| Autor: |
Song, Qi, Singh, Alex, McDonough, John E., Adams, Taylor S., Vos, Robin, De Man, Ruben, Myers, Greg, Ceulemans, Laurens J., Vanaudenaerde, Bart M., Wuyts, Wim A., Yan, Xiting, Schuppe, Jonas, Hagood, James S., Kaminski, Naftali, Bar-Joseph, Ziv |
| Zdroj: |
PLoS Computational Biology; 12/19/2024, Vol. 20 Issue 12, p1-20, 20p |
| Abstrakt: |
Age prediction based on single cell RNA-Sequencing data (scRNA-Seq) can provide information for patients' susceptibility to various diseases and conditions. In addition, such analysis can be used to identify aging related genes and pathways. To enable age prediction based on scRNA-Seq data, we developed PolyEN, a new regression model which learns continuous representation for expression over time. These representations are then used by PolyEN to integrate genes to predict an age. Existing and new lung aging data we profiled demonstrated PolyEN's improved performance over existing methods for age prediction. Our results identified lung epithelial cells as the most significant predictors for non-smokers while lung endothelial cells led to the best chronological age prediction results for smokers. Author summary: Aging is characterized by several changes at the cellular and molecular levels. The type and rate of these changes varies between individuals and does not always correspond to their chronological age. Determining the 'molecular age' of an individual can therefore provide critical information about their susceptibility to various diseases and enable better treatment via personalized medicine. With accumulated data profiling the gene expressions for human lungs at various ages, we have developed machine learning methods to learn the individual molecular age. As we show, the transcriptome in an individual lung allows our method to accurately predict chronological age of the donor. We identified different cell types that correlate well with aging and showed that these cells consistently display aging artifacts across individuals and datasets. Specifically, we found that lung epithelial cells provide the best aging predictions for non-smokers and endothelial/smooth muscle cells as the best aging predictor for smokers. Our approach further revealed important apoptotic genes involved in the aging process of lung tissue. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
