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Related Experiment Video

Updated: Jun 21, 2025

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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
PubMed
Summary
This summary is machine-generated.

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.

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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.