Flow Cytometry for Bioprocess Control Fredrik Wållberg, Royal Institute of Technology, Department of Biotechnology, Stockholm, Sweden 2004.

Abstract

During bio-technical processing it is important to monitor biological parameters such as cell growth, viability and product formation. Many of the analyses traditionally used are slow to perform and provide only average data for the population. Flow cytometry is a laser-based technique, which measures physical properties of a cell in a flowing stream, at a rate of several thousand cells per second. It offers the prospect of an at-line, multi-parameter analysis of individual microorganisms in a population. In this project several methods for at-line measurements of bioprocesses were developed such as protocol’s for measuring cell concentration, viability and product formation. The primary focus was on prokaryotic organisms (E. coli) but eukaryotic organisms (P. pastoris) were included. The possibility to use volumetric cell counting to measure cell concentration (cell number) was evaluated. It was shown that the method was applicable for high cell density processes of both E. coli and P. pastoris. The combination of Bis- (1,3-dibutylbarbituric acid) trimethine oxonol (depolarised membranes) and propidium iodide (loss of membrane integrity) as fluorescent markers was useful to measure viability at-line of cells in high cell density processes. The protocol was shown to be reproducible for E. coli and P. pastoris. The viability staining was used to study the kinetics of weak organic acids (food preservatives). The protocol provided data about cell functions such as membrane depolarisation and loss of membrane integrity caused by introducing weak organic acids to shake flask cultures of E. coli. Labeling inclusion bodies with fluorescent antibodies provided a method, which could specifically monitor the increased accumulation of recombinant promegapoetin protein with process time. This technique was further developed for intracellular staining by application of a permeabilising step before labeling with antibodies. Staining of inclusion bodies directly inside permeabilised cells gave information about the distribution of protein expression in the cell population. In conclusion, flow cytometry provides an at-line, single cell technique for measurement of several biological parameters in bioprocesses.

Key words flow cytometry, Partec PAS, propidium iodide (PI), bis- (1,3-dibutylbarbituric acid) trimethine oxonol (BOX), Alexa fluor 488, bioprocess, E. coli, P. pastoris, inclusion body, food preservatives, viability, membrane potential

List of papers

This thesis is based on the following papers.

I. Wållberg, F. Sundström, H. Ledung, E. Hewitt, C. J. and Enfors, S-O. (2004) Monitoring of inclusion body formation in Escherichia coli by flow cytometry (Submitted)

II. Wållberg, F. Hewitt, C. J. and Enfors, S-O. (2004) Multi-parameter flow cytometry analysis of inactivation of Escherichia coli by weak organic acids (Manuscript for publication)

Data on flow cytometry in the following papers have also been included in the thesis.

Jahic, M. Wållberg, F. Bollok, M. Garcia, P. and Enfors, S-O. (2003) Temperature limited fed-batch technique for control of proteolysis in Pichia pastoris bioreactor cultures. Microbial Cell Factories, 2. www.microbialcellfactories.com

Sundström, H. Wållberg, F. Ledung, E. Norrman, B. Hewitt, C. J. and Enfors, S-O. (2004) Segregation to non-dividing cells in E.coli processes (Manuscript for publication)

Contents

1. AIM OF THE STUDY 3

2. INTRODUCTION 4

2.1 SINGLE CELL ANALYSIS 4 2.2 FLUORESCENCE 4 FIGURE 1. 5 FIGURE 2. 5 2.3 LIGHT SCATTER 5 FIGURE 3. 6 2.4 HISTORY OF FLOW CYTOMETRY 6 2.5 FLOW CYTOMETRY SYSTEMS 6 FIGURE 4. 7 2.6 FLUORESCENT MARKERS USED IN FLOW CYTOMETRY 8 2.6.1 OVERVIEW 8 2.6.2 PROPIDIUM IODIDE (PI) 8 2.6.3 BIS- (1,3-DIBUTYLBARBITURIC ACID) TRIMETHINE OXONOL (BOX) 8 2.6.4 ALEXA FLUOR 488 9 TABLE 1. 9 2.7 APPLICATIONS OF FLOW CYTOMETRY IN MICROBIOLOGY 10 2.7.1 FLOW CYTOMETRIC ANALYSIS OF VIABILITY IN MICROBIAL POPULATIONS 10 2.7.2 COUNTING CELLS IN A POPULATION BY FLOW CYTOMETRY 10 2.7.3 DETECTION AND IDENTIFICATION OF MICROORGANISMS IN COMPLEX SAMPLES 11 2.7.4 FLOW CYTOMETRIC ANALYSIS OF MEMBRANE POTENTIAL IN CELLS 12 2.7.5 FLOW CYTOMETRIC ANALYSIS OF PRODUCT CONCENTRATION IN BIOPROCESSES 12 2.7.6 INTERACTION OF DRUGS, ANTIBIOTICS AND FOOD PRESERVATIVES WITH MICROBIAL CELLS 12

3. RESULTS AND DISCUSSION 14

3.1 ANALYSING CONCENTRATION OF CELLS DURING HIGH CELL DENSITY PROCESSES 14 FIGURE 5. 14 FIGURE 6. 15 FIGURE 7. 15 FIGURE 8. 16 FIGURE 9. 17 3.2 ANALYSING VIABILITY OF CELLS DURING HIGH CELL DENSITY PROCESSES 17 FIGURE 10. 18 FIGURE 11. 19 FIGURE 12. 20 FIGURE 13. 20 3.3 ANALYSIS OF CELL INACTIVATION INDUCED BY WEAK ORGANIC ACIDS (FOOD PRESERVATIVES) 21 FIGURE 14 22 FIGURE 15. 22 FIGURE 16. 23 3.4 MONITORING OF INCLUSION BODY FORMATION IN E. COLI BY FLOW CYTOMETRY 24 FIGURE 17. 25 FIGURE 18. 25 FIGURE 19. 26 FIGURE 20. 26 FIGURE 21 27 FIGURE 22 28 FIGURE 23 28 FIGURE 24 29

4. CONCLUSIONS 30

5. ABBREVIATIONS 31

6. ACKNOWLEDGEMENTS 32

7. REFERENCES 33

2

1. Aim of the study

The goal of this study was to introduce flow cytometry at the department and develop protocols for routine monitoring of physiology parameters in bioprocesses. The physiological information that was expected from these analyses was cell viability, cell concentration and intracellular protein concentration. Both prokaryotic (E. coli) and eukaryotic (P. pastoris) organisms were used for this purpose. The staining reactions that were employed were staining with PI (permeabilised cell membrane), BOX (depolarised cell membrane) and Alexa flour 488 antibody complex (inclusion bodies).

Cell concentration With flow cytometry it is possible to count the number of cells in a sample. The most common method to use is ratiometric counting but in this study volumetric counting was tested. The results was evaluated against cell dry weight and the hypothesis is that flow cytometry will provide a faster (at-line) method which also can analyse subpopulations.

