Antibody validation

Abstract

Antibodies are among the most frequently used tools in basic science research and in clinical assays, but there are no universally accepted guidelines or standardized methods for determining the validity of these reagents. Furthermore, for commercially available antibodies, it is clear that what is on the label does not necessarily correspond to what is in the tube. To validate an antibody, it must be shown to be specific, selective, and reproducible in the context for which it is to be used. In this review, we highlight the common pitfalls when working with antibodies, common practices for validating antibodies, and levels of commercial antibody validation for seven vendors. Finally, we share our algorithm for antibody validation for immunohistochemistry and quantitative immunofluorescence.

Figure 1. Examples of non-specific antibodies for HoxA1 and phospho-4EBP1

(A) Cell lysates were denatured and separated by SDS PAGE, transferred to nitrocellulose and blotted with a mouse polyclonal antibody against HoxA1. A band of the expected molecular weight is seen in CaCo2 lysate (marked by the arrow). Note the numerous bands in all cell lines at unexpected molecular weights. (B) A representative example of IHC staining on FFPE breast carcinoma tissue for HoxA1. Note the cytoplasmic staining for a nuclear transcription factor. (C) Western blot for phospho-4-EBP1. Arrow denotes band at the expected molecular weight; again, all lysates show numerous bands at unexpected molecular weights. (D) A representative example of IHC staining on FFPE lung carcinoma tissue for p-4EBP1. Note the predominantly nuclear localization for a protein expected to localize to the cytoplasm and nucleus.

Figure 2. Lack of reproducibility is seen with the VEGF VG-1 clone antibody

(A) IHC on FFPE lung carcinoma tissue with VG-1 (1:50 dilution) demonstrates specific staining of expected localization. (B) Serial sections of our lung TMA stained with VG-1 (1:50) do not correlate with each other. (C,D) IHC staining of serial sections of the same patient tissue showing a high level of positivity in Run 1 (C) is negative for VEGF in Run 2 (D). Inset represents cytokeratin staining of the tumor for reference.

Figure 3. The Rimm Lab Algorithm for antibody validation of IHC/QIF

Step 1 of antibody validation is using cell lines in vitro to test antibody specificity. Step 2 (below the dotted line) involves further validation of antibody on tissue microarray (TMA) for expected target localization and reproducibility between assay runs and different antibody lots.

Figure 4. Stathmin expression is reduced by siRNA

(A) WB of BT-20 lysates 24 h post-mock; control scrambled siRNA or Stathmin siRNA transfection demonstrates specific reduction in Stathmin levels. GAPDH serves as a loading control. (B) BT-20 cells were fixed in 4% PFA 24 h post-transfection with scrambled control or Stathmin siRNA, and IF with Stathmin demonstrates a reduction in Stathmin expression.

Figure 5. ER-α antibody Clone 1D5 as an example of strong correlation between quantitative WB and IHC methods

(A) FFPE MCF7 cells stained with 1D5 (1:50 dilution) demonstrate expected nuclear localization for ER-α. (B) WB with 1D5 of a panel of breast cancer cell lines demonstrating varying expression of ER-a. Puro cells are MCF7 cells stably expressing an inducible ER-α. β-tubulin serves as a loading control. (C) ER-α expression by WB was quantified using ImageJ and correlated to AQUA scores corresponding to the cell lysates demonstrating a strong correlation.

Figure 6. Examples of reproducible antibodies between assay repeats and antibody lots

(A) Lot-to-lot reproducibility is shown using a MAP-tau monoclonal antibody on a breast index TMA. (B) MENA has an R² value of 0.932 when stained on serial sections of our lung TMA demonstrating a high level of reproducibility among assays run on different days using the same lot and dilution of antibody.