Name: determination of high-affinity antibody-antigen binding kinetics using four biosensor platforms
Uploaded: 2017-04-17T15:00:00
Duration: 15 min 27 s
Description: Watch this Scientific Journal Video about Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms at JoVE.com

The authors thank Noah Ditto and Adam Miles for technical assistance on the IBIS MX96.

<ol>
	<li>Cooper, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+biosensors+in+drug+discovery.">Optical biosensors in drug discovery.</a> Nat Rev Drug Discov. 1 (7), 515-528 (2002).</li><li>Van Regenmortel, M. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+of+antigen-antibody+interactions+with+biosensors.">Measurement of antigen-antibody interactions with biosensors.</a> J Mol Rec. 11 (1-6), 163-167 (1998).</li><li>Canziani, G. A., Klakamp, S., Myszka, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinetic+screening+of+antibodies+from+crude+hybridoma+samples+using+Biacore.">Kinetic screening of antibodies from crude hybridoma samples using Biacore.</a> Anal Biochem. 325 (2), 301-307 (2004).</li><li>Katsamba, P. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinetic+analysis+of+a+high-affinity+antibody/antigen+interaction+performed+by+multiple+Biacore+users.">Kinetic analysis of a high-affinity antibody/antigen interaction performed by multiple Biacore users.</a> Anal Biochem. 352 (2), 208-221 (2006).</li><li>Abdiche, Y. N., Malashock, D. S., Pinkerton, A., Pons, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploring+blocking+assays+using+Octet,+ProteOn,+and+Biacore+biosensors.">Exploring blocking assays using Octet, ProteOn, and Biacore biosensors.</a> Anal Biochem. 386 (2), 172-180 (2009).</li><li>Abdiche, Y. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-Throughput+Epitope+Binning+Assays+on+Label-Free+Array-Based+Biosensors+Can+Yield+Exquisite+Epitope+Discrimination+That+Facilitates+the+Selection+of+Monoclonal+Antibodies+with+Functional+Activity.">High-Throughput Epitope Binning Assays on Label-Free Array-Based Biosensors Can Yield Exquisite Epitope Discrimination That Facilitates the Selection of Monoclonal Antibodies with Functional Activity.</a> PLoS ONE. 9 (3), e92451 (2014).</li><li>Ecker, D. M., Jones, S. D., Levine, H. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+therapeutic+monoclonal+antibody+market.">The therapeutic monoclonal antibody market.</a> mAbs. 7 (1), 9-14 (2014).</li><li>Rich, R. L., Myszka, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Higher-throughput,+label-free,+real-time+molecular+interaction+analysis.">Higher-throughput, label-free, real-time molecular interaction analysis.</a> Anal Biochem. 361 (1), 1-6 (2007).</li><li>Keighley, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+need+for+high+throughput+kinetics+early+in+the+drug+discovery+process.">The need for high throughput kinetics early in the drug discovery process.</a> Drug Disc World. 12 (3), 39-45 (2011).</li><li>Lad, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+kinetic+screening+of+hybridomas+to+identify+high-affinity+antibodies+using+bio-layer+interferometry.">High-throughput kinetic screening of hybridomas to identify high-affinity antibodies using bio-layer interferometry.</a> J Biomol Screen. 20 (4), 498-507 (2015).</li><li>Bravman, T., Bronner, V., Nahshol, O., Schreiber, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ProteOn+XPR36TM+Array+System-High+Throughput+Kinetic+Binding+Analysis+of+Biomolecular+Interactions.">The ProteOn XPR36TM Array System-High Throughput Kinetic Binding Analysis of Biomolecular Interactions.</a> Cell Mol Bioeng. 1 (4), 216-228 (2008).</li><li>Eddings, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+continuous-flow+print+head+for+micro-array+deposition.">Improved continuous-flow print head for micro-array deposition.</a> Anal Biochem. 382 (1), 55-59 (2008).</li><li>Abdiche, Y., Malashock, D., Pinkerton, A., Pons, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determining+kinetics+and+affinities+of+protein+interactions+using+a+parallel+real-time+label-free+biosensor+the+Octet.">Determining kinetics and affinities of protein interactions using a parallel real-time label-free biosensor the Octet.</a> Anal Biochem. 377 (2), 209-217 (2008).