membrane signals.

Table 12 Parameters calculated using the 3D module of the LSM 510 software


a tailor-made solution for our application, which includes both topography calculations of the membrane and biofoulant structure analysis, using the curved membrane as a reference surface. This is not possible using LSM 510 software. We therefore decided to develop a software (see 8.3), which is more suited for analyzing images of biofoulant on curved surfaces.

8.2 Image analysis of biofilms

8.2.1 Software A number of different software packages have been designed to analyze digital biofilm images. Kuehn et al. (1998) calculated the biovolume of biofilms. The software COMSTAT (Heydorn et al. 2000) was developed for calculation of several parameters including biovolume, but requires manual thresholding. The COMSTATE parameters include:

• Biovolume • Surface area coverage in each layer • Biofilm thickness distribution • Average biofilm thickness • Volumes of micro-colonies identified at the substratum • The fractal dimension of each microcolony identified at the

substratum • Roughness coefficient • Distributions of diffusion distance • Maximum diffusion distance • Surface to volume ration (from a 3D stack of images)

Software developed by Xavier et al. (2003) has the advantage of automatic thresholding of the images, but only four parameters are calculated:

• Biovolume • Substratum coverage • Average height of microcolonies • Interfacial area

ISA (Image Structure Analyzer) is a program developed by the Biofilm Structure and Function Research Group, Center for Biofilm Engineering at Montana State University (Yang et al. 2000; Beyenal et al. 2004a ; Beyenal et al. 2004b). Parameters and structural features calculated by ISA are listed in Table 13.


Areal parameters Volumetric parameters Textural parameters

Additional Parameters

Areal porosity Average diffusion distance Maximum diffusion distance Perimeter Fractal dimension Average run lengths (X, Y)

Biovolume Biomass volume to surface area Porosity Average diffusion distance Maximum diffusion distance Surface area biomass to void Fractal dimension Average run lengths (X, Y and Z)

Textural entropy Energy Homogeneity

Mean biofilm thickness Maximim biofilm thickness Biofilm roughness Mean biomass thickness Maximim biomass thickness Biofilm roughness

The textural parameters are based on the variation of pixel intensities in the three-dimensional stack of images:

• Textural entropy: a measure of randomness in the gray scale of the image

• Energy: a measure of directionally repeating patterns of pixels • Homogeneity: a measure of the spatially repeating patterns of pixels

8.2.2 Biofilm structure parameters The parameters considered to give a best description of biofilm structures (Heydorn et al. 2000; Heydorn et al. 2002; Battin et al. 2003; Christensen et.al 2002; Martiny et al. 2003) and also used by the ISA software, can be further described:

• Biovolume: the total volume of the biomass in the biofilm • Surface area: the area of the interface between the biomass and the

liquid • Biomass volume to surface area: the ratio Biovolume/Surface area • Porosity: the ratio of void volume in the biofilm to total volume • Mean biofilm and biomass thicknesses (Eq. 3): the sum of all

thicknesses divided by total surface area or surface area covered by biofilm, respectively

NBiomass Thnn = Σ thnn (x,y) (3) 1

Table 13 Biofilm textural and volumetric parameters calculated by ISA

dxy Ann


• The subscript nn is bf for biofilm or bm for biomass, respectively. • The area Abf is the total surface area, used in the computation of the mean biofilm

thickness Thbf . • The area Abm is the surface covered by the biomass, used in the computation of the

mean biomass thickness Thbm. • thbf and thbm are the individual biofilm and biomass thicknesses for each location

(x,y), respectively. • NBiomass is the total number of biomass thickness measurements, • dxy = dz = pixel size. • Maximum biofilm and biomass thickness MaxThbf and MaxThbm : the average of a

fraction of the local maximum biofilm and biomass thicknesses, respectively (the fraction is a variable in the range [0,1] set by the user when running the software)

Biofilm and biomass roughness (Eq. 4): the variation in biofilm and biomass thickness: NBiomass Rnn = Σ |thnn (x,y) – Thnn | (4) 1 Thnn Thbf and the thbf ’s are used to calculate the biofilm roughness Rbf , Thbm and the thbm ’s are used to calculate the biomass roughness Rbm.

8.2.3 ISA reference surface The ISA calculations are based on the assumption that the reference surface (the surface or substratum supporting biofilm growth) is horizontally aligned at the top or the bottom of the biofilm stack (Figure 29). There is no signal from the reference surface in the stack of images.

Figure 29 Biofilm growing on a horizontal reference surface located at z = 0

x

y

z

membrane

stack of images (biofilm)

voids

biofilm clusters

dxy Ann


In our case, however, the reference surface is curved. Some of the parameter calculations done by ISA will therefore be erroneous when applied to biofilms on curved surfaces.

8.3 CMem To solve the problem described above, a Matlab program called CMem (Curved Membrane) was developed, taking the curved surface into account when calculating biofilm parameters. All parameters computed by CMem is listed below:

• Biovolume BV • Area of reference surface (membrane area) A • Biovolume below reference surface BVbelowA • Mean biomass thickness Th • Maximum biomass thickness MaxTh • Biomass roughness R • Porosity P

CMem is specially designed for our application, but shear some of the features of ISA, making it possible to combine the results computed by both programs when describing the biofilm structure. CMem is a graphical user interface (GUI) with several GUI components, as shown in Figure 30. The user must enter some parameters before running the program (Table 14)

Parameter Description

Result File The file name is entered in the box, and the results are written to the this file (.txt)

GL filter Defines the size k of the median or average filter [k x k] applied to the gray level images

BW filter Defines the size k of the median or average filter [k x k] applied to the binary images

dxy (um) The pixels size biofilm dz/dxy

The ratio dz/dxy for the biofilm stack, an integer in the range [1,4]

membrane dz/dxy

The ratio dz/dxy for the membrane stack, an integer in the range [1,4]

thresholding Otsu’s method or a user defined value

pmaxt A fraction in the range [0,1] of the maximum biomass thicknesses

Table 14 Parameters in CMem GUI


A filter size of [3 x 3] is often used as default. If pmaxt is set to 0.05, the maximum biomass thickness calculated is an average of the 5 % of the maximum biomass thicknesses. If there is noise in the biofilm images caused by membrane signals, a user-entered threshold value in the range [0, 1] can be employed to eliminate this noise. Median filters with user-entered filter sizes are applied to the gray level and binary images. ISA offers two thresholding methods: Otsu’s method and iterative selection. We use Otsu’s method for thresholding, the threshold computed minimizes the intraclass variance of the black and white pixels.

