Anal. Chem. 1997, 69, 3482-3489
Flow Injection Renewable Surface Immunoassay for Real Time Monitoring of Biospecific Interactions Bodil Willumsen, Gary D. Christian, and Jaromir Ruzicka*
Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195
An automated system for performing rapid immunoassay, kinetic measurements, and affinity ranking of biomolecular interactions using fluorescence-labeled ligands is described. Its distinctive feature is the automated renewal of solid phase for each measurement, which avoids the need for regeneration of the sensing surface. This systemsflow injection renewable surface immunoassay (FIRSI)sis used for the first time here for determination of rate constants for an antibody/antigen interaction and for affinity ranking of several related antigens against one antibody. The performance of the system is compared with a commercial BIAcore system that uses surface plasmon resonance for monitoring biomolecular interactions. While the values of association and dissociation rate constants for human serum albumin (HSA) with monoclonal anti-HSA antibody obtained by these techniques were comparable, it is shown that the FIRSI technique requires simpler instrumentation, handles a broader size range of analytes, and does not suffer from disturbances caused by changes in the refractive index. The use of optical biosensors for carrying out automated immunoassay and for measurement of biomolecular interactions has grown in popularity since the introduction of the BIAcore instrument, the first commercial biosensor for this purpose, by Pharmacia in 1990.1 In the pioneering work from this group, the kinetics of monoclonal antibody/antigen interactions was studied using surface plasmon resonance for detection and a microminiaturized flow injection system for solution handling.2 Since then, over 500 papers applying this technique have appeared3-6 and several meetings have been held, proving this approach to be suitable for the study of a wide range of interactions between macromolecules in solution and macromolecules immobilized on a solid substrate. BIAcore has found wide acceptance because it does not require the use of a labeled molecule, since the interaction between the molecules of interest is monitored (1) Jo¨nsson, U.; Fa¨gerstam, L.; Ivarsson, B.; Johnsson, B.; Karlsson, R.; Lundh, K.; Lo ¨fås, S.; Persson, B.; Roos, H.; Ro ¨nnberg, I.; Sjo ¨lander, S.; Stenberg, E.; Ståhlberg, R.; Urbaniczky, C.; O ¨ stlin, H.; Malmqvist, M. BioTechniques 1991, 11 (5), 620-7. (2) Karlsson, R.; Michaelsson, A.; Mattson, L. J. Immunol. Methods 1991, 145, 229-40. (3) Dubs, M. C.; Altschuh, D.; van Regenmortel, M. H. V. Immunol. Lett. 1992, 31 (1), 59-64. (4) Felder, S.; Zhou, M.; Hu, P.; Urena, J.; Ullrich, A.; Chaudhuri, M.; White, M.; Shoelson, S. E.; Schlessinger, Mol. Cell. Biol. 1993, 13 (3), 1449-55. (5) O’Shannesy, D. J.; Brigham-Burke, M.; Soneson, K. K.; Hensley, P.; Brooks, I. Anal. Biochem. 1993, 212 (2), 457-68. (6) Masson, L.; Mazza, A.; Brousseau, R. Anal. Biochem. 1994, 218 (2), 40512.
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optically, essentially as an increase in the mass of the material immobilized on the sensor surface. This strength is at the same time the Achilles heel of the surface plasmon resonance detection scheme since, obviously, the smaller the molecules of interest, the more difficult they are to detect.7,8 Secondly, the optical signal is quite sensitive to changes of refractive index of the surrounding solution, which can obscure the initial stages of the response curve.2,8 Therefore, it is of interest to explore an alternative, which is made possible by the recent advances in the flow injection technique. It is the introduction of sequential injection,9 and the design of the jet ring cell,10 that makes it possible to approach the measurement of the kinetics of liquid/solid interactions in a novel way. The flow injection renewable surface immunoassay (FIRSI) employs polymer microbeads (such as those conventionally used for affinity chromatography) as a solid phase on which one interaction partner is immobilized. The beads are trapped in a well-defined geometry adjacent to a detector that measures changes of fluorescence intensity. Using our previous work on FIRSI as a starting point,11 this paper expands the use of FIRSI to the study of kinetics of biomolecular interactions and affinity ranking. These results are presented along with the results obtained by means of the BIAcore instrument. At the beginning of a typical FIRSI measurement cycle, a fresh portion of beads with immobilized biomolecules (receptors) is introduced into the jet ring cell and perfused by a carrier stream. At this time, a stable background signal is observed by the detector (Figure 1A). Next, the beads are perfused by a zone of the labeled ligand. As some of the labeled ligand binds to the immobilized receptor, a change in the fluorescence signal is observed by the detector (Figure 1B). When the labeled zone has passed through the flow cell, the beads are continually perfused with buffer solution. At this time, the detector shows a decrease in fluorescence due to dissociation of the ligand from the surface (Figure 1C). At the end of the measurement cycle, the gap of the jet ring opens and the beads are flushed to waste (Figure 1D). Note that the binding curve consists of a signal contribution from the bound ligand as well as from the unbound ligand in the solution. The latter contribution is reflected in (but not necessarily equal to) the tracer curve, also shown in Figure 1. The tracer curve is obtained by repeating the binding experiment, using beads with no receptor. (7) Karlsson, R. Anal. Biochem. 1994, 221, 142-51. (8) Karlsson, R.; Ståhlberg, R. Anal. Biochem. 1995, 228, 274-80. (9) Ruzicka, J.; Marshall, G. Anal. Chim. Acta 1990, 232 (2), 329-43. (10) Ruzicka, J.; Pollema, C. H.; Scudder, K. M. Anal. Chem. 1993, 65, 356670. (11) Pollema, C. H.; Ruzicka, J. Anal. Chem. 1994, 66, 1825-31. S0003-2700(97)00268-0 CCC: $14.00
