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
基本信息
- 批准号:10684751
- 负责人:
- 金额:$ 55万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:ATAC-seqAgingAtlasesBiological AssayBiological ModelsBiologyBiomedical ResearchBrainCell AgingCell Culture TechniquesCell physiologyCellsClinical ResearchCollaborationsComplexConfocal MicroscopyDataDevelopmentDimensionsDiseaseDissociationEndoscopesEnvironmentFoundationsFutureGene ExpressionGeneral HospitalsGenetic TranscriptionGenomicsHeterogeneityHumanHuman BioMolecular Atlas ProgramImageIn VitroInflammatoryInstitutional Review BoardsInterventionInvestigationKnowledgeLabelLifeLightLinkLungMachine LearningMalignant NeoplasmsMapsMassachusettsMicroscopeMicroscopyMissionModelingMolecularMolecular ProfilingMorphologyMultiomic DataMusNew EnglandPatternPhasePropertyProteomicsProtocols documentationRaman Spectrum AnalysisRegulationReportingResearch PersonnelResolutionScanningServicesSignal TransductionSkinSpecimenSpeedStressStructure of parenchyma of lungSystemTechnologyTestingTimeTissuesTrainingTranslationsUnited States National Institutes of HealthUniversitiesWhole Organismagedcomputational platformcomputer frameworkdata integrationgenome-widegenomic biomarkerhuman tissueimaging biomarkerimaging modalityimaging systemimprovedin situ sequencingin vivoinnovationinnovative technologieslipidomicsmedical schoolsmetabolomicsmethylomicsmicroscopic imagingmolecular dynamicsmouse modelmultimodal datamultimodalitymultiple omicsnew technologynovelnovel markerpredictive markersenescencesingle-cell RNA sequencingtissue mappingtranscriptomicstumor
项目摘要
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
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Peter T. So其他文献
Plasmin Antagonizes Positive Feedback Between TGF-β1 and TSP1 : Steady States and Dynamics
- DOI:
10.1016/j.bpj.2011.11.3964 - 发表时间:
2012-01-31 - 期刊:
- 影响因子:
- 作者:
Lakshmi Venkatraman;Ser-Mien Chia;B.C. Narmada;Liang Siang Poh;Jacob K. White;Sourav Saha Bhowmick;C. Forbes Dewey;Peter T. So;Hanry Yu;Lisa Tucker-Kellogg - 通讯作者:
Lisa Tucker-Kellogg
Peter T. So的其他文献
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{{ truncateString('Peter T. So', 18)}}的其他基金
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识别
- 批准号:
10552453 - 财政年份:2022
- 资助金额:
$ 55万 - 项目类别:
ECI Advances in Optics for Biotechnology, Medicine and Surgery Conference
ECI 生物技术、医学和外科光学进展会议
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9396291 - 财政年份:2017
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$ 55万 - 项目类别:
Characterizing mechanisms of sickle cell crisis via dynamic optical assay
通过动态光学测定表征镰状细胞危机的机制
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8762091 - 财政年份:2014
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Characterizing mechanisms of sickle cell crisis via dynamic optical assay
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激光在医学和生物学 2008 年戈登研究会议
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7533625 - 财政年份:2008
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