Characterizing solute binding to macroporous ion exchange membrane adsorbers using confocal laser scanning microscopy

Journal of Membrane Science 281 (2006) 609–618

Characterizing solute binding to macroporous ion exchange membrane adsorbers using confocal laser scanning microscopy S.R. Wickramasinghe a,b,c,∗ , J.O. Carlson d , C. Teske e , J. Hubbuch e , M. Ulbricht b a

Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523, USA b Lehrstuhl f¨ ur Technische Chemie II, Universit¨at Duisburg-Essen, 45117 Essen, Germany c Division of Chemical and Biomolecular Engineering, 16 Nanyang Drive, Nanyang Technological University, Singapore 637722, Singapore d Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO 80523, USA e Institut f¨ ur Biotechnologie II, Forschungszentrum J¨ulich, 52425 J¨ulich, Germany Received 18 November 2005; received in revised form 20 April 2006; accepted 21 April 2006 Available online 28 April 2006

Abstract Purification of virus particles for viral vaccines and applications of gene therapy is a major large scale separations challenge. Here adsorption of Aedes aegypti densonucleosis virus by anion and cation exchange membranes has been investigated. As the virus particles are viable at pH values above and below their isoelectric point, adsorption by anion and cation exchange membranes is feasible. The capacity of the membranes for virus particles is orders of magnitude less than the manufacturer’s stated capacity for model proteins such as BSA and lysozyme indicating that binding patterns are very different for different sized solutes. In order to visualize solute binding confocal laser scanning microscopy (CLSM) has been used to observe binding of thyroglobulin, BSA and lysozyme to cation exchange membranes. The results are in qualitative agreement with the measured capacities for virus particles, BSA and lysozyme. However, the results also indicate that the binding patterns for thyroglobulin, BSA and lysozyme are very different. It appears that compared to lysozyme and BSA, only a few pores are available for thyroglobulin binding, in agreement with the much lower measured binding capacity for thyroglobulin compared to BSA and lysozyme. Therefore, CLSM could be a useful visualization technique when designing membranes with optimized pore structures. © 2006 Elsevier B.V. All rights reserved. Keywords: Aedes aegypti densonucleosis virus; Membrane adsorber; Ion exchange membranes; Confocal laser scanning microscopy; Virus purification

1. Introduction Packed bed chromatography is commonly used in the biotechnology industry to isolate and purify compounds of interest. The term chromatography is frequently used in the biotechnology industry to describe any packed bed process. The feed containing the solute of interest flows through the bed. The solute is transported between the resin particles by convective flow. In order to increase the surface area and hence the number of available binding sites (capacity of the resin), chromatographic particles are usually porous. Thus the majority of the binding sites are located on the surface of the internal pores. In order to reach these pores, the solute must diffuse from the bulk feed solution across a liquid film layer at the particle surface and into

∗

Corresponding author. Tel.: +1 970 491 5276; fax: +1 970 491 7369. E-mail address: [email protected] (S.R. Wickramasinghe).

0376-7388/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.memsci.2006.04.032

the pores. Next the solute diffuses by pore diffusion through the pores and attaches to binding sites at the surface of the pores [1]. Conventional chromatography suffers from a number of limitations. The pressure drop across the bed is generally high and tends to increase during operation due to media deformation. Pore diffusion is often slow leading to increases in processing time and possible degradation of fragile biological product molecules [2,3]. Scale up of chromatography columns is often difficult. If the column is not packed carefully, non-uniform resin distribution will lead to channeling of the feed flow. A separation device with ideal properties is a very short column for high flow rates at very low pressure drop, but wide enough for high throughput without channeling. For maximum speed of separation, the separation should only be limited by kinetics, i.e. the diffusion path length accounting for most of the mass transfer resistance has to be negligible. Stacks of membrane adsorbers combine these properties [2,4,5].

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Membrane chromatography which refers to the use of membranes as chromatographic supports uses macroporous membranes that contain functional groups attached to the surface of the internal pores. The feed is pumped through the membrane pores. Thus transport of the solute to the binding site occurs predominantly by convection and the processing time is greatly reduced. The pressure drop is significantly lower compared to packed beds as the flow path, even for a stack of multiple membranes, is much shorter. An important practical advantage is that scale up of membrane devices is easier than packed beds [2–6]. Over the last 25 years numerous investigators have reported protein separations using membranes [2–6]. Though most of these studies indicate the tremendous potential of membrane adsorbers, commercialization of membrane adsorbers has been slow for a number of reasons. The gel based resin capacity for small solutes is higher than the capacity of membrane adsorbers. Further, hindered pore diffusion is often not a major concern for small solutes. However a number of previous investigators have focused on model systems consisting of small protein species as they are usually experimentally simple to operate. Their results confirm the advantages of using membranes as chromatographic supports. However given the efficiency of gel based resin chromatography for isolation and purification of small solutes, there has been little incentive for companies to change an existing manufacturing process in order to incorporate membrane based chromatographic steps. Thus membrane adsorbers will have the most impact on separations that cannot be performed efficiently by resin based chromatography. Microfiltration membranes are often used as the base material for the preparation of membrane adsorbers. Though these membranes are rated using a nominal pore size, they contain a rather wide pore size distribution. The presence of pores that are too large could result in radial concentration gradients within the pores which in turn could lead to early breakthrough resulting in the dynamic capacity being less than the static capacity. The presence of a pore size distribution also means that there will be a residence time distribution since in laminar flow the flow rate depends upon the pore radius to the fourth power. Consequently, if the residence time is less than the characteristic time for binding, again dynamic capacities less than the static capacity will be obtained. Suen and Etzel [7] indicate that by increasing the bed depth, by increasing the number of membranes that are stacked in a module, the effects of the pore size distribution in each membrane will tend to be averaged out leading to sharper breakthrough curves. While this is true, using membranes with optimized pore structures, will lead to higher capacity membrane adsorbers that are likely to be of greater commercial significance. Early attempts to commercialize membrane adsorbers have been reported. Roper and Lightfoot [5] list some of the early commercially available membrane adsorbers whose performance behaviour has been published. As indicated by Klein [3] part of the commercialization problems faced by manufacturers of early membrane adsorbers stems from the fact that in the biotechnology industry any significant manufacturing change

