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Introduction
One unifying issue impacting all antibody driven diagnostic formats is the inevitable signal generation interference effects that sample matrix components impose on assay signal generation processes. The generic term, “Matrix Effect” was assigned to represent the sum of the effects of “all” sample composition components impacting the results of the final assay reading1,2. The severity of the signal interference impact can, depending upon the nature of the interfering agent, be greatly reduced by sample dilution factors > 20X.
With respect to the sample viscosity mediator of signal inhibition/interference that is the subject of this article, the degree of signal suppression varies with the amount by which the bio-physiological sample type (serum, plasma, urine etc.) can be diluted out prior to assay. In their native undiluted form, these sample types present an environment known to suppress antibody-antigen binding kinetics. Obviously, the level of binding kinetics suppression is by no means, complete. But, when quantitatively accurate results are needed, the resulting signal inhibition can often result in underreporting of actual analyte concentrations in bio-physiological sample types. The probability of underreporting target analyte concentrations is the greatest when analyte detection sensitivity requirements limit your ability to dilute your samples by more than three-fold using your sample diluent formulation.
Sample matrix complexity differentials between the e.g. serum sample and the calibrator diluent used for standard curve construction, can be exacerbated when the calibrator diluent is something simple like PBS or PBS–1% BSA. It is a generally accepted fact that the more complex the physical composition of the sample matrix, the greater potential there is for assay signal suppression leading to underreporting actual analyte concentrations in bio-physiological sample-types.
In the discussion topic sections below, we lay out a logical explanation of the chemical basis behind this frustrating sample matrix inhibition phenomenon. In order to devote a majority of the discussion content in this article to matrix viscosity inhibition issues, we chose to bypass the more well documented immune driven matrix-inhibition mechanisms. These include, low binding affinity heterophilic antibodies, IgM-rheumatoid factors, high binding affinity human anti-mouse antibodies (HAMA) and human anti-animal antibodies (HAAA). Complement, an additional potentiator of signal inhibition is not elaborated on to any great degree as it too does not involve a matrix viscosity driven mechanism.
We do include a brief explanation about dealing with complement inhibition issues within the ICT sample diluent product options section at the end of this article.
So, if one were able to selectively remove most of the above immune mechanism-based inhibitor proteins from your serum/plasma samples, you would still be left to contend with the remaining signal inhibition problems arising from sample viscosity differentials.
So, the question that we would like to answer going forward is, how does a physical feature of the serum/plasma solvent environment like sample matrix viscosity, negatively impact antibody-antigen binding kinetics within complex bio-physiological samples?
After reviewing publications describing the various molecular entities known to cause sample matrix inhibition, it becomes readily apparent that sample viscosity, as a common source of immunoassay signal suppression, is a largely overlooked subject. That said, what really elevates the importance of sample matrix viscosity interference is its potential to occur to a variable degree in “any” bio-physiological sample solution. To begin to address the chemical basis for and consequences of, sample viscosity driven immunoassay or enzyme immunoassay (EIA) signal suppression, we needed to draw upon some published conclusions from the few publications willing to address this matrix viscosity inhibition subject3-7.
Chemical equilibrium constants as useful indicators of matrix inhibition activity - Defining the physicochemical terminology associated with assessing antibody-antigen binding kinetics
As a prelude to our sample matrix discussion, a brief review of several equilibrium constant parameter terms would be helpful. The binding affinity by which a designated antibody binds to its respective target analyte can be defined by three different but related terms. These consist of, the equilibrium dissociation constant (KD), the equilibrium association constant (KA), and the general equilibrium constant for the reaction (Keq) when equilibrium for the overall reaction is achieved. Only once equilibrium conditions have been established does KA = Keq. Putting this equilibrium constant terminology into an antibody (A) + antigen (B) à AB complex formation reaction context, [A] and [B] = concentration of the reactants A (antibody) and concentration of reactants B (antigen) respectively. Correspondingly, [AB] = concentration of the antibody antigen complex. The antibody dissociation constant is KD = [A][B] / [AB] and the antibody association constant is KA = [AB] / [A][B] which is also Keq once the antibody-antigen binding reaction has reached equilibrium, are all chemical representations of antibody binding affinity at equilibrium (especially KD) as well as a chemical representation of the relative amount (concentration) of reactants in the complexed versus the free state (Keq) at equilibrium. The chemical association constant KA provides a picture of the rate at which antigen can bind to antibody over a particular time point or reach equilibrium state. Ideally, the greatest assay detection capabilities are achieved when the concentration ratio of complex over unbound A and B are as large as possible8.
