Multimodal data analysis reveals asynchronous aging dynamics across female reproductive organs.

Area of Science:

• Reproductive biology
• Gerontology
• Genomics

Background:

• Female reproductive aging has broad health impacts, but its tissue-level mechanisms are unclear.
• Understanding organ-specific aging is crucial for addressing late-life health concerns.

Purpose of the Study:

• To investigate tissue-level aging dynamics across female reproductive organs.
• To identify molecular and morphological changes associated with aging and menopause.

Main Methods:

• Integrated deep learning on 1,112 histology images and RNA sequencing from 659 samples across seven reproductive organs.
• Analyzed plasma proteomics data from a large cohort to validate findings.
• Utilized multi-omics factor analysis to link histological and gene-expression changes.

Main Results:

• Discovered asynchronous aging trajectories: gradual ovarian aging versus abrupt uterine shifts around menopause.
• Identified myometrium as highly age-affected, with extracellular matrix remodeling and immune activation.
• Observed coordinated epithelial remodeling, including a sharp menopausal transition in the vaginal epithelium.
• Linked histological changes to nonlinear gene expression shifts impacting reproductive traits like pelvic organ prolapse.

Conclusions:

• Menopause represents a key inflection point in female aging.
• Established a tissue-resolved, multi-dataset framework for studying female aging and late-life health.