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Sun ED, Zhou OY, Hauptschein M, Rappoport N, Xu L, Navarro Negredo P, Liu L, Rando TA, Zou J, Brunet A. Spatiotemporal transcriptomic profiling and modeling of mouse brain at single-cell resolution reveals cell proximity effects of aging and rejuvenation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.16.603809. [PMID: 39071282 PMCID: PMC11275735 DOI: 10.1101/2024.07.16.603809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk1. Brain aging is complex and accompanied by many cellular changes2-20. However, the influence that aged cells have on neighboring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in aging tissues have not yet been developed. Here, we generate spatiotemporal data at single-cell resolution for the mouse brain across lifespan, and we develop the first machine learning models based on spatial transcriptomics ('spatial aging clocks') to reveal cell proximity effects during brain aging and rejuvenation. We collect a single-cell spatial transcriptomics brain atlas of 4.2 million cells from 20 distinct ages and across two rejuvenating interventions-exercise and partial reprogramming. We identify spatial and cell type-specific transcriptomic fingerprints of aging, rejuvenation, and disease, including for rare cell types. Using spatial aging clocks and deep learning models, we find that T cells, which infiltrate the brain with age, have a striking pro-aging proximity effect on neighboring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating effect on neighboring cells. By developing computational tools to identify mediators of these proximity effects, we find that pro-aging T cells trigger a local inflammatory response likely via interferon-γ whereas pro-rejuvenating neural stem cells impact the metabolism of neighboring cells possibly via growth factors (e.g. vascular endothelial growth factor) and extracellular vesicles, and we experimentally validate some of these predictions. These results suggest that rare cells can have a drastic influence on their neighbors and could be targeted to counter tissue aging. We anticipate that these spatial aging clocks will not only allow scalable assessment of the efficacy of interventions for aging and disease but also represent a new tool for studying cell-cell interactions in many spatial contexts.
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Affiliation(s)
- Eric D. Sun
- Department of Biomedical Data Science, Stanford University, CA, USA
- Department of Genetics, Stanford University, CA, USA
| | - Olivia Y. Zhou
- Department of Genetics, Stanford University, CA, USA
- Stanford Biophysics Program, Stanford University, CA, USA
- Stanford Medical Scientist Training Program, Stanford University, CA, USA
| | | | | | - Lucy Xu
- Department of Genetics, Stanford University, CA, USA
- Department of Biology, Stanford University, CA, USA
| | | | - Ling Liu
- Department of Neurology, Stanford University, CA, USA
- Department of Neurology, UCLA, Los Angeles, CA, USA
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Biology, UCLA, Los Angeles, CA, USA
| | - Thomas A. Rando
- Department of Neurology, Stanford University, CA, USA
- Department of Neurology, UCLA, Los Angeles, CA, USA
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Biology, UCLA, Los Angeles, CA, USA
| | - James Zou
- Department of Biomedical Data Science, Stanford University, CA, USA
- These authors contributed equally: James Zou, Anne Brunet
| | - Anne Brunet
- Department of Genetics, Stanford University, CA, USA
- Glenn Center for the Biology of Aging, Stanford University, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, CA, USA
- These authors contributed equally: James Zou, Anne Brunet
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2
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Reichmann R, Schulze MB, Pischon T, Weikert C, Aleksandrova K. Biomarker signatures associated with ageing free of major chronic diseases: results from a population-based sample of the EPIC-Potsdam cohort. Age Ageing 2024; 53:ii60-ii69. [PMID: 38745490 DOI: 10.1093/ageing/afae041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND A number of biomarkers denoting various pathophysiological pathways have been implicated in the aetiology and risk of age-related diseases. Hence, the combined impact of multiple biomarkers in relation to ageing free of major chronic diseases, such as cancer, cardiovascular disease and type 2 diabetes, has not been sufficiently explored. METHODS We measured concentrations of 13 biomarkers in a random subcohort of 2,500 participants in the European Prospective Investigation into Cancer and Nutrition Potsdam study. Chronic disease-free ageing was defined as reaching the age of 70 years within study follow-up without major chronic diseases, including cardiovascular disease, type 2 diabetes or cancer. Using a novel machine-learning technique, we aimed to identify biomarker clusters and explore their association with chronic disease-free ageing in multivariable-adjusted logistic regression analysis taking socio-demographic, lifestyle and anthropometric factors into account. RESULTS Of the participants who reached the age of 70 years, 321 met our criteria for chronic-disease free ageing. Machine learning analysis identified three distinct biomarker clusters, among which a signature characterised by high concentrations of high-density lipoprotein cholesterol, adiponectin and insulin-like growth factor-binding protein 2 and low concentrations of triglycerides was associated with highest odds for ageing free of major chronic diseases. After multivariable adjustment, the association was attenuated by socio-demographic, lifestyle and adiposity indicators, pointing to the relative importance of these factors as determinants of healthy ageing. CONCLUSION These data underline the importance of exploring combinations of biomarkers rather than single molecules in understanding complex biological pathways underpinning healthy ageing.