requires regulatory approval from authorities such as the Food and Drug Administration in the United States. Given the significant expense involved in obtaining approval for process changes, biotechnology companies are often very conservative when considering the introduction of new unit operations which do not have a history of commercial use. As commercial processes incorporating adsorptive membrane based separations are established, a history of commercial use will evolve [8]. The lack of commercial scale data on membrane adsorber based separations, often results in designers of purification trains lacking confidence that an adsorptive membrane based system will perform as expected. These design and scale up concerns are being addressed by membrane manufacturers. For example, Sartorius makes radial flow membrane adsorber modules with surface areas ranging from 125 to 19,200 cm2 . Thus experiments conducted on a laboratory scale device may be directly scaled up to manufacturing scale using devices with the same geometry. Recently Yang et al. [9] showed that the dynamic capacity of a large protein molecule (thyroglobulin, 20 nm diameter) decreased rapidly with flow rate while that of a small protein molecule (␣-lactalbumin, 3.5 nm diameter) was less sensitive to flow rate for Q sepharose beads. By contrast, the dynamic capacity for large proteins was the same as the static capacity for Q membranes. These results indicate that pore diffusion of large proteins is severely restricted for resins and suggests that commercially available adsorptive membrane based separations are likely to focus on the isolation and purification of large solute species. Knudsen et al. [10] investigated the use of macroporous ion exchange membranes for production of recombinant monoclonal antibodies. Though conventional ion exchange columns are effective, they have low product throughputs and require large resin volumes in order to overcome the effects of hindered pore diffusion. Further due to hindered pore diffusion the dynamic capacity is a strong function of flow rate. Ion exchange membranes on the other hand exhibit flow rate independent dynamic capacities over a large range of flow rates. Further the capacities of ion exchange membranes for large solutes are often comparable to ion exchange resins [11]. Levy et al. [12] reported binding capacities for a 6.9 kilo base (kb) pair plasmid using 10 different anion exchange resins. While globular proteins typically have equivalent sphere diameters between 3 and 10 nm depending on the molecular weight of the protein and the solution conditions such as pH and ionic strength, the extended shape of a 5 kb plasmid could result in a hydrodynamic diameter as large as 150–250 nm. Capacities of less than 5 mg mL−1 adsorbent were reported for the 6.9 kb plasmid. These capacities are more than an order of magnitude lower than the binding capacities of proteins. Further the highest binding capacity of 5 mg mL−1 occurred for a highly porous resin with pores having a diameter of ∼650 nm. In general adsorbents with pore sizes of 100 nm exhibit capacities that decrease with increasing target compound size, suggesting that large compounds only bind to the outside of the resin. In the future processing of plasmids of up to 50 kb pair may be required further reducing the viability of resin based chromatography.

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Teeters et al. [11] determined the capacity of commercially available macroporous anion exchange membranes (Mustang – Q membranes, Pall), for purification of a 6.1 kb pair plasmid DNA. Unlike anion exchange resins, the dynamic capacity of the membranes adsorbers was found to be independent of flow rate over a large range of flow rates. Further the membrane capacity was found to be 10 mg mL−1 , twice the capacity reported by Levy et al. [12] for highly porous resins and five times greater than the capacity of 15 ␮m particles. These results suggest that membrane adsorbers may be ideally suited for capture of large biomolecules and viruses. Here we have investigated the feasibility of virus capture by ion exchange membranes. Large scale purification of virus vectors for gene therapy based treatments and vaccine production as well as commercial scale purification of large biomolecules such as plasmid DNA is essential if the advances in modern molecular biology are to have clinical value. Results have been obtained for capture of Aedes aegypti densonucleosis virus (AeDNV) using commercially available Sartobind® cation and anion exchange membrane adsorbers provided by Sartorius AG (G¨otttingen, Germany). AeDNV is a mosquito specific parvovirus (non enveloped single stranded DNA). Virus particles are about 20 nm in size and icosohedral in shape with an isoelectric point (pI) of 5.6. AeDNV could find potential applications in integrated vector borne disease control programs. The capacities obtained for AeDNV using macroporous anion and cation exchange membranes are many orders of magnitude less than the capacities reported by Sartorius for binding model proteins such as BSA (MW 67,000, pI 4.9) and lysozyme (MW 14,300, pI 11). While these results are in qualitative agreement with the observations of Levy et al. [12] they indicate that the proportion of the total ion exchange groups used for binding small proteins and AeDNV is very different. Consequently careful design of the membrane pore structure and location of the ion exchange groups will be essential in order to develop optimized membrane adsorbers for virus capture. Developing a method to visualize the actual location of solute binding to the membrane is likely to be very helpful when designing these optimized membranes. Confocal laser scanning microscopy (CLSM) has been used to determine membrane pore structure [13–16]. More recently Reichert et al. [17] have used this technique to visualize protein binding to ion exchange membranes. Here we observe binding of fluorescently labelled thyroglobulin, BSA and lysozyme. Thyroglobulin is similar in size to AeDNV, while BSA and lysozyme are much smaller and are frequently used as model proteins by manufacturers in order to quantify the capacity of macroporous ion exchange membranes.

were grown in 28 ◦ C water that contained 50/50 mixture of tetra fin gold fish flakes and mouse or rat food at a concentration of 0.5 mg mL−1 water. Pupuae and mosquitoes were removed by centrifugation at 3750 rpm and 15 min at 4 ◦ C. The virus water solution was then filtered through a 0.22 ␮m filter (Millipore, Bedford, MA).