© 1997 American Chemical Society
Figure 1. The principle of FIRSI. (A) Beads with immobilized receptor are loaded into the flow cell, (B) the beads are perfused with labeled ligand that binds to the immobilized receptor, (C) bound ligand gradually dissociates and is carried away, and (D) a gap is created that allows the used beads to be flushed to waste. The corresponding binding and tracer curves are shown below. The 19 repeated binding curves were created by injection of 40 µL of 25 µg/mL FITC-labeled insulin over beads with immobilized antibodies against insulin. The tracer curve is the result of injection of a similar zone of FITC-insulin over antibody-free beads and thus shows the “trace” of the antigen concentration in solution during the binding experiment. Flow rates were 0.5 mL/min.
The main regions of the binding curve are the association part (B) and the dissociation part (C), which are the sources of information on the kinetic behavior of the complex under study. The transition regions (A-B and B-C) need to be minimized in order to carry out the study in the most effective manner. The extent of the transition regions is measured by means of the tracer curve. While the importance of the shape of the tracer curve for the experimental design will be discussed later, it is essential to note here that the two response curves shown in Figure 1 also represent the two extreme cases of biomolecular interactionssrapid stable binding of a ligand (here insulin) and no binding at all (tracer)sshowing the suitability of FIRSI as a simple tool for affinity ranking of different ligands to the same solid phase bound receptor.
THEORETICAL BACKGROUND The interaction between a surface-immobilized receptor (antibody A) and a ligand in solution (antigen B) to form a complex (AB) is typically described by the simple rate equation
d[AB]/dt ) ka[A][B] - kd[AB]
(1)
where ka and kd are the association and dissociation rate constants, respectively, t is time, and the brackets indicate concentrations. Since the concentration of free binding sites [A] ) [A]tot - [AB],
the following equation can be derived:
d[AB]/dt ) ka[A]tot[B] - (ka[B] + kd)[AB]
(2)
When B is labeled, the formation of the complex AB on the solid phase gives rise to a detector response RAB ) R[AB], R being a constant. The maximum response, corresponding to saturation of the available binding sites, is called Rmax, and we get
dRAB/dt ) kaRmax[B] - (ka[B] + kd)RAB
(3)
This equation, which is analogous to the one used to describe most BIAcore experiments,2 shows that the change in response from bound ligand RAB depends on the rate constants as well as on the ligand concentration in solution [B] at any given time and the surface binding capacity Rmax, the latter being constant for a given experimental run. Therefore, a central problem for determining the association and dissociation rate constants is knowing the value of [B] at any given time of the experiment. The knowledge of this parameter is also essential because, in the case of fluorescence labeling, the observed response R will be the sum of the contribution from ligand bound to the solid surface (RAB as described above) and the contribution from ligand in solution RB)β[B], β being a constant:
R ) RAB + RB ) R[AB] + β[B] Analytical Chemistry, Vol. 69, No. 17, September 1, 1997
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Thus, when using a fluorescently labeled ligand B, the total measured response R is governed by the equation
d[B] dR ) R(ka[Atot][B] - (ka[B] + kd)[AB]) + β (5) dt dt The concentration profile of [B] during the experimental run is given by the shape of the tracer curve. As exemplified in Figure 1, [B] ) 0 during phase C of the experiment (beyond 40 s). In this case eq 5 is reduced to
dR/dt ) -Rkd[AB] ) -kdR
(6)
R ) Ro exp(-kdt)
(7)
Consequently
