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背后的细胞和分子机制的理解- 衰老的特定表型，包括潜在的收敛机制，已经被 无法在所需的范围内收集从亚细胞到整个器官的原位“组学”数据 多种模式，并有足够的深度和吞吐量来定位一小部分细胞 表现出衰老的表型。为了解决这一未得到满足的需求，生物分析核心(BAC)将产生 并深入分析特定细胞类型的转录组和蛋白质组特征的单细胞水平空间图谱 足以识别稀有和异质的衰老细胞群。这将会实现的 在年龄从20到80岁的健康个体的完整组织中，这代表了完整的 人类成年的寿命。BAC的最终目标是识别和验证一组健康的衰老 我们目标组织中每种细胞类型的生物标记物，以及它们在单细胞水平上的空间背景，并评估 衰老细胞的细胞自主性和非自主性对细胞组成和功能的影响 组织微环境的分子特征。我们将利用先进的技术， 工作流程，以及在哥伦比亚大学衰老中心开发和建立的新型计算工具 组织图谱(CUSTMAP)中心和我们在生成多模式组学数据方面的专业知识/经验 在脑、脊髓和皮肤三个靶组织中的单细胞水平。我们将通过以下方式实现我们的研究目标 使用：1)我们团队首创的空间转译(ST)方法，以生成公正、 在完整的人类组织背景下的转录组范围的和细胞类型特定的衰老图谱，在 结合单核RNA-SEQ(SNRNA-SEQ)和数据发展的新的计算方法 分析核心(DAC)，以发现异质衰老细胞的特征；以及2)迭代间接 基于免疫荧光成像(4I)的蛋白质组图谱能够解释空间分辨 单细胞水平上特定细胞类型衰老相关变化的基因表达数据。 这种结合，允许在单细胞的全基因组分子和细胞衰老的特征 在太空中的分辨率，从未在这种规模上进行过尝试，这是CUSTMAP的主要优势之一 中心进场。生物质学家委员会将与生物质学家核心(BIO)和 DAC，使用优化的组织制备和ST/SnRNA-seq/4I参数。与发援会合作， BAC将把这些数据与脑脊液和血液(与这些组织相关的生物液)的分析相结合，以 将SASP因子与组织水平的信号联系起来。BAC还将进行关键的验证研究 识别组织和细胞类型特异的衰老标志物和衰老相关的分子和细胞 特点，使用尖端的基于IPSC的体外模型和来自小鼠遗传和 药理感官分解。

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.