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