AgingPrint
AgingPrint is a perturbation-informed immune aging clock that decomposes CD4+ T cell aging into three independent biological programs: pro-aging transcription, anti-aging transcription, and naive T cell composition.
Core trick
Weights aging-signature genes by CRISPRi knockdown phenocopy strength and splits the score into pro-aging, anti-aging, and naive-composition components.
Description
AgingPrint is a perturbation-informed immune aging clock that decomposes CD4+ T cell aging into three independent biological programs: pro-aging transcription, anti-aging transcription, and naive T cell composition. AgingPrint weights were derived from 305 genes associated with aging in two independent cohorts then weighted by causal evidence from genome-wide perturbation data. Existing clocks optimize weights to predict chronological age, then call the residual biological aging. AgingPrint inverts this –– the gap from chronological age is the finding, not the error.
Applied to 91 unseen donors, AgingPrint retains 98% of its performance. In healthy donors, biological immune age is normally distributed with a standard deviation of nearly 10 years meaning only 20% of “healthy” donors fall within two years of their chronological age. This variation appears to be genuine biology, not measurement error.
Applied to disease, the three-component attribution reveals mechanistic distinctions invisible to single-score clocks: lupus accelerates all three aging programs, tracking from managed disease to flare-ups. Severe COVID suppresses pro-aging transcription while depleting naive T cells. Alzheimer's shows almost no peripheral CD4+ T cell aging signal.
This framework extends to any immune cell type with matched CRISPRi screening data, enabling multi-cell-type biological aging assessment from a standard blood draw.
README excerpt
# AgingPrint **A perturbation-informed immune aging clock for CD4⁺ T cells.** AgingPrint predicts biological age from the transcriptome of CD4⁺ T cells and decomposes each prediction into interpretable components — transcriptional aging and shifts in cell composition. Unlike a purely statistical age predictor, its 305 genes are weighted by causal evidence from a genome-scale CRISPRi perturbation screen. --- ## Overview Most transcriptomic aging clocks are fit by regressing age on expression: they are accurate in-sample but brittle out-of-sample, and their weights are opaque. AgingPrint takes the opposite approach. It starts from an independently replicated CD4⁺ aging signature, weights each gene by how strongly its genome-scale CRISPRi knockdown *phenocopies* aging, and separates transcriptional aging from the naive→memory compositional shift that accompanies it. The result is a clock with only three free parameters fit to age, trained on 166 healthy donors (Terekhova et al. 2023), that reaches Pearson r = 0.82 and barely degrades when applied cold to a completely independent cohort. The pipeline is built in explicit layers, each adding one unit of biological reasoning, so the contribution of every choice is measurable: 1. **Baseline** — every expressed gene weighted by its raw age coefficient. 2. **Filtered** — keep only significantly age-associated genes. 3. **AgingPrint expression layer** — cross-cohort consensus + perturbation weighting. 4. **AgingPrint** — add the