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One-Step Ligand Immobilization and Single Sample Injection for Regeneration-Free Surface Plasmon Resonance Measurements of Biomolecular Interactions Xiaoying Wang, Zhiqiang Li, Nguyen Ly, and Feimeng Zhou Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b00121 • Publication Date (Web): 22 Feb 2017 Downloaded from http://pubs.acs.org on February 22, 2017
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Analytical Chemistry
One-Step Ligand Immobilization and Single Sample Injection for Regeneration-Free Surface Plasmon Resonance Measurements of Biomolecular Interactions Xiaoying Wang1,2,‡, Zhiqiang Li1,2,‡, Nguyen Ly3, Feimeng Zhou2,* 1
College of Chemistry and Chemical Engineering, Central South University, Changsha, Hunan, P. R. China 410083 Department of Chemistry and Biochemistry, California State University, Los Angeles, Los Angeles, California 90032 3 Biosensing Instrument Inc., Tempe, AZ 85284 2
ABSTRACT: Surface plasmon resonance (SPR) has been well established as a method of choice for label-free kinetic measurements of biomolecular interactions. The conventional approach involves multiple injections of an analyte of different concentrations into a fluidic channel covered with a fixed ligand density. Optimization of the experimental conditions and assessment of the data quality can be complicated by issues such as disruption of the ligand structure by the regeneration step and the limited availability of the sample solution. By sequentially closing fluidic channels on a five-channel SPR instrument, different densities of a ligand can be immobilized and determined in one step. With a subsequent injection of a single sample solution, SPR sensorgrams can be simultaneously collected to yield binding and dissociation rate constants (ka and kd) and dissociation constant (KD) between the ligand and analyte. For biomolecular interactions that obey the Langmuir isotherm, we show that the fidelity of the kinetic data can only be reliably confirmed when there exists a strong linear correlation between the SPR signals and the ligand densities. The use of a multi-channel SPR instrument also obviates the regeneration step, allowing the binding kinetics between the green fluorescent protein and its antibody to be measured. In comparison to the conventional approach, the method simplifies the experimental procedure, reduces costs associated with sensor chips and biological samples, expedites kinetic measurements, and allows affinity constants to be determined more straightforwardly.
Surface plasmon resonance (SPR) has been well established as a label-free method for studying biomolecular interactions.18 The attractive features of SPR include high sensitivity for trace analysis of biomolecules and real-time kinetic measurements between an analyte in solution and a ligand immobilized on the sensor surface.9-13 Despite the developments of highly advanced fluidic systems and automated procedures and publications of numerous papers on SPR measurements of biomolecular interactions, optimization of experimental SPR parameters for obtaining accurate kinetic parameters is not trivial and requires good knowledge about issues such as surface chemistry, mass transfer, kinetic theory, and stabilities and structures of the ligand/receptor pair.14 Without a deep understanding of these issues and how they impact the observed binding behaviors and the subsequent simulations, erroneous conclusions are often made with inaccurate kinetic and thermodynamic data.14,15 In an SPR experiment on the “one-to-one” reaction,16,17 a ligand B is preimmobilized onto a sensor chip and its interaction with an analyte A in solution can be expressed as: ೌ
ሱۛሮ ܣ+ ܤርۛሲ ܤܣ
(1)
During the association phase (association rate constant ka), the SPR signal (R) is given by ܴ=
ೌ ோೌೣ ೌ ା
൫1 − ݁ ିሺೌାሻ௧ ൯
(2)
where C is the analyte concentration, kd the dissociation rate constant, Rmax the maximum analyte coverage at the sensor, and t the reaction time. To initiate the dissociation reaction, the injected sample is rapidly replenished with a running buffer. The SPR signal decays exponentially with time from R0, the signal at the time when buffer replaces the injected sample: ܴ = ܴ ݁ ି௧ (3) In the conventional SPR approach, a series of analyte concentrations are injected into and replenished out of an experimental channel pre-immobilized with a specific ligand density.2,17 The resultant, background-corrected sensorgrams are overlaid and simulations are carried out according to equations 2 and 3 to fit sensorgrams to yield the kinetic and thermodynamic (dissociation constant KD) parameters. The injections of multiple analyte concentrations, in conjunction with the global simulation of corresponding sensorgrams, are to ensure that the simulated results are reliable and the experimental conditions, such as ligand density, the analyte concentrations, and the absence of mass transfer, have been optimized or avoided.18,19 To obtain a series of sensorgrams, a regeneration step is essential, which is accomplished by introducing a reagent such as acid, base, surfactant or other denaturants to denature the bioconjugate at the surface to desorb the analyte. However, exposure to such harsh reagents can lead to discernible changes in the structures or conformations of the immobilized ligand or receptor, which might not recover even after being allowed
