Measuring an Antibody Affinity Distribution Molecule by Molecule

Anal. Chem. 2008, 80, 8642–8648

Measuring an Antibody Affinity Distribution Molecule by Molecule Jamshid P. Temirov,†,‡ Andrew R. M. Bradbury,§ and James H. Werner*,† Center for Integrated Nanotechnologies and Bioscience Division, Los Alamos National Laboratories, Los Alamos, New Mexico 87545 Single molecule fluorescence microscopy was used to observe the binding and unbinding of hapten decorated quantum dots to individual surface immobilized antibodies. The fluorescence time history from an individual antibody site can be used to calculate its binding affinity. While quantum dot blinking occurs during these measurements, we describe a simple empirical method to correct the apparent/observed affinity to account for the blinking contribution. The combination of many single molecule affinity measurements from different antibodies yields not only the average affinity, it directly measures the full shape and character of the surface affinity distribution function. Surface immobilized antibodies are important capture elements for sandwich based immunoassays (often termed enzyme linked immunosorbent assays, ELISAs), protein affinity chromatography, and protein recognition arrays. In all of these applications, surface immobilization of the capture antibody enables vigorous washing of both unbound target and unbound reporter antibodies. Moreover, surface immobilization enables target binding to be detected via sensitive, surface-specific spectroscopies, such as surface plasmon resonance1 or evanescent wave excitation.2-4 However, immobilization of the antibodies on a surface inevitably leads to a loss in binding affinity, with the loss being typically an order of magnitude or more.4,5 This loss in affinity is often attributed to steric problems (i.e., a fraction of the antibodies may be oriented improperly, leaving their binding domains inaccessible, or the antibody hinge motion could be hindered by the immobilization protocol). It is often difficult to decouple heterogeneity due to orientation or environment from intrinsic binding differences among immobilized antibodies. This is a classic example where single molecule methods6,7 can be of utility. For example, single molecule techniques could address the * To whom correspondence should be addressed. E-mail: jwerner@ lanl.gov. Phone: 505-667-8842. † Center for Integrated Nanotechnologies. ‡ Current address: Invitrogen Corporation, Carlsbad, CA. § Bioscience Division. (1) Karlsson, R.; Michaelsson, A.; Mattsson, L. J. Immunol. Methods 1991, 145 (1-2), 229–240. (2) Shriver-Lake, L. C.; Breslin, K. A.; Charles, P. T.; Conrad, D. W.; Golden, J. P.; Ligler, F. S. Anal. Chem. 1995, 67 (14), 2431–2435. (3) Temirov, J.; Bradbury, A.; Werner, J. H. Proc. SPIE 2006, 6092, 60920O1– 60920O10. (4) Vijayendran, R. A.; Leckband, D. E. Anal. Chem. 2001, 73 (3), 471–480. (5) Lu, B.; Smyth, M. R.; O’Kennedy, R. Analyst 1996, 121 (3), R29–R32. (6) Weiss, S. Science 1999, 283 (5408), 1676–1683.