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Affiliation(s)
- Robin Reichmann
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Tobias Pischon
- Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group, Berlin, Germany
- Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Biobank Technology Platform, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Core Facility Biobank, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Cornelia Weikert
- Department of Food Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Krasimira Aleksandrova
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
- Faculty of Human and Health Sciences, University of Bremen, Bremen, Germany
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3
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Silva N, Rajado AT, Esteves F, Brito D, Apolónio J, Roberto VP, Binnie A, Araújo I, Nóbrega C, Bragança J, Castelo-Branco P. Measuring healthy ageing: current and future tools. Biogerontology 2023; 24:845-866. [PMID: 37439885 PMCID: PMC10615962 DOI: 10.1007/s10522-023-10041-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/23/2023] [Indexed: 07/14/2023]
Abstract
Human ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.
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Affiliation(s)
- Nádia Silva
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Ana Teresa Rajado
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Filipa Esteves
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - David Brito
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Joana Apolónio
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
| | - Vânia Palma Roberto
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
| | - Alexandra Binnie
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Department of Critical Care, William Osler Health System, Etobicoke, ON, Canada
| | - Inês Araújo
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Clévio Nóbrega
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - José Bragança
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Pedro Castelo-Branco
- Algarve Biomedical Center Research Institute (ABC-RI), Campus Gambelas, Bld.2, 8005-139, Faro, Portugal.
- ABC Collaborative Laboratory, Association for Integrated Aging and Rejuvenation Solutions (ABC CoLAB), 8100-735, Loulé, Portugal.
- Faculty of Medicine and Biomedical Sciences (FMCB), University of Algarve, Gambelas Campus, Bld. 2, 8005-139, Faro, Portugal.
- Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal.
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Bernard D, Doumard E, Ader I, Kemoun P, Pagès J, Galinier A, Cussat‐Blanc S, Furger F, Ferrucci L, Aligon J, Delpierre C, Pénicaud L, Monsarrat P, Casteilla L. Explainable machine learning framework to predict personalized physiological aging. Aging Cell 2023; 22:e13872. [PMID: 37300327 PMCID: PMC10410015 DOI: 10.1111/acel.13872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/17/2023] [Accepted: 05/03/2023] [Indexed: 06/12/2023] Open
Abstract
Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter-parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty-six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age-specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow-up. These data show that PPA is a robust, quantitative and explainable ML-based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.
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Affiliation(s)
- David Bernard
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
- Université Toulouse 1 – Capitole, Institute of Research in Informatics (IRIT) of Toulouse, CNRSToulouseFrance
| | - Emmanuel Doumard
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
| | - Isabelle Ader
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
| | - Philippe Kemoun
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
- Oral Medicine Department and Hospital of ToulouseToulouse Institute of Oral Medicine and Science, CHU de ToulouseToulouseFrance
| | - Jean‐Christophe Pagès
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
- UFR Santé, Département Médecine, Institut Fédératif de Biologie, CHU de ToulouseToulouseFrance
| | - Anne Galinier
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
- UFR Santé, Département Médecine, Institut Fédératif de Biologie, CHU de ToulouseToulouseFrance
| | - Sylvain Cussat‐Blanc
- Université Toulouse 1 – Capitole, Institute of Research in Informatics (IRIT) of Toulouse, CNRSToulouseFrance
- Artificial and Natural Intelligence Toulouse Institute ANITIToulouseFrance
| | - Felix Furger
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
| | - Luigi Ferrucci
- Biomedical Research Centre, National Institute on AgingNIHBaltimoreMarylandUSA
| | - Julien Aligon
- Université Toulouse 1 – Capitole, Institute of Research in Informatics (IRIT) of Toulouse, CNRSToulouseFrance
| | | | - Luc Pénicaud
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
| | - Paul Monsarrat
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
- Oral Medicine Department and Hospital of ToulouseToulouse Institute of Oral Medicine and Science, CHU de ToulouseToulouseFrance
- Artificial and Natural Intelligence Toulouse Institute ANITIToulouseFrance
| | - Louis Casteilla
- RESTORE Research CenterUniversité de Toulouse, INSERM 1301, CNRS 5070, EFS, ENVTFrance
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5
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Silva N, Rajado AT, Esteves F, Brito D, Apolónio J, Roberto VP, Binnie A, Araújo I, Nóbrega C, Bragança J, Castelo-Branco P, Andrade RP, Calado S, Faleiro ML, Matos C, Marques N, Marreiros A, Nzwalo H, Pais S, Palmeirim I, Simão S, Joaquim N, Miranda R, Pêgas A, Sardo A. Measuring healthy ageing: current and future tools. Biogerontology 2023. [DOI: https:/doi.org/10.1007/s10522-023-10041-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/23/2023] [Indexed: 09/01/2023]
Abstract
AbstractHuman ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.