2. Materials and methods

2.3. Ion exchange testing

2.1. Production of AeDNV particles

All ion exchange experiments were conducted in triplicate at 20 ◦ C. Average results are reported. Experiments were conducted to determine breakthrough curves and the dynamic capacity of the membranes. Ion exchange membranes containing strong anion and cation exchange groups in a grafted hydrogel

AeDNV particles were produced by exposing newly hatched A. aegypti larvae to transducing particles by introducing them into previously infected water containing AeDNV. The larvae

2.2. RTPCR assay A real time PCR (RTPCR) based assay was used to determine the virus titre in the infective solutions as AeDNV does not show cytopathic effects (CPE). The primers and probe were designed within a conserved region of the viral NS1 gene. Primer Express® oligo design software (Applied Biosystems, Foster City, CA) was used to design forward primer: CAT ACT ACA CAT TCG TCC TCC ACA A, reverse primer: CTT GCT GAT TCT GGT TCT GAC TCT T, and TaqMan probe: FAMCCA GGG CCA AGC AAG CGC CTAMRA. The reaction was performed in 96 well format skirted v-bottomed polypropylene microplates (MJ Research Inc., Waltham, MA) with optical caps (Applied Biosystems, Foster city, CA). RTPCR master mix was prepared using the Brilliant Quantitative PCR Core Reagent Kit (Strategene, La Jolla, CA). Each well consisted of 4 ␮L of unknown sample or standard control DNA pUCA plasmid, 0.3 ␮L of 0.002 mmol/L dye, 9.7 ␮L master mix, 2 ␮L of 0.05 mmol/L forward primer, 2 ␮L of 0.05 mmol/L reverse primer, and 2 ␮L of 5 × 10−3 mmol/L probe. The thermal cycling conditions were: stage 1 = 50 ◦ C for 2 min; stage 2 = 95 ◦ C for 10 min; stage 3 = 95 ◦ C for 15 s, then 60 ◦ C for 1 min (40 repetitions). All reactions were performed in the opticon 2 DNA Engine (MJ Research Inc., Waltham, MA). All samples were analyzed three times and average results are reported. The accuracy of the PCR assay was determined by analyzing 12 samples of the same infective solution and found to be within ±0.5 log units. A PCR based method was used for the quantification of AeDNV virus since more conventional biological assays are not straightforward [18,19]. The quantitative PCR assay is a rapid, sensitive and efficient way to compare samples. Though similar results could be obtained with naked viral genomic DNA, when batches of AeDNV prepared from cell culture or mosquito larvae as described in the manuscript, are exposed to pancreatic DNase prior to quantitative PCR, there is little or no reduction in signal. Also quantitative PCR on pellet fractions after ultracentrifugation under conditions that should pellet virus particles indicates that most of the DNA is pelleted. These results give us confidence that we are measuring DNA from virus particles in these preparations rather than DNA from plasmid transfections or replicative forms.

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Table 1 Properties of Sartobind® membranes as provided by the manufacturer Membrane Q S

pKa 11 1

Functional groups -N+ -(CH

R-CH2 R-CH2 -SO3 −

3 )3

layer on the pore surface were obtained from Sartorius AG and tested. Details of these regenerated cellulose based membranes are given in Table 1. Stacks of these membranes consisted of 15 discs, 25 mm diameter, overall thickness 4 mm and nominal pore size larger than 3 ␮m (as specified by the manufacturer). The frontal area was (25/2)2 π = 490 mm2 . The Q membranes were tested at a feed pH of 7.0 while the S membranes were tested at pH 3.5. The pH of the feed suspension was adjusted by adding HCl solution. The membranes were initially rinsed in TE buffer (10 mmol/L Tris adjusted to pH 7.0 with HCl, spiked with 1 mmol/L EDTA) at a flow rate of 1 mL/min. Next, the feed was introduced at a flow rate of 1 mL/min. Totally 10–50 mL of feed suspension was pumped through the membranes. The membranes were then washed using 4–25 mL of TE buffer pumped at a flow rate of 1 mL/min. The bound virus particles were eluted using 5–25 mL 0.5 mol/L NaCl solution at a flow rate of 1 mL/min. The virus titre in the permeate was determined by collecting 100 ␮L samples for PCR analysis at regular intervals. Finally the membranes were regenerated using 20–25 mL 1 mol/L NaCl solution. The membranes were stored wet in a 1 mol/L KCl solution containing 20% ethanol. Static binding capacity experiments were conducted by cutting samples of Q membranes into small pieces consisting of frontal areas of 2 and 25 cm2 , which were then incubated in 20 mL of water containing AeDNV particles at pH 7.0. The suspension was gently stirred at 290 rpm. The change in virus titre in the suspension was determined by removing 100 ␮L samples at regular intervals for PCR analysis. Experiments were conducted using initial virus titres ranging from 104 to 108 viruses ␮L−1 . Static binding capacities were determined from the difference between the initial solute concentration and the measured solute concentration in solution. Static binding capacities for BSA and lysozyme binding were determined by the manufacturer by contacting the membrane for 3 h with the protein in a buffer solution adjusted to a salt concentration of about 50 mmol. The solution was mixed by placing the samples on a shaker table. In addition, dynamic binding experiments were performed ¨ using an Akta Purifier (Amersham Pharmacia Biotech) with a UV detector set at 280 nm and with five membrane discs (diameter 12 mm) as a stack in a CIM® module (BIA Separations, Ljubljana, Slovenia) according to a procedure described earlier [20]. The feed consisted of 1 mL of protein solution (5 mg mL−1 in feed buffer). The proteins tested were thyroglobulin, BSA and lysozyme (see Table 2). The feed buffers were identical to the buffers used in CLSM experiments (see below). The protein was eluted by increasing the NaCl concentration in the feed buffer to 1 mol/L. Experiments were performed at flow rates ranging from 0.25 to 2.5 mL/min.

