The transcriptome of a cell is an assessment of gene expression at a moment in time, specifically which genes have RNA transcripts under production, and the relative amounts of those transcripts. Like all such detailed cell data, the transcriptome changes with age in characteristic ways, a reaction to the presence of ever greater amounts of cell and tissue damage.
Those changes can be used to produce clocks that measure biological age, very similar to the more established epigenetic clocks based on DNA methylation. The transcriptome varies by cell type, and here researchers note that combining transcriptomes from different tissues produces a more accurate result than is the case for single tissue clocks.
Studying transcriptome chronological change from tissues across the whole body can provide valuable information for understanding aging and longevity. Although there has been research on the effect of single-tissue transcriptomes on human aging or aging in mice across multiple tissues, the study of human body-wide multi-tissue transcriptomes on aging is not yet available.
In this study, we propose a quantitative model to predict human age by using gene expression data from 46 tissues generated by the Genotype-Tissue Expression (GTEx) project. Specifically, the biological age of a person is first predicted via the gene expression profile of a single tissue. Then, we combine the gene expression profiles from two tissues and compare the predictive accuracy between single and two tissues.
The best performance as measured by the root-mean-square error is 3.92 years for single tissue (pituitary), which deceased to 3.6 years when we combined two tissues (pituitary and muscle) together. Different tissues have different potential in predicting chronological age. The prediction accuracy is improved by combining multiple tissues, supporting that aging is a systemic process involving multiple tissues across the human body.
Source: Fight Aging!