8.3.1 The reference surface When the program starts running, the user must enter the membrane images on request. It is not necessary or recommended to load the entire stack, but only the frames with the membrane signal (image 543_Memii12.tif to 543_Memii28.tif in Figure 31, corresponding to the green signal in image 13 to 29 in Figure 27).

Figure 30 The CMem GUI window


The image order is inverted, image number 29 (WGA1128.tif) becomes image number 1, located at z = 1 (pixel). If membrane dz/dxy > 1, frames between the existing frames are calculated by linear interpolation. The stack is converted into a three dimensional matrix. Each matrix element (x,y,z) is assigned the gray value or intensity at the corresponding location in the stack. The membrane surface z(x,y) is computed using the Maximum method described on page 45 (Topography module in the LSM 510 software), The z-coordinate for the absolute maximum in the intensity profile I(z) is determined for all (x,y). The intersection of the membrane with the yz-plane at x = x1 and x = x2 yields cross sections for the membrane surface data series zx1(y) and zx2(y). The surface data for x = 1 and x = 512 (the number of x pixels in an image) is plotted in Figure 32 (left half) together with the second order polynomial (Eq. 5) used to fit or smooth the data, zpoly(y) = p1 y2 + p2 y + p3 (5)

y is ranging from ymin to ymax ,where ymin and ymax are the y positions of the two peaks in the intensity profile, corresponding to the intersections of the membrane surface and z = 1. The two intensity profiles for x = 1 and x = 512 is plotted in Figure 32 (right half).

Figure 31 Dialog box for input of membrane stack


The fitting is performed inside a loop ( Figure 33), and new fittings are performed with shifted x values if the results are not approved. The loop is terminated when the goodness of the fitting is ok. The resulting ymin and ymax for x1 and x2 are used to calculate ymin = a + bx and ymax = c + dx for all x in the range 1 to 512. The constants a, b, c and d for the linear relations are determined from the measured data points (ymin ,x1) and (ymax , x2). The reference (smoothed membrane) surface is shown in Figure 34.

Figure 32 Left) Plots of the z-positions of maximum intensities along y, for the cross sections at x =1 and x = 512, together with the corresponding polynoms. Right) The intensity profiles along y, for x = 1 and x = 512,

Second order polynom

ymin ymax

max I’s


Figure 33 Flowchart for the membrane surface smoothing process

Figure 34 Reference surface: smoothed membrane surface


8.3.2 Processing of the biofilm stack The next user input is the last image in the biofilm stack (Figure 35).

The software automatically reads all the other images. The threshold value for each image is checked using Otsu’s method, because the thresholding fails if there are very few pixels of low intensity in the image. This can be the case for the first images in the stack (top of biofilm). After an elimination of these images, the resulting stack is inverted and interpolated if the biofilm dz/dxy > 1. (Frames between the existing frames are calculated by linear interpolation.) The images are filtered, using a median filter of size [GL Filter x GL Filter] and converted to binary images. The threshold value is either user-entered or computed, using Otsu’s method. The final operation is a median filtering of the binary images, using a filter size of [BW Filter x BW Filter]. Results are displayed in Figure 36.

Figure 35 Dialog box for input of last image in biofilm stack


8.3.3 Calculating biofilm parameters Biovolume BV is the number of biomass pixels, multiplied with the dimension of the voxel (dxdydz) in μm3. Area of reference surface A is the area of the membrane in μm2, located above the xy-plane at z = 1 (Figure 34) Biovolume below reference surface BVbelowA is the number of biomass pixels below the reference surface A local Biomass thickness thbm is calculated in two different ways (Figure 37B): a) thbm is the vertical distance from the biomass pixel to the reference surface. b) thbm is the minimum or Euclidean distance from the biomass pixel to the reference surface.

Figure 36 Filtered gray level image and the corresponding binary image (first and last image in stack, corresponding to image nr 7 and 29 in Figure 27


Figure 37 A) The intersection of an yz-plane (x = 103) and the membrane and biofilm (polysaccharide stained by WGA) stacks. B) Three different ways of calculating the biomass thickness (from slice shown in A). ISA calculates the distance from the biomass pixel to z = 1, CMem calculates distance a) or b)

ISA-distance

b) minimum distance

a) vertical distance

A

B


We assume that the tilting of the membrane is negligible and that the straight line from the biomass pixel (x,y,z) to the reference surface (xRef, yRef, zRef) is in the yz-plane (x = xRef). It is possible, but time consuming, to calculate the real, minimum distance from a biomass pixel to the reference surface (Eq. 6) thbm = min [ (x - xRef)2 + (y - yRef)2 + (z - zRef)2 ]1/2 (6)

where xRef can have any value from 1 to 512. The mean biomass thickness Thbm is the average of all thbm’s (Eq. 3). The maximum biomass thickness MaxThbm is the average of a fraction (pmaxt) of the maximum biomass thicknesses. Eq. 4 is used to calculate the Biomass roughness Rbm. The Porosity is the number of void pixels divided by the total number of pixels in the biofilm, which is void and biomass pixels in a layer or shell with thickness MaxThbm above the reference surface. The porosity value depends on which procedure, a) or b) in Figure 37B, was used to calculate the biomass thickness: a) MaxThbm is the thickness of the layer, measured vertically from the

reference surface at all points (x,y). b) MaxThbm is the thickness of the layer, measured along the normal to the

reference surface at all points (x,y). The results are stored in the file (“FileName”.txt). Image analysis results for a biofoulant (Figure 27) by use of CMem are shown in Table 15. The user can now close the GUI, or continue to analyze other stacks.

