Development of machine learning software to quantitatively map telomere induced senescence in tissue sections during aging

开发机器学习软件以定量绘制衰老过程中组织切片中端粒诱导的衰老图谱

• 批准号: 10907055
• 负责人: Joao Passos
• 金额: $ 72.2万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10907055
• 关键词: 3-Dimensional; Age; Aging; Atlases; Autophagocytosis; Biological; Biopsy; Cell Aging; Cell Communication; Cell Nucleus; Cells; Chronic; Collaborations; Communities; Computer software; Computing Methodologies; Cytometry; DNA Damage; Data; Dedications; Detection; Development; Disease; Emerging Technologies; Exclusion; Extracellular Space; Fluorescence Microscopy; Functional disorder; Generations; Hour; Human; Image; Impairment; Inflammation; Intervention; Knowledge; Laboratories; Length; Machine Learning; Maps; Measurement; Measures; Mediating; Methods; Morphologic artifacts; Mus; Nature; Obesity; Phenotype; Play; Process; Proteins; Reproducibility; Research; Resources; Role; Signal Transduction; Signaling Protein; Spatial Distribution; Stains; Suspensions; Technology; Time; Tissues; Treatment Efficacy; age related; clinical practice; deep learning; human tissue; imaging modality; innovation; innovative technologies; insight; learning network; microscopic imaging; mitochondrial dysfunction; multiplexed imaging; neural network; next generation; prognostic tool; response; senescence; single cell technology; technology validation; telomere; tissue mapping; tissue resource

PROJECT SUMMARY/ABSTRACT Cellular senescence is a pillar of aging, acting as a key driver of aging and age-related diseases. Telomeres play a major role in cellular senescence. When telomeres become critically short or damaged, they elicit a DNA damage response (DDR) that drives senescence. Our laboratory developed sophisticated methods to detect senescent cells in tissues based on the co-localization between telomeres and the DDR. Furthermore, we recently developed SenoQuant, a software that simplifies the measure of Telomere associated foci (TAF), reducing quantification time from weeks to hours. We now propose to apply emerging technologies such as machine learning and deep learning to map TAF more accurately and robustly in human tissue sections. This will address several challenges inherent to TAF analysis, including nuclei detection, staining artifacts, and quantification time. Furthermore, in collaboration with the Tissue Mapping Centers we will tailor SenoQuant to the analysis of specific human tissues. Finally, we propose to integrate TAF with multiplexed imaging methods such as Imaging Mass Cytometry (IMC), allowing the detection of multiple senescence markers in tissues simultaneously. We anticipate that this technology will greatly advance the spatially-resolved mapping of senescent cells in human tissues and will be a great resource for the aging and cell senescence community.

项目总结/文摘