A high throughput machine-learning driven analysis of Ca2+ spatio-temporal maps

Abstract

High-resolution Ca²⁺ imaging to study cellular Ca²⁺ behaviors has led to the creation of large datasets with a profound need for standardized and accurate analysis. To analyze these datasets, spatio-temporal maps (STMaps) that allow for 2D visualization of Ca²⁺ signals as a function of time and space are often used. Methods of STMap analysis rely on a highly arduous process of user defined segmentation and event-based data retrieval. These methods are often time consuming, lack accuracy, and are extremely variable between users. We designed a novel automated machine-learning based plugin for the analysis of Ca²⁺ STMaps (STMapAuto). The plugin includes optimized tools for Ca²⁺ signal preprocessing, automated segmentation, and automated extraction of key Ca²⁺ event information such as duration, spatial spread, frequency, propagation angle, and intensity in a variety of cell types including the Interstitial cells of Cajal (ICC). The plugin is fully implemented in Fiji and able to accurately detect and expeditiously quantify Ca²⁺ transient parameters from ICC. The plugin's speed of analysis of large-datasets was 197-fold faster than the commonly used single pixel-line method of analysis. The automated machine-learning based plugin described dramatically reduces opportunities for user error and provides a consistent method to allow high-throughput analysis of STMap datasets.