Procedure a Procedure b average biofilm thickness (μm) 5.3787 5.2743 max biofilm thickness (μm) 10.5873 10.3932 biofilm roughness 0.3244 0.3265 Porosity 0.6202 0.6019 Membrane area (μm2) 3.330563e+004 3.330563e+004 total biofilm volume (μm3) 1.719530e+005 1.719530e+005 biofilm volume below membrane (μm3) 3.282414e+003 3.282414e+003

Table 15 Image analysis of biofoulant stacks shown in Figure 25, calculated using procedure a) and b) (see Figure 37B).


9 Fluoro-staining and confocal imaging of membrane biofoulant – examples and protocol development

Several operational cycles have been performed in the test unit (Figure 3). Based on results from these experiments, stains and staining conditions to be used for image analysis and quantification of membrane biofoulant components have been selected. Examples of results from quantitative characterisation of biofoulant by use of CMem are presented.

9.1 Operational cycle 1

9.1.1 3 days old biofoulant In an initial operational cycle of the test unit, the biofouling of both cleaned and new membranes were inspected by CLSM, and different stains were used for staining of cells and polysaccharides of the biofoulant (Table 16).

1 staining incubation time: S9 and lectins: 60 min. 2 staining incubation time: S9: 30 min, lectins: 35 min. 3 staining incubation time S59: 30 min, lectins: 45 min. Cells stained with S9 were present on membranes after three days of water filtration, and more cells were observed on a cleaned membrane than on a new membrane (results not shown). Traces of polysaccharide (stained with ConA) could be seen after 3 days on the cleaned membrane (see arrows, Figure 38). Con A is specific for glucose and mannose (see Table 8). No ConA stained spots was observed on the new membrane after three days, and the traces observed on the used membranes were traces of biofoulant that had not been removed by the membrane cleaning. Cell-bound WGA, which is specific for sialic acid and N-acetyl-glucosamine (Table 8) was also observed (results not shown).

Table 16 Staining of new and cleaned membranes and subsequent observations of biofoulant on PES membranes in the confocal microscope

Cell stain (μM)

Polysaccharide stain: Lectin (mg/ml) Biofoulant Age (days)

S59 S9 ConA WGA PNA UEA1 SBA 31 5 0.2 0.1 72 5 0.2 0.1 0.2 0.1 103 20 0.2 0.1 0.2 0.2 0.1


The characteristic membrane structure is visible in Figure 38 A and B and should not be confused with the biofilm.

9.1.2 7 days old biofoulant After seven days, WGA was clearly present on a cleaned membrane sample (Figure 39).

Figure 38 CLSM images of biofoulant (age 3 days) stained with ConA (horizontal sections of 202 μm x 202 μm). A) Used, cleaned membrane (xz-slice and yz-slice of Δz=10.14 μm above and to the left). B) New membrane (xz-slice and yz-slice of Δz=of 9.36 μm above and to the right). Excitation 543 nm, emission 580 nm (filter: LP 560)

Figure 39 CLSM image of biofoulant (age 7 days) stained with WGA and ConA on a used and cleaned membrane (horizontal section of 146 μm x 146 μm. WGA-spots: blue; No ConA staining( yellow) is seen; yellow signals from membrane are visible. Excitation at 488 and 543 nm, emission at 519 and 580 nm (filters: BP 505-530 and BP 548-570), respectively xz-slice and yz-slice of Δz= 9.86 μm above and to the right of the horizontal section.

A B

traces of old biofilm


ConA-, PNA- and SBA - stained polysaccharides were not observed. PNA is reported to be specific for galactose. Interestingly both SBA and WGA are specific for N-acetyl-glucosamine (see Table 8), but only WGA was staining the biofoulant. This initial result indicated that WGA could be a useful stain for detection of cell-bound polysaccharide.

9.1.3 10 days old biofoulant After ten days, more cells were seen on both new (Figure 40) and used membranes than for 3 days (results not shown). A co-localisation of cells and WGA stained polysaccharide was observed when cells were present in clusters (Figure 40 C), whereas single cells were not surrounded by WGA.

Figure 40 CLSM images of biofoulant (age 10 days) on a new membrane, stained with WGA and Syto59 (horizontal sections of 202 μm x 202 μm). A) WGA-spots(blue), B) cells (white), C) merged signals. Excitation at 488 and 633 nm, emission at 519 and 645 nm (filters: BP 505-570 and LP 650), respectively

A B

C

chain of cells

single cell


Similar to WGA, SBA stained spots in clusters of (a few) cells, but SBA also stained polysaccharide localized to single cells (Figure 41). The spots were not as clearly defined as the WGA-spots, and this can favour use of WGA instead of SBA for detection of polysaccharide on cells in clusters.

After ten days of filtration, the biofoulant consisted mainly of cells and N-acetyl-glucosamine/fucose, a matrix that anchors the cells to the membrane. Only few spots of mannose and glucose (stained by ConA) were detected in 7 and 10 days old biofoulants. The coverage was not very uniform, and the biofilm thickness varied from 0 to 10 μm along the membrane. Single cells were observed, as well as clusters and chains of cells. Chain lengths of up to 200 μm were observed.

9.2 Operational cycle 2 This cycle started 10.10.06. The characteristics of the membrane bundles in the test unit are described in the Appendix (12.6). CLSM of the membrane biofouling was performed on the dates shown in Table 17. Some of the results are reported here.

Figure 41 CLSM crop image of biofoulant (age 10 days) on a used, cleaned membrane, stained with SBA and Syto59 (horizontal section of 102 μm x 102 μm). The signals from SBA-spots (blue) and cells (white) are merged. Excitation at 488 and 633 nm, emission at 519 and 645 nm (filters: BP 505-570 and LP 650), respectively.


