Physiological Interrogation of Reactive Astrocytes

项目摘要 反应性星形胶质细胞增生症是脑部疾病中最常见的神经病理表现。不幸的是， 对于反应性星形胶质细胞增生症对细胞功能的影响，我们缺乏基本的分子认识。虽然 我们推测，在反应性星形胶质细胞增生症中，星形胶质细胞的重要生理作用是功能失调的。 缺乏关键的、基本的工具来深刻地测试这些细胞如何表现出功能障碍的假说。这 转录分析工作流和生理分析工作流之间的能力差距代表着 神经胶质生物界理解脑胶质细胞增多症的后果的能力面临重大障碍 星形胶质细胞的功能。通过机器学习/人工智能(ML/AI)弥补这一能力差距 方法代表了发表在《自然神经科学》杂志上的一篇共识社论所描绘的具体目标 最近由著名的神经胶质生物学家提出。提出研究的科学前提是以利用为基础 星形胶质细胞内钙瞬变([Ca++]i)的活细胞成像以捕捉星形胶质细胞的生理反应 对外界刺激的反应。需要检验的基本假设是，视频图像的有效分割可以 以及包括像素强度视频图像特征提取， 物体纹理、物体形状和星形胶质细胞[Ca++]i瞬变的方向性将允许丰富的聚集 [Ca++]i瞬变波型分析。在我们的初步数据中，我们已经捕获了超过312 GB的 用于执行建议分析的活细胞星形胶质细胞钙离子成像数据。这些视频记录了 脑干星形胶质细胞对体外低氧及内毒素处理的反应 脂多糖(LPS)。因此，我们将首次评估培养的脑干星形胶质细胞的([Ca++]i 无血清基线、低氧和恢复，加和不加内毒素治疗。该计划的目标 建议的研究是对这一数据集进行二次分析，以便制定客观的分析 在生理挑战期间捕捉更完整的星形细胞表型的工作流程。至 实现这一目标，我们将实现三个目标。我们将首先开发一个高效、无偏见的图像分割工作流 使用基于UNT的CNN算法在生理挑战期间捕获活跃的星形胶质细胞。到时候我们会的 确定星形胶质细胞[Ca++]i在不同生理挑战下的瞬变波形类型。最后， 作为为后续R01应用程序奠定基础的未来方向，我们将修改我们的 星形胶质细胞成像工作流通过整合以促进与空间转录分析的兼容性 照片可转换的记者和图像配准过程中使用的空间转录平台。在 总结拟议的研究，我们将首次拥有快速、客观的图像分析工作流程，以 询问星形胶质细胞生理学。

Project Summary Reactive astrogliosis represents the most common neuropathological finding in brain diseases. Unfortunately, we lack fundamental molecular insight into the consequences of reactive astrogliosis on cell function. Although we presume that astrocytes’ vital physiological roles are dysfunctional in reactive astrogliosis, our community lacks key, fundamental tools that can incisively test hypotheses as to how these cells show dysfunction. This capability gap between transcriptomic analytical workflows and physiological analytical workflows represents a significant barrier for the glial biology community’s capability to understand the consequences of gliosis on astrocyte cell function. Addressing this capability gap through machine learning/artificial intelligence (ML/AI) approaches represents a specific goal delineated by a consensus editorial published in Nature Neuroscience recently by prominent glial biologists. The scientific premise of the proposed research is based on the utilization of live cell imaging of astrocytic intracellular Ca++ transients ([Ca++]i) to capture astrocyte physiological responses to external stimuli. The underlying hypothesis to be tested is that efficient segmentation of video images can occur using convolutional neural networks, and that video image feature extraction that includes pixel intensity, object texture, object shape, and directionality of astrocyte [Ca++]i transients will permit enriched clustering analysis of [Ca++]i transient wave-form types. In our preliminary data, we have already captured over 312 GB of live cell astrocyte Ca++ imaging data upon which to perform the proposed analyses. These videos capture brainstem astrocyte responses to hypoxia in vitro as well as following treatment with the endotoxin lipopolysaccharide (LPS). Thus, we will assess, for the first time, ([Ca++]i from brainstem astrocytes cultured without serum at baseline, hypoxia, and recovery, with and without LPS treatment. The objectives of the proposed research are to perform a secondary analysis of this dataset so as to develop objective analytical workflows that capture a more complete picture of astrocytic phenotypes during physiological challenges. To achieve this we will achieve three aims. We will first develop an efficient, unbiased image segmentation workflow to capture active astrocytes during physiological challenges using the UNET-based CNN algorithm. We will then identify clusters of astrocyte [Ca++]i transients wave-form types under distinct physiological challenges. Lastly, as a future direction that will lay the groundwork for our subsequent R01 application, we will modify our astrocyte imaging workflows to promote compatibility with spatial transcriptomic analysis by integrating photoconvertible reporters and image registration processes using a spatial transcriptomic platform. At the conclusion of the proposed research we will, for the first time, have a rapid, objective image analysis workflow to interrogate astrocyte physiology.