TIME-Seq Enables Scalable and Inexpensive Epigenetic Age Predictions

Abstract

Epigenetic "clocks" based on DNA methylation (DNAme) have emerged as the most robust and widely employed aging biomarkers, but conventional methods for applying them are expensive and laborious. Here, we develop Tagmentation-based Indexing for Methylation Sequencing (TIME-Seq), a highly multiplexed and scalable method for low-cost epigenetic clocks. Using TIME-Seq, we applied multi-tissue and tissue-specific epigenetic clocks to over 1,600 mouse DNA samples. We also discovered a novel approach for age prediction from shallow sequencing (e.g., 10,000 reads) by adapting scAge for bulk measurements. In benchmarking experiments, TIME-Seq performed favorably against prevailing methods and could quantify the effects of interventions thought to accelerate, slow, and reverse aging in mice. Finally, we built and validated a highly accurate human blood clock from 1,056 demographically representative individuals. Our methods increase the scalability and reduce the cost of epigenetic age predictions by more than 100-fold, enabling accurate aging biomarkers to be applied in more large-scale animal and human studies.