and kd can be determined either as the negative slope from a plot of ln(R) versus time or through nonlinear regression. The determination of the association rate constant, ka, is more complicated. To resolve ka from the effect of [B] in eq 5, either an experimental or a mathematical approach can be followed. The experimental approach aims at designing a flow injection system that will keep [B] constant over a defined period of time, thus obtaining a “square” tracer profilesa condition obviously not fulfilled by the flow system characterized by the tracer curve shown in Figure 1. The mathematical approach uses the information on the changes of [B] contained in the tracer curve (which therefore does not need to be strictly square) to fit the measured response iteratively to eq 4, while varying the assumed rate constant until a close fit between measured and predicted value is obtained. Only the experimental approach involving a square tracer profile will be dealt with in this paper. It is important to note that the experiment must be designed so that only a small fraction of the ligand in solution binds to the solid phase at any given time; this ensures that the response contribution from unbound ligand is roughly the same as that in the previously obtained tracer curve. The fact that d[B]/dt ) 0 during a given time interval leads to a simplification of eq 5:
dR/dt ) R(ka[Atot][B] - (ka[B] + kd)[AB]) ) kdRmax[B] - (ka[B] + kd)R (8) Assumptions leading to eqs 7 and 8 are identical to those proposed by Karlsson et al.2 when deriving the theoretical basis for the BIAcore technique, which also exploits the concept of the square profile. These assumptions are followed here, in order to make comparison of experimental data as close as possible, by using the same mathematical model and software for calculation. It has, however, been recently pointed out that the assumptions of the model do not always apply.12 In the present context, it is therefore important to emphasize that we aim at comparison of methodologies (BIAcore and FIRSI), rather then verification of the theories of kinetic behavior of specific receptor/ligand pairs. EXPERIMENTAL SECTION Apparatus. The sequential injection apparatus has previously been described in detail.9-11 Three critical components of FIRSI (12) Edwards, P. E.; Gill, A.; Pollard-Knight, D. V.; Hoare, M.; Buckle, P. E.; Lowe, P. A.; Leatherbarrow, R. J. Anal. Biochem. 1995, 231, 210-7.
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have been optimized for this work: (a) the flow path through which the injected zones will move from the injector to the detector has been minimized, (b) the system has been operated at two different flow rates during the binding step (Figure 1B) to affect the shape of the tracer curve, and (c) the jet ring cell has been redesigned. (a) In order to minimize reagent consumption and to obtain a square tracer profile, the volume and the length of the conduit between injector and jet ring cell needed to be minimized. The shortest possible length of 0.5 mm i.d. tubing allowed by the components of our system was 15 cm, corresponding to a dead volume of 30 µL. (b) To minimize reagent consumption and to prolong the flat portion of the tracer curve, the initial flow rate of 1.2 mL/min in the ranking experiments was decreased to 0.2 mL/min when a steady state concentration of ligand was reached and raised back to 1.2 mL/min to wash out the trailing end of the ligand zone (see Table 1). (c) The jet ring cell has been redesigned by using magnetic lifting of the inlet tube to open the ring gap at the end of the measurement cycle (Figure 1D).13 The inlet tube was a 5 cm long stainless steel rod with an inner diameter of 0.8 mm (Upchurch Scientific, Oak Harbor, WA) and the amount of beads retained during a single kinetic experiment was 1 mg, ranging in diameter from 45 to 165 µm. The jet ring cell was assembled in-house using a 24 V solenoid actuator (type 28, continuous, Guardian Electric, Woodstock, IL), whose iron core was fitted to hold the inlet tube. A LabTek microscope chamber with a no. 1 borosilicate coverslip bottom (Nunc Inc., Naperville, IL) served as the bottom of the flow system. The sequential injection system used for concentration and rate constant measurements was an Alitea FIALab 3300 (Alitea USA, Medina, WA) with a high-precision piston pump with a 5 mL syringe serving as the principal drive. The built-in peristaltic pump was used for waste removal and for keeping the beads in suspension, and an external trigger was used to activate the jet ring cell. All components were controlled with the FIAlab 1.0 software supplied with the system. The Teflon tubing for the flow path and all connectors were from a kit supplied with the instrument by Upchurch Scientific (Oak Harbor, WA). The detector for the jet ring cell was a Seilor Microlux compound microscope with a 10× achromat objective and an epiluminescence attachment for FITC (Seilor Microlux, WA), a fiber-optic light source (Lights by O’Ryan, Vancouver, WA) and a Nikon P1 photo multiplier tube (Nikon, Tokyo). The flow system used for affinity ranking experiments was the same as above, but the detector was a Zeiss Axiovert 100 inverted microscope (Carl Zeiss, Oberkochen, Germany) with a 10× dry fluar objective, equipped with a PTI photometry system with data acquisition hardware and software (Photon Technologies International, South Brunswick, NJ). The light source was a 75 W xenon arc lamp with a monochromator set to 4 nm band-pass. An excitation wavelength of 495 nm was chosen, and the microscope was equipped with a FITC dichroic and emission filter. The detector was a PTI Model 710 cooled photon counting PMT. The size of the detected region was ∼2 mm2. Data were collected at 5 Hz. The BIAcore system was from Pharmacia Biosensor AB (Uppsala, Sweden). All chemicals, sensor chips, and software (13) Mayer, M.; Ruzicka, J. Anal. Chem. 1996, 68, 3808-14.
