Spatial aging clocks reveal how T cells and neural stem cells shape brain aging

Discover how advanced spatial aging clocks decode the aging brain and reveal the dual role of immune and stem cells in reshaping our understanding of cognitive decline and rejuvenation.

Study: Spatial transcriptomic clocks reveal cell proximity effects in brain aging. Image credits: VectorMine / Shutterstock

This is evident from a recent study published in the journal Natureresearchers developed spatial aging clocks using single-cell transcriptomics to investigate cell type-specific interactions and their impact on brain aging, rejuvenation and disease.

Background

Aging of the brain significantly increases the risk of neurodegenerative diseases such as Alzheimer’s disease (a progressive brain disease that causes memory loss) and dementia (a decline in cognitive skills). Although previous research has examined molecular changes in the aging brain at single-cell resolution, these studies lack spatial context, especially at scale. Without a systematic understanding of spatiotemporal changes, including local cell neighborhoods and cell-cell interactions, crucial insights are missed. High-throughput spatial omics holds promise for advancing this understanding, but current studies fail to capture both spatial and temporal resolution at the single-cell level, especially in geriatric ages when cognitive decline is most evident is. This study addresses these gaps by introducing spatial aging clocks, which provide a new computational framework to predict cell-specific aging and investigate cell proximity effects. Further research is needed to develop advanced computational tools to analyze these spatial interactions.

About the study

In the current study, male C57BL/6JN mice were used for the aging and exercise cohorts, while male inducible OSKM (POU class 5 homeobox 1 (Oct4), SRY (sex determining region Y)-box 2 (Sox2), Kruppel-like factor 4 (Klf4) and Myelocytomatosis oncogene (c-Myc)) mice were used for the partial reprogramming experiment. Mice were housed in groups under standard conditions, with at least three weeks of acclimatization prior to experiments. The aging cohorts included mice of different ages, ranging from 3 to 34 months, with coronal and sagittal brain sections collected for transcriptomic analysis. The exercise experiment included young and old sedentary and exercise mice, while the partial reprogramming experiment used young and old OSKM mice with doxycycline treatment. All animal procedures were approved by the Stanford University Institutional Animal Care and Use Committee (IACUC) and the Veterans Affairs Palo Alto Committee on Animal Research.

For sample collection, mice were euthanized and the brains were snap frozen in optimal cutting temperature (OCT) compound. Ribonucleic acid (RNA) sequence data were obtained using the Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) platform with a custom panel of 300 genes. The panel included markers for different cell types and aging-related genes. Brain sections were processed for MERFISH with tissue permeabilization, hybridization and imaging according to the Vizgen protocol. After image collection, cell segmentation and transcript assignment were performed using Cellpose. Data were preprocessed by filtering out low quality cells and gene expression normalization was applied.

Machine learning models were trained on the transcriptomic data of spatial aging clocks to predict age based on spatial gene expression patterns. The proximity effects of T cells and neural stem cells on neighboring cells were analyzed by comparing transcriptomic changes in nearby and distant cells. Statistical analyzes included Pearson correlation and Mann-Whitney U test, where visualization was performed using different plotting tools.

Study results

A spatial transcriptomics atlas of the aging mouse brain was created to map gene expression across the lifespan. The dataset included 2.3 million high-quality cells from different brain regions, ranging in age from 3.4 to 34.5 months. The MERFISH method identified 18 cell types, including neurons, glial cells and immune cells, and showed how these cells localized to their respective regions.

The study revealed significant changes in cell proportions with age. For example, microglia and T cells increased with age, while neural stem cells (NSCs) and oligodendrocyte progenitor cells (OPCs) decreased. T cells showed a substantial increase in number in all regions, while NSCs were mainly found in the neurogenic niche and decreased over time. These changes were consistent in both coronal and sagittal brain sections. Notably, T cells exerted a pro-aging influence on nearby cells, often spreading their effects over longer spatial ranges than NSCs, which showed localized pro-rejuvenation effects.

In addition to changes in cellular composition, gene expression also varied with age. For example, microglia showed the greatest number of age-related gene changes, especially in the immune response pathways. The study also identified specific patterns of gene expression changes in different brain regions, with white matter tracts showing the greatest changes. The findings highlight that immune-related genes increase with age in microglia, in contrast to metabolic and developmental genes, which showed age-related decline.

To further investigate the dynamics of aging, the researchers developed ‘spatial aging clocks’ to predict the biological age of individual cells based on gene expression. This method accurately predicted cell age in several brain regions and cell types, including rare ones such as NSCs and T cells. The clocks generalized effectively across lineages, data sets, and even other single-cell technologies, underscoring their robustness.

The effects of rejuvenation interventions have also been investigated using the spatial aging clocks. Voluntary exercise and partial reprogramming were tested for their impact on brain aging. Exercise showed strong rejuvenating effects, especially on the cerebral vasculature, while partial reprogramming had more modest effects, especially rejuvenating NSCs and neuroblasts. Exercise had a broader impact, rejuvenating multiple cell types in brain regions, while partial reprogramming mainly benefited NSCs and neuroblasts with limited region-specific effects. Finally, the study examined how specific cells influence the aging of nearby cells, finding that T cells have a pro-aging effect, while NSCs have a pro-rejuvenating impact on neighboring cells.

Conclusions

This study provides high-resolution spatiotemporal profiling of the aging mouse brain, monitoring gene expression in different regions and cell types. By generating spatial aging clocks, it quantifies the effects of rejuvenating interventions and disease models. These clocks enable rapid assessment of aging and temporal processes with single-cell resolution. Importantly, the study shows that T cells and NSCs play a crucial role in modulating the aging process, influencing their neighbors through long- and short-term effects. The machine learning framework can be adapted to other tissues and species. The study also examines the effects of cell proximity and identifies potential mediators.