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Analytical Chemistry
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to renature in a buffer for an extended period.2,5 Thus, the reliability of the SPR data can be seriously compromised as the signals in many cases become irreproducible after the “regeneration” step.8 Even with biomolecules that can completely renature back to the original conformation, injections of multiple samples upon repeated regeneration steps are both timeand sample-consuming. Alternative approaches have been suggested to address the limitations inherent in the conventional approach. Schuck and co-workers developed a method in which step-wise injections of increasing analyte concentrations is followed by a single dissociation.20 Such a method is time-efficient and bypasses the regeneration step, though loss of kinetic information (especially during the association phase) has been noted.8 Karlsson and Fält were the first to realize that sensorgrams can also be obtained by using different ligand densities and a single analyte concentration.17 However, experimental parameters, such as the ranges of ligand densities, the suitable analyte concentration, and the criteria for evaluating the data quality, were not provided and no kinetic analysis of a biomolecular interaction was performed. In recent years, multi-channel SPR instruments including imaging SPR (SPRi) have been developed with the major goal of increasing the SPR sample throughput for screening effective drugs or ligands from a large number of candidates.9,11,13,21-28 Krishnamoorthy and co-workers created multiple ligand spots of varying densities and used SPRi to record sensorgrams with a single injection of an analyte solution.29,30 While their work demonstrated that it is possible to carry out SPRi kinetic measurements by varying the spotting ligand concentrations to attain different ligand densities, the validity of the approach hinges on the assumption that Rmax is proportional to ligand density.29 As will be shown in this study, for interactions that follow the Langmuir isotherm, the SPR response is always proportional to Rmax, but can deviate from the linear dependence on the ligand density. As the ligand density increases, artifacts8,17,31 caused by “secondary effects” such as steric hindrance and mass transfer limitation become problematic. Thus, it is crucial to immobilize a ligand with known densities to exclude these secondary effects. Based on this consideration, the off-line spotting method used by Krishnamoorthy and co-workers is not desirable as the ligand density cannot be determined. Moreover, the ligand densities resulted from the spotting method are not uniform32 and SPRi in general is not as sensitive as SPR instruments with focused laser beams.2,5 These factors affect the accuracy and reproducibility of the kinetic and thermodynamic data and pose difficulty to the assessment of the SPR data quality. We envision that, with an SPR instrument that can open and close fluidic channels sequentially in a controllable way, different ligand densities obtained on-line through a single injection of a ligand solution can be accurately determined. In this work, we established the experimental procedure in terms of ligand density, analyte concentration, and flow rate using an SPR instrument equipped with five fluidic channels arranged in series. The unique advantages of varying ligand densities are contrasted to the approach that alters the analyte concentration. These advantages, demonstrated with representative examples, include measurement of biomolecular binding that is impossible to accomplish with surface regeneration, simultaneous attainment of binding equilibria in all fluidic channels, and rapid validation of the method without the ambiguity associated with secondary effects. As a result, the overall SPR
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procedure is considerably simplified and the range of SPR applications is expanded. EXPERIMENTAL SECTION Chemicals and Materials. N-hydroxysuccinimide (NHS), N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride (EDC), ethanolamine hydrochloride, CH3COONa, KH2PO4, K2HPO4, NaCl, NaOH, bovine serum albumin (BSA; 96%), and monoclonal anti‒BSA antibody were acquired from Sigma (St. Louis, MO). Green fluorescence protein (GFP) and monoclonal anti-GFP antibody were purchased from Thermo Fisher Scientific (Tustin, CA). Prostate specific antigen (PSA; 98%) from human seminal fluid and high-purity human antiPSA antibody (molecular weight 150 kDa) were purchased from Fitzgerald Industries International (Concord, MA). Other reagents are of analytical purity and used as received. Solutions were prepared daily with deionized water treated with a water purification system (Simplicity 185, Millipore Corp, Billerica, MA). Carboxymethylated dextran (CM-dextran with an average molecular weight of 500,000) sensor chips were obtained from Biosensing Instrument Inc. (Tempe, AZ). Surface plasmon resonance. The SPR measurements were conducted on a BI-SPR 4500 system (Biosensing Instrument Inc.) equipped with five fluidic channels connected in series. The instrument is capable of closing the downstream channels sequentially for predetermined periods. It was connected to an autosampler programmed to avoid dispersions of injected sample plugs. Samples were preloaded into a 500-µL sample loop and then injected to the flow cell by a dual syringe pump. The kinetics analysis software was used for the data simulation. Conversion of SPR angular changes in degrees to response units (1 RU = 1 pg/mm2) follows our previous published paper.33 Procedure. The running buffer was 10 mM phosphate buffered saline (10 mM K2HPO4/KH2PO4, 150 mM NaCl, 0.005 % (V/V) Tween 20, pH 7.4) degassed under vacuum for 30 min prior to all experiments. In a typical experiment, the CM-dextran molecules in all five fluidic channels were activated by 0.4 M EDC and 0.1 M NHS at 20 µL/min for 10 min. Anti-PSA (dissolved in 10 mM acetate buffer, pH 5.0), GFP (dissolved in 10 mM acetate buffer, pH 5.8) or BSA (dissolved in 10 mM acetate buffer, pH 5.0) was immobilized at 20 µL/min with the first four fluidic channels sequentially closed for different times. This step was followed by injecting 1.0 M ethanolamine (pH 8.0) at 20 µL/min for 10 min to block the remaining activated sites. RESULTS AND DISSCUSSION
Figure 1. (A) Immobilization of anti-PSA via sequential closures of the channels from downstream up. The direction of the solution flow is shown by the curved arrows and the ports for channel closure and opening are denoted by the black dots. (B) Injections of 100 nM anti-PSA into channels 1, 2, 3, and 4 for 378, 315, 256,
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and 160 s, respectively, resulted in immobilization of 3.471 (black curve), 2.549 (green), 1.920 (magenta), and 0.909 ng/mm2 (blue) anti-PSA, as determined from the elevations from the dashed line extended from the original baseline.
Figure 1A is a schematic of the fluidics of the five-channel SPR instrument we used. The individual channels can be opened or closed via the upstream ports for pre-programmed durations, which, if necessary, can be aborted in real time. This program allows a wide range of ligand densities to be controllably attained and accurately determined. Figure 1B shows the typical responses from which the immobilization densities of a ligand can be deduced from the net elevations of the original baseline. The abrupt decreases at the beginning and end of the injection are caused by the combined effect of refractive index difference between the ligand solution and the running buffer and electrostatic attraction/repulsion of the protein molecules by the dextran surface at different pH values. The more gradual increases associated with the attachment of a ligand and the programmable port opening and closure provide an easy, real-time regulation and determination of the immobilization density. 60 (A)
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Analytical Chemistry
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Figure 2. (A) Overlaid experimental (black) and simulated (red) sensorgrams obtained via a single injection of 33 nM PSA from channels pre-immobilized with anti-PSA at densities of 0.492, 1.124, 1.821, and 3.063 ng/mm2. (B) The same as in (A) except 300 nM PSA was injected and the anti-PSA densities were 0.431, 1.633, 2.549, and 4.995 ng/mm2. (C) SPR responses plotted against Rmax. Responses were extracted from panel A at t = 305 s (blue filled circles) and 740 s (blue empty circles) and from panel B at t = 45 s (black filled circles) and 85 s (black empty circles). R2 values are shown next to the regressions. (D) Linear regression plots of Req vs. a wide range of immobilization densities of antiPSA (filled circles) and BSA (empty circles). (E) Sensorgrams obtained with a single injection of 300 nM anti-BSA onto a sensor chip pre-immobilized with 0.078, 0.148, 0.268, and 0.458 ng/mm2 BSA. The vertical lines indicate the onsets of Req. (F) Overlaid sensorgrams showing 1 (black curve, bottom), 5 (black, middle), 25 (black, top), 125 (red), 250 (blue), and 430 nM (green) antiBSA binding to and dissociation from pre-immobilized BSA (density = 0.286 ng/mm2). The arrows indicate the onsets of Req when 125 (red), 250 (blue), and 400 nM (green) anti-BSA were introduced.
