Fig. 5: CRISPR-epigenetic editing in human primary T cells.

a, Scheme of multiplexed epigenetic editing at five genomic regions that gain DNAm with aging. b, Scatter-plot of Illumina BeadChip data showing clear mean DNAm changes at FHL2 and KLF14 (n = 2). c,d, Gaussian kernel density estimates and cumulative distribution function showing significantly different methylation profiles at hyper- and hypo-CpGs (two-sided, two-sample Kolmogorov–Smirnov test P < 10⁻¹⁵). e, Scheme of multiplexed epigenetic editing at five genomic regions that lose DNAm with aging. f, Scatter-plot of Illumina BeadChip data revealing no mean methylation change after 10 days (n = 3). g,h, Gaussian kernel density estimates and cumulative distribution function. The genome-wide epigenetic landscape is less affected when targeting age-hypomethylated CpGs (10 days of culture). i,j, Estimation of epigenetic age with eight different epigenetic clocks:: Zhang³³, Vidal-Bralo³⁴, Lin⁴, Horvath 1 (multi-tissue)¹⁰, Horvath 2 (skin and blood)³⁵, Hannum²⁹ and PhenoAge and updated PhenoAge⁷. Bar plots depict the deviation of epigenetic age predictions upon targeted modification at the age-hypermethylated (i) and the age-hypomethylated CpGs (j), as compared to the scramble guide RNA controls (difference in years). k–n, Density plots of DNAm profiles for different strata of age-relatedness. Pearson correlation of individual CpGs with chronological age was determined in four T cell datasets^36,37,38,39. Upon targeting the hyper-CpGs or hypo-CpGs there is a clear enrichment of bystander modifications at sites with increasing positive (k,m), and negative (l,n) correlation with age (all P < 10⁻¹⁵, Kruskal–Wallis test).