Most people are familiar with the aging process in how it effects our outward appearance; graying hair, crow’s feet and wrinkles, limited mobility, and more. In a new study by Jude Phillip, the Institute for NanoBioTechnology’s newest core member and assistant professor in the Department of Biomedical Engineering, is taking a closer look at how age can impact the behavior of cells, specifically, the way in which they move around the body.

In previous research by Phillip, his team found that aging information is encoded within the biophysical properties of cells, or how the cells look, how they move, and the forces they generate. By looking at this information, researchers can determine a person’s age based on properties of individual cells. Once cellular age is determined, it can be linked to characteristics and defects related to the overall aging process and age-associated diseases. By identifying characteristics and defects in aging cells, researchers hope to make predicting the manifestation of age-related disease more effective.

In the new study published in Communications Biology, Phillip and his team wanted to examine what about a cell’s locomotion capabilities may change with age. With fibroblast samples taken from healthy individuals ages 2-92, they found that in the same way we may need the assistance of a walker or a loved ones’ arm to steady ourselves in our older years, individual cells move less and at slower speeds than younger cells. While on average, the researchers found decreased movement in older cells, it was not universal. By studying individual cell trajectories, Phillip’s team hypothesized that the decreased motility of aging cells is related to how cells that move less are distributed among cells that are more active.

“We found eight motility patterns and four activity patterns that describe all the cells, across all ages,” said Phillip. “We wanted to know how the representation of cells as a function of age and age groups were using those clusters.”

The analysis revealed that there was, indeed, a redistribution among the different activity patterns or motility states. Samples from young donors (2 – 20 years old) were described more frequently as fast moving with steady directionality, while samples from older donors (>65 years old) were described as slow moving and frequently changing direction. Interestingly, cells from donors between the ages of 35 and 65 did not seem to show a bias towards either end of the speed and directionality spectrums, which indicates there is a mixture of heterogeneity among them. Looking closer at the older cells’ deviations from younger cells’ healthy trends could yield new insight on the aging process.

“By studying the heterogeneity of cells, we hope to improve upon what we know about aging trends and with this new insight, potentially develop better biomarkers of aging,” said Phillip.

Story by Amy Weldon