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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

基本信息

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
项目类别：
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

项目摘要

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.

项目总结