</li><li>Abdiche, Y. N., Lindquist, K. C., Pinkerton, A., Pons, J., Rajpal, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Expanding+the+ProteOn+XPR36+biosensor+into+a+36-ligand+array+expedites+protein+interaction+analysis.">Expanding the ProteOn XPR36 biosensor into a 36-ligand array expedites protein interaction analysis.</a> Anal Biochem. 411 (1), 139-151 (2011).</li><li>Rich, R. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+global+benchmark+study+using+affinity-based+biosensors.">A global benchmark study using affinity-based biosensors.</a> Anal Biochem. 386 (2), 194-216 (2009).</li><li>Yang, D., Singh, A., Wu, H., Kroe-Barrett, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+biosensor+platforms+in+the+evaluation+of+high+affinity+antibody-antigen+binding+kinetics.">Comparison of biosensor platforms in the evaluation of high affinity antibody-antigen binding kinetics.</a> Anal Biochem. 508, 78-96 (2016).</li><li>Yang, D., Singh, A., Wu, H., Kroe-Barrett, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dataset+of+the+binding+kinetic+rate+constants+of+anti-PCSK9+antibodies+obtained+using+the+Biacore+T100+ProteOn+XPR36,+Octet+RED384,+and+IBIS+MX96+biosensor+platforms.">Dataset of the binding kinetic rate constants of anti-PCSK9 antibodies obtained using the Biacore T100 ProteOn XPR36, Octet RED384, and IBIS MX96 biosensor platforms.</a> Data in Brief. 8, 1173-1183 (2016).</li><li>Bouvier, E. S. P., Koza, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+size-exclusion+separations+of+proteins+and+polymers+by+UHPLC.">Advances in size-exclusion separations of proteins and polymers by UHPLC.</a> Trends Anal Chem. 63, 85-94 (2014).</li></ol>

Publication fees for this video-article were paid by&#160;Boehringer Ingelheim Pharmaceuticals.

Our head-to-head comparison study shows that each biosensor platform has its own strengths and weaknesses. Even though the binding profiles of the antibodies are similar by visual comparison (Figures 3 - 6), and the rank order of the acquired kinetic rate constants are highly consistent across the instruments (Figure 7), our results show that SPR-based instruments with continuous flow fluidics) are better at resolving high-affinity interactions with slow dissociation rates. Upward drift during the dissociation phase is observed in datasets (e.g., mAb 2, mAb 5, and mAb 9; Figure 5) generated by the BLI fluidics-free instrument. This finding can be attributed in large part to sample evaporation over time in the microplate, which is a primary limitation of the system. With this inherent limitation, the experimental time is also restricted to less than 12 h; the experiments were therefore programmed with shorter times (500 s association and 30 min dissociation) compared to those of the other platforms (10 min association and 45 min dissociation). However, shortening the experimental time did not appear to mitigate the impact of evaporation on data quality/consistency, as the rate constants generated by the BLI-based instrument show less linearity as a result of fluctuations in some of the off-rates (Figure 8C). In addition to sample evaporation, differences in the capture reagents used may have also contributed to the differences in the results obtained. While protein A/G was used in all three fluidics-based SPR platforms, AHC sensors were used in the non-fluidics BLI platform. Since protein A/G is likely to have a weaker affinity for the mAbs than does the AHC, an antibody-based biosensor surface, the off-rates of the mAb-antigen complex from the protein A/G surfaces may artificially appear faster than those obtained from the AHC surface. This possibility is supported by the experimental data showing that the off-rate values generated by the BLI platform were consistently lower than those obtained from the other instruments (Figure 7, red line). Nevertheless, the BLI platform has different advantages over the other platforms. For example, it is highly flexible with regard to sensor choice and assay configuration due to the various pre-coated sensors for immediate use. In our experiments, use of the AHC sensors eliminated the need for ligand immobilization steps, reducing the preparation time. Furthermore, the BLI platform requires much less maintenance compared to the other fluidics SPR platforms, which feature complicated tubing and value switch configurations. This feature is an advantage for experiments involving crude samples that can cause clogging and contamination issues.
