Semiautomated analysis of an optical ATP indicator in neurons.

Significance: The firefly enzyme luciferase has been used in a wide range of biological assays, including bioluminescence imaging of adenosine triphosphate (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. Aim: Our goal was to create a semiautomated pipeline for analysis of bioluminescence imaging to improve measurements of ATP content in nerve terminals. Approach: We 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. Results: Side-by-side comparison of manual and semiautomated image analysis demonstrated that the latter improves precision and accuracy of ATP measurements. Conclusions: Our method streamlines data analysis and reduces user-introduced bias, thus enhancing the reproducibility and reliability of quantitative ATP imaging in nerve terminals.