Cyril Lagger*
Kyiv School of Economics
clagger [at] kse.org.ua
Abstract
Ageing is the main risk factor for cancer, neurodegenerative diseases and cardiovascular diseases. Deciphering the mechanisms underlying this complex phenomenon may have tremendous biomedical and global health implications. Despite impressive advances over the last few decades, a major challenge remains to connect the diverse hallmarks of ageing across biological scales.
Technological and computational advances in single-cell omics, notably scRNA-seq, have enabled the characterisation of ageing tissues at unprecedented resolution. Yet, most analyses remain largely data-driven, focusing on identifying statistical patterns in high-dimensional data, often with limited integration of mechanistic principles. We argue that complementary approaches grounded in biophysical modelling and prior biological knowledge may help bridge descriptive observations and causal, interpretable mechanisms of ageing.
Along these lines, using published single-cell datasets, we investigated age-associated alterations in cell-cell communication networks and identified widespread rewiring of ligand-receptor interactions across tissues and cell types, including increased immune and inflammatory signalling and reduced extracellular matrix organisation. We then leveraged spike-in normalisation in the Tabula Muris Senis dataset to quantify age-related changes in absolute transcript abundance independently of transcriptome composition. This analysis revealed a widespread decline in total mRNA abundance across non-immune cell types, in contrast to increased transcript abundance in many immune populations, consistent with a broad decline in transcriptional and metabolic activity during ageing.
To further characterise changes in transcriptional dynamics, we are currently developing physics-informed inference methods of burst kinetic parameters from omics data. On one hand, we leverage established stochastic telegraph models to infer gene expression dynamics from ageing datasets. In parallel, we are developing novel regulatory frameworks inspired by stochastic thermodynamics to move beyond independent-gene models and integrate epigenetic regulation.
Together, these approaches illustrate how single-cell omics, combined with mechanistic and physics-informed modelling, may help bridge ageing mechanisms across molecular, cellular and tissue scales.
Keywords: ageing, scRNA-seq, communication, transcription, physics-informed