Viability of cells in bioreactors The traditional method to measure viability for a cell population has been and still is viable count on agar plates. A common feature in E. coli high cell density cultures is that they segregate to a non-dividing state, which means that viable count measurements don’t give a measure of living cells, only a measure of dividing cells (cfu). In some E. coli cultures the drop in cfu can be as large as 90-99% while the biomass dry cell weight is doubled and the recombinant protein production proceeds at a constant rate. Flow cytometry offers a method to analyse viability based on other characteristics than reproducibility of the target organism. With this technique it is also possible to analyse a population in a non-dividing state. PI and BOX was used as fluorescent markers to monitor the membrane permeabilisation and membrane depolarisation, respectively. The method was also used to study the effects food preservatives have on microbes.

Intracellular protein product concentration The possibility to use flow cytometry for analysis of the intracellular protein product concentration was investigated. For this purpose antibodies against inclusion bodies and secondary antibodies labelled with Alexa fluor 488 was used. The hypothesis is that flow cytometry and labelled antibodies will provide a fast method to monitor the formation of inclusion bodies and protein distribution in the cell population.

3

2. Introduction

2.1 single cell analysis

Single cell analysis has always played a central role in microbiology. There are many reasons why one wants to analyse a specimen. It could be to detect a specific organism, count or characterise them as to its growth, metabolism and viability. Many of the analyses traditionally used to study cell populations are often slow to perform and it could take several days before it is possible to evaluate the results. These methods don’t give much information about the physiological state of a cell and they make the erroneous assumption that a microbial population is homogenous with respect to its physiological state. Techniques and applications for measuring single cells have progressed enormously in the past decade. Up to the late 1970s there were no other means than microscopy for observation of a single cell. Today it is possible to measure several different parameters simultaneously. New techniques such as fluorescence-based techniques have been developed. These techniques are often very fast and can produce data about individual organisms in a culture (1-6).

2.2 Fluorescence

See figure 1 for spectral properties of visible light. Fluorescence occurs when a molecule absorbs and later emits light. Absorption causes an electron to rise to a higher energy level, called excitation. Because of internal vibrations in the molecule the electron looses energy before the remaining energy is released as light, called emission (figure 2). Excitation takes about a femtosecond and the period between absorption and emission is called fluorescent lifetime, it is typically a few nanoseconds for most organic substances but can be longer for other fluorescent molecules. Because the electron looses energy before returning to the ground state the wavelength of emitted light will be longer and less energy rich than absorbed energy. The difference is called stokes shift and is often in the order of a few tens of nanometers for most compounds. Many fluorescent compounds are polyaromatic hydrocarbons. Fluorescent molecules absorb and emit light at specific wavelengths called absorption and emission spectra. These are characteristic for each compound. Many emission spectra have a tail towards longer wavelengths. The extinction coefficient of a fluorophore is a measure of the amount of light it can absorb at a specific wavelength. Most of the molecules used have extinction coefficient between 5000 and 200.000 cm-1 M-1. The quantum yield of a molecule is the number of photons it emits per photon absorbed. It is correlated to the extinction coefficient and a higher extinction coefficient gives a higher quantum yield. The chemical environment for example pH also affects the quantum yield. Most of the molecules used have a quantum yield in the range of 0.05 to 1. Quenching and bleaching are two common problems related to fluorescent measurements. If a molecule interacts with and transfers its excessive energy to other molecules, it is said to be quenched. Absorption of energy can alter the structure in a molecule and make further cycles impossible this is called bleaching. This problem normally increases with the intensity of the light, which means that in order to increase absorption and thereby sensitivity it is not always possible to use a more powerful laser. However bleaching is a more common problem in other fluorescent techniques where the specimen is held in the light source for a longer period of time, for example, in fluorescence microscopy. It is not only the stains used, which are fluorescent in a cell. Many natural occurring cellular components including NADPH are fluorescent and thereby give a background signal called auto fluorescence. In flow cytometry fluorescence is measured at 90 degrees from the incoming light (7-13).

4

Excitation of PI, BOX and Emission of BOX and Alexa fluor 488 at 488 nm Alexa fluor 488 at 520-525 nm Emission of PI at 636 nm

UV Infrared

380 nm 550 nm 750 nm

Figure 1. Spectral properties of visible light and dyes used in this project.

2

1

3 Energy

Figure 2. Principle of fluorescence. 1) Excitation of an electron from the ground state to a more energy rich sate. 2) Loss of energy caused by vibrations in the molecule. 3) Emission of energy as light and decay of the electron back to the ground state.

2.3 Light scatter

A particle will scatter light when it intercepts a light beam. How the light is distributed to the surroundings depends on the physical properties of the particle; e.g. its shape, size and internal complexity (membrane, DNA, ribosome’s and other intracellular particles). In flow cytometry scattered light are measured at small angles (FALS) and at 90 degrees from the incoming light (SALS). FALS is affected mostly by the size of the particle, but other factors also affect FALS signals. SALS is mostly affected by internal components of the particle (figure 3) (8, 9, 11, 12, 14).

5 Light registered by SALS detector

E.coli Light source

Light registered by FALS detector

Figure 3. Light scattered by an E. coli intercepting a light beam.

2.4 History of flow cytometry

Flow cytometry is based on the laws of physics; i.e. the laws of fluidics, optics and electronics. The fluidics of flow cytometry is dependent on hydrodynamic focusing. This process was discovered by Bernoulli (1738) and Euler (1755) and their results were used when the first flow cell was developed. In 1833 Savart showed that a jet of water could be broken up into droplets by imparting mechanical disruption, this was later used to develop the first cell sorter. The first reference to refraction can be traced back to Socrates (370 BC) but Ptolomy conducted the first measurements, 400 years later. Many well known scientists throughout time have carried on their work including Kepler, Hooke, Young and Newton (15). In 1934 Moldovan reported a technique for counting cells flowing through a capillary tube. This system suffered from many technical problems such as the narrow tubes had a

tendency to block easy (16). Thirteen years later Gucker et al developed a technique to analyse dust particles and airborne spores for the United States armed forces (17). This system is often quoted to be the first flow cytometer (8, 18). In 1965 Fulwyler developed the first flow sorter (19). He used technology, which had been developed for jet ink printers. With this system he could sort a mixture of human and mouse erythrocytes with high viability (8). In the early 1970s, several applications were available for eukaryotic cells but not for prokaryotic cells because of their much smaller size and low concentration of intracellular particles. It was first in the late 1970s that Steen and co-workers developed an instrument designed to analyse bacteria. The first sorting of bacterial cells was probably conducted by Paau et al, who separated algae from bacteria (1, 2, 8). Today there is a wide range of applications available for prokaryotic organisms; e.g. viability and cell cycle analysis.

2.5 Flow cytometry systems

A flow cytometer measures physical properties of a cell in a flowing stream at a rate of several thousand cells per second (20). There are two types of instruments, the ones that can analyse a sample and the ones that also can sort out cells from a complex sample (20, 21). Modern instruments can analyse more than 10 parameters simultaneously and it is likely that it will increase in the near future (20, 22). There are two definitions for sensitivity of a flow cytometer, threshold and resolution. The threshold is the lowest signal from a molecule that can be distinguished from the background. The resolution is the degree to which an

6 instrument can separate subpopulations in a mixed sample (12, 23-25). A flow cytometer is made up of three main systems, see figure 4.

