Bjørnstad Daniel M, Åbjørsbråten Knut S, Hennestad Eivind, Cunen Céline, Hermansen Gudmund Horn, Bojarskaite Laura, Pettersen Klas H, Vervaeke Koen, Enger Rune
GliaLab at the Letten Centre, Division of Anatomy, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Lab for Neural Computation, Division of Physiology, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Front Cell Neurosci. 2021 May 20;15:681066. doi: 10.3389/fncel.2021.681066. eCollection 2021.
Imaging the intact brain of awake behaving mice without the dampening effects of anesthesia, has revealed an exceedingly rich repertoire of astrocytic Ca signals. Analyzing and interpreting such complex signals pose many challenges. Traditional analyses of fluorescent changes typically rely on manually outlined static region-of-interests, but such analyses fail to capture the intricate spatiotemporal patterns of astrocytic Ca dynamics. Moreover, all astrocytic Ca imaging data obtained from awake behaving mice need to be interpreted in light of the complex behavioral patterns of the animal. Hence processing multimodal data, including animal behavior metrics, stimulation timings, and electrophysiological signals is needed to interpret astrocytic Ca signals. Managing and incorporating these data types into a coherent analysis pipeline is challenging and time-consuming, especially if research protocols change or new data types are added. Here, we introduce Begonia, a MATLAB-based data management and analysis toolbox tailored for the analyses of astrocytic Ca signals in conjunction with behavioral data. The analysis suite includes an automatic, event-based algorithm with few input parameters that can capture a high level of spatiotemporal complexity of astrocytic Ca signals. The toolbox enables the experimentalist to quantify astrocytic Ca signals in a precise and unbiased way and combine them with other types of time series data.
对清醒行为小鼠的完整大脑进行成像，而不受麻醉的抑制作用，这揭示了极其丰富的星形胶质细胞钙信号库。分析和解释这些复杂信号带来了许多挑战。传统的荧光变化分析通常依赖于手动勾勒的静态感兴趣区域，但这种分析无法捕捉星形胶质细胞钙动力学的复杂时空模式。此外，从清醒行为小鼠获得的所有星形胶质细胞钙成像数据都需要根据动物的复杂行为模式进行解释。因此，需要处理多模态数据，包括动物行为指标、刺激时间和电生理信号，以解释星形胶质细胞钙信号。将这些数据类型管理并整合到一个连贯的分析流程中具有挑战性且耗时，特别是当研究方案发生变化或添加新的数据类型时。在这里，我们介绍了秋海棠（Begonia），这是一个基于MATLAB的数据管理和分析工具箱，专为结合行为数据分析星形胶质细胞钙信号而设计。该分析套件包括一种自动的、基于事件的算法，输入参数很少，能够捕捉星形胶质细胞钙信号的高度时空复杂性。该工具箱使实验人员能够以精确且无偏差的方式量化星形胶质细胞钙信号，并将它们与其他类型的时间序列数据相结合。