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following scenarios: Does the affinity drop by an order of magnitude because 1 in 10 antibodies deposited binds? Or rather, do all antibodies deposited function similarly, just that all work less effectively when bound to a surface? Single molecule analysis can directly answer these questions, providing the feedback needed for better immobilization strategies. Here, we describe the techniques and methods necessary to calculate a surface affinity distribution function on a molecule by molecule basis. We detail covalent antibody attachment protocols that both preserve antibody activity and block nonspecific binding to the levels necessary for the detection of single molecule binding and unbinding. We describe software to automatically extract fluorescence time histories at individual antibody sites. These fluorescence time histories are used to calculate individual antibody binding affinities. Combining the results from a number of different, spatially isolated, individual antibodies yields a surface affinity distribution function. Finally, quantitative methods to correct the observed affinities to account for quantum dot blinking8,9 are presented. MATERIALS AND METHODS Antibody Immobilization. We used a covalent attachment chemistry for antibody immobilization that is detailed in ref 3. In brief, our immobilization protocol uses vapor deposition of 3-amino propyl methyl diethoxy silane (Gelest) onto a standard number 1.5 coverslip (Fisher Scientific) to create an amine terminated glass surface of dense, uniform coverage.10,11 We note that this silanizing agent yielded more uniform surface coverage than the more commonly employed APTES (3-amino propyl triethoxy silane). The next step of our immobilization protocol is to react the heterobifunctional cross-linker, SFAD, sulfosuccinimidyl[perfluoroazidobenzamido] ethyl-1,3′-dithiopropionate (Pierce), with the amine-terminated surface, which attaches the cross-linker via the amine-reactive end (NHS ester) of the SFAD. After the unreacted cross-linker was washed away, 100 nM of monoclonal (2F5) antibiotin mouse IgG1 antibodies (Invitrogen) were intro(7) Ambrose, W. P.; Goodwin, P. M.; Jett, J. H.; Van Orden, A.; Werner, J. H.; Keller, R. A. Chem. Rev. 1999, 99 (10), 2929–2956. (8) Nirmal, M.; Dabbousi, B. O.; Bawendi, M. G.; Macklin, J. J.; Trautman, J. K.; Harris, T. D.; Brus, L. E. Nature 1996, 383 (6603), 802–804. (9) Kuno, M.; Fromm, D. P.; Hamann, H. F.; Gallagher, A.; Nesbitt, D. J. J. Chem. Phys. 2000, 112 (7), 3117–3120. (10) Moon, J. H.; Shin, J. W.; Kim, S. Y.; Park, J. W. Langmuir 1996, 12 (20), 4621–4624. (11) Anderson, A. S.; Dattelbaum, A. M.; Montan ˜o, G. A.; Price, D. N.; Schmidt, J. G.; Martinez, J. S.; Grace, W. K.; Grace, K. M.; Swanson, B. I. Langmuir 2008, 24 (5), 2240–2247. 10.1021/ac8015592 CCC: $40.75  2008 American Chemical Society Published on Web 10/11/2008

Figure 1. Schematic of the experimental setup. Prism-based total internal reflection was used to confine the excitation laser beam to within 100 nm of the top surface, ensuring quantum dots free in solution did not contribute to the background. Individual antigen-antibody association and dissociation events were recorded with an EM-CCD. This time history can be used to calculate a binding affinity for each antibody.

duced, and the surface was irradiated with the light from a UV (300-400 nm) curing gun for 60 s, which activates the free phenyl azide end of the SFAD to a highly reactive nitrene intermediate. This nitrene intermediate is capable of covalent attachment to nucleophilic residues (e.g., lysines) present on the antibody and can event insert into “inert” aliphatic bonds.12 After antibody immobilization, the surface is washed multiple times in phosphate buffered saline (PBS) and the surface is blocked with Super Block buffer (Pierce) overnight in a Petri dish. After blocking, the surface is rinsed multiple times with PBS with a final antigen solution of 605 nm emission biotinylated quantum dots (Invitrogen) replacing the wash buffer. For some control experiments, to determine the level of nonspecific binding of the antigens to the blocked surface, an anti-GFP rabbit polyclonal antibody (Invitrogen) was immobilized rather than antibiotin. Single Molecule Fluorescence Imaging. A schematic of the experimental setup is shown in Figure 1. A thin (120 µm) aqueous sample chamber containing the fluorescent antigen (qdot 605 nm from Invitrogen) was created using a secure seal imaging chamber (Grace BioLabs SS20) that sandwiched an antibody coated number 1.5 coverslip against a number 1.0 Fisher coverslip. Fluorescence excitation was provided by 4 mW of 488 nm radiation of an argon ion laser (model 2040 Spectra Physics). This light was spatially filtered by a single mode optical fiber before being brought to a focus (∼200 µm 1/e2 diameter) at the antibody surface of the chamber through a fused-silica dove prism (Del Mar Photonics) used for prism-based total internal reflection (TIR) microscopy.3,13 The sample chamber is 120 µm in height, enabling observation of the prism-coupled antibody surface with a high numerical aperture water immersion microscope objective (60× 1.2 NA Olympus). Prism-based TIR microscopy was chosen over a through-objective TIR geometry, as prism TIR excitation yields better signal to background ratios.14 Fluorescence was filtered using a 510 long pass filter (Chroma Technologies). Images were recorded with 50 ms exposure and a 10 Hz framing rate using a Princeton Instruments PhotonMax. (12) Hermanson, G. Bioconjugate Techniques; Academic Press: San Diego, CA, 1996. (13) Axelrod, D.; Burghardt, T. P.; Thompson, N. L. Annu. Rev. Biophys. Bioeng. 1984, 13, 247–268. (14) Ambrose, W. P.; Goodwin, P. M.; Nolan, J. P. Cytometry 1999, 36 (3), 224– 231.