Given the theme of this article, it is appropriate to mention ICT’s sample and assay diluent products. Both the sample and assay diluent products provide opportunities for assay development people to begin to address matrix viscosity disparities. This is the universal challenge of all assay developers that, due to the need to maximize assay sensitivity, must contend with only being able to dilute their serum/plasma samples no more than two or three-fold.
ICT offers an Assay Diluent Optimization Pack (Cat. No. 958) containing four different assay diluent formulations. We also offer a Sample Diluent Optimization Pack (Cat. No. 959) containing three different sample diluent formulations.
Employment of both an assay diluent as well as a sample diluent in your assay wells allows you to employ two different matrix complexity equalization reagents within a single sample well. A key feature of all our assay diluent products is their ability to inhibit complement interference. Identifying the ideal sample diluent composition to equalize the sample/calibrator diluent matrix viscosity with the serum/plasma sample matrix viscosity, may require additional additives to be included within the sample diluent product(s).
ICT’s sample diluent products provide an ideal sample diluent platform for the further addition of other matrix viscosity enhancing components. This can simplify the trial and error process to obtain a “perfect match” between sample/calibrator diluent matrix and the serum/plasma sample matrix. We stand ready to assist you with any matrix equalization questions that you may have going forward.
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References
1. Wood, W. G. “Matrix effects” in immunoassays. Scand J Clin Lab Invest Suppl 205, 105-112 (1991).
2. Weber, T. H., Kapyaho, K. I. & Tanner, P. Endogenous interference in immunoassays in clinical chemistry. A review. Scand J Clin Lab Invest Suppl 201, 77-82 (1990).
3. Xavier, K. A. & Willson, R. C. Association and dissociation kinetics of anti-hen egg lysozyme monoclonal antibodies HyHEL-5 and HyHEL-10. Biophys J 74, 2036-2045 (1998).
4. Morgan, C. L., Newman, D. J., Burrin, J. M. & Price, C. P. The matrix effects on kinetic rate constants of antibody-antigen interactions reflect solvent viscosity. J Immunol Methods 217, 51-60 (1998).
5. May L. Chiu, W. L., Steven T. Snyder, Pak Kin Wong, Joseph C. Liao, and Vincent Gau. Matrix effects – A challenge towards automation of molecular analysis. JALA 15, 233-242, doi:10.1016 (2010).
6. Wienken, C. J., Baaske, P., Rothbauer, U., Braun, D. & Duhr, S. Protein-binding assays in biological liquids using microscale thermophoresis. Nat Commun 1, 100, doi:10.1038/ncomms1093 (2010).
7. Papaneophytou, C. P., Grigoroudis, A. I., McInnes, C. & Kontopidis, G. Quantification of the effects of ionic strength, viscosity, and hydrophobicity on protein-ligand binding affinity. ACS Med Chem Lett 5, 931-936, doi:10.1021/ml500204e (2014).
8. Reverberi, R. & Reverberi, L. Factors affecting the antigen-antibody reaction. Blood Transfus 5, 227-240, doi:10.2450/2007.0047-07 (2007).
9. Kesmarky, G., Kenyeres, P., Rabai, M. & Toth, K. Plasma viscosity: a forgotten variable. Clin Hemorheol Microcirc 39, 243-246 (2008).
10. Zapf, S. & Loos, M. Effect of EDTA and citrate on the functional activity of the first component of complement, C1, and the C1q subcomponent. Immunobiology 170, 123-132, doi:10.1016/S0171-2985(85)80085-1 (1985).