An Activity-Based Fluorogenic Probe Enables Cellular and in Vivo Profiling of Carboxylesterase Isozymes.
Shi-Yu LiuRen-Yu QuRong-Rong LiYao-Chao YanYao SunWen-Chao YangGuang-Fu YangPublished in: Analytical chemistry (2020)
Carboxylesterases (CEs) exist as multiple types of isomers in humans, and two major types are CE1 and CE2. They are widely distributed in human tissues and well-known for their important roles in drug metabolism and pathology of various diseases. Thus, the detection of CEs in living systems could provide efficient proof in disease diagnostics, as well as important information regarding chemotherapeutic effects of antitumor drugs and prognosis. To develop a specific probe to discriminate CEs from other hydrolases, especially cholinesterases, is quite challenging due to their structural similarities and substrate specificity. To date, almost all of the fluorescent probes developed for CEs have been constructed with an acetyl group as the recognition unit. Herein we proposed a new design strategy of probe-cavity matching, which led to the identification of a new fluorogenic substrate (termed as HBT-CE) with high specificity toward both CE isomers and improved sensitivity, considering the higher binding affinity and catalysis efficiency. The promising capability of HBT-CE was further demonstrated for endogenous CEs imaging in living cells, zebrafish, and nude mice. In addition, HBT-CE was successfully applied in kinetically monitoring drug-induced CE regulation in cancer cells. All of these findings suggest that HBT-CE is a valuable tool for tracking and imaging endogenous CEs in complex biological systems.
Keyphrases
- living cells
- fluorescent probe
- drug induced
- energy transfer
- liver injury
- single molecule
- quantum dots
- endothelial cells
- high resolution
- gene expression
- type diabetes
- fluorescence imaging
- adipose tissue
- metabolic syndrome
- adverse drug
- social media
- label free
- single cell
- structural basis
- transcription factor
- sensitive detection
- binding protein
- insulin resistance
- neural network
- dna binding