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qMAP enabled microanatomical mapping of human skin aging.
Kyu Sang Han1,2, Inbal B Sander3, Jacqueline Kumer4
1Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.
Biorxiv : the Preprint Server for Biology
|July 15, 2024
Summary
Researchers developed a new method, quantitative micro-anatomical phenotyping (qMAP), to analyze tissue images and identify aging features. Skin microanatomy can predict biological age, offering new biomarkers for aging research.
Area of Science:
- Gerontology
- Biomarkers
- Computational Biology
Background:
- Aging is a primary risk factor for human diseases, necessitating identification of aging features for interventions and biomarkers.
- Current research often focuses on molecular or whole-body scales, neglecting the crucial tissue (meso-scale) level, hindering translation of findings.
- Understanding tissue-level changes is vital for a comprehensive view of aging.
Purpose of the Study:
- To establish a tissue image analysis workflow for quantitatively profiling age-related microanatomical changes.
- To identify and validate novel aging biomarkers at the tissue microanatomical scale.
- To explore the multi-scale relationship between molecular and tissue microanatomy in aging.
Main Methods:
- Developed quantitative micro-anatomical phenotyping (qMAP), a deep learning and machine vision workflow for comprehensive tissue and cellular compartment labeling.
- Optimized qMAP for skin tissue analysis.
- Applied qMAP to a cohort of 99 donors (ages 14-92) and extracted 914 microanatomical features.
Main Results:
- Identified a broad spectrum of 914 microanatomical features, represented by 10 core processes, strongly associated with aging in skin tissues.
- Demonstrated that skin microanatomical features can predict chronological age with a mean absolute error (MAE) of 7.7 years, rivaling epigenetic clocks.
- Showcased significant correlations between tissue-level architectural changes and aging.
Conclusions:
- Tissue-level architectural changes are robust indicators of aging and represent a novel class of biomarkers.
- qMAP provides an interpretable feature set for quantitatively profiling age-related microanatomical changes.
- The study underscores the importance of meso-scale (tissue level) analysis in aging research and highlights its complementary role to molecular markers.