Membrane charge

Comments

pH < 11, positive pH > 1, negative

Strongly basic anion exchanger Strongly acidic cation exchanger

2.4. Confocal microscopy 2.4.1. Labelled protein preparation Table 2 summarizes the various solute species used. Proteins were dissolved at a concentration of approximately 10 mg mL−1 in 100 mmol/L sodium carbonate buffer at pH 9.3. Protein concentration was determined by UV-spectrophotometry. Approximately 1 mL (exactly the amount to give 10 mg of total protein) was added to a frozen aliquot of Cy5-NHS ester (0.2 mg of Cy5 dissolved in 10 ␮L of DMSO) and allowed to react (mixing end-over-end) for 30 min. After 30 min, the protein-dye conjugate was separated from unreacted dye on a Sephadex G-15 gel filtration column. The gel filtration step was also used to change from the conjugation buffer (100 mmol/L carbonate, pH 9.3) to the running buffers for CLSM experiments (cf. below). The molar dye to protein ratio (D/P) was determined using UV–vis spectrophotometry, measuring at the absorption maximum of the dye (650 nm for Cy5 and 280 nm for the protein). D/P for the lysozyme, BSA, and thyroglobulin were 0.22 and 0.98, and 9, respectively. 2.4.2. The following buffers were used in CLSM experiments A 20 mmol/L sodium phosphate buffer, pH 7, adjusted to 50 mmol/L total ionic strength with NaCl for lysozyme, and a 20 mmol/L sodium acetate buffer, pH 4, adjusted to 50 mmol/L total ionic strength with NaCl for BSA and thyroglobulin. 2.4.3. Labelled protein dilution Labelled protein solutions were diluted with unlabelled protein in the running buffers to give a final molar D/P ratio of 0.01 in Table 2 Solutes tested Solute

Properties

Tests

AeDNV

d ∼20 nm, pI 5.6

Capacity using Q and S membranes

Thyroglobulin

d ∼20 nm, pI 4.6

CLSM using S membranes Dynamic binding capacity using S membranes

BSA

MW 67,000, pI 4.9

Lysozyme

MW 14,300, pI 11

CLSM using S membranes Dynamic binding capacity using S membranes Manufacturer’s static capacity data for Q membranes CLSM using S membranes Dynamic binding capacity using S membranes Manufacturer’s static capacity data for S membranes

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Fig. 1. Breakthrough curve for virus particles at pH 7.0 for Q membranes. Feed flow rate was 1.0 mL/min.

the case of lysozyme, or 0.05 for BSA. The total protein concentration was adjusted to be 2.0 mg mL−1 for both proteins. These solutions were then used for CLSM experiments. Thyroglobulin tended to precipitate from the buffer solution pH 4 and the cy5-thyroglobulin conjugate was used directly after purification without dilution by unlabelled thyroglobulin. 2.5. CLSM experiments A small piece of membrane was cut using a hole punch and the mass was determined on a balance. The piece of membrane was put into a well on a 96-well plate. 50 ␮L of buffer was added to the well. Then 200 ␮L of the protein solution was added to the well. Based on the manufacturer’s stated static capacity, the membrane samples were contacted with excess protein. CLSM images were acquired with the membrane in the protein solution using a Zeiss LSM510 CLSM equipped with a C-Apochromat 63x/1.2Wcorr objective lens. Cy5 was excited using a He–Ne laser at 633 nm. 3. Results Experimental breakthrough curves for Q and S membranes are given in Figs. 1 and 2, respectively. The feed pH was chosen in order to ensure the virus and ion exchange groups on the membrane were oppositely charged. AeDNV is viable over a pH range of 1–12. Consequently by changing the feed pH adsorption above and below the pI of the virus, binding to anion and cation exchange membranes is obtained.

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Fig. 2. Breakthrough curve for virus particles at pH 3.5 for S membranes. Feed flow rate was 1.0 mL/min.

In Figs. 1 and 2 the virus titre in the permeate is plotted as a function of the total permeate collected. As can be seen both membranes successfully adsorb AeDNV particles from the feed solution. Based on these results the dynamic capacity of the membranes for virus binding was determined. The breakthrough volume was defined as the volume of feed solution that resulted in the virus titre in the permeate being 10% of the feed titre. In industrial practice, a much lower breakthrough titre is likely to be used. Using a lower titre would reduce the dynamic capacities reported here but will not affect our conclusions. Table 3 summarizes the capacity of the membranes for viruses. In addition, manufacturer’s values of the static capacity for BSA for the Q membrane and lysozyme for the S membrane are included. As can be seen the static protein capacity results are many orders of magnitude higher than the dynamic capacity for virus particles. Since the Q membrane displayed a higher capacity for AeDNV, the static capacity of these membranes was also determined. The static binding capacity of the membrane is defined as the maximum binding capacity of the membrane under non flow through conditions. The static binding capacity will depend on the virus titre in solution. Figs. 3 and 4 give typical results for membrane surface areas of 2 and 25 cm2 (equivalent to 0.055 and 0.69 mL bed volume, respectively). In these figures the left hand side y-axis gives the virus titre in solution as a function of time while the right hand side y-axis gives the number of virus particles adsorbed per mL of membrane volume. The membrane volume is the total volume, solid plus pore volume. The number of virus particles adsorbed onto the membrane was determined from the difference between the feed titre and

Table 3 Summary of virus and protein binding results Membrane

Binding target

pH

Solute charge

Membrane charge

Static capacity (mg mL−1 )*

Dynamic capacity (mg mL−1 )

Q

AeDNV

7.0

Negative

Positive

29 (2.61 × 1017 molecules mL−1 ) BSA

9.51 × 10−5 (1.35 × 1010 viruses mL−1 )

S

AeDNV Lysozyme BSA Thyroglobulin

3.5 7.0 4.0 4.0

Positive Positive Positive Positive

Negative Negative Negative Negative

29 (1.22 × 1018 molecules mL−1 ) lysozyme

*

Manufacturer’s data.

