Editorial pubs.acs.org/acssensors
Biomolecular Sensors: Benchmarking Basics and impact of the biomolecular sensing field continue to be elevated by the publication of new discoveries. We have several great examples of publications in this area in the December issue of ACS Sensors. Liedberg and co-workers report a gold nanoparticle-based assay for troponinan important cardiac markerand demonstrate the performance of their assay in serum at physiological concentrations. Detection limits as a function of nanoparticle size are reported, highlighting that larger nanoparticles produce more background signal and higher detection limits. Szunerits and co-workers have a paper in this month’s issue targeting an important application for monitoring food allergens, and show that gliadin can be specifically detected using an antibody-functionalized graphene oxide sensor. Gliadin causes a severe allergic reaction in those affected with celiac disease, and therefore testing food for its presence may help celiac sufferers avoid contaminated foods. This paper shows that small quantities of gliadin can be detected when spiked into different types of flour, demonstrating the suitability of the approach for food monitoring. At ACS Sensors, benchmarking the sensitivity and specificity of biomolecular sensors, with careful studies substantiated with detailed statistical analysis, is a mustour editorial team believes that we can maximize the impact of work in this area if we push the rigor of studies characterizing new sensors.
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he development of new biomolecular sensing systems is an important and very active area within measurement sciencebut one where new advances need to clear a high bar in order to make an impact. Sensitivity is key for the application of new biomolecular sensors, as reaching clinically relevant detection limits is a critical requirement that will make these systems useful. A high level of analytical sensitivity underlies the achievement of a high level of clinical sensitivity, which is a metric reporting on the number of false negative results generated. Specificity is equally essential. Observing stringent specificity during analytical studies is good evidence that clinical specificity will be observed, which reports on the number of false positives an approach produces. Only methods that exhibit minimal false positives and negatives have utility in real-world applications, so establishing that new methods have suitable levels of sensitivity and specificity is really critical. At ACS Sensors, we aim to publish work on new biomolecular sensing systems that have a shot at being used as clinical diagnostics, or for other relevant applications, including environmental monitoring and food analysis. So, as we evaluate submitted manuscripts, we look for a few key things. Establishing detection limits using the accepted standard of achieving a biomolecule-specific signalat least three standard deviations above that observed with a relevant blank signalis an important set of experiments for the editorial team to see. As well, controls indicating that specific signals can be generated in the presence of a large excess (>1000-fold) of nontarget molecules establish that a sensor exhibits the ability to discriminate the molecule of interest. Even better are studies testing performance in relevant biological matrices or samples. For example, if the target molecular species is something that would be found in blood, then detection should be demonstrated with this matrix present. This is the best way to test sensor specificity, but it is also important because many detection strategies lose a significant amount of sensitivity in heterogeneous samples, because of interference caused by the excess of nontarget species present. Providing context for the levels of a specific target molecule that may be found in an appropriate sample, and establishing that sufficient sensitivity and specificity can be obtained, provides very strong justification for publication of a new sensing approach. As well, the inclusion of comparative data generated with a “goldstandard” technique, and showing that the new method provides enhanced performance, is very compelling. We also look carefully at the quality of data and the statistical analyses presented. Demonstrating sensor performance requires that the reproducibility of the approach is tested, and that variability is clearly documented. The more independent replicates of experimental trials are performed, the more error analysis provides an indicator of the robustness of a new approach. We also look at the quality of data, linearity/ nonlinearity of trends relative to known relationships, and the simplicity/complexity of new approaches. Identifying highquality efforts, to establish new high-performance methods, is our main goal at ACS Sensors, as we would like to see the profile © 2016 American Chemical Society
Shana Kelley, Associate Editor
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University of Toronto, Toronto, Canada
AUTHOR INFORMATION
ORCID
Shana Kelley: 0000-0003-3360-5359 Notes
Views expressed in this editorial are those of the author and not necessarily the views of the ACS.
Received: November 30, 2016 Published: December 23, 2016 1380
DOI: 10.1021/acssensors.6b00775 ACS Sens. 2016, 1, 1380−1380
