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

项目总结/摘要 细胞衰老是衰老的支柱，是衰老和年龄相关疾病的关键驱动力。 端粒在细胞衰老中起着重要作用。当端粒变得非常短， 一旦受损，它们就会引发DNA损伤反应（DDR），从而导致衰老。本实验室 开发了复杂的方法来检测组织中的衰老细胞， 端粒和DDR之间的联系此外，我们最近开发了SenoQuant软件， 这简化了端粒相关病灶（TAF）的测量，减少了从 几周到几小时我们现在建议应用机器学习等新兴技术， 通过深度学习，在人体组织切片中更准确、更稳健地映射TAF。这将 解决了TAF分析固有的几个挑战，包括细胞核检测，染色伪影， 和量化时间。此外，与组织映射中心合作，我们将 定制SenoQuant以分析特定的人体组织。最后，我们建议将TAF 与多重成像方法，如成像质谱细胞计数法（IMC），允许检测 多种衰老标志物同时存在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映射，并将成为 是衰老和细胞衰老社区的重要资源。

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.