Biological Analysis Core

生物分析核心（BAC）：项目总结 到目前为止，我们对异质性细胞类型的细胞和分子机制的理解- 衰老的特定表型，包括潜在的趋同机制，已经受到阻碍， 无法收集从亚细胞到整个器官的所需尺度范围内的原位“组学”数据， 多种形式，并具有足够的深度和吞吐量，以定位小部分细胞， 表现出衰老表型。为了解决这一未满足的需求，生物分析核心（BAC）将生成 并分析细胞类型特异性转录组和蛋白质组特征的单细胞水平空间图谱， 足以允许鉴定稀有和异质的衰老细胞群体。完成这项工作的方法是 在20 - 80岁的健康个体的完整组织中，这代表了 人类的成年寿命。BAC的最终目标是识别和验证一组强大的衰老 我们的靶组织中每种细胞类型的生物标志物，以及它们在单细胞水平上的空间背景， 衰老细胞对细胞组成的细胞自主和细胞非自主作用， 组织微环境的分子特征。我们将利用先进的技术， 工作流程，以及哥伦比亚大学衰老研究所开发和建立的新型计算工具 组织绘图（CUSTMAP）中心和我们在生成多模态组学数据方面的专业知识/经验， 在三种靶组织-脑、脊髓和皮肤中的单细胞水平。我们将通过以下方式实现研究目标： 采用：1）空间转录组学（ST）的方法，由我们的团队开创，以产生一个公正的， 在完整人体组织中，转录组范围和细胞类型特异性衰老图谱， 结合单核RNA-seq（snRNA-seq）和数据开发的新计算方法， 分析核心（DAC）以揭示异质衰老细胞的特征;以及2）迭代间接 基于免疫荧光成像（4 i）的蛋白质组学分析，能够解释空间分辨的 在单细胞水平上细胞类型特异性衰老相关变化背景下的基因表达数据。 这种组合，允许在单细胞衰老的全基因组分子和细胞表征， 空间分辨率，还没有尝试在这个规模，是一个主要的优势CUSTMAP 中间进场。BAC将与生物样本核心（BIO）和 DAC，使用优化的组织制备和ST/snRNA-seq/4 i参数。与发展援助委员会一起， BAC将这些数据与CSF和血液（与这些组织相关的生物流体）的分析相结合， 将SASP因子与组织水平特征联系起来。BAC还将进行关键验证研究， 鉴定的组织和细胞类型特异性衰老标记物和衰老相关的分子和细胞标记物， 功能，使用尖端的iPSC为基础的体外模型和组织从小鼠模型的遗传和 药理性衰老

BIOLOGICAL ANALYSIS CORE (BAC): PROJECT SUMMARY Thus far, our understanding of the cellular and molecular mechanisms underlying the heterogenous, cell type- specific phenotypes of senescence, including potentially convergent mechanisms, has been hindered by the inability to collect in situ ‘omics’ data across the range of scales required, from subcellular to whole organ, in multiple modalities, and with sufficient depth and throughput to locate the small percentage of cells that would exhibit a senescence phenotype. To address this unmet need, the Biological Analysis Core (BAC) will generate and analyze a single-cell level spatial atlas of cell type-specific transcriptome and proteome signatures at a depth sufficient to permit identification of the rare and heterogeneous senescent cell population. This will be achieved in intact tissues from healthy individuals ranging in age from 20 – 80 years, which represents a full spectrum of the human adult lifespan. The ultimate goals of the BAC are to identify and validate a panel of robust senescence biomarkers for each cell type in our target tissues, and their spatial context, at the single-cell level, and to assess the cell autonomous and cell non-autonomous effects of senescent cells on the cellular composition and molecular signatures of the tissue microenvironment. We will take advantage of the advanced technologies, workflows, and novel computational tools developed and established in the Columbia University Senescence Tissue Mapping (CUSTMAP) Center and our expertise/experience in generating multimodal omics data at the single-cell level in three target tissues – brain, spinal cord, and skin. We will achieve our research goals by employing: 1) the Spatial Transcriptomics (ST) approach pioneered by our team to generate an unbiased, transcriptome-wide, and cell type-specific map of senescence in the context of intact human tissues, in conjunction with single-nucleus RNA-seq (snRNA-seq) and novel computational methods developed by the Data Analysis Core (DAC) to uncover the signatures of heterogeneous senescent cells; and 2) Iterative Indirect Immunofluorescence Imaging (4i)-based proteomic profiling to enable the interpretation of spatially resolved gene expression data in the context of cell type-specific senescence-associated changes at the single-cell level. This combination, allowing for genome-wide molecular and cellular characterization of senescence at single-cell resolution in space, has not been attempted at this scale and is one of the primary strengths of the CUSTMAP Center approach. The BAC will achieve its goals in close coordination with the Biospecimen Core (BIO) and the DAC, using optimized tissue preparation and ST/snRNA-seq/4i parameters. In conjunction with the DAC, the BAC will integrate these data with analyses of CSF and blood (the biofluids associated with these tissues), to relate SASP factors with tissue-level signatures. The BAC will also carry out critical validation studies of the identified tissue- and cell type-specific senescence markers and senescence-associated molecular and cellular features, using both cutting-edge iPSC-based in vitro models and tissues from mouse models of genetic and pharmacological senolysis.