Yale Murine-TMC on Immune Cell Senescence Derived Inflammation

摘要 耶鲁-小鼠TMC(MTMC)通过应用高含量和高通量的单细胞和空间组学 技术，我们的目标是加速发现衰老的生物标记物，将它们应用于小鼠模型，以及 产生假说来测试生物衰老的机制。我们提出的不偏不倚的分析也将回答 衰老细胞(基质或造血系)是否存在足够的数量来改变 炎性景观和生物。耶鲁-mTMC将使用有血统标记的老鼠来分类类型或亚型 衰老细胞的空间异质性，以及这些细胞如何影响组织环境。耶鲁-mTMC 将组建一个多学科团队来生成细胞衰老的分子和细胞图谱 胸腺、骨髓、脾、PBMC、肠系膜和腹股沟脂肪组织。2)开发部署套件 高分辨率、高含量和高通量的单细胞和空间组学技术 这些标本和3)执行综合信息学以识别衰老细胞异质性的生物标记物 并构建全面的细胞衰老分子和细胞图谱，包括静脉注射CD45 抗体标记/分选方法研究非淋巴组织中的组织驻留免疫细胞。我们将利用 通过耶鲁人类TMC建立的分析平台能够实现跨物种的验证 生物标志物。三个主要的生物分析管道是：(A)多路成像(MI)，包括Codex，IMC， 和SMI，辅以清除组织的3D光片显微镜，(B)单细胞分析(SCA)，包括 ScCITE-seq用于蛋白质和信使核糖核酸分析，CyTOF用于高复合免疫表型，以及单细胞蛋白 分泌组图谱以测量SASP异质性，以及(C)使用空间多组体测序(SMOS) 基于DBiT的空间引证序列用于基因组水平的空间分辨蛋白质转录图谱。这个 这些管道的组合允许对衰老细胞进行高灵敏度和单细胞分辨率的标测 以及相关的组织环境。耶鲁-mTMC计划组建一个由Pi：Dixit领导的多学科团队 (耶鲁大学老龄化研究中心主任)，MPI：Montgomery(免疫学家，耶鲁大学副院长 科学事务)与Core领导Fan博士(生物工程师)、Kluger博士(信息学和数据分析)和Key 人员布斯博士(老鼠病理学家)，卢卡斯博士，哈伯曼博士(免疫学家，宠物店老鼠模型专家， 成像)，IAB由科学领袖Medzhitov博士、Iwa aki博士和Ruddle博士组成。我们在以下方面有经验 大型、多组件计划的管理，以及在生成显著高质量项目方面的经验 成像和组学数据作为财团的一部分。专职的项目经理将管理和协调所有 在整个中心和与Sennet财团的活动。互补性和多学科的科学 团队的专业知识将转化为TMC的集体能力，以促进多个 维度、多参数数据生成以及与Sennet和其他TMC的协调，以实现更大的影响

SUMMARY Yale-murine TMC (mTMC), through application of high-content and high throughput single-cell and spatial omics technologies, we aim to accelerate the discovery of senescent biomarkers, apply them to mouse models, and generate hypotheses to test mechanisms of organismal aging. Our proposed unbiased analyses will also answer the question whether senescent cells (stromal or hematopoietic lineage) exist in sufficient quantities to alter the inflammatory landscape and biology. Yale-mTMC will use lineage-marked mouse to classify types or subtypes of senescent cells, their spatial heterogeneity and how these cells impact the tissue environments. Yale-mTMC will assemble 1) a multidisciplinary team to generate the molecular and cellular maps of cellular senescence in Thymus, Bone marrow, Spleen, PBMCs, Mesenteric and Inguinal adipose tissue. 2) develop and deploy a suite of high-resolution, high-content and high throughput single-cell and spatial omics technologies to characterize these specimens and 3) perform integrated informatics to identify biomarkers of senescent cell heterogeneity and to construct comprehensive molecular and cellular maps of cellular senescence and including an i.v CD45 antibody labeling/sorting approach to study tissue resident immune cells in non-lymphoid tissues. We will utilize the analysis platforms established through the Yale human TMC to enable cross-species verification of biomarkers. Three major biological analysis pipelines are: (A) Multiplex Imaging (MI) including CODEX, IMC, and SMI, complemented by 3D light sheet microscopy of cleared tissues, (B) Single Cell Analysis (SCA) including scCITE-seq for protein and mRNA profiling, CyTOF for high-plex immunophenotyping, and single-cell protein secretome profiling to measure SASP heterogeneity, and (C) Spatial Multi-Omics Sequencing (SMOS) using DBiT-based spatial-CITE-seq for spatially resolved proteo-transcriptomic mapping at genome scale. The combination of these pipelines allows for highly sensitive and single-cell resolution mapping of senescent cells and associated tissue environments. Yale-mTMC aims to assemble a multidisciplinary team led by PI: Dixit (Director, Yale Center for Research on Aging), and MPI: Montgomery (Immunologist, Associate Dean for scientific affairs) with Core Leads Dr. Fan (Bioengineer), Dr. Kluger (informatics and data analytics) and Key personnel Dr. Booth (mouse pathologist), Dr. Lucas, Haberman (Immunologists, expert in pet store mouse model, imaging) with IAB composed of scientific leaders Drs. Medzhitov, Iwasaki and Ruddle. We have experience in management of large, multi-component programs and prior experience with generating significant high-quality imaging and omics data as part of a consortium. A dedicated program manager will manage and coordinate all activities across the center and with the SenNet consortium. Complementary and multidisciplinary scientific expertise of the team will translate into the collective capability of the TMC to foster integration of multi- dimensional, multiparameter data generation and coordination with SenNet and other TMCs for greater impact