A transcriptome-based single-cell biological age model and resource for tissue-specific aging measures
- Shulin Mao1,2,7,
- Jiayu Su2,3,7,
- Longteng Wang2,4,
- Xiaochen Bo5,
- Cheng Li2,6 and
- Hebing Chen5
- 1Yuanpei College, Peking University, Beijing 100871, China;
- 2Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China;
- 3Department of Systems Biology, Columbia University, New York, New York 10032, USA;
- 4School of Life Sciences, Joint Graduate Program of Peking-Tsinghua-NIBS, Peking University, Beijing 100871, China;
- 5Institute of Health Service and Transfusion Medicine, Beijing 100850, China;
- 6Center for Statistical Science, Peking University, Beijing 100871, China
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↵7 These authors contributed equally to this work.
Abstract
Accurately measuring biological age is crucial for improving healthcare for the elderly population. However, the complexity of aging biology poses challenges in how to robustly estimate aging and interpret the biological significance of the traits used for estimation. Here we present SCALE, a statistical pipeline that quantifies biological aging in different tissues using explainable features learned from literature and single-cell transcriptomic data. Applying SCALE to the “Mouse Aging Cell Atlas” (Tabula Muris Senis) data, we identified tissue-level transcriptomic aging programs for more than 20 murine tissues and created a multitissue resource of mouse quantitative aging-associated genes. We observe that SCALE correlates well with other age indicators, such as the accumulation of somatic mutations, and can distinguish subtle differences in aging even in cells of the same chronological age. We further compared SCALE with other transcriptomic and methylation “clocks” in data from aging muscle stem cells, Alzheimer's disease, and heterochronic parabiosis. Our results confirm that SCALE is more generalizable and reliable in assessing biological aging in aging-related diseases and rejuvenating interventions. Overall, SCALE represents a valuable advancement in our ability to measure aging accurately, robustly, and interpretably in single cells.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.277491.122.
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Freely available online through the Genome Research Open Access option.
- Received November 19, 2022.
- Accepted July 12, 2023.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
