qMAP enabled microanatomical mapping of human skin aging.
| Title: | qMAP enabled microanatomical mapping of human skin aging. |
|---|---|
| Authors: | Han KS; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Sander IB; Department of Dermatology, Johns Hopkins University, Baltimore, MD.; Kumer J; Department of Illustration Practice, Maryland Institute College of Art, Baltimore, MD.; Resnick E; Department of Medicine, University of Maryland School of Medicine, Baltimore, MD.; Booth C; Center for Cancer Research, National Cancer Institute, Frederick, MD.; Cheng G; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Im Y; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Starich B; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Kiemen AL; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD.; Phillip JM; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD.; Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD.; Reddy S; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Department of Plastic and Reconstructive Surgery, Johns Hopkins School of Medicine, Baltimore, MD.; Joshu CE; Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD.; Sunshine JC; Department of Dermatology, Johns Hopkins University, Baltimore, MD.; Walston JD; Department of Medicine, Division of Geriatrics and Gerontology, Johns Hopkins School of Medicine, Baltimore, MD.; Wirtz D; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD.; Wu PH; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD. |
| Source: | BioRxiv : the preprint server for biology [bioRxiv] 2024 Jul 06. Date of Electronic Publication: 2024 Jul 06. |
| Publication Type: | Journal Article; Preprint |
| Language: | English |
| Journal Info: | Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE |
| Abstract: | Aging is a major driver of diseases in humans. Identifying features associated with aging is essential for designing robust intervention strategies and discovering novel biomarkers of aging. Extensive studies at both the molecular and organ/whole-body physiological scales have helped determined features associated with aging. However, the lack of meso-scale studies, particularly at the tissue level, limits the ability to translate findings made at molecular scale to impaired tissue functions associated with aging. In this work, we established a tissue image analysis workflow - quantitative micro-anatomical phenotyping (qMAP) - that leverages deep learning and machine vision to fully label tissue and cellular compartments in tissue sections. The fully mapped tissue images address the challenges of finding an interpretable feature set to quantitatively profile age-related microanatomic changes. We optimized qMAP for skin tissues and applied it to a cohort of 99 donors aged 14 to 92. We extracted 914 microanatomic features and found that a broad spectrum of these features, represented by 10 cores processes, are strongly associated with aging. Our analysis shows that microanatomical features of the skin can predict aging with a mean absolute error (MAE) of 7.7 years, comparable to state-of-the-art epigenetic clocks. Our study demonstrates that tissue-level architectural changes are strongly associated with aging and represent a novel category of aging biomarkers that complement molecular markers. Our results highlight the complex and underexplored multi-scale relationship between molecular and tissue microanatomic scales. |
| Competing Interests: | Conflict of Interest The authors declare no financial/commercial conflicts of interest. |
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| Grant Information: | UH3 CA275681 United States CA NCI NIH HHS; P30 AG021334 United States AG NIA NIH HHS; U54 CA268083 United States CA NCI NIH HHS; U01 AG060903 United States AG NIA NIH HHS; UG3 CA275681 United States CA NCI NIH HHS; U54 AR081774 United States AR NIAMS NIH HHS; U54 CA143868 United States CA NCI NIH HHS |
| Entry Date(s): | Date Created: 20240715 Latest Revision: 20251004 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC11244916 |
| DOI: | 10.1101/2024.04.03.588011 |
| PMID: | 39005293 |
| Database: | MEDLINE |
Journal Article; Preprint