Cell stain (μM)1 Polysaccharide stain: Lectin (mg/ml)1 date S59 S9 PI ConA WGA PNA UEA1 SBA DBA

13.10 20 0.2 0.1 0.2 16.10 20 0.2 0.1 0.2 0.1 17.10 0.1 0.2 0.1 0.1 19.10 20 0.2 0.1 0.2 0.1 23.10 20 0.1 0.1 30.10 20 5 0.2 0.1 0.2 0.1 6.11 20 0.2 0.1 0.2 9.11 20 5 0.2 0.1 0.2 0.2 13.11 20 0.2 0.1 0.2

17.11 3.3 20 0.02 0.2

0.02 0.1

20.11 3.3 20 0.02 0.1 0.2

0.02 0.1 0.2

1Staining time: 30-45 min

9.2.1 3 days old biofoulant A different biofouling development was observed for new and old, cleaned membranes, respectively. After three days of membrane filtration, a monolayer of cells (thickness ranging from 0 to 8 μm) was observed on the both new and used, cleaned membrane (Figure 42). These observations confirm results obtained in the initial operational cycle. The WGA-binding to polysaccharides was still quite sparse and appeared in spots, “penetrating” 3-4 μm into the membrane (Figure 42).

9.2.2 6 days old biofoulant ConA bound to traces of old biofilm that were not removed by cleaning. After six days of operation, some fresh spots of ConA were observed, but WGA bound to N-acetylglucosamine was the dominating lectin (Figure 43A).

Table 17 Staining of new and used membranes and subsequent observations of biofilm on PES membranes in the confocal microscope.

Figure 42 A xz-slice 146 μm x 14.5 μm) from a CLSM image stack of biofoulant (age 3 days) on a used, cleaned membrane, stained with WGA and Syto59. Excitation at 488 and 633 nm, emission at 519 and 645 nm (filters: BP 500-530 and BP 634-655), respectively. WGA-spots are blue, cells are white spots.

membrane cell WGA


9.2.3 9days old biofoulant PNA staining of the biofoulant was observed (Figure 43B) . The PNA-patterns were looking like clouds (in contrast to the more defined WGA-spots), and PNA stained non-cellbound polysaccharide. Very little ConA was seen (results not shown).

9.3 Operational cycle 3 This cycle started 17.04.2007. The membranes were all new. Stains, concentrations used and the TMP when membranes were sampled for microscopy, are shown in Table 18. Staining times: 30-45 min. Quantitative calculation of biofoulant characteristics was done by use of CMem (9.4)

Cell stain (μM) Lectin (mg/ml) Lipid (mM) Date TMP S9 S59 ConA WGA PNA D3823 20.04 1.06 5 7.5 0.1 0.1 0.125 25.04 1.07 4.5 0.125 26.04 1.07 4.5 6.7 0.1 0.1 0.1 0.063 11.05 1.25 5 0.1 0.1

Figure 43 A) CLSM image of biofoulant (age 6 days) on a used, cleaned membrane stained with WGA (blue spots) and ConA (yellow spots) (horizontal section: 143 μm x 143 μm). Excitation at 488 and 543 nm, emission at 519 and 580 nm (filters: BP 505-570 and BP 634-655), respectively. B) Used, cleaned membrane (age 9 days) stained with WGA (blue spots), S59 (white spots) and PNA (green clouds), horizontal section: 146 μm x 146 μm. Excitation at 488, 633 and 543 nm, emission at 519, 645 and 575 nm (filters: BP 500-530, BP 634-655 and BP 565-615 nm), respectively.

Table 18 Staining of virgin membranes (17.04.07), TMP registrations and subsequent observations of biofilm on PES membranes in the confocal microscope.

traces of old biofilm (ConA)

ConA-spot

Traces of old biofilm

A B


Examples of CLSM images are shown in Figure 44 (polysaccharides) and Figure 45 (cells).

Figure 44 ConA stained polysaccharide (yellow clouds), biofoulant age 24 days. Excitation at 543nm, emission at 580 nm (filter: BP 565-615). Horizontal section of 230 μm x 230 μm. xz-section and yz-section of Δz = 12.6 μm above and to the right of the horizontal section

Figure 45 S9 stained (white) cells, biofoulant age 24 days. Excitation 488nm, emission 500 nm (filter: BP 500-530) (horizontal section of 230 μm x 230 μm). xz-section and yz-section of Δz = 36 μm, above and to the right of the horizontal section, respectively


9.4 Quantification of biofoulant components using CMem After having developed CMem, a new test unit operational cycle was started (cycle 3, see 9.3) and CMem was used for calculation of biofoulant characteristics, based on image analysis of stained cells and polysaccharides.

9.4.1 Effect of sample tilting on biomass thickness calculation The biomass calculations are based on a situation where membranes are not tilted. However, some tilting can occur. For the case shown in Figure 46, the tilting is 11.6 pixels or 5.2 μm (dz = 0.45 μm) from x = 1 to x = 512. The size of the image is 230.4 μm x 230.4 μm. The tilting is therefore tan-1 (5.2/320.4) = 1.3º, and the errors introduced by the tilting are negligible.

9.4.2 Quantitative image analysis The results from image analysis (CMem) of image stacks recorded 20.04.07 and 11.05.07 are listed in Table 19 and Table 20. Two methods, a) and b) (see 8.3.3), were used for calculating biofilm thickness.

Figure 46 Left: The cross-zections of a membrane at x = 1 and x = 512. Maximum z is 31.05 pixels for x = 1 and 19.41 pixels for x = 512.


