The Matrix Effect:
Any trustworthy approach must be developed with appropriate matrix management aimed at reducing or correcting these impacts. Certain sample-preparation techniques have been extremely important in minimizing matrix effects. As the need for assay sensitivity increases due to the assessment of increasingly strong medications and the identification of biomarkers at incredibly low concentrations, the demand for more efficient sample-preparation techniques grows. The specimen type and the molecular analysis platform employed for analyte detection have a significant impact on the sample preparation choice used to reduce the matrix effect. In the end, complete integration of inline sample preparation functionality within the molecular analysis unit would be ideal for laboratory automation. The throughput, reagent volume, flow rate, and various protocol parameters of matrix-reduction sample preparation should be compatible with the downstream molecular analysis. Additionally, these techniques must be quick, economical, and able to handle high throughput.
Immunoassays are susceptible to an inherent complication of the Matrix effect which is the sum of all the interference caused in the system and it disrupts the quantitative analysis of the serum or protein under study. It causes a deviation from the expected result and interferes with the ability of the protein to bind specifically to the target, caused by various components present in the analytes such as the proteins, lipids, and carbohydrates (Stavnsbjerg et al., 2022). Substances such as proteins including lysozymes, albumin, fibrinogen, and various complement proteins, lipids such as steroids, and acids such as bile acids all lead to the interference (Selby, 1999).
What causes the Matrix effect
There are many additional components that attribute to the matrix effect which are as follows:
Evaluating Matrix effect
The increase or decrease in the concentration of the analyte can be observed due to the matrix effect. The increase of it results in a false positive signal due to the bridge formation between the sample substance, signal, and the capture antibody. However, the decreased concentration of the analyte is due to the binding of a substance to the analyte or antibody thus reducing the interaction of both the antibody and analyte to each other (Miller and LEVINSON, 1996).
The evaluation of the matrix effect can be done by evaluating the readings of optical density or the measure of absorbance at a particular wavelength. It is indicated by the lower reading values than what was expected. However, other factors might also lead to the low optical density values, so in order to confirm the presence of the matrix effect, a spike and recovery assay is performed (Lugos et al., 2019).
The effect of Matrix interference:
It has been reported that matrix effects perturb the analysis of measurement in the immunoassays for cytokines and it varies between individual samples extensively (Stavnsbjerg et al., 2022). Moreover, it has also been reported in various biological matrices including serum, plasma (Rosenberger and Finlay, 2003), and urine samples (Taylor et al., 2012). It involves the use of the dilution method to estimate the interference caused by the matrix and expressed as relative antibody binding. By fortifying with known concentration, the blank samples, and fluids of the tissue homogenate, treated, diluted, and tested in the ELISA appropriately to obtain a ratio that corresponded to the recovery of the assay (Burkin et al., 2018).
Spike and Recovery Measurement:
Spike and recovery assessment measures the interference of the sample substances with the ability of the capture and detection or signal antibodies that must bind with the protein target of interest. It is thus essential for the accurate and proficient evaluation of the ELISA methodology against a specific type of sample (Scientific, 2007). A predetermined concentration of analyte or the recombinant standard is added or spiked to the sample under test and the assay is run to compare this to the concentration to the standard diluent and measure the response of interference or the recovery of the samples that were spiked in the matrix. Ideally, the recovery would be 100% but practically it should be considered somewhere within the range of 80-120% (Walker, 2009).
How to measure the Matrix effect?
If the measured concentration is the same, relatively close, or is very different for both the spiked sample and the spiked standard diluent, then the matrix may not be a problem, a slight problem with 80-120% recovery or maybe a serious problem respectively after taking the levels of endogenous into consideration.
How to prevent Matrix Interference?
ELISA findings may be affected by the matrix interference. Matrix effects, which are interactions between your protein of interest and other elements in the sample, might lead to inaccurate results. To encounter matrix interference, several ways are described below:
Dilution is the ideal technique for resolving sample matrix issues, provided a Minimum Required Dilution (MRD) can be empirically established for your sample types. In sample dilution buffer, dilute your samples two to five times. Hopefully, this will sufficiently dilute the matrix so that you can reliably quantify your target protein.
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Use the same buffer when diluting samples for your standard curve.
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When determining the final protein concentration, you take the dilution factor into consideration.
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Ensure that your readings remain within the assay's linear range.
By diluting the sample into a more assay-compatible solution, one of the simplest approaches to prevent interference from the sample matrix is achieved. The substance used to make the kit standards serves as a suitable diluent. As the sample is diluted, its matrix starts to resemble the kit standards, which tends to increase the assay's specificity and accuracy. Matrix Calibration:
Standards and samples should be diluted in the sample matrix. For instance, dilute them both in normal serum. Any negative impacts the matrix may be having on the test will be made up for by this. This approach, however, is restricted to appropriate and representative blank samples/matrices. Without proper sample preparation, matrix-matched calibration curve methods will lose their sensitivity due to matrix effects (Zhou, Yang and Wang, 2017).
To solve pH issues, think about neutralizing your samples simply. In general, if the sample pH is higher or lower than the neutral range, 8.5, ELISA may not perform as expected. Without much dilution, adding a buffering concentration can neutralize your sample to the optimal pH range of 7.0–7.5.
The interference caused by the sample matrix can also be reduced by modifying the ELISA procedure. It may also be successful to utilise fewer samples, longer incubation periods, or a simultaneous incubation approach in which the sample is incubated in the coated capture well concurrently with the enzyme-attached antibody. (Zhang et al., 2022).
