Platform-independent estimation of human physiological time from single blood samples.
Platform-independent estimation of human physiological time from single blood samples.
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DOI：
10.1073/pnas.2308114120
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发表时间：
2024-01-16
影响因子：
11.1
通讯作者：
Braun, Rosemary
中科院分区：
文献类型：
--
作者：
Huang, Yitong;Braun, Rosemary
The importance of circadian rhythm in health is evident in studies from metabolic disorders to Alzheimer’s disease. However, translating these observations to the clinic remains stymied due to the burden of measuring physiological time. Methods to assess physiological time using blood biomarkers address this issue, but they must be accurate and generalizable across protocols and platforms. Here, we present TimeMachine, an algorithm that can estimate the circadian phase from a single blood sample. Validation on four distinct datasets shows TimeMachine accurately recovers phase estimates from a single-timepoint gene expression profile of human peripheral blood mononuclear cells across varied protocols and technologies, an important advance over current methods. This algorithm offers a feasible approach for incorporating circadian biomarkers in research and clinical care. 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 TimeMachine, 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, TimeMachine predicted circadian time with a median absolute error ranging from 1.65 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 TimeMachine against competing approaches and identify the algorithmic features that contribute to its performance.
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影响因子：
12.3
作者：
Hughey JJ
通讯作者：
Hughey JJ
影响因子：
8.8
作者：
Maas MB;Iwanaszko M;Lizza BD;Reid KJ;Braun RI;Zee PC
通讯作者：
Zee PC
影响因子：
13.9
作者：
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通讯作者：
Takahashi JS
DOI：
10.1126/science.aax7621
发表时间：
2019-08-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
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通讯作者：
Hogenesch JB
影响因子：
3.5
作者：
Hughes ME;Hogenesch JB;Kornacker K
通讯作者：
Kornacker K