20.04.07 (1) 20.04.07 (2) 11.05.07 Cells (stain S9) a) b) a) b) a) b) Average biofilm thickness (μm) 2.61 2.45 3.21 3.01 8.15 8.11

Roughness 0.41 0.44 0.46 0.48 0.67 0.66

Maximum biofilm thickness (μm) 7.01 6.87 10.52 10.01 27.44 26.54

Porosity 0.99 0.99 0.99 0.99 0.98 0.98

Membrane area (μm2) 3.91e+004 3.67e+004 3.20e+004

Biovolume (μm3) 4.28e+003 3.90e+003 3.27e+004

Biovolume below membrane area (μm3) 1.20e+002 5.90e+001 4.52e+003

(1) and (2) are calculated for different positions along the same membrane sample

20.04.07 (a) 20.04.07 (b) 11.05.07 11.05.07 PNA WGA WGA ConA

Polysaccharide stain: Lectin a) b) a) b) a) b) a) b) Average biofilm thickness (μm) 4.04 3.96 2.49 2.43 5.29 5.22 6.58 6.44

Roughness 0.45 0.45 0.44 0.44 0.34 0.34 0.41 0.40

Maximum biofilm thickness (μm) 15.13 14.60 6.33 6.19 10.91 10.74 16.25 15.47

Porosity 1.00 1.00 0.99 0.99 0.67 0.65 0.82 0.81

Membrane area (μm2) 3.81e+004 3.87e+004 3.33e+004 3.60e+004

Biovolume (μm3) 1.12e+003 1.65e+003 1.48e+005 1.39e+005

Biovolume below membrane area (μm3) 7.61e+001 3.42e+001 3.47e+003 9.18e+003

For one sample (11.05.97), the biofilm parameters based on staining of cells, was also calculated by use of the ISA software, i.e. average biofilm thickness = 13.65 μm2 , biovolume=32700 μm3, porosity=0.99, roughness = 0.38. The average thickness calculated by ISA were higher than when calculated with

Table 19 Quantification of cells from membrane samples 20.04 and 11.05.07. a) vertical distance, b) minimum (Euclidean) distance

Table 20 Quantification of polysaccharides from membrane samples 20.04 and 11.05.07. a) vertical distance, b) minimum(Euclidean) distance.


CMem, wheras biovolume and porosity were approximately the same. The roughness was less than the value calculated by CMem.

The results of the CMem calulations demonstrated that an increase of both cells and polysaccharides occurred at the end of the operational cycle. The higher biofoulant thickness observed for the cell-based calculations as compared to the polysaccharide based calculations, indicates that total cell volume were more aboundant in the biofoulant than the total polysaccharide volume. The increase of biofoulant thickness and volume support the observed increase of TMP that started after 10 days of operation (Figure 6).

9.5 Summary

9.5.1 Staining Staining time Incubation for 30 minutes in the dark will be selected for the protocol for all stains. Cell staining Concentrations of S59 and S9 in the upper range of recommendations given by the supplier (Table 7), i.e. 20 and 5 μM, respectively, will be used to minimize the noise signal effect. S59 and S9 stained equally well (same intensity). PI is candidate stain for potential dead/viable cell differentiation of biofoulant cells. Acridine orange is not suitable for multiple staining and will not be selected for the protocol. Hoeschst 33342 requires use of CLSM 2 photon lasers, and will not be selected for the protocol Polysaccharide staining WGA and SBA lectins generally appear in spots and cell-bound. Other lectins appear as clouds. WGA and ConA/PNA will be used for detection of cell bound polysaccharide and EPS polysaccharide, respectively. The staining pattern (i.e. cell-bound or not cell-bound/clouds) is similar for all lectin concentrations tested, but the signal to noise ratio decreases with decreasing concentration, due to a higher Detector Gain. Therefore, a lectin concentration of 0.1 mg/ml will be used for detection of polysachharides. Lipid staining Lipids have been detected in the extracellular matrix using a fatty acid probe (D3823). Lipid is not expected to be a major EPS component, but some biofoulant lipid analysis will be considered in the next project period. A concentration of 0.13 mM will be used.

9.5.2 Image acquisition It can be difficult to get good images throughout the stack of thick biofilm, because upper layers are brighter than deeper layers, and the


Detector Gain and Amplifier Offset can not be changed during image acquisitions. For good statistics, at least six different stacks (recorded at different spots on the membrane sample) should be investigated. We have observed biofilm variations along one membrane, among membranes in one bundle and among bundles. These variations should be taken into account. For thick biofilms, it can be difficult to get images of good quality of the membrane. If the biofilm is opaque, the laser light will have problems reaching the membrane. Autofluorescent microorganisms are registered as noise. Adjustment of laser power and filter settings, can improve image quality. If the membrane autofluoresce or is non-specifically stained, the membrane signals can interfere with the signals from the stained biofilm components. The contribution from the membrane can be removed, but this may affect the signals from biofoulant components.


10 Conclusions

• A membrane biofouling test unit has been constructed and has been operated for more than a year.

• Procedures for sampling and staining of biofoulant components are

established

• Assay conditions for enzyme activity measurements have been established

• Procedures for CLSM of membrane biofoulant are established

• A new image analysis software for quantification of biofoulant

components and structure on curved membrane surfaces (CMem) has been developed


11 References

AWWA (2005) Research committee report: Recent advances and research needs in membrane fouling. JAWWA 97 no 8 pp 79-89 Battin, T.J., Kaplan, L.A, Newbold, J.D., Cheng, X.H. and Hansen, C. (2003) Effects of current velocity on the nascent architecture of stream microbial biofilms. Appl. Environmental Microbiol. 69, pp. 5443–5452 Beyenal, H., Lewandowski, Z., Harkin, G. (2004a) Quantifying biofilm structure: facts and fiction. Biofouling 20 pp 1-23 Beyenal, H., Donovan, C., Lewandowski, Z., Harkin, G. (2004b) Three-dimensional biofilm structure quantification. J. Microbiol. Methods 59 pp 395-413 Billén, G. (1991) Protein degradation in aquatic environments. In: Chróst, R.J.(Ed.) Microbial enzymes in auquatic environments. Springer –Verlag, New York, pp 123-143 Butterfield, P. W., Bargmeyer, A. M., Camper, A. K. and Biederman, J. A. (2002) Modified enzyme assay to determine biofilm biomass. J. Microbiol. Methods 50 pp 23-21 Christensen, B.B., Haagensen, J.A.J., Heydorn, A. and Molin, S. (2002) Metabolic commensalism and competition in a two-species microbial consortium. Appl. Environmental Microbiol. 68, pp. 2495–2502 Chróst, R. J. (1991) Environmental control of the synthesis and activity of aquatic microbial ectoenzymes. In: In: Chróst, R.J.(Ed.) Microbial enzymes in auquatic environments. Springer –Verlag, New York, pp 29-59 Diaper, P. and Edwards, C. (1994) The use of fluorogenic esters to detect viable bacteria by flow cytometry. J. Appl. Bacteriol. 77 pp 221–228.

