An open source tool for automatic spatiotemporal assessment of calcium transients and local ‘signal-close-to-noise’ activity in calcium imaging data
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| Yayımlandı: | PLoS Computational Biology vol. 14, no. 3 (Mar 2018), p. n/a |
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| Diğer Yazarlar: | , , , , |
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Public Library of Science
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| Online Erişim: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 2025711067 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1553-734X | ||
| 022 | |a 1553-7358 | ||
| 024 | 7 | |a 10.1371/journal.pcbi.1006054 |2 doi | |
| 035 | |a 2025711067 | ||
| 045 | 2 | |b d20180301 |b d20180331 | |
| 084 | |a 174831 |2 nlm | ||
| 100 | 1 | |a Prada, Juan | |
| 245 | 1 | |a An open source tool for automatic spatiotemporal assessment of calcium transients and local ‘signal-close-to-noise’ activity in calcium imaging data | |
| 260 | |b Public Library of Science |c Mar 2018 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a Germany | |
| 653 | |a Neurons | ||
| 653 | |a Signal detectors | ||
| 653 | |a Receptors | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Spatial analysis | ||
| 653 | |a Glutamic acid receptors (ionotropic) | ||
| 653 | |a Dendrites | ||
| 653 | |a Bioinformatics | ||
| 653 | |a Tetrodotoxin | ||
| 653 | |a Neurosciences | ||
| 653 | |a Noise | ||
| 653 | |a Wavelet analysis | ||
| 653 | |a Calcium | ||
| 653 | |a Environmental | ||
| 653 | |a Computer graphics | ||
| 653 | |a Neurobiology | ||
| 653 | |a Software | ||
| 653 | |a Calcium signalling | ||
| 653 | |a Funding | ||
| 653 | |a Windows (computer programs) | ||
| 653 | |a Two dimensional analysis | ||
| 653 | |a Pharmacology | ||
| 653 | |a Time series | ||
| 653 | |a Action potential | ||
| 653 | |a Continuous wavelet transform | ||
| 653 | |a Data processing | ||
| 653 | |a Axons | ||
| 653 | |a Neural networks | ||
| 653 | |a Inhibitors | ||
| 653 | |a Calcium imaging | ||
| 653 | |a Loci | ||
| 653 | |a Sliding | ||
| 653 | |a Automation | ||
| 653 | |a Open source software | ||
| 700 | 1 | |a Sasi, Manju | |
| 700 | 1 | |a Martin, Corinna | |
| 700 | 1 | |a Jablonka, Sibylle | |
| 700 | 1 | |a Dandekar, Thomas | |
| 700 | 1 | |a Blum, Robert | |
| 773 | 0 | |t PLoS Computational Biology |g vol. 14, no. 3 (Mar 2018), p. n/a | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2025711067/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2025711067/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2025711067/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |