Three-dimensional maps of senescence in the human pancreas

总结 细胞衰老的标志是细胞不能在细胞周期中进展。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， 例如动脉粥样硬化、糖尿病、肺病和许多其他疾病。衰老既是一种生理上的 基本的和病理相关的程序，其作用取决于上下文和具体的 形势已经出现了将衰老与常见的与年龄相关的人类疾病联系起来的证据。尽管有这些 尽管有了这些进展，但对它们的丰度、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。主要的挑战是暂停了异常复杂的异质性， 胰腺等组织的3D结构，即使在非疾病条件下，也会使 衰老细胞的空间分辨鉴定。在这个项目中，我们将使用胰腺作为试验平台， 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腺和卵巢 异质性空间组学方法的发展，如空间转录组学/蛋白质组学， 有助于克服3D组织异质性带来的挑战。但这些方法只能提供高- 在2D组织切片中以空间分辨的方式提供分子信息。在此，我们建议建立 一个新的集成3D成像平台，可以映射大体积（> 1cm 3）组织的3D结构， 并且可以确定3D组织样本内衰老细胞的位置。鉴于其多功能性，我们建议 集成平台可以容易地用于研究其它组织中的空间衰老（除了胰腺， 乳腺和卵巢），并结合基于蛋白质和RNA的衰老特征， 将被SenNet网络的其他中心和项目发现。制作第一个全面的3D 多尺度衰老图谱的组织跨越年龄，我们提出了以下目标，在UG 3和UH 3 阶段。在UG 3阶段，在一小部分胰腺和乳腺组织中，我们将首先整合我们的新AI- 基于免疫荧光的3D组织重建平台CODA（CODA+IF平台） 未患病的人胰腺和乳腺的FFPE组织样品中衰老标志物的标记。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 的组织样品中，是穷人和丰富的细胞衰老，如所确定的CODA+IF。在UH 3阶段， 我们将把这些3D细胞衰老绘图方法应用于胰腺、乳腺和卵巢癌的大型队列， 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 在非患病组织体积中的不同组织隔室中作为年龄的函数。

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.