1. The fluidics system carries the particle to the light source. It is important that the stream transporting the particle is aligned so that the particle will pass in the centre of the light beam and that only one particle at the time passes. To accomplish this the sample is injected into a stream of sheath fluid, which accelerates the sample and restricts the particles to the centre of the moving stream, called hydrodynamic focusing. The most common method to dissolve aggregates of cells is ultrasonic treatment but chemical methods including trypsin-EDTA are available. To reduce the background signals all solutions are normally filtered before use (6, 11, 12, 26-28).

2. The optical system consists of one or more light sources, filters and mirrors to separate and direct the light to the right detectors. The most common light source to use in flow cytometry is the argon laser, which emits light at 488 nm. Other common light sources are red lasers or UV lamps, but many more types of lasers have been used in combination with flow cytometry (6, 9, 10, 12, 25, 29-33).

3. The electronics system converts optical signals into electronic signals, which can be processed by a computer. Sorting systems use electronics to charge droplets to be sorted. There are two types of sorting systems available, the fluid switching and droplet cell sorter. The most efficient modern sorters can separate more than 20.000 cells per second (9-11, 18, 20, 21, 34).

Sample Sheat fluid

Flow cell

Laser beam Detection of FALS

Droplets

Detection of SALS and fluorescence

High voltage plate High voltage plate

Left sort Right sort

Waste

Figure 4. A schematic picture of a flow cytometry system

7 2.6 Fluorescent markers used in flow cytometry

2.6.1 Overview

The number of fluorophores available for flow cytometry has increased dramatically in recent years and today several thousand is commercially available. Despite this fast development, fluorescent probes are probably one of the areas, which will develop most in the near future. Especially dyes, which can be used in multicolour experiments including tandem dyes and bead arrays. Fluorophores used for flow cytometry should posses several important features. They should have a high extinction coefficient close to the wavelength of the light source used and a high quantum yield. They should possess narrow emission spectra especially if they are intended for use in multi colour experiments. For practical reasons, it is good if they are easy to dissolve and aren’t toxic. Light has three properties colour, intensity and polarisation. Colour and intensity are routinely measured in flow cytometry. However polarisation can only be measured with specific equipment even though it has been shown that most stains used suffer from polarisation. The most common molecule to stain is DNA. There are many commercial stains for DNA available. Most of them enhance their fluorescence several times upon binding and binds DNA in a loose equilibrium, hence a wash step should not be included in the protocol. Fluorescent proteins including GFP, mutated forms of GFP and homologues have been used extensively in combination with flow cytometry. Many interesting protocols have been developed to date and it is very likely that the fast development will continue even more rapidly. A new and promising technique is to use fluorescent beads together with flow cytometry. They posses very narrow emission spectra, which makes it possible to design multi colour protocols with far more parameters than ever before. See table 1 for an overview of stains available (3, 6, 7, 9, 11, 12, 18, 24, 25, 28, 35-53).

2.6.2 Propidium iodide (PI)

PI absorbs light at 493 nm and emits light at 636 nm (figure 1). It has the molecular formula -1 -1 C27H34I2N4, molecular weight 668.4 DA and excitation coefficient 5400 cm M . When PI binds to DNA its fluorescence is enhanced 20-30 times, its excitation maximum is shifted 30- 40 nm towards red light and emission maximum are shifted 15 nm towards blue light. PI binds with little or no sequence preference at a stoichiometry of one PI molecule per 4-5 base pairs of nucleic acid. It is soluble in water and then carries two positive charges. The most important feature of this molecule is that it cannot cross an intact cell membrane and is probably the most common stain used to measure viability of single cells with flow cytometry. A cell is determined as viable if it can reject uptake of PI (4, 12, 28, 53-58).

2.6.3 Bis- (1,3-dibutylbarbituric acid) trimethine oxonol (BOX)

BOX is a lipophilic and anionic compound. It accumulates in depolarised cells by binding to lipid-rich intracellular compounds. It is commonly used for viability and membrane potential experiments. BOX absorbs light at 493 or 488 nm and emits light at 516 or 525 nm (figure 1). It has the molecular formula C27H40I2N4O6, molecular weight 516.6 DA and excitation coefficient 140.000 cm-1 M-1. It has been used extensively in combination with PI to determine a pre-stage to cell death indicated by the loss of the membrane potential in cells (4, 53, 56, 57, 59-66).

8 2.6.4 Alexa fluor 488

Alexa fluor 488 is one dye in a series of stains that span the spectra of visible light. The most common application is to use it conjugated to an antibody, and it is a good candidate to replace FITC. It can also be conjugated to nucleic acid probes and therefore is a good candidate for FISH applications. One study has shown that it is possible to conjugate Alexa fluor 488 to lipopolysaccharides (LPS) with maintained biological activity. Alexa fluor 488 antibody complex is very bright and insensitive to changes in pH (from pH 4 to 10). Conjugated to an antibody it absorbs light at 495 nm and emits light at 519 nm (figure 1). It has the molecular formula C25H15LI2N3O13S2, molecular weight 643.4 DA and fluorescence life time 4.1 nanoseconds (53, 67-69).

Fluorescent Dye Excitation wavelength Emission wavelength Main application

(nm) (nm)

BCECF-AM 503 528 Intracellular pH

BOX 488 516 Membrane potential

DAPI 350 460 Nucleic acid

EGFP 489 508 Fluorescent protein

FITC 495 519 Antibody conjugate

Hoechst 33342/33258 343 483 Nucleic acid

Nile red 540 610 Lipids

PI 493 636 Nucleic acid, viability

Phycoerythrin (PE) 480 578 Antibody conjugate

Alexa fluor 488 495 519 Antibody conjugate

TOTO 488 530 Nucleic acid

SNARF-1 548 635 Intracellular pH

Rhodamine 123 500 550 Viability

Mithramycin 425 550 Nucleic acid

PicoGreen 500 523 Nucleic acid

Sytox Green 504 523 Viability

7-AAD 546 647 Apoptosis, cell cycle

Cy5 and Cy7 488 Depends on conjugate Conjugate to PE & APC

Table 1. Common fluorophores used in flow cytometry

9 2.7 Applications of flow cytometry in microbiology

The first applications for flow cytometry were developed for eukaryotic cells and today the most common organisms to use in combination with flow cytometry are eukaryotic cells. Flow cytometry is, for example, used extensively in medical research and diagnostics. This thesis however has it main focus on applications for prokaryotic organisms and therefore applications for eukaryotic cells will not be discussed in this section. Several of the methods can be applied on eukaryotic cells as well.

