Details

Autor(en) / Beteiligte

• Leigh, Wesley A.
• Del Valle, Guillermo
• Kamran, Sharif Amit
• Drumm, Bernard T.
• Tavakkoli, Alireza
• Sanders, Kenton M.
• Baker, Salah A.

Titel

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

Ist Teil von

• Cell calcium (Edinburgh), 2020-11, Vol.91, p.102260-102260, Article 102260

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• [Display omitted] •We designed a new automated machine-learning based plugin for the analysis of Ca2+ Spatio-Temporal Maps (STMaps).•The plugin is fully implemented in Fiji and able to accurately detect and quantify a variety of Ca2+ transient signals.•The plugin includes optimized tools for automated extraction of key Ca2+ events.•The plugin is extremely fast and provides an efficient method in the analysis of Ca2+ large-datasets.•The automated plugin reduces user error and provides a consistent high-throughput analysis. 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.