Image Analysis. Image processing was fully automated using software written in Igor Pro (Wavemetrics). Flat-field and background corrections are applied to the data prior to analysis. In our quantum dot search algorithm, the average pixel intensity is calculated for a test 4 × 4 pixel grid of the image that is slightly larger than a single quantum dot (a single pixel on the EMCCD is ∼130 nm wide, and a single quantum dot takes up roughly a 3 by 3 pixel area). We note that while the fluorescence image of the quantum dot appears to be ∼ 400 nm in diameter, this is due to diffraction of the fluorescence image. The dots used in these studies have a diameter of ∼16 nm determined by fluorescence correlation spectroscopic measurements calibrated with 41 nm diameter microspheres (YG microspheres, Polysciences). For image processing, we compare the ratio of the brightest pixel in this test grid to the average intensity of all the pixels in the test grid. If that ratio exceeds a threshold value (1.5), then further analysis is performed on that pixel. In particular, we examine the total intensity of the pixel and ensure that the ratio of this brightest pixel to his nearest neighbor falls in the range of 1.2 to 1.7, which eliminates aggregates from being selected. The 4 × 4 test grid is raster scanned across the entire 512 by 512 pixel image to find all possible locations of individual quantum dots bound by surface immobilized antibodies. The fluorescence time history is reported as a sum of a 3 by 3 pixel box centered on the pixel of maximal intensity. For ease of comparison among different intensity time traces, the minimum count value for each trace is subtracted from the data stream. Approximately 20% of the antibody sites detected in this fashion appeared to be from two quantum dots bound simultaneously to the same antibody. The majority of the traces found in the automated search method showed monovalent binding behavior, most likely due to having only one accessible binding domain. The discrimination between monovalent and bivalent binding was performed based upon the maximum count rate observed in the fluorescence time history. Intensity traces that exceeded a maximum count rate, >300 counts in 50 ms, were assumed to come from two quantum dots bound to the same antibody (bivalent binding) whereas those traces that never exceeded this threshold were classified as monovalent binders. Safety Considerations. Laser eyewear rated to an OD > 7 at 488 nm (the wavelength used for fluorescence excitation) was worn during the microscopy experiments. Chemical handling was performed with the appropriate personal protective equipment (nitrile gloves, protective labcoat, and safety glasses). The quantum dots used in these experiments were disposed of in a liquid waste stream approved for cadmium and other heavy metals. RESULTS AND DISCUSSION We have developed the protocols needed to covalently attach antibodies to a surface that retain some antibody binding activity and block nonspecific binding to levels needed to see individual antigen-antibody binding and unbinding events over the background of nonspecific binding (see Figure 2). In particular, we chose a covalent attachment strategy for the following two reasons: First, we desired to be able to vigorously wash the surface, and second, we wanted to have the antibodies immobilized for as long as necessary (days to weeks if needed), as blocking the surface for several hours was necessary to reduce the level of nonspecific binding to levels suitable to observe individual binding events. Analytical Chemistry, Vol. 80, No. 22, November 15, 2008

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Figure 2. Top: The importance of surface blocking. (A) Without blocking the surface, individual quantum dots can carpet the surface, even at sub-100 nM concentrations. (B) With 8 h of surface passivation in a commercial blocking buffer (Pierce SuperBlock), surface association due to nonspecific binding is reduced to acceptable levels to observe specific antibody-antigen interactions. Bottom: Blocked surfaces retain accessibility to antibody binding sites. (C) Binding of biotinylated quantum dots to surface-immobilized antibiotin antibodies. (D) Control image demonstrating the level of nonspecific binding of ∼100 nM of biotinylated quantum dots to a surface where anti-GFP instead of antibiotin antibodies were immobilized on the surface.

