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Updated: Jun 7, 2025

Detecting Glycogen in Peripheral Blood Mononuclear Cells with Periodic Acid Schiff Staining
Published on: December 23, 2014
Boronic Acid-Based Glucose Detection for Liver Three-Dimensional Cellular Models
Wessal Hussein1, Doron Goldberg1, Guy Tenzer1
1BioAI Drug Safety Prediction Platform, Quris, HaNatsiv St 6, Tel Aviv-Yafo 6701033, Israel.
Abstract:
Liver 3D cell models are regularly employed as a screening platform for predicting the metabolic safety of drugs, by monitoring the physiological responses of the spheroids, through the measurement of relevant markers of normal liver physiology, notably glucose. Measuring glucose levels within the spheroids and their surroundings provides insight into the metabolic homeostasis of liver cells and may be employed as an indication of potential drug-induced toxicity. Several ortho-aminomethyl phenylboronic acid (PDBA) glucose sensors have been developed. Most recently, Mc-CDBA ((((((2-(methoxycarbonyl)anthracene-9,10-diyl)bis(methylene)) bis(methylazanediyl))bis(methylene))bis(4-cyano-2,1-phenylene))diboronic acid) was reported. Although Mc-CDBA exhibits good water solubility and sensitivity toward glucose, its ability for intra- and extracellular glucose monitoring in spheroids has not been determined. Here, we present a set of Mc-CDBA derivatives: carboxylic (BA) and amide (BA 5)-based Mc-CDBA sensors for extra- and intracellular glucose monitoring, respectively. Both sensors exhibit superior spectroscopic features. BA 5 showed selective intracellular accumulation in liver spheroids and exhibited more than 3-fold higher basal fluorescence sensitivity compared to Mc-CDBA. These observations led to the development of an extracellular hydrogel-embedded sensor (HG-BA 21) to monitor extracellular glucose levels under persistent solution flow mimicking physiological conditions. We have therefore demonstrated that the sensors developed by our team are suitable for a variety of assays, notably with liver spheroids, and provide powerful new tools for organ-on-a-chip applications predicting drug-induced liver injury in the early stages of drug development.
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