Single-cell label-free identification of senescence by Raman microscopy and spatial genomics

通过拉曼显微镜和空间基因组学进行单细胞无标记衰老鉴定

• 批准号: 10684751
• 负责人: Peter T. So
• 金额: $ 55万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-05 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10684751
• 关键词: ATAC-seq; Aging; Atlases; Biological Assay; Biological Models; Biology; Biomedical Research; Brain; Cell Aging; Cell Culture Techniques; Cell physiology; Cells; Clinical Research; Collaborations; Complex; Confocal Microscopy; Data; Development; Dimensions; Disease; Dissociation; Endoscopes; Environment; Foundations; Future; Gene Expression; General Hospitals; Genetic Transcription; Genomics; Heterogeneity; Human; Human BioMolecular Atlas Program; Image; In Vitro; Inflammatory; Institutional Review Boards; Intervention; Investigation; Knowledge; Label; Life; Light; Link; Lung; Machine Learning; Malignant Neoplasms; Maps; Massachusetts; Microscope; Microscopy; Mission; Modeling; Molecular; Molecular Profiling; Morphology; Multiomic Data; Mus; New England; Pattern; Phase; Property; Proteomics; Protocols documentation; Raman Spectrum Analysis; Regulation; Reporting; Research Personnel; Resolution; Scanning; Services; Signal Transduction; Skin; Specimen; Speed; Stress; Structure of parenchyma of lung; System; Technology; Testing; Time; Tissues; Training; Translations; United States National Institutes of Health; Universities; Whole Organism; aged; computational platform; computer framework; data integration; genome-wide; genomic biomarker; human tissue; imaging biomarker; imaging modality; imaging system; improved; in situ sequencing; in vivo; innovation; innovative technologies; lipidomics; medical schools; metabolomics; methylomics; microscopic imaging; molecular dynamics; mouse model; multimodal data; multimodality; multiple omics; new technology; novel; novel marker; predictive marker; senescence; single-cell RNA sequencing; tissue mapping; transcriptomics; tumor

PROJECT SUMMARY The molecular and cellular heterogeneity of senescent cells remains poorly characterized. The knowledge gap is mainly due to the lack of proper technology to characterize the cell states, types, and circuits in intact tissues. Thus, we will need novel technologies to map the multidimensional parameters of senescence across diverse tissue environments at molecular, cellular, and morphological levels and over longitudinal time frames. Single cell multi-omics and molecular profiling assays (e.g., single-cell RNA-seq, single-cell ATAC-seq, single-cell proteomics, methylomics, metabolomics) have opened new windows into understanding the properties, regulation, dynamics, and function of cells at unprecedented resolution and scale. However, these assays are inherently destructive. Cells need to be dissociated, fixed, or lysed for these molecular profiling assays. Raman microscopy offers a unique opportunity to comprehensively report on the vibrational energy levels of molecules in a label-free, nondestructive manner with subcellular spatial resolution. With recent advances in Raman microscopy, single-cell and spatial multi-omics, and machine learning, we have developed “Raman2RNA” (R2R), an experimental and computational framework to infer single-cell expression profiles in live cells through label- free hyperspectral Raman microscopy images combined with multi-modal data integration and domain translation. In this proposal, we aim to develop “SenNetRaman”, an innovative experimental and computational platform to character the molecular heterogeneity of senescent cells through label-free hyperspectral Raman microscopy, single cell and spatial genomics, and machine learning. In the UG3 phase, we aim to develop “SenNetRaman” for characterizing single cells in lung tissues corresponding to young, naturally aged or stress- induced senescence states from well-established mouse models. We will develop a high-throughput Raman microscopy system for label-free characterization of the molecular heterogeneity of senescent cells and identify Raman signals/markers predictive of gene expression and corresponding to various senescent cell states and types. In the UH3 phase, we will demonstrate “SenNetRaman” for characterizing senescent cells across multiple senescence model systems including human lungs, brains, and skins from an established human senescence tissue mapping center. Overall, “SenNetRaman” is a modular and universal framework to link imaging data with single-cell multi-omics data for building quantitative biomolecular tissue maps of human senescent cells. Our application is innovative in the approach to study senescence by leveraging the recent advances in imaging, single-cell genomics, and machine learning. The results of this project will help identify novel markers and reveal new biology of senescence. “SenNetRaman” builds upon the SenNet Initiative and can be readily adapted to existing NIH single-cell tissue mapping efforts, including the Human Tumor Atlas (HTAN), Human Biomolecular Atlas Program (HuBMAP), and Human Cell Atlas (HCA) that will transform future biomedical and clinical research.

项目总结 衰老细胞的分子和细胞异质性仍然没有得到很好的描述。知识鸿沟 主要是由于缺乏适当的技术来表征完整组织中的细胞状态、类型和电路。 因此，我们将需要新的技术来绘制不同类型的衰老的多维参数 分子、细胞和形态水平以及纵向时间范围内的组织环境。单人 细胞多组学和分子图谱分析(例如，单细胞RNA-seq、单细胞atac-seq、单细胞 蛋白质组学、甲基组学、代谢组学)为理解其性质打开了新的窗口， 以前所未有的分辨率和规模对细胞的调节、动态和功能。然而，这些化验是 本质上是破坏性的。这些分子图谱分析需要对细胞进行分离、固定或裂解。拉曼 显微镜提供了一个全面报告分子振动能级的独特机会 以亚细胞空间分辨率的无标记、无损的方式。随着拉曼光谱的最新进展 在显微镜、单细胞和空间多组学以及机器学习的基础上，我们开发出了“Raman2RNA”(R2R)， 一种通过标记来推断活细胞中单细胞表达谱的实验和计算框架 结合多模式数据集成和域的自由高光谱拉曼显微图像 翻译。在这个方案中，我们的目标是开发一种创新的实验和计算工具--SenNetmann 无标记高光谱拉曼表征衰老细胞分子异质性的平台 显微镜、单细胞和空间基因组学，以及机器学习。在UG3阶段，我们的目标是开发 用于表征肺组织中单个细胞与年轻、自然衰老或应激- 从成熟的小鼠模型中诱导衰老状态。我们将开发一种高通量拉曼 无标记鉴定衰老细胞分子异质性的显微系统 预测基因表达并对应于各种衰老细胞状态的拉曼信号/标记 类型。在UH3阶段，我们将演示用于表征多个 衰老模型系统包括人的肺、脑和已建立的人衰老的皮肤 组织测绘中心。总体而言，“SenNetmann”是一个模块化的通用框架，可以将成像数据与 用于构建人类衰老细胞定量生物分子组织图谱的单细胞多组学数据。我们的 应用在通过利用成像方面的最新进展来研究衰老的方法方面是创新的， 单细胞基因组学和机器学习。该项目的结果将有助于识别新的标记并揭示 衰老的新生物学。“SenNetmann”建立在SenNet倡议的基础上，可以很容易地适应 现有的NIH单细胞组织图谱工作，包括人类肿瘤图谱(Htan)、人类生物分子 Atlas计划(HuBMAP)和人类细胞图谱(HCA)将改变未来的生物医学和临床研究。