Figure 2 shows the level of nonspecific binding present on these surfaces and highlights the importance of surface blocking. Without blocking the surface for ∼10 h, quantum dots carpet the surface nonspecifically, even at nanomolar concentrations. In contrast, a “blocked” silanized surface (Figure 2B) lowers nonspecific binding to levels suitable for single molecule detection of individual antigen-antibody binding events. The lower portion of this figure demonstrates that our covalent immobilization protocol and surface blocking procedures preserve antibody function. Biotinylated quantum dots are recognized and captured by surface immobilized antibiotin antibodies (Figure 2C). In contrast, when a different antibody is immobilized (anti-GFP), binding is reduced to nonspecific levels. As further proof that most of the individual quantum dots shown in Figure 2C are bound to the surface through antibiotin antibody recognition, we varied the surface coverage of the antibodies over 2 orders of magnitude, by varying the concentration of the SFAD cross-linker (Figure 3A-C) while keeping the concentration of the introduced quantum dot labeled antigen constant. This experiment (Figure 3) further suggests the spots observed in Figure 2C are due primarily to antigen-antibody interactions and not nonspecific adsorption to the surface. A time series of images such as that shown in Figure 2C can be used to calculate a single molecule binding affinity, as individual antigens can associate and dissociate over time from their surface capture element. For antibodies spanning an affinity range between micromolar and nanomolar, the dissociation rates can 8644

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span a range from 10-2 to 102 per second,15 enabling numerous associations and dissociation events to be recorded over the course of an experiment. As discussed above, we have written software to automatically extract fluorescence time histories from individual fluorescent spots. We note here that the fluorescence intensity at selected antibody immobilization sites is quantized (i.e., zero, one, or two quantum dots bound). With our random covalent cross-linking strategy, many (∼20%) of the antibody locations appear capable of binding two antigens simultaneously (e.g., the fluorescence time histories showed three state behavior, zero, one, or two quantum dots bound). Two examples of this three state behavior coming from a single diffraction limited spot are shown in Figure 4. Again, ∼20% of the antibodies immobilized displayed such three state (bivalent binding) behavior. The vast majority (∼80%) of antibodies appear capable of only binding an individual quantum dot. A randomly selected time history from an immobilized antibody that displays only monovalent binding behavior is shown in the top of Figure 5. The discreet changes in the fluorescence time history shown in Figure 4 and the top part of Figure 5 contain contributions both from antibody binding/unbinding and from quantum dot “blinking”8,9 at an immobilized antibody site. For comparison purposes, a randomly selected fluorescence time history for a quantum dot stuck by hydrophobic interactions to a silanized surface, taken under identical excitation conditions, is shown in the lower panel of Figure 5. This time-trace only contains contributions due to blinking (we have found vigorous washing incapable of quantum dot removal from an unblocked silanized surface). For this randomly selected time-trace of a surface immobilized quantum dot, the quantum dot blinks on and off as expected. However, for this one randomly selected example, the quantum dot that is associated with an antibiotin antibody (top trace in Figure 5) has longer and more “dark” or “off” periods than the lower trace, where on/off events are simply due to blinking. This is shown in the normalized fluorescence time histories on the right-hand side of Figure 5. This suggests that, for this one randomly selected example, binding/unbinding from the antibody-antigen surface is occurring in addition to quantum dot blinking. While binding and unbinding appears to be ongoing on the antibody immobilized surface based upon this one pair of randomly selected fluorescence time histories, an analysis of the entire data set of automatically selected fluorescence time traces (blinking data from the silanized surface and binding/unbinding plus blinking data from the monovalent antibody population) strengthens this conclusion (Figure 6). In particular, the aggregate histograms of the normalized intensity distribution obtained for ∼434 surface immobilized monovalent-binding antibodies and for 392 quantum dots hydrophobically stuck to the silanized surface show that under identical excitation conditions, the antibody surface has more “dark” periods, suggesting binding and unbinding from this surface, in addition to quantum dot blinking, is ongoing. Moreover, as the experiments and controls of Figures 2 and 3 suggest, most of the spots found in our automated search algorithm are due to specific antigen-antibody interactions. Taken together, we conclude that much of the on/off behavior shown in the antibody-antigen fluorescence time histories is due to (15) Foote, J.; Milstein, C. Nature (London, U.K.) 1991, 352 (6335), 530–532.

