DNA methylation is an important epigenetic mark spanning all of life's kingdoms. In humans, DNA methylation has been associated with a wide range of age-related pathologies, including type II diabetes and cancer. More recently, in humans, changes in DNA methylation at specific positions in the genome have been found to be predictive of chronological age. Interestingly, DNA methylation age is also predictive of health status and time-to-death. A better understanding of what these DNA methylation changes represent and whether they might be causative in the ageing process will be important to ascertain. However, at present there is no animal model system with which this process can be studied at a mechanistic level. Furthermore, it is becoming increasingly apparent that many disease states that increase in prevalence with age are not caused by all cells within the individual, but are often the result of changes to a subset of cells. This underscores the importance of studying these processes at the single cell level. The recent advances in single cell sequencing approaches now mean that we can study multiple layers of biology within the same single cell, such as the epigenome and the transcriptome (scM&T-Seq). Unfortunately, we are still only able to probe these important aspects of single cell biology in a static sense. This is a major limitation in the study of ageing because ageing and age-related disease processes are inherently dynamic. As such, it is incumbent upon us to develop approaches to assay single cell biology in a dynamic manner. 
In this thesis, I describe an epigenetic age predictor in the mouse. This predictor is tissue-independent and can accurately predict age (with an error of 3.33 weeks) and can record deviations in biological age upon interventions including ovariectomy and high fat diet both of which are known to reduce lifespan. Next, I describe the analysis of a homogeneous population of muscle satellite cells (MuSCs) that I have interrogated at the single cell level, using single cell combined transcriptome and methylome sequencing (scM&T-seq). I found that with age there was increased global transcriptional variability and increased feature-specific methylome variability. These findings explain the loss of functionality of these cells with age. Lastly, I describe two imaging approaches to study DNA methylation dynamically in single cells. Using these methods, I demonstrate that it is possible to accurately determine methylation status across a wide spectrum of global methylation levels and that by using such approaches novel information about dynamic methylation processes can be obtained. These methods represent the first to study DNA methylation dynamically.