(422c) Human Ageing at Cell Resolution

Ageing is inevitable, and while we gain one chronological year with the passing of each birthday, the biological outlook among individuals are quite variable. As we age, the physiology of our bodies change and eventually dysfunctions propagate that lead to the manifestations of age-associated defects and diseases. Recently, my lab has become very interested in these ageing transitions and the question of why we age differently. Why is it that among three eighty-five-year-old individuals, one can run a marathon, one is bedridden, and the other is in-between? Questions like these drive the notion that the chronological and biological ages of a person may be defined by distinct characteristics.

When one thinks of ageing, we can think of it in terms of scales, where each age-associated change can be linked to a corresponding length- scales, namely the molecular, cellular, clinical and epidemiological; and a corresponding time-scale. We have recently demonstrated that ageing information is encoded within biophysical properties of cells, and as such, can be used as robust biomarkers of ageing. This added insights stems from the fact that cells are integrators of molecular signals, with cellular dysfunctions likely preluding the manifestation of age-associated diseases. As such, cell-based measures of ageing may provide the lead time needed for therapeutic interventions and optimal course correction. Furthermore, since cells display dynamic phenotypes, both in health and disease, we postulated that analyzing cellular dynamics may provide significantly more mechanistic insights relative to the standard “snapshot” data, based on cell-based perturbation-responses.

Typically, cell motility is measured at single-cell resolution, however, they are reported as bulk parameters, e.g. average speeds and displacements. Here, we have developed a novel single-cell analysis approach that defines patterns of cell movements, and classifies them as a function of age-associated motility states. Using cell motility data of primary dermal fibroblasts derived from healthy donors spanning an age-range from 2-96 years old, we observe a shift in the phenotypic heterogeneity as a function of age, which was previously unseen based on standard motility analysis approaches. Our results revealed, that the global age-dependent decrease in bulk motility is not due to decreased motility for all cells, but rather from the fractional re-distribution among motility states. Using the anisotropic persistent random walk together with non-linear clustering analyses, we have defined spatial patterns that describe cell movements. From this analysis we found that, (1) Age does not solely define spatial clusters, (2) donors separate into polarized spatial clusters as a function of age, and (3) there is a significant decrease in the cellular heterogeneity (Entropy) for older adults. This work reveals that ageing information is encoded within the intrinsic cellular variations in motility. Furthermore, we have extended this methodology to analyze and classify cells across conditions in health and disease, and provide an innovative solution to quantify cellular dynamics.

Together, our study presents a general framework to analyze single-cell motility based on spatio-temporal patterns of cells. This analysis provides novel insights into the ageing process and beyond, with potential applications for precision health based on the development of phenotypic sensors for donor stratification in health and disease.