https://orcid.org/0000-0002-9853-8815
Figures & data
Figure 1. Determinants of the use of poorly selective antibodies in research.
The behavior of end-users is a key component, in part because inattention to the issue of reagent validation maintains a marketplace that includes a high proportion of non-selective antibodies. The factors that determine their capability, opportunity and motivation for best practices are a focus for this program. However, the issue is also maintained by a lack of efficient quality control mechanisms. Robust quality control experiments can be expensive and slow, and the number of antibodies used in research makes this difficult to address. End-users are also not aware of the necessity of quality controls and frequently do not perform this themselves, nor find and use robust data from others. Polyclonal antibody technologies, currently popular amongst end-users, have inherently high lot-to-lot variation, although newer recombinant antibodies have less variation, their adoption by end-users has been slow. These issues are discussed in more detail within the main text.
Figure 2. A strategy for improving the use of antibodies in research.
Summary of potential areas for consideration for stakeholders to develop action plans toward making best practices more feasible, easy, and rewarding.
Figure 3. Working with stakeholders across the scientific community to develop consensus solutions.
Research institutions and end users: UK Reproducibility Network (UKRN) and the Only Good Antibodies community (OGA); research funding agencies: National Centre for the Replacement, Refinement and Reduction of Animals in Research and the Michael J Fox Foundation for Parkinson’s Research; experts in antibody production and validation: Genetex, the Antibody Society, Abcam, Cell Signalling Technology, YCharOS (Antibody Characterization through Open Science), AstraZeneca, and the Institute for Protein Innovation; antibody database providers: the RRID initiative, CiteAb and Biocompare; publishers and journals: eLife, F1000, Nature protocols and Wiley.