1.33 × 10−6 (1.90 × 108 viruses mL−1 ) 24.8 (1.04 × 1018 molecules mL−1 ) 7.9 (7.10 × 1016 molecules mL−1 ) 4.5 (8.89 × 1015 molecules mL−1 )

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S.R. Wickramasinghe et al. / Journal of Membrane Science 281 (2006) 609–618 Table 4 Static adsorption capacity results Initial feed titre (viruses ␮L−1 ) Membrane area (cm2 ) Total virus adsorbed Virus adsorbed/mL membrane (viruses mL−1 )

2.0 × 106 2.0 3.9 × 1010 7.1 × 1011

6.0 × 105 25 7.5 × 109 3.1 × 1010

Fig. 3. Static adsorption results for Q membranes, surface area 2 cm2 incubated in 20 mL water containing virus particles at pH 7.0. The left hand side y-axis gives the virus concentration in solution as a function of incubation time while the right hand side y-axis gives the number of bound virus particles per mL of apparent membrane volume. The adsorbed virus concentration was determined by subtracting the concentration in solution form the initial feed concentration.

the residual virus titre in solution. The static capacity gives the amount of adsorbed virus in equilibrium with the residual virus in solution. In Figs. 3 and 4 the static capacity may be determined after about 500 min of incubation when the virus titre in solution is approximately constant. Figs. 3 and 4 are examples of the many static binding experiments that have been conducted. In earlier work [21] we have used the results of these static binding experiments to determine an adsorption isotherm for virus particles. The initial virus titre is different in the two figures. Further the initial virus concentration in solution will affect the number of virus particles that bind to the membrane. Based on these results static binding capacities for Q membranes were determined. The results are given in Table 4. CLSM was used to visualize binding of thyroglobulin, BSA and lysozyme (see Table 2). The results are shown in Figs. 5–7 for thyroglobulin, BSA and lysozyme. The pI of lysozyme is the same as the pKa of the Q membrane. Thus adsorption of lysozyme onto Q membranes is impossible. However it is possible to adsorb the cationic form of all three solutes onto S membranes. Therefore S membranes were investigated. In Figs. 5–7

Fig. 4. Static adsorption capacity results for Q membranes, surface area 25 cm2 incubated in 20 mL water containing virus particles at pH 7.0. The left hand side y-axis gives the virus concentration in solution as a function of incubation time while the right hand y-axis gives the number of bound virus particles per mL of apparent membrane volume. The adsorbed virus concentration was determined by subtracting the concentration in solution form the initial feed concentration.

Fig. 5. CLSM image of thyroglobulin bound to Sartorius S membrane. This image, 146 ␮m × 146 ␮m was taken at a depth 59 ␮m from the surface of the membrane after 12 h of incubation of the membrane with the thyroglobulin solution at pH 4.0.

Fig. 6. CLSM image of BSA bound to Sartorius S membrane. This image, 146 ␮m × 146 ␮m was taken at a depth 59 ␮m from the surface of the membrane after more than 12 h of incubation of the membrane with the BSA solution at pH 4.0.

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Fig. 7. CLSM image of lysozyme bound to Sartorius S membrane. This image, 146 ␮m × 146 ␮m was taken at a depth 59 ␮m from the surface of the membrane after more than 12 h of incubation of the membrane with the lysozyme solution at pH 7.0.

the amounts and distributions of protein as depicted by the fluorescently labelled solute species is very different indicating qualitatively the effect of pore structure on binding for different sized solutes. All images were taken for a single membrane at a depth of 59 ␮m. On the one hand, it was the intention of this work to analyze protein distribution inside the membranes; on the other hand, it is well known that detection sensitivity and resolution of CLSM decrease with increasing depth. It was found that at the selected depth good resolution was obtained. Dynamic capacities for thyroglobulin, BSA and lysozyme on the Sartobind S membranes and using the same buffer as for the CLSM experiments were also determined. These results are given in Table 3. The dynamic capacity for lysozyme was similar to the manufacturer’s static capacity but it was lower for BSA and thyroglobulin. The protein recovery was 100% for lysozyme. However for BSA and thyroglobulin the recoveries were 90%, and about 85% respectively. For lysozyme and BSA, the binding capacities were unchanged when the flow rate was increased by a factor of 10, but for thyroglobulin the capacity dropped to 3.5 mg mL−1 . 4. Discussion The results obtained in Figs. 1 and 2 indicate that providing the virus particles and membrane are oppositely charged, adsorption by anion and cation exchange membranes is possible. In earlier work, we have shown that adsorbed virus particles may be eluted at a higher concentration than the feed titre [21]. Thus membrane adsorbers could be used to purify and concentrate virus particles. Figs. 1 and 2 indicate that the virus concentration in the permeate after breakthrough initially increases rapidly. However

