Development of a deep neural network model for simultaneous analysis of extracellular analyte gradients for a population of cells

Abstract

Detecting the spatial release of extracellular nitric oxide (NO) is essential for understanding the dynamics in cell communication for physiological and pathological processes. This study presents an innovative methodology that integrates fluorescence-based sensing platforms utilizing single walled carbon nanotubes (SWNT) with machine learning models to expedite the spatial data analysis of extracellular analytes. The deep learning model You Only Look Once (YOLOv8) segmentation achieves accurate cell identification across diverse morphologies and clustered cell groups, with a recall of 98% and a precision of 83%. The spatial analysis of extracellular NO is achieved by extracting the cell contour coordinates from the YOLO-identified cells and translocating the boundaries onto SWNT fluorescence files. The model enables rapid analysis for multiple cells across numerous images, with 100 image pairs completed in just 68 s. The combination of nanotechnology with automated neural network-based cell detection establishes a robust sensing framework with pixel-level spatial resolution of NO dynamics, delivering critical insights into cellular communication and holding promising implications for diagnostic and therapeutic applications.