We performed an SPR binding assay via a single injection of one prostate specific antigen (PSA) sample (33 nM) into channels wherein different anti-PSA densities were attained via the graduated immobilization scheme depicted in Figure 1. Four sensorgrams were simultaneously obtained, and, with
subtraction of the background from the reference channel (no anti-PSA immobilized), they could be well fitted with the pseudo-first-order kinetics (Figure 2A). In a different experiment, 300 nM PSA was injected, and the four seensorgrams collected again agreed well with the simulated sensorgrams (Figure 2B). The unpaired t test34 proved that ka of (5.2 ± 0.5) × 104 M−1s−1, kd of (4.6 ± 0.2) × 10−5 s−1, and KD of 0.9 ± 0.1 nM measured with our method are not statistically different from those obtained with the conventional SPR approach31. The kinetic measurements are independent of the analyte concentration, but it is evident that a higher analyte concentration is more advantageous in that greater signal/noise ratios can be obtained and the binding equilibria can be attained more rapidly. The latter advantage is expected from eq. 2. Note that negligible depletion of analyte concentration occurs after the sample flows from channels 1 to 4. For example, even if all four channels are immobilized with 4.995 ng/mm2 anti-PSA (the highest density in Figure 2B), binding of PSA onto the cumulative channel area (9.06 mm2) by 120 RU or 120 pg/mm2 from a 300 nM PSA sample at 60 µL/min (cf. topmost sensorgram in Figure 2B) will reduce the bulk concentration by less than 0.14 % when the injected sample reaches the fifth channel. We should also note that all curves in Figures 2A and 2B reach binding equilibria simultaneously, again consistent with the predication by eq. 2. Although the two trends are good indications of the Langmuir isotherm, the validity can only be authenticated from the linearity between the SPR binding signals and Rmax throughout the association phase. As shown in Figure 2C, data extracted from any given time within the curved portions of the association phases (blue and black lines) are indeed proportional to Rmax. In addition, all plots must go through the origin of the coordinates. These are again expected from eq. 2, as for any fixed C and t, all sensorgrams should have the same value of 1 − exp(−kaC − kd)t. To ensure that the Langmuir isotherm is strictly obeyed, we suggest that a closeto-one R2 value (>0.995) and a zero intercept should be generated from the linear regression. To further test the rigor of the above criteria, we collected additional sensorgrams with higher anti-PSA immobilization densities. In comparison to Figure 2C, the R2, ka, kd, and KD values remain essentially unchanged for the PSA/anti-PSA system. However, when sensorgrams of the bovine serum albumin (BSA)/anti-BSA interaction were collected from channels with immobilization densities in the same range, the signals increases more gradually from the ligand density of 3.370 ng/mm2 and higher (open circles and in Figure 2D). Furthermore, this segment does not extrapolate through the origin. The agreement between the experimental and simulated sensorgrams (data not shown) is also substantially worse than the good fit shown in Figure 2E. The good fit shown in Figure 2E allowed ka, kd, and KD to be determined as (9.2 ± 0.5) × 104 M−1s−1, (1.1 ± 0.1) × 10−4 s−1, and (1.2 ± 0.5) nM, respectively, which are all consistent with the literature values.29,35 Therefore, it is obvious that Rmax is not necessarily proportional to the ligand density, as higher ligand densities could impose steric hindrance and mass transfer limitation to the ligand/analyte interaction. We caution that, only when throughthe-origin and strong linear regression is obtained across the SPR data plotted against the ligand density can one ascertain that the kinetic data are reliable. In other words, without prior knowledge of the ligand densities, artifacts cannot be confidently excluded.