615

as the feed concentration is approached, the rate of adsorption appears to slow down significantly, thus the virus concentration in the permeate increases very slowly. For the Q membrane (Fig. 1) permeate samples were only analyzed up to a total volume of 20 mL. As can be seen the virus titre in the flow through after 20 mL of permeate have been collected is still significantly less than the original feed titre. Similar effects have been observed by others for adsorption of proteins and plasmid DNA [22,23]. The slow approach of the titre in the flow through to the initial feed titre for proteins has been explained using the ‘car parking’ model [24]. Since initial binding occurs in a random manner geometric blockage and steric hindrance by particles already bound to the surface slows the rate of binding as saturation is approached. Thus the maximum surface coverage is less than monolayer coverage since the space between randomly adsorbed solutes is often too small for adsorption of another large solute. Clearly this effect will be greatest when the solute species is large relative to the spacing between binding sites. This is exactly the situation in our experiments. Thus it is likely that steric hindrance effects are also to be observed for virus binding. Comparing Figs. 1 and 2 it can be seen that at pH 7.0, the virus titre in the flow through for the Q membrane appears to rises to the initial feed titre much more slowly than the titre in the flow through for the S membrane at pH 3.5. In this work our focus was to study virus binding to anion and cation exchange membranes. Thus pH values above and below the pI of the virus (pI = 5.6) were chosen. We have not investigated the effect of pH on virus binding and elution. Each virus particle will contain a number of charge groups with different pIs. Hence, the pI of the virus is an average value. Further competitive binding effects due to the presence of other charged species in the feed solution are likely to be different at pH 7.0 and 3.5. It is not surprising therefore that the breakthrough curves at pH 3.5 and 7.0 appear to reach the feed titre at different rates. Static binding capacities for the Q membrane, determined using the data in Figs. 3 and 4 are given in Table 4. Table 4 indicates that the higher the initial feed titre the higher the capacity of the membrane, as expected. Comparing Figs. 1 and 4 it can be seen that the initial feed titre was similar. The dynamic capacity in Fig. 1, expressed as virus particles adsorbed per membrane volume is 1.35 × 1010 mL−1 . The static capacity in Table 4 for 25 cm2 of membrane frontal area at the same feed titre as in Fig. 1 is 3.1 × 1010 mL−1 (see Table 3). The dynamic capacity is within a factor of 2.5 of the static capacity for AeDNV particles. This result is agreement with the observations of Yang et al. [9] for thyroglobulin. Increasing the flow rate 10 fold had no effect on the dynamic binding capacity for lysozyme and BSA but did lead to a decrease in the binding capacity for thyroglobulin. There are many possible explanations for this observation. It could represent a mass transfer limitation. However it could also be that adsorption of thyroglobulin occurs more slowly than lysozyme and BSA. Specht et al. [21] have shown that by calculating the first and second Damk¨ohler numbers, it is possible to differentiate between these two mechanisms. This was not possible in the present study; nevertheless, the results indicate that solute size has an

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impact on capacity and that – as suggested by Yang and Etzel [22] – thyroglobulin is a much better model for virus particles than smaller proteins. Choosing appropriate operating conditions such as linear velocities for a membrane adsorber module can be complicated. Since the bed depth is usually very different to that of a packed bed using the same linear velocity as the packed column will result in very low residence times for the membrane module. Thus optimal performance for a membrane module may be obtained at very different operating conditions when compared to a packed bed. The Thomas model provides a basis for an initial comparison of membrane and resin chromatography [25,26]. Important assumptions in the Thomas model include a Langmuir type adsorption isotherm and negligible axial diffusion. The Thomas model predicts that the breakthrough curves are functions of three dimensionless parameters, the dimensionless concentration (outlet concentration divided by the feed concentration), the relative mass throughput (ratio between the mass in the feed that has been loaded at time t and the mass that the matrix can adsorb at equilibrium with the feed concentration) and the performance parameter which is similar to the first Damk¨ohler number. Appropriate operating conditions for a membrane module could be estimated by determining the values of the dimensionless parameters for a packed bed and then back calculating the values for the linear velocity, etc. for a membrane module. The manufacturer’s procedure for determining the static capacity of the membranes for BSA and lysozyme is similar to the procedure we have used to determine the static capacity of the membrane for AeDNV (see Section 2). Comparing the capacity for BSA and lysozyme for the Q and S membranes respectively with the capacity for AeDNV, the capacity for virus particles is many orders of magnitude lower. This result is in qualitative agreement with the observation of Levy et al. [12]. Table 4 indicates that the dynamic capacity is significantly higher for thyroglobulin compared to AeDNV even though both species are similar in size. This could be due to the fact that thyroglobulin is more flexible than virus particles. This flexibility could lead to more contacts between the thyroglobulin molecules and ion exchange sites compared to rigid virus particles which leads to the higher observed dynamic capacity. Yang and Etzel [22] studied the binding of ␣-lactalbumin and thyroglobulin to flat sheet macroporous Q ion exchange membranes. They noted that for both proteins there is a very slow approach to saturation well after initial breakthrough. This is in agreement with our results for AeDNV (see Figs. 1 and 2). They further noted that while the car parking model is able to describe the asymmetric breakthrough curve for ␣-lactalbumin, it was less successful for thyroglobulin. Yang and Etzel [22] propose an alternative spreading model to explain the breakthrough curve of thyroglobulin. The spreading model assumes that proteins can change their orientation and conformation upon adsorption. Thus initially a protein may bind to just one ion exchange site. However it can then flatten and spread and attach to multiple ion exchange sites. Consequently in the case of thyroglobulin it is proposed that initially the protein binds in its unspread form. However as time passes

the protein begins to spread and bind to multiple sites on the membrane. The dynamic capacity experiments for thyroglobulin, BSA and lysozyme resulted in recoveries of 85%, 90% and 100%, respectively. Thyroglobulin that is adsorbed in the spread form is much more tightly bound to the membrane. Consequently recovery of the bound protein is likely to be lower compared to proteins that do not exhibit the same degree of multiple adsorption points to the membrane. Since BSA is much smaller than thyroglobulin and lysozyme is even smaller than BSA, one would expect recoveries to increase in the order thyroglobulin, BSA, lysozyme as indicated by the experimental recoveries obtained here. The Q and S microporous membranes tested here contain a pore size distribution [21]. It is likely that smaller pores available for lysozyme and BSA binding are not available for AeDNV binding. In addition attachment of a large AeDNV particle is likely to block binding sites close to it. Qualitatively this should be seen using CLSM. Figs. 5–7 indicate that the binding patterns of thyroglobulin, BSA and lysozyme are very different. In the original figures the bound protein appears blue due to the dye staining and indicates the location of the ion-exchange binding sites in the membrane pores. In these experiments only a fraction of the protein molecules was labeled. Further as indicated by Charcosset and Bernengo [14,15] and Charcosset et al. [16], CLSM is unable to resolve individual proteins. Thus bright regions in Figs. 5–7 do not represent individual protein molecules. Nevertheless, Fig. 5 indicates that there are only relatively few pores that are available for thyroglobulin binding. Fig. 6 indicates that there are many more pores available for BSA binding while Fig. 7 indicates that there are many interconnecting pores as well as smaller pores that are available for lysozyme binding. In this work we focus on binding patterns for proteins of different sizes. Charcosset and Bernengo [14,15] and Charcosset et al. [16] have shown that by labelling the membrane with gold colloidal particles the three dimensional structure of the membrane pores can be obtained by taking a series of images at different depths by changing the position of the focal plane. Reichart et al. [17] used a double labelling technique. The membrane was labelled with DTAF, a FITC derivative from Molecular Probes Europe BV (Leiden, The Netherlands). Their model proteins, lysozyme and BSA, were labelled with Cy5 and Cy3. In their results the membranes are stained green. Membrane pores appear black. Areas where protein binding occurs appear orange/red in colour. In our experiments different membrane samples are used for binding the different proteins. Consequently Figs. 5–7 represent different membrane samples with different pore sizes, and only the labelled protein that is bound to the membrane is identified. Since macroporous membranes are not uniform, and the three images are for three different locations on membranes from the same batch, the results are not for exactly the same pore sizes and size distributions. However by observing a sufficiently large area, the effect of local variations will be minimized. The CLSM images shown in Figs. 5–7 indicate qualitatively the differences in binding patterns for proteins of different size. However, in the lysozyme experiments the fluorescence intensi-