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6
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Escourrou E, Joyeux T, Guilhem M, Oustric S, Gardette V. How general practitioners perceive the aging trajectory of oldest-old - A qualitative study. BMC PRIMARY CARE 2023; 24:5. [PMID: 36624382 PMCID: PMC9829226 DOI: 10.1186/s12875-023-01964-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 01/02/2023] [Indexed: 01/11/2023]
Abstract
BACKGROUND A new population of older people is growing: the oldest-old. The care of the oldest-old (individuals aged 90 and over) is a new challenge in primary care. This study aimed to analyze the perception of General Practitioners (GP) on (1) the aging process of their patients up to a very advanced age, (2) how to adapt their practice to the care of these patients. METHODS We conducted a qualitative study using focus group (face to face) and individual (video call) interviews of GPs in southwest France. The sampling was purposive. We analyzed the interviews using an inductive approach based on the phases of thematic analysis. We used researchers' triangulation during the process. Collection was concluded when saturation was reached. RESULTS Three focus groups and one individual interview were conducted with a total of seventeen general practitioners. GP perception concerning aging and very advanced age were based on their personal experience and their daily clinical practice. Aging was perceived as an individual, unconscious, unpredictable and irreversible phenomenon. The shift towards "very old age" appeared inevitable. It could be a physical or psychological shift, or patients neglecting themselves or lacking a project. The care of the oldest-old became more specific and individual, adapted to the wishes of the patient. Those adaptations involve medical disengagement to focus on the most essential outcomes. The objectives of health care needed to be less strict with limited invasive practices. Prevention needed to focus mainly on prevention of falls and limitation of functional decline. CONCLUSION GPs identified an inevitable and unpredictable shift from old age to very old age. The adaption of the theory of disengagement allowed us to identify a medical disengagement of the GPs in the care of their oldest patients.
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Affiliation(s)
- Emile Escourrou
- grid.15781.3a0000 0001 0723 035XDépartement Universitaire de Médecine Générale, Faculté de Médecine Rangueil, Université Paul Sabatier Toulouse III, 133 route de Narbonne, 31400 Toulouse, France ,grid.15781.3a0000 0001 0723 035XMaintain Aging Research team, CERPOP, Université de Toulouse, Université Paul Sabatier, Inserm, Toulouse, France ,Maison de Santé Pluriprofessionnelle Universitaire La Providence, Toulouse, France
| | - Thomas Joyeux
- grid.15781.3a0000 0001 0723 035XDépartement Universitaire de Médecine Générale, Faculté de Médecine Rangueil, Université Paul Sabatier Toulouse III, 133 route de Narbonne, 31400 Toulouse, France
| | - Matthieu Guilhem
- grid.15781.3a0000 0001 0723 035XDépartement Universitaire de Médecine Générale, Faculté de Médecine Rangueil, Université Paul Sabatier Toulouse III, 133 route de Narbonne, 31400 Toulouse, France
| | - Stéphane Oustric
- grid.15781.3a0000 0001 0723 035XDépartement Universitaire de Médecine Générale, Faculté de Médecine Rangueil, Université Paul Sabatier Toulouse III, 133 route de Narbonne, 31400 Toulouse, France ,grid.15781.3a0000 0001 0723 035XMaintain Aging Research team, CERPOP, Université de Toulouse, Université Paul Sabatier, Inserm, Toulouse, France ,Maison de Santé Pluriprofessionnelle Universitaire La Providence, Toulouse, France
| | - Virginie Gardette
- grid.15781.3a0000 0001 0723 035XMaintain Aging Research team, CERPOP, Université de Toulouse, Université Paul Sabatier, Inserm, Toulouse, France ,grid.411175.70000 0001 1457 2980Centre Hospitalier Universitaire de Toulouse, Toulouse, France
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7
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Buckley MT, Sun ED, George BM, Liu L, Schaum N, Xu L, Reyes JM, Goodell MA, Weissman IL, Wyss-Coray T, Rando TA, Brunet A. Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain. NATURE AGING 2023; 3:121-137. [PMID: 37118510 PMCID: PMC10154228 DOI: 10.1038/s43587-022-00335-4] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 11/14/2022] [Indexed: 12/24/2022]
Abstract
The diversity of cell types is a challenge for quantifying aging and its reversal. Here we develop 'aging clocks' based on single-cell transcriptomics to characterize cell-type-specific aging and rejuvenation. We generated single-cell transcriptomes from the subventricular zone neurogenic region of 28 mice, tiling ages from young to old. We trained single-cell-based regression models to predict chronological age and biological age (neural stem cell proliferation capacity). These aging clocks are generalizable to independent cohorts of mice, other regions of the brains, and other species. To determine if these aging clocks could quantify transcriptomic rejuvenation, we generated single-cell transcriptomic datasets of neurogenic regions for two interventions-heterochronic parabiosis and exercise. Aging clocks revealed that heterochronic parabiosis and exercise reverse transcriptomic aging in neurogenic regions, but in different ways. This study represents the first development of high-resolution aging clocks from single-cell transcriptomic data and demonstrates their application to quantify transcriptomic rejuvenation.