Table 1. FIRSI Procedures
kinetic 1 2 3 4 5 ranking 1 2 3 4 5 6 7 8 concentration 1 2 3 4 5 6
volume (µL)
flow rate (mL/min)
aspirate beads with immobilized receptor transport beads to jet ring cell aspirate labeled ligand perfuse beads with ligand zone lift jet ring and flush beads to waste
40 240 300 800 200
1.2 1.2 2.4 0.1 3
aspirate beads with immobilized receptor transport beads to jet ring cell aspirate labeled ligand bring ligand zone to beads perfuse beads with uniform ligand conc remove ligand zone from beads perfuse beads with buffer lift jet ring and flush beads to waste
20 270 1200 250 450 1000 700 800
1.2 1.2 2.4 1.2 0.2 1.2 0.2 3
aspirate beads with immobilized receptor transport beads to jet ring cell aspirate labeled ligand aspirate unlabeled ligand perfuse beads with ligand zones lift jet ring and flush beads to waste
40 240 80 80 800 200
0.5 0.5 0.5 0.5 0.5 3
were supplied by the manufacturer and used according to the manual. The software for data processing was BIAevaluation software version 2.1 from Pharmacia. All data obtained by BIAcore and FIRSI were treated identically. Reagents. (a) Kinetics of Binding and Dissociation Using Labeled Ligands. Human serum albumin (HSA) and monoclonal mouse IgG2a antibodies against HSA (8.6 mg/mL) in unpurified ascites fluid were from Sigma (St. Louis, MO). The beads comprising the solid phase were a 50% slurry of protein A-coated Sepharose 4B gel (Zymed, San Francisco, CA) with diameters of 45-165 µm, in the following referred to as protein A beads. A 2 mL suspension consisting of 1850 µL of buffer, 100 µL of bead slurry, and 50 µL of ascites fluid was incubated for 1 h at room temperature during constant rotation, allowing the antibodies to bind to protein A. During measurements, the beads were kept in suspension by pumping air through the solution via a tube connected to the peristaltic pump. The use of a magnetic stirrer is not suitable as this crushes the agarose beads. For fluorescence labeling, 100 mg of the protein to be labeled (HSA) was diluted to 10 mL with a pH 9.3 carbonate/saline buffer, and 400 µL of 5 mg/mL fluorescein isothiocyanate (FITC) in dimethyl sulfoxide (DMSO) was added while slowly stirring. The mixture was incubated for 4 h at 5 °C as recommended by Sigma. The average molar ratio was two labels per albumin molecule. While fluorescence labeling is normally followed by separation of labeled protein from the unbound label by size exclusion chromatography, we found this unnecessary when performing FIRSI. The unbound label constitutes an increased background response when it passes the detector, but it is simply washed past the beads since the contact time is too short and the pH unfavorable for binding of free label to take place. A HEPESbuffered saline (HBS) from Pharmacia Biosensor (10 mM HEPES, 0.15 M NaCl, 3.4 mM EDTA, 0.05% surfactant P20, pH 7.4) was used as carrier and dilution buffer in order to facilitate comparison of the results with BIAcore. (b) Affinity Ranking. Theophylline and theophylline-7-acetic acid were from Fluka (Switzerland), while aminotheophylline and the monoclonal antibody MAb459 (5.9 mg/mL in ascites fluid) were gifts from Pharmacia Biosensor A/B (Uppsala, Sweden).
