Temporal changes in the gene expression heterogeneity during brain development and aging
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Abstract
Cells in largely non-mitotic tissues such as the brain are prone to stochastic (epi-)genetic alterations that may cause increased variability between cells and individuals over time. Although increased inter-individual heterogeneity in gene expression was previously reported, whether this process starts during development or if it is restricted to the aging period has not yet been studied. The regulatory dynamics and functional significance of putative aging-related heterogeneity are also unknown. Here we address these by a meta-analysis of 19 transcriptome datasets from three independent studies, covering diverse human brain regions. We observed a significant increase in inter-individual heterogeneity during aging (20 + years) compared to postnatal development (0 to 20 years). Increased heterogeneity during aging was consistent among different brain regions at the gene level and associated with lifespan regulation and neuronal functions. Overall, our results show that increased expression heterogeneity is a characteristic of aging human brain, and may influence aging-related changes in brain functions.
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Ultrastructural studies of human tissues have shown similar results, and different human brain regions exhibit different gene expression trajectories with age.
“Ultrastructural studies of human tissues157 have shown similar results, and even different human brain regions exhibit different gene expression trajectories with age158.”
From Measuring biological age using omics data (2022) · cites this paper as [158]
Transcriptomic studies strongly agree that tissues, organs, and even sub-regions within an organ such as the brain age at different rates.
“As mentioned above, transcriptomic studies strongly agree that tissues, organs, and even sub-regions within an organ such as the brain age at different rates.32,33”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [33]
In humans, a meta-analysis of >300 brains and 17 brain regions showed broad non-linear changes in gene expression which varied across brain regions with age.
“In humans, a meta-analysis of >300 brains and 17 brain regions showed broad non-linear changes in gene expression which varied across brain regions with age.33”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [33]
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BackgroundMethodInfluentialSmurfness‐based two‐phase model of ageing helps deconvolve the ageing transcriptional signature
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“…increasing the gene expression heterogeneity in a variety of organisms, tissues and cell types (Bahar et al., 2006; Brinkmeyer-Langford et al., 2016; Enge et al., 2017; Işıldak et al., 2020 ; Kedlian et al., 2019; Martinez-Jimenez et al., 2017 ; Perez-Gomez et al., 2020; Somel et al., 2006) (ATH6).”
BackgroundSchneider, A. L., Martins-Silva, R., Kaizeler, A., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2023) · eLife · doi:10.7554/elife.88623 · Cited by 29 · see citing works
“…; Jozsi et al., 2000 , kidney Rodwell et al., 2004 , brain Galatro et al., 2017 ; Olah et al., 2018 ; Berchtold et al., 2008 ; Lu et al., 2004 ; I ş ıldak et al., 2020 , skin Haustead et al., 2016 ; Holzscheck et al., 2020 , blood Nakamura et al., 2012 ; Harries et al., 2011 , liver Thomas et…”
BackgroundCellBiAge: Improved single-cell age classification using data binarization
Yu, D., Li, M., Linghu, G., Hu, Y., Hajdarovic, K. H., Wang, A., Singh, R., & Webb, A. E. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113500 · Cited by 17 · see citing works
“36 Recent developments in single-cell ‘omics technologies enable the age prediction or classification of single cells using transcriptomics 8,37–39 and methylation profiles.”
Kokorudz, C., Radford, B. N., Dean, W., & Hemberger, M. (2022) · Cells · doi:10.3390/cells12010076 · Cited by 14 · see citing works
Kim, N., Ko, Y., Shin, Y., Park, J., Lee, A. J., Kim, K. W., & Pyo, J. (2023) · Biology · doi:10.3390/biology12070970 · Cited by 14 · see citing works
“Multivariate Survival Analysis for Prognostic Effect of PGF Expression and Tumor Infiltrating Lymphocytes We evaluated the impact of tumor infiltrating lymphocyte (TIL) level on survival along with PGF expression level in various cancers [29].”
MethodAge-dependent increase of cytoskeletal components in sensory axons in human skin
Metzner, K., Darawsha, O., Wang, M., Gaur, N., Cheng, Y., Rödiger, A., Frahm, C., Witte, O. W., Perocchi, F., Axer, H., et al. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.965382 · Cited by 13 · see citing works
“Aging is broadly associated with increased heterogeneity of gene expression in the human brain (Isildak et al., 2020), possibly due to substantial epigenetic variations (Cheung et al.”
BackgroundGasperi, R. D., Sosa, M. A. G., Garcia, G. S. P., Perez, G. M., Abutarboush, R., Kawoos, U., Statz, J. K., Patterson, J., Hof, P. R., Katsel, P., et al. (2022) · Journal of Neurotrauma · doi:10.1089/neu.2022.0282 · Cited by 12 · see citing works
Single cell spatial biology over developmental time can decipher pediatric brain pathologies
Nussinov, R., Yavuz, B. R., & Jang, H. (2024) · Neurobiology of Disease · doi:10.1016/j.nbd.2024.106597 · Cited by 11 · see citing works
“Another example concerns the accumulated mutations over time increasing expression heterogeneity during development, and ageing (Isildak et al., 2020).”
