Session: Session 3

Quantifying and mapping ATP distribution within tissues to inform targeted therapeutic strategies in drug development

Mathieu GAUDIN¹, Paul DUHAMEL¹, Aurore TOMEZYK¹, David BONNEL¹, Corinne RAMOS¹

¹Aliri, LOOS, France

INTRODUCTION

ATP levels indicate cellular metabolic activity and are crucial for understanding tumor cell viability and growth, making them potential therapeutic targets. This study aims to determine the most suitable model for targeted drug development by exploring ATP distribution and quantification in various mouse tumor models using Quantitative Mass Spectrometry Imaging (QMSI).

METHODOLOGY

Fifteen tumor biopsies from three distinct cancer models were sectioned using a cryostat and mounted on ITO or SuperFrost slides for mass spectrometry imaging (MSI) and Hematoxylin-Eosin (H&E) staining, respectively.

ATP-13C10 calibrants were spotted control tissues prior to spraying with MALDI matrix spiked with ATP-13C10,15N5 (TM sprayer). The slides were then analyzed using a MALDI FTICR. Data analysis was processed with Multimaging software. Adjacent sections on Superfrost slides were stained with Hematoxylin and Eosin to overlay histological images with the MSI data.

RESULTS

ATP was consistently detected across the entire tissue sections in all three tumor models. The metabolite exhibited markedly higher concentrations in the tumor region versus the necrotic and stromal regions. Among the models, Model 1 exhibited the highest average concentration of ATP, with an average (n=5) of 550.7 µg/g of tissue. Conversely, the lowest average concentration of ATP in the tumor region was observed in Model 2 at 312.3 µg/g of tissue. This trend persisted in the necrotic regions, with Model 1 displaying the highest ATP concentration and Model 2 demonstrating the lowest.

CONCLUSION

This study highlights the technical feasibility and potential applicability of utilizing QMSI methodologies to study ATP for steering therapeutic targeting in drug development. It pinpoints regions characterized by heightened metabolic activity, thus aiding in model selection and the optimization of targeted therapies.