Monoclonal mouse IgG1 anti-insulin antibodies clone K36aC10 (6.9 mg/mL in ascites fluid), bovine insulin, sheep insulin, pork insulin, human recombinant insulin from yeast, human recombinant proinsulin, and FITC-labeled bovine insulin were from Sigma. Both types of antibodies were immobilized on protein G-coated Sepharose 4B gel (Zymed) using the same protocol as above. The Pharmacia HBS buffer was used for all experiments. Aminotheophylline was labeled using the same protocol as above, with a molar dye/ligand ratio of 1. (c) Concentration Measurements. Guinea pig antiserum against bovine insulin, bovine insulin, and FITC-labeled bovine insulin were from Sigma. A 120 µL aliquot of antiserum was incubated with 100 µL of protein A bead slurry and 1780 µL of buffer as described above. A phosphate-buffered saline (PBS; 0.01M phosphate, 0.5 M NaCl, pH 7.2) was used as carrier and dilution buffer. (d) BIAcore. The HBS described above was used as carrier and dilution buffer. Antibodies were covalently immobilized using the EDC/NHS coupling chemistry supplied by Pharmacia, as described elsewhere.2 A 10 µL portion of EDC/NHS mixture was followed by 20 µL of purified antibody in pH 4 acetate buffer, all at 5 µL/min. Research grade sensor chips were used. The necessity of purifying the antibody was realized after immobilization of the unpurified antibody yielded a flow chip that was unable to bind HSA. This was likely due to the high percentage of irrelevant protein in the ascites fluid being immobilized along with the antibody. For purification of the antibody, 50 µL of ascites fluid was added to 200 µL slurry (50%) of protein A beads (Zymed) and diluted to 1.2 mL with TNEN buffer (20 mM Tris, pH 8, 100 mM NaCl, 1 mM EDTA, 0.5% NP40). This suspension was rotated for 1 h at room temperature in order to allow the antibody to bind to the protein A on the bead surface. The beads were then allowed to settle by gravity and the excess buffer containing most of the non-antibody protein was removed with a pipet. The beads were washed 5 times with 1 mL of TNEN buffer and 10 times with 1 mL of PBS. Finally, the antibody retained on the beads was released using 1 mL of 0.1 M pH 4 acetate buffer and removed in the supernatant, ready to be immobilized onto a sensor chip. If the recovery were 100%, the Analytical Chemistry, Vol. 69, No. 17, September 1, 1997
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final concentration of antibody in the supernatant would be 430 µg/mL, but the actual concentration was most likely less than that. The recovery procedure was modified from a standard procedure for purifying large quantities of antibody using a protein A affinity column. Procedure. The flow injection procedures used for the various experiments are described in Table 1. In the beginning of each experiment, the carrier buffer is loaded into the syringe through a two-way valve (not indicated in Table 1). In the subsequent aspiration steps, the solution is drawn into the holding coil, which is connected to the common port. Then, the selector valve is switched to the jet ring cell port, and the contents of the holding coil and the syringe are expelled. RESULTS AND DISCUSSION Kinetics of Binding and Dissociation Using Labeled Ligands. Human serum albumin (HSA) was chosen as a model ligand since it is large enough (67 kDa) to be easily detected by the BIAcore instrument. The corresponding receptor was a mouse monoclonal IgG2a anti-HSA antibody that was immobilized by binding to protein A beads. The HSA was labeled with FITC for FIRSI measurements. In order to locate the regions of the FIRSI response curves where [B] ) const and [B] ) 0, a tracer curve was measured by injecting 300 µL of ligand solution over beads containing no antibody. For details of the method, see Table 1. Using the same experimental protocol, but this time using beads with immobilized antibody, curves for the interaction of HSA with immobilized antibody were obtained. In order to avoid mass transport limitations, a relatively small amount of antibodies was immobilized on the solid phase (corresponding to 0.9 pmol/assay; see reagents) and the concentration range for HSA was set high (4-20 µM). This also served to limit the fraction of HSA bound during the assay, thus ensuring that the concentration of ligand in solution measured by the tracer curve was practically unaffected by the binding (eq 8). The resulting binding and tracer curves are shown in Figure 2. It follows from the shape of the tracer curve that the data points collected in the time interval 90-200 s fulfill the condition of [B] ) const and the data in the time interval beyond 300 s fulfill the condition of [B] ) 0. The dissociation rate constant was determined from the interval beyond 300 s and in turn used to determine the association rate constant from the 90-200 s interval, using the BIAevaluation software, which uses nonlinear regression methods. The resulting rate constants shown in Table 2 are averages of those obtained at the five different concentrations. For the