Platform-independent estimation of human physiological time from single blood samples

• 成果类型: Article
• 署名作者: Huang, Yitong; Braun, Rosemary
• 署名单位: Northwestern University; Northwestern University; Northwestern University; Northwestern University; Northwestern University; The Santa Fe Institute
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12640
• DOI: 10.1073/pnas.230811412
• 发表日期: 2024-01-16
• 关键词: peripheral circadian clocks; gene-expression; rhythms; sleep; point

摘要

Abundant epidemiological evidence links circadian rhythms to human health, from heart disease to neurodegeneration. Accurate determination of an individual's circadian phase is critical for precision diagnostics and personalized timing of therapeutic interventions. To date, however, we still lack an assay for physiological time that is accurate, minimally burdensome to the patient, and readily generalizable to new data. Here, we present Time Machine, an algorithm to predict the human circadian phase using gene expression in peripheral blood mononuclear cells from a single blood draw. Once trained on data from a single study, we validated the trained predictor against four independent datasets with distinct experimental protocols and assay platforms, demonstrating that it can be applied generalizably. Importantly, Time Machine predicted circadian time with a median absolute error ranging from1.65 to 2.7 h, regardless of systematic differences in experimental protocol and assay platform, without renormalizing the data or retraining the predictor. This feature enables it to be flexibly applied to both new samples and existing data without limitations on the transcriptomic profiling technology (microarray, RNAseq). We benchmark Time Machine against competing approaches and identify the algorithmic features that contribute to its performance.