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ties in some areas are very high (“clustered”) while the intensities in other areas were much lower. For BSA on the other hand, the fluorescent spots were far more evenly distributed (assuming a similar morphology of the membrane samples). This result was observed in repeated experiments. The binding experiments were performed under static conditions, i.e. via diffusion of the proteins in the membrane. Considering the nominal binding capacities of the membranes, the experimental conditions were selected so that a depletion of the (bulk) protein solution is unlikely. Nevertheless, it may be speculated that the local capacities might be much higher and the resulting protein clustering may cause a local depletion in nearby pores. It should also be kept in mind that the different affinity of labeled proteins compared to their native counterparts has been shown to have a significant influence on the apparent protein distribution deduced from CSLM experiments as described below [27]. Overall, the results with respect to different bound amounts for the different proteins are in agreement with the measured binding capacities given in Table 3. The largely reduced capacity for BSA compared to lysozyme could be explained by the fact that binding took place at a pH very close to the pI of the protein (Table 2). These conditions are not ideal for measuring the maximum capacity of an ion-exchange membrane. The same arguments are valid for thyroglobulin, but the even lower capacities and the influence of flow rate on capacity indicate that the larger size as compared to BSA also has an influence on binding. Nevertheless, the values for lysozyme confirm manufacturer’s data, and they also support the assumption that the overall intensities in the CLSM images correlate with the amount of bound protein. However to fully explaining the differences between the patterns for lysozyme on the one hand (Fig. 7), and for BSA and thyroglobulin (Figs. 6 and 5) on the other hand will require further variations of experimental conditions. Since all samples were incubated for more than 12 h, the results represent static binding results. Several investigators have used CLSM to observe binding patterns in chromatographic resins [28–30]. Flow cells have also been designed in order to study dynamic binding patterns. Importantly, due to the necessity to match the refractive index of the resin material with that of the bulk fluid, experiments are usually conducted at viscosities higher than that of water. Furthermore, competitive binding between the labelled and unlabelled protein can lead to experimental artifacts in dynamic protein uptake measurements using CLSM [27]. The addition of a dye to a small protein such as lysozyme will have a greater effect on the charge, hydrophobicity and size of the protein compared to a larger protein such as thyroglobulin and virus particles. It is likely that artifacts due to competitive binding would be less evident for fluorescently labelled virus particles. On the other hand, we believe the interpretation of the CLSM data for our static binding experiments would not be influenced very much by labeling effects. The most important result is the indication that a large fraction of the membrane pore volume can be used for smaller proteins but is not available for thyroglobulin binding (Fig. 5). Qualitatively, this is similar to the very largely reduced virus binding

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capacities. Clearly, optimization of the pore structure and the three dimensional arrangements of the ion exchange groups will be essential to maximize the capacity for viruses. A membrane adsorber with large interconnected pores and a very narrow size distribution would be optimal. Further the surface of the membrane and the pores should be covered by an ion-exchange layer with a rather open three-dimensional structure. Models that have been derived to describe the breakthrough curves for large proteins, indicate that it is important that the spacing between the ion exchange groups relative to the size of the target solute, is carefully chosen in order to avoid asymmetric breakthrough curves. CLSM could provide rational guidelines for such a further development of functional macroporous membranes. 5. Conclusion Dynamic capacity data for Sartobind® Q and S membranes have been determined for AeDNV, lysozyme, BSA and thyroglobulin. By changing the pH of the feed solution above and below the pI of AeDNV particles, binding to anion and cation exchange membranes is observed. As the size of the adsorbed species increases, the capacity of the membrane decreases. Importantly, dynamic and static capacities for virus and protein are similar suggesting that membrane adsorbers may be well suited for isolation and purification of virus particles. This finding along with the large differences between virus and protein capacities suggests that not the mass transfer in the pore structure but the accessibility of binding sites in small pores and (presumably) in three-dimensional ion-exchange layers is critical for the membrane adsorber performance for binding larger bioparticles. Binding of fluorescently labelled thyroglobulin, BSA and lysozyme has been observed using CLSM. The results are in qualitative agreement with the capacity data and their tentative interpretation. The results suggest that CLSM could be a useful tool when designing optimized membrane adsorbers. Acknowledgements Funding for this work was provided by the National Institutes of Health (N01-A125489), National Science Foundation (CAREER program BES 9984095) and the Monfort Foundation. Dr Volkmar Thom, Sartorius AG, provided the membranes. Ms Anne Wolf and Rachel Specht conducted some of the virus binding experiments. Claudia Schenk performed the dynamic protein experiments at UDuE. References [1] P.A. Belter, E.L. Cussler, W.-S. Hu, Bioseparations Downstream Processing for Biotechnology, John Wiley & Sons, New York, 1988. [2] R. Gosh, Review: protein separation using membrane chromatography: opportunities and challenges, J. Chromatogr. A 952 (2002) 13–27. [3] M. Kaufmann, Review: unstable proteins: how to subject them to chromatographic separation procedures, J. Chromatogr. B 699 (1997) 347–369. [4] E. Klein, Review: affinity membranes: a 10-year review, J. Membr. Sci. 179 (2000) 1–27.