2.7.1 Flow cytometric analysis of viability in microbial populations

Viability is one of the most fundamental properties of a cell. A cell is said to be viable when it has shown to reproduce (4, 8). The traditional method to determine the viability of cells has been and still is viable count on agar plates. This is a slow method, often days are required to detect colonies and for many organisms it fails completely because the cells cannot reproduce in an artificial environment. The need for a method that is fast, can be applied to a wide range of organisms and are able to analyse properties of a cell other than reproducibility is obvious. With flow cytometry it is possible to measure parameters such as metabolic activity and membrane potential, hence with this method it is possible to show activity in a cell different from reproducing (1, 5, 54, 70). Probably the most common approach is to use PI. This molecule can’t enter a cell with an intact membrane and a cell is said to be viable if it can reject PI. Many successful studies have been reported for both eukaryotic and prokaryotic cells (18, 54, 71, 72). Other common markers for viability and bacteria are Rhodamine 123, BOX, TOTO and Sytox green (3, 60, 62, 63, 73-75). The traditional approach to measure viability has been to stain dead cells. Cells that have been disrupted by severe methods including strong detergents and freeze thawing etc can normally be detected by their inability to exclude certain molecules and hence be classified as dead cells. However, cells that have been incubated with cytotoxic drugs to render them incapable of reproducing may still exclude these dyes for a long time even though they would not be accounted for as viable cells. There are also examples of viable cells, which will take up PI with out any treatment. Another problem with looking at dead cells rather than the amount of living cells is that cells will disintegrate and disappear. Therefore staining live cells or both live and dead cells in a population could provide better information about the viability. Today there are several commercial kits and published academic protocols available for staining both live and dead cells in the same sample (57, 76-80).

2.7.2 Counting cells in a population by flow cytometry

One of the key parameters in bioprocess control is to measure the concentration of cells and the biomass. Many of the mathematical methods used to describe cell growth contain biomass. It is also an important parameter to determine if a population is in steady state or not. Several methods to count, determine the size and the biomass of cells in a population are available to date. Microscopy is a classic method for counting cells. With this method it is only possible to count a small number of cells due to the tedious work involved, which make this method statistically unreliable. This problem escalates because of the manual error of the operator. The coulter particle counter is a common approach to determine the number of cells in a population. This method relies on two electrodes immersed in solution and when cells are introduced into the system they generate an electrical pulse. This is considered to be a fast and statistically reliable method but it is not possible to discriminate between subpopulations. The most common method to measure biomass is to use the dry weight. However, this method cannot discriminate between subpopulations and the results are only available one or several

10 days after sampling. A faster method is to use the wet weight but it is considered to be less accurate than the dry weight and is not so often used. One of the few on-line methods available is optical density. But this method, as many others fails to recognise subpopulations and is normally considered too be less accurate than the dry weight. It is often used to monitor cell growth during a process but is replaced by the dry weight for the final evaluation of the results. With flow cytometry the number of cells in a population can be determined. It is also possible to recognise and analyse subpopulations. Moreover a large amount of cells can be counted in a short period of time, which makes the method statistically reliable. There are different protocols, which can be used but the method of choice for most laboratories is ratiometric counting. Fluorescent beads are used as an internal standard and a known concentration of beads in a fixed volume is added to the sample. The concentration of cells can be determined by calculating the ratio of cells to beads and multiply it to the known concentration of the beads. Partec (GmbH, Münster, Germany) has developed an alternative method. They use on their instruments a fixed volume method. The benefit with this system is that the rather high cost for fluorescent beads can be omitted. But the negative aspect of using this method is the large amount of cells, which has to be counted. This makes it difficult to determine subpopulations in the same sample as concentration. If it is possible to measure the size of cells in a population and hence biomass with flow cytometry have been debated for more than a decade. Light scattered by particles and measured at a small angle doesn’t only depend on size, refractive index and the surface structure of the cell also affect it. Many published studies have reported that they can measure size of a cell by relating FALS to a standard made with fluorescent beads. But probably as many reports have come to the conclusion that there doesn’t exist a linear relationship between FALS signals and bacterial size. Many publications have also claimed that a correlation exists but only under certain conditions, for example for unfixed cells but not for fixed cells. There is no clear opinion and this discussion is likely to go on for some time. Probably the most accepted option is to use FALS to monitor changes in a culture (1, 14, 65, 70, 81-91).

2.7.3 Detection and identification of microorganisms in complex samples

Detection of a micro-organism in a mixed sample can be very complex but, flow cytometry has proven to be successful for this purpose. In some cases FALS, RALS and auto fluorescence have been enough to discriminate and identify a subpopulation of bacteria from others present in the sample. The more common approach is to label with fluorescent nucleic acid probes or fluorescent antibodies. The most common technique for nucleic acid probes is fluorescent in situ hybridisation (FISH). FISH protocols usually target the 16s rRNA of a cell but reports have been published were either 5s or 23s rRNA has been the target. Ribosomal RNA is used because the cells normally contain an amount high enough for detection. In recent years however, the development have been to combine FISH with other techniques; e.g. PCR to increase the signal and thereby be able to detect specific genes or mRNA in single cells. The most common stain used in combination with FISH protocols is FITC. Antibody based detection system have become very widespread during the last decades. The major advantage of using antibody based systems is the high specificity even in a very complex sample. The most common antibodies used for flow cytometry is IgG, they are small and relatively cheap. It is normally easy to purchase the same IgG antibodies conjugated to different fluorophores. As for FISH applications the traditionally used fluorophore is FITC but other options are available; e.g. Alexa fluor 488 have proved to be a good candidate (3, 5, 8, 11, 12, 22, 27, 34, 92-114).

11 2.7.4 Flow cytometric analysis of membrane potential in cells

The membrane potential is of most importance to the cell; e.g. it mediates transport of compounds and is involved in the formation of ATP. A membrane potential is generated and maintained by concentration gradients of ions across the membrane. The most important ions involved in this process are sodium, potassium, chloride and hydrogen ions. Bacteria normally maintain a membrane potential in the range of 100 mV, with the interior negative. It is possible to measure if a cell have a membrane potential or not. Positively charged dyes for example Rhodamine 123 have been used and they will accumulate inside the cell if the membrane potential is maintained. Another common approach to use is negatively charged oxonol dyes including BOX. They will stain the cell if the membrane potential has been lost. Attempts have been made to measure the membrane potential with micro electrodes but this becomes difficult with cells as small as bacteria and with this method it is not possible to measure any larger amount of cells (9, 61, 70, 115-117).

2.7.5 Flow cytometric analysis of product concentration in bioprocesses

Product concentration is perhaps the most important variable to measure during a bioprocess. There are many methods available to date. However many of them suffer from the fact that they are expensive, labour demanding, results are only available long after the process is terminated and they only produce average information about the population and not data for each individual cell. Flow cytometry however, is a relatively cheap method after the initial investment in the instrument, it is not so labour demanding, it is relatively fast and it can produce data for each individual cell. Several reports have been published were flow cytometry have been used to study product concentration. FALS has been used to monitor formation of inclusion bodies in bioprocesses (118-120). β-galactosidase is an important reporter protein in modern biology. It can be measured with several different techniques for example spechtrophotometric techniques with the substrate ONPG. It is also possible to measure the concentration of β-galactosidase with flow cytometry. The two most common substrates used are fluorescein di-β-galactopyranoside (FDG) and resorufin-β- galactopyranoside (RG), both are commercially available (121). The use of Green fluorescent protein (GFP) and other fluorescent proteins in control of and to measure concentration of product in bioprocesses is being explored. The expectation is that the use of GFP will give an easy, fast and reliable method, which can provide information about single cells in the process. Several successful reports has been published were this reporter system have been evaluated against other reporter systems in both bacteria and yeast cells. Today it is also possible to quantify the fluorescence of GFP and there is beads available, which makes it possible to determine a standard curve for the calculations. (18, 38, 120, 122-150). Several mutants of GFP have been developed to emit light at different wavelengths, yellow fluorescent protein (YFP), blue fluorescent protein (BFP) and cyano fluorescent protein (CFP) is available to date and can all be used in combination with GFP. Additionally a fluorescent protein, which emits red light, has been discovered in a coral (Discosoma) (106, 134, 151- 175).

