Yale Murine-TMC on Immune Cell Senescence Derived Inflammation

耶鲁小鼠-TMC 对免疫细胞衰老引起的炎症的研究

• 批准号: 10675111
• 负责人: VISHWA DEEP DIXIT
• 金额: $ 165.13万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-02 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10675111
• 关键词: Acceleration; Adipocytes; Adipose tissue; Aging; Agreement; Anti-Inflammatory Agents; Antibodies; B-Lymphocytes; Biological; Biological Markers; Biology; Biomedical Engineering; Bone Marrow; CCL2 gene; CCL3 gene; Cell Aging; Cell Cycle Arrest; Cells; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Chronic; Classification; Communities; Complement 3d; Complex; Data; Data Analytics; Dedications; Dimensions; Environment; Fibroblasts; Fostering; Generations; Geroscience; Growth Factor; HMGB1 gene; Hematopoietic; Heterogeneity; Homeostasis; Human; Human Resources; IL8 gene; Image; Immune; Immune system; Immunologics; Immunologist; Immunophenotyping; Inflammation; Inflammatory; Informatics; Interleukin-1 beta; Interleukin-6; Iron; Label; Leukocytes; Light; Lipolysis; Lymphoid Tissue; Macrophage; Maps; Measures; Mesentery; Messenger RNA; Metabolism; Microscopy; Molecular; Mus; Natural Killer Cells; Normal tissue morphology; Organ; PTPRC gene; Pathologist; Peripheral Blood Mononuclear Cell; Phenotype; Proliferating; Property; Proteins; Reporter; Research; Resolution; Sorting; Specimen; Spleen; T memory cell; TNF gene; Technology; Testing; Thymic epithelial cell; Thymus Gland; Tissues; Translating; aged; analysis pipeline; biomarker identification; chemokine; cytokine; data resource; experience; exposed human population; functional decline; genome-wide; mouse model; multidisciplinary; multiple omics; multiplexed imaging; pathogen; pathogen exposure; programs; response; senescence; single cell analysis; single cell proteins; synergism; tissue injury; tissue mapping; transcriptomics

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

概括 耶鲁鼠 TMC (mTMC)，通过应用高内涵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，以及 产生假设来测试有机衰老的机制。我们提出的公正分析也将回答 衰老细胞（基质细胞或造血细胞谱系）是否存在足够数量以改变衰老细胞的问题 炎症景观和生物学。耶鲁-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) 一个多学科团队来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、高内涵和高通量的单细胞和空间组学技术来表征 这些样本和 3) 进行综合信息学识别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，包括静脉注射 CD45 抗体标记/分选方法用于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，以实现跨物种验证 生物标志物。三个主要的生物分析流程是： (A) 多重成像 (MI)，包括 CODEX、IMC、 和 SMI，辅以透明组织的 3D 光片显微镜检查，(B) 单细胞分析 (SCA)，包括 用于蛋白质和 mRNA 分析的 scCITE-seq、用于高强度免疫表型分析的 CyTOF 以及单细胞蛋白质 分泌蛋白组分析来测量 SASP 异质性，以及 (C) 使用空间多组学测序 (SMOS) 基于 DBiT 的空间 CITE-seq，用于基因组规模的空间解析蛋白质转录组图谱。这 这些流程的组合可以实现衰老细胞的高灵敏度和单细胞分辨率图谱 和相关的组织环境。 Yale-mTMC 旨在组建一支由 PI 领导的多学科团队：Dixit （耶鲁大学衰老研究中心主任）和 MPI：蒙哥马利（免疫学家、副院长） 科学事务），核心领导者为 Fan 博士（生物工程师）、Kluger 博士（信息学和数据分析）和 Key 人员 Booth 博士（小鼠病理学家）、Lucas、Haberman 博士（免疫学家、宠物店小鼠模型专家、 成像）与由科学领袖 Drs 组成的 IAB。梅德泽托夫、岩崎和拉德尔。我们有经验 管理大型、多组成部分的项目以及产生显着高质量的先前经验 成像和组学数据作为联盟的一部分。专门的项目经理将管理和协调所有 整个中心以及与 SenNet 联盟的活动。互补和多学科的科学 团队的专业知识将转化为 TMC 的集体能力，以促进多方整合 多维、多参数数据生成以及与 SenNet 和其他 TMC 的协调，以产生更大的影响