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:
- 金额:$ 18.09万
- 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:2023-08-03 至 2025-07-31
- 项目状态:未结题
- 来源:
- 关键词:AgingAlgorithmsBioinformaticsBiological AssayCOVID-19 patientCellsClinicClinicalCollectionComputer AnalysisConsumptionCytometryDataDendritic CellsDiagnostic testsDiseaseFRAP1 geneFaceFoundationsFrequenciesGraphImmuneImmune signalingImmune systemImmunologic StimulationImmunologicsInterferonsJointsLearningLeftLigandsLinkMachine LearningManualsMapsMeasurementMethodsModelingOutcomePatientsPopulationProductionResearchRunningSamplingSignal PathwaySignal TransductionTechnologyTherapeutic InterventionTimeToll-like receptorsTraumaVaccine DesignVirus DiseasesWorkautomated analysiscell typeclinical phenotypeclinical predictive modelclinical predictorsclinically relevantcohortcombinatorialcytokineexperimental studyfrontierfunctional disabilityimmunoregulationinnovationlearning strategynovelpost SARS-CoV-2 infectionprotein biomarkersresponse
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
项目成果
期刊论文数量(0)
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