Back

Attractor Landscape Analysis Distinguishes Aging Markers from Rejuvenation Targets in Human Keratinocytes

Copes, N.; Canfield, C.-A. E.

2026-02-03 bioinformatics
10.64898/2026.02.01.703159 bioRxiv
Show abstract

Cellular aging is characterized by progressive changes in gene expression that contribute to tissue dysfunction; however, identifying genes that regulate the aging process, rather than merely serve as biomarkers, remains a significant challenge. Here we present PRISM (Pseudotime Reversion via In Silico Modeling), a computational pipeline that integrates pseudotime trajectory analysis with Boolean network analysis to identify cellular rejuvenation targets from single-cell RNA sequencing data. We applied PRISM to a published dataset of human skin comprising 47,060 cells from nine donors aged 18 to 76 years. Analysis of keratinocytes revealed two distinct aging trajectories with fundamentally different regulatory architectures. One trajectory (labeled Y_272) exhibited "aging as convergence," where cells were driven toward a single dominant aged attractor (aging score +2.181). A second trajectory (labeled Y_308) exhibited "aging as departure," where cells escaped from a dominant youthful attractor basin (aging score -0.536). Systematic perturbation analysis revealed a critical distinction between genes exhibiting age-related expression changes (phenotypic markers) and genes controlling attractor landscape architecture (regulatory controllers). Switch genes marking the aging trajectories proved largely ineffective as intervention targets, while master regulators operating at higher levels of the regulatory hierarchy produced substantial rejuvenation effects. BACH2 knockdown was identified as the dominant intervention for Y_272, shifting the aging score by {Delta}= -3.746 (98.9% improvement). ASCL2 knockdown was identified as the top target for Y_308, with synergistic enhancement observed through combinatorial perturbation with ATF6. These findings demonstrate that attractor-based analysis identifies different and potentially superior therapeutic targets compared to expression-based approaches and provide specific hypotheses for experimental validation of cellular rejuvenation strategies in human skin.

Matching journals

The top 8 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.