Divergent age-related methylation patterns in long and short-lived mammals
Haghani, A., Wang, N., Lu, A. T., Mozhui, K., Mammalian Methylation Consortium, Raj, K., Yang, X. W., & Horvath, S. (2022) · doi:10.1101/2022.01.16.476530 · Cited by 9 · see citing works
Loeffler‐Wirth, H., Hopp, L., Schmidt, M., Zakharyan, R., Arakelyan, A., & Binder, H. (2022) · Cells · doi:10.3390/cells11030362 · Cited by 9 · see citing works
“Increased variance in late adults shows that increased expression heterogeneity is a characteristic of the aging human brain [36].”
BackgroundAlmeida, D. M., & Turecki, G. (2022) · Methods · doi:10.1016/j.ymeth.2022.08.013 · Cited by 8 · see citing works
Genetic Influence on Gyral Peaks
Huang, Y., Zhang, T., Zhang, S., Zhang, W., Yang, L., Zhu, D., Liu, T., Jiang, X., Han, J., & Guo, L. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120344 · Cited by 7 · see citing works
“Therefore, it could be inferred that the markedly variable genetic influences on gyral peaks may result from the heterogeneous expression genes in different cortical regions, or even different VZ regions (Deco et al., 2021; Naumova et al., 2013; Collado-Torres et al., 2019; Somel et al., 2020).”
Nodal properties of the resting‐state brain functional network in childhood and adolescence
Yu, T., Xu, G., Zhang, J., Chen, K., & Liu, S. (2023) · Journal of Neuroimaging · doi:10.1111/jon.13155 · Cited by 7 · see citing works
Arakelyan, A., Avagyan, S., Kurnosov, A., Mkrtchyan, T., Mkrtchyan, G., Zakharyan, R., Mayilyan, K. R., & Binder, H. (2024) · Schizophrenia · doi:10.1038/s41537-024-00443-7 · Cited by 6 · see citing works
DNA damage-induced stalling of transcription drives aging through gene expression imbalance
Sims, A., & Gurkar, A. U. (2023) · DNA repair · doi:10.1016/j.dnarep.2023.103483 · Cited by 5 · see citing works
Symonenko, A. V., Roshina, N. V., Krementsova, A. V., Rybina, O. Y., & PASYUKOVA, E. G. (2022) · Biochemistry (Moscow) · doi:10.1134/s0006297922120161 · Cited by 4 · see citing works
“At the same time, the fact that gene expression continues for some time after the organism’s death [9], and especially of those groups of genes activity of which increases significantly during aging [10, 11], suggests that on the molecular level aging process is similar to the beginning of decay, which occurs after th…”
BackgroundNikopoulou, C., Kleinenkuhnen, N., Parekh, S., Sandoval, T., Schneider, F., Giavalisco, P., Bozukova, M., Vesting, A. J., Altmüller, J., Wunderlich, T., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.14.472593 · Cited by 4 · see citing works
Li, Z., Liu, J., Dai, Y., Zhu, H., Gao, Y., Ning, A., Zhang, X., Hou, Y., Xu, J., Wu, Y., et al. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103056 · Cited by 3 · see citing works
Single cell analysis of the aging female hypothalamusPreprint
Hajdarovic, K. H., Yu, D., Hassell, L.-A., Evans, S. A., Packer, S., Neretti, N., & Webb, A. E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.07.434282 · Cited by 3 · see citing works
Fard, A. T., Leeson, H., Perez, J. A., Pietrogrande, G., Power, D., Inclan, C. L. G., Zheng, H., Nelson, C., Soheilmoghaddam, F., Glass, N., et al. (2022) · doi:10.1101/2022.01.24.476823 · Cited by 3 · see citing works
Schneider, A. L., Martins-Silva, R., Kaizeler, A., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.22.521681 · Cited by 3 · see citing works
“…aimed at profiling ageing-68 related gene expression changes in human tissues focused on a single tissue (e.g. muscle 19– 21 , kidney 22 , brain 23–27 , skin 28,29 , blood 30,31 , liver 32 , retina 33 ) and/or are limited to a comparison between young and old individuals 20,21,28,32,33 , failing…”
BackgroundSchneider, A. L., Martins-Silva, R., Kaizeler, A., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2024) · eLife · doi:10.7554/elife.88623.3 · Cited by 2 · see citing works
Genome-wide transcript and protein analysis reveals distinct features of aging in the mouse heartPreprint
Gyuricza, I. G., Chick, J. M., Keele, G. R., Deighan, A. G., Munger, S. C., Korstanje, R., Gygi, S. P., & Churchill, G. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.28.272260 · Cited by 2 · see citing works
“Even though both subcomplexes show significant changes in correlation with age, PSMD14, from the regulatory particle, was found in the greatest number of protein-pairs that change in correlation with age (14), followed by proteins PSMD13 (10) and PSMD11 (9).”
