Protocol for measuring anti-fentanyl antibodies in mouse serum by enzyme-linked immunosorbent assay

Abstract

Enzyme-linked immunosorbent assay (ELISA) offers an effective, inexpensive, and reliable approach for the analysis of humoral immune responses. Here, we describe a protocol for measuring anti-fentanyl antibodies generated by the immunization of mice with novel opioid vaccine candidates. We describe steps for coating BSA-fentanyl antigen and standard wells. We then detail procedures for preparing buffers and performing assays. This protocol is adaptable for use with either BALB/c or C57BL/6J mice strains and can measure antibody isotypes to elucidate class switching. For complete details on the use and execution of this protocol, please refer to Stone et al.¹.

Keywords: antibody; cell biology; immunology.

Graphical abstract

Figure 1

IgG2a, IgG2c, and IgA coating template Wells shaded green are reserved for standards (Std) and plate blanks. Wells shaded blue correspond to 2 μg/mL BSA-Fentanyl Conjugate.

Figure 2

Assay optimization curves Total IgG detection antibody was tested against increasing concentrations of BSA-FEN coatings of (A). The 1:500 dilution of IgG detection antibody with the 2 μg/mL coating antigen yielded the strongest correlation (r² = 0.9953). IgG1 detection antibody was tested against increasing concentrations of BSA-FEN coatings (B). The 1:1000 dilution of IgG1 detection antibody with the 2 μg/mL coating antigen yielded the strongest correlation (r² = 0.9987). IgG2a was coated directly to the ELISA plate at standard curve concentrations (C). No condition for direct IgG2a detection yielded satisfactory correlation coefficients. IgG2a capture antibody was tested at increasing concentrations with various IgG2a detection antibodies (D). When detecting IgG2a standards through the capture method the 2 μg/mL coating antibody with 1:5000 detection gave the strongest correlation coefficient (r² = 0.9983). Direct detection of IgG2c standards with an anti-IgG2c detection antibody was optimized at a detection concentration of 1:5000 (r² = 0.9968) (E). Direct detection of IgA found a detection concentration of 1:1000 yielded the strongest correlation (r² = 0.9989) (F).

Figure 8

Edging effect occurs when the outer wells of an ELISA have higher absorbance value than wells in the center of the plate or even their replicates This may be caused by contamination of the outer wells or uneven temperature across the plate.

Figure 9

Lack of colorimetric development in any well may be the result of one or more process errors including using the wrong substrate, reagents not added or added in the wrong order, low antibody, or lack of coating

Figure 10

Lack of colorimetric development in sample wells may be the result of low antibody concentrations in tested samples, if antibody presence is expected

Figure 11

High background can have several causes including insufficient washing, too much substrate, insufficient blocking, or incubation times being too long

Figure 3

Graphical representation of the standard curve mathematical model

Figure 4

GraphPad Prism start screen to begin data analysis, related to Quantification and statistical analysis step 2 Select an XY Table with enough Y replicates to match the number used for the samples.

Figure 5

Data entry in XY datafile for ELISA analysis, related to Quantification and statistical analysis step 3 Standard curve concentrations are added to the X column with OD values for the concentration matched by row in Group A column. Unknown values are listed below the standards and sample ID for the unknowns can be listed in the row title square to the far left of the page.

Figure 6

Statistical analysis option window in GraphPad Prism for ELISA analysis, related to Quantification and statistical analysis step 4 To interpolate unknowns from a standard curve, select “Hyperbola (X is concentration)”.

Figure 7

Statistical analysis output from standard curve interpolation, related to step 4s Model statistics can be found under the “Table of Results” tab. Interpolated results for unknowns are listed under “Interpolated X mean values” tab in the order they were entered into the datafile.