Physiological Interrogation of Reactive Astrocytes

反应性星形胶质细胞的生理学询问

• 批准号: 10555444
• 负责人: CATHERINE CZEISLER
• 金额: $ 14.95万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-26 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10555444
• 关键词: Address; Algorithms; Artificial Intelligence; Astrocytes; Behavior; Biology; Blood Vessels; Brain Diseases; Brain Stem; Calcium; Cell Culture Techniques; Cell physiology; Cells; Cellular biology; Characteristics; Cluster Analysis; Collaborations; Communities; Consensus; Data; Data Set; Development; Endotoxins; Functional disorder; Future; Generations; Genes; Genetic Transcription; Gliosis; Goals; Homeostasis; Human; Hypoxia; Image; Image Analysis; In Vitro; Informatics; Knowledge; Laboratories; Lipopolysaccharides; Machine Learning; Mission; Molecular; Nature; Neurosciences; Ontology; Organism; Oxygen; Pathology; Pathway interactions; Phenotype; Physiological; Physiology; Play; Process; Public Health; Publishing; Recording of previous events; Recovery; Reporter; Research; Role; Sensory; Serum; Sleep Fragmentations; Stimulus; Techniques; Testing; Texture; Time; United States National Institutes of Health; Validation; astrogliosis; base; brain cell; cellular imaging; comparative; convolutional neural network; editorial; experimental study; feature extraction; hindbrain; image registration; imaging Segmentation; in vivo; insight; interest; live cell imaging; neuropathology; object shape; respiratory; response; secondary analysis; sensor; tool; transcriptomics

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.

项目摘要 反应性星形胶质细胞增生代表了脑疾病中最常见的神经病理学发现。很遗憾， 我们缺乏对反应性星形胶质症对细胞功能的后果的基本洞察力。虽然 我们假定星形胶质细胞的重要身体角色在反应性星形胶质细胞增多症中功能失调，我们的社区 缺乏关键的基本工具，可以敏锐地检验这些细胞如何显示功能障碍的假设。这 转录组分析工作流与物理分析工作流之间的能力差距代表 神经胶质生物学界了解神经胶质的后果的重大障碍 星形胶质细胞功能。通过机器学习/人工智能（ML/AI）来解决此能力差距 方法代表了由自然神经科学发表的共识社论所描述的特定目标 最近由著名的神经胶质生物学家。拟议研究的科学前提是基于利用率 星形细胞内Ca ++瞬变的活细胞成像（[Ca ++] i）捕获星形胶质细胞物理反应 进行外部刺激。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，以及该视频图像特征提取，包括像素强度， 星形胶质细胞的对象纹理，对象形状和方向性[Ca ++] i瞬变将允许浓缩聚类 [CA ++] I瞬态波形类型的分析。在我们的初步数据中，我们已经捕获了312 GB的 活细胞星形胶质细胞CA ++成像数据，以执行所提出的分析。这些视频捕获 脑干星形胶质细胞对体外缺氧的反应，并遵循内毒素治疗 脂多糖（LPS）。这是我们第一次评估（[[Ca ++] i的脑干星形胶质细胞培养 在基线时没有血清，缺氧和恢复，有和没有LPS治疗。目标的目标 拟议的研究是对该数据集进行次要分析，以开发客观分析 在物理挑战期间，捕获星形胶质细胞表型的工作流程。到 实现这一目标，我们将实现三个目标。我们将首先开发高效，公正的图像分割工作流程 使用基于UNET的CNN算法在物理挑战期间捕获活跃的星形胶质细胞。然后我们会 在不同的物理挑战下识别星形胶质细胞[Ca ++] i瞬态波形类型的簇。最后， 作为将为我们后续R01申请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程通过整合来促进与空间转录组分析的兼容性 使用空间转录组平台的可光转换记者和图像注册过程。在 结论拟议的研究的结论我们将首次拥有快速，客观的图像分析工作流程 询问星形胶质细胞生理学。