Figure 3. The number of observed antigen-antibody binding events depends upon the concentration of antibodies cross-linked to the surface (0, 1, and 100 µM of cross-linker).

Figure 4. The fluorescence time history at approximately 20% of the antibody immobilization sites displayed behavior indicative of bivalent binding activity. Two examples of this type of behavior are shown.

association and dissociation of hapten decorated quantum dots with the antihapten surface. While the fluorescence time history shown in the top of Figure 5 clearly contains fluctuations due to quantum dot blinking, we first discuss the methodology used to calculate the binding affinity disregarding the blinking contribution. Later, we discuss empirical methods to correct these affinity calculations to account for the blinking contribution. Consider a simple association/dissociation reaction between an antigen (Ag) and a monovalent antibody (Ab):

From the simple kinetic rate scheme (eq 1a), one can express the rate of change in the bulk population of free antibodies (eq 1b) in terms of concentrations and kinetic rate constants. For a population in steady-state equilibrium, the macroscopic rate of change of the entire population is zero, leading to an expression for the dissociation constant (the inverse of the equilibrium constant) in terms of either the ratio of the kinetic rate constants or in terms of the relative concentration of the two populations in

equilibrium, or KD ) [Ab][Ag]/[Ab · Ag] (eq 1c). From the fluorescence time trace at a single monovalent antibody site, one can directly extract [Ab]/[Ab · Ag], as this is the ratio of the time that the trace appears to have no antigen, toff, divided by the time that the trace has an attached antigen, ton. For the monovalent binding population, we have taken a value of 0.4 in the normalized fluorescence intensity distribution (see Figure 6) to represent the distinction between an “off” and “on” state. This threshold value was selected based upon Gaussian fits to the aggregate fluorescence histogram shown in the top of Figure 6. With this as a threshold value, for the fluorescence time history shown in the top of Figure 5, the antigen appears to be “off” for 151 frames (7.55 s) and “on” for 849 frames (42.45 s). The KD for this antibody would then be calculated as KD ) (7.55/42.45)[Ag], or ∼18 nM. Repeating this calculation for all 434 monovalent antibody locations yields the surface affinity distribution shown in Figure 7A. For bivalent antibodies, one can also readily calculate a binding affinity from the fluorescence time history. For a bivalent binder, there are three possible fluorescent intensity levels, zero, one, or two antibodies bound, with these intensity levels corresponding to one of four states of the antibody (antigen-free, one antigen on the “left” arm, one antigen on the “right” arm, or both binding domains occupied). The kinetic equilibrium scheme between all four of these states is given below in eq 2a.

As is the case for monovalent binders, one can generate a differential rate equation for any population and assume in the steady state the net rate of change of this population is zero. Doing this for the antigen-free population, one arrives at an expression for the dissociation constant (the ratio of the off-rate to the onrate) for the antibody expressed as a ratio of 2 times the time the antibody appears to be totally unoccupied divided by the time the antibody appears to have only a single antigen. Again, these times are readily obtained from the available bivalent binding fluorescence time histories (see Figure 4). The affinity distribution constructed from the 102 “bivalent” antibodies measured over the course of our experiment is shown in Figure 7B. Analytical Chemistry, Vol. 80, No. 22, November 15, 2008

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Figure 5. Top: Fluorescence time history for a randomly selected individual surface-immobilized antibody. Bottom: Fluorescence time history for a randomly selected quantum dot nonspecifically bound by an unblocked, silanized surface (such as that shown in Figure 2A) taken under identical excitation conditions. Histograms of the normalized fluorescence time history are shown to the right. For this one randomly selected example, the antibody-antigen trace shows longer periods of being in an “off” or dark state.

