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

项目总结/文摘

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.