Back

Acute priming using elevated fluid viscosity recovers young-like single-cellsurveillance behaviors in aged human T cells

Dance, Y.; Amitrano, A.; Saffron, A.; Min, C.; Thompson, L.; Ge, Z.; Smith, I.; Macaluso, N.; Ezenwanne, C.; Milcik, N.; Fennell, A.; Pyndell, K.; Stroka, K.; Walston, J.; Sun, S.; Konstantopoulos, K.; Phillip, J. M.

2026-01-14 bioengineering
10.64898/2026.01.13.699374 bioRxiv
Show abstract

Aging is a complex biological process, often characterized by increased vulnerability to disease, infection, and death. This increased vulnerability is mechanistically linked to a progressive and functional decline of the immune system. In humans, aged lymphocytes lose their capacity to effectively surveil within diverse microenvironments, decreasing their capability for clearing infections and maintaining physiological homeostasis. However, specific mechanisms by which aged lymphocytes, specifically T cells, lose this capacity to surveil remain unclear. We profiled three core characteristics of T cell surveillance at single-cell resolution, specifically migration, deformability, and sensing. While aged T cells retained their capacity for spontaneous migration, they exhibited impaired cellular deformability and deficiencies in sensing local signaling cues. To modulate this surveillance defect, we performed mechanical reprogramming using elevated fluid viscosity. Results showed that acute priming of aged T cells with elevated fluid viscosity recovered a transient young-like surveillance phenotype, which was mechanistically linked to membrane tension, cortical F-actin, and Arp3 expression. These findings reveal a key source of surveillance defects in aged T cells and provide an effective mechanical approach to tuning their single-cell behaviors. TeaserRecovery of young-like surveillance phenotypes in aging human T cells via viscosity priming

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.