Emtiazi, F., Schwartz, T., Marten, S. M, Krolla-Sidenstein, P. and Obst, U. (2004). Investigation of natural biofilms formed during the production of drinking water from surface water embankment filtration. Water Res. 38 pp 1197-1206 Flemming, H. C. (2002) Biofouling in water systems-cases, causes and countermeasures. Appl. Microbiol. Biotechnol. 59 pp 629-640 Flemming, H. C. (2003) Role and levels of real-time monitoring for successful anti-fouling strategies - an overview. Water Sci. Technol. 47 pp 1-8


Hammes, F. A. and Egli, T. (2005) New method for assimilable organic carbon using flow-cytometric enumeration and a natural microbial consortium as inoculum. Environ. Sci. Technol. 39 pp 3289-3294 Haugland, R. (2006) Handbook of fluorescent probes and research chemicals. 10th ed. Molecular Probes. Section 15.2 http://probes.invitrogen.com/handbook/ Heydorn, A. Nielsen, A.T. , Hentzer, M., Sternberg, C., Givskov, M., Ersboll, B.K. and Molin, S. (2000) Quantification of biofilm structures by the novel computer program COMSTAT, Microbiology (UK) 146 pp 2395–2407. Heydorn, A., Ersboll, B., Kato, J., Hentzer, M., Parsek, M.R., Tolker-Nielsen, T., Givskov, M. and Molin, S. (2002) Statistical analysis of Pseudomonas aeruginosa biofilm development: Impact of mutations in genes involved in twitching motility, cell-to-cell signaling, and stationary-phase sigma factor expression. Appl. Environmental Microbiol. 68, pp. 2008–2017. Hoppe, H.G. (1983) Significance of exoenzymatic activity in brackish water: measurements by means of methylumbelliferyl substrates. Mar. Ecol. Progr. Ser. 11 299-308 Kappelhof, J.W.N.M., Vrouvenwelder, J.S., Schaap, M., Kruithof, J.C. , van der Koij, D. and Schippers, J.C. (2003) An in situ biofouling monitor for membrane systems. Water Sci. Technol. Water Supply 3 pp 205-210 Keevil, C. W. (2003) Rapid detection of biofilms and adherent pathogens using scanning confocal laser microscopy and episcopic differential interference microscopy. Water Sci. Technol. 47 pp 105-116 Kuehn, M., Hausner, M., Bungartz, H.J. Wagner, M., Wilderer, P.A. and Wuertz, S. (1998) Automated confocal laser scanning microscopy and semiautomated image processing for analysis of biofilms, Applied and Environmental Microbiology 64 pp 4115–4127. Laurent, P. and Servais, P. (1995) Fixed bacterial biomass estimated by potential exoproteolytic activiy. Can. J. Microbiol. 41 pp 749-752 Martiny, A.C., Jorgensen, T.M., Albrechtsen, H.J., Arvin, E. and Molin, S. (2003) Long-term succession of structure and diversity of a biofilm formed in a model drinking water distribution system. Appl. Environmental Microbiol. 69, pp. 6899–6907 Neu, T., Swerhone, G. D. W. and Lawrence, J. R. (2001) Assessment of lectin-binding analysis for in situ detection of glycoconjugates in biofilm systems. Microbiology 147 pp 299-313 NCIUBM (2006) Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB), Enzyme Nomenclature.


Recommendations, http://www.chem.qmul.ac.uk/iubmb/enzyme/EC3/ . Last update 24 July 2006 Palmer, Jr. R. J., Haagensen, J. A. J., Neu, T. R. and Sternberg, C. (2006) Confocal microscopy of biofilms- Spatiotemporal approaches. In: Handbook of Confocal Microscopy, ed. J. B. Pawley, 3rd ed., Springer, New York Schaule, G. Griebe, T. and Flemming, H-C. (2000) Steps in biofilm sampling and characterisation in biofouling cases pp 1-21 In: Biofilms- Investigative Methods & Applications Eds. H-C Flemming, U. Szewzyk and T. Griebe, Technomic Publishing co. Lancaster Somville, M. and Billen, G. (1983) A method for determining exoproteolytic activity in natural waters. Limnol. Oceanogr. 28 pp 190-193 Staudt, C., Horn, H., Hempel, D. C. and Neu, T. R. (2004) Volumetric measurement of bacterial cells and extracellular polymeric substance glycoconjugates in biofilms. Biotechnol. Bioeng. 88 pp 585-592 Tryland, I. and Fiksdal, L. (1998) Rapid enzymatic detection of heterotrophic activity of environmental bacteria. Water Sci. Tech. 38 95-101 Van der Kooij, D., Visser, A. And Hijnen, W. A. M. (1982) Determining the concentration of easily assimilable organic carbon in drinking water. J. Am. Water Works Assoc. 74 pp 540-545 Van der Kooij, D., Veenendaal, H. R., Baars-Lorist, C., van der Klift, D.W. and Drost, Y. C. (1995) Biofilm formation on surfaces of glass ans teflon exposed to treated water. Water Res. 29 pp 1165-1662 Vrouvenwelder, J. S., Kappelhof, J. W. N M., Heiijman, S. G. J., Schippers, J C. and van der Kooij, D. (2003) Tools for fouling diagnosis of NF and RO membranes and assessment of the fouling potential of feed water. Desalination 157 pp 361-365 Vrouwenvelder, J. S. and Van der Koij, D. (2003) Integral diagnosis of fouling problems by analysing biomass and inorganic compounds in membrane elements used in water treatment. Water Sci. Technol. : Water Supply 3 pp 211-215 Xavier, J.B., White, D.C. and Almeida, J.S. (2003) Automated biofilm morphology quantification from confocal laser scanning microscopy imaging, Water Science and Technology 47 pp 31–37 Yang, X.M., Beyenal, H., Harkin, G. and Lewandowski, Z. (2000) Quantifying biofilm structure using image analysis, J. Microbiol. Met. 39, pp. 109–119



