Physiological Interrogation of Reactive Astrocytes

项目摘要 反应性星形胶质细胞增生是脑疾病中最常见的神经病理学发现。不幸的是， 我们对反应性星形胶质细胞增生对细胞功能的影响缺乏基本的分子洞察力。虽然 我们假设星形胶质细胞的重要生理作用在反应性星形胶质细胞增生中功能失调， 缺乏关键的，基本的工具，可以尖锐地测试假设，这些细胞如何显示功能障碍。这 转录组学分析工作流程和生理学分析工作流程之间的能力差距代表了 这是神经胶质生物学社区理解神经胶质增生的后果的能力的一个重大障碍， 星形胶质细胞功能通过机器学习/人工智能（ML/AI）解决这一能力差距 《自然神经科学》上发表的一篇共识社论描绘了一个具体的目标 最近由著名的神经胶质生物学家。建议研究的科学前提是基于利用 星形胶质细胞内Ca++瞬变（[Ca++]i）的活细胞成像，以捕获星形胶质细胞的生理反应 外部刺激。要测试的基本假设是，视频图像的有效分割可以 使用卷积神经网络发生，并且包括像素强度的视频图像特征提取， 对象纹理、对象形状和星形胶质细胞[Ca++]i瞬变的方向性将允许富集聚类 [Ca++]i瞬态波形类型分析。在我们的初步数据中，我们已经捕获了超过312 GB的 进行拟定分析的活细胞星形胶质细胞Ca++成像数据。这些视频记录了 脑干星形胶质细胞对体外缺氧以及内毒素处理后的反应 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 基线时无血清，缺氧和恢复，有和无LPS治疗。的目标 建议的研究是对该数据集进行二次分析，以便制定客观的分析方法。 在生理挑战期间捕获星形胶质细胞表型的更完整图片的工作流程。到 要做到这一点，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程 使用基于UNET的CNN算法在生理挑战期间捕获活跃的星形胶质细胞。然后我们将 在不同生理挑战下鉴定星形胶质细胞[Ca++]i瞬变波形类型簇。最后， 作为未来的发展方向，将为我们随后的R 01应用奠定基础，我们将修改我们的 星形胶质细胞成像工作流程，通过整合 使用空间转录组学平台的光可转化报告子和图像配准过程。在 结论所提出的研究，我们将首次有一个快速，客观的图像分析工作流程， 研究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.