As the demand for the efficient, rapid, and accurate identification of therapeutic candidates increases, the need for biosensor throughput is also rising. Among the four biosensor platforms, the throughput from the biosensor capable of 96-ligand array printing is the highest, followed by the biosensor coupled to a crisscrossing 36-ligand format and the BLI-based biosensor with 16-channel simultaneous readout, which ultimately increase the number of interactions measured in a single binding cycle to 96, 36 and 16, respectively. These throughput capabilities are significantly higher than that of the traditional SPR platform, which is limited by having only four flow cells connected by a single serial flow. Since our experiments involved a relatively small sample set of 10 mAbs evaluated at multiple surface densities with long dissociation times, the instrumental throughputs played a moderate role in determining the efficiency of the experiments. There were no significant differences in the experimental times of the three high-throughput platforms, and in all cases the experiments were completed in one day. On the other hand, the traditional serial flow SPR experiments required 3 days to complete, despite the walk-away automation of data acquisition after setup. In other studies that involve a large number of samples (i.e. in the hundreds or thousands), for off-rate ranking/kinetic screening or epitope binning purposes, the throughput becomes a critical factor.
Although the throughput in the IBIS MX96 is orders of magnitude higher than that of the other biosensors and is therefore an optimal choice for these purposes, it has a few shortcomings. In particular, the array printing by the CFM shows large surface inconsistencies (Figure 1) and reduced data reproducibility (Figure 8D and 8E). For accurate kinetic measurements, the amount of ligand on the biosensor surface is a critical parameter that needs to be controlled to ensure that the binding responses are not disturbed by secondary factors such as mass transfer or steric hindrance. For the Biacore T100 and ProteOn XPR36, the optimal RL levels were determined based on the standard calculation of set Rmax values as described in the research article17. On the other hand, for the Octet RED384 and IBIS MX96 platforms, the mAb capture levels were attained empirically using a series of 2-fold serially diluted antibodies at constant times. The lack of knowledge or control of the capture step resulted in a high-density surface and high binding response signals (Figure 2) that may have compromised the accuracy of the kinetic rate constants. Furthermore, the separation of the printer from the SPR detector also presents a challenge when conducting multiple binding cycle measurements involving regenerations. The only way to perform the multi-cycle kinetics setup was through direct amine coupling of the mAbs, as opposed to capture through the mAb Fc by the immobilized protein A/G surface in the other three biosensor platforms. As a result, an additional regeneration scouting experiment was required to determine the optimal regeneration conditions. The outcome of this setup was linked to a ~90% lower observed surface activity compared to the Fc capture method (Figure 9), in addition to a longer experimental time. To execute the Fc capture method, an alternative single-cycle kinetic approach was adopted. This approach involves the sequential injection of analyte at increasing concentrations without regeneration between each injection (Figure 2). This highly convenient, but less commonly applied approach not only shortened the experimental time and reduced reagent consumption, but also produced kinetic rate constants highly similar to those from the other biosensors (Figure 7). Therefore, applying this single-cycle kinetics approach overcomes the inherent configuration limitation of the instrument and presents an opportunity for obtaining high-resolution kinetic rate constants in a high-throughput manner.
