Ins-ATP: Deep Estimation of ATP for Organoid Based on High Throughput Microscopic Images

TL;DR

Ins-ATP is a novel deep learning model that non-invasively estimates ATP levels in organoids from microscopic images, enabling long-term viability monitoring without disrupting drug response assessments.

Contribution

This work introduces the first organoid ATP estimation model based on high-throughput microscopy, overcoming limitations of traditional bioluminescence methods.

Findings

Ins-ATP predictions align well with bioluminescence measurements.

The model enables long-term, non-invasive ATP monitoring.

Improves reliability of drug efficacy evaluation in organoids.

Abstract

Adenosine triphosphate (ATP) is a high-energy phosphate compound and the most direct energy source in organisms. ATP is an essential biomarker for evaluating cell viability in biology. Researchers often use ATP bioluminescence to measure the ATP of organoid after drug to evaluate the drug efficacy. However, ATP bioluminescence has some limitations, leading to unreliable drug screening results. Performing ATP bioluminescence causes cell lysis of organoids, so it is impossible to observe organoids' long-term viability changes after medication continually. To overcome the disadvantages of ATP bioluminescence, we propose Ins-ATP, a non-invasive strategy, the first organoid ATP estimation model based on the high-throughput microscopic image. Ins-ATP directly estimates the ATP of organoids from high-throughput microscopic images, so that it does not influence the drug reactions of organoids. Therefore, the ATP change of organoids can be observed for a long time to obtain more stable results. Experimental results show that the ATP estimation by Ins-ATP is in good agreement with those determined by ATP bioluminescence. Specifically, the predictions of Ins-ATP are consistent with the results measured by ATP bioluminescence in the efficacy evaluation experiments of different drugs.