PIXEL-seq-based spatial, multi-omic profiling for senescent cell mapping with single-cell resolution

摘要 形态完整的人体组织中衰老细胞的综合鉴定和特征 对了解体内衰老和靶向移除这些细胞以改善健康寿命具有重要意义 和寿命。这项任务一直具有挑战性，因为缺乏普遍和明确的标志来表征 衰老状态，反映了衰老表型的复杂性和高度的 不同的衰老程序。发现衰老标记的首选途径是在空间上 在单个细胞分辨率下绘制不同组织和生命阶段细胞类型的组学状态图。的总目标是 该项目旨在(I)开发一种空间、单单元分辨率、多峰方法，同时分析 转录组、开放染色质和蛋白质组(或分泌组)，以及(Ii)优化和缩放它以用于作图 人体组织中的衰老细胞。PI的实验室最近开发了一种新的技术-像素序列 (Polony索引文库测序)，并将其应用于1微米分辨率和高分辨率的空间剖面转录组 RNA捕获效率。认识到其在体内衰老机制和生产规模研究中的潜力 数据生成，将追求三个具体目标：1)在UG3年第1年，演示基于像素序列的空间 转录组、蛋白质组和单细胞分辨率的ATAC-SEQ分析；2)在UH3第二年，优化和 将这些分析结合起来用于人体组织图谱；以及3)在UH3年3-4年，扩大对人体心脏的应用， 肝脏和肺组织图。在第一个目标下，将开发像素序列以实现单单元分辨率。 通过图像引导的细胞分割(目标1A)并扩展到空间蛋白质组(目标1B)和开放染色质 可及性分析(目标1C)通过提供DNA标记的抗体和经Tn5处理的染色体DNA， 分别用Polony凝胶捕获。对于第二个目标，蛋白质组分析将被优化和扩大到 使用多克隆微型结合剂的200-plex，允许衰老标记和相关的交叉验证 异构体和翻译后修饰(目标2A)。这些分析将针对多模式数据进行整合 使用人体组织捕获和验证(目标2B)。在第三个目标中，应用程序的规模将扩大到 提高Polony凝胶制造的生产能力(Aim 3A)，并交付CODCC公开释放高... 来自几个个体器官捐赠者的多个器官的几个部位的高质量数据(目标3B和3C)。这个 调查人员还将参与财团共同项目和其他尚未形成的合作。 提出的项目是创新的，因为这种方法将首次产生空间多模式人类 前所未有的深度和分辨率的组织数据。这一点很重要，因为检测不需要专门的 并可广泛应用于SENNET等单电池联合体中。

ABSTRACT Comprehensive identification and characterization of senescent cells in morphologically intact human tissues is important for understanding senescence in vivo and the targeted removal of these cells to improve healthspan and lifespan. This task has been challenging due to the lack of universal and unequivocal markers characterizing the senescence state, which reflects the complexity of the senescence phenotype and the existence of highly heterogeneous senescence programs. A preferred avenue for discovering senescence markers is to spatially map ‘omics’ states of cell types in different tissues and life stages at single cell resolution. The overall goal of this project is to (i) develop a spatial, single-cell-resolution, multimodal method that simultaneously analyze transcriptome, open chromatin, and proteome (or secretome), and (ii) optimize and scale it for mapping senescent cells in human tissues. The PI’s laboratory has recently developed a novel technique PIXEL-seq (polony-indexed library-sequencing) and applied it to spatially profile transcriptome with 1-µm resolution and high RNA capture efficiency. To realize its potential for studying in vivo senescence mechanism and production-scale data generation, three specific aims will be pursued: 1) In UG3 Year 1, demonstrate PIXEL-seq-based spatial transcriptome, proteome, and ATAC-seq assays with single-cell resolution; 2) In UH3 Year 2, optimize and combine these assays for human tissue mapping; and 3) In UH3 Years 3-4, scale up application to human heart, liver, and lung tissue mapping. Under the first aim, PIXEL-seq will be developed to achieve single-cell resolution by image-guided cell segmentation (Aim 1A) and expanded to spatial proteome (Aim 1B) and open chromatin accessibility assays (Aim 1C) by rendering DNA-tagged antibodies and Tn5-treated chromosomal DNAs, respectively, to capture by polony gels. For the second aim, the proteome assay will be optimized and scaled to 200-plex using polyclonal mini-binders, allowing the cross-validation of senescence markers and associated isoforms and post-translational modifications (Aim 2A). These assays will be integrated for multimodal data capture and validated using human tissues (Aim 2B). In the third aim, the application will be scaled up by increasing throughput of polony gel fabrication (Aim 3A) and to deliver to the CODCC for public release of high- quality data on several sites of multiple organs from several individual tissue donors (Aim 3B and 3C). The investigators will also participate in the Consortium common project and other collaborations yet to be formed. The proposed project is innovative in that this method will for the first time generate the spatial multimodal human tissue data at unprecedented depth and resolution. It is significant because the assays do not require specialized equipment and can be widely implemented in the SenNet and other single cell consortia.