Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data.
Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data.
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DOI：
10.3389/fninf.2017.00048
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发表时间：
2017
影响因子：
3.5
通讯作者：
Yu G
中科院分区：
文献类型：
--
作者：
Wang Y;Shi G;Miller DJ;Wang Y;Wang C;Broussard G;Wang Y;Tian L;Yu G
最近发现星形胶质细胞对神经信息处理发挥积极的调节作用，并且它们深深地参与了正常的脑发育和疾病病理，这激发了人们对理解星形胶质细胞在脑回路中的功能作用的广泛兴趣。测量星形胶质细胞的功能状态现在在技术上是可行的，由于现代显微镜和超灵敏的细胞类型特异性遗传编码的Ca2+指标的慢性成像的最新进展。然而，通过钙成像生成大数据集的能力与用于解码星形胶质细胞功能的复杂分析工具的可用性之间存在很大差距。目前的实践基本上是手动的，这不仅限制了分析吞吐量，而且还存在引入偏见和丢失复杂动态大数据中潜在的重要信息的风险。在这里，我们报告了一套计算工具，称为功能性星形胶质细胞表型分析（FASP），用于自动量化星形胶质细胞的功能状态。考虑到星形胶质细胞中Ca2+信号传导的复杂性和低信噪比，FASP设计为数据驱动和概率原则，以灵活地解释各种模式，并在噪声数据下稳健地执行。特别是，FASP明确地对信号传播进行建模，这排除了为其他类型的数据设计的工具的适用性。我们使用广泛的合成和真实的数据集证明了FASP的有效性。通过人工检查验证了FASP的发现。FASP还检测到纯手动分析遗漏的信号，但可以在自动分析的指导下通过更仔细的手动检查进行确认。所有算法和分析管道都打包到Fiji的插件（ImageJ）中，源代码可在https://github.com/VTcbil/FASP上免费获得。
Recent discoveries that astrocytes exert proactive regulatory effects on neural information processing and that they are deeply involved in normal brain development and disease pathology have stimulated broad interest in understanding astrocyte functional roles in brain circuit. Measuring astrocyte functional status is now technically feasible, due to recent advances in modern microscopy and ultrasensitive cell-type specific genetically encoded Ca2+ indicators for chronic imaging. However, there is a big gap between the capability of generating large dataset via calcium imaging and the availability of sophisticated analytical tools for decoding the astrocyte function. Current practice is essentially manual, which not only limits analysis throughput but also risks introducing bias and missing important information latent in complex, dynamic big data. Here, we report a suite of computational tools, called Functional AStrocyte Phenotyping (FASP), for automatically quantifying the functional status of astrocytes. Considering the complex nature of Ca2+ signaling in astrocytes and low signal to noise ratio, FASP is designed with data-driven and probabilistic principles, to flexibly account for various patterns and to perform robustly with noisy data. In particular, FASP explicitly models signal propagation, which rules out the applicability of tools designed for other types of data. We demonstrate the effectiveness of FASP using extensive synthetic and real data sets. The findings by FASP were verified by manual inspection. FASP also detected signals that were missed by purely manual analysis but could be confirmed by more careful manual examination under the guidance of automatic analysis. All algorithms and the analysis pipeline are packaged into a plugin for Fiji (ImageJ), with the source code freely available online at https://github.com/VTcbil/FASP.
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