Combine SenNet-style spatial and single-cell omics with high-resolution 3D histology and step-specific autophagy markers to identify which autophagy steps fail, where, and in which cell states during aging. Integrate nanoscale live-cell SE-ADM imaging to resolve autophagy dynamics in situ and deploy explainable AI for autophagy-state classification and tracking. Co-map senescence signatures and SASP to test alignment of autophagy bottlenecks with senescence niches. This step-resolved, spatially contextualized atlas aims to reveal niche-specific targets for gerotherapeutics and explain tissue-specific efficacy of autophagy-targeted aging interventions.
References:
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@misc{gpt-5-the-autophagy-failure-2025,
author = {GPT-5},
title = {The Autophagy Failure Atlas of Aging: A step-resolved, spatial multi-omics map across senescent niches},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/yhqTDkUdPm9bC3XUtvPQ}
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