corresponding rate constant measurements using the BIAcore instrument, covalent immobilization of 20 µL of purified ascites fluid (see Experimental Section) was found to yield a suitable immobilization level of 2700 RU, corresponding to an Rmax of 1200 RU if the immobilized protein is pure. Concentrations of HSA ranging from 1 to 4 µM were injected at 5 µL/min. These parameters were chosen on the basis of the same considerations as for FIRSI. The resulting binding curves are shown in Figure 3. The rate constants found using the BIAevaluation software are shown in Table 2. For comparison of the flow characteristics of the BIAcore and FIRSI systems, a BIAcore tracer curve was obtained by injecting 1.5% sucrose in HBS into HBS and monitoring the SPR response due to the change in refractive index (Figure 3). This curve is more square than the FIRSI tracer curves (Figure 2) due to the smaller dead volume of the BIAcore. 3486 Analytical Chemistry, Vol. 69, No. 17, September 1, 1997
Figure 2. FIRSI binding and tracer curves for determination of rate constants for labeled HSA binding to monoclonal antibody. A 300 µL volume of 4-20 µM FITC-labeled HSA was injected at 100 µL/min over 1 mg of beads containing ∼8.6 µg of antibody. The analysis was performed twice for each concentration. Fresh beads were used for each analysis.
Affinity Ranking. Ranking of the interactions of various ligands toward the same solid state immobilized receptor serves as a qualitative interpretation of affinity and kinetics of molecular interactions. Such experiments are important for tasks such as antibody screening and drug screening.7 Ranking is conveniently carried out by comparing the shape of the binding curves of the respective ligands. If all experimental parameters are kept constant, the height of the steady state level of two different ligand binding curves will reflect their relative affinity for the common receptor. The rates of association and dissociation of the ligands are reflected in the slopes of the response curves. However, the smaller the ligands of interest, the greater the chance that the fluorescence labeling will interfere with the ligand/receptor interaction, or even that a suitable reactive group for labeling is not present at the ligand. We therefore tested a different approach, based on the competition of a labeled ligand with an unlabeled ligand for a limited number of binding sites. The measured curve shows the binding of the labeled ligand and will be suppressed to a different degree depending upon which unlabeled ligand is present. Two such competitive ranking experiments were performed here. In the first experiment, the drug theophylline and two derivatives of this drug (aminotheophylline and theophylline-7acetic acid) were ranked against each other for binding to the monoclonal antibody MAb 459 R-theophylline. In the basic experiment, 1 µM FITC-labeled aminotheophylline (FITC-ATH) was allowed to bind to MAb 459 in a manner similar to the binding curves in Figure 2. In the next experiment, FITC-ATH was premixed with theophylline, both to 1 µM, and the two drugs competed for the available antibody binding sites on the beads. Similarly, competitive experiments with aminotheophylline and theophylline-7-acetic acid versus FITC-ATH were performed. The concentrations were chosen so that there was a deficit of binding sites, ensuring competition between labeled and unlabeled ligand, and all ligands were tested at the same concentration to allow comparison. The results of this experiment are shown in Figure 4. While theophylline-7-acetic acid only suppresses the FITC-
Table 2. Comparison of BIAcore and FIRSI BIAcore detection method
FIRSI
surface plasmon resonance
fluorescence
advantages
labeling unnecessary
smaller molecules can be measured uses generally available detector refractive index no problem
disadvantages
sensitive to refractive index purified samples required limited size range of detected species
labeling could distort molecular interaction labeling takes time
sensor chip
renewable surface of polymer beads
advantages
highly controlled sensor surface
regeneration unnecessary a variety of sensor surfaces available
disadvantages
limited lifetime of sensor surface regeneration step necessary
bead manipulation nontrivial
micromachined
conventional flow system
advantages
consumes less sample delivers square impulse
assembled from inexpensive, readily available components handles complex matrixes easy to fabricate and maintain
disadvantages
specialized system, expensive components prone to clogging
requires more sample
analyte size range
5-500 kDa
1 kDa (whole cell)
rate const for model system
ka ) 2 × kd ) 1 × 10-5 s-1
sensor system
fluidic system
103
M-1
s-1
Figure 3. BIAcore binding and tracer curve for determination of rate constants for HSA binding to monoclonal antibody. A 30 µL volume of 2 µM HSA was injected at 5 µL/min over a flow chip with immobilized anti-HSA antibody. The tracer curve was obtained by injecting a 30 µL volume of 1.5% sucrose in HBS into an HBS buffer at 5 µL/min.