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In studying biomolecular interactions, many researchers often only need to determine KD. In such applications, knowledge about ka and kd is not crucial. In the conventional SPR approach, Req values are recorded at different analyte concentrations to construct a linear plot of 1/Req vs. 1/C so that KD can be deduced from the slope and Rmax from the intercept of the following equation:15 ଵ
ோ
=ቀ
ವ
ோೌೣ
ଵ
ቁ +
ଵ
ோೌೣ
= ሺ
ାವ
ሻ
ଵ
ோೌೣ
(4)
Eq. 2 predicts that, when C is the variable, the time required to reach Req varies with the analyte concentration. This is evident from the absence of Req in the black curves and the different arrow positions in the colored sensorgrams in Figure 2F. This renders the conventional approach difficult to automate. As a result, multiple sensor chips might be needed to develop the SPR method. Such a problem is mitigated by our method as Rmax is the variable and all sensorgrams reach equilibria at the same time (cf. the vertical lines in Figure 2E and eq. 2). Such a procedure can be easily programmed and implemented, reducing the costs associated with sensor chips and reagents and simplifying the experimental procedure for determining KD. Thus, with a single injection of a relatively high analyte concentration, four Req values can be simultaneously obtained without surface regeneration. The plot of 1/Req against 1/Rmax as shown in eq. 4 is linear, with a slope of (C+ KD)/C and a zero intercept. Although it has been recognized that a single sample injection into fluidic channels of different immobilization densities could obviate the need for surface regeneration, experimental data demonstrating this unique capability for interactions that cannot be studied by the conventional SPR approach have not been reported. Shown in Figure 3A is the binding of a monoclonal antibody to the green fluorescent protein (GFP). An injection of 100 nM GFP into a fluidic channel preimmobilized with a high anti-GFP density (0.792 ng/mm2) did not produce any binding signal (red curve in Figure 3), most
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We also investigated on the suitable analyte concentration for kinetic measurements using the BSA/anti-BSA system. The ka, kd and KD values measured with our method from 4 to 600 nM anti-BSA concentrations are within the ranges of 6.3−7.9 × 104 M−1s−1, 6.5−7.9 × 10−5 s−1, and 0.8−1.1 nM, respectively, which all agree well with the data obtained with the conventional approach.29,35 This is not surprising, as the conventional approach can also employ a wide range of analyte concentrations. For our method, we recommend the use of a moderateto-high analyte concentration (e.g. 100−300 nM for the BSA/anti-BSA system) as a higher signal-to-noise ratio can be obtained without a substantial sample consumption. For sensors whose surfaces are regenerable, after one regeneration all four sensorgrams can be replicated with again only one sample injection. For sensor surfaces that cannot be regenerated, replicate measurements can be easily performed with a new chip because the same ligand densities in the four channels can be precisely attained (cf. Figure 1). To examine the effect of flow rate on the kinetic data quality, 5, 60, and 120 µL/min were used, and the ka, kd, and KD values are not statistically different from the literature values. The data suggest that the BSA/antiBSA interaction is not controlled by mass transfer. Analogous behaviors were observed for the PSA/anti-PSA interaction.
