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Reverse engineering zonation-specific and age-specific iPSC-derived cerebrovascular models based on transcriptomic profiling of the human brain

Reverse engineering zonation-specific and age-specific iPSC-derived cerebrovascular models based on transcriptomic profiling of the human brain
基于人脑转录组分析的逆向工程分区特异性和年龄特异性 iPSC 衍生脑血管模型
批准号:
10321473
负责人:
Myriam Heiman
金额:
$83.31万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-15 至 2023-08-31

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中文摘要
翻译
项目摘要 脑血管功能障碍正在成为许多脑部疾病的常见病理,包括 神经退行性疾病、脑血管疾病以及衰老。因此,理解这个角色 脑血管功能障碍在疾病进展和衰老中的作用将是长期维持大脑的关键 健康。随着组织工程和干细胞技术的发展,体外脑血管模型可以 在了解脑血管功能障碍在疾病进展和发展中的作用 衰老。 下一代脑血管模型应考虑三个关键因素:(1) 脑微血管内皮细胞沿动静脉轴线的表型,(内在因素),(2) 动静脉轴上微环境线索的差异(外在因素),以及(3) 衰老过程中的区带特异性脑血管表型以及对衰老血清的反应。因此, 这个项目的总体目标是使用分区和年龄特定的内在和外在线索来进行逆向工程 人类脑血管模型,并利用这些模型来了解脑血管表型在 衰老。我们将首先进行集合基因筛查,以确定哪些转录因子组合 能够驱动源细胞向人脑微血管内皮细胞的基因表达谱迈进 沿着动静脉轴(目标1)。三种候选诱导的脑微血管内皮细胞 每个区域都将使用慢病毒转导产生。届时,每个地区的最佳候选人将是 从基因和蛋白质表达谱分析中选出(目标2)。然后,我们将使用三个iBMEC来 在分区特定模型中演示分区特定的脑血管表型(小动脉、毛细血管、 小静脉)(目标3)。接下来,我们将评估年轻和老年血清对脑血管表型的影响。 分区专用模式(目标4)。最后,我们将使用相同的方法来创建一个 在一个区域建立脑血管模型。我们将创造出与人类转录因子谱相匹配的iBMEC 脑微血管内皮细胞在老年脑血管系统中的作用 微环境线索和青年/老年血清对脑血管表型的影响。 该项目是Searson Group(JHU)与具有组织工程学专业知识的合作 微血管模型,以及海曼小组(MIT)在基因组学和分子机制方面的专长 神经退行性疾病。该项目建立在两个实验的主要最新成果的基础上:(1) 脑微血管内皮细胞区带特异性转录因子文库的建立 从人脑中,(2)识别关键的转录因子,以实现区带的反向工程- 特定的人脑微血管内皮细胞,以及(3)用于分区特定的组织工程平台 脑血管模型。
英文摘要
Project Summary Cerebrovascular dysfunction is emerging as a common pathology in many diseases of the brain, including neurodegenerative diseases, cerebrovascular diseases, as well as in aging. Therefore, understanding the role of cerebrovascular dysfunction in disease progression and aging will be key to long-term maintenance of brain health. With developments in tissue engineering and stem cell technology, in vitro cerebrovascular models can play an important role in understanding the role of cerebrovascular dysfunction in disease progression and aging. Next-generation cerebrovascular models should take into account three key factors: (1) differences in phenotype of brain microvascular endothelial cells along the arterio-venous axis, (intrinsic factors), (2) differences in microenvironmental cues along the arterio-venous axis (extrinsic factors), and (3) changes in zonation-specific cerebrovascular phenotype during aging and in response to aged serum. Therefore, the overall goal of this project is to use zonation- and age-specific intrinsic and extrinsic cues to reverse engineer human cerebrovascular models, and to use these models to understand cerebrovascular phenotype during aging. We will first perform a pooled genetic screen to identify transcription factor combinations that are capable of driving source cells towards gene expression profiles of human brain microvascular endothelial cells along the arterio-venous axis (Aim 1). Three candidate induced brain microvascular endothelial cells (iBMECs) for each zone will be generated using lentiviral transduction. The top candidate for each zone will then be selected from analysis of gene and protein expression profiles (Aim 2). We will then use the three iBMECs to demonstrate zonation-specific cerebrovascular phenotype in zonation-specific models (arteriole, capillary, venule) (Aim 3). Next, we will assess the influence of young and old serum on cerebrovascular phenotype in the zonation-specific models (Aim 4). Finally, we will use the same approach to create an aged cerebrovascular model in one zone. We will create iBMECs that match the transcription factor profile of human brain microvascular endothelial cells in the aged cerebrovasculature, and then assess the role of microenvironmental cues and young/old serum on cerebrovascular phenotype. This project is a collaboration between the Searson group (JHU) with expertise in tissue-engineered microvascular models, and the Heiman group (MIT) with expertise in genomics and molecular mechanisms of neurodegenerative disease. This project builds upon key recent accomplishments from the two labs: (1) the creation of a library of zonation-specific transcription factor profiles for brain microvascular endothelial cells from the human brain, (2) identification of key transcription factors to enable reverse engineering of zonation- specific human brain microvascular endothelial cells, and (3) tissue-engineered platforms for zonation-specific cerebrovascular models.
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