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Interpretable Aging Signatures in Human Retinal Cell Types Revealed by Single-Cell RNA Sequencing and Sparse Logistic

Luning Yang1, Sen Lin1, Yiwen Tao1

  • 1Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo, China.

Ophthalmology Science
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

This study reveals cell-specific aging signatures in the human retina using machine learning. It identifies shared aging hallmarks and unique cellular vulnerabilities to guide future retinal aging therapies.

Keywords:
AgingMachine learningRetinaSingle-cell RNA sequencingTranscriptional regulation

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Area of Science:

  • Ophthalmology
  • Genomics
  • Computational Biology

Background:

  • Human retinal aging involves complex cellular and molecular changes.
  • Understanding cell-type-specific alterations is crucial for addressing age-related vision loss.

Purpose of the Study:

  • To characterize cell-type-specific transcriptional changes during human retinal aging.
  • To develop a machine learning (ML) model for discriminating cellular age in a Chinese cohort.

Main Methods:

  • Single-cell RNA sequencing of 223,612 cells from young and old Chinese donors.
  • Identification of age-related signatures using differentially expressed genes (DEGs).
  • Development of ML classifiers and analysis of transcription factor (TF) regulon activity.

Main Results:

  • Eleven major retinal cell populations were identified with distinct aging patterns.
  • Machine learning models achieved 80-96% accuracy in classifying cellular age.
  • Shared aging signatures included mitochondrial dysfunction and inflammation; cell-specific vulnerabilities were identified in rods, cones, and horizontal cells.

Conclusions:

  • This study presents the first ML-derived, cell-type-specific aging signatures for the human retina in a Chinese cohort.
  • Findings reveal conserved aging hallmarks and distinct cellular vulnerabilities.
  • These insights can inform targeted therapeutic strategies for retinal aging.