Despite the throughput being a major limitation in the Biacore T100, our results collectively show that it generated the most consistent data with the highest quality. This was followed by the ProteOn XPR36, which has ~ 10-fold higher throughput. Their ability to generate high-quality data becomes an advantage when characterizing high-affinity antibody-antigen interactions that can be technically challenging when the instruments&#39; detection limits are reached. While the systematic limitations in instrumentation of the Octet RED384 hinder the accurate measurement of dissociation rate constants (i.e., falling short of the sensitivity to resolve sufficient signal decay for slow off-rates), both the Biacore T100 and ProteOn XPR36 can provide sensitive and reliable detection for differentiation.

The acquisition of reliable kinetic parameters for characterizing antibody-antigen interactions is an essential component of the drug discovery and development process1. Optical Surface Plasmon Resonance (SPR) biosensors, the "gold standard" for real-time detection of these interactions, have been used for approximately two decades to enable early selection of criteria-meeting therapeutic antibody candidates2,3. In addition to providing an affinity ranking of antibody candidates by the rapid binding screening of crude supernatants4, and rigorous kinetic constant determinations of purified preparations5, biosensors can further differentiate the functional activity of lead candidates via epitope binning studies6,7. 
To meet the growing demand of antibody-based products for various therapeutic indications8, a wide variety of innovative biosensor instruments have been developed in recent years that increase the efficiency of candidate identification and characterization9,10. These instruments differ in microfluidic channel configuration design and/or in the optical principles involved in detecting biomolecular interactions. Specifically, the Octet RED38411, ProteOn XPR3612, and IBIS MX967 have expanded the number of interactions measured in a single binding cycle to 16, 36, and 96 respectively, representing significant throughput improvements over the traditional Biacore platform. Although these various biosensor platforms all provide essential kinetic data for characterizing antibody-antigen interactions, they differ in experimental setup and operational procedures due to variations in instrumental configurations. For example, in the IBIS MX96's SPR imaging array platform, the multiplex ligand immobilization step is performed in an off-line mode in an external printing device separate from the detector7,13; in contrast, the other three biosensor platforms utilize an "all-in-one" setup, where the addition of component onto the sensor surface, whether it is the activation reagent, ligand, or analyte, is recorded in real time and through pre-programmed commands in sequential order. In the Octet RED384, a unique feature of this BioLayer Interferometry (BLI)-based platform is the availability of pre-coated optical fiber biosensors for immediate "dip-and-read" use14, eliminating the need for ligand surface preparation and initiation/conditioning steps, which are often required for the sensor chips in SPR-based platforms. This instrument's fluidic-free design also simplifies the mechanics and avoids clogging and contamination concerns when dealing with crude samples. With the novel 6 x 6 crisscrossing fluidic design in the ProteOn XPR36, a "one-shot" kinetics approach can be implemented by switching the flow channels between horizontal and vertical directions to create 36 interaction spots15. Instead of assessing binding kinetics one ligand or analyte concentration at a time, as is done in the traditional serial flow Biacore T100 platform, this approach offers the ability to monitor up to 36 different interactions simultaneously in a single binding cycle. 
Despite their differences, these four biosensor platforms are all widely used by many laboratories worldwide. For new users with little hands-on biosensor experience, deciding which instrument to use can be a challenging task given the differences in instrumental design. To determine the most appropriate instrument for their research purposes, factors such as data quality, performance consistency, throughput, ease of operation, and material consumption need to be considered collectively. While several benchmark studies have explored the variability of kinetic rate constants obtained from multiple laboratories and biosensors5,16, a recently published head-to-head comparison study further addresses the systematic factors that influence data reliability from the instrumental performance point of view17,18. The protocols supplied in this video focus on the experimental setup and procedures in detail, and are accompanied by the research article entitled, "Comparison of biosensor platforms in the evaluation of high affinity antibody-antigen binding kinetics"17. These protocols are intended not only to help new biosensor users implement these instruments for their research purposes, but also to provide additional insights for current biosensor users regarding technical challenges and considerations in experimental designs for evaluating high-affinity antibody-antigen interactions.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Biacore T100 System</td>
			<td>GE Healthcare</td>
			<td>28975001</td>
			<td>The T100 system has been upgraded to T200</td>
		</tr>
		<tr>
			<td>CM5 Sensor Chip&#160;</td>
			<td>GE Healthcare</td>
			<td>BR100012</td>
			<td></td>
		</tr>
		<tr>
			<td>BIAevaluation&#160;</td>
			<td>GE Healthcare</td>
			<td></td>
			<td>Version 4.1</td>
		</tr>
		<tr>
			<td>Biacore T100 Control Software</td>
			<td>GE Healthcare</td>
			<td></td>
			<td>The T100 control software has been upgraded to T200</td>
		</tr>
		<tr>
			<td>Amine Coupling Kit</td>
			<td>GE Healthcare</td>
			<td>BR100050&#160;</td>
			<td>It contains: 750 mg 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC), 115 mg N-hydroxysuccinimide (NHS), 10.5 ml 1.0 M ethanolamine-HCl pH 8.5&#160;</td>
		</tr>
		<tr>
			<td>HBS-EP+ Buffer 10&#215;</td>
			<td>GE Healthcare</td>
			<td>BR100669</td>
			<td>Concentrated stock solution&#160;</td>
		</tr>
		<tr>
			<td>Plastic Vials, o.d. 7 mm</td>
			<td>GE Healthcare</td>
			<td>BR100212&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>Rubber Caps, type 3</td>
			<td>GE Healthcare</td>
			<td>BR100502</td>
			<td></td>
		</tr>
		<tr>
			<td>Plastic Vials and Caps, o.d. 11 mm</td>
			<td>GE Healthcare</td>
			<td>BR100214&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>ProteOn XPR36 Protein Interaction Array System</td>
			<td>Bio-Rad</td>
			<td>1760100</td>
			<td></td>
		</tr>
		<tr>
			<td>ProteOn Manager Software&#160;</td>
			<td>Bio-Rad</td>
			<td>1760200</td>
			<td>Version 3.1.0.6</td>
		</tr>
		<tr>
			<td>GLM Sensor Chip</td>
			<td>Bio-Rad</td>
			<td>1765012</td>
			<td></td>
		</tr>
		<tr>
			<td>Amine Coupling Kit</td>
			<td>Bio-Rad</td>
			<td>1762410</td>
			<td>It includes EDAC (EDC), sulfo-NHS, ethanolamine HCl&#160;</td>
		</tr>
		<tr>
			<td>Regeneration and Conditioning Kit and Buffers</td>
			<td>Bio-Rad</td>
			<td>1762210</td>
			<td>It includes 1 each glycine buffer (pH 1.5, 2.0, 2.5, 3.0), and NaOH, SDS, HCl, phosphoric acid, NaCl; 50 ml each solution&#160;</td>
		</tr>
		<tr>
			<td>2 L PBS/Tween/EDTA buffer</td>
			<td>Bio-Rad</td>
			<td>1762730</td>
			<td>It includes hosphate buffered saline (PBS), pH 7.4, 0.005% Tween 20, 3 mM EDTA&#160;</td>
		</tr>
		<tr>
			<td>Octet RED384 System</td>
			<td>Fort&#233;Bio</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Data Analysis Software</td>
			<td>Fort&#233;Bio</td>
			<td></td>
			<td>Version 9.0.0.4</td>
		</tr>
		<tr>
			<td>Dip and Read Anti-hIgG Fc Capture (AHC) Biosensors</td>
			<td>Fort&#233;Bio</td>
			<td>18-5060</td>
			<td>One tray of 96 biosensors&#160;</td>
		</tr>
		<tr>
			<td>384-Well Tilted-Bottom Plate&#160;</td>
			<td>Fort&#233;Bio</td>
