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.

项目概要 衰老细胞的分子和细胞异质性仍然知之甚少。知识差距 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，我们需要新技术来绘制不同领域衰老的多维参数。 分子、细胞和形态水平以及纵向时间范围内的组织环境。单身的 细胞多组学和分子谱分析（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）为理解这些特性打开了新的窗口， 以前所未有的分辨率和规模研究细胞的调控、动力学和功能。然而，这些测定是 本质上具有破坏性。需要对细胞进行解离、固定或裂解才能进行这些分子分析测定。拉曼 显微镜提供了一个独特的机会来全面报告分子的振动能级 以无标记、非破坏性的方式，具有亚细胞空间分辨率。随着拉曼技术的最新进展 显微镜、单细胞和空间多组学以及机器学习，我们开发了“Raman2RNA”（R2R）， 一个实验和计算框架，通过标签推断活细胞中的单细胞表达谱 结合多模态数据集成和域的免费高光谱拉曼显微图像 翻译。在本提案中，我们的目标是开发“SenNetRaman”，一种创新的实验和计算方法 通过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 “SenNetRaman”用于表征肺组织中与年轻、自然衰老或压力相对应的单细胞 从成熟的小鼠模型中诱导衰老状态。我们将开发高通量拉曼 用于无标记表征衰老细胞分子异质性并识别的显微镜系统 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“SenNetRaman”，用于表征多个衰老细胞 衰老模型系统，包括来自已建立的人类衰老的人类肺、大脑和皮肤 组织绘图中心。总体而言，“SenNetRaman”是一个模块化的通用框架，可将成像数据与 用于构建人类衰老细胞定量生物分子组织图的单细胞多组学数据。我们的 该应用程序利用成像领域的最新进展，在研究衰老的方法上具有创新性， 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。 “SenNetRaman”建立在 SenNet Initiative 的基础上，可以很容易地适应 NIH 现有的单细胞组织图谱工作，包括人类肿瘤图谱 (HTAN)、人类生物分子 图谱计划 (HuBMAP) 和人类细胞图谱 (HCA) 将改变未来的生物医学和临床研究。