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CellBiAge: Improved single-cell age classification using data binarization.

Doudou Yu1, Manlin Li2, Guanjie Linghu2

  • 1Molecular Biology, Cell Biology, and Biochemistry Graduate Program, Brown University, Providence, RI 02912, USA; Data Science Institute, Brown University, Providence, RI 02912, USA.

Cell Reports
|November 30, 2023
PubMed
Summary
This summary is machine-generated.

We developed CellBiAge, a machine learning tool to accurately predict single-cell age from mouse brain transcriptomics. This method improves aging research and helps evaluate rejuvenation strategies by analyzing cellular aging.

Keywords:
CP: Cell biologyagingbrainhypothalamusmachine learningsingle-cell RNA-seq

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Area of Science:

  • Computational Biology
  • Neuroscience
  • Genomics

Background:

  • Aging is a primary risk factor for numerous diseases, necessitating accurate methods to assess cellular age.
  • Understanding cellular aging heterogeneity and evaluating rejuvenation interventions requires precise age prediction at the single-cell level.
  • Classifying organismal age from single-cell transcriptomics presents challenges due to data sparsity and noise.

Purpose of the Study:

  • To develop a robust and user-friendly machine learning pipeline, CellBiAge, for classifying the age of single cells in the mouse brain using single-cell transcriptomics.
  • To enhance the accuracy of age prediction by investigating the impact of gene expression binarization on model performance.
  • To identify potential age-related genes contributing to accurate age classification and to assess the pipeline's utility in detecting rejuvenation.

Main Methods:

  • Development of CellBiAge, a machine learning pipeline for single-cell age classification.
  • Application of gene expression binarization to highly variable genes to improve model performance.
  • Testing the pipeline across diverse mouse brain regions, sexes, and using various machine learning models.
  • Validation of CellBiAge's ability to detect exercise-induced rejuvenation in neural stem cells.

Main Results:

  • Gene expression binarization significantly improved the test performance of age classification models across different conditions.
  • CellBiAge successfully classified the age of single cells in the mouse brain with high accuracy.
  • Potential age-related genes were identified, contributing to the predictive power of the models.
  • The pipeline demonstrated sensitivity in capturing exercise-induced rejuvenation effects in neural stem cells.

Conclusions:

  • CellBiAge offers a broadly applicable and robust approach for classifying the organismal age of single cells in the mouse brain.
  • This method can aid in dissecting the complexities of the aging process at a cellular level.
  • CellBiAge provides a valuable tool for evaluating the efficacy of rejuvenation strategies and interventions.