Synapse Calcium Image Analysis (SCIA): A Groundbreaking Toolkit for Real-Time Analysis of synaptic Calcium Signals

With the continuous advancement of fluorescence microscopy technology and improved fluorescent indicators, we can now record neuronal calcium activity with unprecedented resolution and signal- to-noise ratio. By using calcium sensors to monitor and track calcium signals, we can observe changes in pixel intensity over time within regions of interest, thereby capturing neuronal calcium activity, including synaptic calcium activity. Custom tools were initially developed within individual labs for neuronal image analysis and shared across the research community. In the past, data analysis using open-source toolboxes like CaImAn and SIMA, previous image processing toolkits often subtract a universal background signal during image procession, which is inaccurate for delicate synapse calcium signals, neglecting the background heterogeneity of synapse. Additionally, those toolboxes require a certain level of programming proficiency, which could hinder some researchers lacking programming experience. We have proposed and designed a more streamlined and user-friendly synapse calcium imaging analysis toolbox to overcome these challenges. Synapse Calcium Image Analysis (SCIA) employs various signal extraction methods. It incorporates local background processing techniques, including detection, comparison, and subtraction, enabling rapid and precise identification of active calcium signal regions in neurons. Meanwhile, the toolbox provides a user- friendly GUI, allowing users to quickly and easily obtain accurately matched signal regions. As a result, it provides users with a simple yet efficient synapse calcium imaging analysis tool, saving them time and effort.

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Publication Details

Journal
BIO Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/bioconf/202517401011
Citations
1
Primary Topic
CCD and CMOS Imaging Sensors
Type
article
Field-Weighted Citation Impact
0.73
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article

Synapse Calcium Image Analysis (SCIA): A Groundbreaking Toolkit for Real-Time Analysis of synaptic Calcium Signals

Ting Li, Huanhuan Wei, Congjian Zhao
1 citations
BIO Web of Conferences
CCD and CMOS Imaging Sensors
0.73
article

Synapse Calcium Image Analysis (SCIA): A Groundbreaking Toolkit for Real-Time Analysis of synaptic Calcium Signals

Ting Li, Huanhuan Wei, Congjian Zhao
article en
1 citations

Abstract

With the continuous advancement of fluorescence microscopy technology and improved fluorescent indicators, we can now record neuronal calcium activity with unprecedented resolution and signal- to-noise ratio. By using calcium sensors to monitor and track calcium signals, we can observe changes in pixel intensity over time within regions of interest, thereby capturing neuronal calcium activity, including synaptic calcium activity. Custom tools were initially developed within individual labs for neuronal image analysis and shared across the research community. In the past, data analysis using open-source toolboxes like CaImAn and SIMA, previous image processing toolkits often subtract a universal background signal during image procession, which is inaccurate for delicate synapse calcium signals, neglecting the background heterogeneity of synapse. Additionally, those toolboxes require a certain level of programming proficiency, which could hinder some researchers lacking programming experience. We have proposed and designed a more streamlined and user-friendly synapse calcium imaging analysis toolbox to overcome these challenges. Synapse Calcium Image Analysis (SCIA) employs various signal extraction methods. It incorporates local background processing techniques, including detection, comparison, and subtraction, enabling rapid and precise identification of active calcium signal regions in neurons. Meanwhile, the toolbox provides a user- friendly GUI, allowing users to quickly and easily obtain accurately matched signal regions. As a result, it provides users with a simple yet efficient synapse calcium imaging analysis tool, saving them time and effort.

BIO Web of ConferencesVol. 174
Chongqing University of Posts and Telecommunications (CN)
Openalex Percentile: Top 29%
CCD and CMOS Imaging Sensors
0.73
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