			<td>18-5080</td>
			<td></td>
		</tr>
		<tr>
			<td>Biosensor Dispenser</td>
			<td>Fort&#233;Bio</td>
			<td>18-5016</td>
			<td></td>
		</tr>
		<tr>
			<td>Kinetics Buffer 10X</td>
			<td>Fort&#233;Bio</td>
			<td>18-1092</td>
			<td>10X concentration. Contains ProClin 300.&#160;</td>
		</tr>
		<tr>
			<td>IBIS MX96 SPRi System</td>
			<td>Wasatch Microfluidics</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Microfluidics Continuous Flow Microspotter (CFM) Printer</td>
			<td>Wasatch Microfluidics</td>
			<td></td>
			<td>Version 2.0</td>
		</tr>
		<tr>
			<td>SensEye COOH-G chip</td>
			<td>Wasatch Microfluidics</td>
			<td></td>
			<td>1-09-04-004</td>
		</tr>
		<tr>
			<td>Data Analysis Software</td>
			<td>Wasatch Microfluidics</td>
			<td></td>
			<td>Version 6.19.3.17</td>
		</tr>
		<tr>
			<td>SPRint Data Analysis Software</td>
			<td>Wasatch Microfluidics</td>
			<td></td>
			<td>Version 6.15.2.1</td>
		</tr>
		<tr>
			<td>Scrubber 2</td>
			<td>BioLogic Software</td>
			<td></td>
			<td>Version 2</td>
		</tr>
		<tr>
			<td>Pierce Recombinant Protein A/G</td>
			<td>ThermoFisher Scientific</td>
			<td>21186</td>
			<td></td>
		</tr>
	</tbody>
</table>

determination of high-affinity antibody-antigen binding kinetics using four biosensor platforms

Label-free optical biosensors are powerful tools in drug discovery for the characterization of biomolecular interactions. In this study, we describe the use of four routinely used biosensor platforms in our laboratory to evaluate the binding affinity and kinetics of ten high-affinity monoclonal antibodies (mAbs) against human proprotein convertase subtilisin kexin type 9 (PCSK9). While both Biacore T100 and ProteOn XPR36 are derived from the well-established Surface Plasmon Resonance (SPR) technology, the former has four flow cells connected by serial flow configuration, whereas the latter presents 36 reaction spots in parallel through an improvised 6 x 6 crisscross microfluidic channel configuration. The IBIS MX96 also operates based on the SPR sensor technology, with an additional imaging feature that provides detection in spatial orientation. This detection technique coupled with the Continuous Flow Microspotter (CFM) expands the throughput significantly by enabling multiplex array printing and detection of 96 reaction sports simultaneously. In contrast, the Octet RED384 is based on the BioLayer Interferometry (BLI) optical principle, with fiber-optic probes acting as the biosensor to detect interference pattern changes upon binding interactions at the tip surface. Unlike the SPR-based platforms, the BLI system does not rely on continuous flow fluidics; instead, the sensor tips collect readings while they are immersed in analyte solutions of a 384-well microplate during orbital agitation.
Each of these biosensor platforms has its own advantages and disadvantages. To provide a direct comparison of these instruments&#39; ability to provide quality kinetic data, the described protocols illustrate experiments that use the same assay format and the same high-quality reagents to characterize antibody-antigen kinetics that fit the simple 1:1 molecular interaction model.

Figure 1 shows the antibody array image from the CFM and the spotted mAb levels by the two immobilization methods, and Figure 2 shows the corresponding binding sensorgrams generated in the MX96 from the multi-cycle kinetic and the single-cycle kinetic measurements on these mAb arrays. The real-time binding and kinetically fitted curves from multiple antibody-coated surfaces generated in the four biosensor platforms are shown in Figures 3 - 6. Figure 7 shows the comparison of the final kinetic rates and equilibrium binding constants obtained from the global analysis of these binding curves. The individual association (ka), dissociation (kd), and equilibrium (KD) binding constants are presented in Table 1. To demonstrate the variability of datasets generated within the same instrument, Figure 8 shows plots of ka, kd, and KD at different mAb surface densities, and Figure 9 further compares the calculated binding activities of the mAb surfaces across the biosensor platforms.
