Semi-automated analysis of an optical ATP indicator in neurons

Abstract

SignificanceThe firefly enzyme luciferase has been used in a wide range of biological assays, including bioluminescence imaging of ATP. The biosensor Syn-ATP utilizes subcellular targeting of luciferase to nerve terminals for optical measurement of ATP in this compartment. Manual analysis of Syn-ATP signals is challenging due to signal heterogeneity and cellular motion in long imaging sessions. Here, we have leveraged machine learning tools to develop a method for analysis of bioluminescence images. AimOur goal was to create a semi-automated pipeline for analysis of bioluminescence imaging to improve measurements of ATP content in nerve terminals. ApproachWe developed an image analysis pipeline that applies machine learning toolkits to distinguish neurons from background signals, and excludes neural cell bodies, while also incorporating user input. ResultsSide-by-side comparison of manual and semi-automated image analysis demonstrated that the latter improves precision and accuracy of ATP measurements. ConclusionsOur method streamlines data analysis and reduces user-introduced bias, thus enhancing the reproducibility and reliability of quantitative ATP imaging in nerve terminals.