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[5] D.K. Roper, E.N. Lightfoot, Review: separation of biomolecules using adsorptive membranes, J. Chromatogr. A 702 (1995) 3–26. [6] W.S.W. Ho, K.K. Sirkar, Membrane Handbook, van Nostrand Reinhold, New York, 1992. [7] S.-Y. Suen, M.R. Etzel, A mathematical analysis of affinity membrane bioseparations, Chem. Eng. Sci. 47 (6) (1992) 1355–1364. [8] W. Demmer, D. Nussbaumer, Large-scale membrane absorbers, J. Chromatogr. A 852 (1999) 73–81. [9] H. Yang, C. Viera, J. Fischer, M.R. Etzel, Purification of a large protein using ion-exchange membranes, Ind. Eng. Chem. Res. 41 (2002) 1597–1602. [10] H.L. Knudsen, R.L. Fahrner, Y. Xu, L.A. Norling, G.S. Blank, Membrane ion-exchange chromatography for process-scale antibody purification, J. Chromatogr. A 907 (2001) 145–154. [11] M.A. Teeters, S.E. Conrardy, B.L. Thomas, T.W. Root, E.N. Lightfoot, Adsorptive membrane chromatography for purification of plasmid DNA, J. Chromatogr. A 989 (2003) 165–173. [12] M.S. Levy, R.D. O’Kennedy, P. Ayazi-Shamlou, P. Dunnill, Biochemical engineering approaches to the challenges of producing pure plasmid DNA, Trends Biotechnol. 18 (2000) 296–305. [13] C. Charcosset, F. Yousefian, J.-F. Thovert, P.M. Adler, Calculation of flow and solute deposition through three dimensional reconstructed model for microporous membranes, Desalination 145 (2002) 133–138. [14] C. Charcosset, J.-C. Bernengo, Comparison of microporous membrane morphologies using confocal scanning laser microscopy, J. Membr. Sci. 168 (2000) 53–62. [15] C. Charcosset, J.-C. Bernengo, Characterization of microporous membranes using confocal scanning laser microscopy in fluorescence mode, Eur. Phys. J. AP. 12 (2000) 195–199. [16] C. Charcosset, A. Cherfi, J.-C. Bernengo, Characterization of microporous membrane morphology using confocal scanning laser microscopy, Chem. Eng. Sci. 55 (2000) 5351–5358. [17] U. Reichert, T. Linden, G. Belfort, M.-R. Kula, J. Th¨ommes, Visualizing protein adsorption to ion exchange membranes by confocal microscopy, J. Membr. Sci. 199 (2002) 161–166. [18] J.P. Ledermann, E.L. Suchman, W.C. Black IV, J.O. Carlson, Infection and pathogenicity of the mosquito densoviruses AeDNV, HeDNV, and APeDNV in Aedes aegypti mosquitoes (Diptera: Culicidae), J. Econ. Entomol. 97 (2004) 1827–1835.

[19] T.W. Ward, M.S. Jenkins, B.N. Afanasiev, M. Edwards, B.A. Duda, E. Suchman, M. Jacobs-Lorena, B.J. Beaty, J.O. Carlson, Aedes aegypti transducing densovirus pathogenesis and expression in Aedes aegypti and Anopheles gambiae larvae, Insect. Mol. Biol. 10 (2001) 397– 405. [20] M. Ulbricht, H. Yang, Porous polypropylene membranes with different carboxyl polymer brush layers for reversible protein binding via surfaceinitiated graft copolymerization, Chem. Mater. 17 (2005) 2622–2631. [21] R. Specht, B. Han, S.R. Wickramasinghe, J.O. Carlson, P. Czermak, A. Wolf, O.-W. Reif, Densonucleosis virus purification by ion exchange membranes, Biotechnol. Bioeng. 88 (2004) 465–473. [22] H. Yang, M.R. Etzel, Evaluation of three kinetic equations in models of protein purification using ion-exchange membranes, Ind. Eng. Chem. 42 (2003) 890–896. [23] H.H. Endres, J.A.C. Johnson, C.A. Ross, J.K. Welp, M.R. Etzel, Evaluation of ion-exchange membrane for the purification of plasmid DNA, Biotechnol. Appl. Biochem. 37 (2003) 259–266. [24] J. Talbot, G. Tarjus, P.R. van Tassel, P. Viot, From car parking to protein adsorption: an overview of sequential adsorption processes, Colloid. Surf. A 165 (2000) 287–324. [25] N.C. Thomas, Heterogeneous ion exchange in a flowing system, J. Am. Chem. Soc. 66 (1944) 1664–1666. [26] C. Charcosset, Z. Su, S. Karoor, G. Duan, C.K. Colton, Protein A immunoaffinity hollow fiber membranes for immunoglobulin G purification: experimental characterization, Biotech. Bioeng. 54 (4) (1995) 415–427. [27] C. Teske, M. Schr¨oder, R. Simon, J. Hubbuch, Protein-labelling effects in Confocal laser scanning microscopy, J. Phys. Chem. B 109 (2005) 13811–13817. [28] V. Kasche, M. de Boer, C. Lazo, M. Gad, Direct observation of interparticle equilibration and the rate limiting step in adsorption of proteins in chromatographic adsorbents with confocal laser scanning microscopy, J. Chromatogr. B 290 (2003) 115–129. [29] U. Tallarek, E. Rapp, H. Sann, U. Reichl, A. Seidel-Morgenstern, Quantitative study of electrokinetic transport in porous media by confocal laser scanning microscopy, Langmuir 19 (2003) 4527–4531. [30] A. Ljungl¨of, J.J. Th¨ommes, Visualizing intraparticle protein transport in porous adsorbents by confocal microscopy, J. Chromatogr. A 813 (1998) 387–395.