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A Spatially Resolved Molecular Atlas of Human Endothelium

A Spatially Resolved Molecular Atlas of Human Endothelium
人类内皮细胞的空间分辨分子图谱
批准号：
10411809
负责人：
Long Cai
金额：
$10.0万
依托单位：
CALIFORNIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
项目状态：
已结题
起止时间：
2018-09-20 至 2022-06-30
来源：
https://reporter.nih.gov/project-details/10411809
关键词：
AddressAlgorithmsAreaAtlasesBiologicalBiological AssayCategoriesCellsCharacteristicsCollaborationsCollectionColorCommunitiesComputer AnalysisComputer softwareCytometryDataData SetDetectionDevelopmentDimensionsDiseaseEndotheliumGenomicsHealthHumanHuman BioMolecular Atlas ProgramImageImage AnalysisImmunofluorescence ImmunologicIndividualInvestigationLabelLibrariesMachine LearningMechanicsMethodsModelingMolecularNuclearPositioning AttributeProteomeProteomicsResearch PersonnelResolutionSemanticsSoftware ToolsSpottingsTechnologyTissue imagingTissuesTrainingUnited States National Institutes of HealthWorkbasecell typecrowdsourcingdata modelingdeep learningdesignfallsgenomic datahigh dimensionalityhuman imaginghuman tissuehuman-in-the-looplearning strategymembermultiplexed imagingnext generationopen sourceoperationpreservationsingle cell analysissingle moleculeterabytetranscriptometranscriptomicstwo-dimensional

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中文摘要
翻译
项目摘要 成像和基因组学的最新进展正在彻底改变我们对人体组织的理解。就像这些 两种技术变得更加相互交织和高吞吐量，对可伸缩性的需求越来越大 用单元格分辨率解释这些数据的方法。在本副刊中，我们寻求在正在进行的基础上 致力于为空间基因组数据的单细胞分析开发可扩展和准确的算法。我们建议 开发注解软件，用于注解单个细胞、它们的细胞类型和功能组织单元 多路传输的成像数据。该软件将是产生训练数据为下一代提供动力所必需的 用于分析多路传输成像数据的深度学习模型。
英文摘要
Project Abstract Recent advances in imaging and genomics are revolutionizing our understanding of human tissues. As these two technologies become more intertwined and high-throughput, there is an increasing need for scalable methods to interpret these data with single-cell resolution. In this Supplement, we seek to build on ongoing work to develop scalable and accurate algorithms for single-cell analysis of spatial genomics data. We propose to develop annotation software for annotating individual cells, their cell types, and functional tissue units in multiplexed imaging data. This software will be essential to produce training data to power the next generation of deep learning models for analyzing multiplexed imaging data.
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