Prof Tamir Chandra; Mayo Clinic
Tamir Chandra is Professor of Biomedical Informatics and Director of AI and Quantitative Biology at the Robert and Arlene Kogod Center on Aging, Mayo Clinic. His research addresses a central question in biology: how can population-scale molecular data reveal the mechanisms of human aging, and how can those mechanisms be translated into clinically actionable tools? His laboratory develops computational and statistical methods at the intersection of epigenomics, somatic mosaicism, and quantitative inference. Professor Chandra trained at the University of Cambridge and the Babraham Institute before holding a tenured faculty position at the University of Edinburgh, where he led work with the Generation Scotland and Lothian Birth Cohort resources. He joined Mayo Clinic in 2024. He holds two US provisional patents for clinical applications in clonal haematopoiesis detection and senescence burden prediction. He has trained10 PhD students and 5 postdoctoral fellows, three of whom hold faculty positions internationally.
Ageing is not a single process but an accumulation of molecular changes that unfold across decades, vary between individuals, and ultimately determine health trajectories. My laboratory uses DNA methylation as a lens through which to study these changes at scale, exploiting its dual role as a faithful recorder of cellular history and a dynamic readout of biological state. I will present three interconnected lines of work. First, I will describe how coordinated shifts in methylation across the genome can betray the presence of clonal cell populations expanding silently within an otherwise heterogeneous tissue, revealing a form of somatic evolution that standard sequencing approaches miss entirely. Second, I will show how the statistical architecture of methylation variation across individuals encodes information about haematopoietic stem cell population dynamics, allowing us to infer how individual stem cell pools have been stressed and reshaped over a lifetime. Finally, I will present evidence that DNA methylation carries a probabilistic signature of biological age that is mechanistically distinct from chronological time, reflecting not just how long a cell has existed, but how it has been altered by the forces of ageing itself. Together, these findings argue that the epigenome is not merely a correlate of ageing but a structured record of the stochastic and selective processes that drive it. Understanding this record, and learning to read it quantitatively, opens a window onto the biology of ageing that is both mechanistically grounded and measurable at the level of the individual.
Event Time & Dates
Event Details