2.7.6 Interaction of drugs, antibiotics and food preservatives with microbial cells

In today’s society, cytotoxic drugs are used extensively. They are used in a diverse range of applications such as to prevent infections in medical applications, prolong expire dates for food products, as research tools etc. Many of the compounds used only suppress growth of the cell; i.e. they are not lethal for the target organism. The traditional method to measure effects

12 of antibiotics, food preservatives and other cytotoxic drugs has been and still is viable count on agar plates. This is a slow method, often days are required to detect colonies and for many organisms it fails completely because the cells cannot reproduce in an artificial environment (1, 5, 55, 70, 176, 177). The need for a method that is fast, can be applied to a wide range of organisms and are able to analyse properties of a cell other than reproducibility is obvious. This has provided another fruitful area for flow cytometric methods. The first attempts to analyse the effects of antibiotics were made by Steen and co-workers in 1982. Many successful studies have been published after that were the effects of antibiotics have on microorganisms have been analysed by flow cytometry, see Davey et al 1996 for review (8, 48, 59, 63, 64, 178-183). Flow cytometry has not yet been explored to analyse the effects of chemical food preservatives in the same manner as for antibiotics. Most of the studies published to date involve physical food preservation methods such as cold treatment, heat treatment and high-pressure treatment (63, 184-201).

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3. Results and discussion

3.1 Analysing concentration of cells during high cell density processes

The common method to measure concentration with flow cytometry is ratiometric counting (1, 70). Partec instruments however, utilise a built in function called true volumetric absolute cell counting (volumetric counting). Two electrodes are used to start and stop the measurement respectively. In between the electrodes there is a fixed volume and the number of cells in a sample can be calculated by the formula C=N/V, were N=number of cells and V=volume (figure 5). This study evaluated the possibility to use flow cytometry to determine concentration of cells during a process instead of dry cell weight and optical density. Flow cytometry was expected to provide an at-line method, the analysis takes about 20 min to perform. Furthermore it provides additional information about the population such as viability and product concentration. See Jahic et al., for a detailed description of the method (71).

Air Sample Flow cuvette

Start electrode Stop electrode

Fixed volume in between electrodes

Figure 5. The method for Partec true volumetric absolute cell counting. Air is used to push the sample into the sheath fluid, transporting the sample to the flow cuvette. Two electrodes are used to start and stop the measurement respectively. In between them cells are counted in a fixed volume and converted into concentration with the formula C=N/V, N=number of cells and V=volume.

A study was conducted were volumetric counting was evaluated against ratiometric counting. A stock solution consisting of E. coli w3110 wt (obtained from the Genetic Stock Centre, Yale University, New Haven, CT) was diluted in sequential steps representing 75, 50 and 25% of the original concentration. Samples were spiked with fluorescent beads before the analysis. Both methods gave a linear correlation but volumetric measurements had a slightly better R2 value after linear regression analysis (data not shown), 0.9977 compared to 0.988 obtained with ratiometric counting (figure 6). The results showed that both methods could be used to determine concentration with similar precision.

14 Counts/ml

0 20 40 60 80 100 120 % diluted from original solution

Figure 6. A comparison of E. coli cells in solution between ratiometric counting and Partec true volumetric absolute cell counting. R2 value for Ratiometric counting and Partec true volumetric absolute cell counting was 0.988 and 0.9977 respectively.

Flow cytometry was then used to monitor concentration during a temperature limited fed batch process (TLFB) of P. pastoris (71). Concentration was measured through out the process (240 h) and evaluated against dry cell weight data. Figure 7 shows the correlation for flow cytometry data plotted against dry cell weight data. The results shown in figure 7 are average values of three measurements. To further investigate the method flow cytometry data was plotted against time and evaluated with standard deviation (figure 8). For most of the points in figure 8 the deviation was between 0.5 and 2%. Flow cytometry was used to measure concentration for three TLFB and two methanol limited processes (MLFB) with similar results as in figure 7.

250 y = 8E-09x + 37.691 R2 = 0.9094 Dry cell weigth (g/l)

0 0.00E+00 5.00E+09 1.00E+10 1.50E+10 2.00E+10 2.50E+10 Cells/ml

Figure 7. Concentration of P. pastoris measured during a TLFB process. Dry cell weight data plotted against flow cytometry results.

15

2.50E+10 Cells/ml

0.00E+00 0 50 100 150 200 250 300 Time (h)

Figure 8. Concentration of P. pastoris measured during a TLFB process. Flow cytometry results plotted against time. Error bars are based on three measurements for each point.

When the method had shown to be reproducible for P. pastoris it was tested on a glucose limited fed batch process with E. coli (202). Concentration was measured through out the process (30 h) and plotted against dry cell weight data. Flow cytometry and dry cell weight correlates well up to about 20 h. There is no correlation between the two methods for the last hours of the process (figure 9). One problem encountered was aggregation of cells, which resulted in a rather large spread for the values, obtained with flow cytometry as well as for dry cell weight results. Flow cytometry was used to monitor concentration during three glucose limited lab scale fermentation’s (8 l) and one glucose limited pilot plant fermentation (600 l) with similar results as can be seen in figure 9.

16 45 y = 2E-09x + 1.4088 R2 = 0.9906 Dry cell weigth (g/l)

0 0.00E+00 5.00E+09 1.00E+10 1.50E+10 2.00E+10 Cells/ml

Figure 9. Concentration of E. coli measured during a fed-batch process. Dry cell weight data plotted against flow cytometry results.

From this study it was concluded that flow cytometry provides an at-line method for measuring concentration of cells in fed batch processes. It could be applied to both prokaryotic (E. coli) and eukaryotic (P. pastoris) organisms with a reasonable correlation to dry cell weight. The possibility to count large amounts of cells in a short time makes the method statistically reliable. Moreover it provides important information about the cell population and subpopulations such as viability.

3.2 analysing viability of cells during high cell density processes

This study intended to establish a method for measuring viability of cells at-line (the protocol takes about 10 min to perform). There are many probes available to analyse viability of cells with flow cytometry. We decided to use BOX (depolarised membranes) and PI (loss of membrane integrity). The combination of BOX and PI as fluorescent markers is a well- established method for analysing viability of microorganisms in bioreactors (5, 54, 55, 176, 177). The information that was expected from this protocol is a live dead discrimination of cells based on the uptake of PI and detection of a reversible pre-stage to cell death, were the cell cannot maintain its membrane potential. If the cell is healthy it won’t bee stained by PI or BOX (Population 1, figure 10). However if the cell cannot uphold its membrane potential BOX alone (Population 2, figure 10) will stain the cell. If the cell has lost its membrane integrity both BOX and PI (Population 3, figure 10), will stain it. The cell is then considered dead and will eventually disintegrate and disappear.

