Yale Murine-TMC on Immune Cell Senescence Derived Inflammation

总结 耶鲁-小鼠TMC（mTMC），通过应用高含量和高通量单细胞和空间组学 技术，我们的目标是加速衰老生物标志物的发现，将其应用于小鼠模型， 提出假说来检验生物体衰老的机制。我们提出的无偏见的分析也将回答 衰老细胞（基质或造血谱系）是否存在足够的数量来改变 炎症景观和生物学。Yale-mTMC将使用谱系标记的小鼠对类型或亚型进行分类 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1）一个多学科团队，以生成细胞衰老的分子和细胞图谱， 胸腺、骨髓、脾脏、PBMC、肠系膜和腹股沟脂肪组织。2)开发和部署套件 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）进行综合信息学以鉴定衰老细胞异质性的生物标志物 并构建细胞衰老的全面分子和细胞图谱，包括i.v CD 45 抗体标记/分选方法来研究非淋巴组织中的组织驻留免疫细胞。我们将利用 通过耶鲁大学人类TMC建立的分析平台， 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，辅以透明组织的3D光片显微镜，（B）单细胞分析（SCA），包括 scCITE-seq用于蛋白质和mRNA分析，CyTOF用于高复合免疫表型分析，单细胞蛋白 （C）空间多组测序（SMOS），使用 基于DBiT的spatial-CITE-seq用于基因组规模的空间分辨蛋白质转录组学作图。的 这些管道的组合允许衰老细胞的高灵敏度和单细胞分辨率映射 以及相关的组织环境。Yale-mTMC旨在组建一个由PI领导的多学科团队： （耶鲁大学衰老研究中心主任）和MPI：蒙哥马利（免疫学家， 科学事务）与核心负责人范博士（生物工程师），克鲁格博士（信息学和数据分析）和关键 Booth博士（小鼠病理学家），Lucas博士，Haberman（免疫学家，宠物店小鼠模型专家， 成像）与由科学领导人Medzhitov博士，Iwasaki和Ruddle组成的IAB。已有成熟经验的 管理大型，多组件计划和先前的经验，产生重大的高质量 成像和组学数据作为联盟的一部分。一个专门的项目经理将管理和协调所有 在整个中心和SenNet联盟的活动。互补和多学科科学 团队的专业知识将转化为TMC的集体能力，以促进多个 多维、多参数数据生成，并与SenNet和其他TMC协调，以产生更大的影响

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