<img alt="Figure 1" src="/files/ftp_upload/55659/55659fig1.jpg" /> 
Figure 1. Multiplex Ligand Arrays of Amine-coupled (A) and Fc-captured (B) Antibody Surfaces from CFM Printing in the IBIS MX96. Images of the printed arrays are shown in the left panels, where the grey areas enclosed by red squares indicate the presence of antibody. The darker interspots located between the active antibody spots are used for reference subtraction. Each column contains an antibody printed at the titration concentrations identified below and to the left of the image. The amounts of the printed antibodies quantified by calculating the difference in bulk shifts between the active and reference locations are shown in the right panels. <a href="http://cloudflare.jove.com/files/ftp_upload/55659/55659fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/55659/55659fig2.jpg" /> 
Figure 2. Comparison of Real-time Binding Sensorgrams from Multi-cycle (A) and Single-cycle (B) Kinetic Experiments in the IBIS MX96. The sequential injections of human PCSK9 at increasing concentrations across the 10 x 8 antibody arrays are noted above each corresponding sensorgram segment. <a href="http://cloudflare.jove.com/files/ftp_upload/55659/55659fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/55659/55659fig3.jpg" /> 
Figure 3. Binding Sensorgrams of the Captured Antibodies Interacting with Human PCSK9 and 1:1 Kinetic Model Fit Overlays in the Biacore T100. The interactions are evaluated over high- (top panels), medium- (middle panels), and low- (bottom panels) density surfaces. The overlaid smooth black lines represent the kinetic fit of the binding response signals at different human PCSK9 concentrations (colored lines) to a 1:1 interaction model. <a href="http://cloudflare.jove.com/files/ftp_upload/55659/55659fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/55659/55659fig4.jpg" /> 
Figure 4. Binding Sensorgrams of the Captured Antibodies Interacting with Human PCSK9 and 1:1 Kinetic Model Fit Overlays in the ProteOn XPR36. The interactions are evaluated over high- (top panels), medium- (middle panels), and low- (bottom panels) density surfaces. The overlaid smooth black lines represent the kinetic fit of the binding response signals at different human PCSK9 concentrations (colored lines) to a 1:1 interaction model. <a href="http://cloudflare.jove.com/files/ftp_upload/55659/55659fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/55659/55659fig5.jpg" /> 
Figure 5. Binding Sensorgrams of the Captured Antibodies Interacting with Human PCSK9 and 1:1 Kinetic Model Fit Overlays in the Octet RED384. The interactions are evaluated over high- (top panels), medium- (middle panels), and low- (bottom panels) density surfaces. The overlaid red lines represent the kinetic fit of the binding response signals at different human PCSK9 concentrations (colored lines) to a 1:1 interaction model. <a href="http://cloudflare.jove.com/files/ftp_upload/55659/55659fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/55659/55659fig6.jpg" /> 
Figure 6. Binding Sensorgrams of the Amine-coupled (A) and Fc-captured (B) Antibodies Interacting with Human PCSK9 and 1:1 Kinetic Model Fit Overlays in the IBIS MX96. The binding profiles are organized as 10 x 8 panels that follow the array plate map in Figure 1. The black lines represent the recorded binding response signals at different human PCSK9 concentrations, and the overlaid red lines represent the fitted curves. <a href="http://cloudflare.jove.com/files/ftp_upload/55659/55659fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" src="/files/ftp_upload/55659/55659fig7.jpg" /> 