Figure 6. A histogram of the normalized fluorescence intensity for 434 antibiotin antibody sites in the presence of 100 nM biotinylated quantum dots (top) and a similar histogram for 392 quantum dots stuck to a silanized surface (bottom). These histograms show that, on average, the antibody surface has more dark periods, suggesting that binding and unbinding from the surface is occurring in addition to quantum dot blinking.

Figure 7 enables one to directly compare the affinity distributions obtained from the two different antibody populations: those that appear to only have one accessible binding domain (Figure 8646

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7A) and those that are competent bivalent binders (Figure 7B). We first note that the average affinities of the two distributions are quite similar (70 nM for the monovalent distribution and 62 nM for the bivalent distribution). We further mention that the average affinity of both distributions is comparable to the solution phase affinity determined by fluorescence correlation spectroscopy (FCS), which places the affinity at roughly 100 nM3. While the average affinity of the bivalent and monovalent distributions are similar, the shapes of the distributions have measurable differences. In particular, the bivalent distribution appears to have fewer outliers from the mean affinity, with a standard deviation about the average of ∼55 nM. In contrast, the standard deviation about the average for the monovalent affinity distribution is substantially broader, ∼120 nM. The most likely reason the monovalent distribution has a broader range of apparent affinities than the bivalent binders is due to heterogeneity and steric hindrance introduced by the surface immobilization protocol. For bivalent binders, the antibodies are presumably attached to the surface through their Fc region and potentially have less surface interference with their binding domains. However, in addition to heterogeneity created through surface immobilization, there are other potential noise sources in these single molecule measures of affinity, such as differences in binding kinetics among “identical” monoclonal antibodies,16-18 or simply having the width of the distribution dominated by technical noise sources, in particular, quantum dot blinking.8 We consider this last possibility in detail before further assigning any physical significance to the breadth of the affinity distribution. (16) Foote, J.; Milstein, C. Proc. Natl. Acad. Sci. U.S.A. 1994, 91 (22), 10370– 10374. (17) Rini, J. M.; Schulzegahmen, U.; Wilson, I. A. Science 1992, 255 (5047), 959–965. (18) James, L. C.; Roversi, P.; Tawfik, D. S. Science 2003, 299 (5611), 1362– 1367.

Figure 7. (A) The affinity distribution obtained from 434 surface-immobilized antibody molecules that appear to only have one active binding domain. The average of the distribution is ∼70 nM, with a standard deviation of ∼120 nM. (B) The affinity distribution obtained from 102 surfaceimmobilized antibody molecules that appear to be competent bivalent binders. The average of the distribution is ∼62 nM, with a standard deviation of ∼55 nM.

As far as the uncertainty or error introduced by quantum dot blinking is concerned, one can directly account for the contribution of blinking to the width of the measured affinity distribution. In particular, if we observe an antigen-antibody complex that appears on for a time Ton-obs, then we know the total time the antigen is bound must be greater than Ton-obs, due to the fact that some of the time the antigen is bound to the antibody, the dot is in a dark state. To account for this discrepancy, a correction factor can be obtained from biotinylated quantum dots immobilized to a surface under identical excitation conditions of that used during the binding and unbinding studies. Figure 1 of the Supporting Information shows a histogram of the ratio the time a dot is “on” to the total observation time for 392 quantum dots immobilized on a silanized surface. The average of this distribution is 0.85 (i.e., they are “on” for 85% of the time), while the standard deviation of this distribution is 0.13. We denote the ratio Ton/(Ton + Toff) as cf, for correction factor. With this correction factor, we can correct the observed “on” time in our binding/unbinding data to what we would expect if blinking was not occurring. The corrected “on-time” is simply Ton-corr ) Ton-obs/cf, whereas the corrected off-time is Toff-corr ) [Toff-obs + Ton-obs(cf - 1)/ cf]. We note that this correction procedure yields unphysical results for antibodies that have an “uncorrected” or apparent affinity