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: 10376395
• 负责人: Joao Passos
• 金额: $ 55.65万
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10376395
• 关键词: 3-Dimensional; Address; Age; Aging; Atlases; Biological; Biopsy; Cell Aging; Cell Nucleus; Cells; Chronic; Collaborations; Communities; Computer software; Computing Methodologies; Cytometry; DNA Damage; Data; Detection; Development; Disease; Emerging Technologies; Fluorescence Microscopy; Functional disorder; Generations; Hour; Human; Image; Impairment; Intervention; Knowledge; Laboratories; Length; Machine Learning; Maps; Measurement; Measures; Mediating; Methods; Mitochondria; Morphologic artifacts; Mus; Nature; Phenotype; Play; Process; Proteins; Research; Resources; Role; Signal Transduction; Signaling Protein; Spatial Distribution; Stains; Suspensions; Technology; Time; Tissues; Treatment Efficacy; age related; base; clinical practice; deep learning; extracellular; human tissue; imaging modality; innovation; innovative technologies; insight; learning network; microscopic imaging; multiplexed imaging; neural network; next generation; prognostic tool; response; senescence; single cell technology; telomere; 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.

项目摘要/摘要 细胞衰老是衰老的支柱，是衰老和衰老相关疾病的关键驱动因素。 端粒在细胞衰老过程中起着重要作用。当端粒变得非常短或 受损后，它们会引发DNA损伤反应(DDR)，从而推动衰老。我们的实验室 开发了基于共定位的复杂方法来检测组织中的衰老细胞 在端粒和DDR之间。此外，我们最近开发了SenoQuant软件 这简化了端粒相关灶(TAF)的测量，将量化时间从 几周到几个小时。我们现在建议应用新兴技术，如机器学习和 深度学习在人体组织切片中更准确和更稳健地标测TAF。这将是 解决TAF分析固有的几个挑战，包括核检测、染色伪影、 和量化时间。此外，我们将与组织测绘中心合作， 量身定做SenoQuant以分析特定的人体组织。最后，我们建议集成TAF 利用多路成像方法，如成像质量细胞术(IMC)，允许检测 多个衰老标记物同时存在于组织中。我们预计这项技术将 极大地推进了人体组织中衰老细胞的空间分辨图谱，并将成为 为衰老和细胞衰老社区提供极好的资源。