Summary: Researchers have created a computational “aging clock” that measures the biological age of brain cells and pinpoints compounds with potential to rejuvenate them. By analyzing gene expression from healthy and neurodegenerative human brain tissue, the team identified 453 interventions predicted to reverse cell-level aging. Early animal tests showed behavioral, cognitive, and molecular improvements in older mice, opening new avenues for therapies to protect against neurodegeneration and support brain health in aging populations.
As populations grow older worldwide, age-related brain disorders are becoming more common. Extending healthy brain function is a major public-health goal, and this study offers a data-driven route to discover treatments that could slow or reverse cellular aging in the brain.
Key Facts:
- Aging clock: A machine-learning tool that predicts the biological age of brain cells using gene expression data.
- Rejuvenation candidates: 453 compounds and genetic perturbations were predicted to reverse age-related decline in neural cell types.
- Proof of concept: Selected compounds produced molecular signatures of younger tissue and improved behavior in aged mice.
Source: CIC bioGUNE
Could aging brain cells be made functionally younger?
An international team based in Spain and Luxembourg tackled this question by developing a brain-specific aging clock and using it to search for interventions that shift cellular age toward a younger state. The computational platform, described in the journal Advanced Science, offers a systematic method to screen for treatments with neuroprotective and rejuvenating potential.
Population aging means a rising number of people at risk for dementia and other neurodegenerative conditions. Identifying interventions that preserve cognitive function and reduce disease risk is essential to improving quality of life and reducing the burden on health systems. A validated, cell-type-specific aging clock can accelerate discovery by predicting which compounds may reverse molecular signs of aging in the brain.
Led by Prof. Antonio Del Sol and colleagues at CIC bioGUNE and the Luxembourg Centre for Systems Biomedicine (LCSB) at the University of Luxembourg, the team applied machine-learning to transcriptomic data to build a clock tailored to brain tissue. This approach leverages differences in gene activity to estimate a cell’s biological age, which can diverge from chronological age because of genetics, environment, and disease processes.
Constructing a brain-specific biological clock
The aging clock developed by the researchers uses expression levels from 365 genes to predict biological age. Trained on samples from healthy donors aged 20 to 97, the model accurately estimated age and was able to assess the biological age of different brain cell types, with particular sensitivity for neurons. When applied to samples from patients with neurological disease, the clock estimated higher biological ages compared with healthy controls, supporting the idea that neurodegeneration is linked to accelerated cellular aging.
Dr. Guillem Santamaria, first author of the study, notes that the clock’s predictions reflect functional decline observed clinically, especially during the critical ages between 60 and 70, and correlate with the degree of neurodegeneration. This relationship suggests that compounds predicted to reduce biological age could serve as neuroprotective agents.
Discovering compounds with rejuvenating potential
The researchers used the clock to screen tens of thousands of transcriptional profiles from chemical and genetic perturbations—over 43,800 profiles in total—and identified 453 unique interventions predicted to rejuvenate neural progenitor cells and neurons. Some of these candidates are already known to extend lifespan in animal models or are used clinically for neurological disorders, while many others represent novel leads not previously studied for rejuvenation or lifespan effects.
Prof. Del Sol emphasizes two key points: first, the fact that the platform recovered drugs with established effects on brain function validates the approach; second, the large set of new candidates provides a rich resource for follow-up studies to evaluate efficacy and safety across biological systems.
Initial animal validation and therapeutic prospects
To validate the computational predictions, teams collaborated to test three of the identified compounds in aged mice. Treatment reduced anxiety-like behavior and produced modest improvements in spatial memory—two common features of brain aging. Transcriptomic analysis of the treated mouse cortex showed a shift toward younger gene-expression profiles, demonstrating molecular rejuvenation in addition to behavioral benefits.
These results provide proof of concept that computationally predicted rejuvenating interventions can translate to physiological and molecular improvements in vivo. The study, published in Advanced Science, positions the brain aging clock as a discovery tool to prioritize candidates for further preclinical and clinical testing against neurodegenerative disease.
Prof. Del Sol concludes that the hundreds of predicted compounds will require extensive validation across different models to determine their therapeutic potential and safety, but they offer a broad pipeline for future development of neuroprotective and rejuvenating treatments.
About this neurogenesis research news
Author: Jana Sendra, CIC bioGUNE
Source: CIC bioGUNE
Contact: Jana Sendra – CIC bioGUNE
Image credit: Neuroscience News
Original Research: Open access. “A Machine-Learning Approach Identifies Rejuvenating Interventions in the Human Brain” by Antonio Del Sol Mesa et al., Advanced Science.
Abstract
A Machine-Learning Approach Identifies Rejuvenating Interventions in the Human Brain
Rising life expectancy has increased the prevalence of age-related brain disorders. While brain rejuvenation holds promise to counter functional decline, scalable discovery methods for effective interventions have been lacking. The authors developed a computational platform based on a transcriptional brain aging clock that detects age- and neurodegeneration-related changes in human brain tissue.
Applied to samples with neurodegeneration, the clock reveals that disease presence and severity significantly increase predicted biological age. Screening 43,840 transcriptional profiles of chemical and genetic perturbations identified 453 unique rejuvenating interventions, including compounds previously linked to lifespan extension and drugs used to treat neurological conditions.
A combination of compounds predicted by the platform reduced anxiety, improved memory, and rejuvenated the cortical transcriptome in aged mice. These findings demonstrate the platform’s capacity to discover brain-rejuvenating interventions and support further development of potential treatments for neurodegenerative diseases.