![]() As shown in Figure 3, the X-axis is the Optical Density, and the Y-axis is the Log. ![]() value of the standard (X-axis) against the log of concentration of the standard (Y-axis), as a XY scatter plot (highlighted in green parts shown in Fig. We can see that for the competitive inhibition ELISA, the blank well has the highest OD.ĭifferent from the OD of double-sandwich ELISA, it is no need to minus the absorbance of negative control well in competition inhibition ELISA. Clone's CEA924Ge, ELISA Kit for Cyanocobalamin (CNCbl) as a reference, as shown in Figure 1, the highest 10000 pg/mL concentration of the standard (F1 in Figure 1), diluted to 123.5 pg/mL concentration (B1), negative control (A1). We choose the standard absorbance value of Cloud. Now we will introduce the fitting step of standard curve in competition inhibition ELISA by using EXCEL software. Last time, we have discussed the plotting and fitting of classic double-sandwich ELISA standard curve(Fitting of Standard Curve of Double Sandwich ELISA). small molecule such as hormone generally use this method.įor laboratory personnels who are used to double sandwich ELISA, they often encounter to the problems that how to deal with the data of competitive inhibition ELISA. After addition of the substrate solution, the intensity of color developed is reverse proportional to the concentration of target molecules in the samples. The more amount of antigen in samples, the less labeled antigen be bound on solid phase enzyme, so the final color will be lighter, i.e., The amount of bound HRP conjugate is reverse proportional to the concentration of target molecules in the samples. Competitive inhibition ELISA also can be called blocking ELISA, its main principle is the competive binding of the antigens within the samples and the certain amount of labeled-antigens with antibody on the solid-phase. In this case, competitive inhibition method can be applied to detect target molecules. While for small molecule or hapten, as lacking of more than two binding sites for double sandwich detection, double sandwich method is not suitable. Prism isn't smart enough to adjust the column titles when you transform data.Ĭlick and edit the column title to "Concentration (M)".As we know, for antigens with multiple epitopes, double sandwich ELISA (Double Sandwich ELISA) can be applied to the quantitative detection of target antigens or antibodies. Note that the column title hasn't changed. Now the X column is in molar concentration units. On the Transform dialog check the option to transform X values and choose the transform X=10^X Prism can transform these values to concentration units.Ĭlick the Analyze button and choose Transform at the top of the Analyze dialog. These are in the same units as the X values, so are the logarithm of concentration. The X column of the results table has the interpolated values we want. For this example, we aren't too interested in these results. It tabulates the best-fit values of the parameters and much more. The second page is the table of results for the overall curve fit. The first page shows you the interpolated values. Note that 4PL means four parameter logistic, which is another name for this kind of equation.įor this example, leave all the other settings to their default values.Ĭlick OK to see the curves superimposed on the graph. Choose a modelĬhoose the equation: Sigmoidal, 4PL, X is log(Concentration). Choose the standard curve analysisĬlick the Analyze button and from the list of XY analyses choose: Interpolate a Standard Curve.Īlternatively, you can click the “Interpolate a standard curve” button right on top of the Analyze button. Since the unknowns have no X value, they are not included on the graph. You can also choose to plot the individual duplicates rather than plot the means. You can customize the symbols, colors, axis labels, etc. The graph Prism makes automatically is fairly complete. So a concentration of 1 micromolar (10 -6 Molar) would be entered as -6. Why are X values negative? Because in this example, the X values are the logarithm of concentrations expressed in molar. Note that three of the four unknowns are labeled, so you can later match up the results with the labels. The goal of this analysis is to interpolate the corresponding X values (concentrations) for these unknowns. These have a Y values that you measured, but no X. The first seven rows contain the standard curve, in duplicate. You can move the floating note out of the way, or minimize it. The sample data may be partly covered by a floating note explaining how to fit the data (for people who are not reading this help page). Interpolate unknowns from sigmoidal curve. ![]() From the Welcome or New Table dialog, choose to create an XY data table, and select the sample data set: RIA or ELISA. ![]()
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