Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications.

Antibodies are critical reagents to detect and characterize proteins. It is commonly understood that many commercial antibodies do not recognize their intended targets, but information on the scope of the problem remains largely anecdotal, and as such, feasibility of the goal of at least one potent and specific antibody targeting each protein in a proteome cannot be assessed. Focusing on antibodies for human proteins, we have scaled a standardized characterization approach using parental and knockout cell lines (Laflamme et al., 2019) to assess the performance of 614 commercial antibodies for 65 neuroscience-related proteins. Side-by-side comparisons of all antibodies against each target, obtained from multiple commercial partners, have demonstrated that: (i) more than 50% of all antibodies failed in one or more applications, (ii) yet, ~50-75% of the protein set was covered by at least one high-performing antibody, depending on application, suggesting that coverage of human proteins by commercial antibodies is significant; and (iii) recombinant antibodies performed better than monoclonal or polyclonal antibodies. The hundreds of underperforming antibodies identified in this study were found to have been used in a large number of published articles, which should raise alarm. Encouragingly, more than half of the underperforming commercial antibodies were reassessed by the manufacturers, and many had alterations to their recommended usage or were removed from the market. This first study helps demonstrate the scale of the antibody specificity problem but also suggests an efficient strategy toward achieving coverage of the human proteome; mine the existing commercial antibody repertoire, and use the data to focus new renewable antibody generation efforts

Science Forum: Antibody characterization is critical to enhance reproducibility in biomedical research

Antibodies are used in many areas of biomedical and clinical research, but many of these antibodies have not been adequately characterized, which casts doubt on the results reported in many scientific papers. This problem is compounded by a lack of suitable control experiments in many studies. In this article we review the history of the ‘antibody characterization crisis’, and we document efforts and initiatives to address the problem, notably for antibodies that target human proteins. We also present recommendations for a range of stakeholders – researchers, universities, journals, antibody vendors and repositories, scientific societies and funders – to increase the reproducibility of studies that rely on antibodies.</p

Partial high-level structure of OBI classes.

<p>OBI classes are shown in blue. Classes imported from BFO, IAO and other external ontologies are shown in orange, purple and dark red, respectively. Some example subclasses, such as <i>device</i> and <i>processed specimen</i> are included to illustrate the use of the class <i>processed material</i>.</p

Measuring glucose concentration in blood.

<p>The large boxes represent instances of processes and their participants. The <i>collecting specimen from organism</i> process takes place first. In this process, a <i>syringe</i> is used to draw blood from the mouse. At the end of this process a tube contains the <i>blood specimen</i>. In a second process, this specimen is used in an <i>analyte assay</i>, which measures the concentration of glucose in the blood. A <i>glucometer</i> is used to make this measurement. The <i>analyte role</i> inheres in the <i>glucose molecules</i> scattered throughout the <i>blood specimen</i>. This <i>planned process</i> achieves the <i>analyte measurement objective</i>.</p