SICT: automated detection and supervised inspection of fast Ca2+ transients

Roberta Mancini, Tobias van der Bijl, Quentin Bourgeois-Jaarsma, Rizky Lasabuda, Alexander J. Groffen

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Recent advances in live Ca2+ imaging with increasing spatial and temporal resolution offer unprecedented opportunities, but also generate an unmet need for data processing. Here we developed SICT, a MATLAB program that automatically identifies rapid Ca2+ rises in time-lapse movies with low signal-to-noise ratios, using fluorescent indicators. A graphical user interface allows visual inspection of automatically detected events, reducing manual labour to less than 10% while maintaining quality control. The detection performance was tested using synthetic data with various signal-to-noise ratios. The event inspection phase was evaluated by four human observers. Reliability of the method was demonstrated in a direct comparison between manual and SICT-aided analysis. As a test case in cultured neurons, SICT detected an increase in frequency and duration of spontaneous Ca2+ transients in the presence of caffeine. This new method speeds up the analysis of elementary Ca2+ transients.

Original languageEnglish
Article number15523
Pages (from-to)1-13
Number of pages13
JournalScientific Reports
Volume8
DOIs
Publication statusPublished - 19 Oct 2018

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Signal-To-Noise Ratio
Motion Pictures
Caffeine
Quality Control
Neurons

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Mancini, Roberta ; van der Bijl, Tobias ; Bourgeois-Jaarsma, Quentin ; Lasabuda, Rizky ; Groffen, Alexander J. / SICT : automated detection and supervised inspection of fast Ca2+ transients. In: Scientific Reports. 2018 ; Vol. 8. pp. 1-13.
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SICT : automated detection and supervised inspection of fast Ca2+ transients. / Mancini, Roberta; van der Bijl, Tobias; Bourgeois-Jaarsma, Quentin; Lasabuda, Rizky; Groffen, Alexander J.

In: Scientific Reports, Vol. 8, 15523, 19.10.2018, p. 1-13.

Research output: Contribution to JournalArticleAcademicpeer-review

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