A high throughput machine-learning driven analysis of Ca2+ spatio-temporal maps. (November 2020)

• Record Type: Journal Article
• Authors: Leigh, Wesley A.; Del Valle, Guillermo; Kamran, Sharif Amit; Drumm, Bernard T.; Tavakkoli, Alireza; Sanders, Kenton M.; Baker, Salah A.
• Is Part Of: Cell calcium. Volume 91(2020)
• Publication Date: 2020-11
• Subjects: FOV field of view -- GI gastrointestinal -- ICC interstitial cells of cajal -- ICC-DMP interstitial cells of cajal at the level of the deep muscular plexus -- ICC-IM intramuscular interstitial cells of cajal -- ICC-MY interstitial cells of cajal at the level of the myenteric plexus -- GCaMP genetically encoded Ca2+ indicator composed of a single GFP -- KRB krebs ringer bicarbonate -- ROI region of interest -- STMap spatio-temporal map -- STMapAuto spatial-temporal maps automation plugin; Ca2+ Imaging analysis -- Ca2+ Signaling -- Interstitial cell of cajal; Calcium -- Metabolism -- Periodicals; Vertebrates -- Physiology -- Periodicals; Calcium -- Physiological effect -- Periodicals; Cell physiology -- Periodicals; Calcium in the body -- Periodicals; 572.516
• DOI: 10.1016/j.ceca.2020.102260
• Languages: English
• ISSNs: 0143-4160

Abstract

Graphical abstract: Highlights: We designed a new automated machine-learning based plugin for the analysis of Ca 2+ Spatio-Temporal Maps (STMaps). The plugin is fully implemented in Fiji and able to accurately detect and quantify a variety of Ca 2+ transient signals. The plugin includes optimized tools for automated extraction of key Ca 2+ events. The plugin is extremely fast and provides an efficient method in the analysis of Ca 2+ large-datasets. The automated plugin reduces user error and provides a consistent high-throughput analysis. Abstract: High-resolution Ca 2+ imaging to study cellular Ca 2+ 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 Ca 2+ 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 Ca 2+ STMaps (STMapAuto). The plugin includes optimized tools for Ca 2+ signal preprocessing, automated segmentation, and automated extraction of key Ca 2+ 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 Ca 2+ 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.