Bioimaging Probes Development by DOFLA (Diversity Oriented Fluorescence Library Approach) for in Vitro, in Vivo and Clinical Applications

Abstract

AbstractDue to the remarkable development of bioimaging probes and equipment during the last decades, we are able to see a variety of biological systems with a resolution ranging from centimeters to subnanometers. Bioimaging is now an indispensable tool for basic research and clinical diagnosis. Particularly, the application of fluorescence in optical imaging has enabled us to investigate molecular events as well as the structures in living cells and tissues. Among the fluorescent molecules, low molecular weight chemicals have great potentials to be developed as highly specific and versatile bioimaging probes. Target-specific fluorescent probes have been developed conventionally by a hypothesis-driven approach in which fluorophores are conjugated to already developed molecules such as antibody, peptide or small molecule drug. However, the fluorescence-labeled macromolecules may not be used for the detection of intracellular molecules in living cells and tagging small molecule without affecting its property is relatively challenging. To overcome these problems, we have developed Diversity Oriented Fluorescence Library (DOFL) by exploring the diverse chemical space directly around fluorophores using combinatorial chemistry. By screening DOFL in various platforms such as in vitro, cell, tissue and whole organism, we have successfully developed bioimaging probes which interact specifically with the targets. In this article, we discuss how bioimaging contributes to the development of biomedical science, why the development of new bioimaging probes is necessary and what can be achieved by DOFL approach (DOFLA).KeywordsPositron Emission TomographySingle Photon Emission Compute TomographyImaging ProbeDeep Tissue ImagingOptical Imaging ProbeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.