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Analytical Chemistry
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(D)
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Figure 3. (A) Sensorgram obtained upon injecting 100 nM GFP into a fluidic channel with 0.792 ng/mm2 pre-immobilized antiGFP (red curve), and injections of 100 nM anti-GFP into channels pre-immobilized with GFP before (black), and after regeneration by HCl (pH = 4.0, magenta) and NaOH (pH = 12.0, blue). (B) Fluorescence spectra of 50 nM GFP before (black) and after the addition of HCl (magenta; pH 4.0) or NaOH (blue; pH = 12.0). (C) Sensorgrams of a single injection of 100 nM anti-GFP binding to and dissociate from pre-immobilized GFP at densities of 0.355, 0.571, 0.783, and 1.127 ng/mm2. (D) Linear regression plots of Req vs. ligand densities of GFP at 80 (filled circles) and 200s (empty circles). R2 values are shown next to the regressions.
likely due to the unfavorable orientation of anti-GFP towards the GFP binding site. Cross-linking GFP onto the sensor chip followed by the anti-GFP injection did generate a large binding signal (black curve); however, surface regeneration with dilute HCl (pH 4.2) or NaOH (pH 12.2) drastically attenuated the signals of subsequent injections of the same anti-GFP solution (blue and magenta curves for HCl and NaOH regenerations, respectively). Other commonly used denaturant solutions such as glycine hydrochloride are also not amenable. These results clearly indicate that denaturation of GFP occurred upon exposure to HCl or NaOH. The denaturation was confirmed by a separate fluorescence experiment. As shown in Figure 3B, the GFP fluorescence was diminished ca. 40% and 60% by HCl (magenta curve) and NaOH (blue curve), respectively. The destruction of the GFP β-barrel, which is responsible for the loss of fluorescence intensity,36 apparently affects the appropriate anti-GFP binding. This problem can be easily mitigated with our method. A single injection of 100 nM antiGFP into four channels immobilized with different GFP densities yielded sensorgrams that are essentially congruent with the simulated ones (Figure 3C). The data points extracted using the same procedure as for Figure 2D exhibit a strong linear correlation with the GFP densities (Figure 3D), confirming that the interaction follows the Langmuir isotherm and no secondary effect exists. The ka, kd, and KD values were deduced to be (1.8 ± 0.1) × 105 M−1s−1, (1.1 ± 0.2) × 10−4 s−1, and 0.6 ± 0.1 nM, respectively. In addition to circumventing the ligand structural change resulted from the surface regeneration, the present method also saves time needed for searching a suitable regeneration solution or for optimization of the regeneration procedure. CONCLUSIONS
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Analytical Chemistry
The use of a five-channel SPR instrument for facile and accurate kinetic and thermodynamic measurements is described. In contrast to the conventional SPR approach, different but uniform ligand densities in four of the five channels (the fifth reserved as the reference) can be controllably immobilized in one step. In conjunction with the injection of a single sample solution, the experimental conditions are significantly simplified and optimized in terms of ligand densities, analyte concentrations, flow rates, obviation of the regeneration step, and concurrent attainment of binding equilibria from all channels. These features render the SPR experiments easier to program and implement. To exclude complications caused by secondary effects such as steric hindrance and mass transfer limitation, we demonstrate that it is critical to ascertain a strong linear correlation between the SPR binding signals and the ligand densities. Our measurement of the interaction between the green fluorescent protein and its antibody, which cannot be determined with the conventional SPR approach, demonstrates that SPR measurements for biomolecular interactions can be further expanded with this new method. AUTHOR INFORMATION Corresponding Author *
E-mail:
[email protected].
Author Contributions ‡
Xiaoying Wang and Zhiqiang Li contributed equally and collected the data and performed data analysis, NL provided technical help and critiqued the data, and FZ directed the project and wrote the manuscript. ACKNOWLEDGEMENTS Partial support of this work by a grant from the National Science Foundation (NSF No. 1112105), the NSF-CREST Program at California State University, Los Angeles (NSF HRD1547723), the National Key Basic Research Program of China (2014CB744502), and a 2011 Collaborative and Innovative Grant from Hunan Province of China is gratefully acknowledged.
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