An algorithm for automated detection, localization and measurement of local calcium signals from camera-based imaging

• Published in: Cell calcium (Edinburgh) Vol. 56; no. 3; pp. 147 - 156
• Main Authors: Ellefsen, Kyle L., Settle, Brett, Parker, Ian, Smith, Ian F.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.09.2014
• Subjects: Advanced Basic Science; Algorithm; Algorithms; Automation; Calcium; Calcium - metabolism; Calcium Signaling - physiology; Fluorescence; Humans; Imaging; Microscopy, Fluorescence - instrumentation; Microscopy, Fluorescence - methods; Neuroblastoma - metabolism; Neuroblastoma - pathology; Total internal reflection microscopy; Tumor Cells, Cultured; Fluorescence; Automation; Calcium; Algorithm; Imaging; Total internal reflection microscopy
• ISSN: 0143-4160; 1532-1991; 1532-1991
• DOI: 10.1016/j.ceca.2014.06.003

•Local Ca2+ transients form the basis of cellular Ca2+ signaling.•Here we describe a high-throughput algorithm for rapid detection/analysis of local Ca2+ signals.•Noise filtering allows for the detection of small events in presence of fluctuating baselines.•An intuitive user interface enables swift and precise evaluation of analyzed data.•Amplitudes, kinetics, sub-pixel location and F/F0 ratio traces are output directly into Excel. Local Ca2+ transients such as puffs and sparks form the building blocks of cellular Ca2+ signaling in numerous cell types. They have traditionally been studied by linescan confocal microscopy, but advances in TIRF microscopy together with improved electron-multiplied CCD (EMCCD) cameras now enable rapid (>500framess−1) imaging of subcellular Ca2+ signals with high spatial resolution in two dimensions. This approach yields vastly more information (ca. 1Gbmin−1) than linescan imaging, rendering visual identification and analysis of local events imaged both laborious and subject to user bias. Here we describe a routine to rapidly automate identification and analysis of local Ca2+ events. This features an intuitive graphical user-interfaces and runs under Matlab and the open-source Python software. The underlying algorithm features spatial and temporal noise filtering to reliably detect even small events in the presence of noisy and fluctuating baselines; localizes sites of Ca2+ release with sub-pixel resolution; facilitates user review and editing of data; and outputs time-sequences of fluorescence ratio signals for identified event sites along with Excel-compatible tables listing amplitudes and kinetics of events.