ABT ELISAs tested for Matrix Interference
Our development team has created a Colorimetric Sandwich ELISA system that negates the effects of Matrix Interference and provides a product that is reliable for customer samples. We designed these kits to provide convenient and cost effective means to for researchers without sacrificing quality.
Features:
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Specific Assay Diluent for plasma, serum and cell culture supernatant
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Recovery and Linearity data from samples 80-120% range
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Wide range of targets for Human, Mouse, and Rat
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Recommended Dilution Factors to save your time and samples
Example Recovery Data:
Three concentrations of recombinant protein was spiked into various samples to measure the recovery percentage of recombinant protein in duplicate.
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Sample Type
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Average Recovery %
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Range %
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Plasma (50%)
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90
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87-93
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Serum (50%)
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99
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86-108
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RPMI 10% FBS (25%)
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100
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96-104
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Example Linearity Data:
Human recombinant protein was spiked into various biological samples and serial diluted with Assay Diluent 10WR for serum and plasma samples and Assay Diluent 1TD for RPMI 10% FBS.
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EDTA Plasma
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Serum
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RPMI 10% FBS
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Neat
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pg/ml
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1100.02
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1022.50
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1442.73
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Expected %
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93
|
102
|
96
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1 to 2
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pg/ml
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534.31
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535.95
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777.75
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Expected %
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107
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90
|
104
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1 to 4
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pg/ml
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250.64
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249.59
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390.37
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Expected %
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100
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100
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104
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1 to 8
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pg/ml
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139.96
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142.39
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185.03
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Expected %
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114
|
114
|
99
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Matrix Interference - Assay Biotech Inc.
Our buffers included have been designed in consideration for not just the customer samples but the antibodies that are utilized in the kit. Standard curves are validated on each buffer provided.
References:
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Crowther, J.R., 2001. Systems in ELISA. In The ELISA Guidebook (pp. 9-44). Humana Press.
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Chiu, M.L., Lawi, W., Snyder, S.T., Wong, P.K., Liao, J.C. and Gau, V., 2010. Matrix effects—a challenge toward automation of molecular analysis. JALA: Journal of the Association for Laboratory Automation, 15(3), pp.233-242.
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Engvall, E. and Perlmann, P., 1971. Enzyme-linked immunosorbent assay (ELISA) quantitative assay of immunoglobulin G. Immunochemistry, 8(9), pp.871-874.
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Gonzalez, R.M., Seurynck-Servoss, S.L., Crowley, S.A., Brown, M., Omenn, G.S., Hayes, D.F. and Zangar, R.C., 2008. Development and validation of Sandwich ELISA microarrays with minimal assay interference. Journal of proteome research, 7(6), pp.2406-2414.
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Hutchings, G.H. and Ferris, N.P., 2006. Indirect Sandwich ELISA for antigen detection of African swine fever virus: comparison of polyclonal and monoclonal antibodies. Journal of virological methods, 131(2), pp.213-217.
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Hosseini, S., Vázquez-Villegas, P., Rito-Palomares, M. and Martinez-Chapa, S.O., 2018. General overviews on applications of ELISA. In Enzyme-Linked Immunosorbent Assay (ELISA) (pp. 19-29). Springer, Singapore.
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TIP, T., 2010. ELISA technical guide and protocols. Thermo Fisher Scientific Inc USA, Bartlesville, OK.
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Burkin, M.A., Nuriev, R.I., Wang, Z. and Galvidis, I.A., 2018. Development of Sandwich double-competitive ELISA for sulfonamides. Comparative analytical characteristics and matrix effect resistance. Food analytical methods, 11(3), pp.663-674.
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Lugos, M.D., Damulak, O.D., Perikala, V., Davou, G.I., Obeta, U.M., Banda, J.M., Oluwatayo, B.O. and Okwori, J.A., 2019. Assay linearity and spike-recovery assessment in optimization protocol for the analysis of serum cytokines by Sandwich ELISA platform. Am J Biomed Sci, 3(2).
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Miller, J.J. and LEVINSON, S.S., 1996. Interferences in immunoassays. In Immunoassay (pp. 165-190). Academic Press.
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Monaci, L., Brohée, M., Tregoat, V. and van Hengel, A., 2011. Influence of baking time and matrix effects on the detection of milk allergens in cookie model food system by ELISA. Food Chemistry, 127(2), pp.669-675.
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Rosenberger, C.M. and Finlay, B.B., 2003. Phagocyte sabotage: disruption of macrophage signalling by bacterial pathogens. Nature reviews Molecular cell biology, 4(5), pp.385-396.
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Scientific, T.F., 2007. Spike-and-recovery and linearity-of-dilution assessment. Thermo Scientific Tech Tip, 58.
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Selby, C., 1999. Interference in immunoassay. Annals of clinical biochemistry, 36(6), pp.704-721.
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Zhou, W., Yang, S. and Wang, P.G., 2017. Matrix effects and application of matrix effect factor. Bioanalysis, 9(23), pp.1839-1844.
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Marks, M.A., Wenzel, T., Whitehouse, M.J., Loose, M., Zack, T., Barth, M., Worgard, L., Krasz, V., Eby, G.N., Stosnach, H. and Markl, G., 2012. The volatile inventory (F, Cl, Br, S, C) of magmatic apatite: An integrated analytical approach. Chemical Geology, 291, pp.241-255.
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Zhang, Z., Lin, H., Sui, J., Han, X., Wang, L., Sun, X. and Cao, L., 2022. The effect of chlorophyll on the enzyme‐linked immunosorbent assay (ELISA) of procymidone in vegetables and the way to overcome the matrix interference. Journal of the Science of Food and Agriculture, 102(8), pp.3393-3399.
