Three-dimensional maps of senescence in the human pancreas

人类胰腺衰老的三维图

• 批准号: 10684887
• 负责人: Pei-Hsun wu
• 金额: $ 55万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-05 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10684887
• 关键词: 3-Dimensional; Acinus organ component; Adult; Age; Aging; Algorithms; Architecture; Atherosclerosis; Atlases; Biological Models; Biology; Blood Vessels; Breast; Cell Aging; Cell Cycle; Cells; Complex; Cytometry; Development; Diabetes Mellitus; Disease; Duct (organ) structure; Epithelial Cells; Fatty acid glycerol esters; Fibroblasts; HMGB1 gene; Heterogeneity; Human; Image; Imaging technology; Immune; Immunofluorescence Immunologic; Immunohistochemistry; In Situ; Islets of Langerhans; Label; Link; Liver; Location; Longevity; Lung; Lung diseases; Mammary Gland Parenchyma; Maps; Measures; Methods; Molecular; Molecular Profiling; Natural History; Nature; Nerve; Organ; Ovarian Tissue; Ovary; Pancreas; Pathologic; Phase; Physical shape; Physiological; Proteins; Proteomics; RNA; Resolution; Role; Sampling; Skin; Spatial Distribution; Techniques; Technology; Thick; Three-Dimensional Imaging; Tissue Sample; Tissue atlas; Tissues; age related; cell type; cohort; deep learning; digital; human disease; human tissue; imaging platform; islet; multi-scale atlas; novel; pathogen; programs; repaired; response; senescence; technology platform; tissue mapping; tissue reconstruction; tool; transcriptomics

Summary The hallmark of cellular senescence is the inability of cells to progress through the cell cycle. With increasing age, senescent cells accumulate in multiple tissues; their abundance increases in aging and several age-related diseases such as atherosclerosis, diabetes, lung disease, and many others. Senescence is both a physiologically fundamental and pathologically relevant program, with its role depending on the context and the specific situation. Evidence linking senescence to common age-associated human diseases has emerged. Despite these advances, little is known about the abundance, spatial distribution, precise nature, and functional relevance of senescent cells in human tissues. The main challenge is paused by the extraordinarily complex heterogeneity of the 3D architecture of tissues like the pancreas, even in non-diseased conditions, which greatly complicates the spatially resolved identification of senescent cells. In this project, we will use the pancreas as a testbed and will expand our 3D spatial analysis of cellular senescence to the human breast and ovaries of similar complex heterogeneity. The development of spatial -omics approaches such as spatial transcriptomics/proteomics may help overcome the challenge paused by 3D tissue heterogeneity. But these approaches only provide high- content molecular information in a spatially resolved manner in 2D tissue sections. Here, we propose to establish a new integrated 3D imaging platform that can map both the 3D architecture of large volumes (> 1cm3) of tissues and can determine the location of senescent cells within a 3D tissue sample. Given its versatility, our proposed integrated platform could readily be used to study spatial senescence in other tissues (besides the pancreas, breast, and ovaries studied here) and to incorporate protein-based and RNA-based senescence signatures that will be discovered by other centers and projects of the SenNet network. To produce the first comprehensive 3D multiscale senescence atlas of tissues across ages, we propose the following aims in the UG3 and the UH3 phases. In the UG3 phase, in a small set of pancreatic and breast tissues, we will first integrate our new AI- based 3D tissue reconstruction platform CODA with immunofluorescence (the CODA+IF platform) for multiple labeling of senescence markers in FFPE tissue samples of non-diseased human pancreas and breast. We will then integrate CODA with DBiT-seq (the CODA+DBiT-seq platform) and apply this integrated platform in regions of the tissue sample that are poor and rich in cellular senescence, as identified by CODA+IF. In the UH3 phase, we will apply these 3D cellular senescence mapping methods to large cohorts of pancreatic, breast, and ovarian tissue. We will measure the distribution and variability and associated molecular signatures of senescent cells within different tissue compartments in non-diseased tissue volumes as a function of age.

概括 细胞衰老的标志是细胞无法完成细胞周期。随着增加 随着年龄的增长，衰老细胞在多个组织中积累；它们的丰度随着年龄的增长而增加，并且一些与年龄有关的 动脉粥样硬化、糖尿病、肺部疾病等疾病。衰老既是一种生理现象 基本和病理相关的计划，其作用取决于背景和具体情况 情况。将衰老与常见的与年龄相关的人类疾病联系起来的证据已经出现。尽管有这些 的进展，但人们对这些物质的丰度、空间分布、精确性质和功能相关性知之甚少。 人体组织中的衰老细胞。主要挑战因极其复杂的异质性而暂停 即使在非疾病条件下，胰腺等组织的 3D 结构也使 衰老细胞的空间分辨识别。在这个项目中，我们将使用胰腺作为试验台，并将 将我们对细胞衰老的 3D 空间分析扩展到类似复杂的人类乳房和卵巢 异质性。空间组学方法（例如空间转录组学/蛋白质组学）的发展可能会 帮助克服 3D 组织异质性带来的挑战。但这些方法仅提供高 在二维组织切片中以空间分辨的方式包含分子信息。在此，我们建议建立 全新集成 3D 成像平台，可绘制大体积 (> 1cm3) 组织的 3D 架构 并可以确定 3D 组织样本中衰老细胞的位置。鉴于其多功能性，我们建议 集成平台可以很容易地用于研究其他组织的空间衰老（除了胰腺， 乳房和卵巢），并结合基于蛋白质和基于 RNA 的衰老特征 将被 SenNet 网络的其他中心和项目发现。制作第一个综合 3D 跨年龄组织的多尺度衰老图谱，我们在 UG3 和 UH3 中提出以下目标 阶段。在 UG3 阶段，在一小组胰腺和乳腺组织中，我们将首先整合我们的新 AI- 基于 3D 组织重建平台 CODA 与免疫荧光（CODA+IF 平台）的多个 在未患病的人类胰腺和乳房的 FFPE 组织样本中标记衰老标记。我们将 然后将CODA与DBiT-seq集成（CODA+DBiT-seq平台）并将该集成平台应用于区域 通过 CODA+IF 鉴定细胞衰老程度低和细胞衰老程度高的组织样本。在UH3阶段， 我们将把这些 3D 细胞衰老图谱方法应用于大量胰腺、乳腺癌和卵巢 组织。我们将测量衰老细胞的分布和变异性以及相关的分子特征 作为年龄的函数，在非患病组织体积的不同组织隔室内。