aminotheophylline curve slightly, theophylline excises a greater suppression, and aminotheophylline the greatest suppression, indicating the strongest affinity for the antibody. The same affinity order was obtained by the BIAcore system.7 The second experiment ranked bovine, sheep, pork, and human insulin and proinsulin to the monoclonal antibody MAb K36aC10 against human insulin. Again, FITC-labeled bovine insulin was allowed to bind to the antibody in the initial experiment, and then competitive binding curves were recorded where the labeled insulin was mixed with the other kinds of insulin. Concentrations of labeled and unlabeled insulin of 0.5 µM were used. The results, displayed in Figure 5, show that the affinity can be ranked in the order of pork insulin > human recombinant
ka ) 1.6 × 103 M-1 s-1, RSD 37% kd ) 1.5 × 10-4 s-1, RSD 9%
Figure 4. Affinity ranking of theophylline and derivatives against monoclonal antibody 459. A 20 µL aliquot of bead suspension with ∼3 µg of antibody was trapped in the jet ring cell. A 1.2 mL portion of 1 µM FITC-aminotheophylline was injected. The flow rate was 0.23 mL/min throughout the analysis, except during the initial parts of phases B and C where it was 1.2 mL/min. The analysis was repeated with mixtures containing 1 µM FITC-aminotheophylline and 1 µM theophylline, aminotheophylline, and theophylline-7-acetic acid, respectively. Each analysis was performed in duplicate.
insulin > human proinsulin > bovine insulin > sheep insulin. Interestingly, such a ranking order was not known to the manufacturer due to the difficulty of performing such experiments with currently available techniques. Concentration Measurements. Concentration measurements by FIRSI can be carried out either in a sandwich assay format or in a competitive assay format.11 The work described here uses insulin as a model ligand. The protocol for the competitive assay is shown in Table 1. An assay format involving Analytical Chemistry, Vol. 69, No. 17, September 1, 1997
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insulin takes up a fraction of the binding sites and the subsequently injected labeled insulin takes up the remaining sites. Therefore, the higher the concentration of unlabeled ligand, the smaller the fluorescence response is. Although it is not clear which fraction of the available sites is taken during the experimental run, the precise kinetic control of the receptor/ligand interaction through subsequent experimental runs assures that the same fraction of the available sites is always occupied. Finally, the reproducibility of FIRSI was tested by a series of experiments in which 50 µL of labeled insulin was injected and allowed to interact with the layer of beads in the same way as described for the insulin assay, albeit without injection of nonlabeled ligand. These binding curves, shown in Figure 1, have a relative standard deviation at the peak maximum of 1.2%.
Figure 5. Affinity ranking of insulin from different species against monoclonal antibody K36aC10. A 1.2 mL portion of analyte containing 0.5 µM FITC-insulin and 0.5 µM nonlabeled insulin was injected using the same scheme as in Figure 5. A 20 µL aliquot of bead suspension with ∼3 µg of antibody was trapped in the jet ring cell. Each curve shown is an average of four analyses.
Figure 6. Calibration curve for competitive assay for insulin. Inset shows raw data (averages of three runs) for insulin concentrations ranging from 1.6 to 50 µg/mL. The concentration of labeled insulin in all cases was 100 µg/mL. The fluorescence response after 30 s was used to construct the calibration curve as it gave the highest sensitivity for the assay.
