Biological Analysis Core

生物分析核心

• 批准号: 10683324
• 负责人: Hemali Phatnani
• 金额: $ 102.17万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683324
• 关键词: 21 year old; 3-Dimensional; Address; Adult; Age; Atlases; Biological; Biological Markers; Biological Models; Biology; Blood; Brain; Cells; Clinical assessments; Computing Methodologies; Data; Data Analyses; Elderly; Exhibits; Experimental Models; Freezing; Gene Expression; Genetic Models; Goals; Image; In Situ; Indirect Immunofluorescence; Individual; Longevity; Maps; Modality; Modeling; Molecular; Molecular Profiling; Mus; Organ; Organoids; Phenotype; Physiological; Population; Prefrontal Cortex; Proteome; Proteomics; Research; Resolution; Skin; Spinal Cord; Technology; Testing; Tissues; Universities; brain tissue; cell type; cohort; computerized tools; data integration; design; experience; genome-wide; human tissue; in vitro Model; in vivo; induced pluripotent stem cell; middle age; mouse model; multi-scale atlas; multimodality; novel; pharmacologic; senescence; single nucleus RNA-sequencing; spatial integration; tissue mapping; tissue preparation; transcriptome; transcriptomics; validation studies; young adult

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.

生物分析核心（BAC）：项目摘要 那遥远的是，我们对异质，细胞类型的基础的细胞和分子机制的理解 特定的感应表型，包括潜在的收敛机制，已受到 无法在所需量表范围内收集原位“ OMICS”数据，从亚细胞到整个器官， 多种方式，并具有足够的深度和吞吐量，以定位少数的细胞 表现出感应表型。为了满足这种未满足的需求，生物分析核心（BAC）将产生 并在深度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集 足以识别罕见和异质的感觉细胞群体。这将被实现 在健康个体的完整组织中，年龄在20至80岁之间，这代表了一系列 人类的成人寿命。 BAC的最终目标是识别和验证一个健壮的衰老面板 在我们的目标时间和它们的空间环境中，在单细胞水平上的每种单元类型的生物标志物，并评估 感官细胞对细胞组成和 组织微环境的分子特征。我们将利用高级技术， 工作流以及在哥伦比亚大学衰老中开发和建立的新型计算工具 组织映射（Custmap）中心以及我们在生成多模式OMICS数据的专业知识/经验 三个目标时机的单细胞水平 - 大脑，脊髓和皮肤。我们将通过 雇用：1）我们团队开创的空间转录组学（ST）方法，以产生无偏见的， 在完整的人体组织中，整个转录组和细胞类型的感应图，在 与单核RNA-Seq（SNRNA-Seq）和数据开发的新型计算方法的结合 分析核心（DAC）发现异质感觉细胞的特征； 2）迭代间接 免疫荧光成像（基于4i）的蛋白质组学分析以实现空间分辨的解释 基因表达数据在单细胞水平上与细胞类型特异性相关的变化的上下文中。 这种组合允许全基因组分子和细胞在单细胞处的传感表征 在太空中的分辨率尚未按照这种规模尝试，并且是Custmap的主要优势之一 中心方法。 BAC将与Biospecimen Core（BIO）和 DAC，使用优化的组织制备和ST/SNRNA-SEQ/4I参数。与DAC结合 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因子与组织级特征相关联。 BAC还将对 鉴定的组织和细胞类型特异性的感应标记以及与传感相关的分子和细胞 特征，使用基于IPSC的最先进的体外模型和来自遗传和遗传模型的组织 药理学鼻溶解。