Figure 7. Comparison of the Association ka (A), Dissociation kd (B), and Equilibrium KD (C) Binding Constants Generated by the Four Biosensor Platforms. The kinetic parameters are derived from global analysis of the binding curves in Figures 3 - 6. The instruments are represented as follows: Biacore T100 (blue), ProteOn XPR36 (green), Octet RED384 (red), IBIS MX96, amine-coupled (purple), and IBIS MX96, Fc-captured (orange). <a href="http://cloudflare.jove.com/files/ftp_upload/55659/55659fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" src="/files/ftp_upload/55659/55659fig8.jpg" /> 
Figure 8. Comparison of the Consistency of Kinetic Rate Constants Over Multiple Antibody Surface Densities in the Biacore T100 (A), ProteOn XPR36 (B), Octet RED384 (C), IBIS MX96, amine-coupled (D), and IBIS MX96, Fc-captured (E). The kinetic parameters ka (top sub-panels), kd (middle sub-panels), and KD (bottom sub-panels) are derived from group analysis at individual antibody surface density. <a href="http://cloudflare.jove.com/files/ftp_upload/55659/55659fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" src="/files/ftp_upload/55659/55659fig9.jpg" /> 
Figure 9. Binding Activities of the Antibody Surfaces and Their Standard Deviations (error bars) in the Four Biosensor Platforms. The values are calculated using equations described in the research articles17. <a href="http://cloudflare.jove.com/files/ftp_upload/55659/55659fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2"></td>
			<td>ka</td>
			<td>kd</td>
			<td>KD</td>
		</tr>
		<tr>
			<td>(M-1s-1)</td>
			<td>(s-1)</td>
			<td>(nM)</td>
		</tr>
		<tr>
			<td>mAb 1</td>
			<td>11.7 (7.8) x 105</td>
			<td>4.89 (4.18) x 10-5</td>
			<td>0.052 (0.05)</td>
		</tr>
		<tr>
			<td>mAb 2</td>
			<td>1.53 (0.28) x 105</td>
			<td>1.30 (1.54) x 10-5</td>
			<td>0.092 (0.12)</td>
		</tr>
		<tr>
			<td>mAb 3</td>
			<td>11.4 (8.3) x 105</td>
			<td>27.6 (11.0) x 10-5</td>
			<td>0.333 (0.20)</td>
		</tr>
		<tr>
			<td>mAb 4</td>
			<td>3.61 (1.6) x 105</td>
			<td>20.1 (12.3) x 10-5</td>
			<td>0.659 (0.54)</td>
		</tr>
		<tr>
			<td>mAb 5</td>
			<td>0.59 (0.21) x 105</td>
			<td>3.46 (2.50) x 10-5</td>
			<td>0.663 (0.46)</td>
		</tr>
		<tr>
			<td>mAb 6</td>
			<td>1.19 (0.78) x 105</td>
			<td>7.21 (3.83) x 10-5</td>
			<td>0.894 (0.64)</td>
		</tr>
		<tr>
			<td>mAb 7</td>
			<td>1.29 (0.53) x 105</td>
			<td>16.4 (5.63) x 10-5</td>
			<td>1.57 (1.02)</td>
		</tr>
		<tr>
			<td>mAb 8</td>
			<td>4.02 (2.23) x 105</td>
			<td>22.8 (10.7) x 10-5</td>
			<td>0.768 (0.68)</td>
		</tr>
		<tr>
			<td>mAb 9</td>
			<td>4.20 (2.13) x 105</td>
			<td>6.67 (4.3) x 10-5</td>
			<td>0.197 (0.16)</td>
		</tr>
		<tr>
			<td>mAb 10</td>
			<td>1.20 (0.49) x 105</td>
			<td>12.5 (7.64) x 10-5</td>
			<td>1.27 (0.80)</td>
		</tr>
	</tbody>
</table>
Table 1: Kinetic Rates and Equilibrium Binding Constants of 10 mAbs Obtained by Global Fitting of Binding Curves from Four Biosensor Instruments to the 1:1 Interaction Model.

Watch this Scientific Journal Video about Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms at JoVE.com

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