12 Appendix

12.1 Procedure for membrane module preparation The procedure is described under and illustrated in Figure 47. Two pieces of tape (ca. 2 cm) were placed >15 cm apart with the sticky side up. 6 - 8 hollow fibre membranes are placed on the pieces of tape, immobilizing the membranes at both ends. Tape pieces are rolled to form cylinders. Each end of the bundle is inserted into flexible tubing 1 (OD 6 mm, ID 4 mm, length 6 cm) . The distance between the two tubings/the length of the fiber bundle should be 9.5 – 10 cm The fiber ends are bent over tubing 1 and immobilized with tubing 2 (OD < 8mm, ID < 6 mm, length ~2 cm) to ensure that no potting material enters the lumen. Epoxy glue is injected into tubing 2 (Figure 47) , open arrow) and 1, using a syringe. Tubing 2 is clamped to avoid leakage of glue. After curing of the glue, Tubing 1 is cut with a knife. The length of tubing 1 should be 2.5 cm and the length of the whole fiber module ~15 cm. The quality of the cutting is controlled by inspection.

12.2 Water quality and transmembrane pressure data logging Sensors for monitoring of water quality and transmembrane pressure are connected to the terminals of a Field Point I/O module FP-AI-110 (National Instruments) (Table 21).

Figure 47 Potting of capillary membranes and module manufacturing

cross-section: tubing 1

fiber bundle/ module

tubing 1 filled with glue

tubing 2 filled with glue

knife cut fiber glue


Channel VIN IIN VSUP COM 0 1 2 17 18 1 3 4 19 20 2 5 6 21 22 3 7 8 23 24 4 9 10 25 26 5 11 12 27 28 6 13 14 29 30 7 15 16 31 32 The FP-AI-110 item mounts on the network module FP 1000, which is connected to the computer’s USB port (COM3). The software Measurement & Automation Explorer (MAX) (National Instruments) provides access to the two Field Point items. The item configuration is specified in an -.iak file. The correct device types and settings are chosen, and devices and channels are configured. A filter setting of 60 Hz and an input range of 0.0035 to 0.021 Amps is chosen for channel 0-4.

12.3 LabVIEW operation. A virtual instrument (VI) is created in LabVIEW, and a LabVIEW function can be used to read data from the Field Point item created in MAX (see12.2). The front panel of the virtual instrument is shown in Figure 48. Before the program is run, the operator has to follow the INSTRUCTIONS shown on the screen. First, the name of the .iak file must be entered in the box named “FieldPoint IO Point”. The interval between the measurements must then be specified and entered in the box “Control Rate (ms)”. When the operator presses the Run button (date and time for this action is registered and shown in “Timestamp init.”), a dialog box pops up and a file name for recorded data must be entered. The file name is shown in the box “File Name (Data Out)”. The recorded parameters are shown on the computer screen and stored in succession.

Table 21 Terminal assignments for the signals associated with each channel. VIN and IIN are input terminals for voltage and current, respectively. Voltage and current inputs are referenced to the COM terminals. The VSUP terminals (for external power supply to power supply to power field devices) are internally connected to each other.


Part of the content of a typical data file is shown in Figure 49. The information of interest is in the six columns, the column titles reflect the content (time or parameter). The data can be imported to e.g. Excel or SigmaPlot and visualized graphically.

Figure 48 Front panel of virtual instrument (LabVIEW) for registration of temperature, pH and conductivity of the incoming water, and for trans membrane pressure recordings.


LabVIEW Measurement Writer_Version 0.92 Reader_Version 1 Separator Tab Multi_Headings No X_Columns One Time_Pref Absolute Operator bjorkoy Description Sampling of: conductivity - pressure (bar) - pH Date 2006/11/28 Time 09:40:40,725999 ***End_of_Header*** Channels 4 Samples 1 1 1 1 Date 2006/11/28 2006/11/28 2006/11/28 2006/11/28 Time 09:40:40,736 09:40:40,736 09:40:40,736 09:40:40,736 X_Dimension Time Time Time Time X0 0.0000000000000000E+0 0.0000000000000000E+0 0.0000000000000000E+0 0.0000000000000000E+0 Delta_X 1.000000 1.000000 1.000000 1.000000 ***End_of_Header***

Time after start (sec) Temp (ºC) Pressure

(bar) pH Conductivity (mS/cm) Date and time

0.000000 15.981113 0.907351 16.164000 0.258366 28.11.06 9:40 7.660000 15.981113 0.907226 16.164000 0.258366 28.11.06 9:40 37.452000 15.981113 0.907226 16.164000 0.258366 28.11.06 9:41 277.425000 15.986168 0.906475 16.164000 0.258366 28.11.06 9:45 457.461000 15.986168 0.908102 16.164000 0.258366 28.11.06 9:48 637.521000 15.991223 0.907936 16.164000 0.258366 28.11.06 9:51 817.520000 15.989539 0.907936 16.164000 0.258366 28.11.06 9:54 997.521000 15.989539 0.907518 16.164000 0.258366 28.11.06 9:57 1297.497000 15.986168 0.907644 16.164000 0.259035 28.11.06 10:02 1597.484000 15.991223 0.907936 16.164000 0.258366 28.11.06 10:07 1957.463000 15.981113 0.906976 16.164000 0.258366 28.11.06 10:13 2257.450000 15.979426 0.907101 16.164000 0.258366 28.11.06 10:18 2497.443000 15.974371 0.906976 16.164000 0.258366 28.11.06 10:22 2677.433000 15.977742 0.907268 16.164000 0.258366 28.11.06 10:25 2917.416000 15.974371 0.907393 16.164000 0.258366 28.11.06 10:29 3157.409000 15.972684 0.907644 16.164000 0.258366 28.11.06 10:33 3397.392000 15.974371 0.907185 16.164000 0.258366 28.11.06 10:37 3637.385000 15.977742 0.907393 16.164000 0.258366 28.11.06 10:41

. . . . . .

