Abstract
Local and spontaneous calcium signals play important roles in neurons and neuronal networks. Spontaneous or cell-autonomous calcium signals may be difficult to assess because they appear in an unpredictable spatiotemporal pattern and in very small neuronal loci of axons or dendrites. We developed an open source bioinformatics tool for an unbiased assessment of calcium signals in x,y-t imaging series. The tool bases its algorithm on a continuous wavelet transform-guided peak detection to identify calcium signal candidates. The highly sensitive calcium event definition is based on identification of peaks in 1D data through analysis of a 2D wavelet transform surface. For spatial analysis, the tool uses a grid to separate the x,y-image field in independently analyzed grid windows. A document containing a graphical summary of the data is automatically created and displays the loci of activity for a wide range of signal intensities. Furthermore, the number of activity events is summed up to create an estimated total activity value, which can be used to compare different experimental situations, such as calcium activity before or after an experimental treatment. All traces and data of active loci become documented. The tool can also compute the signal variance in a sliding window to visualize activity-dependent signal fluctuations. We applied the calcium signal detector to monitor activity states of cultured mouse neurons. Our data show that both the total activity value and the variance area created by a sliding window can distinguish experimental manipulations of neuronal activity states. Notably, the tool is powerful enough to compute local calcium events and ‘signal-close-to-noise’ activity in small loci of distal neurites of neurons, which remain during pharmacological blockade of neuronal activity with inhibitors such as tetrodotoxin, to block action potential firing, or inhibitors of ionotropic glutamate receptors. The tool can also offer information about local homeostatic calcium activity events in neurites.
Highlights
Calcium ions mediate fast signaling to regulate neuronal development, synaptic transmission, and synaptic plasticity [1,2,3,4,5]
Continuous Wavelet Transform (CWT)-guided peak detection was used to compute calcium signals, because we found that it targets critical problems in signal-close-to-noise computing: (1.) Baseline removal and signal smoothing is not needed and even small signals are accurately computed. (2.) The algorithm can be directly applied to raw data. (3.) Low amplitude peaks can still be real, and peak detection through local signal-to-noise ratio computation above a heuristic threshold may become an insufficient criterion to detect small signals, or to discriminate real signals from high amplitudes of noise signals
We asked whether the tuning parameters (SAT and signalto-noise ratio (SNR); Fig 1b) are robust enough to assess the activity state of a motoneuron
Summary
Calcium ions mediate fast signaling to regulate neuronal development, synaptic transmission, and synaptic plasticity [1,2,3,4,5]. Calcium signaling of neurons is well investigated, calcium-dependent mechanisms underlying spontaneous or cell-autonomous excitability are not well understood [8,9,10,11]. Either spontaneous excitation is ligand-dependent and caused by the non-synaptic release of transmitters such as glutamate or GABA [12,13]. Excitability is part of a developmental program and is triggered by the neuron itself, meaning by cell-autonomous excitation using subthreshold active ion channels, or is caused by selfenhancement of intrinsic excitability through autocrine signaling [10,14,15]. Three major explanations of why signaling of spontaneous or cell-autonomous excitability is not well investigated can be proposed: (1) Calcium transients can appear at very local places, and their spatiotemporal footprint is not predictable. Three major explanations of why signaling of spontaneous or cell-autonomous excitability is not well investigated can be proposed: (1) Calcium transients can appear at very local places, and their spatiotemporal footprint is not predictable. (2) Proteins involved in neuronal excitation show a high functional diversity, depending on their locus of action. (3) Neuronal signals can be very fast and ‘small’, making it difficult to identify real signaling events due to the unavoidable measurement noise
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