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Principles of Curve Fitting for Multiplex Sandwich Immunoassays, Rev B

on may not be as useful when analyzing samples in a multiplex setting (e.g., Bio-Plex cytokine assays are available in an 18-plex panel). Each analyte exhibits a different response and resulting linear range, and as a result it is difficult to select a universal set of standards that covers all of the analytes in a panel. This problem may be circumvented in the data analysis step of Bio-Plex Manager by deleting specific standards for each analyte; however, selection of standards that yield an R2 value >0.99 for each analyte is a tedious and time-consuming task. It is also important to analyze the backcalculated standards when using linear regression. Even if the R2 value is very high (>0.99), the accuracy of the fit as determined by standards recovery may indicate otherwise. For example, Figure 6 shows a cytokine assay exhibiting an R2 value of 1.000, indicating an excellent fit through the points; however, the associated backcalculated standards data (Table 1) indicate that the goodness of fit is not optimal throughout the entire range of the standards.

Logistic regression yields accurate quantitation across a wider range of concentrations compared to linear regression. This is the primary advantage of using a logistic regression. In Table 2, the standards recovery of a mouse cytokine assay using both linear and logistic regression methods is shown. The cells corresponding to 70130% standards recovery for linear and logistic regression are shaded. The range using logistic regression is much broader compared to the linear regression range. These data are shown graphically in Figure 7. The red bars indicate the range of concentrations showing 70130% recovery using linear re
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