BackgroundVision transformers for age prediction from facial images in a wild primate
Renoult, J. P., Karpinski, R., Sauvadet, L., Kreyer, M., Richard, M., Roura‐Torres, B., Baniel, A., & Charpentier, M. J. E. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70187 · Cited by 1 · see citing works
“…of age (Figure 1 ), which corresponds to the typical age of adult body size attainment in mandrills (Setchell et al., 2001 where the transition from adolescence to adulthood is marked by increasing and then plateauing variance in biological aging (Işıldak et al., 2020 ; Kuznetsov et al., 2024).”
Physiological System Dysregulation in Gene Expression Correlates Negatively with AgePreprint
Dufour, F., Jacques, P., & Cohen, A. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.09.289389 · Cited by 1 · see citing works
“Some studies found a majority of genes that increase variance (Işıldak et al., 2020; Li et al., 2009; Kedlian et al., 2019), other studies found the variance mostly decreased with age (Viñuela et al., 2018), while other studies show there is an equal amount of genes that increase or decrease…”
BackgroundPeripheral Expression Levels of Selected Oxidative Stress-Related Genes in Alzheimer’s Disease
Köseoğlu, P. K., Güven, G., Lohmann, E., Hanağası, H., Gürvit, İ., Bılgıç, B., Diker, I. Y., & Erginel‐Ünaltuna, N. (2021) · Experimed · doi:10.26650/experimed.2021.911956
Discovering Molecular Regulators of Ageing Using Mixture Models With RNA-sequencing DataPreprint
Loganathan, S., Kulkarni, A. S., Fard, A. T., & Mar, J. C. (2022) · Research Square · doi:10.21203/rs.3.rs-1390473/v1
Schneider, A. L., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2023) · eLife · doi:10.7554/elife.88623.1
Schneider, A. L., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2023) · eLife · doi:10.7554/elife.88623.1.sa1
Schneider, A. L., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2023) · eLife · doi:10.7554/elife.88623.1.sa2
Schneider Arthur, L., Nuno, S.-A., & Barbosa-Morais Nuno, L. (2023) · eLife · doi:10.7554/elife.88623.1.sa0
Schneider, A. L., Martins-Silva, R., Kaizeler, A., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2024) · eLife · doi:10.7554/elife.88623.2
Isoswitching in human brain development and agingPreprint
Erdogdu, B., Ji, H. J., Rudnick, Z., Pertea, M., & Salzberg, S. L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.05.652255
“Unlike development, where milestones occur in a relatively uniform pattern, aging progresses at different rates in different individuals(49).”
Khosravani, H., Disfani, R. A., Rabori, P. M., Zabihi, M. R., Akhoondian, M. J., Emami, A., & Salehi, R. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000004025
“Thus, age was deemed a significant variable in our analysis to examine whether age-related changes could account for some of the gene expression heterogeneity in burn patients [ 24 , 25 ] .”
Neurological aging and neurodegenerative disease in humans and great apes
Edler, M. K., Hearn, C. J., Guevara, E. E., Mhatre‐Winters, I., Varghese, M., Freire‐Cobo, C., Sherwood, C. C., Richardson, J. R., Raghanti, M. A., & Hof, P. R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-27380-3.00074-9
Embracing non-linearity in human ageing
Grolaux, R., Teschendorff, A. E., & Eynon, N. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00993-9
- Authors
- Işıldak, U., Somel, M., Thornton, J. M., & Dönertaş, H. M.
- Affiliations
- Middle East Technical University (TR)
- European Bioinformatics Institute (GB)
- Corresponding author
- Dönertaş, Handan Melike
- Venue
- Scientific Reports
- Published
- 2020-03-05
- Volume
- 10
- Issue
- 1
- Pages
- 4080-4080
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7058021
- OpenAlex
- W3010435021
- Cited by
- 53
- References
- 67
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 6 · 2023: 15 · 2022: 15 · 2021: 6 · 2020: 5
- FWCI
- 3.32
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-06-22)
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OpenMETU (Middle East Technical University) (submittedVersion, cc-by-nc-nd)
- Funders
- European Molecular Biology Laboratory
- Wellcome Trust (098565/Z/12/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- gene expression heterogeneity, brain aging, brain development, interindividual variability, transcriptome, lifespan regulation, neuronal function, human brain regions
- MeSH terms
- Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Aging, Brain, Child, Child, Preschool, Female, Humans, Infant, Infant, Newborn, Male, Middle Aged, Biomarkers, Gene Expression Regulation, Developmental, Gene Expression Profiling, Young Adult, Transcriptome
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