Data infrastructure for single-cell multiplex imaging in Bioconductor

Bioconductor 中单细胞多重成像的数据基础设施

• 批准号: 10831240
• 负责人: Sashank K Reddy
• 金额: $ 9.58万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-26 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10831240
• 关键词: Address; Atlases; Bioconductor; Cells; Characteristics; Computer software; Data; Data Analyses; Data Set; Diagnosis; Disease; Face; Gene Expression; Goals; Health; Human; Human BioMolecular Atlas Program; Imaging technology; Individual; Process; Prognosis; Proteins; Research Personnel; Signal Transduction; Technology; Tissue Sample; Tissues; cell type; complex data; data infrastructure; data standards; imaging platform; insight; multiplexed imaging; precision medicine; treatment response

Project Summary/Abstract Understanding the spatial landscape of gene expression in tissues is a fundamental question for human health. Applications range from identifying the spatial organization of cell types to dysregulation of spatial-dependent gene expression associated with disease. Advances in technologies, such as multiplex imaging (MI), provide a wealth of data to investigate these questions. Images from these platforms are all preprocessed to produce a tabular dataset for each tissue sample where each row is an individual and each column contains characteristics of that cell including spatial coordinates and protein / cell signaling expression. Despite these similarities, these data often are not used to their full potential by new users and software developers due to existing challenges, including a lack of a standardized data infrastructure across MI platforms to enable robust and modular downstream analyses. To address this, we propose to develop standardized data infrastructure for single-cell and spatial multiplex imaging data in Bioconductor scalable for the analysis of large atlas-scale data. This data infrastructure will allow users and developers to quickly and efficiently extract deeper insights from HuBMAP multiplex imaging data.

项目概要/摘要 了解组织中基因表达的空间景观是人类健康的一个基本问题。 应用范围从识别细胞类型的空间组织到空间依赖性的失调 与疾病相关的基因表达。多重成像 (MI) 等技术的进步提供了 大量的数据来调查这些问题。来自这些平台的图像都经过预处理以生成 每个组织样本的表格数据集，其中每行都是一个个体，每列包含特征 该细胞的特征包括空间坐标和蛋白质/细胞信号表达。尽管有这些相似之处，但这些 由于现有的挑战，新用户和软件开发人员通常无法充分利用数据的潜力， 包括缺乏跨 MI 平台的标准化数据基础设施来实现稳健和模块化 下游分析。为了解决这个问题，我们建议开发单细胞标准化数据基础设施 Bioconductor 中的空间多重成像数据可扩展用于分析大型图谱数据。这个数据 基础设施将允许用户和开发人员快速有效地从 HuBMAP 中提取更深入的见解 多重成像数据。