Fluorescence-Based Calcium Imaging in Primary Human Airway Epithelial Cultures Using Automated Cell Segmentation

Calcium signaling is critical in a multitude of biological processes, reinforcing the need to develop methods to study calcium flux in primary tissue. The study of calcium signaling at a single-cell resolution in complex primary epithelial cultures is challenging and therefore remains poorly studied. This study presents methods adapted for monitoring live calcium signaling in primary airway epithelial cultures and novel approaches for machine learning based analyses. Using patient-derived primary airway epithelial cultures differentiated at ALI (air-liquid interface) loaded with an extrinsic fluorescent indicator dye, calcium mobilization was measured at single-cell resolution using an epifluorescent microscope. We developed a novel software that utilizes machine learning-based cell segmentation to assign fluorescence intensities to distinct cells over time. Overall, the adapted imaging setup and software enable a rapid and unbiased approach to analyzing single cells within the primary epithelial cultures. The step-by-step protocol presented here will enable the future study of calcium in individual cells and cell types that make up the primary epithelial cultures.