Automating the Discovery of Clinically-Relevant Intracellular Signaling Responses in Immune Cell-Types

自动发现免疫细胞类型中临床相关的细胞内信号转导反应

• 批准号: 10741148
• 负责人: Natalie M Stanley
• 金额: $ 18.09万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-03 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10741148
• 关键词: Aging; Algorithms; Bioinformatics; Biological Assay; COVID-19 patient; Cells; Clinic; Clinical; Collection; Computer Analysis; Consumption; Cytometry; Data; Dendritic Cells; Diagnostic tests; Disease; FRAP1 gene; Face; Foundations; Frequencies; Graph; Immune; Immune signaling; Immune system; Immunologic Stimulation; Immunologics; Interferons; Joints; Learning; Left; Ligands; Link; Machine Learning; Manuals; Maps; Measurement; Methods; Modeling; Outcome; Patients; Population; Production; Research; Running; Sampling; Signal Pathway; Signal Transduction; Technology; Therapeutic Intervention; Time; Toll-like receptors; Trauma; Vaccine Design; Virus Diseases; Work; automated analysis; cell type; clinical phenotype; clinical predictive model; clinical predictors; clinically relevant; cohort; combinatorial; cytokine; experimental study; frontier; functional disability; immunoregulation; innovation; learning strategy; novel; post SARS-CoV-2 infection; protein biomarkers; response

Project Summary Single-cell immune profiling technologies, such as cytometry by time of flight (CyTOF) enable broad and comprehensive characterization of diverse immune cell-types. Moreover, such technologies are being increasingly applied in clinical settings to gain a holistic view of the immune system. Ex vivo stimulation is a common perturbation applied to immune cells and assayed through CyTOF, which elicits functional responses that may be clinically predictive. Such experiments generate single-cell measurements for a large number of cells, causing manual analysis to become time-consuming and biased towards studying immune cell-types and their functional responses that have already been well-characterized. Existing bioinformatics approaches for automating manual analysis are limited in that they 1) primarily focus only on partitioning cells into cohesive cell-populations, 2) need to be run independently per stimulation and 3) produce several immunological features encoding cell-type specific functional responses to stimulation that are not indicative of canonical immune signaling pathways. In this proposal, we introduce a fully automated approach for automating the analysis of multi-sample, multi-stimulation immune profiling data. In particular, we shall develop algorithms to efficiently identify clinically-predictive functional responses to stimulation in a scalable manner to enable analysis of large clinical cohorts under several stimulation conditions. Uncovered functional responses that are clinically predictive can be used to develop diagnostic tests or to design vaccines to elicit particular cellular responses.

项目摘要 单细胞免疫图谱技术，如飞行时间细胞术(CyTOF)，使广泛的和 对不同免疫细胞类型的全面表征。此外，这样的技术正在被 越来越多地应用于临床环境中，以获得对免疫系统的整体看法。体外刺激是一种 应用于免疫细胞的常见扰动，并通过细胞周期分析，引起功能反应 这可能是临床上的预测。这样的实验产生了大量的单细胞测量 细胞，导致人工分析变得耗时并偏向于研究免疫细胞类型 以及它们的功能反应已经被很好地描述了。现有的生物信息学方法 自动手动分析的局限性在于：1)它们主要集中在将细胞划分为有凝聚力的 细胞群，2)每次刺激需要独立运行，3)产生几种免疫 编码细胞类型对刺激的特定功能反应的特征，而不是典型的 免疫信号通路。在本提案中，我们引入了一种完全自动化的方法来自动化 多样本、多刺激免疫图谱数据分析。特别是，我们将开发算法来 以可扩展的方式高效识别对刺激的临床预测性功能反应，以实现 几种刺激条件下的大型临床队列分析。未涵盖的功能性反应是 临床预测性可用于开发诊断测试或设计疫苗以诱导特定的细胞 回应。