Integrative analysis of spatial transcriptomics with histology images and single cells
空间转录组学与组织学图像和单细胞的综合分析
基本信息
- 批准号:10733815
- 负责人:
- 金额:$ 54.66万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-20 至 2027-07-31
- 项目状态:未结题
- 来源:
- 关键词:AddressBackBar CodesBasic ScienceBehaviorBenchmarkingCell CommunicationCellsCellular MorphologyClinicalClinical ResearchCollaborationsCompanionsComputer softwareDataData AnalysesData SetDetectionDiseaseEnvironmentEye diseasesFrontotemporal DementiaGene ExpressionGene Expression ProfilingGenesGenomicsHematoxylin and Eosin Staining MethodHistologicHistologyImageJointsKidney DiseasesKnowledgeLearningLocationMalignant neoplasm of lungMapsMeasuresMethodologyMethodsModelingMotivationPathologyPerformanceResearch PersonnelResolutionSpottingsStainsStrokeStructureTechnologyTissue imagingTissuesTrainingTranslationsVisioncell typecostdisease diagnosishuman diseasemachine learning algorithmmachine learning methodmalignant stomach neoplasmmethod developmentmultimodal datanext generation sequencingnovelopen sourcesingle-cell RNA sequencingspatial integrationtranscriptometranscriptome sequencingtranscriptomicsultra high resolutionvirtual
项目摘要
PROJECT SUMMARY
The
function.
relative
disease
tissues in our body consist of diverse cell t ypes with each cell type specialized to carry out a particular
The behavior of a cell is influenced by its surrounding environment within a tissue. Knowledge of the
locations of different cells in a tissue is critical for understanding the spatial organization of cell types and
pathology.Although single-cell RNA sequencing (scRNA-seq) has made it possible to characterize cell
types and states at an unprecedented resolution, the lack of physical relationships among cells has hindered the
study of cell-cell communications within tissue context. Recent technology advances in spatial transcriptomics
(ST) have enabled gene expression profiling while retaining location information in tissues. A popular ST
technology is based on spatial barcoding followed by next-generation sequencing in which transcriptome-wide
gene expression is measured in spatially barcoded spots. Data from such ST technologies often include a high-
resolution hematoxylin and eosin (H&E)-stained histology image of the tissue section from which the gene
expression data are obtained. Although ST is powerful, such data are still expensive to generate. On the other
hand, it is relatively cheaper to generate H&E-stained histology images and scRNA-seq data. The main
motivation of this project is to leverage information in ST to gain additional knowledge from the relatively easy-
to-obtain histology images and scRNA-seq data. Building upon our expertise in statistical genomics, we propose
to develop novel machine learning methods to address key computational challenges when performing
integrative analysis of ST, histology images, and single cells. Our methods will jointly model gene expression
and histology to characterize the spatial organization of tissues and predict spatial gene expression from
histology images. The resulting spatial map from these analyses will further enable the spatial mapping of single
cells back to tissues. The proposed methods will be applied to public data and data generated from ongoing
collaborations in various diseases to evaluate their performance. The successful completion of this project will
allow researchers to take advantage of advanced machine learning algorithms to integrate ST, histology, and
single-cell data to gain a holistic view of the spatial organization of tissues.
项目总结
这个
功能。
相对的
疾病
我们身体中的组织由不同的细胞类型组成,每种细胞类型专门执行特定的
细胞的行为受到组织内周围环境的影响。了解以下内容
组织中不同细胞的位置对于理解细胞类型的空间组织和
病理学。尽管单细胞RNA测序(scRNA-seq)已经使鉴定细胞成为可能
类型和状态以前所未有的分辨率,细胞之间缺乏物理关系阻碍了
研究组织环境中的细胞-细胞通讯。空间转录组学的最新技术进展
(ST)实现了基因表达谱分析,同时保留了组织中的位置信息。一条受欢迎的街
技术是基于空间条形码,然后是下一代测序,其中转录组范围
基因的表达是在空间条形码的点上测量的。来自这类ST技术的数据通常包括高度的
组织切片的分辨率苏木精-伊红(H&E)染色的组织学图像
获得表情数据。尽管ST功能强大,但生成此类数据的成本仍然很高。另一方面
另一方面,生成H&E染色的组织学图像和scRNA-seq数据相对便宜。主
该项目的动机是利用ST中的信息从相对容易的-
获取组织学图像和scRNA-seq数据。基于我们在统计基因组学方面的专业知识,我们建议
开发新的机器学习方法,以解决执行以下任务时的关键计算挑战
ST、组织学图像和单细胞的综合分析。我们的方法将联合模拟基因表达
和组织学来描述组织的空间组织并预测空间基因表达
组织学图像。通过这些分析得到的空间地图将进一步支持单个
细胞回到组织中。拟议的方法将适用于公共数据和正在进行的
在各种疾病方面的合作,以评估其表现。这一项目的成功完成将
允许研究人员利用高级机器学习算法将ST、组织学和
单细胞数据,以获得组织的空间组织的整体视图。
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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Mingyao Li其他文献
Mingyao Li的其他文献
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{{ truncateString('Mingyao Li', 18)}}的其他基金
The Penn Human Precision Pain Center (HPPC): Discovery and Functional Evaluation of Human Primary Somatosensory Neuron Types at Normal and Chronic Pain Conditions
宾夕法尼亚大学人类精准疼痛中心 (HPPC):正常和慢性疼痛条件下人类初级体感神经元类型的发现和功能评估
- 批准号:
10806545 - 财政年份:2023
- 资助金额:
$ 54.66万 - 项目类别:
Integrative analysis of bulk and single-cell RNA-seq data for cardiometabolic disease
心脏代谢疾病的批量和单细胞 RNA-seq 数据的综合分析
- 批准号:
10448317 - 财政年份:2021
- 资助金额:
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Computational and functional strategies to decipher lncRNAs in human atherosclerosis
破译人类动脉粥样硬化中 lncRNA 的计算和功能策略
- 批准号:
10347301 - 财政年份:2020
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Computational and functional strategies to decipher lncRNAs in human atherosclerosis
破译人类动脉粥样硬化中 lncRNA 的计算和功能策略
- 批准号:
10557797 - 财政年份:2020
- 资助金额:
$ 54.66万 - 项目类别:
Computational and functional strategies to decipher lncRNAs in human atherosclerosis
破译人类动脉粥样硬化中 lncRNA 的计算和功能策略
- 批准号:
10091516 - 财政年份:2020
- 资助金额:
$ 54.66万 - 项目类别:
Integrative analysis of bulk and single-cell RNA-seq data from human retina for age-related macular degeneration
对来自人类视网膜的大量和单细胞 RNA-seq 数据进行综合分析,以了解与年龄相关的黄斑变性
- 批准号:
10241966 - 财政年份:2020
- 资助金额:
$ 54.66万 - 项目类别:
Single-Cell Transcriptomic Analysis of Human Retina
人类视网膜的单细胞转录组分析
- 批准号:
10159930 - 财政年份:2019
- 资助金额:
$ 54.66万 - 项目类别:
Single-Cell Transcriptomic Analysis of Human Retina
人类视网膜的单细胞转录组分析
- 批准号:
10119528 - 财政年份:2019
- 资助金额:
$ 54.66万 - 项目类别:
Single-Cell Transcriptomic Analysis of Human Retina
人类视网膜的单细胞转录组分析
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9920150 - 财政年份:2019
- 资助金额:
$ 54.66万 - 项目类别:
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