. . . . . .

. . . . . .

. . . . . .

Figure 49 Part of the content of data file 281106_1.


12.4 Biofoulant sampling for CLSM The membrane is removed from the frame (Figure 3) by the following procedure:

• the pump is turned off • the frame is lifted up, ensuring that the membranes are not damaged

by the tubings in the tank • the membrane is cut with a scissors and left hanging, with one end

fastened/supported. • pieces of the membrane are cut and inserted into staining solutions . • membrane remainings in the frame are sealed by burning with a

flame. • the frame is remounted into the chamber • pumping is started.

It is possible to fix the biofilm prior to staining by adding formaldehyde (3% v/v) for 30 minute. Mechanical shocks during incubation and transport to the microscope should be avoided. After incubation, the staining solution is carefully removed with a pipette, and the biofilm is washed three times with 0.2 ml filter-sterilized water from the tank.

Figure 50 Sampling of biofilm: Pieces of the membrane are cut and transferred into staining solution. Cutting positions are marked with red arrows.

2 3 4

Eppendorf PCR tube with staining solution 1

Staining solutions

tape

4 3 2 1

membrane


12.5 Specification of LSM 510 META confocal laser microscope Microscopes

Models Upright: Axio Imager.M1, Axio Imager.Z1, Axioskop FS 2 MOT; inverted: Axio Observer (Base Port) or Axiovert 200 M SP (Side Port)

Z drive Smallest z-increment Axio Imager.Z1m, Axio Imager.M1: < 25 nm Axio Observer.Z: < 25 nm; Axioshop 2 FS : 100 nm; fast piezo objective focus attachment (option)

XY stage (option) Motorized XY scanning stage, with Mark & Find (xyz) and Tile Scan (mosaic scan) functions, smallest increment 1 µm (Axio Observer) or 0.2 µm (Axio Imager)

Accessories (option) Digital microscope camera AxioCam, incubation chambers, micromanipulators, etc.

Scanning module Models META scanning module with two single-channel detectors and a polychromatic

multichannel detector (each genuinely confocal with selected, high-sensitivity PMTs) prepared for lasers from UV to NIR

Scanner Two independent galvanometric scanning mirrors providing ultrashort line and frame flyback times

Scanning resolution 4x1 to 2048x2048 pixels, also for several channels, continuous adjustment Scanning speed 13 x 2 speed stages;

up to 5 frames/s with 512x512 pixels (max. 77 frames/s with 512x32 pixels); min. 0.38 ms for a line of 512 pixels

Scanning zoom 0.7x to 40x, digital, variable in steps of 0.1 Scanning rotation Free 360 degrees rotation, variable in steps of 1 degree, free xy offset Scanning field 18 mm diagonal field (max.) in the intermediate image plane, homogeneous

illumination Pinholes Pinholes for each epi-illumination channel (single-channel detector or META

multichannel detector), individual adjustment of size and position, preadjusted Detection Standard: three confocal epi-illumination channels simultaneously (META

detector + 2 single-channel detectors), each with a high-sensitivity PMT detector. Option: transmitted-light channel with PMT; Option: monitor diode for measuring the excitation intensity. Simultaneous acquisition of up to 8 channels.

META detector Polychromatic 32-channel detector for fast acquisition of Lambda Stacks and Metatracking; also in combination with time series

Data depth Selectable between 8 bit and 12 bit Laser Module VIS laser module Polarization-preserving single-mode fiber, temperature-stabilized VIS-AOTF for

simultaneous intensity control of all visible-light laser lines, switching time < 5 µs; AOTF reprogramming via the LSM software; Diode laser (405 nm) 30 mW; Ar laser (458, 477, 488, 514 nm) 30 mW; HeNe laser (543 nm) 1 mW; DPSS-Laser (561 nm) 10 mW HeNe laser (594 nm) 2 mW; HeNe laser (633 nm) 5 mW (end-of-lifetime specification)

UV laser module Polarization-preserving single-mode fiber, temperature-stabilized UV-AOTF for simultaneous intensity control of two ultraviolet laser lines, switching time < 5 µs Ar laser (351, 364 nm) 80 mW (end-of-lifetime specification)

Electronics Module LSM 510 Control Control of the microscope, the VIS and UV laser modules, the scanning module

and further accessories. Monitoring of data acquisition and synchronization by Realtime Electronics. Data exchange between Realtime Electronics and computer via Gigabit Ethernet Interface.

Computer High-end PC with ample RAM and hard disk storage capacity, ergonomic high-resolution TFT flat-panel display, many accessories; Windows XP operating system with multi-user capability


12.6 Description of membranes/bundles used in operational cycles. During the test unit operational periods, membranes are replaced or cleaned. Table 22 contains information about the 18 membrane modules in two operational periods (10.10.06 - 31.10.06 and 24.11.06– 09.01.07), i.e. which modules were cleaned and which were virgin, and numbers of membranes removed for CLSM studies.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Module1 a a a a a a c c c c a c c a b b c c

Initial # 10.10.1

6 6 6 6 5 6 7 7 7 7 6 7 7 5 7 7 7 7

Analyzed 1 1 3 1 1 2 1 1 2 1 1 1 1 Initial # 23.11.2

5 4 4 5 3 6 5 6 6 6 6 6 6 5 6 6 7 7

Analyzed 3 1 1 1 2 3 2 1 Module a: manufactured.05.06. Cleaned twice 10.10.06, three times 23.11.06 Module b: manufactured 22.09.06. Cleaned once 10.10.06, three times 23.11.06 Module c: manufactured 09.10.06. Cleaned once 23.11.06 2 Per 10.10.06: a total of 116 membranes in the modules. Flow: 60 l/m2h. Per 23.11.06: a total of 99 membranes in the modules. Flow: 44.7 l/m2h.

Table 22 Initial total number (#) of membranes in module 1 – 18 per 10.10.06 and 23.11.06, and number of membranes removed for CLSM analyzes/viable cell count during the two operational periods.

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