17

2 3 BOX 1

PI

Figure 10. The combination of PI and BOX as viability markers for a cell population. Subpopulation 1 contains cells stained by neither BOX nor PI. Subpopulation 2 Contains cells stained by BOX alone. Subpopulation 3 Contains cells stained by both BOX and PI.

The method was tested on cells (E. coli w3110wt, obtained from the Genetic Stock Centre, Yale University, New Haven, CT) that had been washed with 70% ethanol or treated with heat (50 and 60 degrees C respectively) to facilitate uptake of BOX and PI. Almost 100% of the population were stained with both BOX and PI, when cells were washed in 70% ethanol. The small amount of cells left unstained after ethanol treatment can be a consequence of difficulties resuspending the pellet and therefore some of the cells in the population were protected against the ethanol. Moreover background signals in the area where healthy cells occur can be caused by contaminants in the sample or sheath fluid. Background noise can also originate from the instrument optics, internal background noise exists in even the most advanced flow cytometer. It is possible to reduce the background with methods such as gating the data and filtering solutions. Figure 11 shows healthy cells (E. coli) mixed with ethanol washed cells and stained with a BOX/PI solution. Population 1, contains non-stained cells and population 2, represents cells stained by both BOX and PI. A pre-stage to cell death indicated by BOX-staining alone don’t exist under these conditions, indicating that ethanol causes instant loss of membrane integrity. Both methanol and ethanol are commonly used to permeabilise microbes, often in combination with cross-linking agents such as formaldehyde or glutaraldehyde. Alcohol’s exhibit strong effects on a cell, they are believed to denature protein, extract phospholipids from the membrane and permeabilise the cell wall completely (28, 66). When cells were treated with 60 degrees C, both PI and BOX stained the majority of the population within 10 min (data not shown). However a test was conducted in a fermentor were the temperature was set to 50 degrees C, after 90 min, only 50% of the population was stained by BOX and less than 15% was stained by PI (data not shown). This indicates that there is a critical point between 50 and 60 degrees C for the viability of E. coli.

18 2

1 BOX

PI

Figure 11. A mixture of E. coli 3110 wt treated with EtOH and healthy E. coli 3110 wt. subpopulation 1, contains healthy cells not stained by BOX or PI. Subpopulation 2, Represents cells stained by both PI and BOX.

Two protocols were initially tested when PI was used to monitor viability of P. pastoris during high cell density processes (BOX-staining showed a very high separation between stained and unstained cells therefore it was not possible to fit them into the same histogram. BOX was not used for P. pastoris, data not shown) (71). The same protocol, which had been tested out for E. coli and one published protocol specific for P. pastoris and PI-staining (72). The method Martinet et al, described includes a wash step to wash out excess PI. It is not obvious why this step is included, PI binds DNA in a loose equilibrium and to wash out unbound PI from the medium can lead to reduced staining (12, 53). The centrifugation step included, caused in our experiments loss of cells and damage to cells. Data obtained with this protocol showed very uncertain results and was not used further. Another drawback of this protocol is that the wash step gives a more labour demanding protocol. However the protocol developed for E. coli was proven to be valid for P. pastoris and was used to analyse viability in three TLFB processes and two MLFB processes. Figure 12 shows that the amount of stained cells were several percent lower in TLFB than for MLFB processes and this is believed to be one reason for the higher product yield and less proteolytic activity in TLFB processes, see Jahic et al, for a more extensive discussion (71).

19 15 PI stained cells (%)

0 0 50 100 150 200 250 300 Time (h)

Figure 12. % PI-stained cells of the entire population in three TLFB processes (filled symbols) and two MLFB processes at 30°C (open symbols).

In figure 13, three laboratory scale (8 l) glucose limited fed batch processes (E. coli) were analysed with flow cytometry. One glucose limited pilot plant (600 l) fed batch for E. coli was also analysed with similar results (data not shown). The outcome is different from results obtained with P. pastoris (figure 12). In E. coli processes, the percentage of dead cells was highest at inoculation of the fermentor, decreased to almost zero during the batch phase and remained low throughout the rest of the process. In this study viability analysed with flow cytometry was compared to viable count on agar plates. The results showed that flow cytometry is a more accurate method to analyse viability than viable count on agar plates. For a more extensive discussion, see Sundström et al, (202).

10 Stained cells (%)

0 0 5 10 15 20 25 30 35 Time (h)

Figure 13. Analysis of viability in three E. coli fed batch processes. Open symbols BOX stained cells. Filled symbols PI stained cells.

20 This study showed that using flow cytometry in combination with PI and BOX as fluorescent markers provides a fast method to analyse viability of high cell density processes. It provides data at-line, which is important to make accurate decisions during the processes. The protocol could be applied to both prokaryotic (E. coli) and eukaryotic (P. pastoris) organisms with good reproducibility. One possible improvement to the protocol described above would be to counter stain live cells to provide more information about the population. In conclusion knowledge about the viability of a population is of most importance, flow cytometry provides a fast, easy and reproducible method, it should therefore be applied routinely to high cell density processes.

3.3 Analysis of cell inactivation induced by weak organic acids (food preservatives)

This study investigated the use of BOX and PI as fluorescent markers to analyse the effects weak organic acids have on microbes. The combination PI and BOX was expected to provide information about the lethal effects of food preservatives and a pre-stage to cell death indicated by depolarisation of cells. Moreover it was expected that the method could analyse a non-dividing population. E. coli w3110 wt (obtained from the Genetic Stock Centre, Yale University, New Haven, CT) was used because it is a microbe, which is a potential target for food preservatives. Weak organic acids used are acetic acid, sorbic acid, calcium L-lactate hydrate (lactic acid), methyl 4-hydroxybenzoate (paraben) and benzoic acid. They were all added to a final concentration of 10 g l-1, which is a common concentration in food processing. Experiments were carried out close to the pKa value for each compound, to have the same amount of dissociated and undissociated acid present between experiments. It is the undissociated acid, which penetrates the membrane and causes damage to the cell (203). Therefore experiments were conducted at different pH but tests showed that the effects of pH could be neglected apart from the fact that it is the pH that determines the amount of dissociated and undissociated acid present (see paper II). The actions of acetic acid is of special interest to bioprocesses with E. coli, during a fermentation the concentration of acetic acid can increase to relatively high concentrations as a consequence of overflow metabolism and the reactions triggered by high levels of acetic acid are not well studied. Lactic acid is produced by bacteria used in fermentation of milk products such as cheese and yoghurts. It is a small water-soluble molecule and the undissociated form can enter cells via the porins. Inside cells, lactic acid lowers the pH and thereby disturbs the proton motive force, vital for formation of ATP. It has been shown that both acetic acid and lactic acid can have a permeabilising function on bacteria. They are thought to be involved in release of LPS from the outer membrane and thereby disrupt the first protective layer of gram negative bacteria (193). See paper II for more about method and materials. Figure 14 shows the effects of 10 g l-1 acetic acid added to a shake flask culture and incubated for 27 h at pH 5 (pKa for Acetic acid is 4.76). Before acid was introduced to the culture, PI or BOX only stained a small fraction of the population. Staining increased directly after addition of acetic acid and the amount of stained cells increased with time through out the experiment. No growth was observed at these conditions and pH was stable during the experiment.

