(174r) Defining Effects of Senescence on Single-Cell Motility States with Aging

Background. Aging is a physiological process characterized by an accumulation of damage and dysfunctions that increases the risk of disease^1,2. Census data indicates that the US population 65 years or older is to increase from ~16% to ~20% by 2050³. There is a need to develop robust aging biomarkers, which are encoded within the propensity of cells to move upon 2D substrates, that can be used to map baseline aging trajectories and improve precision medicine^4,5. In this study, we use ~6,000 single-cell trajectories over baseline and senescence conditions to characterize cell motility states with age.

Cellular senescence is a state of cell cycle arrest during which cells are unable to proliferate while secreting pro-inflammatory factors known as senescence-associated secretory phenotype (SASP)⁶. In humans, senescent cells accumulate with age and are induced in response to stressors such as mitochondrial/DNA damage. We hypothesize that the observed decrease in cell motility in aging and matched senescent cell samples is not due to a population-wide decrease in cell motility but rather from a differential redistribution of cells among distinct cell motility patterns upon 2D microenvironments.

Methods. To elucidate the notions of redistribution in cell motility states in cells during aging, we will create a high-throughput motility reference that connects distinct single-cell aging trajectories and motility characteristics. A balanced cohort of healthy human dermal fibroblasts from individuals in age ranging from 20 to 90 years old, both male and female, were seeded upon Collagen-I coated plates. These were imaged using live-cell confocal microscopy for 8 hours in 5-minute intervals at 10x magnification. To evaluate the impact of stressors on cell motility, senescence was induced on biological replicates of the cells. Cells were exposed to a 600 µM H2O2 media for 4 hours and then returned to standard high-glucose DMEM for 7 days to induce mitochondrial dysfunction. These cells were also imaged under the same conditions. The movies were then analyzed with the commercial tracking software Metamorph to gather the x-y-coordinates of individual cells. The coordinates were then analyzed under APRW parameters to elucidate key motility parameters and with CaMI⁷ which is a computational pipeline that utilizes single-cell motility data to identify and classify spatio-temporal behaviors of single cells.

Results. Preliminary experiments conducted on 2D collagen surfaces have indicated the effect of aging on motility trends. Current data shows that there is a decrease in the displacement of cells prior to their persistence with aging and occurs in a differential shift. We observe an overall redistribution in cell motility states in aging and matched senescence cell samples. With this, we are gaining a single-cell level analysis of the age-associated patterns of cell motility and the impact of cellular stressors on the cell response as a function of its motility state.

Conclusion. From this work, we are building a framework to classify subtypes of senescence response with potential applications in improving our understanding of cellular determinants of aging and precision medicine.

[1] Phillip, et al., Annual Review of Biomedical Engineering (2015) [2] Vigetti, et al., Journal of Biological Chemistry (2008) [3] U.S. Department of Health and Human Services (2020) [4] López-Otín, et al., Cell (2013) [5] Phillip, et al., Nature Biomedical Engineering (2017) [6] Lozano-Torres, et al., Nature Reviews Chemistry (2019) [7] Maity, et al., bioRxiv (2022)