Fractional re-distribution among cell motility states during ageing

Abstract

Ageing in humans is associated with the decreased capacity to regulate cell physiology. Cellular properties, such as cell morphology and mechanics, encode ageing information, and can therefore be used as robust biomarkers of ageing. Using a panel of dermal fibroblasts derived from healthy donors spanning a wide age range, we observe an age-associated decrease in cell motility. By taking advantage of the single-cell nature of our motility data, we classified cells based on spatial and activity patterns to define age-dependent motility states. We show that the age-dependent decrease in cell motility is not due to the reduced motility of all cells, but results from the fractional re-distribution among motility states. These findings highlight an important feature of ageing cells characterized by a reduction of cellular heterogeneity in older adults relative to post-adolescent/adults. Furthermore, these results point to a mechanistic framework of ageing, with potential applications in deciphering emergent ageing phenotypes and biomarker development.

Targeted Therapeutics Delivery by Exploiting Biophysical Properties of Senescent Cells

“…[114] Recently, it was discovered that this overall decrease in cell motility is due to the fractional re-distribution of motility states of cells, rather than a decrease in cell motility of all cells. [143] Beyond these biophysical features above, various emerging methods have been developed for cellular biophysical characterization. [144][145][146] This will potentially open up possibilities for the discovery of novel and useful biophysical markers.…”

Profiling Dynamic Patterns of Single-cell Motility

“…To develop a computational framework to classify universal patterns and behaviors of single cells across multiple conditions, we combined existing and newly acquired cell motility datasets 8,15–20 . We tracked cells using MetaMorph, and x-y trajectories across all experimental conditions had total imaging durations ranging between 2.5 to 16 hours, with image acquisition intervals ranging from 2 to 10 minutes.…”

“…Using this new harmonized dataset ( Supplementary dataset 1 ), we asked whether we could identify distinct patterns of cell movements across all conditions. Using the 74,253 single-cells, we computed eight motility parameters based on the APRW model (see Methods ), which we previously showed to describe critical characteristic of cell movements 8,15,21 . These parameters include, short and long time-scale mean squared displacements (MSD10, MSD100), persistence times in the primary and secondary axes of migration (Pp, Pnp), total diffusivities and diffusivities in the primary and secondary axes of migration (Dtot, Dp, Dnp), and the anisotropic index that describes the spatial persistence (ϕ) (see Methods ).…”

“…However, conventional cell motility data captured in vitro or in vivo are often performed on small numbers of homogeneous cells and tend to be reported as averaged values for populations of cells, e.g ., average speeds and average displacements. While in some ways averaging motility parameters offer biological simplicity, it limits the ability to assess cellular heterogeneity and infer generalizable cell motility patterns that define single-cell behaviors per condition 8 .…”

Unravelling cell migration: defining movement from the cell surface

“…Whatsmore, we still need to fully understand how other biophysical factors, such as the stiffness and the microarchitecture of the ECM, influence migratory behaviors. Phillip and colleagues [ 372 ] recently study single-cell motility patterns of primary dermal fibroblasts from healthy donors. Their results demonstrated an age-associated decrease in overall cell migration and cellular heterogeneity.…”