21

Cell number Cell number

) ) (h h e ( m Relat e i ive flu m Rela T orescenc i tive fluo e T rescence Figure 14. BOX staining (left panel) and PI staining (right panel) of cells incubated in LB medium after addition of 10 g l-1 acetic acid and pH adjustment to 5. Samples withdrawn from the culture at 30 min before addition of acid, 0 min, 1 h and 15 min, 3 h and 15 min, 4 h and 45 min, 5 h and 45 min 23 h and 27 after addition of acid.

Figure 15 shows the effects of three compounds acetic acid, lactic acid and sorbic acid, which all exhibited a similar pattern of staining. Sorbic acid resulted in the fastest initial cell membrane depolarisation, which reached 50% of the population within about 7 hours. The membrane permeabilisation (PI staining) lagged behind this reaction, and also did not reach more than 40% of the population. Incubation with acetic acid resulted in considerably slower membrane damage, but the levels after 29 hours were higher, about 60% of the population and was similar for both BOX staining and PI staining. The staining of lactic acid reached the highest end value, about 70% for both BOX and PI.

80 % stained cells

0 0 5 10 15 20 25 30 35 Time (h)

Figure 15. Kinetics of membrane damage caused by acetic acid (), lactic acid (ο) and sorbic acid (∆). Open symbols represent the population that was stained by BOX and filled symbols corresponding staining by PI.

22 In figure 16, the effects of 10 g l-1 paraben and 10 g l-1 benzoic acid incubated with shake flask cultures of E. coli for 25 hours is shown. Both displayed very different staining patterns compared to results in figure 15. Paraben resulted in a slow disruption of the membrane potential and after 24 hours BOX stained the majority of the population. However, even after more than 24 hour of incubation PI did not stain the cells. It has been shown that parabens can block the phosphotransferase system (PTS) that controls the glucose uptake (204). This could explain the depolarisation of cells but experiments where cells were incubated in glucose free minimal media showed that cells could retain their membrane potential in absence of a carbon source for a long time (paper II). This suggests that paraben has a more direct effect on the membrane depolarisation without causing the permeabilisation exhibited by all other compounds investigated here. Benzoic acid had a more dramatic effect on the culture and both BOX and PI stained the majority of the population within 90 min.

Figure 16. BOX staining (upper left panel) and PI staining (upper right panel) of cells incubated in LB medium with 10 g l-1 paraben at pH 8. BOX staining (lower left panel) and PI staining (lower right panel) of cells incubated in LB medium with 10 g l-1 benzoic acid at pH 4. Samples withdrawn from the culture at 1 h before addition of acid, 1 h and 30 min, 3 h, 4 h, 24 h and 25 after addition of acid.

23 Results presented here showed that flow cytometry in combination with PI/BOX staining can be used to monitor depolarisation and loss of membrane integrity of microorganisms caused by food preservatives even in a non-dividing population. Moreover it was shown that the effects measured was a direct consequence of introducing weak organic acids to the system and not pH alone, but pH plays an important role because that determines the amount of undissociated acid present compared to the dissociated form. The information obtained can be extended with other flourophores. For example intracellular pH can be monitored with stains such as BCECF (46, 205-208).

3.4 Monitoring of inclusion body formation in E. coli by flow cytometry

The possibility to use flow cytometry to monitor formation of inclusion bodies was investigated. The expected outcome was an at-line method to monitor inclusion body formation and information about the protein distribution in a cell population. An E. coli w3110 strain producing promegapoetin (PMP) inclusion bodies derived from E. coli K-12 strain W3110 (209) was used (see paper I). Inclusion bodies are dense (1.3 kg m-3) particles composed of recombinant proteins that miss fold and precipitate as a complex with 5-50% co-precipitated proteins. Inclusion bodies can make up as much as 40% of the cellular protein and they can have the size approaching that of the cell, which may alter their light scattering properties (210). Measurements based on FALS signals have been used to monitor formation of inclusion bodies (118-120). The possibility to use FALS and RALS was investigated as shown in figure 17 and 18. Figure 17 shows FALS signals for two shake flasks cultures, one was induced (nalidixid acid) to produce recombinant protein and hence inclusion bodies, one was not induced (negative control). The induced culture showed an increase in FALS signals after induction, absent in the uninduced culture, which displayed a decreasing trend throughout the experiment. In figure 18, FALS and RALS signals were obtained during a glucose limited fed batch process. Both signals had an increasing trend during the period when the inclusion bodies accumulated (after induction at 12.15 h). However, during the initial phase, when almost no inclusion bodies were formed, the signals showed the opposite trend; i.e. declining with time. Thus from these experiments it was not possible to determine if the increase in FALS and RALS signals is caused by formation of inclusion bodies or as a consequence of introducing nalidixid acid to the system, which is know to inhibit DNA replication (211, 212).

24 90 Induced culture Induction

Un-induced culture

Relative intensity

0 0123456 Sample

Figure 17. The FALS signals from E. coli cells during two shake flask experiments, One was induced for production of PMP inclusion bodies and one was not induced.

20

FALS

RALS Relative intensity Feed start 8.57 h Induction 12.15 h 0 0 5 10 15 20 25 30 35 Time (h)

Figure 18. The FALS and RALS signals from E. coli cells during a fed batch process for production of PMP inclusion bodies

Since FALS and RALS signals had proven to be unreliable for analysing inclusion body formation, a more direct approach was investigated, labelling with antibodies. The inclusion bodies were labelled with a primary antibody, specific for the target protein and then with a secondary fluorescent antibody, specific for the primary antibody (figure 19).

25 Fluorescent secondary antibody Primary antibody Genome

Inclusion body

Outer membrane Cell wall Inner membrane

Figure 19. Staining of inclusion bodies inside permeabilised E. coli cells with a primary and a secondary antibody.

Staining was performed both on inclusion bodies released from the cells by disintegration (high pressure) and on inclusion bodies that were retained inside permeabilised cells (DMSO+lysozyme). A test was conducted to use a system with only a primary fluorescent antibody (data not shown). This would provide a less labour demanding and faster protocol but the resolution was not satisfactory and the results outlined in figure 20, 21, 22, 23 and 24 were obtained with both a primary and a secondary antibody. The method takes 1.5 h to perform. Staining of inclusion bodies released to the media by disintegration (high pressure) of the cells can be seen in figure 20. Two peaks appear in figure 20, No 1, the high fluorescent peak is inclusion bodies labelled with antibodies. The low fluorescent peak is likely cell debris because it appears in the same position as untreated cell debris, figure 20, No 2. The specificity of the secondary anti bodies were tested in figure 20, No 3 and the specificity of the primary anti bodies were tested in figure 20, No 4, it was shown that they don’t cause any unspecific binding, hence any unspecific signal.

