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The Revolution of Real-Time, Label-Free Biosensor Applications
Rebecca L. Rich and David G. Myszka
Center for Biomolecular Interaction Analysis, University of Utah, Salt Lake City, UT, USA
1.1 Introduction
1.2 SPR Pessimists
1.3 Setting Up Experiments
1.4 Data Processing and Analysis
1.5 The Good News
References
1.1 Introduction
Initially, we had planned to discuss the revolution of real-time, label-free biosensor applications. This revolution has been monumental. In the early days, biosensors were used as immunosensors to characterize antibody/antigen interactions. It didnât take long for researchers to exploit the technologyâs capabilities to examine other biological systems, including receptors, nucleic acids, and lipids. Once people recognized that low intensity signals were reliable, the biosensor quickly became a tool for characterizing small molecules and even membrane-associated systems.
Upon reflection, we realized a greater development was in usersâ understanding of how to apply biosensor technology. How we design experiments and analyse data today is different than in years past. Improvements in data processing and global fitting have eliminated much, but not all, of the confusion biosensor users experience when interpreting binding responses. With these advances it is now easier to recognize well performed experiments. So a better title for this discussion may be âEvolution in Our Understanding of Biosensor Analysisâ.
When we look at how people use biosensors today, we realize that many users still donât know what they are doing with the technology and the problems are not because of the biosensor (itâs a poor craftsman that blames his tools). Instead, far too often, users donât employ basics tenets of the scientific method. They donât include controls, test replicates, or even show data when presenting results. As a result, they end up publishing experimental artifacts or misinterpreting the interaction. Unfortunately, poor quality analysis gives all biosensor technology a bad name. In fact, based on the published data, we wonder if a better title for this chapter might be âWhy are Biosensor Users Such Poor Scientists?â
Before we examine why most biosensor users arenât good scientists, letâs have a short review of where the technology came from. In 1990, a Swedish company called Pharmacia released Biacore, the first commercially viable biosensor. As depicted in Figure 1.1a, the system was operated by a 486 Hz personal computer (PC for short) â boy, does that bring back memories. To put things into perspective, Figures 1.1bâ1.1f pictorially depict other significant advances that occurred in 1990. You might not remember it but the World Wide Web (Figure 1.1b) was launched then and changed forever how we gather information and communicate. The Super Nintendo Entertainment System (Figure 1.1c) revolutionized home video gaming, making it possible to play sports without going outside. Researchers who had been using Perrier water as a solvent in their chromatography systems (presumably because of its high level of purity) found some bottles were actually contaminated with benzene (Figure 1.1d). In one of the biggest upsets in boxing history, James Buster Douglas knocked out Mike Tyson (Figure 1.1e). And Pons and Fleischmann discovered cold fusion (Figure 1.1f); thanks to them we now have an endless supply of cheap, clean energy but of course the cost of Perrier has skyrocketed.
Since the release of the first biosensor, we have seen an explosion in the number and variety of commercial biosensors. Today there are around twenty different instrument manufacturers and about forty different platforms available. These numbers fluctuate as established companies offer new products, old companies falter, and new companies acquire old companiesâ products (the circle of biosensors cannot be broken). This diversity in instrumentation is a godsend for bench-top scientists because it means there is a system available to meet each userâs sensitivity, throughput, and cost requirements.
While it is true that todayâs biosensors often employ a variety of detection methods (e.g., surface plasmon resonance, reflectometric interference, evansescent wave, acoustic wave, and dual polarization interferometry to name a few), we think people are too often distracted by a particular platformâs detection method. It is not necessary to understand the physics of how a detector works to use it properly. It is far more important to understand how to set up a biosensor experiment and analyse the data properly.
1.2 SPR Pessimists
Unfortunately, there is still significant skepticism in the general scientific community about the validity of biosensor data. Most people can be classified into one of the three categories (Figure 1.2). There are the naysayers who say biosensors donât work (Figure 1.2a), users who think they are experts (Figure 1.2b), and scientists who really love the technology and will do what it takes to get reliable biosensor data (Figure 1.2c).
Letâs start with the first group. The naysayers often declare the biosensor has insurmountable problems with instrument drift, nonspecific binding, mass transport, and avidity effects. (Actually, these effects can be minimized and/or accounted for if an experiment is performed properly.) But their fundamental claim is that immobilizing one binding partner on a surface produces artificial binding constants. Sure, taking something in solution, as shown in Figure 1.3a, and putting it on a surface could change its entropic properties; perhaps then it cannot freely rotate and would be accessible in only two dimensions (Figure 1.3b) rather than three dimensional space by an approaching binding partner. But, for the vast majority of binding studies the immobilized partner is not actually stuck directly on the flat surface. It is suspended in a dextran layer (Figure 1.3c), which provides a solution-like environment. Maybe the problem with understanding this concept is the word âimmobilizeâ. When the ligand is linked to the dextran-coated surface, the binding partner is not immobile. Instead, it is tethered: it is still free to rotate and is accessible in three dimensions for binding.
Relying on its experience using dextran in column chromatography resins, Pharmacia recognized the advantages of using this surface matrix. The dextran layer provides a hydrophilic environment and reduces nonspecific binding. Often the dextran layer is illustrated as a homogeneous forest of seaweed but in reality it is more like cotton candy, whose height depends on buffer conditions, for example, salt concentration. Not only does the dextran layer permit target mobility, but it also introduces a âpre-concentration effectâ (1), which allows targets to be readily immobilized, um,⌠we mean tethered. Coupling a protein on a planar carboxyl surface, for example, requires a higher protein concentration, but with the dextranâs capacity to pre-concentrate material through charge effects, a protein could be extracted from a solution of comparably lower concentration and still immobilized at high surface densities. Of course, high densities may not always be optimal (read on).
Coating the sensor surface with dextran was a brilliant decision by Pharmacia when it was developing the biosensor for commercial release. It turned out that the dextran layer is one of the primary reasons its technology has been so successful. Several manufacturers have produced novel biosensor detection systems but have stumbled in surface chemistry development. Pharmaciaâs (later Biacore, now GE Healthcare) longevity in the biosensor field is due to its proprietary dextran surfaces. As patents on the use of dextran surfaces begin to expire in 2010, we should see other manufacturers quickly adopt this surface chemistry.
Naysayers often claim that solution- and sensor-determined binding parameters do not match up. To counter this charge, we demonstrated that rate constants and affinities determined using the two approaches do in fact agree when the experiments are done properly. In one study, we determined the kinetics of a small molecule binding to an enzyme using both Biacore technology and a stopped-flow fluorescence instrument (2). The rate constants obtained from the two experiments correlated well. We expanded this investigation to include other biosensor platforms and a panel of compounds that display different affinities for the enzyme and compared results with those obtained from calorimetry measurements (3â8).
A few years ago we began a series of benchmark studies to show that other users can get reliable data from biosensors (2, 3, 7â10). In each study, a panel of participants tested the same interaction. For example, in one study, twenty-two different biosensor users determined the affinities of four compound/target interactions at six temperatures. From these numbers we calculated interaction enthalpies and entropies and compared these values with thermodynamic parameters determined using calorimetry (8). Once again, results from the two approaches matched and the coefficient of variation in the biosensor-determined rate constants was about 10%.
In another benchmark study, we examined a high-affinity antibody/antigen system (9) to demonstrate that even systems with slow off rates could be interpreted reliably. Others have also compared the binding constants for mAb/antigen interactions obtained from Biacore and Kinexa (11), again demonstrating the k...
