To label or not: the need for validation in label-free imaging.
Joseph M Szulczewski, Filiz Yesilkoy, Tyler K Ulland, Randy Bartels, Bryan A Millis, Stephen A Boppart, Kevin W Eliceiri
Author Information
Joseph M Szulczewski: University of North Carolina-Chapel Hill, Department of Pharmacology, Chapel Hill, North Carolina, United States.
Filiz Yesilkoy: University of Wisconsin-Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States. ORCID
Tyler K Ulland: University of Wisconsin-Madison, Department of Pathology and Laboratory Medicine, Madison, Wisconsin, United States. ORCID
Randy Bartels: University of Wisconsin-Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States. ORCID
Bryan A Millis: Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States.
Stephen A Boppart: University of Illinois Urbana-Champaign, Beckman Institute for Advanced Science and Technology, Urbana, Illinois, United States. ORCID
Kevin W Eliceiri: University of Wisconsin-Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States. ORCID
Significance: Advances in label-free imaging have impacted many areas of biological and biomedical imaging ranging from cell biology and cancer to pathology and neuroscience. Despite the great progress and advantages of these methods, it is clear that to realize their full potential, validation by extrinsic labels and probes is critically needed. Aim: This perspective calls for developing and applying innovative labels and probes to validate both existing and emerging label-free imaging methods. Approach: Major representative types of label-free imaging methods are briefly presented discussing their advantages and differing contrasts. Their biological applications are also reviewed with a focus on how validation of label-free methods with carefully developed labeling approaches will greatly aid in further intrinsic contrast imaging adoption and likely lead to more sophisticated image-based biomarkers and a better understanding of the underlying signals. Conclusions: Expanded efforts in extrinsic label validation will significantly push forward the utilization and adoption of label-free methods both in basic research and clinical models.