Cell number

4 3

2

R 1 elative flu orescence

Figure 20. Specificity of the antibody staining of PMP inclusion bodies in disintegrated cell suspension. 1: Staining with both primary and secondary antibodies 2: Control without antibody treatment 3: Control treated with only the fluorescent secondary antibody 4: Control with cells lacking inclusion bodies but treated with both antibodies

26

Up till now it had been shown that it was possible to detect labelled inclusion bodies and that the staining was specific. But to label inclusion bodies released to the media cannot provide information about the distribution of protein expression in the population, such information can only be obtained if the inclusion bodies are kept inside the cells. A protocol to permeabilise the cells to the antibodies was tested. It was important that the protocol would permeabilise the membranes and cell wall but it should not be too harsh, which would disintegrate the cells. DMSO was used to permeabilise the membranes and lysozyme to digest the cell wall. In figure 21 (top panels), The staining of several cells is shown and as can be seen the majority of the cells have been labelled with antibodies. In figure 21 (bottom panels), one or possibly two cells are shown. It can be seen that the inclusion bodies are kept inside and that the staining is specific for the inclusion bodies.

Figure 21. Fluorescence microscopy of cells containing PMP inclusion bodies and stained with Alexa fluor 488. Top left picture shows cells illuminated with visible light. Top right picture shows the same cells illuminated with blue light. Bottom left picture shows a cell illuminated with visible light. Bottom right picture shows the same cell illuminated with blue light.

The protocol was used to analyse product formation during a high cell density process of E. coli. In figure 22, results of analysis of inclusion bodies released from the cells by disintegration are shown. It is evident that the high fluorescent peak is gradually moving towards higher fluorescence with process time. Similar results was obtained if the staining were performed intracellular on permeabilised cells (figure 23) but with a decrease in the fluorescence towards the end of the process. A decrease in product concentration could also be detected from data obtained with HPLC (figure 24). Moreover it was established that in this process nearly 100 % of the cells was expressing the recombinant protein (figure 23) hence producing inclusion bodies (this could also be seen in fluorescence microscopy, figure 21 top panels). Flow cytometry results were plotted against data obtained with HPLC and a correlation did exist between the two methods (figure 24). In al, three lab-scale fermentation’s (8 l) and one pilot plant fermentation (600 l) was monitored for inclusion body formation with similar results as shown here (data not shown).

27 Cell number 4

3

2 Process time 1

Relative fluorescence

Figure 22. Monitoring of inclusion body formation during a process. Analyses were made by antibody staining of disintegrated cells at. 1: 2 h before induction, 2: 1.5 h after induction, 3: 3 h after induction and 4: 18 h after induction. 2 hrs before induction, the cells contained about 6.0 PMP per cell dry weight (mg g-1), due to the “leaky” promoter. The final sample contained 48 PMP per cell dry weight (mg g-1).

Cell number 5 4 3

2 Process time Process 1

Relative fluorescence

Figure 23. Monitoring of inclusion body formation during a process. Analyses were made by antibody staining of permeabilised cells at. 1: control that shows the signal from cells without the PMP-plasmid. 2: Sample taken 4.6 h before induction. 3: samples taken 3.7 h after induction. 4: samples taken 7.2 h after induction. 5: samples taken 10.7 after induction.

28 10 60 Fluorescence PMP/cell (%, w/w)

0 20 0 5 10 15 20 25 30 Time (h)

Figure 24. Correlation between the relative fluorescence from permeabilised cells treated with both a primary antibody against the inclusion body and a secondary fluorescent antibody against the primary antibody (filled symbols) and specific PMP concentration (mg/g cell dry weight) according to HPLC analysis (open symbols).

In this study it was shown that the protocol provided the possibility to specifically monitor accumulation of the target protein with process time. Staining of inclusion bodies directly inside permeabilised cells gave information about the distribution of protein expression in the cell population. In addition it was possible to directly measure other particles besides cells with flow cytometry, namely inclusion bodies released from cells by disintegration. Moreover it was shown that antibodies could be used intracellulary in prokaryotic organisms with high specificity, an application most often restricted to eukaryotic organisms.

29

4. Conclusions

Flow cytometry can be used to monitor concentration (cell number) of cells during high cell density processes. It could be applied to both prokaryotic and eukaryotic organisms. The benefit with flow cytometry over dry cell weight analysis, which is the most accepted method, is that it is fast, data can be collected at-line and subpopulations can be analysed. The high number of cells counted in each measurement provides statistics about the population and additional information such as viability can be obtained simultaneously. Viability analysis with PI and BOX as fluorescent markers is reproducible for glucose limited fed-batch processes (E. coli), including pilot plant fermentations. PI proved to be a reproducible marker of viability for P. pastoris processes. The protocol for PI/BOX staining is useful to analyse the kinetics of cell inactivation by weak organic acids, often used as food preservatives. Cell functions such as membrane depolarisation and loss of membrane integrity caused by weak organic acids can be monitored. The information obtained in this study can be extended if other flourophores are used, for example BCECF to analyse intracellular pH. Labelling inclusion bodies with fluorescent antibodies provides a method to specifically monitor accumulation of recombinant proteins. Staining of inclusion bodies directly inside permeabilised cells gave information about the distribution of recombinant protein production in the cell population. In addition, with this technique it was possible to directly measure other particles besides cells with flow cytometry, namely inclusion bodies released from cells by disintegration. Moreover the use of antibodies intracellulary have mostly been applied to eukaryotic cells but here it was demonstrated that it can be used with high specificity on prokaryotic organisms as well. This thesis showed that flow cytometry is a robust method and can routinely be applied to obtain physiological information about microorganisms in a culture. The benefits with using flow cytometry are, it is a fast method, and multi-parameter data can be collected at-line during the process. It can measure more than a thousand cells per second, which makes it possible to collect data from a large number of cells and thereby obtain statistics about the population and subpopulations. Many methods fail to measure properties for single cells and can therefore only provide mean values for the population. However flow cytometry collects data for single cells, this makes it possible to analyse subpopulations with excellent precision.

30

5. Abbreviations

Bis- (1,3-dibutylbarbituric acid) trimethine oxonol BOX Blue fluorescent protein BFP Colony forming units cfu Cyano fluorescent protein CFP Dimethyl sulfoxide DMSO Escherichia coli E. coli Fluorescent in situ hybridisation FISH Forward angle light scatter FALS Fluorescein isothiocyanate FITC Green fluorescent protein GFP Luria-Bertani medium LB Lipopolysacharides LPS Methanol limited fed batch MLFB Polymerase chain reaction PCR Pichia pastoris P. pastoris Promegapoetin PMP Propidium Iodide PI Red fluorescent protein RFP Side angle light scatter SALS Temperature limited fed batch TLFB Yellow fluorescent protein YFP

31

6. Acknowledgements

The work presented in this thesis was performed at the Department of Biotechnology, Royal Institute of Technology, Sweden. I would like to thank my supervisor professor Sven Olof Enfors, for being an excellent supervisor; i.e. conducting every responsibility I think an academic supervisor should and more. I would also like to acknowledge Helene, Erika, Chris Hewitt, Bato, Percy (which I worked together with in different projects), Ela, Tommy (technical staff) Marita and Gunilla (administrative staff). And of course also the other members, present and former of the department. The Swedish Centre for Bioprocess Technology (CBioPT) and the VINNOVA project Biomann supported this project.

32

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