Ins-ATP: Deep Estimation of ATP for Organoid Based on High Throughput Microscopic Images
This work addresses the need for reliable, long-term drug screening in organoid research by providing a non-invasive alternative to ATP bioluminescence, though it appears incremental as it applies deep learning to a specific domain problem.
The paper tackles the problem of non-invasively estimating adenosine triphosphate (ATP) levels in organoids to evaluate drug efficacy, proposing Ins-ATP, a model that uses high-throughput microscopic images to predict ATP without cell lysis, achieving results consistent with traditional ATP bioluminescence methods.
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.