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Ultra-cheap and scalable epigenetic age predictions with TIME-Seq

View ORCID ProfilePatrick T Griffin, View ORCID ProfileAlice E Kane, View ORCID ProfileAlexandre Trapp, Jien Li, Maeve S McNamara, Margarita V Meer, View ORCID ProfileMichael R MacArthur, Sarah J Mitchell, View ORCID ProfileAmber L Mueller, Colleen Carmody, Daniel L Vera, View ORCID ProfileCsaba Kerepesi, View ORCID ProfileNicole Noren Hooten, View ORCID ProfileJames R Mitchell, Michele K Evans, Vadim N Gladyshev, View ORCID ProfileDavid A Sinclair
doi: https://doi.org/10.1101/2021.10.25.465725
Patrick T Griffin
1Blavatnik Institute, Dept. of Genetics, Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Boston, MA 02115 USA
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Alice E Kane
1Blavatnik Institute, Dept. of Genetics, Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Boston, MA 02115 USA
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Alexandre Trapp
2Brigham and Women’s Hospital, Division of Genetics, Department of Medicine, Harvard Medical School, Boston, MA, 02115 USA
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Jien Li
1Blavatnik Institute, Dept. of Genetics, Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Boston, MA 02115 USA
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Maeve S McNamara
1Blavatnik Institute, Dept. of Genetics, Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Boston, MA 02115 USA
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Margarita V Meer
2Brigham and Women’s Hospital, Division of Genetics, Department of Medicine, Harvard Medical School, Boston, MA, 02115 USA
3Yale University School of Medicine, Department of Pathology, New Haven, CT
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Michael R MacArthur
4Department of Health Sciences and Technology, ETH Zurich, Zurich 8005 Switzerland
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Sarah J Mitchell
4Department of Health Sciences and Technology, ETH Zurich, Zurich 8005 Switzerland
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Amber L Mueller
1Blavatnik Institute, Dept. of Genetics, Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Boston, MA 02115 USA
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Colleen Carmody
1Blavatnik Institute, Dept. of Genetics, Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Boston, MA 02115 USA
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Daniel L Vera
1Blavatnik Institute, Dept. of Genetics, Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Boston, MA 02115 USA
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Csaba Kerepesi
2Brigham and Women’s Hospital, Division of Genetics, Department of Medicine, Harvard Medical School, Boston, MA, 02115 USA
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Nicole Noren Hooten
5Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD
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James R Mitchell
4Department of Health Sciences and Technology, ETH Zurich, Zurich 8005 Switzerland
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Michele K Evans
5Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD
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Vadim N Gladyshev
2Brigham and Women’s Hospital, Division of Genetics, Department of Medicine, Harvard Medical School, Boston, MA, 02115 USA
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David A Sinclair
1Blavatnik Institute, Dept. of Genetics, Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Boston, MA 02115 USA
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  • For correspondence: david_sinclair@hms.harvard.edu
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ABSTRACT

Epigenetic “clocks” based on DNA methylation (DNAme) are the most robust and widely employed aging biomarker. They have been built for numerous species and reflect gold-standard interventions that extend lifespan. However, conventional methods for measuring epigenetic clocks are expensive and low-throughput. Here, we describe Tagmentation-based Indexing for Methylation Sequencing (TIME-Seq) for ultra-cheap and scalable targeted methylation sequencing of epigenetic clocks and other DNAme biomarkers. Using TIME-Seq, we built and validated inexpensive epigenetic clocks based on genomic and ribosomal DNAme in hundreds of mice and human samples. We also discover it is possible to accurately predict age from extremely low-cost shallow sequencing (e.g., 10,000 reads) of TIME-Seq libraries using scAge, a probabilistic age-prediction algorithm originally applied to single cells. Together, these methods reduce the cost of DNAme biomarker analysis by more than two orders of magnitude, thereby expanding and democratizing their use in aging research, clinical trials, and disease diagnosis.

Competing Interest Statement

P.T.G. and D.A.S. are named inventors on a patent application related to TIME-Seq methods filed by Harvard Medical School and licensed to Longevity Sciences. P.T.G. is an equity owner of Longevity Sciences. D.A.S is a consultant to, inventor of patents licensed to, and in some cases board member and investor in MetroBiotech, Cohbar, Life Biosciences and affiliates, Zymo, EdenRoc Sciences and affiliates, Alterity, InsideTracker, Immetas, Segterra, and Galilei Biosciences. He is also an inventor on patent applications licensed to Bayer Crops, Merck KGaA, and Elysium Health. Additional info at sinclair.hms.harvard.edu/david-sinclairs-affiliations. A.T., C.K, V.N.G. are named inventors on a patent application related to scAge filed by Brigham and Women's Hospital. S.J.M, A.E.K, M.R.M and J.R.M. have nothing to disclose.

Footnotes

  • ↵# Deceased

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 28, 2021.
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Ultra-cheap and scalable epigenetic age predictions with TIME-Seq
Patrick T Griffin, Alice E Kane, Alexandre Trapp, Jien Li, Maeve S McNamara, Margarita V Meer, Michael R MacArthur, Sarah J Mitchell, Amber L Mueller, Colleen Carmody, Daniel L Vera, Csaba Kerepesi, Nicole Noren Hooten, James R Mitchell, Michele K Evans, Vadim N Gladyshev, David A Sinclair
bioRxiv 2021.10.25.465725; doi: https://doi.org/10.1101/2021.10.25.465725
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Ultra-cheap and scalable epigenetic age predictions with TIME-Seq
Patrick T Griffin, Alice E Kane, Alexandre Trapp, Jien Li, Maeve S McNamara, Margarita V Meer, Michael R MacArthur, Sarah J Mitchell, Amber L Mueller, Colleen Carmody, Daniel L Vera, Csaba Kerepesi, Nicole Noren Hooten, James R Mitchell, Michele K Evans, Vadim N Gladyshev, David A Sinclair
bioRxiv 2021.10.25.465725; doi: https://doi.org/10.1101/2021.10.25.465725

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