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Updated: Dec 4, 2025
Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes
Published on: December 28, 2011
Cross-Sectional Transcriptional Analysis of the Aging Murine Heart
Matthew Greenig1, Andrew Melville2, Derek Huntley1
1Department of Life Sciences, Imperial College London, London, United Kingdom.
Insights
This study reveals how gene expression in the heart changes throughout a mouse's life. It identifies specific genes linked to aging and suggests aging involves both new and re-activated gene programs.
Area of Science:
- Cardiovascular Biology
- Genomics
- Aging Research
Background:
- Cardiovascular disease is a leading global cause of mortality.
- The relationship between aging and heart function requires further elucidation.
- A comprehensive understanding of cardiac aging across the lifespan is lacking.
Purpose of the Study:
- To investigate cardiac gene expression patterns throughout the murine lifespan.
- To identify genes differentially expressed during aging and specific age transitions.
- To synthesize a holistic view of cardiac aging from development to old age.
Main Methods:
- Repurposed RNA-sequencing data from wild-type mice across different age groups.
- Utilized DESeq2's generalized linear model for differential gene expression analysis.
- Applied unsupervised machine learning to identify gene expression trajectories.
Main Results:
- Identified genes associated with specific age transitions and the broader aging process.
- Discovered gene expression patterns with both linear and non-linear trajectories.
- Found evidence suggesting aging involves both unique and re-activated gene expression programs.
Conclusions:
- Cardiac aging is a complex process involving dynamic gene expression changes.
- Gene expression patterns across the lifespan offer insights into heart aging mechanisms.
- This transcriptomic analysis provides a comprehensive resource for cardiac aging research.
Abstract:
Cardiovascular disease accounts for millions of deaths each year and is currently the leading cause of mortality worldwide. The aging process is clearly linked to cardiovascular disease, however, the exact relationship between aging and heart function is not fully understood. Furthermore, a holistic view of cardiac aging, linking features of early life development to changes observed in old age, has not been synthesized. Here, we re-purpose RNA-sequencing data previously-collected by our group, investigating gene expression differences between wild-type mice of different age groups that represent key developmental milestones in the murine lifespan. DESeq2's generalized linear model was applied with two hypothesis testing approaches to identify differentially-expressed (DE) genes, both between pairs of age groups and across mice of all ages. Pairwise comparisons identified genes associated with specific age transitions, while comparisons across all age groups identified a large set of genes associated with the aging process more broadly. An unsupervised machine learning approach was then applied to extract common expression patterns from this set of age-associated genes. Sets of genes with both linear and non-linear expression trajectories were identified, suggesting that aging not only involves the activation of gene expression programs unique to different age groups, but also the re-activation of gene expression programs from earlier ages. Overall, we present a comprehensive transcriptomic analysis of cardiac gene expression patterns across the entirety of the murine lifespan.