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Affiliation(s)
- Matthew T Buckley
- Department of Genetics, Stanford University, Stanford, CA, USA
- Genetics Graduate Program, Stanford University, Stanford, CA, USA
| | - Eric D Sun
- Department of Genetics, Stanford University, Stanford, CA, USA
- Biomedical Informatics Graduate Program, Stanford University, Stanford, CA, USA
| | - Benson M George
- Stanford Medical Scientist Training Program, Stanford University, Stanford, CA, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Ling Liu
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Department of Neurology, UCLA, Los Angeles, CA, USA
| | - Nicholas Schaum
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Lucy Xu
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Jaime M Reyes
- Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Margaret A Goodell
- Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Irving L Weissman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Ludwig Center for Cancer Stem Cell Research and Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Tony Wyss-Coray
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Glenn Center for the Biology of Aging, Stanford University, Stanford, CA, USA
| | - Thomas A Rando
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Glenn Center for the Biology of Aging, Stanford University, Stanford, CA, USA
- Neurology Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Neurology, UCLA, Los Angeles, CA, USA
- Broad Stem Cell Research Center, UCLA, Los Angeles, CA, USA
| | - Anne Brunet
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Glenn Center for the Biology of Aging, Stanford University, Stanford, CA, USA.
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8
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An evaluation of aging measures: from biomarkers to clocks. Biogerontology 2022; 24:303-328. [PMID: 36418661 DOI: 10.1007/s10522-022-09997-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/21/2022] [Indexed: 11/25/2022]
Abstract
With the increasing number of aged population and growing burden of healthy aging demands, a rational standard for evaluation aging is in urgent need. The advancement of medical testing technology and the prospering of artificial intelligence make it possible to evaluate the biological status of aging from a more comprehensive view. In this review, we introduced common aging biomarkers and concluded several famous aging clocks. Aging biomarkers reflect changes in the organism at a molecular or cellular level over time while aging clocks tend to be more of a generalization of the overall state of the organism. We expect to construct a framework for aging evaluation measurement from both micro and macro perspectives. Especially, population-specific aging clocks and multi-omics aging clocks may better fit the demands to evaluate aging in a comprehensive and multidimensional manner and make a detailed classification to represent different aging rates at tissue/organ levels. This framework will promisingly provide a crucial basis for disease diagnosis and intervention assessment in geroscience.
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9
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Chen BH. Minimum standards for evaluating machine-learned models of high-dimensional data. FRONTIERS IN AGING 2022; 3:901841. [PMID: 36176975 PMCID: PMC9513121 DOI: 10.3389/fragi.2022.901841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022]
Abstract
The maturation of machine learning and technologies that generate high dimensional data have led to the growth in the number of predictive models, such as the “epigenetic clock”. While powerful, machine learning algorithms run a high risk of overfitting, particularly when training data is limited, as is often the case with high-dimensional data (“large p, small n”). Making independent validation a requirement of “algorithmic biomarker” development would bring greater clarity to the field by more efficiently identifying prediction or classification models to prioritize for further validation and characterization. Reproducibility has been a mainstay in science, but only recently received attention in defining its various aspects and how to apply these principles to machine learning models. The goal of this paper is merely to serve as a call-to-arms for greater rigor and attention paid to newly developed models for prediction or classification.
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Affiliation(s)
- Brian H. Chen
- FOXO Technologies Inc, Minneapolis, MN, United States
- The Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, United States
- *Correspondence: Brian H. Chen,
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