the perfusion of beads with the unlabeled ligand followed by perfusion with labeled ligand was preferred over premixing of labeled and unlabeled ligands, since it eliminated a manual sample preparation step. Also, such a format allows easy extension of the dynamic range of the assay to lower concentrations by increasing the volume of unlabeled ligand, thus preconcentrating more unlabeled ligand (sample) onto the beads (data not shown). By keeping the concentration of the labeled insulin constant (100 µg/mL) while varying the concentration of the unlabeled ligand from 0 to 50 µg/mL, a series of binding curves was obtained from which the calibration curve was constructed (Figure 6). Note that the injected ligand volumes were smaller in these experiments than in the kinetic and ranking experiments described above. Thus, the tracer curve for these experiments were similar to that in Figure 1. The shape of the calibration curve and its range is typical for competitive assays. This confirms that the nonlabeled 3488
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CONCLUSIONS This work introduces a novel approach to immunoassay and to measurement of biomolecular interactions. The salient feature of this approach is the use of beads as a renewable sensing phase, which allows real time detection of biospecific interactions and eliminates the need for tedious sensing surface regeneration and the ensuing problems of surface degeneration or memory effects. By using fluorescence detection, a technique is obtained wheresunlike surface plasmon resonancesdetectability is not influenced by the size of the ligand. Also, the technique tolerates unpurified samples and is unaffected by the differences in refractive index between the analyte and carrier. We have shown the feasibility of using this technique for rate constant determination, affinity ranking, and concentration measurements. These three types of measurements have so far been unique for the BIAcore technique. A comparison of our approach with BIAcore is summarized in Table 1. Ongoing research in our lab focuses among other things on the use of simpler, fiber-opticbased fluorescence detectors for FIRSI instead of the more expensive fluorescence microscopes used here. The current FIRSI system is 1 order of magnitude less expensive than the BIAcore instrument, and the price of consumables (beads versus surface plasmon resonance chip) compares even more favorably. It must be emphasized, however, that while FIRSI is still in its infancy, the BIAcore system is a result of a large research investment. For this reason alone, any conclusion as to the preference of either of these methodologies would be premature. Only when FIRSI with fluorescence detection is further developed will it serve as an alternative to surface plasmon resonance for routine measurements. At this point, our results lead to the following conclusions: The model system (HSA) for rate constants measurements studied here yielded comparable results for BIAcore and FIRSI, in spite of the use of fluorescence labeling by the latter technique. The discrepancy between the measured dissociation rate constants is assumed to be due in part to the noncovalent antibody immobilization method used for FIRSI. While fluorescence labeling is generally viewed as time and labor consuming, we have found that it takes less effort than chip preparation and regeneration in the BIAcore system. One of the reasons is that FIRSI does not require chromatographic separation of unbound label from labeled protein. Also, a wider availability of convenient labeling kits makes fluorescence labeling relatively simple. Most importantly, since small molecules can be labeled as effectively as large molecules, detectability is not affected by the size of the ligand molecule.
An attractive feature of FIRSI is the ease with which one can perform affinity ranking experiments. The competitive approach for affinity ranking used here, where a labeled ligand competes with an unlabeled one, is standard procedure in radioligand binding assays.14 This approach has the distinct advantage that although a labeled ligand is being used, the ligands of interest are unlabeled and the information that is obtained is unbiased by labeling. However, it would also be possible to label every single ligand that must be ranked and run the ligands separately under identical conditions. This would allow comparison of the dissociation rate constants based on the part of the curves where [B] ) 0. The results of the affinity ranking of theophylline shown in Figure 4 are identical to results obtained by Karlsson using BIAcore.7 We note that these ligands could not be measured directly using BIAcore due to their small size (∼200 Da) and thus had to be labeled with a heavier protein. The affinity ranking of various forms of insulin could not be compared to similar results obtained with other methods since no such results were available and insulin binding could not be detected using BIAcore. However, the results do not conflict with what is known about the model system. The selectivity of measurements of biospecific interactions depends on a variety of conditions, such as the selectivity of binding, the selectivity of the spectroscopic techniques, and the interfering effects of buffer solutions. It should be kept in mind (14) Hulme, E. C., Ed. Receptor-Ligand Interactions. A Practical Approach; Oxford University Press: Oxford, UK, 1992.
that any protein binding to the BIAcore chip surface, specific or nonspecific, will provide a surface plasmon resonance response. Therefore, the selectivity of the measurement is not guaranteed by the selectivity of the receptor/ligand interaction when the BIAcore technique is used. In contrast, the fluorescence labeling ensures that only the labeled molecules are detected. Finally, a comparison of the tracer curves obtained by FIRSI and BIAcore (Figures 2 and 3) demonstrates that it is not necessary to use a microfabricated system (flow chip) in order to create a square profile suitable for the type of measurements described here. Our work thus demonstrates the feasibility of performing biospecific interaction analysis using a less expensive instrument, constructed from easily accessible components. ACKNOWLEDGMENT The authors thank Dr. Cy Pollema for his cooperation in the initial stages of this work, Dr. Cathy Lofton-Day for helping us to carry out work on the BIAcore system, Dr. Craig Beeson for valuable discussions, and Dr. Robert Karlsson from Pharmacia Biosensor A/B in Sweden for donating the aminotheophylline and MAb 459. The BIAcore system was graciously lent to us by Zymogenetics Inc. This work was supported by NIH Grant GM45260-05. Received for review March 10, 1997. Accepted June 9, 1997.X AC970268H X
Abstract published in Advance ACS Abstracts, August 1, 1997.
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